Lipidomics reveals biomarkers of the efficacy of first-line ICIs therapy combined with chemotherapy in NSCLC

Lipidomics reveals biomarkers of the efficacy of first-line ICIs therapy combined with chemotherapy in NSCLC | 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 Lipidomics reveals biomarkers of the efficacy of first-line ICIs therapy combined with chemotherapy in NSCLC Jia Yu, Hanyan Xu, Fen Xiong, Lingfei Meng, Xiling Liu, Hongchang Gao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5662148/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 Immune checkpoint inhibitors (ICIs) plus chemotherapy have become the first-line standard therapy for patients with the gene-negative advanced non-small cell lung cancer (NSCLC). There is a lack of reliable biomarkers to predict treatment outcomes. This study aimed to identify relevant lipids that can predict treatment outcomes in NSCLC patients receiving first-line ICIs plus chemotherapy via lipidomics. Plasma samples were collected from Forty-nine patients with stage IIIB/IV NSCLC before the start of treatment, and patients were categorized into a long-term benefit group (progression-free survival [PFS] < 12 months) and a short-term benefit group (PFS ≥ 12 months). We identified13 lipids (FA-18:0, FA-18:2, FA-15:0, PS-36:3, PE-P-34:2, LPC-18:0, FA-20:3, LPI-18:1, LPC-14:0, PI-40:4, PE-O-34:2, FA-20:4, FAHFA-37:5) to predict the therapeutic efficacy of ICIs plus chemotherapy with high specificity and sensitivity We further investigated the role of linoleic acid (LA) (FA-18:2), a pivotal lipid involved in immune regulation, in animal and cellular models and explored its potential in enhancing NSCLC immunotherapy. Our results showed that LA can assist PD-1 inhibitors in exerting immune anti-tumor effects, slowing down tumor growth in mouse models, and suppressing the expression of PD-L1 proteins in both Lewis cells and tumor tissues. This study found that lipids were important biomarkers for predicting the efficacy of first-line ICIs plus chemotherapy in NSCLC patients, of which LA is an important adjuvant therapy for immune response. Biomarkers Non-small cell lung cancer Lipidomics Immune checkpoint inhibitors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Intrudction Lung cancer remains the most prevalent cancer globally, characterized by a high mortality rate. By the time of diagnosis, a significant proportion of patients are already in advanced and unresectable disease stages, leading to a poor prognosis[ 1 ]. Tumor immunotherapy employing immune checkpoint inhibitors (ICIs) has brought new hope to advanced non-small cell lung cancer (NSCLC) patients[ 2 ]. However, there is considerable variability in the response to immunotherapy among patients, affecting both the benefits and long-term survival outcome[ 3 ]. To date, various potential biomarkers have been investigated to identify patients who are more likely to respond favorably to ICIs[ 4 ]. Programmed cell death ligand-1 (PD-L1) has been recognized as a biomarker with some predictive value[ 5 , 6 ]. However, numerous studies indicated that PD-L1 expression levels cannot accurately predict the treatment outcomes of ICIs therapy in NSCLC, especially when used in combination with chemotherapy[ 7 – 9 ]. This variability is partly due to individual heterogeneity[ 10 ]. In addition, blood tumor mutational burden (bTMB) has emerged ascandidate biomarker for immunotherapy that can be evaluated by blood testing[ 6 , 11 , 12 ], but testing is expensive. The bTMB thresholds used in different studies are quite different, and the standardization of bTMB is another challenge that needs to be addressed[ 11 , 13 ]. Tumor-infiltrating lymphocytes, the neutrophil/lymphocyte ratio[ 14 ], and gut microbes[ 15 ] have also recently been found to have potential predictive value in assessing immune responses in a wide range of cancers but are still in the exploratory stage. Tumor immunity is a complex process, and single biomarkers have varying degrees of limitations. Compared with monotherapy, combination therapy has the potential to enhance the efficacy of immunotherapy by promoting immune system activation. The American Cancer Society recommends the use of ICIs in combination with chemotherapy as the first-line standard treatment for patients with advanced NSCLC with negative driver genes[ 16 , 17 ]. Current studies have focused on the discovery of predictive markers of single-agent immunity, and there is no effective biomarker to predict patients who can long-term benefit from chemotherapy combined with ICIs treatment. Therefore, the discovery of predictive biomarkers is one of the most urgent research needs in chemoimmunotherapy. Lipidomics aims to elucidate metabolic pathways associated with various diseases and is increasing utilized for biomarker discovery, disease diagnosis, and disease development[ 18 ]. Therefore, this approach also provides novel insights and directions for understanding disease mechanisms. Many studies have demonstrated that lipid metabolism play a critical role in the development and progression of NSCLC. A study revealed that a set of four lipids identified in the lipidomic profile of plasma from patients with early-stage lung cancer could be used to predict early-stage cancer with high accuracy and predictive power compared with healthy subjects[ 19 ]. Single-cell RNA sequencing of early lung cancer revealed a general imbalance in lipid metabolism among different cell types, and nine lipids can serve as important features for early cancer screening[ 20 ]. Elevated level of cholesterol and long-chain fatty acid in serum had been correlated with better progression-free survival (PFS) and overall survival (OS) in NSCLC patients who have received ICIs treatment[ 21 ]. We believed that the response of NSCLC patients to ICIs treatment in combination with chemotherapy is an innate ability, which can be represented by a specific metabolic phenotype. Zheng[ 22 ] et al. revealed that higher serum concentrations of N-(3-indoleacetyl)-L-alanine and methotrexate metabolites correlate with poorer prognosis in NSCLC patients receiving ICI combination chemotherapy. In this study, plasma was collected from NSCLC patients and divided into two groups based on PFS. The plasma samples underwent lipid detection using liquid chromatography coupled with mass spectrometry (LC-MS)-based untargeted lipidomics for multivariate analysis to identify differentially abundant lipids distinguishing the two groups. Semi-targeted lipidomics were then used to quantify key lipids and screen for potential biomarkers distinguishing the two groups by significant difference and receiver operating characteristic (ROC) curve analysis. Many researchers believe that ICIs combination therapy with chemotherapy can promote antitumor immunity, increase remission rates, and produce durable remission, leading to better outcomes. 2. Material and Methods 2.1 Study design and patient This study was conducted in the First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China. Patients with histologically confirmed advanced NSCLC at stage IIIB/IV according to the eighth edition of TNM classification who received first-line chemotherapy plus ICIs were enrolled from May 2019 to November 2023 in this study. The follow-up period ended in November 2023, and plasma samples were collected before ICIs treatment. Patients with obvious infection symptoms or severe autoimmune disease and those who stopped treatment due to various toxic adverse reactions within 12 months or who lacked follow-up data were excluded. The tumor response to therapy was evaluated based on the response evaluation criteria for solid tumors version 1.1 (RECIST v1.1). According to the literature on NSCLC immunotherapy, the 12-month PFS may be a crucial endpoint for patient prognosis. Therefore, patients were further classified into a short-term benefit group (PFS < 12 months) and a long-term benefit group (PFS ≥ 12 months)[ 23 – 25 ]. By comparing the two groups, we looked for potential markers of the efficacy of chemotherapy combined with ICIs. This study received approval from the Medical Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University (No. 2020084). All patients signed a written informed consent form for participation. 2.2 Plasma sample collection Patient blood was collected at baseline (pretreatment). Early morning fasting blood was collected in EDTA. The samples were left to stand at 25°C for approximately 30 minutes. The blood sample underwent centrifugation at 4000 × g for 15 minutes at a temperature of 4°C. The upper layer of plasma was carefully collected and rapidly stored at -80°C until LC-MS analysis, without repeated freezing and thawing prior to lipid extraction. 2.3 Lipid extraction The plasma samples were first thawed in a 4°C refrigerator for approximately 10 minutes. To deproteinize and extract lipids, 400 µL of precooled (to -4°C) isopropanol solution was added to each 100-µL plasma sample, mixed thoroughly on a homogenizer (60 Hz, 2 min), and allowed to stand for about 15 min to ensure complete proteins precipitate. Following this, the mixture underwent centrifugation. Approximately 400 µL of the supernatant was collected in a new Eppendorf tube and dried under nitrogen for approximately 1 hour. Subsequently, 100 µL of isopropanol was added to the dried sample to dissolve and homogenize the mixture, which was then followed by centrifugation at 15,000 × g for 20 min at 4°C. From the supernatant, 50 µL was removed in a liquid-phase vial containing a glass-lined tube, pending subsequent LC‒MS analysis. To monitor the instrument’s stability, 10 µL of each supernatant was aspirated to prepare a quality control (QC) sample. 2.4 lipidomic detection and data analysis Nontargeted lipidomics and semitargeted lipidomic acquisition and data analysis were performed according to previously published methods[ 26 ]. Detailed methods can be found in the supplementary material 2.5 Cell experiments The Lewis lung cancer cells (LLCs) selected for this experiment were mouse lung cancer cells purchased from Procell Life Science & Technology Co., Ltd. (Wuhan, China). LLC cells were cultured semiadherently in DMEM supplemented with 10% FBS and 1% antibiotics in a humidified incubator with 5% CO 2 at 37°C. LLC cells were replaced once a day and passaged at a ratio of 1:3 every 2–3 days. Cells in the logarithmic growth cycles were selected for passaging and counting. A total of 3×10 6 cells were inoculated in 6-well plates and incubated at 37°C for 24 hours to allow for stable attachment. The effects of different concentrations of LA on the PD-L1 expression level in LLC cells were investigated. LA at concentrations of 0, 25, 50, 100, 150, and 200 µM was added to the cells, and the cells were collected after 24 h for subsequent experiments. 2.7 Animal experiments The animal care and experimental protocols used in this study were approved by Wenzhou Medical University, which is the Experimental Animal Center Use Committee. First, the LLC cells were diluted to a concentration of 5x10 6 /mL with PBS and placed on ice for backup. Six- to eight-week-old female C57BL/6 mice were selected, 0.1 mL of single-cell suspension was aspirated and injected subcutaneously into the right axilla of each mouse, and the needle was rotated back to observe whether there was any fluid outflow. Tumor volumes were assessed every other day. When the average tumor volume reached approximately 75 mm 3 , the mice were randomly divided into 4 groups: the control group (200 µg/kg of IgG antibody (Selleck, China) administered intraperitoneally every 3 days and subjected to daily saline infusion), the LA group (100 mg/kg of LA (Sigma, USA) administered by gavage every day, as well as 200 µg/kg of IgG antibody administered intraperitoneally every 3 days), and the anti-PD-1 antibody(αPD-1) group (200 µg/kg of αPD-1 (Selleck, China) administered intraperitoneally every 3 days and subjected to daily saline infusion) and the LA combined with αPD-1 group (100 mg/kg LA administered by gavage every day, as well as 200 µg/kg of αPD-1 administered by intraperitoneal injection every 3 days), which were treated for a total of 2 weeks. The growth of the subcutaneously grafted tumors in the mice was observed every 2 days, and the tumor volume was measured via Vernier calipers. The tumor volume was calculated according to the following formula: tumor volume mm 3 = (length) × (width) 2 × 0.5. At the end of the experiment, the mice were euthanized by isoflurane anesthesia, after which the tumors were carefully removed, the volume and weight of the tumors were measured, and the tumors were photographed. Finally, we stored the tissues at -80°C. 2.8 Protein immunoblotting experiments Protease inhibitors (Thermo Fisher Scientific, MA) and lysates (Thermo Fisher Scientific) were added to tumor tissues and cells to extract proteins. The protein concentration was detected via a BCA protein assay kit (Bio-Rad, CA, USA). Equal amounts of protein samples were added to a 10% polyacrylamide gel, and protein separation was performed via sodium dodecyl sulfate‒polyacrylamide gel electrophoresis (SDS‒PAGE). The proteins were subsequently transferred to a polyvinylidene difluoride membrane (0.45 mm, Millipore, Germany). The membrane was blocked with 10% nonfat milk for 1 h, after which it was incubated with primary antibody overnight at 4°C. The next day, the membranes were incubated with secondary antibody at room temperature for 1 h. Finally, the blots were incubated with enhanced chemiluminescence (ECL) luminescent reagent (Amersham Pharmacia Biotech, Piscataway, NJ) to visualize the immunoreactive bands, and the intensity of the bands was quantified via ImageJ software. All the following antibodies were used: PD-L1 (1:1000, Proteintech), anti-DAPI (1:1000, Proteintech), and secondary antibodies (Thermo Fisher Scientific, 1:5000). 2.10 Statistical analysis In this study, the grouping and processing of animals and cells, the collection and extraction of samples and other steps were carried out in strict accordance with the guidelines of randomized division. All the statistical analyses were performed via SPSS version 19.0 and GraphPad Prism version 8.00. The clinical data are expressed as the means ± standard deviations or medians. Comparisons between two groups were accomplished via independent samples t tests, and one-way ANOVA was used to determine differences between multiple groups, with p < 0.05 considered to indicate a statistically significant difference. 3. Results 3.1 Study design and clinical characteristics of NSCLC patients The clinical characteristics of the NSCLC patients are summarized in Table 1 . Clinical information on patients was obtained approximately 1 week before blood collection. We prospectively enrolled 120 patients; 60 patients were excluded from this study because of discontinuation of immunotherapy due to adverse immune reactions, and 11 patients did not have sufficient plasma samples for testing. Therefore, 49 patients with evaluable plasma samples were included in the study. As detailed in Table 1 , there were no statistically significant differences between the two groups of patients in terms of age, sex, body mass index (BMI), tumor stage, or underlying metabolic diseases. Table 1 The clinical characteristics of NSCLC patients Characteristics PFS < 12moth(n = 25) PFS ≥ 12moth(n = 24) P value Age, (years) 0.09 Median (rang) 62(57–67) 66.5(62–70) Mean ± SD 62.24 ± 7.04 65.65 ± 6.65 Sex 0.68 Male 22(88%) 22(92%) Female 3(12%) 2(8%) BMI,kg/m 2 0.17 Median(rang) 23.56(20.83–25.02) 21.65(20.80-23.93) Mean ± SD 23.19 ± 3.54 21.98 ± 2.36 Disease stage 0.78 IIIB-IIIC 7(28%) 9(38%) IV 18(72%) 15(62%) Tumor subtype 0.99 Adenocarcinoma 13(52%) 12(50%) Squamous 12(48%) 12(50%) Hypertension (%) 5(20%) 5(21%) 0.79 Diabetes (%) 3(12%) 4(17%) 0.65 3.2 Identification of lipid profiling in the plasma of NSCLC patients In our study, comprehensive untargeted lipidomics analysis was performed on plasma samples using LC‒MS. Our analysis revealed a significant number of characteristic peaks, totaling 1902 peaks identified in the positive ionization mode (ESI+) and 2420 peaks in the negative ionization mode (ESI-). To further examine the differences in lipid profiles between patients in the PFS < 12-month group and the PFS ≥ 12-month group at baseline, we performed principal component analysis (PCA), and the results in Fig. 1 A and B show that there is a trend of separation in the lipid profile between the two groups. In order to better differentiate the metabolic differences between the two groups, we established an (Orthogonal Partial Least Squares Discriminant Analysis) OPLS-DA model that could effectively distinguish the differences in plasma lipid metabolism between the two groups. In the positive model, the OPLS-DA plot yielded an R 2 Y value of 0.811 and a Q 2 value of 0.465 (Fig. 1 C), and in the negative model, R 2 Y = 0.721 and Q 2 = 0.471 (Fig. 1 D). Then, we performed 200 alignment tests, which showed that the model had interpretability, reliability, and predictive ability. (Fig. 1 E, 1 F). Therefore, we concluded that there was a significant difference in the plasma lipid mzetabolism phenotype between the two groups of patients before first-line ICIs combined with chemotherapy. We hypothesized that in vivo lipid levels may be a key determinant of the different prognoses of NSCLC patients after immunotherapy. The results of volcano plot analysis in the ESI + mode and ESI- mode with FDR 1 as screening conditions, as shown in Fig. 2 A and 2 B, revealed that 87 metabolite peaks were upregulated and 37 were downregulated in the positive ion mode compared with those in the PFS < 12-month group. In the negative ion mode, 364 metabolite peaks were upregulated and 1 was downregulated compared with those in the PFS < 12-month group. Based on m/z, RT, and MS/MS fragment data, 56 lipids were identified as known substances, as shown in Table S1 . A total of 56 different lipids were identified in both groups, including fatty acids (FAs), branched fatty acid esters of hydroxy fatty acids (FAHFAs), lysophosphatidylcholine (LPC), lysophosphatidylinositol (PE), phosphatidylserine (PS), lysophosphatidylinositol (LPE), phosphatidylcholine (PC), lysophosphatidylinositol (LPI), phosphatidylglycerol ester (PG), phosphatidic acid (PA), and phosphatidylinositol (PI). The heatmap shown in Fig. 2 C depicts the relative intensities of differential lipids across the two groups. We noted that compared with the PFS < 12-month group, the PFS ≥ 12-month group had higher concentrations of FA (including FA-15:0, FA-16:0, FA-16:1, FA-18:0, FA-18:1, FA-18:2, FA-20:1, FA-20:3, FA-20:4, FA-22:4, FA-22:5, and FA-22:6), FAHFA (including FAHFA-21:3, FAHFA-222:2, FAHFA-14:2, FAHFA-32:3, FAHFA-32:1, FAHFA-27:4, FAHFA-28:0, FAHFA-28:4, FAHFA-28:5, FAHFA-30:0, FAHFA-32:0, FAHFA-32:2, FAHFA-32:3, FAHFA-32:4, FAHFA-35:5, FAHFA-37:5, FAHFA-38:4), and LPC (LPC-14:0, LPC-16:0). These findings revealed differences in lipid metabolism between patients in the PFS < 12-month group and those in the PFS ≥ 12-month group before ICIs plus chemotherapy. 3.3 Effectiveness of the use of multiple lipid biomarkers to predict the efficacy of first-line chemotherapy plus ICIs treatment In order to detect the concentration level of lipids in the blood, we monitor the mass of precursors and two characteristic fragments of 56 lipids via LC-MS in combination with this MRM method, of which 18 specific lipids were highly responsive and significantly different. However, the remaining 38 lipids were not significantly different between the PFS < 12-month group and the PFS ≥ 12-month group. LC‒MS-based semitargeted semiquantitative analysis revealed 18 species of lipids, including FA-18:0, FA-18:2, FA-15:0, PS-36:3, PE-P-34:2, LPC-18:0, FA-20:3, LPI-18:1, LPC-14:0, PI-40:4, PE-O-34:2, FA-20:4, FAHFA-37:5, PA-36:2, FAHFA-35:5, LPC-20:2, FAHFA-32:0, and PG-36:3. The quantitative concentrations of the 18 lipids were subsequently statistically analyzed between the PFS < 12 and PFS ≥ 12-month groups, and the concentrations of the 18 lipids in the plasma are shown in Fig. 3 . The results revealed that patients in the PFS ≥ 12-month group had significantly higher levels of 18 lipids compared with those in the PFS < 12-month group. As shown in Fig. 4 , univariate ROC curves of these 18 lipids were used as biomarkers to establish a predictive model that was designed to predict long-term survival in NSCLC patients receiving first-line ICIs therapy in combination with chemotherapy. The results showed that FA-18:0 (AUC = 0.813, P = 0.0002), FA-18:2 (AUC = 0.751, P = 0.0025), FA-15:0 (AUC = 0.765, P = 0.0015), PS-36:3 (AUC = 0.75, P = 0.0027), PE-P-34:2 (AUC = 0.739, P = 0.0041), LPC-18:0 (P = 0.733, P = 0.0053), FA-20:3 (AUC = 0.722, P = 0.0078), LPI-18:1 (AUC = 0.721), LPC-14:0 (AUC = 0.710, P = 0.0117), PI-40:4 (AUC = 0.628, P = 0.028), PE-O-34:2 (AUC = 0.708, P = 0.0124), FA-20:4 (AUC = 0.708, P = 0.0124), FAHFA-37:5 (AUC = 0.701, P = 0.0117), PA-36:2 (AUC = 0.698, P = 0.0173), FAHFA-35:5 (AUC = 0.688, P = 0.0244), LPC-20:2 (AUC = 0.652, P = 0.0670), FAHFA-32:0 (AUC = 0.659, P = 0.0561), PG-36:3 (AUC = 0.657, P = 0.0600).The prediction model was valid when P 0.7 is clinically significant, we chose an AUC > 0.7 as the screening condition for lipid biomarkers, and we identified these 13 lipids as potential biomarkers for prediction: FA-18:0, FA-18:2, FA-15:0, PS-36:3, PE-P-34:2, LPC-18:0, FA-20:3, LPI-18:1, LPC-14:0, PI-40:4, PE-O-34:2, FA-20:4, and FAHFA-37:5. To assess the predictive value of the combination of these 13 lipid biomarkers in predicting the efficacy of first-line chemotherapy in patients with NSCLC in combination with ICIs, we used a binary logistic regression equation to obtain a joint predictive probability index P value. A ROC curve was built based on the P value to assess the predictive efficacy of the model built for these 13 lipid biomarker groups. As shown in Fig. 5A, the AUC of the 13 lipid biomarker groups was 0.905 (sensitivity 83.31%; specificity 92%; 95% confidence interval (CI): 0.890–1.000; P < 0.0001). These findings indicate that this model has high accuracy and sensitivity in predicting the efficacy of first-line chemotherapy plus ICIs treatment. Next, the optimal cutoff value could be obtained based on the highest Youden index of these 13 lipid combinations, as shown in Fig. 5B. When the cutoff value was 0.371, 4 (16%) patients in the PFS < 12-month group were incorrectly categorized as patients in the PFS ≥ 12-month group (false-negatives), and 1 (4.2%) patient in the PFS ≥ 12-month group was misclassified as being in the PFS < 12-month group (false-positives). Therefore, the model constructed with these 13 biomarker groups predicted a prediction rate of 89.8% for patients with NSCLC who could survive for a long period of time treated with first-line chemotherapy plus ICIs. 3.4 Linoleic acid (LA) reduces PD-L1 expression in lung cancer cells The results of our experiments revealed that various differential lipids were significantly elevated in patients in the PFS ≥ 12-month group. This led us to speculate that lipid metabolism may play a crucial role in the immunotherapy for NSCLC. Many reports have demonstrated the multiple biological functions of PUFAs, especially ω-3 and ω-6 polyunsaturated fatty acids. Recently, LA (FA-18:2) was shown to enhance the antitumor function of cytotoxic T lymphocytes (CTLs) through metabolic reprogramming[ 27 ]. This reprogramming prevents the depletion of T cells and promotes their conversion to a memory phenotype with enhanced cytotoxicity. Recently, oral supplementation with LA combined with PD-L1 blockade was found to enhance the inhibition of pancreatic cancer and reverse cancer resistance to immunotherapy in mice[ 28 ]. However, the role of LA in lung cancer immunotherapy is not yet known. Therefore, in the present study, based on the abovementioned lipidomics results, we ultimately selected LA as a representative lipid to further investigate its mechanism related to immunotherapy for lung cancer. As PD-L1 engages with PD-1 on T cells to dampen T-cell activity, it enables cancer cells to evade immune surveillance. Therefore, we subjected LLC mouse lung cancer cells to varying concentrations of LA (0, 25, 50, 100, 150, and 200 µM) for 24 hours and assessed PD-L1 expression via protein blotting. As shown in Fig. 6 (A) and (B), the surface PD-L1 levels on tumor cells exhibited a gradual decrease with increasing concentration of LA. The reduction in PD-L1 expression results in a decrease in the interaction between PD-L1 and PD-1, which could reverse PD-L1-mediated immunosuppression and, potentially, impending tumor growth. These findings suggest the potential of LA in regulating PD-L1-mediated immunosuppression. 3.5 LA mediated growth inhibition and immune response in LLC xenografts In cell culture models, we found that LA has inhibitory effects on PD-L1 expression, and we next speculated that these effects could be realized in vivo. To investigate this further, we established a xenograft model by subcutaneously injecting LLC mouse lung cancer cells into C57BL/6 mice. Subsequently, these mice were randomly divided into four groups: the control group, LA group, αPD-1 group, and LA plus the αPD-1 group. Our results, as illustrated in Fig. 7 (A) and (B), demonstrate a significant reduction in the tumor volume in the LA group, αPD-1 group, and the combination group compared to the control group, indicating the antitumor potential of LA. Particularly noteworthy is the synergistic antitumor effect observed in the LA combined with the αPD-1 group, as evidenced by a substantial decrease in tumor volume compared to the αPD-1 group alone. Furthermore, consistent with our in vitro findings, the protein expression of PD-L1 in tumor tissues was notably decreased in the LA group compared to the control group, as depicted in Fig. 7 (C) and (D). These results suggest that LA not only exhibits antitumor effects in vivo but also downregulates PD-L1 expression, supporting its potential as a therapeutic agent in promoting PD-L1-mediated antitumor. 4. Discussion It has been reported that elevated serum cholesterol and long-chain fatty acid levels correlate with good efficacy of nivolumab in patients with NSCLC[ 21 ]. A recent study analyzing the pretreatment serum of NSCLC patients treated with pembrolizumab revealed that serum eicosapentaenoic acid (EPA) levels and the ratio of EPA to arachidonic acid (AA) (EPA/AA) were greater in long-term survivors than in short-term survivors[ 29 ]. Our experiments has identified 13 lipids (FA-18:0, FA-18:2, FA-15:0, PS-36:3, PE-P-34:2, LPC-18:0, FA-20:3, LPI-18:1, LPC-14:0, PI-40:4, PE-O-34:2, FA-20:4, FAHFA-37:5) that were enriched in patients exhibiting favorable outcome, serving as potential biomarkers with high sensitivity and specificity for predicting the efficacy of first-line ICIs in combination with chemotherapy. Therefore, we posit that the distinct metabolic profiles of patients prior to treatment may influence the response to first-line ICIs combined with chemotherapy. Given the ease and non-invasiveness of plasma lipid level assessments and the safe and accessible nature of lipid-based interventions, these natural componds hold promise as adjunct therapeutic agents to enhance the efficacy of ICIs in combination with chemotherapy. In the tumor microenvironment, cancer cells compete with immune cells immune cells for lipids, which are are less active due to the inability of immune cells to undergo normal metabolism [ 30 ]. Qin et al[ 31 ]found that increasing exogenous lipids can restimulate the activation of immune cells. They also found that natural killer (NK) cells increased in number and activity in high cholesterol-fed mice, and that elevated serum cholesterol levels also promoted the activation of cellular immune signaling and cellular antitumor effects. Therefore, we suggest that elevated serum lipid levels may have the potential to enhance anti-tumor immunity. Phospholipids make up about 1/3 of blood lipids and are widely found in the human body. Phospholipids are major components of biological membranes and have biological activities involved in various signal transduction. Phospholipids can induce immune cell migration, activation, and regulation of immune function[ 32 ]. Several studies have shown that phospholipids bind to and activate the CD1 receptor on the surface of natural killer T (NKT) cells, which are innate T-lymphocytes that rapidly produce large amounts of cytokines when stimulated by antigens and play an important role in the host immune response to cancer[ 33 , 34 ]. Derosa et al[ 15 ]. have found that Akkermansia muciniphila (AKK) is associated with clinical benefit in lung cancer patients treated with PD-1 inhibitors. AKK colonies were enriched in the feces of patients who responded well to immunotherapy. Recently, Bae et al[ 35 ]. found the identification of a phospholipid from AKK cell membranes that promotes immune homeostasis by stimulating the TLR2-TLR1 signaling pathway. And based on our lipidomics results, we found a significant increase in phospholipids in the PFS ≥ 12 months group, so we believe that phospholipids may be a potential in predictive biomarker. PUFAs that promote apoptosis and inhibit cell proliferation in many malignant tumors, including breast, liver, and pancreatic tumors[ 36 ]. Excessive accumulation of PUFAs is mediated by the induction of the ER stress response, the activation of caspase-3, and the activation of tumor necrosis factor-alpha (TNF-α). TNF-α signaling promotes apoptosis[ 37 ]. PUFAs (i.e., eicosapentaenoic acid, LA, and EPA) increase the chemosensitivity of tumor cells. In addition, they reduce the side effects of chemotherapy and protect target tissues without any adverse effects on nontargeted tissues. Tumor cells exhibit reduced total PUFA content, contributing to their resistance to chemotherapy and lipid peroxidation[ 38 ]. Conjugated linoleic acid (CLA) in combination with gemcitabine improves anticancer activity as well as bioavailability against breast cancer in vitro and in vivo[ 39 ].Studies have revealed a negative correlation between the intake or blood levels of PUFAs and cancer risk[ 40 ]. Studies have shown that PUFAs reduce the growth of human lung tumor cells in a concentration-dependent manner[ 36 ]. PUFAs are known to promote proinflammatory responses, which are potentially beneficial in the context of immunotherapy. PUFAs acts as an essential fatty acid because of its wide range of anti-inflammatory and anti-proliferative activities in vivo and in vitro[ 38 ], and PUFAs inhibits the IL-6-induced JAK2/STAT3 signaling pathway in human breast cancer cells and has synergistic antitumor effects in combination with chemotherapeutic compounds[ 41 ]. Oral administration of LA in combination with an anti-PD-L1 antibody in a mouse model of pancreatic cancer significantly increased CD8 + T-cell infiltration and reduced the proportion of depleted PD-1 + CD8 + T cells in the tumor. Recent studies have shown that after being ingested into the body, LA is converted via gut microbes into CLA, which is required to induce the production of a specific type of immune cell, CD4 + CD8α + intraepithelial lymphocytes, in the small intestine[ 42 ]. Most of the current studies on the effects of LA on immunity have focused on its effects on immune cells (e.g., T cells). To identify lipids that can promote the efficacy of ICIs, we selected LA for cell and animal experiments. Our results revealed that the tumor growth of mice treated with a PD-1 inhibitor combined with LA slowed in the LLC model, indicating the efficacy of the anti-PD-1 inhibitor. Mechanistically, LA can inhibit the expression of the PD-L1 receptor on tumor cells and the surface of tissues, thus promoting the immune response of tumors. Our findings reveal a potential role that LA may play in cancer immunity and may be a particularly promising cancer treatment strategy in combination with ICIs. However, there are some limitations of this work. First, because of the limited sample size, we need to conduct a large-scale cohort study to verify the results. Second, our study was limited to plasma samples, so supplementation with a combination of urine, tissue, and other samples can further confirm the metabolic changes in the process of disease occurrence and development. Finally, further studies to explore the effects of LA on the tumor microenvironment and how it modulates the immune system to enhance the antitumor effect remain crucial. 5. Conclusion In conclusion, this study revealed that 13 lipids comprising biomarker groups (FA-18:0, FA-18:2, FA-15:0, PS-36:3, PE-P-34:2, LPC-18:0, FA-20:3, LPI-18:1, LPC-14:0, PI-40:4, PE-O-34:2, FA-20:4, and FAHFA-37:5) could predict long-term survival in advanced NSCLC patients treated with first-line chemotherapy combined with ICIs. LA can regulate the expression of PD-L1 on the surface of lung cancer cells and has synergistic antitumor effects with anti-PD-1 antibodies. LA was found to have potential value as a combined immunotherapy in cell and animal experiments, but further in-depth studies are needed. Declarations Ethics approval and consent to participate Our study was approved by the ethics committee of the First Affiliated Hospital of Wenzhou Medical University (ethics approval No. 2020084) and complied with the declaration of Helsinki. Informed consent was obtained from all participants. Consent for publication We promise to agree to publish our article in this magazine Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This study was supported by Wenzhou Municipal Science and Technology Bureau (No. Y2020001). Author Contribution Jia Yu, Yuping Li, and Hongchang Gao design the experimental. Yuping Li and Hongchang Gao provision of study materials and patients. Lingfei Meng contributed to sample collection and clinical information collection. Jia Yu, Feng Xiong and xiling Li contributed to sample preparation and metabolomic analysis. Jia Yu and Fen Xiong contributed to the data analysis and manuscript writing. Jia Yu, Fen Xiong, Hanyu Xu and Yuping Li contributed to the result discussion and interpretation. All authors have read and approved the final manuscript. Acknowledgement We would like to thank all patients in this study. We thank for all participants for collecting the data. We also thank the Scientific Research Center of Wenzhou Medical University is acknowledged for its technical services. Availability of data and materials The data used and/or analyzed are available from the corresponding author on a reasonable request. References Marrugal Á, Ojeda L, Paz-Ares L, Molina-Pinelo S, Ferrer I. Proteomic-Based Approaches for the Study of Cytokines in Lung Cancer. Dis Markers 2016, 2016:2138627. Doroshow DB, Sanmamed MF, Hastings K, Politi K, Rimm DL, Chen L, Melero I, Schalper KA, Herbst RS. Immunotherapy in Non-Small Cell Lung Cancer: Facts and Hopes. Clin Cancer Res. 2019;25(15):4592–602. Passaro A, Brahmer J, Antonia S, Mok T, Peters S. Managing Resistance to Immune Checkpoint Inhibitors in Lung Cancer: Treatment and Novel Strategies. J Clin Oncol. 2022;40(6):598–610. 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Cancer, obesity and immunometabolism - Connecting the dots. Cancer Lett. 2018;417:11–20. Qin WH, Yang ZS, Li M, Chen Y, Zhao XF, Qin YY, Song JQ, Wang BB, Yuan B, Cui XL, et al. High Serum Levels of Cholesterol Increase Antitumor Functions of Nature Killer Cells and Reduce Growth of Liver Tumors in Mice. Gastroenterology. 2020;158(6):1713–27. O'Donnell VB, Rossjohn J, Wakelam MJ. Phospholipid signaling in innate immune cells. J Clin Invest. 2018;128(7):2670–9. Nair S, Dhodapkar MV. Natural Killer T Cells in Cancer Immunotherapy. Front Immunol. 2017;8:1178. Courtney AN, Tian G, Metelitsa LS. Natural killer T cells and other innate-like T lymphocytes as emerging platforms for allogeneic cancer cell therapy. Blood. 2023;141(8):869–76. Bae M, Cassilly CD, Liu X, Park SM, Tusi BK, Chen X, Kwon J, Filipčík P, Bolze AS, Liu Z, et al. Akkermansia muciniphila phospholipid induces homeostatic immune responses. Nature. 2022;608(7921):168–73. Trombetta A, Maggiora M, Martinasso G, Cotogni P, Canuto RA, Muzio G. Arachidonic and docosahexaenoic acids reduce the growth of A549 human lung-tumor cells increasing lipid peroxidation and PPARs. Chem Biol Interact. 2007;165(3):239–50. Koundouros N, Poulogiannis G. Reprogramming of fatty acid metabolism in cancer. Br J Cancer. 2020;122(1):4–22. Venn-Watson S, Lumpkin R, Dennis EA. Efficacy of dietary odd-chain saturated fatty acid pentadecanoic acid parallels broad associated health benefits in humans: could it be essential? Sci Rep. 2020;10(1):8161. Tao XM, Wang JC, Wang JB, Feng Q, Gao SY, Zhang LR, Zhang Q. Enhanced anticancer activity of gemcitabine coupling with conjugated linoleic acid against human breast cancer in vitro and in vivo. Eur J Pharm Biopharm. 2012;82(2):401–9. Kim Y, Kim J. N-6 Polyunsaturated Fatty Acids and Risk of Cancer: Accumulating Evidence from Prospective Studies. Nutrients 2020, 12(9). To NB, Truong VN, Ediriweera MK, Cho SK. Effects of Combined Pentadecanoic Acid and Tamoxifen Treatment on Tamoxifen Resistance in MCF-7/SC Breast Cancer Cells. Int J Mol Sci 2022, 23(19). Wu C, Chen H, Mei Y, Yang B, Zhao J, Stanton C, Chen W. Advances in research on microbial conjugated linoleic acid bioconversion. Prog Lipid Res. 2024;93:101257. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5662148","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392133731,"identity":"e61701ed-457a-4381-b3bb-5f1932e21dfa","order_by":0,"name":"Jia Yu","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Yu","suffix":""},{"id":392133732,"identity":"42b2c17c-9373-4c07-b2f7-f803118eadcb","order_by":1,"name":"Hanyan Xu","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hanyan","middleName":"","lastName":"Xu","suffix":""},{"id":392133733,"identity":"005f2a91-1935-49d0-8654-74d0f8805b3e","order_by":2,"name":"Fen Xiong","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fen","middleName":"","lastName":"Xiong","suffix":""},{"id":392133734,"identity":"33749ebb-ce55-4b22-8da7-10e3b09e93e8","order_by":3,"name":"Lingfei Meng","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lingfei","middleName":"","lastName":"Meng","suffix":""},{"id":392133735,"identity":"a87a262e-a3fd-4789-b864-c1a2c2bacf5e","order_by":4,"name":"Xiling Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiling","middleName":"","lastName":"Liu","suffix":""},{"id":392133736,"identity":"50e8e40b-28a7-4871-88af-2a802e1cee09","order_by":5,"name":"Hongchang Gao","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongchang","middleName":"","lastName":"Gao","suffix":""},{"id":392133737,"identity":"fdf9127f-59cb-487e-b39a-b24963c87dba","order_by":6,"name":"Yuping Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIie3RMQrCMBSA4cSAU0LmgIfIpAjFXqUgOHmIVwqurnXwGtLxhYBTsAfoGYR0dLPpCV43wfzDy/K+4RHGcrmfTAAyVkitgUx4IqeNaXEBmYYvLFREYHtXe9X10jLkcTxTCDrwKgxyJ0CY24NAtq6eyGWQe8C1UCTieSIvabGikudMcAEpAwd3D0dpWtfQbjFX7+O7O5RaNy6OFJJaKUjP/EHE+GfBci6Xy/1hX4V4O+mb0jIiAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yuping","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-12-17 13:23:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5662148/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5662148/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72362620,"identity":"de3c8886-e0f1-42bd-955b-9eb92dd6f95a","added_by":"auto","created_at":"2024-12-26 06:09:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1058606,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlasma lipid profile analysis at baseline.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLipidomics analysis of human plasma PCA and OPLS-DA score maps by comparing plasma samples in the PFS\u0026lt;12-months group and PFS≥12-months group. (A) ESI+: PCA maps, (B) ESI-: PCA maps, (C) OPLS-DA score maps in ESI+ (R2Y=0.72, Q2=0.4542), and (D) ESI-: (R2Y=0.745, Q2=0.456) patterns. (E) (F) Validation plots obtained from 200 alignment tests. ESI+: positive electrospray ionization mode, ESI-: negative electrospray ionization mode\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5662148/v1/7a2d22fb2296cc084ba41cfa.jpg"},{"id":72364448,"identity":"3c787890-637c-44fb-8ed5-4dfd6b9ca5cc","added_by":"auto","created_at":"2024-12-26 06:25:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2098506,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe lipid profile discriminated between the two groups of patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLipids with VIP\u0026gt;1 and FDR\u0026lt;0.05 were selected and volcano plot analysis was used to identify significant lipids characterized in ESI+ (A) and ESI- (B). (C) Heatmap showing the relative intensity of 56 different lipids in the PFS \u0026lt; 12-months and PFS ≥ 12-months groups in non-target lipidomics.VIP: variable important in prediction, FDR: false discovery rate. Fatty acids (FA), hydroxy fatty acid branched chain fatty acid esters (FAHFA), lysophosphatidylcholine (LPC), lysophosphatidylinositol (PE), phosphatidylserine (PS), phosphatidylcholine (PC), lysophosphatidylinositol (LPI), phosphatidylglycerol (PG), phosphatidic acid (PA), phosphatidylinositol (PI)\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5662148/v1/afc6ef6d788ef1267caf2bf8.jpg"},{"id":72364449,"identity":"e945c0c0-a21b-4a65-b239-7bb81ade44f1","added_by":"auto","created_at":"2024-12-26 06:25:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2381923,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantification of lipids level in plasma samples by Semitarged.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) FA-18:0, (B) FA-18:2, (C) PS-36:3 (D) PE-P-34:2, (E) FA-20:3, (F) FA-15:0, (G) PE-O-34:2, (H) LPI-18:1, (I) PI-40:4, (J) LPC-18:0, (K) LPC-14:0, (L) FA-20:4, (M) FAHFA-37:5, (N) FAHFA-35:5, (O) PA-36:2, (P) LPC-20:2, (Q) PG-36:3, (R) FAHFA-32:0, Fatty acids (FA), hydroxyfatty acid branched fatty acid esters (FAHFA), lysophosphatidylcholine (LPC), lysophosphatidyl Inositol (LPE), Phosphatidylinositol (PE) Phosphatidylserine (PS), Phosphatidylcholine (PC), Phosphatidylglycerol (PG), Phosphatidic Acid (PA), Phosphatidylinositol (PI), Lysophosphatidylinositol (LPI)\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5662148/v1/d1a504478338466a938794d7.jpg"},{"id":72363400,"identity":"aa5ee1df-ea55-4a88-97ae-680ef1f0eddb","added_by":"auto","created_at":"2024-12-26 06:17:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1874753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUnivariate ROC analysis of significantly different lipids.\u003c/strong\u003e\u003cbr\u003e\n (A) FA-18:0, (B) FA-18:2, (C) PS-36:3, (D) PE-P-34:2, (E) FA-20:3, (F) FA-15:0, (G) PE-O-34:2, (H) LPI-18:1, (I) PI-40:4, (J) LPC-18:0, (K) LPC-14:0, (L) FA-20:4, (M) FAHFA-37:5, (N) FAHFA-35:5, (O) PA-36:2, (P) LPC-20:2, (Q) PG-36:3, (R) FAHFA-32:0, Fatty acids (FA), hydroxyfatty acid branched chain fatty acid esters (FAHFA), lysophosphatidylcholine (LPC), phosphatidylinositol (PE), lysophosphatidylinositol (LPE), phosphatidylserine (PS), phosphatidylcholine (PC), phosphatidylglycerol (PG), phosphatidic acid (PA), phosphatidylinositol (PI), lysophosphatidylinositol (LPI)\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5662148/v1/3fc4c966eba604c9cf1effe3.jpg"},{"id":72362616,"identity":"b3257769-a9c0-4851-9a1a-6967c7ae2164","added_by":"auto","created_at":"2024-12-26 06:09:08","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":376351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC prediction of efficacy based on the combination of 13 lipid biomarkers \u003c/strong\u003e(A) ROC plot used to differentiate the PFS\u0026lt;12-months group from the PFS≥12-months group (AUC=0.950, p\u0026lt;0.0001). (B) To compare the predictive accuracy of biomarker groups in the PFS\u0026lt;12-months group and the PFS≥12-months group. PFS: progression-free survival\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5662148/v1/6ff76d4085d55a4e0f476a76.jpg"},{"id":72362622,"identity":"cafcf626-0821-4310-ad14-0d161071b8cd","added_by":"auto","created_at":"2024-12-26 06:09:08","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":399917,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLA inhibits PD-L1 expression in vitro. \u003c/strong\u003eLLC cells were treated with different concentrations of LA (0, 25, 50, 100, 150, or 200 μM) for 24 h. (A) PD-L1 protein expression was detected via a protein immunoblotting assay. (B) Bar graph showing the relative quantification of PD-L1 levels. GAPDH was used as a loading control. The data are expressed as the means ± standard deviations. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5662148/v1/0e770a929a90b2d30d8d329b.jpg"},{"id":72362626,"identity":"a861cdcb-715e-4a19-b008-044310f0b9c6","added_by":"auto","created_at":"2024-12-26 06:09:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":971493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLA inhibits the growth of tumor xenografts in vivo.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLLC cells were subcutaneously inoculated into female C57BL/6 WT mice. After 7 days, the mice bearing xenografts were randomly divided into four groups (n=5), which were orally administered saline (control) or LA (100 mg/kg) every day for 14 consecutive days and intraperitoneally injected with αPD-1 (200 μg) or IgG (control) every 3 days before sacrificeand tumor resection. (C) Ex vivo observation of tumors in mice. (D) Bar graph of ex vivo tumor volume in hormone-treated mice. (E) Tumortissues were lysed,and PD-L1 expression was detected by immunoblotting. (F) Bar graph showing the relative quantification of PD-L1 levels. GAPDH was used as a loading control. The data are expressed as the means ± standard deviations. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5662148/v1/39a9a8178158abfd5d8dc3f6.jpg"},{"id":72557896,"identity":"57c66b69-6a35-4e89-8e32-4c40a8cdd3b1","added_by":"auto","created_at":"2024-12-29 18:01:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9849385,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5662148/v1/5a7972e0-408e-45a9-8ce4-0ae8a0b8adb6.pdf"},{"id":72362617,"identity":"dd3e4d81-336d-48ee-8510-95cf03eaca36","added_by":"auto","created_at":"2024-12-26 06:09:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27268,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5662148/v1/0d559cb6937da364307a0682.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lipidomics reveals biomarkers of the efficacy of first-line ICIs therapy combined with chemotherapy in NSCLC","fulltext":[{"header":"1. Intrudction","content":"\u003cp\u003eLung cancer remains the most prevalent cancer globally, characterized by a high mortality rate. By the time of diagnosis, a significant proportion of patients are already in advanced and unresectable disease stages, leading to a poor prognosis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Tumor immunotherapy employing immune checkpoint inhibitors (ICIs) has brought new hope to advanced non-small cell lung cancer (NSCLC) patients[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, there is considerable variability in the response to immunotherapy among patients, affecting both the benefits and long-term survival outcome[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To date, various potential biomarkers have been investigated to identify patients who are more likely to respond favorably to ICIs[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Programmed cell death ligand-1 (PD-L1) has been recognized as a biomarker with some predictive value[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, numerous studies indicated that PD-L1 expression levels cannot accurately predict the treatment outcomes of ICIs therapy in NSCLC, especially when used in combination with chemotherapy[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This variability is partly due to individual heterogeneity[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, blood tumor mutational burden (bTMB) has emerged ascandidate biomarker for immunotherapy that can be evaluated by blood testing[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], but testing is expensive. The bTMB thresholds used in different studies are quite different, and the standardization of bTMB is another challenge that needs to be addressed[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Tumor-infiltrating lymphocytes, the neutrophil/lymphocyte ratio[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and gut microbes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] have also recently been found to have potential predictive value in assessing immune responses in a wide range of cancers but are still in the exploratory stage. Tumor immunity is a complex process, and single biomarkers have varying degrees of limitations.\u003c/p\u003e \u003cp\u003eCompared with monotherapy, combination therapy has the potential to enhance the efficacy of immunotherapy by promoting immune system activation. The American Cancer Society recommends the use of ICIs in combination with chemotherapy as the first-line standard treatment for patients with advanced NSCLC with negative driver genes[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Current studies have focused on the discovery of predictive markers of single-agent immunity, and there is no effective biomarker to predict patients who can long-term benefit from chemotherapy combined with ICIs treatment. Therefore, the discovery of predictive biomarkers is one of the most urgent research needs in chemoimmunotherapy.\u003c/p\u003e \u003cp\u003eLipidomics aims to elucidate metabolic pathways associated with various diseases and is increasing utilized for biomarker discovery, disease diagnosis, and disease development[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, this approach also provides novel insights and directions for understanding disease mechanisms. Many studies have demonstrated that lipid metabolism play a critical role in the development and progression of NSCLC. A study revealed that a set of four lipids identified in the lipidomic profile of plasma from patients with early-stage lung cancer could be used to predict early-stage cancer with high accuracy and predictive power compared with healthy subjects[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Single-cell RNA sequencing of early lung cancer revealed a general imbalance in lipid metabolism among different cell types, and nine lipids can serve as important features for early cancer screening[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Elevated level of cholesterol and long-chain fatty acid in serum had been correlated with better progression-free survival (PFS) and overall survival (OS) in NSCLC patients who have received ICIs treatment[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We believed that the response of NSCLC patients to ICIs treatment in combination with chemotherapy is an innate ability, which can be represented by a specific metabolic phenotype. Zheng[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] et al. revealed that higher serum concentrations of N-(3-indoleacetyl)-L-alanine and methotrexate metabolites correlate with poorer prognosis in NSCLC patients receiving ICI combination chemotherapy.\u003c/p\u003e \u003cp\u003eIn this study, plasma was collected from NSCLC patients and divided into two groups based on PFS. The plasma samples underwent lipid detection using liquid chromatography coupled with mass spectrometry (LC-MS)-based untargeted lipidomics for multivariate analysis to identify differentially abundant lipids distinguishing the two groups. Semi-targeted lipidomics were then used to quantify key lipids and screen for potential biomarkers distinguishing the two groups by significant difference and receiver operating characteristic (ROC) curve analysis. Many researchers believe that ICIs combination therapy with chemotherapy can promote antitumor immunity, increase remission rates, and produce durable remission, leading to better outcomes.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and patient\u003c/h2\u003e \u003cp\u003eThis study was conducted in the First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China. Patients with histologically confirmed advanced NSCLC at stage IIIB/IV according to the eighth edition of TNM classification who received first-line chemotherapy plus ICIs were enrolled from May 2019 to November 2023 in this study. The follow-up period ended in November 2023, and plasma samples were collected before ICIs treatment. Patients with obvious infection symptoms or severe autoimmune disease and those who stopped treatment due to various toxic adverse reactions within 12 months or who lacked follow-up data were excluded.\u003c/p\u003e \u003cp\u003eThe tumor response to therapy was evaluated based on the response evaluation criteria for solid tumors version 1.1 (RECIST v1.1). According to the literature on NSCLC immunotherapy, the 12-month PFS may be a crucial endpoint for patient prognosis. Therefore, patients were further classified into a short-term benefit group (PFS\u0026thinsp;\u0026lt;\u0026thinsp;12 months) and a long-term benefit group (PFS\u0026thinsp;\u0026ge;\u0026thinsp;12 months)[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. By comparing the two groups, we looked for potential markers of the efficacy of chemotherapy combined with ICIs. This study received approval from the Medical Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University (No. 2020084). All patients signed a written informed consent form for participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Plasma sample collection\u003c/h2\u003e \u003cp\u003ePatient blood was collected at baseline (pretreatment). Early morning fasting blood was collected in EDTA. The samples were left to stand at 25\u0026deg;C for approximately 30 minutes. The blood sample underwent centrifugation at 4000 \u0026times; g for 15 minutes at a temperature of 4\u0026deg;C. The upper layer of plasma was carefully collected and rapidly stored at -80\u0026deg;C until LC-MS analysis, without repeated freezing and thawing prior to lipid extraction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Lipid extraction\u003c/h2\u003e \u003cp\u003eThe plasma samples were first thawed in a 4\u0026deg;C refrigerator for approximately 10 minutes. To deproteinize and extract lipids, 400 \u0026micro;L of precooled (to -4\u0026deg;C) isopropanol solution was added to each 100-\u0026micro;L plasma sample, mixed thoroughly on a homogenizer (60 Hz, 2 min), and allowed to stand for about 15 min to ensure complete proteins precipitate. Following this, the mixture underwent centrifugation. Approximately 400 \u0026micro;L of the supernatant was collected in a new Eppendorf tube and dried under nitrogen for approximately 1 hour. Subsequently, 100 \u0026micro;L of isopropanol was added to the dried sample to dissolve and homogenize the mixture, which was then followed by centrifugation at 15,000 \u0026times; g for 20 min at 4\u0026deg;C. From the supernatant, 50 \u0026micro;L was removed in a liquid-phase vial containing a glass-lined tube, pending subsequent LC‒MS analysis. To monitor the instrument\u0026rsquo;s stability, 10 \u0026micro;L of each supernatant was aspirated to prepare a quality control (QC) sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 lipidomic detection and data analysis\u003c/h2\u003e \u003cp\u003eNontargeted lipidomics and semitargeted lipidomic acquisition and data analysis were performed according to previously published methods[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Detailed methods can be found in the supplementary material\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Cell experiments\u003c/h2\u003e \u003cp\u003eThe Lewis lung cancer cells (LLCs) selected for this experiment were mouse lung cancer cells purchased from Procell Life Science \u0026amp; Technology Co., Ltd. (Wuhan, China). LLC cells were cultured semiadherently in DMEM supplemented with 10% FBS and 1% antibiotics in a humidified incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e at 37\u0026deg;C. LLC cells were replaced once a day and passaged at a ratio of 1:3 every 2\u0026ndash;3 days. Cells in the logarithmic growth cycles were selected for passaging and counting. A total of 3\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells were inoculated in 6-well plates and incubated at 37\u0026deg;C for 24 hours to allow for stable attachment. The effects of different concentrations of LA on the PD-L1 expression level in LLC cells were investigated. LA at concentrations of 0, 25, 50, 100, 150, and 200 \u0026micro;M was added to the cells, and the cells were collected after 24 h for subsequent experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Animal experiments\u003c/h2\u003e \u003cp\u003eThe animal care and experimental protocols used in this study were approved by Wenzhou Medical University, which is the Experimental Animal Center Use Committee. First, the LLC cells were diluted to a concentration of 5x10\u003csup\u003e6\u003c/sup\u003e/mL with PBS and placed on ice for backup. Six- to eight-week-old female C57BL/6 mice were selected, 0.1 mL of single-cell suspension was aspirated and injected subcutaneously into the right axilla of each mouse, and the needle was rotated back to observe whether there was any fluid outflow. Tumor volumes were assessed every other day. When the average tumor volume reached approximately 75 mm\u003csup\u003e3\u003c/sup\u003e, the mice were randomly divided into 4 groups: the control group (200 \u0026micro;g/kg of IgG antibody (Selleck, China) administered intraperitoneally every 3 days and subjected to daily saline infusion), the LA group (100 mg/kg of LA (Sigma, USA) administered by gavage every day, as well as 200 \u0026micro;g/kg of IgG antibody administered intraperitoneally every 3 days), and the anti-PD-1 antibody(αPD-1) group (200 \u0026micro;g/kg of αPD-1 (Selleck, China) administered intraperitoneally every 3 days and subjected to daily saline infusion) and the LA combined with αPD-1 group (100 mg/kg LA administered by gavage every day, as well as 200 \u0026micro;g/kg of αPD-1 administered by intraperitoneal injection every 3 days), which were treated for a total of 2 weeks. The growth of the subcutaneously grafted tumors in the mice was observed every 2 days, and the tumor volume was measured via Vernier calipers. The tumor volume was calculated according to the following formula: tumor volume mm\u003csup\u003e3\u003c/sup\u003e = (length) \u0026times; (width)\u003csup\u003e2\u003c/sup\u003e \u0026times; 0.5. At the end of the experiment, the mice were euthanized by isoflurane anesthesia, after which the tumors were carefully removed, the volume and weight of the tumors were measured, and the tumors were photographed. Finally, we stored the tissues at -80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Protein immunoblotting experiments\u003c/h2\u003e \u003cp\u003eProtease inhibitors (Thermo Fisher Scientific, MA) and lysates (Thermo Fisher Scientific) were added to tumor tissues and cells to extract proteins. The protein concentration was detected via a BCA protein assay kit (Bio-Rad, CA, USA). Equal amounts of protein samples were added to a 10% polyacrylamide gel, and protein separation was performed via sodium dodecyl sulfate‒polyacrylamide gel electrophoresis (SDS‒PAGE). The proteins were subsequently transferred to a polyvinylidene difluoride membrane (0.45 mm, Millipore, Germany). The membrane was blocked with 10% nonfat milk for 1 h, after which it was incubated with primary antibody overnight at 4\u0026deg;C. The next day, the membranes were incubated with secondary antibody at room temperature for 1 h. Finally, the blots were incubated with enhanced chemiluminescence (ECL) luminescent reagent (Amersham Pharmacia Biotech, Piscataway, NJ) to visualize the immunoreactive bands, and the intensity of the bands was quantified via ImageJ software. All the following antibodies were used: PD-L1 (1:1000, Proteintech), anti-DAPI (1:1000, Proteintech), and secondary antibodies (Thermo Fisher Scientific, 1:5000).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, the grouping and processing of animals and cells, the collection and extraction of samples and other steps were carried out in strict accordance with the guidelines of randomized division. All the statistical analyses were performed via SPSS version 19.0 and GraphPad Prism version 8.00. The clinical data are expressed as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations or medians. Comparisons between two groups were accomplished via independent samples t tests, and one-way ANOVA was used to determine differences between multiple groups, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered to indicate a statistically significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Study design and clinical characteristics of NSCLC patients\u003c/h2\u003e\n \u003cp\u003eThe clinical characteristics of the NSCLC patients are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Clinical information on patients was obtained approximately 1 week before blood collection. We prospectively enrolled 120 patients; 60 patients were excluded from this study because of discontinuation of immunotherapy due to adverse immune reactions, and 11 patients did not have sufficient plasma samples for testing. Therefore, 49 patients with evaluable plasma samples were included in the study. As detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, there were no statistically significant differences between the two groups of patients in terms of age, sex, body mass index (BMI), tumor stage, or underlying metabolic diseases.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe clinical characteristics of NSCLC patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePFS\u0026thinsp;\u0026lt;\u0026thinsp;12moth(n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePFS\u0026thinsp;\u0026ge;\u0026thinsp;12moth(n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian (rang)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e62(57\u0026ndash;67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.5(62\u0026ndash;70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e62.24\u0026thinsp;\u0026plusmn;\u0026thinsp;7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.65\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e22(88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3(12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI,kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian(rang)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23.56(20.83\u0026ndash;25.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.65(20.80-23.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIIIB-IIIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7(28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15(62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12(50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSquamous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12(50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5(21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4(17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\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\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Identification of lipid profiling in the plasma of NSCLC patients\u003c/h2\u003e\n \u003cp\u003eIn our study, comprehensive untargeted lipidomics analysis was performed on plasma samples using LC‒MS. Our analysis revealed a significant number of characteristic peaks, totaling 1902 peaks identified in the positive ionization mode (ESI+) and 2420 peaks in the negative ionization mode (ESI-). To further examine the differences in lipid profiles between patients in the PFS\u0026thinsp;\u0026lt;\u0026thinsp;12-month group and the PFS\u0026thinsp;\u0026ge;\u0026thinsp;12-month group at baseline, we performed principal component analysis (PCA), and the results in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA and B show that there is a trend of separation in the lipid profile between the two groups. In order to better differentiate the metabolic differences between the two groups, we established an (Orthogonal Partial Least Squares Discriminant Analysis) OPLS-DA model that could effectively distinguish the differences in plasma lipid metabolism between the two groups. In the positive model, the OPLS-DA plot yielded an R\u003csup\u003e2\u003c/sup\u003eY value of 0.811 and a Q\u003csup\u003e2\u003c/sup\u003e value of 0.465 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC), and in the negative model, R\u003csup\u003e2\u003c/sup\u003eY\u0026thinsp;=\u0026thinsp;0.721 and Q\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.471 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). Then, we performed 200 alignment tests, which showed that the model had interpretability, reliability, and predictive ability. (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE, \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF). Therefore, we concluded that there was a significant difference in the plasma lipid mzetabolism phenotype between the two groups of patients before first-line ICIs combined with chemotherapy. We hypothesized that in vivo lipid levels may be a key determinant of the different prognoses of NSCLC patients after immunotherapy. The results of volcano plot analysis in the ESI\u0026thinsp;+\u0026thinsp;mode and ESI- mode with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 as screening conditions, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, revealed that 87 metabolite peaks were upregulated and 37 were downregulated in the positive ion mode compared with those in the PFS\u0026thinsp;\u0026lt;\u0026thinsp;12-month group. In the negative ion mode, 364 metabolite peaks were upregulated and 1 was downregulated compared with those in the PFS\u0026thinsp;\u0026lt;\u0026thinsp;12-month group. Based on m/z, RT, and MS/MS fragment data, 56 lipids were identified as known substances, as shown in Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e. A total of 56 different lipids were identified in both groups, including fatty acids (FAs), branched fatty acid esters of hydroxy fatty acids (FAHFAs), lysophosphatidylcholine (LPC), lysophosphatidylinositol (PE), phosphatidylserine (PS), lysophosphatidylinositol (LPE), phosphatidylcholine (PC), lysophosphatidylinositol (LPI), phosphatidylglycerol ester (PG), phosphatidic acid (PA), and phosphatidylinositol (PI). The heatmap shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC depicts the relative intensities of differential lipids across the two groups. We noted that compared with the PFS\u0026thinsp;\u0026lt;\u0026thinsp;12-month group, the PFS\u0026thinsp;\u0026ge;\u0026thinsp;12-month group had higher concentrations of FA (including FA-15:0, FA-16:0, FA-16:1, FA-18:0, FA-18:1, FA-18:2, FA-20:1, FA-20:3, FA-20:4, FA-22:4, FA-22:5, and FA-22:6), FAHFA (including FAHFA-21:3, FAHFA-222:2, FAHFA-14:2, FAHFA-32:3, FAHFA-32:1, FAHFA-27:4, FAHFA-28:0, FAHFA-28:4, FAHFA-28:5, FAHFA-30:0, FAHFA-32:0, FAHFA-32:2, FAHFA-32:3, FAHFA-32:4, FAHFA-35:5, FAHFA-37:5, FAHFA-38:4), and LPC (LPC-14:0, LPC-16:0). These findings revealed differences in lipid metabolism between patients in the PFS\u0026thinsp;\u0026lt;\u0026thinsp;12-month group and those in the PFS\u0026thinsp;\u0026ge;\u0026thinsp;12-month group before ICIs plus chemotherapy.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.3 Effectiveness of the use of multiple lipid biomarkers to predict the efficacy of first-line chemotherapy plus ICIs treatment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn order to detect the concentration level of lipids in the blood, we monitor the mass of precursors and two characteristic fragments of 56 lipids via LC-MS in combination with this MRM method, of which 18 specific lipids were highly responsive and significantly different. However, the remaining 38 lipids were not significantly different between the PFS\u0026thinsp;\u0026lt;\u0026thinsp;12-month group and the PFS\u0026thinsp;\u0026ge;\u0026thinsp;12-month group. LC‒MS-based semitargeted semiquantitative analysis revealed 18 species of lipids, including FA-18:0, FA-18:2, FA-15:0, PS-36:3, PE-P-34:2, LPC-18:0, FA-20:3, LPI-18:1, LPC-14:0, PI-40:4, PE-O-34:2, FA-20:4, FAHFA-37:5, PA-36:2, FAHFA-35:5, LPC-20:2, FAHFA-32:0, and PG-36:3. The quantitative concentrations of the 18 lipids were subsequently statistically analyzed between the PFS\u0026thinsp;\u0026lt;\u0026thinsp;12 and PFS\u0026thinsp;\u0026ge;\u0026thinsp;12-month groups, and the concentrations of the 18 lipids in the plasma are shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The results revealed that patients in the PFS\u0026thinsp;\u0026ge;\u0026thinsp;12-month group had significantly higher levels of 18 lipids compared with those in the PFS\u0026thinsp;\u0026lt;\u0026thinsp;12-month group. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, univariate ROC curves of these 18 lipids were used as biomarkers to establish a predictive model that was designed to predict long-term survival in NSCLC patients receiving first-line ICIs therapy in combination with chemotherapy. The results showed that FA-18:0 (AUC\u0026thinsp;=\u0026thinsp;0.813, P\u0026thinsp;=\u0026thinsp;0.0002), FA-18:2 (AUC\u0026thinsp;=\u0026thinsp;0.751, P\u0026thinsp;=\u0026thinsp;0.0025), FA-15:0 (AUC\u0026thinsp;=\u0026thinsp;0.765, P\u0026thinsp;=\u0026thinsp;0.0015), PS-36:3 (AUC\u0026thinsp;=\u0026thinsp;0.75, P\u0026thinsp;=\u0026thinsp;0.0027), PE-P-34:2 (AUC\u0026thinsp;=\u0026thinsp;0.739, P\u0026thinsp;=\u0026thinsp;0.0041), LPC-18:0 (P\u0026thinsp;=\u0026thinsp;0.733, P\u0026thinsp;=\u0026thinsp;0.0053), FA-20:3 (AUC\u0026thinsp;=\u0026thinsp;0.722, P\u0026thinsp;=\u0026thinsp;0.0078), LPI-18:1 (AUC\u0026thinsp;=\u0026thinsp;0.721), LPC-14:0 (AUC\u0026thinsp;=\u0026thinsp;0.710, P\u0026thinsp;=\u0026thinsp;0.0117), PI-40:4 (AUC\u0026thinsp;=\u0026thinsp;0.628, P\u0026thinsp;=\u0026thinsp;0.028), PE-O-34:2 (AUC\u0026thinsp;=\u0026thinsp;0.708, P\u0026thinsp;=\u0026thinsp;0.0124), FA-20:4 (AUC\u0026thinsp;=\u0026thinsp;0.708, P\u0026thinsp;=\u0026thinsp;0.0124), FAHFA-37:5 (AUC\u0026thinsp;=\u0026thinsp;0.701, P\u0026thinsp;=\u0026thinsp;0.0117), PA-36:2 (AUC\u0026thinsp;=\u0026thinsp;0.698, P\u0026thinsp;=\u0026thinsp;0.0173), FAHFA-35:5 (AUC\u0026thinsp;=\u0026thinsp;0.688, P\u0026thinsp;=\u0026thinsp;0.0244), LPC-20:2 (AUC\u0026thinsp;=\u0026thinsp;0.652, P\u0026thinsp;=\u0026thinsp;0.0670), FAHFA-32:0 (AUC\u0026thinsp;=\u0026thinsp;0.659, P\u0026thinsp;=\u0026thinsp;0.0561), PG-36:3 (AUC\u0026thinsp;=\u0026thinsp;0.657, P\u0026thinsp;=\u0026thinsp;0.0600).The prediction model was valid when P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, so only 15 of the 18 lipids could be used as biomarkers for predicting the efficacy of first-line chemotherapy plus ICI treatment. Since an AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 is clinically significant, we chose an AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 as the screening condition for lipid biomarkers, and we identified these 13 lipids as potential biomarkers for prediction: FA-18:0, FA-18:2, FA-15:0, PS-36:3, PE-P-34:2, LPC-18:0, FA-20:3, LPI-18:1, LPC-14:0, PI-40:4, PE-O-34:2, FA-20:4, and FAHFA-37:5. To assess the predictive value of the combination of these 13 lipid biomarkers in predicting the efficacy of first-line chemotherapy in patients with NSCLC in combination with ICIs, we used a binary logistic regression equation to obtain a joint predictive probability index P value. A ROC curve was built based on the P value to assess the predictive efficacy of the model built for these 13 lipid biomarker groups. As shown in Fig.\u0026nbsp;5A, the AUC of the 13 lipid biomarker groups was 0.905 (sensitivity 83.31%; specificity 92%; 95% confidence interval (CI): 0.890\u0026ndash;1.000; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). These findings indicate that this model has high accuracy and sensitivity in predicting the efficacy of first-line chemotherapy plus ICIs treatment.\u003c/p\u003e\n \u003cp\u003eNext, the optimal cutoff value could be obtained based on the highest Youden index of these 13 lipid combinations, as shown in Fig. 5B. When the cutoff value was 0.371, 4 (16%) patients in the PFS\u0026thinsp;\u0026lt;\u0026thinsp;12-month group were incorrectly categorized as patients in the PFS\u0026thinsp;\u0026ge;\u0026thinsp;12-month group (false-negatives), and 1 (4.2%) patient in the PFS\u0026thinsp;\u0026ge;\u0026thinsp;12-month group was misclassified as being in the PFS\u0026thinsp;\u0026lt;\u0026thinsp;12-month group (false-positives). Therefore, the model constructed with these 13 biomarker groups predicted a prediction rate of 89.8% for patients with NSCLC who could survive for a long period of time treated with first-line chemotherapy plus ICIs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Linoleic acid (LA) reduces PD-L1 expression in lung cancer cells\u003c/h2\u003e\n \u003cp\u003eThe results of our experiments revealed that various differential lipids were significantly elevated in patients in the PFS\u0026thinsp;\u0026ge;\u0026thinsp;12-month group. This led us to speculate that lipid metabolism may play a crucial role in the immunotherapy for NSCLC. Many reports have demonstrated the multiple biological functions of PUFAs, especially \u0026omega;-3 and \u0026omega;-6 polyunsaturated fatty acids. Recently, LA (FA-18:2) was shown to enhance the antitumor function of cytotoxic T lymphocytes (CTLs) through metabolic reprogramming[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. This reprogramming prevents the depletion of T cells and promotes their conversion to a memory phenotype with enhanced cytotoxicity. Recently, oral supplementation with LA combined with PD-L1 blockade was found to enhance the inhibition of pancreatic cancer and reverse cancer resistance to immunotherapy in mice[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, the role of LA in lung cancer immunotherapy is not yet known. Therefore, in the present study, based on the abovementioned lipidomics results, we ultimately selected LA as a representative lipid to further investigate its mechanism related to immunotherapy for lung cancer.\u003c/p\u003e\n \u003cp\u003eAs PD-L1 engages with PD-1 on T cells to dampen T-cell activity, it enables cancer cells to evade immune surveillance. Therefore, we subjected LLC mouse lung cancer cells to varying concentrations of LA (0, 25, 50, 100, 150, and 200 \u0026micro;M) for 24 hours and assessed PD-L1 expression via protein blotting. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e (A) and (B), the surface PD-L1 levels on tumor cells exhibited a gradual decrease with increasing concentration of LA. The reduction in PD-L1 expression results in a decrease in the interaction between PD-L1 and PD-1, which could reverse PD-L1-mediated immunosuppression and, potentially, impending tumor growth. These findings suggest the potential of LA in regulating PD-L1-mediated immunosuppression.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 LA mediated growth inhibition and immune response in LLC xenografts\u003c/h2\u003e\n \u003cp\u003eIn cell culture models, we found that LA has inhibitory effects on PD-L1 expression, and we next speculated that these effects could be realized in vivo. To investigate this further, we established a xenograft model by subcutaneously injecting LLC mouse lung cancer cells into C57BL/6 mice. Subsequently, these mice were randomly divided into four groups: the control group, LA group, \u0026alpha;PD-1 group, and LA plus the \u0026alpha;PD-1 group. Our results, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e(A) and (B), demonstrate a significant reduction in the tumor volume in the LA group, \u0026alpha;PD-1 group, and the combination group compared to the control group, indicating the antitumor potential of LA. Particularly noteworthy is the synergistic antitumor effect observed in the LA combined with the \u0026alpha;PD-1 group, as evidenced by a substantial decrease in tumor volume compared to the \u0026alpha;PD-1 group alone. Furthermore, consistent with our in vitro findings, the protein expression of PD-L1 in tumor tissues was notably decreased in the LA group compared to the control group, as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e(C) and (D). These results suggest that LA not only exhibits antitumor effects in vivo but also downregulates PD-L1 expression, supporting its potential as a therapeutic agent in promoting PD-L1-mediated antitumor.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIt has been reported that elevated serum cholesterol and long-chain fatty acid levels correlate with good efficacy of nivolumab in patients with NSCLC[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A recent study analyzing the pretreatment serum of NSCLC patients treated with pembrolizumab revealed that serum eicosapentaenoic acid (EPA) levels and the ratio of EPA to arachidonic acid (AA) (EPA/AA) were greater in long-term survivors than in short-term survivors[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our experiments has identified 13 lipids (FA-18:0, FA-18:2, FA-15:0, PS-36:3, PE-P-34:2, LPC-18:0, FA-20:3, LPI-18:1, LPC-14:0, PI-40:4, PE-O-34:2, FA-20:4, FAHFA-37:5) that were enriched in patients exhibiting favorable outcome, serving as potential biomarkers with high sensitivity and specificity for predicting the efficacy of first-line ICIs in combination with chemotherapy. Therefore, we posit that the distinct metabolic profiles of patients prior to treatment may influence the response to first-line ICIs combined with chemotherapy. Given the ease and non-invasiveness of plasma lipid level assessments and the safe and accessible nature of lipid-based interventions, these natural componds hold promise as adjunct therapeutic agents to enhance the efficacy of ICIs in combination with chemotherapy.\u003c/p\u003e \u003cp\u003eIn the tumor microenvironment, cancer cells compete with immune cells immune cells for lipids, which are are less active due to the inability of immune cells to undergo normal metabolism [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Qin et al[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]found that increasing exogenous lipids can restimulate the activation of immune cells. They also found that natural killer (NK) cells increased in number and activity in high cholesterol-fed mice, and that elevated serum cholesterol levels also promoted the activation of cellular immune signaling and cellular antitumor effects. Therefore, we suggest that elevated serum lipid levels may have the potential to enhance anti-tumor immunity.\u003c/p\u003e \u003cp\u003ePhospholipids make up about 1/3 of blood lipids and are widely found in the human body. Phospholipids are major components of biological membranes and have biological activities involved in various signal transduction. Phospholipids can induce immune cell migration, activation, and regulation of immune function[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Several studies have shown that phospholipids bind to and activate the CD1 receptor on the surface of natural killer T (NKT) cells, which are innate T-lymphocytes that rapidly produce large amounts of cytokines when stimulated by antigens and play an important role in the host immune response to cancer[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Derosa et al[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. have found that Akkermansia muciniphila (AKK) is associated with clinical benefit in lung cancer patients treated with PD-1 inhibitors. AKK colonies were enriched in the feces of patients who responded well to immunotherapy. Recently, Bae et al[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. found the identification of a phospholipid from AKK cell membranes that promotes immune homeostasis by stimulating the TLR2-TLR1 signaling pathway. And based on our lipidomics results, we found a significant increase in phospholipids in the PFS\u0026thinsp;\u0026ge;\u0026thinsp;12 months group, so we believe that phospholipids may be a potential in predictive biomarker.\u003c/p\u003e \u003cp\u003ePUFAs that promote apoptosis and inhibit cell proliferation in many malignant tumors, including breast, liver, and pancreatic tumors[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Excessive accumulation of PUFAs is mediated by the induction of the ER stress response, the activation of caspase-3, and the activation of tumor necrosis factor-alpha (TNF-α). TNF-α signaling promotes apoptosis[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. PUFAs (i.e., eicosapentaenoic acid, LA, and EPA) increase the chemosensitivity of tumor cells. In addition, they reduce the side effects of chemotherapy and protect target tissues without any adverse effects on nontargeted tissues. Tumor cells exhibit reduced total PUFA content, contributing to their resistance to chemotherapy and lipid peroxidation[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Conjugated linoleic acid (CLA) in combination with gemcitabine improves anticancer activity as well as bioavailability against breast cancer in vitro and in vivo[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].Studies have revealed a negative correlation between the intake or blood levels of PUFAs and cancer risk[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Studies have shown that PUFAs reduce the growth of human lung tumor cells in a concentration-dependent manner[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. PUFAs are known to promote proinflammatory responses, which are potentially beneficial in the context of immunotherapy. PUFAs acts as an essential fatty acid because of its wide range of anti-inflammatory and anti-proliferative activities in vivo and in vitro[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and PUFAs inhibits the IL-6-induced JAK2/STAT3 signaling pathway in human breast cancer cells and has synergistic antitumor effects in combination with chemotherapeutic compounds[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Oral administration of LA in combination with an anti-PD-L1 antibody in a mouse model of pancreatic cancer significantly increased CD8\u0026thinsp;+\u0026thinsp;T-cell infiltration and reduced the proportion of depleted PD-1\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cells in the tumor. Recent studies have shown that after being ingested into the body, LA is converted via gut microbes into CLA, which is required to induce the production of a specific type of immune cell, CD4\u0026thinsp;+\u0026thinsp;CD8α\u0026thinsp;+\u0026thinsp;intraepithelial lymphocytes, in the small intestine[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Most of the current studies on the effects of LA on immunity have focused on its effects on immune cells (e.g., T cells). To identify lipids that can promote the efficacy of ICIs, we selected LA for cell and animal experiments. Our results revealed that the tumor growth of mice treated with a PD-1 inhibitor combined with LA slowed in the LLC model, indicating the efficacy of the anti-PD-1 inhibitor. Mechanistically, LA can inhibit the expression of the PD-L1 receptor on tumor cells and the surface of tissues, thus promoting the immune response of tumors. Our findings reveal a potential role that LA may play in cancer immunity and may be a particularly promising cancer treatment strategy in combination with ICIs.\u003c/p\u003e \u003cp\u003eHowever, there are some limitations of this work. First, because of the limited sample size, we need to conduct a large-scale cohort study to verify the results. Second, our study was limited to plasma samples, so supplementation with a combination of urine, tissue, and other samples can further confirm the metabolic changes in the process of disease occurrence and development. Finally, further studies to explore the effects of LA on the tumor microenvironment and how it modulates the immune system to enhance the antitumor effect remain crucial.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study revealed that 13 lipids comprising biomarker groups (FA-18:0, FA-18:2, FA-15:0, PS-36:3, PE-P-34:2, LPC-18:0, FA-20:3, LPI-18:1, LPC-14:0, PI-40:4, PE-O-34:2, FA-20:4, and FAHFA-37:5) could predict long-term survival in advanced NSCLC patients treated with first-line chemotherapy combined with ICIs. LA can regulate the expression of PD-L1 on the surface of lung cancer cells and has synergistic antitumor effects with anti-PD-1 antibodies. LA was found to have potential value as a combined immunotherapy in cell and animal experiments, but further in-depth studies are needed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study was approved by the ethics committee of the First Affiliated Hospital of Wenzhou Medical University (ethics approval No. 2020084) and complied with the declaration of Helsinki. Informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe promise to agree to publish our article in this magazine\u003c/p\u003e\n\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by Wenzhou Municipal Science and Technology Bureau (No. Y2020001).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eJia Yu, Yuping Li, and Hongchang Gao design the experimental. Yuping Li and Hongchang Gao provision of study materials and patients. Lingfei Meng contributed to sample collection and clinical information collection. Jia Yu, Feng Xiong and xiling Li contributed to sample preparation and metabolomic analysis. Jia Yu and Fen Xiong contributed to the data analysis and manuscript writing. Jia Yu, Fen Xiong, Hanyu Xu and Yuping Li contributed to the result discussion and interpretation. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe would like to thank all patients in this study. We thank for all participants for collecting the data. We also thank the Scientific Research Center of Wenzhou Medical University is acknowledged for its technical services.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe data used and/or analyzed are available from the corresponding author on a reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarrugal \u0026Aacute;, Ojeda L, Paz-Ares L, Molina-Pinelo S, Ferrer I. 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Prog Lipid Res. 2024;93:101257.\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":"Biomarkers, Non-small cell lung cancer, Lipidomics, Immune checkpoint inhibitors","lastPublishedDoi":"10.21203/rs.3.rs-5662148/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5662148/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImmune checkpoint inhibitors (ICIs) plus chemotherapy have become the first-line standard therapy for patients with the gene-negative advanced non-small cell lung cancer (NSCLC). There is a lack of reliable biomarkers to predict treatment outcomes. This study aimed to identify relevant lipids that can predict treatment outcomes in NSCLC patients receiving first-line ICIs plus chemotherapy via lipidomics. Plasma samples were collected from Forty-nine patients with stage IIIB/IV NSCLC before the start of treatment, and patients were categorized into a long-term benefit group (progression-free survival [PFS]\u0026thinsp;\u0026lt;\u0026thinsp;12 months) and a short-term benefit group (PFS\u0026thinsp;\u0026ge;\u0026thinsp;12 months). We identified13 lipids (FA-18:0, FA-18:2, FA-15:0, PS-36:3, PE-P-34:2, LPC-18:0, FA-20:3, LPI-18:1, LPC-14:0, PI-40:4, PE-O-34:2, FA-20:4, FAHFA-37:5) to predict the therapeutic efficacy of ICIs plus chemotherapy with high specificity and sensitivity We further investigated the role of linoleic acid (LA) (FA-18:2), a pivotal lipid involved in immune regulation, in animal and cellular models and explored its potential in enhancing NSCLC immunotherapy. Our results showed that LA can assist PD-1 inhibitors in exerting immune anti-tumor effects, slowing down tumor growth in mouse models, and suppressing the expression of PD-L1 proteins in both Lewis cells and tumor tissues. This study found that lipids were important biomarkers for predicting the efficacy of first-line ICIs plus chemotherapy in NSCLC patients, of which LA is an important adjuvant therapy for immune response.\u003c/p\u003e","manuscriptTitle":"Lipidomics reveals biomarkers of the efficacy of first-line ICIs therapy combined with chemotherapy in NSCLC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-26 06:09:04","doi":"10.21203/rs.3.rs-5662148/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":"552b3ef1-06bd-4b2f-8292-a3a28685f31e","owner":[],"postedDate":"December 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-16T22:13:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-26 06:09:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5662148","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5662148","identity":"rs-5662148","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}