Diagnostic and prognostic potential of tissue phospholipidomics in hepatocellular carcinoma: A prospective cohort study

Diagnostic and prognostic potential of tissue phospholipidomics in hepatocellular carcinoma: A prospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diagnostic and prognostic potential of tissue phospholipidomics in hepatocellular carcinoma: A prospective cohort study Tongtong He, Maierhaba Wusiman, Song Shuang, Jie-dong Chen, Meng-chu Li, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4110772/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Previous studies have indicated that hepatocellular carcinoma (HCC) is linked to abnormal phospholipid (PL) metabolism. However, whether alterations of phospholipids in hepatic tissues contribute to the diagnosis and prognosis of HCC remains unclear. Methods: A quantitative and comprehensive phospholipidomic analysis was conducted using hydrophilic liquid chromatography-electrospray ionization-triquadrupole-mass spectrometry (HILIC-ESI-MS/MS). This analysis facilitated the comparison of 214 distinct PLs between paired samples from HCC tissues and tumor-adjacent normal hepatic tissues (NATs) in a prospective cohort (n=87). Differential metabolites were identified through paired t tests and orthogonal partial least-squares discriminant analysis (OPLS-DA). The survival analysis of phospholipids for HCC was assessed using univariate and multivariable Cox regression models. Results: Significant differences were found between HCC and NAT for phospholipid profile, and 85 phospholipids demonstrated a high accuracy in discerning two types of tissue. The increased HCC/ NAT ratio of lysophosphatidylglycerol (LPG) class was associated with greater HCC specific mortality (Hazard ratio (HR) = 6.50, 95% confidence interval (CI): 1.88-22.51, P = 0.002), and the association was still significant (HR = 4.82, 95% CI: 1.34-17.29, P = 0.017) even after adjustment covariances. LPG (18:1) and LPG (18:2) differentiated HCC from NAT with great capacities (the area under the curve (AUC)>0.75) and had prognostic significance for HCC specific mortality before (HR = 5.17 and 5.51, respectively, both of P < 0.01) and after adjustment (HR = 4.14 and 4.15, respectively, both of P < 0.05). Conclusions: Phospholipids could serve as potential biomarkers with significant diagnostic and prognostic implications. A more profound understanding of cancer-associated phospholipid metabolism could pave the way for innovative therapeutic strategies. hepatocellular carcinoma metabolomics phospholipids prognosis liquid chromatography-mass spectrometry Figures Figure 1 Figure 2 Figure 3 1 INTRODUCTION Primary liver cancer (PLC), as per the 2020 GLOBOCAN statistics, stands among the top three leading causes of cancer-related deaths globally for both sexes[ 1 ]. Hepatocellular carcinoma (HCC) constitutes the majority of PLC cases, often diagnosed at advanced stages due to its subtle onset and rapid progression[ 2 ]. Despite surgical or local treatments for HCC patients, high recurrence rates significantly impact their survival[ 3 ]. Thus, there is a pressing need to comprehensively characterize tumors and identify effective therapeutic targets. Dysregulation of lipid metabolism has emerged as a prominent phenotypic hallmark of cancer[ 4 ]. Lipids, a diverse group of biomolecules with varied structures and functions, play crucial roles in energy metabolism, membrane formation, synthesis of signaling molecules, and regulation of gene expression through epigenetic modulation[ 5 ]. Changes in lipid components in biological fluids and tissues are consistently associated with HCC, making lipid metabolism a source for biomarker discovery and potential therapeutic target[ 6 , 7 ]. Despite this understanding, the precise nature of lipid alterations in HCC tissues remains unclear. As fundamental components of cellular membranes, phospholipids (PLs) influence numerous membrane-associated processes such as homeostasis, cell adhesion, cellular signaling, cell-cell interactions, vesicular trafficking, and apoptosis. Changes in PLs composition and distribution in cells, tissues, and biofluids are linked to cancer and explored as potential biomarkers for diagnosis and prognosis in various cancers[ 8 – 10 ]. PL analysis has been applied to study lipid proteomics in HCC research using model hepatocyte lines and three-dimensional culture systems[ 11 – 13 ]. PLs associated with HCC includes lysophospholipids (LPLs), crucial signaling lipids, and ether lipids acting as endogenous antioxidants[ 14 ]. Imaging of PL profiles in HCC tissues enables the localization of specific phosphatidylcholine species in cancer regions, highlighting differences between tumor-adjacent tissues and HCC phenotypes in vivo[ 15 ]. Altered PL metabolism in cancer cells also serves as a potential molecular target for anticancer therapy [ 16 , 17 ]. Thus, a comprehensive understanding of PL dysregulation in HCC is essential. Our study aims to compare phospholipid profiles between HCC tissues and tumor-adjacent normal hepatic tissues (NATs) and assess the potential clinical prognostic utility of defined PLs 2 MATERIALS AND METHODS 2.1 Study Patients This study encompassed 174 paired normal and tumor samples acquired during surgical resection from 87 previously untreated and newly diagnosed HCC patients. Patients were drawn from the Guangdong Liver Cancer Cohort[ 18 ], a prospective cohort study conducted in Guangdong province, southern China. The study, initiated in 2013, enrolled liver cancer Patients within a month of diagnosis and before any cancer treatment. Trained investigators conducted face-to-face interviews using standardized questionnaires to collect information on sociodemographic characteristics, lifestyle factors, dietary intake, and medical history. The study protocol received approval from the Ethics Committee of the School of Public Health at Sun Yat-sen University (ethical approval number: NCT03297255), and all patients provided informed consent at recruitment. Follow-up commenced from the date of cancer diagnosis until the date of death or the last follow-up (January 4, 2023), whichever occurred first. HCC-specific mortality (death from HCC) served as the primary outcome. Participant death information, including survival status and cause of death, was confirmed through the death registration and reporting system of the Guangdong Provincial Center for Disease Control and Prevention or the inpatient and outpatient medical system of the Sun Yat-sen University Cancer Center. Additionally, active follow-up via mail or telephone interviews with patients or their families was conducted to verify their survival status. Tissue samples from the tumor and adjacent areas within the resection margin were collected immediately after surgical resection and processed as follows: 30 mg aliquots of hepatic tissues were homogenized in 180 µl PBS, with 100 µL packed for quantitative PL analysis. A mixed sample was created by combining 100 µL from each individual sample for qualitative analysis using the HILIC-ESI-IT-TOF system. The remaining portion of the mixed sample was divided into 100 µL, serving as a quality control (QC) sample for quantitative analysis. All samples were stored at -80°C until analysis. 2.2 Phospholipidomics profiling Chemicals, including PL standards and deuterium-labeled internal PL standards, were procured from Avanti Polar Lipids (USA). Other chemicals such as chloroform, methanol, acetonitrile, ammonium formate, and formic acid were obtained from Sigma Aldrich (USA) and were of HPLC or MS grade. Ultra-pure water was sourced from a Milli-Q system (Millipore, USA). All samples were thawed at 37°C, and total PL was extracted from tissues using an improved Folch method. Briefly, 100 µL of tissue, 10 µL of deuterium-labeled internal standards solution, and 1.0 mL of chloroform/methanol (2:1, v/v) were vortex-mixed for 30 minutes. Subsequently, 0.2 mL NaCl solution (0.9%) was added to the mixture, mixed for an additional 30 minutes, and then centrifuged at 8,000 rpm for 10 minutes. The chloroform layer was transferred to a new tube and dried under a gentle stream of nitrogen. Before analysis, all samples were re-dissolved in 100 µL chloroform/methanol (2:1, volume ratio). PL molecular species were identified using a hydrophilic liquid chromatography-electrospray ionization-ion trap-time of flight-mass spectrometry (HILIC-ESI-IT-TOF-MS) method outlined in a previous publication[ 19 , 20 ]. In summary, total PL was extracted from the 500 µL mixed sample (as described in Section 2.3 ) and re-dissolved in 50 µL chloroform/methanol (2:1, volume ratio). This concentrated sample was injected into the HILIC-ESI-IT-TOF-MS system. MS, MS 2 and MS 3 scan data were collected under Electrospray Ionization (ESI)- mode in the ranges of 400–1000 m/z, 100–800 m/z, and 100–700 m/z, respectively. PLs were initially identified by comparing m/z values with calculated exact masses using the LIPID MAPS Structure Database. Detailed structures of individual molecular species were confirmed by MS 2 and MS 3 spectra, following the PL cleavage law described in a previous publication. The identified PL molecular species were quantified using a hydrophilic liquid chromatography-electrospray ionization-triquadrupole-mass spectrometry (HILIC-ESI-MS/MS) method, coupling an HPLC system (Shimadzu, Japan) with a quadrupole mass spectrometer (LC-MS 8060, Shimadzu Japan) equipped with an ESI source for PL quantification. In terms of chromatography, we employed a CORTECS HILIC Column (1.6 µm, 2.1 mm x 150 mm, Waters, USA), maintained at 40°C. Eluent A was water, and eluent B was acetonitrile/water (95/5), both containing 0.1% formic acid and 10 mM ammonium formate. The flow rate was set at 0.4 mL/min, and the elution gradient ranged from 99.5% B at 0 min to 99.5% B at 32 min, with intermittent adjustments. Blank injections (10 µL methanol) after every 25 samples showed no significant lipid carryover. For mass spectrometry, instrument parameters included nebulizer gas flow (3 L/min), heat gas flow (12 L/min), desolvation gas flow (8 L/min), interface temperature (250°C), DL temperature (180°C), heat block temperature (250°C), and detector voltage (1.85 kV). Positive ESI mode was used for phosphatidylethanolamine (PE), phosphatidylcholine (PC), phosphatidylserine (PS), lysophosphatidylethanolamine (LPE), lysophosphatidylcholine (LPC), and sphingomyelin (SM), while negative mode was employed for phosphatidylglycerol (PG), phosphatidylinositol (PI), lysophosphatidylinositol (LPI), and lysophosphatidylglycerol (LPG). MS/MS data were acquired in targeted multiple-reaction monitoring (MRM) mode for all identified PL molecular species in the HILIC-ESI-IT-TOF-MS system, with precursor and product ion m/z values calculated based on PL fragmentation laws. In terms of quantification, individual PL molecular species were quantified using the internal standard method. Calibration curves (1/x^2 weighting factor) were constructed with 47 PL standards at varying concentrations, featuring a constant concentration of deuterium-labeled internal standards. Quantification involved using the calibration curve of the PL standard from the same class with the most similar acyl chain length and unsaturation degree. Quality control was maintained with a QC sample injected after every 25 samples. The mixture of standards was injected six times to evaluate the relative standard deviation (RSD) of retention time and peak area ratio. Accuracy and precision were assessed by testing QC samples with added nonendogenous PLs at low, medium, and high concentrations. Metabolites with a deletion rate below 10% were included, with deletion values estimated using 1/5 of the minimum value for each metabolite. A total of 214 metabolites were considered in the final analysis. 2.3 Statistical analysis The differences in baseline characteristics were assessed using chi-square tests or Fisher's exact tests for categorical variables and paired t tests or Wilcoxon's signed-rank tests for continuous variables. Spearman correlation analysis was employed to calculate correlation coefficients between metabolites. To enhance normality, metabolite data underwent log transformation (base 10) and Pareto scaling (mean-centered and divided by the square root of the standard deviation of each variable). Principal component analysis (PCA) and an orthogonal partial least-squares discriminant analysis (OPLS-DA) visualized discrimination between HCC and NAT, and variable importance in the projection (VIP) values were calculated to prioritize differential metabolites. Paired t tests examined differences in metabolite levels between HCC and NAT. The Benjamini-Hochberg procedure, with false discovery rate (FDR) estimation, corrected for multiple testing. To evaluate the strength and robustness of associations between PLs and HCC-specific mortality, univariate and multivariate Cox models calculated hazard ratios (HR) and 95% confidence intervals (CIs) for the ratio of PL. In multivariable analysis, adjusted factors included age (in years), gender (women, men), body mass index (kg/m 2 ), hepatitis B virus (HBV) infection (yes, no), smoking status (never smoker, ever smoker, current smoker), alcohol drinking status (never drinker, ever drinker, current drinker), family history of primary liver cancer, Barcelona Clinic Liver Cancer (BCLC) stage: (0, A, B, C), and treatment (liver resection, radiofrequency ablation, intervention). Stratified analyses were conducted based on gender, HBV infection status, family history of PLC, treatments, and alcohol drinking status. Potential effect modification was assessed by including a product term between PL concentrations and the categorical stratified variable in the multivariate model. Support vector machines (SVM) were established to evaluate the performance of independent predictors selected by the Least Absolute Shrinkage and Selection Operator (LASSO). The predictive performance of metabolites was tested by calculating the area under the curve (AUC) through receiver operating characteristics (ROC) analysis in the cohort. All statistical analyses were conducted using R software (R version 3.6.3) and were two-sided. The significant P value was set at 0.05 for all results. 3 RESULTS 3.1 Basic characteristics of HCC patients The present analysis included 74 (85.1%) men and 13 (14.9%) women. The mean (standard deviation [SD]) age at diagnosis was 50.84 (12.88) years old (Table 1). Males tended to smoke and drink more, with lower total cholesterol (TC), low density lipoprotein cholesterol (LDLC), and high density lipoprotein cholesterol (HDLC) level in comparison to females. After a median follow-up of 2513 days with a total of 181070 person-days, 32 (36.78%) patients died (group HCd), while 55 survived (group HCs). Patients in group HCd tended to smoke more, with higher aspartate amino transferase (AST), AST/alanine amino transferase (ALT), C-reaction protein (CRP) levels and advanced stages in comparison to group HCs (Table S1). Table 1 Baseline characteristics of patients with hepatocellular carcinoma Total (n = 87) Male (n = 74) Female (n=13) P Age (year) a 50.84 (12.88) 50.66 (11.74) 51.86 (18.68) 0.759 Median follow-up time (day) 2513 2512 2513 0.458 No. of deaths (%) a 32 (36.78) 28 (37.84) 4 (30.77) 0.626 BMI (kg/m2) a 22.25 (3.29) 22.30 (3.26) 21.95 (3.60) 0.729 Physical activity (MET-h/d) a 29.7 (26.10,38.25) 30.05 (26.19,39.14) 27.5 (24.58,36.50) 0.946 Residence, n (%) 0.76 Urban 57 (65.5) 48 (64.9) 9 (69.2) Rural 30 (34.5) 26 (35.1) 4 (30.8) Education level, n (%) 0.09 Primary school or below 20 (23.3) 14 (19.2) 6 (46.2) Secondary or technical secondary school 50 (58.1) 44 (60.3) 6 (46.2) College or above 16 (18.6) 15 (20.5) 1 (7.7) Smoking status, n (%) 0.002 Never smoker 48 (55.2) 35 (47.3) 13 (100.0) Former smoker 16 (18.4) 16 (21.6) Current smoker 23 (26.4) 23 (31.1) Alcohol drinking status, n (%) 0.046 Never drinker 62 (71.3) 49 (66.2) 13 (100.0) Former drinker 11 (12.6) 11 (14.9) Current drinker 14 (16.1) 14 (18.9) FLD, n (%) 15 (17.2) 14 (18.9) 1 (7.7) 0.323 Cirrhosis, n (%) 69 (79.3) 61 (82.4) 8 (61.5) 0.086 With family history of PLC, n (%) 18 (20.7) 14 (18.9) 4 (30.8) 0.331 HBV infection b 82 (94.3) 69 (93.2) 13 (100.0) 0.334 ALT (U/L) a 35.40 (26.00,43.50) 35.75 (28.88,44.38) 26 (20.80,34.65) 0.119 AST (U/L) a 33.2 32.85 38.1 0.152 (26.80,42.60) (26.78,42.03) (26.95,52.70) AST/ALT a 1.00 (0.75,1.28) 0.95 (0.74,1.20) 1.33 (0.99,2.00) 0.119 TC (mmol/L) a 4.88 (4.34,5.50) 4.79 (4.32,5.36) 5.88 (4.88,6.58) <0.001 TG (mmol/L) a 0.97 (0.67,1.30) 1.00 (0.69,1.33) 0.80 (0.65,1.21) 0.399 LDLC (mmol/L) a 2.96 (2.53,3.67) 2.89 (2.40,3.45) 3.87 (3.13,4.49) <0.001 HDLC (mmol/L) a 1.31 (1.07,1.51) 1.27 (1.06,1.48) 1.33 (1.18,1.79) 0.017 AFP≥200 ng/L, n (%) 43 (49.4) 37 (50.0) 6 (46.2) 0.798 CRP≥3 mg/L, n (%) 27 (31.0) 23 (31.1) 4 (30.8) 0.982 TNM stage, n (%) 0.543 Ⅰ 41 (47.1) 33 (44.6) 8 (61.5) Ⅱ 26 (29.9) 24 (32.4) 2 (15.4) Ⅲ 16 (18.4) 14 (18.9) 2 (15.4) Ⅳ 4 (4.6) 3 (4.1) 1 (7.7) BCLC stage, n (%) 0.654 0 5 (5.7) 5 (6.8) A 37 (42.5) 31 (41.9) 6 (46.2) B 8 (9.2) 6 (8.1) 2 (15.4) C 37 (42.5) 32 (43.2) 5 (38.5) Treatment, n (%) 0.715 Liver resection 81 (93.1) 69 (93.2) 12 (92.3) Radiofrequency ablation 2 (2.3) 2 (2.7) Intervention 4 (4.6) 3 (4.1) 1 (7.7) Abbreviations: BMI, body mass index; MET, metabolic equivalent; FLD, fatty liver disease; HBV, hepatitis B virus; ALT, alanine amino transferase; AST, aspartate amino transferase; TC, total cholesterol; TG, triglyceride; LDLC, low density lipoprotein cholesterol; HDLC, high density lipoprotein cholesterol; AFP, alpha fetoprotein; CRP, C-reaction protein; TNM stage, tumor-node-metastasis stage; BCLC stage, Barcelona Clinic Liver Cancer stage. a Values are mean ± SD or medians (interquartile range [IQR]). b HBV infection was defined as seropositivity of HBV surface antigen. 3.2 Identification of differential PL classes and species In this study, the concentrations of PLs were represented by nmol/g wet tissues in data analysis. In addition, the percentage of given PL in individual species or classes out of total PLs was used in the calculation of their abundances. The number of individual PL species in 10 PL classes and 4 PL subgroups are listed in Table 2. These PL subgroups are grouped according to similarity of parts in the phosphate head. Table 2 Phospholipids profiles in hepatic tissues Phospholipids (PL) Number of species in PL class Lysophosphatidylcholine (LPC) 20 Lysophosphatidylethanolamine (LPE) 18 Lysophosphatidylglycerol (LPG) 3 Lysophosphatidylinositol (LPI) 5 Phosphatidylcholine (PC) 56 Phosphatidylethanolamine (PE) 47 Phosphatidylglycerol (PG) 1 Phosphatidylinositol (PI) 17 Phosphatidylserine (PS) 21 Sphingomyelin (SM) 29 Total phospholipids 214 Subgroups Choline-containing PLs (LPC, PC) 76 Ethanolamine-containing PLs (LPE, PE) 65 Lyso-containing PLs (LPC, LPE, LPG, LPI) 46 Plasmalogens (pPC, pLPC) 19 The abundances of PLs varied greatly among individual species, classes and subgroups between NAT and HCC. In the level of individual PL species, PC (34:1) was the most abundant among 214 individual PL species in HCC which is lower in HCC than in NAT only in male population (1.14-fold, P = 0.003, data not shown). In the level of PL class, among 10 PL classes, PC was the most abundant, accounting for more than 50% of total PLs, and LPG was the least abundant, accounting for 0.5% or less of total PLs in both NAT and HCC in all studied populations (Figure 1). In the level of subgroup, PC-containing PL was the most abundant among PL subgroups, accounting for more than 60% of total PLs in both NAT and HCC in all studied populations. The sum of the signal intensities of the detected compounds within various groups of complex lipids between HCC and NAT was compared. The total concentration of LPE, LPG, and LPI were lower in HCC than in NAT. By contrast, the total concentration of PC, PE, PI, and PS were higher in tumor tissue (Figure 2). The diagnostic power of total amount of phospholipids classes for HCC and NAT, only total LPG met the condition including VIP derived from the OPLS-DA loadings >1, FDR2 or <0.5 and AUC over 0.8 (Table S2). And multivariate exploratory ROC analysis for each phospholipid class distinguishing HCC and NAT based on SVM algorithm were shown in Figure S1. There were 172 of 214 phospholipids showing statistical significance ( P < 0.05) compared between HCC and NAT in all population. After correction for multiple comparisons, 169 metabolites remained significant (FDR < 0.05). PCA of the whole set of detected lipids showed that the phospholipid profile was different between normal tissue and HCC tissue, although a small overlap was present (Figure S2). The discrimination between NAT and HCC tissue, was better represented by OPLS-DA (Figure 3) and permutation showed the empirical p-values Q2:0.894 ( P < 0.01) and R2Y:0.937 ( P < 0.01). Furthermore, VIP scores derived from the OPLS-DA loadings were created to indicate the variation patterns of these 85 differential metabolites in two types of tissues, showing the tendency of molecules to evolve between HCC and NAT (Table 4), which may be considered as putative biomarkers of discrimination between two tissues. The volcano plots showed the estimated fold change (x-axis) vs the -log 10 P value from paired t -tests (y-axis) for each PL (Figure 3). Subsequent verification (FDR1, FC>2 or <0.5) identified 36 common metabolites as differential metabolites, including 11 PCs, 8 PCPs, 6 LPEs, 2 PEs, 2 PIs, 2 PSs, 2 LPGs, 2 LPIs and 1 LPC. Among them, 18 PL species were significantly increased, and 18 were statistically decreased in HCC than in NAT (Table S3). Pairwise correlation analysis of the 36 PLs showed that most metabolites of the same class tended to cluster together; additionally, there was inverse correlation between PSs and PIs (Figure S3) Table 4 Eighty-five identified differential phospholipid species between HCC tissue and tumor-adjacent normal hepatic tissue Phospholipid species a P FC VIP AUC Tendency b Phospholipid species a P FC VIP AUC Tendency b Lysophosphatidylcholine Phosphatidylcholine LPC (14:0) 1.21E-07 1.862 1.007 0.723 I PC (30:0) 3.49E-25 4.195 1.970 0.932 I LPC (16:0) 2.01E-07 0.640 1.132 0.718 D PC (30:1) 4.08E-20 4.68 1.771 0.903 I LPC (18:0) 1.39E-08 0.574 1.115 0.753 D PC (32:0) 1.02E-07 1.451 1.076 0.760 I LPC (20:0) 4.56E-08 0.546 1.160 0.733 D PC (32:1) 2.85E-14 2.278 1.515 0.832 I LPC (20:1) 1.68E-12 0.446 1.465 0.818 D PC (33:0) 3.26E-21 2.892 1.902 0.917 I Lysophosphatidylethanolamine PC (33:1) 8.96E-14 1.824 1.512 0.818 I LPE (16:0) 3.11E-18 0.386 1.768 0.884 D PC (35:1) 1.36E-07 1.433 1.091 0.731 I LPE (18:0) 8.96E-09 0.570 1.150 0.754 D PC (36:1) 2.09E-09 1.474 1.307 0.756 I LPE (18:3) 2.78E-10 0.475 1.271 0.776 D PC (36:3) 6.48E-08 1.476 1.097 0.749 I LPE (20:1) 1.23E-11 1.860 1.245 0.81 I PC (38:2) 2.14E-23 2.249 1.953 0.912 I LPE (20:2) 1.92E-12 2.042 1.402 0.816 I PC (38:3) 9.76E-16 2.241 1.725 0.856 I LPE (20:3) 2.12E-14 2.566 1.647 0.859 I PC (38:4) 9.26E-11 1.993 1.405 0.792 I LPE (20:4) 2.35E-06 1.734 1.049 0.743 I PC (38:5) 8.05E-09 1.939 1.212 0.765 I LPE (22:4) 1.82E-12 2.375 1.409 0.819 I PC (39:3) 1.01E-22 0.164 1.929 0.911 D LPE (22:5) 1.35E-08 2.424 1.178 0.770 I PC (39:5) 7.26E-12 0.583 1.379 0.789 D Lysophosphatidylglycerol PC (40:1) 4.52E-14 0.591 1.536 0.836 D LPG (18:1) 4.09E-10 0.378 1.281 0.760 D PC (40:2) 8.75E-17 0.359 1.692 0.865 D LPG (18:2) 8.88E-12 0.316 1.485 0.811 D PC (40:3) 8.51E-29 0.167 2.127 0.948 D Lysophosphatidylinositol PC (40:4) 3.33E-16 2.300 1.736 0.867 I LPI (18:0) 1.38E-10 0.417 1.351 0.767 D PC (40:5) 2.44E-08 1.851 1.248 0.744 I LPI (18:2) 1.68E-06 0.305 1.18 0.744 D PC (40:6) 1.91E-06 1.993 1.062 0.712 I Phosphatidylethanolamine PC (40:7) 1.66E-14 3.65 1.589 0.867 I PE (32:0) 2.86E-09 1.759 1.229 0.764 I PC (P-36:2) 2.83E-12 1.717 1.361 0.819 I PE (32:1) 2.44E-09 2.274 1.245 0.762 I PC (P-36:3) 2.25E-13 2.468 1.503 0.836 I PE (33:1) 3.12E-10 1.900 1.267 0.766 I PC (P-38:3) 1.34E-14 2.281 1.633 0.866 I PE (37:2) 5.35E-09 0.551 1.153 0.755 D PC (P-38:4) 3.24E-11 2.198 1.390 0.811 I PE (37:4) 5.35E-12 1.638 1.400 0.803 I PC (P-38:6) 1.07E-07 1.372 1.127 0.722 I PE (38:2) 1.27E-13 1.912 1.554 0.846 I PC (P-40:6) 3.84E-12 0.475 1.412 0.808 D PE (38:3) 2.03E-11 1.961 1.466 0.820 I PC (P-40:7) 2.00E-11 0.736 1.339 0.796 D PE (40:2) 4.06E-10 0.566 1.298 0.794 D PC (P-42:2) 3.00E-09 0.470 1.222 0.761 D PE (40:7) 9.68E-07 1.964 1.013 0.735 I PC (P-42:4) 4.68E-14 0.408 1.580 0.834 D PE (40:9) 2.66E-15 0.541 1.554 0.868 D PC (P-42:5) 1.17E-16 0.333 1.591 0.849 D PE (41:5) 2.28E-09 0.611 1.202 0.781 D PC (P-42:6) 6.35E-17 0.415 1.591 0.850 D PE (41:6) 2.38E-10 0.577 1.235 0.765 D Phosphatidylinositol PE (41:7) 1.54E-20 0.328 1.774 0.889 D PI (36:2) 7.48E-08 0.582 1.135 0.734 D PE (42:4) 2.73E-10 0.536 1.277 0.791 D PI (38:3) 1.70E-08 1.918 1.115 0.734 I PE (42:9) 5.74E-14 0.507 1.371 0.805 D PI (38:5) 5.77E-10 2.140 1.092 0.753 I Sphingomyelin PI (40:4) 1.56E-10 2.395 1.269 0.774 I SM (d36:0) 8.61E-09 0.622 1.140 0.751 D Phosphatidylserine SM (d37:1) 4.63E-09 0.676 1.025 0.73 D PS (35:2) 9.99E-07 0.308 1.050 0.780 D SM (d40:0) 5.96E-08 0.701 1.047 0.721 D PS (36:2) 5.89E-07 1.297 1.072 0.750 I SM (d40:1) 2.14E-14 0.629 1.367 0.794 D PS (38:1) 8.84E-26 0.414 2.024 0.935 D SM (d41:1) 4.10E-11 0.643 1.235 0.774 D PS (38:2) 1.74E-09 0.668 1.222 0.765 D SM (d42:0) 3.72E-10 0.666 1.212 0.767 D PS (38:3) 2.37E-22 1.999 1.944 0.931 I SM (d42:1) 9.74E-11 0.691 1.212 0.772 D PS (38:4) 8.79E-10 1.492 1.231 0.774 I SM (d43:2) 1.23E-08 1.630 1.168 0.756 I PS (40:5) 2.55E-09 1.657 1.290 0.773 I a Significantly differential metabolites for comparison were selected based on P 1. b “D” means downregulated and “I” means upregulated in HCC tissues when compared with NAT tissues. In females, significant differences (based on P 1) in concentrations between NAT and HCC were seen in 77 PL species as shown in Table S4. Forty-one species were significantly higher in HCC than in NAT (raged 1.34–4.70 fold), the other 36 PL species were significantly lower in HCC than in NAT. In males, significant differences in PL concentrations between NAT and HCC were seen in 88 out of 214 individual PL species (Table S5). There were 42 PL species significantly lower in HCC than in NAT, and 46 PL species were significantly increased in HCC (raged 1.33–4.70 fold). 3.3 PL-derived signature is prognostic for hepatocellular carcinoma survival During a follow-up period of 6.9 years, 32 (36.78%) out of 87 patients died. The survival of patients with HCC/ NAT ratio of all phospholipids as single variables was examined, using univariate and multivariate Cox proportional hazard method for death to evaluate the potential for predicting the HCC prognosis. Among the 10 lipid classes, we noted that the ratio of LPG class (HCC/NAT) showed a gradually increased trend in hepatocellular carcinoma tumors with the progression of hepatocellular carcinoma from stage T1 to T3. Unadjusted HRs (95% CIs) for HCC-specific mortality according to tertiles of the ratio of phospholipids classes were listed in Table 5, indicating that only the HCC/ NAT ratio of LPG class was associated with HCC specific mortality (HR = 6.50, 95% CI: 1.88-22.51, P = 0.002). After adjusting for multiple variables, the association was still significant (HR = 4.82, 95% CI: 1.34-17.29, P = 0.017). There was a positive correlation between the HCC/ NAT ratio of PG class (HR = 2.90, 95% CI: 1.09-7.69, P = 0.032) and the risk of death as well. Table 5 Hazard Ratio (95% confidence interval) for HCC-specific mortality according to tertiles of HCC/ NAT ratio of phospholipid classes Univariate Analysis Multivariate Analysis a T1 T2 T3 P trend T1 T2 T3 P trend LPC 1.00 (Ref.) 1.77 (0.69~4.56) 2.27 (0.92~5.63) 0.076 1.00 (Ref.) 1.47 (0.52~4.17) 1.88 (0.71~4.99) 0.200 LPE 1.00 (Ref.) 0.74 (0.29~1.87) 1.37 (0.61~3.08) 0.414 1.00 (Ref.) 0.81 (0.30~2.25) 1.06 (0.44~2.57) 0.883 LPG 1.00 (Ref.) 5.43 (1.56~18.91) 6.50 (1.88~22.51) 0.002 1.00 (Ref.) 4.94 (1.37~17.78) 4.82 (1.34~17.29) 0.017 LPI 1.00 (Ref.) 0.62 (0.25~1.51) 0.96 (0.43~2.13) 0.919 1.00 (Ref.) 0.60 (0.24~1.52) 0.90 (0.40~2.03) 0.820 PC 1.00 (Ref.) 0.97 (0.41~2.34) 1.20 (0.52~2.77) 0.670 1.00 (Ref.) 1.30 (0.52~3.25) 1.48 (0.61~3.57) 0.379 PE 1.00 (Ref.) 1.22 (0.52~2.81) 0.98 (0.41~2.36) 0.967 1.00 (Ref.) 1.32 (0.52~3.32) 1.12 (0.43~2.90) 0.813 PG 1.00 (Ref.) 1.39 (0.55~3.52) 2.01 (0.84~4.79) 0.111 1.00 (Ref.) 1.43 (0.51~3.99) 2.90 (1.09~7.69) 0.032 PI 1.00 (Ref.) 1.02 (0.43~2.46) 1.23 (0.53~2.86) 0.621 1.00 (Ref.) 1.11 (0.44~2.83) 1.54 (0.63~3.75) 0.342 PS 1.00 (Ref.) 1.14 (0.49~2.65) 0.95 (0.40~2.29) 0.913 1.00 (Ref.) 0.78 (0.32~1.90) 0.84 (0.34~2.09) 0.712 SM 1.00 (Ref.) 1.31 (0.54~3.15) 1.51 (0.63~3.58) 0.353 1.00 (Ref.) 1.23 (0.47~3.23) 1.70 (0.69~4.22) 0.250 a Multivariable adjustment included age (in years), gender (women, men), body mass index (kg/m 2 ), HBV infection (yes, no), smoking status (never smoker, ever smoker, current smoker), alcohol drinking status (never drinker, ever drinker, current drinker), with or without family history of primary liver cancer, BCLC stage (0, A, B, C), and treatments (liver resection, radiofrequency ablation, intervention). Table 6 showed unadjusted HRs (95% CIs) for HCC-specific mortality according to tertiles of the HCC/ NAT ratio of PL species. After additional adjustments, the associations remained significant for 3 LPC, 1 LPE,2 LPG, 7 PE, and 3 SM, which were found to be independent markers of HCC prognosis. Higher ratios of LPG species displayed were associated with a higher risk of death from HCC (all the HR>1, P <0.05 for trend), and inverse associations were found between tissue ratios of species in SM class and HCC specific death (all the HR<1, P <0.05 for trend). The data obtained highlights that LPC (14:0), LPG (18:1), LPG (18:2), PE (32:0) and PE (32:1) may predict HCC mortality and constitute important biomarkers. The concentration of PE (32:0) in tumor tissue was positively associated with higher risk of death from HCC (HR= 3.05, 95% CI: 1.25-7.42, P = 0.011 for trend), and the association remained significant after adjustments (HR= 5.62, 95% CI: 1.89-16.68, P = 0.001 for trend). Inversly, concentrations of SM(d37:1) and SM(d36:1) in tumor tissue were negatively correlated with the risk of death from HCC, HR (95% CI): 0.32 (0.12-0.90) and 0.34 (0.13-0.88), respectively (both P trend <0.05). Table 6 Hazard Ratio (95% confidence interval) for HCC-specific mortality according to tertiles of HCC/ NAT ratio of phospholipid species Univariate Analysis Multivariate Analysis a T1 T2 T3 P trend T1 T2 T3 P trend LPC (14:0) 1.00 (Ref.) 0.87 (0.29~2.59) 3.93 (1.65~9.36) 0.001 1.00 (Ref.) 0.74 (0.24~2.30) 3.06 (1.17~8.05) 0.014 LPC (16:0) 1.00 (Ref.) 1.58(0.56~4.45) 3.56 (1.40~9.05) 0.005 1.00 (Ref.) 1.45(0.49~4.3) 2.70 (0.99~7.32) 0.041 LPC (16:1) 1.00 (Ref.) 1.04 (0.39~2.77) 2.73 (1.17~6.39) 0.016 1.00 (Ref.) 1.23 (0.45~3.37) 2.62 (1.05~6.52) 0.040 LPC (18:1) 1.00 (Ref.) 2.29 (0.80~6.61) 4.07 (1.49~11.11) 0.004 1.00 (Ref.) 2.28 (0.77~6.73) 3.29 (1.17~9.25) 0.022 LPC (18:2) 1.00 (Ref.) 1.95 (0.72~5.28) 2.74 (1.06~7.07) 0.034 1.00 (Ref.) 1.40 (0.49~3.98) 2.18 (0.82~5.76) 0.099 LPC (20:0) 1.00 (Ref.) 2.42 (0.91~6.46) 2.73 (1.05~7.12) 0.042 1.00 (Ref.) 2.20 (0.77~6.31) 2.54 (0.94~6.84) 0.073 LPC (20:1) 1.00 (Ref.) 2.71 (0.95~7.69) 3.49 (1.27~9.60) 0.015 1.00 (Ref.) 2.73 (0.92~8.09) 2.77 (0.93~8.27) 0.074 LPC (20:2) 1.00 (Ref.) 1.47 (0.56~3.87) 2.54 (1.03~6.24) 0.036 1.00 (Ref.) 1.05 (0.37~2.98) 2.08 (0.82~5.24) 0.095 LPC (P-18:0) 1.00 (Ref.) 0.89 (0.32~2.46) 2.69 (1.16~6.24) 0.014 1.00 (Ref.) 0.87 (0.31~2.47) 2.14 (0.86~5.33) 0.089 LPE (16:1) 1.00 (Ref.) 1.40 (0.52~3.77) 2.94 (1.21~7.17) 0.013 1.00 (Ref.) 1.57 (0.56~4.42) 3.21 (1.23~8.36) 0.014 LPE (18:1) 1.00 (Ref.) 1.01 (0.38~2.69) 2.57 (1.10~6.02) 0.022 1.00 (Ref.) 0.79 (0.29~2.16) 2.73 (1.04~7.18) 0.056 LPE (18:2) 1.00 (Ref.) 1.15 (0.42~3.16) 3.14 (1.30~7.57) 0.007 1.00 (Ref.) 1.27 (0.44~3.61) 2.78 (1.12~6.88) 0.021 LPG (18:1) 1.00 (Ref.) 3.40 (1.10~10.54) 5.17 (1.72~15.48) 0.002 1.00 (Ref.) 2.81 (0.84~9.33) 4.15 (1.33~12.99) 0.013 LPG (18:2) 1.00 (Ref.) 6.39 (1.85~22.12) 5.51 (1.58~19.2) 0.009 1.00 (Ref.) 5.2 (1.45~18.56) 4.14 (1.15~14.89) 0.049 PC (39:4) 1.00 (Ref.) 1.6(0.61~4.21) 2.69 (1.09~6.59) 0.027 1.00 (Ref.) 1(0.34~2.93) 2.70 (1.00~7.28) 0.039 PE (32:0) 1.00 (Ref.) 1.79 (0.64~5.03) 3.97 (1.56~10.08) 0.002 1.00 (Ref.) 2.78 (0.87~8.86) 7.32 (2.29~23.35) <0.001 PE (32:1) 1.00 (Ref.) 1.38 (0.55~3.50) 2.19 (0.92~5.24) 0.072 1.00 (Ref.) 2.20 (0.81~5.92) 3.04 (1.14~8.13) 0.026 PE (32:2) 1.00 (Ref.) 2.33 (0.86~6.30) 3.30 (1.28~8.52) 0.012 1.00 (Ref.) 2.18 (0.78~6.12) 4.08 (1.41~11.78) 0.008 PE (34:0) 1.00 (Ref.) 1.38 (0.55~3.50) 2.07 (0.87~4.94) 0.095 1.00 (Ref.) 1.63 (0.61~4.35) 3.63 (1.31~10.05) 0.012 PE (34:1) 1.00 (Ref.) 1.38 (0.55~3.51) 2.07 (0.87~4.93) 0.096 1.00 (Ref.) 1.54 (0.58~4.11) 3.51 (1.27~9.74) 0.015 PE (40:3) 1.00 (Ref.) 1.81 (0.7~4.67) 2.39 (0.96~5.92) 0.059 1.00 (Ref.) 2.12 (0.76~5.96) 2.87 (1.08~7.58) 0.034 PE (42:5) 1.00 (Ref.) 0.96(0.36~2.55) 2.39(1.02~5.59) 0.033 1.00 (Ref.) 1.08(0.4~2.93) 2.47(0.98~6.26) 0.048 PG (34:1) 1.00 (Ref.) 1.39 (0.55~3.52) 2.01 (0.84~4.79) 0.111 1.00 (Ref.) 1.43 (0.51~3.99) 2.90 (1.09~7.69) 0.032 PS (35:2) 1.00 (Ref.) 1.09 (0.41~2.90) 2.40 (1.03~5.62) 0.034 1.00 (Ref.) 0.61 (0.21~1.76) 1.90 (0.77~4.68) 0.085 PS (36:3) 1.00 (Ref.) 1.26(0.49~3.26) 2.45(1.04~5.79) 0.035 1.00 (Ref.) 1.01(0.37~2.78) 2.44(0.99~6.03) 0.046 SM (d36:1) 1.00 (Ref.) 0.74 (0.33~1.66) 0.50 (0.21~1.2) 0.119 1.00 (Ref.) 0.57 (0.24~1.34) 0.35 (0.14~0.89) 0.026 SM (d37:1) 1.00 (Ref.) 0.61 (0.26~1.43) 0.71 (0.31~1.62) 0.399 1.00 (Ref.) 0.42 (0.17~1.08) 0.39 (0.15~0.99) 0.048 SM (d41:1) 1.00 (Ref.) 0.68 (0.30~1.56) 0.58 (0.25~1.36) 0.207 1.00 (Ref.) 0.46 (0.18~1.19) 0.36 (0.14~0.93) 0.039 a Multivariable adjustment included age (in years), gender (women, men), body mass index (kg/m 2 ), HBV infection (yes, no), smoking status (never smoker, ever smoker, current smoker), alcohol drinking status (never drinker, ever drinker, current drinker), with or without family history of primary liver cancer, BCLC stage (0, A, B, C), and treatments (liver resection, radiofrequency ablation, intervention). In the stratified analyses by median age, gender, and BMI, the associations of metabolites with HCC were largely consistent across strata (Table S3). For HBV status, we found that the strength of the associations with the HCC/ NAT ratio of total LPG and LPG (18:2) was stronger in HBsAg positive group than in HBsAg-negative group ( P for interaction <0.05). For alcohol drinking status, the strength of the associations with the HCC/ NAT ratio of total LPG was stronger in patients who never drinks than in drinker group ( P for interaction = 0.039). 4 DISCUSSION The primary objective of this study is to discover more sensitive and specific biomarkers for diagnosis and prognosis of hepatocellular carcinoma by profiling the phospholipid features of tumor tissues. In this study, paired comparison of phospholipid profiles was made between tumor and matched normal liver tissues in 87 patients with hepatocellular carcinoma, in order to eliminate individual differences, such as age, gender and HBV status. Results showed that LPE, LPG, LPI, PC, PE, PI and PS were widely disordered in HCC tissue. Then survival analysis was carried out to evaluate the prognostic potential of phospholipids in tissue. Phospholipids have a variety of physiological functions, including cell membranes assembly, metabolic energy storage and as signal molecules in cell metabolism. Previous studies showed that cell canceration has seriously happened in hepatocellular carcinoma tumors, indicating significant metabolic variations., and various membrane lipids were identified as being significantly affected by hepatocellular carcinoma using non-targeted metabolomics. It is noted that an array of phospholipids was significantly altered in hepatocellular carcinoma tumors as well. There is a general trend in which more phospholipids are upregulated in the cancer versus the control sample than downregulated. Over the past two decades, many lysophospholipids have been identified. Studies have shown that their concentration in cells is very low, while they are abundant in extracellular environment[21]. The common feature of lysophospholipids is that they are composed of a long hydrophobic carbon chain and a hydrophilic head group attached to the backbone of glycerol or sphingosine. Therefore, compared with original phospholipids or sphingolipids, they show different properties. In addition, lysophospholipids have a variety of pathophysiological functions[22, 23]. According to reports, the most abundant LPC species (such as LPC 16: 0 and LPC 18: 1) contain non-essential fatty acids. LPC plays an inflammatory, anti-hemostatic and cytotoxic roles. Pro-inflammatory effects, such as the expression of adhesion molecules, release of chemokines and the increase of reactive oxygen species (ROS), have been fully described as saturated (LPC 16: 0 and LPC 18: 0) and monounsaturated LPC 18: 1[24, 25]. Furthermore, several LPCs have shown diagnostic value in HCC[26-28]. In this study, LPC (14:0), LPC (16:0), LPC (18:0), LPC (20:0), andLPC (20:1) showed a high capability to differentiate HCC from NAT, of which AUC scores were greater than 0.7. Associations between higher HCC/ NAT ratio of LPC (14:0), LPC (16:1), and LPC (18:1) and poorer HCC prognosis were also observed. Taken together, we propose LPC metabolism change based on LPC (14:0) that differentiates tumor from nontumor tissue and has prognostic significance. Moreover, LPC (14:0) levels in tumor seem to increase in HCC patients at advanced TNM stage, and thus might represent an important parameter. Patterson et al. showed that HCC patients had lower plasma LPC (20:4/0:0) concentrations than healthy volunteers[29]. This is followed by a targeted lipidomic analysis showing lower concentrations of LPC (20:4/0:0) and higher concentrations of PCs in HCC patients compared to cirrhotic controls[30]. Biologically, decreased LPCs and increased PCs in HCC patients might be attributable to overexpression of lysophosphatidyl-choline acyltransferase 1, which catalyzes the conversion of LPCs into PCs and thereafter promotes hepatic cell proliferation, migration, and invasion[31]. Moreover, PCs appear to stimulate carcinogenesis through their structural function in membrane composition and cell-signaling activities[6, 32]. LPG is another minor lysoglycerophospholipid which has recently been identified as a bioactive lipid. Although the biological role of LPG has not been extensively studied, LPG was reported to be a precursor of the de novo synthesis of anionic phosphatidylglycerol, which accounts for 1% of total phospholipids in most mammalian tissues, and play a vital role in liposome formation[33]. It is the first time that LPG has been found to be related to the differentiation and prognosis of hepatocellular carcinoma. Total amount of LPGs and LPG species showed a high capability to differentiate HCC from NAT samples. We noted that the ratio of LPG (HCC/NAT) showed a gradually increased trend in hepatocellular carcinoma tumors with the progression of hepatocellular carcinoma from stage T1 to T4. Furthermore, in the multivariate Cox regression models, we found that the ratio of LPG class (HR = 4.82, 95% CI: 1.34-17.29, P = 0.017), LPG (18:1) (HR =4.15, 95% CI: 1.33-12.99, P = 0.013), and LPG (18:2) (HR =4.14, 95% CI: 1.15-14.89, P = 0.049), are potentially independent markers of HCC survival. Extensive research is needed to analyze the molecular mechanism involved in the proliferation pathway of this cancer cells and the physiological functions of LPG. LPG is produced through secretory PLA2-mediated hydrolysis of phosphatidylglycerol[21]. Studies have shown a biological effect of LPG in the lung and in ovarian cancer cells[34] but there are no studies to examine the levels and the direct effect of LPG on chronic liver diseases. Yet, LPG has been reported to antagonise the binding of LPA to LPARs and thus to block biological functions induced by LPA such as intracellular calcium increase. Also, LPG was found to inhibit FPRL1-induced cellular responses from human phagocytes and interleukin (IL)-1β production[35]. These in vitro biological effects of LPG speak in favour of a possible protective role of LPG against liver inflammation, but further in vivo studies are required to support this hypothesis[36, 37]. Information about LPE as a bioactive lipid is quite rare as compared with other phospholipids. With regard to carcinogenesis, it was found that LPE induced intracellular calcium mobilization in OVCAR-3 and SK-OV3 human ovarian cancer cells, and induced chemotaxis as well as cell invasion in SK-OV3 cells[38]. LPE (16:0) was defined as one of the "marker metabolites", which can be used to distinguish the different stages of liver cancer[39]. Nevertheless, LPE (16:0) was applied to the classification of the control and diseased animals in this study, and the level of serum LPE (16:0) increased as the cirrhosis developed and reached its peak during the advanced stage of HCC. In our study, LPE (16:0) was higher in NAT than HCC, distinguish two types of tissue with AUC 0.88. The reason may be that the composition and changing trend of serum and tissue metabolites are different. The amount of total LPI was lower in HCC than in NAT in this study, and LPI (18:2) showed a strong ability to distinguish HCC from NAT (VIP>1, FC<0.5). It is known that LPI activates signal pathways related to cell proliferation, migration and tumorigenesis. The production of LPI in cancer is enhanced through autocrine loop[40]. The potential pharmacological effects of LPI range from obesity, inflammatory reaction to cancer. The physiological role of LPI is not well understood, however, an accumulation of LPI as a consequence of malignant cell transformation identified LPI as a biomarker for poor prognosis in cancer patients. In vitro studies have shown that the levels of LPI is significantly increased in highly proliferative cancer cells that overexpress RAS-p21 protein encoded by the members of the RAS family proto-oncogenes[41]. LPI may promote the migration of cancer cell in prostate cancer through the receptor of transient receptor potential cation channel subfamily V member 2 (TRPV 2) and by inducing calcium influx in PC 3 cell line[42]. Glycerophospholipids consist of the following parts: a glycerol backbone, esterified by two long chain fatty acids at carbons 1 and 2 (C1 and C2), phosphoric acid esterified to the C3 hydroxyl group of glycerol and usually an alcohol head group esterified to the phosphate group[43]. Among the glycerophospholipids family a prominent subgroup is PC, a class of phospholipids with choline head group, being a major component of bio-membranes. Besides, the alterations of PC have previously been reported in different cancer types including HCC[44, 45]. A metabolic study on tissue samples showed significantly lower PCs in HCC compared to NAT. As the author explained, decrease in PC and other choline-containing PLs in HCC might mean that the metabolism of these PLs is beneficial to the degradation catalyzed by PC-specific phospholipase in Kennedy pathway[11]. In our study, the total PC class was significantly increased in HCC tissue than in NAT. We found that an array of PCs can distinguish HCC from NAT with high sensitivity, specificity and accuracy at the same time, especially PC (30: 0) with AUC over 0.9. Interestingly, PC (30: 0) was more abundant in breast cancer region than in stroma around cancer, and it was considered as a metabolite produced by abnormal lipid metabolism, which promoted cancer metastasis[46], which was in agreement with current study. PC accumulated in tumor tissue could be used as a biomarker[47]. Smith et al. [48]investigate the suitability of a lipid tumor marker derived from ether-linked PL in normal, benign and neoplastic samples from human breast, lung and prostate tissues. They observed that a biochemical marker derived from PE plasmalogens provides a reliable index capable of distinguishing between benign and neoplastic tissues and it correlates linearly with metastases spreading in vivo. In the diagnosis of HCC, and in monitoring the therapeutic effects of HCC by magnetic resonance spectroscopy, as proved in HCC and other cancers[49, 50]. We found that an array of PE could differentiate tumor from nontumor tissue with high AUC, among which PE (32:0) has prognostic significance for HCC specific mortality (HR =7.32, P < 0.001 for trend). Moreover, the concentration of PE (32:0) in tumor tissue was higher than NAT, and was positively associated with higher risk of death from HCC and the association remained significant after adjustments (HR= 5.62, 95% CI: 1.89-16.68, P = 0.001 for trend), thus might represent an important parameter. It also takes advantage of the difference in structural lipid abundance, and the specific sphingolipids have the potential to be biomarkers. As an example, following increased levels of C16-ceramides, sphingosine-1-phosphate has been shown to distinguish patients with HCC from those with cirrhosis. The decrease of serum SMs in HCC could distinguish HCC patients from healthy controls, while SMs between HCC and CCA changes significantly, so these malignant tumors can be distinguished[51]. In this study, HCC/ NAT ratios of SM(d37:1) and SM(d41:1) in tumor tissue were negatively correlated with the risk of death from HCC, HR (95% CI): 0.32 (0.12-0.90) and 0.34 (0.13-0.88), respectively (both P trend <0.05). The concentration of SM(d37:1) in tumor tissue was lower than NAT, and seems to decrease for HCC patients at advanced TNM stage, which needs further study for the mechanism. The main advantages of our research include a prospective design, long-term follow-up and the ability to control lifestyle factors and HBV infection. However, we also recognize some limitations. First, as is typical of previous studies, metabolites were measured only once, which may not well reflect long-time exposure. Second, the ability to infer causality was limited in the current study because an observational design cannot fully exclude reverse causality. Future metabolomics studies with longitudinal sample collections are needed to confirm our findings and facilitate clinical applications of metabolite biomarkers. 5 CONCLUSIONS In conclusion, we identified specific phospholipid changes in HCC compared with NAT. This study delineates the alterations in phospholipids within HCC tissue, providing insights into substantial modifications in phospholipid metabolism. The findings shed light on biomarkers for diagnosis and prognosis of hepatocellular carcinoma by exploring the phospholipidomic characteristics in liver tumor tissues, as well as into discovering novel therapeutic targets for HCC. Abbreviations PLC Primary liver cancer HCC Hepatocellular carcinoma PL Phospholipid LPL Lysophospholipid NAT tumor-adjacent normal hepatic tissue QC Quality control HILIC-ESI-IT-TOF-MS Hydrophilic liquid chromatography-electrospray ionization-ion trap-time of flight-mass spectrometry HILIC-ESI-MS/MS Hydrophilic liquid chromatography-electrospray ionization-triquadrupole-mass spectrometry ESI Electrospray Ionization PE Phosphatidylethanolamine PC Phosphatidylcholine PS Phosphatidylserine LPE Lysophosphatidylethanolamine LPC Lysophosphatidylcholine SM Sphingomyelin PG Phosphatidylglycerol PI Phosphatidylinositol LPI Lysophosphatidylinositol LPG Lysophosphatidylglycerol MRM multiple-reaction monitoring PCA Principal component analysis OPLS-DA Orthogonal partial least-squares discriminant analysis VIP variable importance in the projection FDR false discovery rate HR Hazard ratios CI Confidence interval BCLC stage Barcelona Clinic Liver Cancer stage HBV Hepatitis B virus SVM Support vector machines LASSO Least Absolute Shrinkage and Selection Operator AUC the area under the curve ROC Receiver operating characteristics SD standard deviation TC Total cholesterol LDLC Low density lipoprotein cholesterol HDLC High density lipoprotein cholesterol AST Aspartate amino transferase ALT Alanine amino transferase CRP C-reaction protein BMI Body mass index MET Metabolic equivalent FLD Fatty liver disease TG Triglyceride AFP Alpha fetoprotein TNM stage Tumor-node-metastasis stage IQR Interquartile range ROS Reactive oxygen species Declarations Author Contributions Conceptualization, T.H. and H.Z.; data curation, A.F., Z.L., and H.Z.; formal analysis, T.H.; funding acquisition, H.Z.; investigation, T.H., M.W., Z.L., M.L., C.W and J.Z.; methodology, S.S. and T.H.; project administration, H.Z.; resources, Y.Z., H.Z. and S.S.; software, T.H. and J.C.; supervision, H.Z.; validation, H.Z.; writing—original draft, T.H.; writing—review and editing, M.L., J.C., T.H. and H.Z. All authors have read and agreed to the published version of the manuscript. Conflicts of interest There are no conflicts to declare. Data availability statements The datasets generated and analysed during the current study are not publicly available but are available from the corresponding author on reasonable request. Acknowledgements The authors thank all the HCC patients who participated in this study, and we also thank the researchers of SYSUCC for their contributions to the data collection, processing and preparation for this study. This work was supported by the National Natural Science Foundation of China (81973016). References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. Vogel A, Meyer T, Sapisochin G, Salem R, Saborowski A. Hepatocellular carcinoma. Lancet. 2022;400(10360):1345–62. 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Lysophosphatidylglycerol inhibits formyl peptide receptorlike-1-stimulated chemotactic migration and IL-1beta production from human phagocytes. Exp Mol Med. 2009;41(8):584–91. Buechler C, Aslanidis C. Role of lipids in pathophysiology, diagnosis and therapy of hepatocellular carcinoma. Biochim Biophys Acta Mol Cell Biol Lipids. 2020;1865(5):158658. Kaffe E, Tisi A, Magkrioti C, Aidinis V, Mehal WZ, Flavell RA, Maccarrone M. Bioactive signalling lipids as drivers of chronic liver diseases. J Hepatol. 2024;80(1):140–54. Park KS, Lee HY, Lee SY, Kim MK, Kim SD, Kim JM, Yun J, Im DS, Bae YS. Lysophosphatidylethanolamine stimulates chemotactic migration and cellular invasion in SK-OV3 human ovarian cancer cells: involvement of pertussis toxin-sensitive G-protein coupled receptor. FEBS Lett. 2007;581(23):4411–6. Tan Y, Yin P, Tang L, Xing W, Huang Q, Cao D, Zhao X, Wang W, Lu X, Xu Z, et al. Metabolomics study of stepwise hepatocarcinogenesis from the model rats to patients: potential biomarkers effective for small hepatocellular carcinoma diagnosis. Mol Cell Proteom. 2012;11(2):M111010694. Piñeiro R, Maffucci T, Falasca M. The putative cannabinoid receptor GPR55 defines a novel autocrine loop in cancer cell proliferation. Oncogene. 2011;30(2):142–52. Grzelczyk A, Gendaszewska-Darmach E. Novel bioactive glycerol-based lysophospholipids: new data -- new insight into their function. Biochimie. 2013;95(4):667–79. Monet M, Gkika D, Lehen'kyi V, Pourtier A, Vanden Abeele F, Bidaux G, Juvin V, Rassendren F, Humez S, Prevarsakaya N. Lysophospholipids stimulate prostate cancer cell migration via TRPV2 channel activation. Biochim Biophys Acta. 2009;1793(3):528–39. Harayama T, Riezman H. Understanding the diversity of membrane lipid composition. Nat Rev Mol Cell Biol. 2018;19(5):281–96. Lee CM, Lu SN, Changchien CS, Yeh CT, Hsu TT, Tang JH, Wang JH, Lin DY, Chen CL, Chen WJ. Age, gender, and local geographic variations of viral etiology of hepatocellular carcinoma in a hyperendemic area for hepatitis B virus infection. Cancer. 1999;86(7):1143–50. Wu Y, Yao N, Feng Y, Tian Z, Yang Y, Zhao Y. Identification and characterization of sexual dimorphism–linked gene expression profile in hepatocellular carcinoma. Oncol Rep. 2019;42(3):937–52. Hosokawa Y, Masaki N, Takei S, Horikawa M, Matsushita S, Sugiyama E, Ogura H, Shiiya N, Setou M. Recurrent triple-negative breast cancer (TNBC) tissues contain a higher amount of phosphatidylcholine (32:1) than non-recurrent TNBC tissues. PLoS ONE. 2017;12(8):e0183724. Messias MCF, Mecatti GC, Priolli DG, de Oliveira Carvalho P. Plasmalogen lipids: functional mechanism and their involvement in gastrointestinal cancer. Lipids Health Dis. 2018;17(1):41. Smith RE, Lespi P, Di Luca M, Bustos C, Marra FA, de Alaniz MJ, Marra CA. A reliable biomarker derived from plasmalogens to evaluate malignancy and metastatic capacity of human cancers. Lipids. 2008;43(1):79–89. Yang Y, Li C, Nie X, Feng X, Chen W, Yue Y, Tang H, Deng F. Metabonomic studies of human hepatocellular carcinoma using high-resolution magic-angle spinning 1H NMR spectroscopy in conjunction with multivariate data analysis. J Proteome Res. 2007;6(7):2605–14. Glunde K, Jie C, Bhujwalla ZM. Molecular causes of the aberrant choline phospholipid metabolism in breast cancer. Cancer Res. 2004;64(12):4270–6. Banales JM, Iñarrairaegui M, Arbelaiz A, Milkiewicz P, Muntané J, Muñoz-Bellvis L, La Casta A, Gonzalez LM, Arretxe E, Alonso C, et al. Serum Metabolites as Diagnostic Biomarkers for Cholangiocarcinoma, Hepatocellular Carcinoma, and Primary Sclerosing Cholangitis. Hepatology. 2019;70(2):547–62. Additional Declarations No competing interests reported. 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PC was the most abundant PL class, and LPG was the least abundant PL class in both NAT and HCC in all studied populations\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4110772/v1/51cd42658a5a19e30f03b0cf.jpeg"},{"id":53082205,"identity":"927a87aa-647c-4cb2-89f7-26f1fd026ab1","added_by":"auto","created_at":"2024-03-20 10:57:29","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":320891,"visible":true,"origin":"","legend":"\u003cp\u003eThe sum of the concentration of 10 phospholipids classes. Green and red violins represent hepatocellular carcinoma tissue (HCC) and tumor-adjacent normal hepatic tissue (NAT), respectively. \u003cem\u003eP\u003c/em\u003e-value from paired, two-tailed\u003cem\u003e t\u003c/em\u003e-Student’s test.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4110772/v1/f6b398cbc8d956fdf94e6a08.jpeg"},{"id":53082206,"identity":"059c4f46-f300-46b1-9031-1a659d0f5945","added_by":"auto","created_at":"2024-03-20 10:57:29","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":652287,"visible":true,"origin":"","legend":"\u003cp\u003e2D scores plot showing the discrimination between HCC and NAT based on OPLS-DA loadings and volcano plot of 36 identified metabolites in HCC and NAT samples (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, log2(FC)\u0026gt;1 or \u0026lt;-1).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4110772/v1/903e20ea5eadd8f314bac5d6.jpeg"},{"id":53270937,"identity":"262bfd93-d534-4bb0-8efb-4bba08413e7f","added_by":"auto","created_at":"2024-03-22 16:35:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":556083,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4110772/v1/e12475e0-4d8c-44b1-b8a2-9e72305d154d.pdf"},{"id":53082204,"identity":"aaba13ef-3ad6-421c-a028-008f0dd48062","added_by":"auto","created_at":"2024-03-20 10:57:29","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1195796,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4110772/v1/67503c5404480398d5f915de.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic and prognostic potential of tissue phospholipidomics in hepatocellular carcinoma: A prospective cohort study","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003ePrimary liver cancer (PLC), as per the 2020 GLOBOCAN statistics, stands among the top three leading causes of cancer-related deaths globally for both sexes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Hepatocellular carcinoma (HCC) constitutes the majority of PLC cases, often diagnosed at advanced stages due to its subtle onset and rapid progression[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite surgical or local treatments for HCC patients, high recurrence rates significantly impact their survival[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Thus, there is a pressing need to comprehensively characterize tumors and identify effective therapeutic targets.\u003c/p\u003e \u003cp\u003eDysregulation of lipid metabolism has emerged as a prominent phenotypic hallmark of cancer[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Lipids, a diverse group of biomolecules with varied structures and functions, play crucial roles in energy metabolism, membrane formation, synthesis of signaling molecules, and regulation of gene expression through epigenetic modulation[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Changes in lipid components in biological fluids and tissues are consistently associated with HCC, making lipid metabolism a source for biomarker discovery and potential therapeutic target[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite this understanding, the precise nature of lipid alterations in HCC tissues remains unclear.\u003c/p\u003e \u003cp\u003eAs fundamental components of cellular membranes, phospholipids (PLs) influence numerous membrane-associated processes such as homeostasis, cell adhesion, cellular signaling, cell-cell interactions, vesicular trafficking, and apoptosis. Changes in PLs composition and distribution in cells, tissues, and biofluids are linked to cancer and explored as potential biomarkers for diagnosis and prognosis in various cancers[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. PL analysis has been applied to study lipid proteomics in HCC research using model hepatocyte lines and three-dimensional culture systems[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. PLs associated with HCC includes lysophospholipids (LPLs), crucial signaling lipids, and ether lipids acting as endogenous antioxidants[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Imaging of PL profiles in HCC tissues enables the localization of specific phosphatidylcholine species in cancer regions, highlighting differences between tumor-adjacent tissues and HCC phenotypes in vivo[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Altered PL metabolism in cancer cells also serves as a potential molecular target for anticancer therapy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Thus, a comprehensive understanding of PL dysregulation in HCC is essential. Our study aims to compare phospholipid profiles between HCC tissues and tumor-adjacent normal hepatic tissues (NATs) and assess the potential clinical prognostic utility of defined PLs\u003c/p\u003e"},{"header":"2 MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Patients\u003c/h2\u003e \u003cp\u003eThis study encompassed 174 paired normal and tumor samples acquired during surgical resection from 87 previously untreated and newly diagnosed HCC patients. Patients were drawn from the Guangdong Liver Cancer Cohort[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], a prospective cohort study conducted in Guangdong province, southern China. The study, initiated in 2013, enrolled liver cancer Patients within a month of diagnosis and before any cancer treatment. Trained investigators conducted face-to-face interviews using standardized questionnaires to collect information on sociodemographic characteristics, lifestyle factors, dietary intake, and medical history. The study protocol received approval from the Ethics Committee of the School of Public Health at Sun Yat-sen University (ethical approval number: NCT03297255), and all patients provided informed consent at recruitment.\u003c/p\u003e \u003cp\u003eFollow-up commenced from the date of cancer diagnosis until the date of death or the last follow-up (January 4, 2023), whichever occurred first. HCC-specific mortality (death from HCC) served as the primary outcome. Participant death information, including survival status and cause of death, was confirmed through the death registration and reporting system of the Guangdong Provincial Center for Disease Control and Prevention or the inpatient and outpatient medical system of the Sun Yat-sen University Cancer Center. Additionally, active follow-up via mail or telephone interviews with patients or their families was conducted to verify their survival status.\u003c/p\u003e \u003cp\u003eTissue samples from the tumor and adjacent areas within the resection margin were collected immediately after surgical resection and processed as follows: 30 mg aliquots of hepatic tissues were homogenized in 180 \u0026micro;l PBS, with 100 \u0026micro;L packed for quantitative PL analysis. A mixed sample was created by combining 100 \u0026micro;L from each individual sample for qualitative analysis using the HILIC-ESI-IT-TOF system. The remaining portion of the mixed sample was divided into 100 \u0026micro;L, serving as a quality control (QC) sample for quantitative analysis. All samples were stored at -80\u0026deg;C until analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Phospholipidomics profiling\u003c/h2\u003e \u003cp\u003eChemicals, including PL standards and deuterium-labeled internal PL standards, were procured from Avanti Polar Lipids (USA). Other chemicals such as chloroform, methanol, acetonitrile, ammonium formate, and formic acid were obtained from Sigma Aldrich (USA) and were of HPLC or MS grade. Ultra-pure water was sourced from a Milli-Q system (Millipore, USA).\u003c/p\u003e \u003cp\u003eAll samples were thawed at 37\u0026deg;C, and total PL was extracted from tissues using an improved Folch method. Briefly, 100 \u0026micro;L of tissue, 10 \u0026micro;L of deuterium-labeled internal standards solution, and 1.0 mL of chloroform/methanol (2:1, v/v) were vortex-mixed for 30 minutes. Subsequently, 0.2 mL NaCl solution (0.9%) was added to the mixture, mixed for an additional 30 minutes, and then centrifuged at 8,000 rpm for 10 minutes. The chloroform layer was transferred to a new tube and dried under a gentle stream of nitrogen. Before analysis, all samples were re-dissolved in 100 \u0026micro;L chloroform/methanol (2:1, volume ratio).\u003c/p\u003e \u003cp\u003ePL molecular species were identified using a hydrophilic liquid chromatography-electrospray ionization-ion trap-time of flight-mass spectrometry (HILIC-ESI-IT-TOF-MS) method outlined in a previous publication[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In summary, total PL was extracted from the 500 \u0026micro;L mixed sample (as described in Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e) and re-dissolved in 50 \u0026micro;L chloroform/methanol (2:1, volume ratio). This concentrated sample was injected into the HILIC-ESI-IT-TOF-MS system. MS, MS\u003csup\u003e2\u003c/sup\u003e and MS\u003csup\u003e3\u003c/sup\u003e scan data were collected under Electrospray Ionization (ESI)- mode in the ranges of 400\u0026ndash;1000 m/z, 100\u0026ndash;800 m/z, and 100\u0026ndash;700 m/z, respectively. PLs were initially identified by comparing m/z values with calculated exact masses using the LIPID MAPS Structure Database. Detailed structures of individual molecular species were confirmed by MS\u003csup\u003e2\u003c/sup\u003e and MS\u003csup\u003e3\u003c/sup\u003e spectra, following the PL cleavage law described in a previous publication.\u003c/p\u003e \u003cp\u003eThe identified PL molecular species were quantified using a hydrophilic liquid chromatography-electrospray ionization-triquadrupole-mass spectrometry (HILIC-ESI-MS/MS) method, coupling an HPLC system (Shimadzu, Japan) with a quadrupole mass spectrometer (LC-MS 8060, Shimadzu Japan) equipped with an ESI source for PL quantification.\u003c/p\u003e \u003cp\u003eIn terms of chromatography, we employed a CORTECS HILIC Column (1.6 \u0026micro;m, 2.1 mm x 150 mm, Waters, USA), maintained at 40\u0026deg;C. Eluent A was water, and eluent B was acetonitrile/water (95/5), both containing 0.1% formic acid and 10 mM ammonium formate. The flow rate was set at 0.4 mL/min, and the elution gradient ranged from 99.5% B at 0 min to 99.5% B at 32 min, with intermittent adjustments. Blank injections (10 \u0026micro;L methanol) after every 25 samples showed no significant lipid carryover.\u003c/p\u003e \u003cp\u003eFor mass spectrometry, instrument parameters included nebulizer gas flow (3 L/min), heat gas flow (12 L/min), desolvation gas flow (8 L/min), interface temperature (250\u0026deg;C), DL temperature (180\u0026deg;C), heat block temperature (250\u0026deg;C), and detector voltage (1.85 kV). Positive ESI mode was used for phosphatidylethanolamine (PE), phosphatidylcholine (PC), phosphatidylserine (PS), lysophosphatidylethanolamine (LPE), lysophosphatidylcholine (LPC), and sphingomyelin (SM), while negative mode was employed for phosphatidylglycerol (PG), phosphatidylinositol (PI), lysophosphatidylinositol (LPI), and lysophosphatidylglycerol (LPG). MS/MS data were acquired in targeted multiple-reaction monitoring (MRM) mode for all identified PL molecular species in the HILIC-ESI-IT-TOF-MS system, with precursor and product ion m/z values calculated based on PL fragmentation laws.\u003c/p\u003e \u003cp\u003eIn terms of quantification, individual PL molecular species were quantified using the internal standard method. Calibration curves (1/x^2 weighting factor) were constructed with 47 PL standards at varying concentrations, featuring a constant concentration of deuterium-labeled internal standards. Quantification involved using the calibration curve of the PL standard from the same class with the most similar acyl chain length and unsaturation degree. Quality control was maintained with a QC sample injected after every 25 samples. The mixture of standards was injected six times to evaluate the relative standard deviation (RSD) of retention time and peak area ratio. Accuracy and precision were assessed by testing QC samples with added nonendogenous PLs at low, medium, and high concentrations.\u003c/p\u003e \u003cp\u003eMetabolites with a deletion rate below 10% were included, with deletion values estimated using 1/5 of the minimum value for each metabolite. A total of 214 metabolites were considered in the final analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe differences in baseline characteristics were assessed using chi-square tests or Fisher's exact tests for categorical variables and paired \u003cem\u003et\u003c/em\u003e tests or Wilcoxon's signed-rank tests for continuous variables. Spearman correlation analysis was employed to calculate correlation coefficients between metabolites. To enhance normality, metabolite data underwent log transformation (base 10) and Pareto scaling (mean-centered and divided by the square root of the standard deviation of each variable). Principal component analysis (PCA) and an orthogonal partial least-squares discriminant analysis (OPLS-DA) visualized discrimination between HCC and NAT, and variable importance in the projection (VIP) values were calculated to prioritize differential metabolites. Paired t tests examined differences in metabolite levels between HCC and NAT. The Benjamini-Hochberg procedure, with false discovery rate (FDR) estimation, corrected for multiple testing.\u003c/p\u003e \u003cp\u003eTo evaluate the strength and robustness of associations between PLs and HCC-specific mortality, univariate and multivariate Cox models calculated hazard ratios (HR) and 95% confidence intervals (CIs) for the ratio of PL. In multivariable analysis, adjusted factors included age (in years), gender (women, men), body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e), hepatitis B virus (HBV) infection (yes, no), smoking status (never smoker, ever smoker, current smoker), alcohol drinking status (never drinker, ever drinker, current drinker), family history of primary liver cancer, Barcelona Clinic Liver Cancer (BCLC) stage: (0, A, B, C), and treatment (liver resection, radiofrequency ablation, intervention). Stratified analyses were conducted based on gender, HBV infection status, family history of PLC, treatments, and alcohol drinking status. Potential effect modification was assessed by including a product term between PL concentrations and the categorical stratified variable in the multivariate model.\u003c/p\u003e \u003cp\u003eSupport vector machines (SVM) were established to evaluate the performance of independent predictors selected by the Least Absolute Shrinkage and Selection Operator (LASSO). The predictive performance of metabolites was tested by calculating the area under the curve (AUC) through receiver operating characteristics (ROC) analysis in the cohort. All statistical analyses were conducted using R software (R version 3.6.3) and were two-sided. The significant \u003cem\u003eP\u003c/em\u003e value was set at 0.05 for all results.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cp\u003e3.1 Basic characteristics of HCC patients\u003c/p\u003e\n\u003cp\u003eThe present analysis included 74 (85.1%) men and 13 (14.9%) women. The mean (standard deviation [SD]) age at diagnosis was 50.84 (12.88) years old (Table 1). Males tended to smoke and drink more, with lower total cholesterol (TC), low density lipoprotein cholesterol (LDLC), and high density lipoprotein cholesterol (HDLC) level in comparison to females. After a median follow-up of 2513 days with a total of 181070 person-days, 32 (36.78%) patients died (group HCd), while 55 survived (group HCs). Patients in group HCd tended to smoke more, with higher aspartate amino transferase (AST), AST/alanine amino transferase (ALT), C-reaction protein (CRP) levels and advanced stages in comparison to group HCs (Table S1).\u003c/p\u003e\n\u003cp\u003eTable 1 Baseline characteristics of patients with hepatocellular carcinoma\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal (n = 87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMale (n = 74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale (n=13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (year)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.84 (12.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.66 (11.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.86 (18.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedian follow-up time (day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo. of deaths (%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32 (36.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28 (37.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (30.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI (kg/m2)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.25 (3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.30 (3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.95 (3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhysical activity (MET-h/d)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.7 (26.10,38.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.05 (26.19,39.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.5 (24.58,36.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eResidence, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57 (65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48 (64.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (69.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation level, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimary school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSecondary or technical secondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50 (58.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44 (60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSmoking status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFormer smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlcohol drinking status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNever drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62 (71.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49 (66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFormer drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFLD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCirrhosis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69 (79.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61 (82.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWith family history of PLC, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHBV infection\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82 (94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69 (93.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALT (U/L)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.40 (26.00,43.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.75 (28.88,44.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26 (20.80,34.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAST (U/L)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(26.80,42.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(26.78,42.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(26.95,52.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAST/ALT\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (0.75,1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95 (0.74,1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.33 (0.99,2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTC (mmol/L)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.88 (4.34,5.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.79 (4.32,5.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.88 (4.88,6.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTG (mmol/L)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97 (0.67,1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00 (0.69,1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.80 (0.65,1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLDLC (mmol/L)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.96 (2.53,3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.89 (2.40,3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.87 (3.13,4.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHDLC (mmol/L)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.31 (1.07,1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.27 (1.06,1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.33 (1.18,1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAFP\u0026ge;200 ng/L, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43 (49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRP\u0026ge;3 mg/L, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTNM stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41 (47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33 (44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBCLC stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31 (41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTreatment, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLiver resection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81 (93.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69 (93.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRadiofrequency ablation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIntervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations:\u0026nbsp;BMI, body mass index; MET,\u0026nbsp;metabolic equivalent;\u0026nbsp;FLD, fatty liver disease;\u0026nbsp;HBV, hepatitis B virus;\u0026nbsp;ALT, alanine amino transferase; AST, aspartate amino transferase; TC, total cholesterol; TG, triglyceride; LDLC, low density lipoprotein cholesterol; HDLC, high density lipoprotein cholesterol;\u0026nbsp;AFP,\u0026nbsp;alpha fetoprotein;\u0026nbsp;CRP, C-reaction protein;\u0026nbsp;TNM stage,\u0026nbsp;tumor-node-metastasis stage;\u0026nbsp;BCLC stage,\u0026nbsp;Barcelona Clinic Liver Cancer stage.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eValues are mean \u0026plusmn; SD\u0026nbsp;or medians (interquartile range [IQR]).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eHBV infection was defined as\u0026nbsp;seropositivity of HBV surface antigen.\u003c/p\u003e\n\u003cp\u003e3.2 Identification of differential PL classes and species\u003c/p\u003e\n\u003cp\u003eIn this study, the concentrations of PLs were represented by nmol/g wet tissues in data analysis. In addition, the percentage of given PL in individual species or classes out of total PLs was used in the calculation of their abundances. The number of individual PL species in 10 PL classes and 4 PL subgroups are listed in Table 2. These PL subgroups are grouped according to similarity of parts in the phosphate head.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Phospholipids profiles in hepatic tissues\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhospholipids (PL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of species in PL\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eclass\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLysophosphatidylcholine (LPC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLysophosphatidylethanolamine (LPE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLysophosphatidylglycerol (LPG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLysophosphatidylinositol (LPI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhosphatidylcholine (PC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhosphatidylethanolamine (PE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhosphatidylglycerol (PG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhosphatidylinositol (PI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhosphatidylserine (PS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSphingomyelin (SM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal phospholipids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCholine-containing PLs (LPC, PC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthanolamine-containing PLs (LPE, PE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLyso-containing PLs (LPC, LPE, LPG, LPI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlasmalogens (pPC, pLPC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe abundances of PLs varied greatly among individual species, classes and subgroups between NAT and HCC. In the level of individual PL species, PC (34:1) was the most abundant among 214 individual PL species in HCC which is lower in HCC than in NAT only in male population (1.14-fold, \u003cem\u003eP\u003c/em\u003e = 0.003, data not shown). In the level of PL class, among 10 PL classes, PC was the most abundant, accounting for more than 50% of total PLs, and LPG was the least abundant, accounting for 0.5% or less of total PLs in both NAT and HCC in all studied populations (Figure 1). In the level of subgroup, PC-containing PL was the most abundant among PL subgroups, accounting for more than 60% of total PLs in both NAT and HCC in all studied populations. The sum of the signal intensities of the detected compounds within various groups of complex lipids between HCC and NAT was compared. The total concentration of LPE, LPG, and LPI were lower in HCC than in NAT. By contrast, the total concentration of PC, PE, PI, and PS were higher in tumor tissue (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe diagnostic power of total amount of phospholipids classes for HCC and NAT,\u0026nbsp;only\u0026nbsp;total\u0026nbsp;LPG met the condition including\u0026nbsp;VIP derived from the OPLS-DA loadings\u0026nbsp;\u0026gt;1, FDR\u0026lt;0.05, FC\u0026gt;2 or \u0026lt;0.5\u0026nbsp;and AUC over 0.8 (Table S2). And multivariate exploratory ROC analysis for each phospholipid class distinguishing HCC and NAT based on SVM algorithm were shown in Figure S1.\u003c/p\u003e\n\u003cp\u003eThere were 172 of 214 phospholipids showing statistical significance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) compared between HCC and NAT in all population. After correction for multiple comparisons, 169 metabolites remained significant (FDR \u0026lt; 0.05). PCA of the whole set of detected lipids showed that the phospholipid profile was different between normal tissue and HCC tissue, although a small overlap was present (Figure S2). The discrimination between NAT and HCC tissue, was better represented by OPLS-DA (Figure 3) and permutation showed the empirical p-values Q2:0.894 (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01) and R2Y:0.937 (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01). Furthermore, VIP scores derived from the OPLS-DA loadings were created to indicate the variation patterns of these 85 differential metabolites in two types of tissues, showing the tendency of molecules to evolve between HCC and NAT (Table 4), which may be considered as putative biomarkers of discrimination between two tissues.\u003c/p\u003e\n\u003cp\u003eThe volcano plots showed the estimated fold change (x-axis) vs the -log\u003csub\u003e10\u003c/sub\u003e \u003cem\u003eP\u003c/em\u003e value from paired \u003cem\u003et\u003c/em\u003e-tests (y-axis) for each PL (Figure 3). Subsequent verification (FDR\u0026lt;0.05, VIP \u0026gt;1, FC\u0026gt;2 or \u0026lt;0.5) identified 36 common metabolites as differential metabolites, including 11 PCs, 8 PCPs, 6 LPEs, 2 PEs, 2 PIs, 2 PSs, 2 LPGs, 2 LPIs and 1 LPC. Among them, 18 PL species were significantly increased, and 18 were statistically decreased in HCC than in NAT (Table S3). Pairwise correlation analysis of the 36 PLs showed that most metabolites of the same class tended to cluster together; additionally, there was inverse correlation between PSs and PIs (Figure S3)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 Eighty-five identified differential phospholipid species between HCC tissue and tumor-adjacent normal hepatic tissue\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.342245989304812%\" valign=\"top\"\u003e\n \u003cp\u003ePhospholipid species\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.342245989304812%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.342245989304812%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.342245989304812%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.342245989304812%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.342245989304812%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTendency\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.877005347593583%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ePhospholipid species\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.7005347593582885%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.342245989304812%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.342245989304812%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.342245989304812%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.342245989304812%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTendency\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.476394849785407%\" colspan=\"2\"\u003e\n \u003cp\u003eLysophosphatidylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\" colspan=\"3\"\u003e\n \u003cp\u003ePhosphatidylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.759656652360515%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPC (14:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.21E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (30:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e3.49E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e4.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPC (16:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.01E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (30:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e4.08E-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPC (18:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.39E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (32:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e1.02E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPC (20:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e4.56E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (32:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e2.85E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPC (20:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.68E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (33:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e3.26E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLysophosphatidylethanolamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (33:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e8.96E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPE (16:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e3.11E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (35:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e1.36E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPE (18:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e8.96E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (36:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e2.09E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPE (18:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.78E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (36:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e6.48E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPE (20:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.23E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (38:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e2.14E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPE (20:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.92E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (38:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e9.76E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPE (20:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.12E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (38:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e9.26E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPE (20:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.35E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (38:5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e8.05E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPE (22:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.82E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (39:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e1.01E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPE (22:5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.35E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (39:5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e7.26E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLysophosphatidylglycerol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (40:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e4.52E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPG (18:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e4.09E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (40:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e8.75E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPG (18:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e8.88E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (40:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e8.51E-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLysophosphatidylinositol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (40:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e3.33E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPI (18:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.38E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (40:5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e2.44E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eLPI (18:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.68E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (40:6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e1.91E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePhosphatidylethanolamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (40:7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e1.66E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (32:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.86E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (P-36:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e2.83E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (32:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.44E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (P-36:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e2.25E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (33:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e3.12E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (P-38:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e1.34E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (37:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e5.35E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (P-38:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e3.24E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (37:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e5.35E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (P-38:6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e1.07E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (38:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.27E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (P-40:6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e3.84E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (38:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.03E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (P-40:7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e2.00E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (40:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e4.06E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (P-42:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e3.00E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (40:7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e9.68E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (P-42:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e4.68E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (40:9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.66E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (P-42:5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e1.17E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (41:5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.28E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePC (P-42:6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e6.35E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (41:6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.38E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePhosphatidylinositol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (41:7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.54E-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePI (36:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e7.48E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (42:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.73E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePI (38:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e1.70E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003ePE (42:9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e5.74E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePI (38:5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e5.77E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eSphingomyelin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePI (40:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e1.56E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eSM (d36:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e8.61E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePhosphatidylserine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eSM (d37:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e4.63E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePS (35:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e9.99E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eSM (d40:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e5.96E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePS (36:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e5.89E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eSM (d40:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.14E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePS (38:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e8.84E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e2.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eSM (d41:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e4.10E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePS (38:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e1.74E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eSM (d42:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e3.72E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePS (38:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e2.37E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eSM (d42:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e9.74E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePS (38:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e8.79E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eSM (d43:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.23E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003ePS (40:5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"3\"\u003e\n \u003cp\u003e2.55E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e1.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" colspan=\"2\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eSignificantly differential metabolites for comparison were selected based on \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, VIP \u0026gt;1.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e \u0026ldquo;D\u0026rdquo; means downregulated and \u0026ldquo;I\u0026rdquo; means upregulated in HCC tissues when compared with NAT tissues.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn females, significant differences (based on \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, VIP \u0026gt;1) in concentrations between NAT and HCC were seen in 77 PL species as shown in\u0026nbsp;Table S4. Forty-one species were significantly higher in HCC than in NAT (raged 1.34\u0026ndash;4.70 fold), the other 36 PL species were significantly lower in HCC than in NAT.\u0026nbsp;In males, significant differences in PL concentrations between NAT and HCC were seen in 88 out of 214 individual PL species (Table S5). There were 42 PL species significantly lower in HCC than in NAT, and 46 PL species were significantly increased in HCC (raged 1.33\u0026ndash;4.70 fold).\u003c/p\u003e\n\u003cp\u003e3.3 PL-derived signature is prognostic for hepatocellular carcinoma survival\u003c/p\u003e\n\u003cp\u003eDuring a follow-up period of 6.9 years, 32 (36.78%) out of 87 patients died. The survival of patients with HCC/ NAT ratio of all phospholipids as single variables was examined, using univariate and multivariate Cox proportional hazard method for death to evaluate the potential for predicting the HCC prognosis. Among the 10 lipid classes, we noted that the ratio of LPG class (HCC/NAT) showed a gradually increased trend in hepatocellular carcinoma tumors with the progression of hepatocellular carcinoma from stage T1 to T3. Unadjusted HRs (95% CIs) for HCC-specific mortality according to tertiles of the ratio of phospholipids classes were listed in Table 5, indicating that only the HCC/ NAT ratio of LPG class was associated with HCC specific mortality (HR = 6.50, 95% CI: 1.88-22.51, \u003cem\u003eP\u003c/em\u003e = 0.002). After adjusting for multiple variables, the association was still significant (HR = 4.82, 95% CI: 1.34-17.29, \u003cem\u003eP\u003c/em\u003e = 0.017). There was a positive correlation between the HCC/ NAT ratio of PG class (HR = 2.90, 95% CI: 1.09-7.69, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e=\u0026nbsp;0.032)\u0026nbsp;and the risk of death as well.\u003c/p\u003e\n\u003cp\u003eTable 5 Hazard Ratio (95% confidence interval) for HCC-specific mortality according to tertiles of HCC/ NAT ratio of phospholipid classes\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.081632653061225%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.95918367346939%\" colspan=\"4\"\u003e\n \u003cp\u003eUnivariate\u0026nbsp;Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.95918367346939%\" colspan=\"4\"\u003e\n \u003cp\u003eMultivariate Analysis\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003csub\u003etrend\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003csub\u003etrend\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\"\u003e\n \u003cp\u003eLPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.77 (0.69~4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e2.27 (0.92~5.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.47 (0.52~4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.88 (0.71~4.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\"\u003e\n \u003cp\u003eLPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.74 (0.29~1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.37 (0.61~3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.81 (0.30~2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.06 (0.44~2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\"\u003e\n \u003cp\u003eLPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e5.43 (1.56~18.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e6.50 (1.88~22.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e4.94 (1.37~17.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e4.82 (1.34~17.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\"\u003e\n \u003cp\u003eLPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.62 (0.25~1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.96 (0.43~2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.60 (0.24~1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.90 (0.40~2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.97 (0.41~2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.20 (0.52~2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.30 (0.52~3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.48 (0.61~3.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\"\u003e\n \u003cp\u003ePE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.22 (0.52~2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.98 (0.41~2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.32 (0.52~3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.12 (0.43~2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\"\u003e\n \u003cp\u003ePG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.39 (0.55~3.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e2.01 (0.84~4.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.43 (0.51~3.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e2.90 (1.09~7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\"\u003e\n \u003cp\u003ePI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.02 (0.43~2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.23 (0.53~2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.11 (0.44~2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.54 (0.63~3.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\"\u003e\n \u003cp\u003ePS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.14 (0.49~2.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.95 (0.40~2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.78 (0.32~1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.84 (0.34~2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.166666666666667%\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.31 (0.54~3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.51 (0.63~3.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.23 (0.47~3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e1.70 (0.69~4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eMultivariable adjustment included age (in years), gender (women, men), body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e), HBV infection (yes, no), smoking status (never smoker, ever smoker, current smoker), alcohol drinking status (never drinker, ever drinker, current drinker), with or without family history of primary liver cancer, BCLC stage (0, A, B, C), and treatments (liver resection, radiofrequency ablation, intervention).\u003c/p\u003e\n\u003cp\u003eTable 6 showed unadjusted HRs (95% CIs) for HCC-specific mortality according to tertiles of the HCC/ NAT ratio of PL species. After additional adjustments, the associations remained significant for 3 LPC, 1 LPE,2 LPG, 7 PE, and 3 SM, which were found to be independent markers of HCC prognosis. Higher ratios of LPG species displayed were associated with a higher risk of death from HCC (all the HR\u0026gt;1, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 for trend), and inverse associations were found between tissue ratios of species in SM class and HCC specific death (all the HR\u0026lt;1, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 for trend). The data obtained highlights that LPC (14:0), LPG (18:1), LPG (18:2), PE (32:0) and PE (32:1) may predict HCC mortality and constitute important biomarkers. The concentration of PE (32:0) in tumor tissue was positively associated with higher risk of death from HCC (HR= 3.05, 95% CI: 1.25-7.42, \u003cem\u003eP\u003c/em\u003e = 0.011 for trend), and the association remained significant after adjustments (HR= 5.62, 95% CI: 1.89-16.68, \u003cem\u003eP\u003c/em\u003e = 0.001 for trend). Inversly, concentrations of SM(d37:1) and SM(d36:1) in tumor tissue were negatively correlated with the risk of death from HCC, HR (95% CI): 0.32 (0.12-0.90) and 0.34 (0.13-0.88), respectively (both \u003cem\u003eP\u003c/em\u003e trend \u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eTable 6 Hazard Ratio (95% confidence interval) for HCC-specific mortality according to tertiles of HCC/ NAT ratio of phospholipid species\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.91836734693877%\" colspan=\"4\"\u003e\n \u003cp\u003eUnivariate Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.89795918367347%\" colspan=\"4\"\u003e\n \u003cp\u003eMultivariate Analysis\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003csub\u003etrend\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003csub\u003etrend\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eLPC (14:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e0.87 (0.29~2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e3.93 (1.65~9.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.74 (0.24~2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e3.06 (1.17~8.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eLPC (16:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e1.58(0.56~4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e3.56 (1.40~9.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e1.45(0.49~4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e2.70 (0.99~7.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e0.041\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eLPC (16:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.04 (0.39~2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.73 (1.17~6.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1.23 (0.45~3.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.62 (1.05~6.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eLPC (18:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.29 (0.80~6.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e4.07 (1.49~11.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e2.28 (0.77~6.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e3.29 (1.17~9.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eLPC (18:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.95 (0.72~5.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.74 (1.06~7.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1.40 (0.49~3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.18 (0.82~5.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eLPC (20:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.42 (0.91~6.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.73 (1.05~7.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e2.20 (0.77~6.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.54 (0.94~6.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eLPC (20:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.71 (0.95~7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e3.49 (1.27~9.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e2.73 (0.92~8.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.77 (0.93~8.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eLPC (20:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.47 (0.56~3.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.54 (1.03~6.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1.05 (0.37~2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.08 (0.82~5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eLPC (P-18:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e0.89 (0.32~2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.69 (1.16~6.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.87 (0.31~2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.14 (0.86~5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eLPE (16:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.40 (0.52~3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.94 (1.21~7.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1.57 (0.56~4.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e3.21 (1.23~8.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eLPE (18:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.01 (0.38~2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.57 (1.10~6.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.79 (0.29~2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.73 (1.04~7.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eLPE (18:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.15 (0.42~3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e3.14 (1.30~7.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1.27 (0.44~3.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.78 (1.12~6.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eLPG (18:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e3.40 (1.10~10.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e5.17 (1.72~15.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e2.81 (0.84~9.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e4.15 (1.33~12.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eLPG (18:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e6.39 (1.85~22.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e5.51 (1.58~19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e5.2 (1.45~18.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e4.14 (1.15~14.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ePC (39:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.6(0.61~4.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.69 (1.09~6.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.027\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1(0.34~2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.70 (1.00~7.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.039\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ePE (32:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.79 (0.64~5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e3.97 (1.56~10.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e2.78 (0.87~8.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e7.32 (2.29~23.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ePE (32:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.38 (0.55~3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.19 (0.92~5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e2.20 (0.81~5.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e3.04 (1.14~8.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ePE (32:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.33 (0.86~6.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e3.30 (1.28~8.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e2.18 (0.78~6.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e4.08 (1.41~11.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ePE (34:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.38 (0.55~3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.07 (0.87~4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1.63 (0.61~4.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e3.63 (1.31~10.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ePE (34:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.38 (0.55~3.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.07 (0.87~4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1.54 (0.58~4.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e3.51 (1.27~9.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ePE (40:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.81 (0.7~4.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.39 (0.96~5.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e2.12 (0.76~5.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.87 (1.08~7.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003ePE (42:5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e0.96(0.36~2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e2.39(1.02~5.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e0.033\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e1.08(0.4~2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"top\"\u003e\n \u003cp\u003e2.47(0.98~6.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\" valign=\"top\"\u003e\n \u003cp\u003e0.048\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ePG (34:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.39 (0.55~3.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.01 (0.84~4.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1.43 (0.51~3.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.90 (1.09~7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ePS (35:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.09 (0.41~2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.40 (1.03~5.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.61 (0.21~1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.90 (0.77~4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003ePS (36:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.26(0.49~3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.45(1.04~5.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.035\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e1.01(0.37~2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.44(0.99~6.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.046\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eSM (d36:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e0.74 (0.33~1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e0.50 (0.21~1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.57 (0.24~1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e0.35 (0.14~0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eSM (d37:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e0.61 (0.26~1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e0.71 (0.31~1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.42 (0.17~1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e0.39 (0.15~0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003eSM (d41:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e0.68 (0.30~1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e0.58 (0.25~1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e1.00 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.46 (0.18~1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e0.36 (0.14~0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eMultivariable adjustment included age (in years), gender (women, men), body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e), HBV infection (yes, no), smoking status (never smoker, ever smoker, current smoker), alcohol drinking status (never drinker, ever drinker, current drinker), with or without family history of primary liver cancer, BCLC stage (0, A, B, C), and treatments (liver resection, radiofrequency ablation, intervention).\u003c/p\u003e\n\u003cp\u003eIn the stratified analyses by median age, gender, and BMI, the associations of metabolites with HCC were largely consistent across strata\u0026nbsp;(Table S3). For HBV status, we found that the strength of the associations with the\u0026nbsp;HCC/ NAT ratio of\u0026nbsp;total LPG and LPG (18:2) was stronger in HBsAg positive group than in HBsAg-negative group (\u003cem\u003eP\u003c/em\u003e for interaction \u0026lt;0.05). For alcohol drinking status, the strength of the associations with the HCC/ NAT ratio of total LPG was stronger in patients who never drinks than in drinker group (\u003cem\u003eP\u003c/em\u003e for interaction = 0.039).\u003c/p\u003e"},{"header":"4 DISCUSSION","content":"\u003cp\u003eThe primary objective of this study is to discover more sensitive and specific biomarkers for diagnosis and prognosis of hepatocellular carcinoma by profiling the phospholipid features of tumor tissues. In this study, paired comparison of phospholipid profiles was made between tumor and matched normal liver tissues in 87 patients with hepatocellular carcinoma, in order to eliminate individual differences, such as age, gender and HBV status. Results showed that LPE, LPG, LPI, PC, PE, PI and PS were widely disordered in HCC tissue. Then survival analysis was carried out to evaluate the prognostic potential of phospholipids in tissue.\u003c/p\u003e\n\u003cp\u003ePhospholipids have a variety of physiological functions, including cell membranes assembly, metabolic energy storage and as signal molecules in cell metabolism. Previous studies showed that cell canceration has seriously happened in hepatocellular carcinoma tumors, indicating significant metabolic variations., and various membrane lipids were identified as being significantly affected by hepatocellular carcinoma using non-targeted metabolomics. It is noted that an array of phospholipids was significantly altered in hepatocellular carcinoma tumors as well. There is a general trend in which more phospholipids are upregulated in the cancer versus the control sample than downregulated. Over the past two decades, many lysophospholipids have been identified. Studies have shown that their concentration in cells is very low, while they are abundant in extracellular environment[21]. The common feature of lysophospholipids is that they are composed of a long hydrophobic carbon chain and a hydrophilic head group attached to the backbone of glycerol or sphingosine. Therefore, compared with original phospholipids or sphingolipids, they show different properties. In addition, lysophospholipids have a variety of pathophysiological functions[22, 23].\u003c/p\u003e\n\u003cp\u003eAccording to reports, the most abundant LPC species (such as LPC 16: 0 and LPC 18: 1) contain non-essential fatty acids. LPC plays an inflammatory, anti-hemostatic and cytotoxic roles. Pro-inflammatory effects, such as the expression of adhesion molecules, release of chemokines and the increase of reactive oxygen species (ROS), have been fully described as saturated (LPC 16: 0 and LPC 18: 0) and monounsaturated LPC 18: 1[24, 25].\u0026nbsp;Furthermore, several LPCs have shown diagnostic value in HCC[26-28]. In this study, LPC (14:0), LPC (16:0), LPC (18:0), LPC (20:0), andLPC (20:1) showed a high capability to differentiate HCC from NAT, of which AUC scores were greater than 0.7. Associations between higher HCC/ NAT ratio of LPC (14:0), LPC (16:1), and LPC (18:1) and poorer HCC prognosis were also observed. Taken together, we propose LPC metabolism change based on LPC (14:0) that differentiates tumor from nontumor tissue and has prognostic significance. Moreover, LPC (14:0) levels in tumor seem to increase in HCC patients at advanced TNM stage, and thus might represent an important parameter.\u0026nbsp;Patterson et al. showed that HCC patients had lower plasma LPC (20:4/0:0) concentrations than healthy volunteers[29]. This is followed by a targeted lipidomic analysis showing lower concentrations of LPC (20:4/0:0) and higher concentrations of PCs in HCC patients compared to cirrhotic controls[30]. Biologically, decreased LPCs and increased PCs in HCC patients might be attributable to overexpression of lysophosphatidyl-choline acyltransferase 1, which catalyzes the conversion of LPCs into PCs and thereafter promotes hepatic cell proliferation, migration, and invasion[31]. Moreover, PCs appear to stimulate carcinogenesis through their structural function in membrane composition and cell-signaling activities[6, 32].\u003c/p\u003e\n\u003cp\u003eLPG is another minor lysoglycerophospholipid which has recently been identified as a bioactive lipid. Although the biological role of LPG has not been extensively studied, LPG was reported to be a precursor of the de novo synthesis of anionic phosphatidylglycerol, which accounts for 1% of total phospholipids in most mammalian tissues, and play a vital role in liposome formation[33]. It is the first time that LPG has been found to be related to the differentiation and prognosis of hepatocellular carcinoma.\u0026nbsp;Total amount of LPGs and LPG species showed a high capability to differentiate HCC from NAT samples. We noted that the ratio of LPG (HCC/NAT) showed a gradually increased trend in hepatocellular carcinoma tumors with the progression of hepatocellular carcinoma from stage T1 to T4. Furthermore, in the multivariate Cox regression models, we found that the ratio of LPG class\u0026nbsp;(HR = 4.82, 95% CI: 1.34-17.29, \u003cem\u003eP\u003c/em\u003e = 0.017), LPG (18:1) (HR =4.15, 95% CI: 1.33-12.99, \u003cem\u003eP\u003c/em\u003e = 0.013), and LPG (18:2) (HR =4.14, 95% CI: 1.15-14.89, \u003cem\u003eP\u003c/em\u003e = 0.049), are potentially independent markers of HCC survival. Extensive research is needed to analyze the molecular mechanism involved in the proliferation pathway of this cancer cells and the physiological functions of LPG.\u0026nbsp;LPG is produced through secretory PLA2-mediated hydrolysis of phosphatidylglycerol[21]. Studies have shown a biological effect of LPG in the lung and in ovarian cancer cells[34]\u0026nbsp;but there are no studies to examine the levels and the direct effect of LPG on chronic liver diseases. Yet, LPG has been reported to antagonise the binding of LPA to LPARs and thus to block biological functions induced by LPA such as intracellular calcium increase. Also, LPG was found to inhibit FPRL1-induced cellular responses from human phagocytes and interleukin (IL)-1\u0026beta; production[35]. These in vitro biological effects of LPG speak in favour of a possible protective role of LPG against liver inflammation, but further in vivo studies are required to support this hypothesis[36, 37].\u003c/p\u003e\n\u003cp\u003eInformation about LPE as a bioactive lipid is quite rare as compared with other phospholipids. With regard to carcinogenesis, it was found that LPE induced intracellular calcium mobilization in OVCAR-3 and SK-OV3 human ovarian cancer cells, and induced chemotaxis as well as cell invasion in SK-OV3 cells[38]. LPE (16:0) was defined as one of the \u0026quot;marker metabolites\u0026quot;, which can be used to distinguish the different stages of liver cancer[39]. Nevertheless, LPE (16:0)\u0026nbsp;was applied to the classification of the control and diseased animals in this study, and the level of serum\u0026nbsp;LPE (16:0) increased as the cirrhosis developed and reached its peak during the advanced stage of HCC. In our study, LPE (16:0) was higher in NAT than HCC, distinguish two types of tissue with AUC 0.88. The reason may be that the composition and changing trend of serum and tissue metabolites are different.\u003c/p\u003e\n\u003cp\u003eThe amount of total LPI was lower in HCC than in NAT in this study, and\u0026nbsp;LPI (18:2) showed a strong ability to distinguish HCC from NAT (VIP\u0026gt;1, FC\u0026lt;0.5). It is known that LPI activates signal pathways related to cell proliferation, migration and tumorigenesis. The production of LPI in cancer is enhanced through autocrine loop[40]. The potential pharmacological effects of LPI range from obesity, inflammatory reaction to cancer. The physiological role of LPI is not well understood, however, an accumulation of LPI as a consequence of malignant cell transformation identified LPI as a biomarker for poor prognosis in cancer patients. In vitro studies have shown that the levels of LPI is significantly increased in highly proliferative cancer cells that overexpress RAS-p21 protein encoded by the members of the RAS family proto-oncogenes[41]. LPI may promote the migration of cancer cell in prostate cancer through the receptor of transient receptor potential cation channel subfamily V member 2 (TRPV 2) and by inducing calcium influx in PC 3 cell line[42].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGlycerophospholipids consist of the following parts: a glycerol backbone, esterified by two long chain fatty acids at carbons 1 and 2 (C1 and C2), phosphoric acid esterified to the C3 hydroxyl group of glycerol and usually an alcohol head group esterified to the phosphate group[43]. Among the glycerophospholipids family a prominent subgroup is PC, a class of phospholipids with choline head group, being a major component of bio-membranes. Besides, the alterations of PC have previously been reported in different cancer types including HCC[44, 45]. A \u003cu\u003emetabolic\u003c/u\u003e study on tissue samples showed significantly lower PCs in HCC compared to NAT. As the author explained, decrease in PC and other choline-containing PLs in HCC might mean that the metabolism of these PLs is beneficial to the degradation catalyzed by PC-specific phospholipase in Kennedy pathway[11]. In our study, the total PC class was significantly increased in HCC tissue than in NAT. We found that an array of PCs can distinguish HCC from NAT with high sensitivity, specificity and accuracy at the same time, especially PC (30: 0) with AUC over 0.9. Interestingly, PC (30: 0) was more abundant in breast cancer region than in stroma around cancer, and it was considered as a metabolite produced by abnormal lipid metabolism, which promoted cancer metastasis[46], which was in agreement with current study. PC accumulated in tumor tissue could be used as a biomarker[47]. Smith et al.\u0026nbsp;[48]investigate the suitability of a lipid tumor marker derived from ether-linked PL in normal, benign and neoplastic samples from human breast, lung and prostate tissues. They observed that a biochemical marker derived from PE plasmalogens provides a reliable index capable of distinguishing between benign and neoplastic tissues and it correlates linearly with metastases spreading in vivo. In the diagnosis of HCC, and in monitoring the therapeutic effects of HCC by magnetic resonance spectroscopy, as proved in HCC and other cancers[49, 50]. We found that an array of PE could differentiate tumor from nontumor tissue with high AUC, among which PE (32:0) has prognostic significance for HCC specific mortality (HR =7.32, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 for trend). Moreover, the concentration of PE (32:0) in tumor tissue was higher than NAT, and was positively associated with higher risk of death from HCC and the association remained significant after adjustments (HR= 5.62, 95% CI: 1.89-16.68, \u003cem\u003eP\u003c/em\u003e = 0.001 for trend), thus might represent an important parameter.\u003c/p\u003e\n\u003cp\u003eIt also takes advantage of the difference in structural lipid abundance, and the specific sphingolipids have the potential to be biomarkers. As an example, following increased levels of C16-ceramides, sphingosine-1-phosphate has been shown to distinguish patients with HCC from those with cirrhosis. The decrease of serum SMs in HCC could distinguish HCC patients from healthy controls, while SMs between HCC and CCA changes significantly, so these malignant tumors can be distinguished[51]. In this study, HCC/ NAT ratios of SM(d37:1) and SM(d41:1) in tumor tissue were negatively correlated with the risk of death from HCC, HR (95% CI): 0.32 (0.12-0.90) and 0.34 (0.13-0.88), respectively (both \u003cem\u003eP\u0026nbsp;\u003c/em\u003etrend \u0026lt;0.05). The concentration of SM(d37:1) in tumor tissue was lower than NAT, and seems to decrease for HCC patients at advanced TNM stage, which needs further study for the mechanism.\u003c/p\u003e\n\u003cp\u003eThe main advantages of our research include a prospective design, long-term follow-up and the ability to control lifestyle factors and HBV infection. However, we also recognize some limitations. First, as is typical of previous studies, metabolites were measured only once, which may not well reflect long-time exposure. Second, the ability to infer causality was limited in the current study because an observational design cannot fully exclude reverse causality. Future metabolomics studies with longitudinal sample collections are needed to confirm our findings and facilitate clinical applications of metabolite biomarkers.\u003c/p\u003e"},{"header":"5 CONCLUSIONS","content":"\u003cp\u003eIn conclusion, we identified specific phospholipid changes in HCC compared with NAT. This study delineates the alterations in phospholipids within HCC tissue, providing insights into substantial modifications in phospholipid metabolism. The findings shed light on biomarkers for diagnosis and prognosis of hepatocellular carcinoma by exploring the phospholipidomic characteristics in liver tumor tissues, as well as into discovering novel therapeutic targets for HCC.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrimary liver cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHepatocellular carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhospholipid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLysophospholipid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor-adjacent normal hepatic tissue\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuality control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHILIC-ESI-IT-TOF-MS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHydrophilic liquid chromatography-electrospray ionization-ion trap-time of flight-mass spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHILIC-ESI-MS/MS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHydrophilic liquid chromatography-electrospray ionization-triquadrupole-mass spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectrospray Ionization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphatidylethanolamine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphatidylcholine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphatidylserine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLysophosphatidylethanolamine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLysophosphatidylcholine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSphingomyelin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphatidylglycerol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphatidylinositol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLysophosphatidylinositol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLysophosphatidylglycerol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emultiple-reaction monitoring\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOPLS-DA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOrthogonal partial least-squares discriminant analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evariable importance in the projection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBCLC stage\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBarcelona Clinic Liver Cancer stage\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHBV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHepatitis B virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSVM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSupport vector machines\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe area under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate amino transferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine amino transferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reaction protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic equivalent\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFatty liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAFP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlpha fetoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNM stage\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor-node-metastasis stage\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReactive oxygen species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, T.H. and H.Z.; data curation, A.F., Z.L., and H.Z.; formal analysis, T.H.; funding acquisition, H.Z.; investigation, T.H., M.W., Z.L., M.L., C.W and J.Z.; methodology, S.S. and T.H.; project administration, H.Z.; resources, Y.Z., H.Z. and S.S.; software, T.H. and J.C.; supervision, H.Z.; validation, H.Z.; writing\u0026mdash;original draft, T.H.; writing\u0026mdash;review and editing, M.L., J.C., T.H. and H.Z. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no conflicts to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the HCC patients who participated in this study, and we also thank the researchers of SYSUCC for their contributions to the data collection, processing and preparation for this study. This work was supported by the National Natural Science Foundation of China (81973016).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVogel A, Meyer T, Sapisochin G, Salem R, Saborowski A. Hepatocellular carcinoma. Lancet. 2022;400(10360):1345\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Zhao Y, Li M, Hou H, Jian Z, Li W, Li P, Ma F, Liu M, Liu H, et al. Conversion of primary liver cancer after targeted therapy for liver cancer combined with AFP-targeted CAR T-cell therapy: a case report. Front Immunol. 2023;14:1180001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan SLW, Israeli E, Ericksen RE, Chow PKH, Han W. The altered lipidome of hepatocellular carcinoma. Semin Cancer Biol. 2022;86(Pt 3):445\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul B, Lewinska M, Andersen JB. Lipid alterations in chronic liver disease and liver cancer. JHEP Rep. 2022;4(6):100479.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall Z, Chiarugi D, Charidemou E, Leslie J, Scott E, Pellegrinet L, Allison M, Mocciaro G, Anstee QM, Evan GI, et al. Lipid Remodeling in Hepatocyte Proliferation and Hepatocellular Carcinoma. Hepatology. 2021;73(3):1028\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewinska M, Santos-Laso A, Arretxe E, Alonso C, Zhuravleva E, Jimenez-Ag\u0026uuml;ero R, Eizaguirre E, Pareja MJ, Romero-G\u0026oacute;mez M, Arrese M, et al. 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Cancer Res. 2004;64(12):4270\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanales JM, I\u0026ntilde;arrairaegui M, Arbelaiz A, Milkiewicz P, Muntan\u0026eacute; J, Mu\u0026ntilde;oz-Bellvis L, La Casta A, Gonzalez LM, Arretxe E, Alonso C, et al. Serum Metabolites as Diagnostic Biomarkers for Cholangiocarcinoma, Hepatocellular Carcinoma, and Primary Sclerosing Cholangitis. Hepatology. 2019;70(2):547\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"hepatocellular carcinoma, metabolomics, phospholipids, prognosis, liquid chromatography-mass spectrometry","lastPublishedDoi":"10.21203/rs.3.rs-4110772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4110772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Previous studies have indicated that hepatocellular carcinoma (HCC) is linked to abnormal phospholipid (PL) metabolism. However, whether alterations of phospholipids in hepatic tissues contribute to the diagnosis and prognosis of HCC remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA quantitative and comprehensive phospholipidomic analysis was conducted using hydrophilic liquid chromatography-electrospray ionization-triquadrupole-mass spectrometry (HILIC-ESI-MS/MS). This analysis facilitated the comparison of 214 distinct PLs between paired samples from HCC tissues and tumor-adjacent normal hepatic tissues (NATs) in a prospective cohort (n=87). Differential metabolites were identified through paired\u003cem\u003e t\u003c/em\u003e tests and orthogonal partial least-squares discriminant analysis (OPLS-DA). The survival analysis of phospholipids for HCC was assessed using univariate and multivariable Cox regression models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Significant differences were found between HCC and NAT for phospholipid profile, and 85 phospholipids demonstrated a high accuracy in discerning two types of tissue. The increased HCC/ NAT ratio of lysophosphatidylglycerol (LPG) class was associated with greater HCC specific mortality (Hazard ratio (HR) = 6.50, 95% confidence interval (CI): 1.88-22.51,\u003cem\u003eP\u003c/em\u003e = 0.002), and the association was still significant (HR = 4.82, 95% CI: 1.34-17.29, \u003cem\u003eP\u003c/em\u003e = 0.017) even after adjustment covariances. LPG (18:1) and LPG (18:2) differentiated HCC from NAT with great capacities (the area under the curve (AUC)\u0026gt;0.75) and had prognostic significance for HCC specific mortality before (HR = 5.17 and 5.51, respectively, both of\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) and after adjustment (HR = 4.14 and 4.15, respectively, both of\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Phospholipids could serve as potential biomarkers with significant diagnostic and prognostic implications. A more profound understanding of cancer-associated phospholipid metabolism could pave the way for innovative therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Diagnostic and prognostic potential of tissue phospholipidomics in hepatocellular carcinoma: A prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 10:57:24","doi":"10.21203/rs.3.rs-4110772/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"586a80c6-ab12-47a6-ba30-5089881e8205","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-22T16:27:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-20 10:57:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4110772","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4110772","identity":"rs-4110772","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}