Comprehensive analyses of metabolism-related lncRNA for LUAD

Comprehensive analyses of metabolism-related lncRNA for LUAD | 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 Comprehensive analyses of metabolism-related lncRNA for LUAD Xinti Sun, Xingqi Huang, Linao Sun, Peng Zhang, Zesheng Li, Xiaojuan Sun, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1593827/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 Lung adenocarcinoma(LUAD), as a tumor with high heterogeneity, strong invasiveness, and high degree of malignancy, has become a hot issue in contemporary medicine. With the deepening of tumor research, the metabolic reprogramming of tumor cells and the regulatory role of lncRNA in tumor progression have gradually become prominent. This article aims to explore the relationship between the above three. In this study, based on LUAD samples downloaded from TCGA and metabolism-related genes downloaded from KEGG database, 8 key metabolism-related lncRNAs (AC068228.1, LINC02390, AC123595.1, AC021016.1, LINC00707, AL132656.2, AL033397.2 and LINC00941) were screened for the construction of prognostic risk models. After multi-dimensional validation, this risk model proved to have good reliability and validity and was closely related to the immune status and drug tolerance of LUAD patients. In addition, to better clarify the molecular mechanism by which these key lncRNAs affect the LUAD process, we performed functional enrichment analysis and found their close relationship with hematopoietic cell lineage, lipid metabolism, and human humoral immunity. To increase the credibility of this study, we verified the expression levels of these eight lncRNAs in BEAS-2B, A549 and H1975 cell lines, and the results of PCR experiments were in good agreement with our risk model. Overall, the above studies aim to improve the understanding of LUAD and open up new ideas for guiding clinical treatment. lncRNA tumor metabolism lung adenocarcinoma tumor immune bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Lung cancer is the malignant tumor with the highest morbidity and mortality worldwide, and lung adenocarcinoma (LUAD) is the primary subtype of non-small cell lung cancer in lung cancer. [ 1 ] [ 2 ]. Due to its undefined histopathological behavior and lack of early predictive biomarkers, the overall prognosis for LUAD patients remains poor [ 3 ]. The 5-year survival rate is lower than 10% [ 4 ]. Additionally, only a minority of them benefit from clinical treatment. Therefore, identifying effective biomarkers for accurate prognostic prediction is imperative. Long non-coding RNAs (lncRNAs), as a type of non-coding RNA sequence measuring approximately 200 nucleotides in length, served as regulators in various biological functions [ 5 , 6 ]. Recently, many studies suggested that lncRNAs are related to oncogenesis, suppression, and metastasis in LUAD and other tumor types [ 7 – 10 ]. For example, lncRNA MALAT1 is associated with lung cancer metastasis by regulating genes expression [ 2 ]. The lncRNA LUADT1 is highly expressed in LUAD and may stimulate cell proliferation in cancer cells by interacting with SUZ12 and modulating H3K27 trimethylation [ 11 ]. Many lncRNAs have emerged as novel biomarkers for predicting cancer prognosis, including lung cancer [ 12 , 13 ]. Li et al. constructed a seven lncRNA model to improve the predictive value for LUAD[ 14 ]. Similarly, Zhou et al. found eight lectures highly associated with LUAD [ 15 ]. These newly signatures related lncRNAs provide guidance to predict prognosis and contribute to better clinical treatment. Tumor cells usually live in an abnormal metabolic environment, depending on the imbalance between the rapid proliferation of tumor cells and nutrient angiogenesis [ 16 ] [ 17 ]. Contemporary thinking holds that tumor cells must change their metabolism to meet increased metabolic and synthetic demands and growth requirements [ 18 ]. Additionally, it is becoming increasingly clear that changes in cellular metabolism are related to cancer development and progression[ 19 ]. For example, Valtorta et al. have demonstrated that combination therapy based on the glutaminase inhibitor CB-839 and the PI3K/aldolase inhibitor NVP-BKM120 significantly reduced tumor growth. [ 20 ]. Deng et al. found that lncRNA GLS-AS inhibits the development of malignant tumors by impairing GLS-mediated metabolism [ 21 ]. However, the regulation of lncRNA on metabolic pathways in LUAD is unknown. Further understanding of the function and mechanism of metabolism-related lncRNAs in LUAD might provide a deep insight into the potential mechanism and find proper treatment. Based on the TCGA database, eight full metabolism-related lncRNA signature linked to the prognosis in LUAD patients was identified in the training set and verified well in the testing and entire collection. We compared the tumor microenvironment between high-risk and low-risk groups using our prognostic model. To further explore how these metabolism-related lncRNAs are involved in LUAD progression, we examined their relationship with LUAD immune signatures and tumor drug resistance. Functional enrichment analysis was also performed to search for potential LUAD progression mechanisms. Ultimately, we confirmed that these eight lncRNAs were differentially expressed between the LUAD cell lines (A549, H1975) and human standard bronchial epithelium cell line BEAS2B by a qRT-PCR test. The model based on eight metabolism-related lncRNAs helps predict prognosis in LUAD patients and promotes more individualized treatment. Materials And Methods Data preparation and Preprocessing Sequencing data of lncRNA, somatic mutation profiles, and clinical information on LUAD were downloaded from the TCGA database [ 22 ]. 816 metabolism-associated genes were acquired from the GSEA database. The differentially expressed genes were obtained by using the “limma” R package ((FDR) 0.4 and P -value < 0.001). LUAD patients with short OS values (< 30days) and missing overall survival (OS) values were deleted. 490 samples were collected and divided into training and testing sets. The training set, including 246 samples, was used to construct the risk model. The testing set, including 244 samples, was used to validate the risk model. Establishment and Assessment of Prognostic Model Eight lncRNAs significantly associated with prognosis were identified by performing univariate Cox, LASSO, and multivariate regression analysis using the "glmnet" package in R. A metabolism-related risk model was constructed. The following formula calculates the risk score: $$Risk score={\sum }_{k=1}^{n}Coef\left(lncRNA\right)*expr\left({lncRNA}^{k}\right)$$ The coef in the formula is the coefficient of correlation between lncRNAs and survival, and expr represents the expression of lncRNAs. Patients in the high and low-risk groups were defined according to the median risk score. The model was confirmed to independently predict the prognosis of LUAD by comparing it with other clinical factors using univariate Cox regression and multivariate Cox regression analyses. The nomogram was established to better predict the LUAD patients’ survival using “rms” packages in R. Using the Harrell concordance index and a calibration curve, the nomogram was verified for accuracy. Kaplan- Meier survival analysis was applied to test the accuracy of the risk model by using the "survival" package. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE)research were further conducted to verify the risk model. Evaluation of Tumor Immune Microenvironment The infiltration status of immune cells among high- and low-risk groups was calculated using the CIBERSORT and ssGSEA algorithms. StromalScore, ImmuneScore, and ESTIMATE Score of patients were calculated using the “ESTIMATE” package to explore TME in LUAD patients further. The R package “maftools” was conducted to measure the tumor mutation burdens (TMBs). Exploration of Clinical Treatment. The differences in tumor immune dysfunction and exclusion (TIDE) between the two groups of LUAD patients were evaluated. We measured common anti-tumor drugs' median inhibitory concentration (IC50) to guide our clinical treatment using the R package “pRRophetic”. The data of the immune subtype was downloaded on TIMER( http://timer.comp-genomics.org/ )[ 23 ]. We also evaluated the expression levels among high-risk and low-risk groups on PD-1, PD-L1, and others ICI-related biomarkers expression. Functional analysis. GO and KEGG enrichment analysis was conducted with the R package “cluster profile” and “enrich plot”. Gene set enrichment analysis was performed to further screen possible enrichment functional pathways using software GSEA 4.2.1 ( http://www.gesa-msigdb.org/gsea/index,jsp ). The software Cytoscape established the co-expression network between lncRNA and mRNA (version 3.6.1). Cell Line and Reagents Human standard lung epithelial cell lines BEAS-2B and LUAD cell lines (NCI-H1975, A549) were received from the American Type Culture Collection (Manassas, VA, USA). All cells were developed in PRMI 1640 medium (Beijing, China), supplemented with 10% fetal bovine serum (10% FBS), and in a humidified environment at 37°C and 5% CO2. Total RNA Extraction and Real-Time Quantitative PCR RNAiso was used to extract total cellular RNA. 1ug of RNA was used to reverse transcribe the cDNA of the 20ul system. Quantitative real-time PCR was performed according to a total of 25ul reaction system of 1ul F-primer, 1ul R-primer, 12.5ul TB green, 1ul cDNA, and 9.5ul DEPC water. GAPDH was used as an internal reference to standardize the relative expression of target genes. The 2-ΔΔCT method was adopted to determine the relative expression of target genes. The PCR primer sequences directly were synthesized (Sangon Biotech, China) and are shown in Supplementary Table S8 . Statistical Analysis R platform (version 4.1.2) and GraphPad Prism 8 are applied to process, analyze and present the data. When no special instructions were given for the above analysis methods, P < 0.05 was considered statistically significant. Result Identification of metabolism-related lncRNAs in LUAD patients The workflow is presented in Figure 1 . We screened 490 LUAD patients and 14056 lncRNAs for this analysis. Table 1 shows the clinical details of samples in the training and testing sets. We analyzed and compared the expression levels of the 816 metabolism-associated genes among normal and tumor samples and identified 185 differentially expressed metabolism genes. Moreover, we calculated the corresponding lncRNA expression matrix of these genes. Of them, 43 were downregulated, and 142 were upregulated. The expression distribution of metabolism-associated differently expressed genes in LUAD was displayed in Figure 2A-2C . Analyses of the GO enrichment indicated that 185 differentially expressed genes participated in many critical physiological processes, especially in the alpha-amino acid metabolic and small molecule catabolic process, mitochondrial matrix, basolateral plasma membrane, anion transmembrane transporter activity, and active transmembrane transporter activity ( Figure 2D ). KEGG analysis discovered that 185 differentially expressed metabolic genes were associated with signaling pathways, including biosynthesis of amino acids, biosynthesis of cofactors, purine metabolism, and carbon metabolism ( Figure 2E ). Finally, the coexpression network between metabolism-related lncRNAs and differentially expressed metabolic genes was shown in the San-key diagram ( Figure 2F ), and 2633 metabolism-related lncRNAs were identified as metabolism-related lncRNAs. The correlation between metabolism-related lncRNAs and corresponding genes, such as ABCA8, PMM2, and lncRNAs, was shown in (Supplementary Table S1 ) and visualized in ( Figure 2G ). Table 1 Sample details of the training group and the test group Covariates Type Total Test Train P -value Age 65 249(50.82%) 129(52.87%) 120(48.78%) unknow 10(2.04%) 5(2.05%) 5(2.03%) Gender FEMALE 262(53.47%) 130(53.28%) 132(53.66%) 1 MALE 228(46.53%) 114(46.72%) 114(46.34%) Stage Stage I-II 378(77.14%) 192(39.18%) 186(37.96%) 0.8448 Stage III-IV 104(21.22%) 48(9.80%) 56(11.43%) unknow 8(1.63%) 4(1.64%) 4(1.63%) T T1-2 426(86.94%) 210(42.86%) 216(44.08%) 0.8287 T3-4 61(12.45%) 32(6.53%) 29(5.92%) unknow 3(0.61%) 2(0.82%) 1(0.41%) M M0 324(66.12%) 161(65.98%) 163(66.26%) 0.5842 M1 24(4.9%) 10(4.1%) 14(5.69%) unknow 142(28.98%) 73(29.92%) 69(28.05%) N N0 317(64.69%) 161(65.98%) 156(63.41%) 0.8888 N1-3 162(33.06%) 77(15.71%) 85(17.35%) unknow 11(2.24%) 6(2.46%) 5(2.03%) Construction and Validation of Risk Model We selected 527 metabolism-related lncRNAs by univariate COX regression analysis ( Figure 3A , Supplementary Table S2 ). LASSO regression analysis was further applied, with the result that 15 lncRNAs showed a strong correlation with the OS of LUAD patients ( Figure 3B-3C ). Finally, we employed multivariate Cox regression analysis for screening the eight metabolism-related lncRNAs ( Table 2 ) to construct the risk model (Figure 3D). Table 2 Multivariate cox regression analysis of 8 hub lncRNAs coef HR HR.95L HR.95H pvalue AC068228.1 1.04537938 3.31067518 2.069563114 5.296079195 5.91E-07 LINC02390 -2.765775643 0.05374742 0.010851902 0.266200811 0.000341925 AC123595.1 -0.612033991 0.305070349 0.148148482 0.62820703 0.001275727 AC021016.1 -0.536593072 0.274268488 0.144707684 0.519828675 7.32E-05 LINC00707 0.409269302 1.955105801 1.532233927 2.494683498 6.98E-08 AL132656.2 -1.227239484 0.125174908 0.039862049 0.393074569 0.00037181 AL033397.2 0.313575844 1.47613924 1.183856926 1.840583105 0.00054189 LINC00941 0.330708502 1.778741168 1.396296046 2.265937908 3.12E-06 A risk score was obtained based on the following formula: risk score= expression of AC068228.1×（1.04537938048222）+ expression of LINC02390×（-2.76577564344421）+ expression of AC123595.1×（-0.612033991186885）+ AC021016.1×（-0.53659307165381）+ expression of LINC00707×（0.40926930226994）+ expression of AL132656.2×（ -1.22723948408621）+ expression of AL033397.2×（0.3135758443623）+ expression of LINC00941×（ 0.330708502412192） We defined these LUAD patients as two groups: low-risk groups and high-risk groups(based on the median value of the predictive risk scores). Figure 3E presented the distributional patterns of risk scores among two subgroups in the training set. We summarized the survival parameters of these patients in Figures3F. The results suggested that as risk score increased, OS time decreased while mortality rise. Figure 3G illustrated the relative expression difference for the eight metabolism-related lncRNAs in the training set. Then, we performed a K-M analysis and found that the high-risk group held an inferior OS rate than that of the low-risk group ( p < 0.001 ) ( Figure 3H ). Additionally, we evaluated the model’s efficacy based on the testing set and the entire set. We summarized the risk scores, survival parameters, and expression of the eight metabolism-related lncRNAs in the abovementioned two sets and listed them in ( Figure 4A-4F). K-M analyses presented the same outcomes based on the testing set and the entire set ( Figure 4G-4H ). All the above results supported the power of our risk model. Independent Prognostic Analysis and Nomogram Using univariate Cox and multivariate Cox regression analyses, we combined metabolism-related lncRNAs with clinical parameters to investigate whether the risk model could be used independent factor for predicting the survival of LUAD. As shown in the univariate cox regression analysis, the hazard ratio (HR) of the risk model was 1.04, and the 95% confidence interval (CI) was 1.029–1.061 (p< 0.001) ( Figure 5A ). Multivariate cox regression analysis showed that the HR was 1.041 and CI was 1.024–1.059 (p< 0.001) ( Figure 5B ). Therefore, the risk model might serve as a prognostic factor independent of other clinical parameters such as age, sex, pathological stage and so on. Furthermore, we constructed a nomogram for better predicting the 1,3,5, -year OS of LUAD patients by combining the risk model with clinical factors, including gender, age, stage, TNM, and risk score ( Figure 5C ). After that, the prediction accuracy of the nomogram was assessed. Observed survival rates are blue, while the optimized survival rates are shown in gray, indicating a good match between them ( Figure 5D ). Assessing the Risk Model. We used receiver operating characteristic (ROC) curve analysis to verify the efficacy of the risk model. The 1-, 3-, and 5-year AUC of entire set was 0.843, 0.816, and 0.814 ( Figure 6A ). The AUC for the risk score was higher than the AUC for any other clinicopathological feature, fully demonstrating that the predictive risk model for LUAD is robust and highly reliable ( Figure 6B ). The concordance index also suggested the accuracy of the risk model ( Figure 6C ). Then, we employed P-principal-component (PCA) and t-SNE analyses to assess the distribution between the high and low-risk groups in training and testing sets ( Figure 6D-6E ). The PCA and t-SNE analysis results confirmed that the metabolism-related lncRNAs model had grouping capabilities. Besides, we also use PCA analysis to verify further the ability of the risk model between two subgroups based on the entire gene expression profiles, 185 differentially expressed metabolic genes, 2633 metabolism-related lncRNAs, and risk model according to the eight metabolism-related lncRNAs ( Figure 6F-6I ). The results confirmed that the distributional patterns of the high-risk and low-risk groups were significantly different, indicating that the risk model was competent to distinguish the two groups with high accuracy. We evaluated the discrepancies between two subgroups based on the universal clinicopathologic characteristics. By further grouping patients of LUAD by gender, age, stage, and TNM, survival analysis found that LUAD low-risk patients also had better OS than high-risk patients ( Figure 7 ). The above results indicated that the risk model maintained its powerful predictive ability among subgroups of different clinical features. Identifying the immune infiltration status We explored the immune infiltration status among two subgroups using the CIBERSORT algorithm ( Supplementary Table S3 ). The proportions of 22 immune cells in every patient were presented in ( Figures 8A-8B ). Furthermore, the results based on the ssGSEA algorithm revealed that some factors reflecting immune functions were upregulated in the low-risk subgroup (e.g., T_cell_co_stimulation, HLA, Type_II_IFN_Response) ( Figure 8C, Supplementary Table S4 ). The infiltration of ads, B_cells, DCS, iDCs, Mast_cells, Neutrophils, TIL, and T_helper_cells, was higher in the low-risk group ( Figure 8D ). These results suggest that the low-risk group has a higher immune infiltration status, combined with a better OS in the low-risk group, which we reasoned might contribute to the antitumor effect. Similarly, LUAD patients in the low-risk group also showed remarkably higher stromal, immune, and ESTIMATE scores, suggesting that the high-risk group held dissimilar TME( Figure 8E-G ). Furthermore, the comparison illustrates that approximately 93.49% of samples exhibited genetic mutations in the high-risk samples, as well as 82.19% of samples exhibited mutations in the low-risk samples were displayed in ( Figure 9A-9B ). Additionally, TMB scores were calculated from the TCGA mutation data. They presented a higher TMB status in the high-risk groups, indicatied that high-risk patients might benefit more from immunotherapy ( Figure 9C ). Therefore, we tested the correlation between the risk model based on metabolism-related lncRNAs and TMB ( Figure 9D , R=0.3, P =1.6e-11 ). The results showed that the metabolism-based classifier index was highly correlated with TMB. To investigate the impact of TMB state on prognosis in LUAD patients, we applied survival analysis based on high and low TMB groups. However, the survival curve of patients with high TMB was similar to patients with low TMB, indicating that the TMB failed to distinguish the survival in LUAD ( Figure 9E ). Additionally, we checked the efficacy of TMB scores related to risk scores based on the model to judge its predictive ability for predicting the OS outcomes ( Figure 9F ). Surprisingly, the model showed a significant predictive power for patients with LUAD. Besides, according to the immune subtype data in TIMER2.0, we tested whether a the-risk model based on the eight metabolism-related lncRNAs could identify the different immune subtypes ( Figure 9G , Supplementary Table S5 ). The result suggested that the risk model effectively distinguished the resistant subtype. Our findings might shield new light on understanding the molecular pathogenesis of LUAD from the perspective of metabolism-lncRNAs. Clinical Treatment and Drug sensitivity analysis Considering the significant differences in the immune microenvironment between the low-risk and high-risk groups, we speculated that responses to drugs, chemotherapy, critical ICPs, and immunotherapy might differ between the two groups. Using the “pRRophetic” package, we then evaluated the therapeutic response using the IC50 values of 138 anti-tumor drug patients obtainable in the GDSC database to explore potential drugs targeting our risk model and improve treatments for patients with LUAD. The IC50 of A.443654, A770041, AMG.706, AUY922, AZ6828, and AZD.0530 were higher in the low-risk group, suggesting high risk patients may respond better to those drugs. Interestingly, the ABT.888, AP.24534, ATRA, and Axitinib showed a higher level in the high-risk group ( Figure 10A ), indicating that low-risk patients were more sensitive to these drugs. Besides, with ICIs have been applied in the treatment of LUAD and other cancers, we further explored the differences in ICI-related biomarkers expression among two subgroups. The results presented that the low-risk group had high PD1, CTLA4, TIGIT, PD−L1, and HAVCR2 expression ( Figure 10B ). Additionally, we counted the IC50 of common anti-lung cancer drugs in two subgroups. Patients in the low-risk groups were related with a higher IC50 of targeted therapy such as erlotinib and gefitinib and chemotherapeutics like cisplatin, paclitaxel, etoposide, which indicated that the risk model served as a promising predictor of anti- tumor drug sensitivity ( Figure 10C ). Besides, we analyzed the correction between 8 metabolism-related lncRNAs and drugs. For example, the correlation coefficient between Sulfatinib and LINC00707 was the highest ( Cor=−0.433, p <0.001 )( Figure 10D ). As a result, we might be able to select the most appropriate drugs for LUAD patients. Besides, a high-risk group with lower TIDE scores may be more sensitive to immunotherapy than the high-risk group. So, our metabolism-based classifier index might serve as a powerful indicator for instructing clinical treatment ( Figure 10E ). Functional analysis To understand the potential biological process involved, we employed enrichment analysis to identify the signature of the eight metabolism lncRNAs ( Supplementary Table S6 ). As shown in Figure 11A , GO analysis revealed that it mainly participates in the humoral immune response, human antimicrobial response, clathrin-coated endocytic vesicle, multivesicular body, receptor-ligand activity, and signaling receptor activator activity. According to KEGG analysis, the signature was connected with Hematopoietic cell lineage, Amoebiasis, Arachidonic acid metabolism, Pancreatic secretion, and so on ( Figure 11B-11C ). Further, we leveraged GSEA software to explore better the differences in biological functions in the KEGG pathways ( Supplementary Table S7 ). The GSEA results illustrated that the high-risk group was enriched in the pathway such as cell cycle, DNA replication, homologous recombination, glycolysis gluconeogenesis, pyrimidine metabolism, and others ( Figure 11D ). In contrast, pathways such as allograft rejection, asthma, B/T cell receptor signaling pathway, and others were enriched in the low-risk group ( Figure 11E ). Besides, we presented metabolism-related differently expressed genes, eight metabolism-related lncRNAs, and risk types in the Sankey network ( Figure 11F ). Finally, with the help of Cytoscape, we presented an interaction network for visualizing the co-expression between the lncRNAs and mRNAs ( Figure 11G ). Verifying the expression Level of eight Prognostic lncRNAs in vitro We performed RT-qPCR to validate the expression Level of eight Prognostic lncRNAs using BEAS-2B and LUAD cells, including A549 and H1975 (Figure 12). three lncRNAs (LINC02390, AC021016.1, AC123595.1) exhibited significant downregulation in both LUAD cells. Given that the high expression of lncRNAs could represent a better survival (Figure 3D), they might function as tumor suppressor factors. TwolncRNAs (LINC00941, LINC00707) were upregulated in both LUAD cells. Similarly, and thus the high expression of the four lncRNAs could be representative of worse survival, suggesting that they may serve as risk factors. Interestingly, AL132656.2 was downregulated in A549 cells while upregulated in H1975 cells, so a deeper understanding of the specific mechanism is required. In addition, we also verified the differences in the expression of these eight critical lncRNAs in LUAD samples and standard tissue samples based on the TCGA database. The expression patterns of the remaining three lncRNAs were reversed. Except for LINC02390, the other lncRNA expression patterns were consistent with our risk model's respective coef coefficients of lncRNAs. Discussion Lung adenocarcinoma is currently the most aggressive and lethal of all human cancers and itself is a complex disease with high heterogeneity [ 24 ]. In addition, lung adenocarcinoma, as a disease with the poor therapeutic effects of radiotherapy and conventional chemotherapy, how to carry out the appropriate treatment for different stages of LUAD is also the most critical problem currently perplexing clinicians [ 25 , 26 ]. Because of these characteristics of lung adenocarcinoma, people are constantly pursuing a deeper understanding of this disease. With the deepening of research, the dimensions of people's insights into tumor occurrence and progression are gradually enriched. For example, the reprogramming of tumor cell metabolism is currently c key role in tumor development [ 27 ] [ 28 ]. From the discovery of the earliest Warburg effect [ 29 ] to the widespread downregulation of metabolic pathways such as AKT, mTOR, and hypoxia-inducible factors (HIFs) in cancer [ 30 ] [ 31 ], when electron transport chain (ETC) [ 32 ], pyruvate carboxylase (PC) [ 33 ] and the generation of reactive oxygen species (ROS) [ 34 ] were considered to be a critical link in the regulation of tumor development, people's understanding of the role of changes in tumor cell metabolic characteristics in tumor progression were constantly being updated and deepened. Today, the focus of tumor metabolism-related research has gradually shifted from individual tumor cell research to in vivo experimental research. The tumor microenvironment (TME), which is currently considered to be the most closely related factor to tumor progression, has also been proved to be inseparable from the metabolic changes of tumor cells [ 35 ] [ 36 ]. In addition, we employed genome-wide association studies (GWAS) to extract the information on tumor-related lncRNAs, and the "protagonist" of non-coding RNAs, lncRNAs, also demonstrate unique tumor progression regulation [ 37 ]. At the same time, we also noticed that lncRNAs could also be involved in the tumor progression by regulating the activity of key enzymes related to metabolism, such as phosphoglycerate kinase 1 (PGK1) [ 38 ], or by changing the activation of classical metabolic signaling pathways, such as AKT/mTOR signaling pathway [ 39 ]. Based on the above evidence, we explored how metabolism-related lncRNAs interact with TME, tumor immunity, and other related features in LUAD and how these lncRNAs affect the LUAD process. Normal tissue and LUAD samples were downloaded from the TCGA database based on the above research background. We obtained metabolic-related lncRNA datasets from the KEGG database. After using univariate, multivariate, and LASSO regression, we identified eight metabolism-related lncRNAs, which were AC068228.1, LINC02390, AC123595.1 AC021016.1, LINC00707, AL132656.2, AL033397.2, and LINC00941, and used them to build a LUAD prognosis prediction model. After multi-dimensional validation of the predictive model, we found that this model shows strong efficacy, which is reliable for prognostic prediction. To apply the model to the clinic, a nomogram was also constructed. We also explored the impact of these eight critical metabolism-related lncRNAs on LUAD progression, their correlations with immune signatures of LUAD, and sensitivity to chemotherapeutic agents. In addition, to further explore how LUAD patients with different risk levels differ in molecular processes, we employed functional enrichment analysis for analyzing genes with an other expressions between the two groups. Finally, to verify the actual face of the eight lncRNAs we predicted and screened in LUAD, we performed real-time quantitative PCR verification using the LUAD cell line. The results proved the accuracy and scientificity of our prediction to a certain extent. Of the 8 selected lncRNAs, 5 (AC068228.1, LINC02390, AC021016.1, AL132656.2, and AL033397.2) were not studied. AC123595.1 was available for predicting the prognostic survival risk of LUAD patients in the studies of Jian-Ping Li [ 40 ] and Yugang Guo [ 41 ], respectively. It was closely related to the tumor immunity and ferroptosis process of LUAD. At present, there are relatively many studies on LINC00707. This lncRNA has been proved to be upregulated in LUAD by multiple studies and shows strong correlations to the high invasiveness and high malignancy of LUAD [ 42 ] [ 43 ]. The study by Hongde Zhang et al. pointed out that LINC00707 can mediate the resistance of LUAD to the first-line chemotherapy drug cisplatin (DDP) [ 44 ]. Similarly, LINC00941 can also prompt the progression of many tumors by regulating VEGFA expression[ 45 ], enhancing endothelial-mesenchymal transition[ 46 ], and changing the level of intercellular link proteins[ 47 ].In addition, the study by Ming Xu et al. pointed out that this lncRNA can enhance the fitness of tumor cells by activating the Hippo pathway and increasing the glycolytic activity of pancreatic ductal adenocarcinoma cells [ 48 ]. Notably, no study has yet explored how this lncRNA affects the metabolic processes of LUAD cells. Unexpectedly, we found that LINC00707 had a cof coefficient > 0 in our risk score model. From the perspective that the high-risk group exhibited poorer survival outcomes than that of the low-risk group, LINC00707 was considered as a risk factor for LUAD, or a tumor-promoting factor. The research of Jun Shao et al. obtained the same results as ours [ 43 ], which also confirms the credibility of our risk model. However, we also noticed that the PCR results and the validation results in the TCGA database were inconsistent with our risk model coef scores. For example, the coef > 0 of AL033397.2 in the risk model means that it should be a risk factor for the prognosis of LUAD, but in PCR, its low expression in LUAD cell lines relative to normal cells is a protective factor. Similarly, LINC02390 also contradict the risk model coef coefficient in the validation based on the TCGA database. In addition, we also found that the expression level of AL132656.2 was significantly different in the two cell lines of LUAD (higher in the H1975 cell line than in the standard cell line, and opposite in the A549 cell line). At the same time, AC068228.1 in the There was no difference in expression levels between regular cell lines and LUAD lines. The possible reasons we conjecture are as follows: 1. The lncRNAs in our study are matched based on metabolism-related genes. There may be some unknown interaction between them, which eventually leads to the difference between the PCR results and the coef coefficients. 2. Compared with the real LUAD, the source of the cell line we used for PCR verification is relatively single, and there is inherent heterogeneity between H1975 and A549 [ 49 ], so there may be two cells, such as AL132656.2. The expression levels vary significantly between strains. Therefore, perhaps we need to use more LUAD cell lines to verify the expression of these 8 lncRNAs’ expression and add more in-depth basic experiments to illustrate. Interestingly, in the functional enrichment analysis based on eight hub lncRNAs, we found that these genes are closely related to hematopoietic cell lineage and arachidonic acid metabolism. Cells derived from hematopoietic cells, including bone marrow and lymphocytes, are important in TEM and constitute an essential lineage of immune cell infiltration in TME [ 50 ]. For example, Min Liu et al. pointed out in their investigation that the deletion of specific c-Maf in bone marrow cells (mainly on the surface of macrophages) inhibits the activation of M2 macrophages, qualifying the tumor cells of non-small cell lung cancer with resistance to PD-1 inhibitors, while also reducing the mutational load of the organism [ 51 ]. Correspondingly, in our ssGSEA analysis, we also observed differential infiltration of different immune cells, including dendritic cells, monocytes in high and low-risk groups, and differential activation of the resistant such as type 2 interferon response, immune checkpoints, etc. in different risk groups. This evidence point to potential targets for the treatment of LUAD. As an essential product of lipid metabolism, arachidonic acid is also essential in tumor progression. As early as a study in 1999, it was found that the release of arachidonic acid contributes to the DNA synthesis of LUAD cells [ 52 ]. In 2016, a heavy study by Zoe Hall et al. pointed out that arachidonic acid phospholipids can also be used as Signaling precursors in the LUAD downregulate the COX/5-LOX pathway to reduce the organism's tumor mutational burden [ 53 ]. However, we also noticed no systematic study on the relationship between hematopoietic cells, arachidonic acid, LAUD, and lncRNA, which may be the direction for the development of new LUAD therapies in the future. Admittedly, there might be some limitations concerning this research. First, all of the analysis is mainly based on the samples and data of the TCGA database, and there may be deviations in data sources or incomplete data. Secondly, this study is based on bioinformatics technology. Although PCA was used to verify the eight key lncRNAs screened, more cell and basic experiments are still needed. In conclusion, a predictive model of lncRNAs related to metabolic processes in LUAD cells was established. Through joint analysis of LUAD samples from TCGA and metabolic-related lncRNA datasets from the KEGG database, we finally screened out eight key lncRNAs that significantly impacted the LUAD process and verified their expression levels by PCR and TCGA database. On this basis, we established a risk model based on these critical lncRNAs. After multi-dimensional verification, the model exhibits reliability and could well distinguish and predict the clinical outcomes of LUAD patients. We also explored the relationship between tumor metabolism and the immunity of LUAD, tumor cell function, and tumor clinical drug resistance based on these critical lncRNAs. The above studies aim to improve the understanding of LUAD and open up new ideas for guiding clinical treatment. Declarations Ethics approval and consent to participate The current study investigated the publicly available data, and no ethical approval was required. All methods were carried out in accordance with the Declaration of Helsinki. Consent for publication This study has been approved by all authors for publication. Availability of data and materials The data used during the study are available online (TCGA database, https://portal.gdc.cancer.gov/; TIDE: http://tide.dfci.harvard.edu/. TIMER: http://timer.comp-genomics.org/; GDSC: https://www.cancerrxgene.org/; GSEA: http://www.gsea-msigdb.org/gsea/index.jsp). PCR data can be obtained by contacting the corresponding author. Competing interests The authors declare that they have no competing interests. Funding This project was supported by the Natural Science Foundation of Tianjin (19JCZDJC35500). Authors' contributions XTS, XQH designed the study. LAS, XJS and ZP ( [email protected] ) analyzed the data, participated in data collection, and prepared the manuscript. PZ ( [email protected] ) and ZSL helped the analysis with constructive discussions and completed the PCR verification. All authors critically revised the manuscript. Xinti Sun, Xingqi Huang, Linao Sun shared the first authorship, and contributed equally to this work. Acknowledgements We thank the investigators who participated and provided data unselfishly in TCGA and GSEA databases. 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Shao, J., et al., Integrated analysis of hypoxia-associated lncRNA signature to predict prognosis and immune microenvironment of lung adenocarcinoma patients. Bioengineered, 2021. 12 (1): p. 6186-6200. Zhang, H., et al., Silencing long intergenic non-coding RNA 00707 enhances cisplatin sensitivity in cisplatin-resistant non-small-cell lung cancer cells by sponging miR-145. Oncol Lett, 2019. 18 (6): p. 6261-6268. Ren, M.H., et al., LINC00941 Promotes Progression of Non-Small Cell Lung Cancer by Sponging miR-877-3p to Regulate VEGFA Expression. Front Oncol, 2021. 11 : p. 650037. Wu, N., et al., LINC00941 promotes CRC metastasis through preventing SMAD4 protein degradation and activating the TGF-β/SMAD2/3 signaling pathway. Cell Death Differ, 2021. 28 (1): p. 219-232. Gugnoni, M., et al., Linc00941 Is a Novel Transforming Growth Factor β Target That Primes Papillary Thyroid Cancer Metastatic Behavior by Regulating the Expression of Cadherin 6. Thyroid, 2021. 31 (2): p. 247-263. Xu, M., et al., LINC00941 promotes glycolysis in pancreatic cancer by modulating the Hippo pathway. Mol Ther Nucleic Acids, 2021. 26 : p. 280-294. Rødland, G.E., et al., Differential Effects of Combined ATR/WEE1 Inhibition in Cancer Cells. Cancers (Basel), 2021. 13 (15). Remark, R., et al., The non-small cell lung cancer immune contexture. A major determinant of tumor characteristics and patient outcome. Am J Respir Crit Care Med, 2015. 191 (4): p. 377-90. Liu, M., et al., Transcription factor c-Maf is a checkpoint that programs macrophages in lung cancer. J Clin Invest, 2020. 130 (4): p. 2081-2096. Schuller, H.M., et al., The tobacco-specific carcinogen 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone is a beta-adrenergic agonist and stimulates DNA synthesis in lung adenocarcinoma via beta-adrenergic receptor-mediated release of arachidonic acid. Cancer Res, 1999. 59 (18): p. 4510-5. Hall, Z., et al., Myc Expression Drives Aberrant Lipid Metabolism in Lung Cancer. Cancer Res, 2016. 76 (16): p. 4608-18. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.xlsx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-1593827","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":103094237,"identity":"72df8883-f477-4d4c-bf13-4cd0c076e388","order_by":0,"name":"Xinti Sun","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xinti","middleName":"","lastName":"Sun","suffix":""},{"id":103094238,"identity":"433b242a-553c-46b8-94c0-b0684f1251b1","order_by":1,"name":"Xingqi Huang","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xingqi","middleName":"","lastName":"Huang","suffix":""},{"id":103094239,"identity":"78e6930d-d8d9-4bcb-92df-dbbb8fd1180c","order_by":2,"name":"Linao Sun","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Linao","middleName":"","lastName":"Sun","suffix":""},{"id":103094240,"identity":"39021dcb-054c-47a5-b02c-7d57a037acc2","order_by":3,"name":"Peng Zhang","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhang","suffix":""},{"id":103094241,"identity":"7544738a-5ae8-44e8-9ac6-196f59fd03a4","order_by":4,"name":"Zesheng Li","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zesheng","middleName":"","lastName":"Li","suffix":""},{"id":103094242,"identity":"4ed3b40c-d18b-4bf4-9510-d50d41347b57","order_by":5,"name":"Xiaojuan Sun","email":"","orcid":"","institution":"Qingdao University Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaojuan","middleName":"","lastName":"Sun","suffix":""},{"id":103094243,"identity":"ae42fe96-0ad1-4d40-913f-d0ccae4003a7","order_by":6,"name":"Peng Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYLACxgYgwczY+ADCTSBWC3tzs8EB0rTwHG+TIEqLOXvv4Zc/d9jlyUcktlV/zDnMwM+eY8DwcwduLZY959IsJM8kFxveSGy7cXDbYQbJnjcGjL1ncGsxuJFjZmDYxpy4cQZUC1DEgJmxDY+W+2/MDBLb6sFaCkBa7AlqucFj/OBg2+HE+TwH2xjAtkgQ0GLZk2PG2Nh2PHEDe2OzxNlt6TwSZ54VHOzFo8Wc/Yzxx59t1Ynzm9kffqjcZi3H35688cFPfA5jYGCTADMOQAR4QMQB3BrAWpg/gBjyDfiUjYJRMApGwYgGAHCfW46rbmmlAAAAAElFTkSuQmCC","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2022-04-25 15:29:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1593827/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1593827/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21284819,"identity":"f2440a24-0a2a-455d-9d43-f77412781728","added_by":"auto","created_at":"2022-05-10 14:13:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1319202,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of complete data analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/bf93cedb72d2e2d7205e2ac3.png"},{"id":21284184,"identity":"602f4e88-9b4e-4aae-b044-cac30a93060b","added_by":"auto","created_at":"2022-05-10 14:08:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8285984,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed metabolic genes and metabolism-related lncRNAs in LUAD patients\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) Volcano plot of 816 metabolic genes. (B) Heat map of differentially expressed metabolic genes. (C) Boxplot of differentially expressed metabolic genes. (D) GO analysis of differentially expressed metabolic genes. (E) KEGG analysis of differentially expressed metabolic genes. (F) Sankey relation diagram for 185 differentially expressed metabolic genes and lncRNAs. (G) Heatmap for the correlations between differentially expressed metabolic genes and the eight prognostic metabolism-related lncRNAs.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/f65aa7b9b05decd621475a06.png"},{"id":21286530,"identity":"b3d219c6-be4f-4c06-90e4-d665c47c2eef","added_by":"auto","created_at":"2022-05-10 14:23:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2613685,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of predictivestic model in the TCGA training set \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u0026nbsp;(A) Univariate Cox regression analysis revealed that the selected lncRNAs significantly correlated with clinical prognosis (show conditions p\u0026lt;0.001). (B) The LASSO coefficient profile of metabolism-related lncRNAs. (C) The 10-fold cross-validation for variable selection in the LASSO model. (D) Multivariate Cox regression analysis showed 8 independent prognostic lncRNAs. (E) Distribution of metabolism-related lncRNAs model-based risk score for the TCGA training set. (F) Different patterns of survival status and survival time between the high-risk and low-risk groups for the TCGA training set. (G) The clustering analysis heatmap shows the expression standards of the eight prognostic lncRNAs for each patient in the TCGA training set. (H) Kaplan-Meier survival curves of the OS of high-risk and low-risk patients in the TCGA training set.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/fb738e545aad677478db4ff9.png"},{"id":21284177,"identity":"0932af99-f49f-4d35-9dec-295f5d6b1a7b","added_by":"auto","created_at":"2022-05-10 14:08:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2958187,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic value of the risk model of the eight metabolism-related lncRNAs in the TCGA testing and entire sets\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) Distribution of metabolism-related lncRNA model-based risk score for the testing set. (B) Patterns of the survival time and survival status between the high-risk and low-risk groups for the testing set. (C) The clustering analysis heatmap shows the display levels of the 8 prognostic lncRNAs for each patient in the testing set. (D) Distribution of metabolism-related lncRNA model-based risk score for the entire set. (E) Patterns of the survival time and survival status between the high-risk and low-risk groups for the whole of the set. (F) The clustering analysis heatmap shows the display levels of the 8 prognostic lncRNAs for each patient in the entire set. (G) Kaplan-Meier survival curves of the OS of patients in the high-risk and low-risk groups in the testing set. (H) Kaplan-Meier survival curves of the OS of patients in the high-risk and low-risk groups in the entire set.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/217ceda67bdbf5abd5f29761.png"},{"id":21286230,"identity":"e85ebbd4-25d9-4bb4-a696-6cfd453e7777","added_by":"auto","created_at":"2022-05-10 14:18:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":910968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of the nomogram\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u0026nbsp;(A) Univariate analysis of the OS's clinical characteristics and risk score. (B) Multivariate analysis of the clinical characteristic and risk score with the OS. (C) The nomogram predicts the probability of the 1-, 3-, and 5-year OS. (D) The calibration plot of the nomogram indicates the likelihood of the 1-, 3-, and 5-year OS.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/7d2646691ae9d1dfd49ba067.png"},{"id":21284182,"identity":"efc0747d-910e-4399-b986-281ce36c7e98","added_by":"auto","created_at":"2022-05-10 14:08:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2847371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssessment of the predictive risk model and Principal component analysis\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u0026nbsp;(A) The entire set's 1-, 3-, and 5-year ROC curves. (B) ROC curves of the clinical characteristics and risk score. (C) Concordance indexes of the risk score and clinical features. (D) Principal component and t-SNE analysis between the high-risk and low-risk groups based on the eight prognostic lncRNAs in the TCGA training set. (E) Principal component and t-SNE between the high-risk and low-risk groups based on the 8 prognostic lncRNAs in the TCGA testing set. (F-I) PCA for all gene expression profiles, 185 differentially expressed metabolic genes, 2633 metabolism-related lncRNAs and risk model sorted by the expression of the eight metabolism-related lncRNAs.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/50416cdfe2e2549d445108e4.png"},{"id":21284820,"identity":"c2af8b1e-0b98-42ff-b096-f11eb344cc5e","added_by":"auto","created_at":"2022-05-10 14:13:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2190803,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves of OS difference stratified by LUAD grade, age, gender, and TNM stage between the high-risk and low-risk groups in the TCGA entire set.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/3a2c0ef2bb69d27955389330.png"},{"id":21286231,"identity":"d1bde5da-bd05-4182-a573-f78d7e214379","added_by":"auto","created_at":"2022-05-10 14:18:33","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4180536,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStratification Analysis of the metabolism-related lncRNA prognostic risk score in immune features\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) Heatmap of 22 tumor-infiltrating immune cell types in low- and high-risk groups. (B) Bar chart of the proportions for 22 immune cell types. (C) The score of immune functions comparing high-risk and low-risk groups by ssGSEA Score. (D) The score of immune cells comparing high-risk and low-risk groups by ssGSEA score. (E)-(G) The comparison of TME-related scores between high- and low-risk groups. \u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/f9f7f5e5b711cc0de935ab73.png"},{"id":21284823,"identity":"846ddd04-e425-4dee-bc56-fdf7e862b7da","added_by":"auto","created_at":"2022-05-10 14:13:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3279296,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExploration of Tumor mutation burden and visualization of lncRNAs networks.\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) Waterfall plot displays mutation information of the 20 genes with high mutation frequencies in the high-risk group. (B) The waterfall plot displays mutation information of the 20 genes with high mutation frequencies in the low-risk group. (C) Tumor mutation burden difference in the high-risk and low-risk groups. (D) The correlation between risk score and TMB. (E) Kaplan-Meier survival curves of the OS of LUAD patients based on high and low TMB. (F) Kaplan-Meier survival curves of the OS of LUAD patients based on TMB and two risk groups. (G) The correlation between risk score and immune subtype.\u003c/p\u003e","description":"","filename":"OnlineFigure9.png","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/420c0ca98208920c054f9db0.png"},{"id":21284185,"identity":"d6869ec7-ea30-447a-9277-eaa5d3d59014","added_by":"auto","created_at":"2022-05-10 14:08:33","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2646758,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe investigation of tumor immune factors and immunotherapy\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u0026nbsp;(A) The immunotherapy prediction of high-risk and low-risk groups. (B) Expression levels of CTLA4, HAVCR2, TIGIT, PD-1 and PD-L1 in the high- and low-risk groups. (C) Investigation of anti-tumor drug sensitivity targeting signature. (D) The correlation between 8 metabolism-related lncRNAs and drugs. (E) TIDE prediction difference in the high-risk and low-risk patients.\u003c/p\u003e","description":"","filename":"OnlineFigure10.png","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/d9846badd732ba82f347ac53.png"},{"id":21284825,"identity":"5c0dc993-41a4-4cd3-b1a2-cff38e35e475","added_by":"auto","created_at":"2022-05-10 14:13:33","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":3363583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional analysis \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) Circle diagram which enriched in GO analysis. (B) Circle diagram which enriched in KEGG analysis. (C) Top 30 classes of KEGG enrichment pathways. (D-E) Gene set enrichment analysis (GSEA)of KEGG pathways in the high- and low-risk groups. (F) The Sankey diagram shows the degree of connection between metabolism-related lncRNAs, metabolic-related genes, and risk types. (G) Construction of 8 metabolism-related lncRNAs and coexpression metabolic genes networks. Green, lncRNAs. Red, mRNAs.\u003c/p\u003e","description":"","filename":"OnlineFigure11.png","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/e9650a2d0b75300e47eb9250.png"},{"id":21284186,"identity":"737eb74b-10c4-4307-b961-3e12d4bcd249","added_by":"auto","created_at":"2022-05-10 14:08:33","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":884669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression levels of 8 metabolism-related lncRNAs. \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e(A) Expression level of 8 metabolism-related lncRNAs between regular lung epithelial cell line (BEAS-2B) and LUAD cell lines (NCI-H1975, A549) by RT-qPCR. (B) Expression levels of 8 metabolism-related lncRNAs among 535 LUAD and 59 normal tissues based on TCGA databases.\u0026nbsp;\u003c/p\u003e","description":"","filename":"OnlineFigures12.png","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/e933fdcf08ebc91d7ec55bbb.png"},{"id":23160909,"identity":"b3f20f98-7881-49f4-82a2-377d4f4e8b7c","added_by":"auto","created_at":"2022-06-28 06:44:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":903391,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/cbdc4e87-75df-4f17-b5c4-eecdf76ed234.pdf"},{"id":21284175,"identity":"a091e3d5-a188-442d-b149-62608bd22b33","added_by":"auto","created_at":"2022-05-10 14:08:32","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1435099,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1593827/v1/d5c1e34717aa3debe32c97e8.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive analyses of metabolism-related lncRNA for LUAD","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is the malignant tumor with the highest morbidity and mortality worldwide, and lung adenocarcinoma (LUAD) is the primary subtype of non-small cell lung cancer in lung cancer. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Due to its undefined histopathological behavior and lack of early predictive biomarkers, the overall prognosis for LUAD patients remains poor [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The 5-year survival rate is lower than 10% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Additionally, only a minority of them benefit from clinical treatment. Therefore, identifying effective biomarkers for accurate prognostic prediction is imperative.\u003c/p\u003e \u003cp\u003eLong non-coding RNAs (lncRNAs), as a type of non-coding RNA sequence measuring approximately 200 nucleotides in length, served as regulators in various biological functions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recently, many studies suggested that lncRNAs are related to oncogenesis, suppression, and metastasis in LUAD and other tumor types [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For example, lncRNA MALAT1 is associated with lung cancer metastasis by regulating genes expression [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The lncRNA LUADT1 is highly expressed in LUAD and may stimulate cell proliferation in cancer cells by interacting with SUZ12 and modulating H3K27 trimethylation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Many lncRNAs have emerged as novel biomarkers for predicting cancer prognosis, including lung cancer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Li et al. constructed a seven lncRNA model to improve the predictive value for LUAD[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, Zhou et al. found eight lectures highly associated with LUAD [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These newly signatures related lncRNAs provide guidance to predict prognosis and contribute to better clinical treatment.\u003c/p\u003e \u003cp\u003eTumor cells usually live in an abnormal metabolic environment, depending on the imbalance between the rapid proliferation of tumor cells and nutrient angiogenesis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Contemporary thinking holds that tumor cells must change their metabolism to meet increased metabolic and synthetic demands and growth requirements [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, it is becoming increasingly clear that changes in cellular metabolism are related to cancer development and progression[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For example, Valtorta et al. have demonstrated that combination therapy based on the glutaminase inhibitor CB-839 and the PI3K/aldolase inhibitor NVP-BKM120 significantly reduced tumor growth. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Deng et al. found that lncRNA GLS-AS inhibits the development of malignant tumors by impairing GLS-mediated metabolism [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the regulation of lncRNA on metabolic pathways in LUAD is unknown. Further understanding of the function and mechanism of metabolism-related lncRNAs in LUAD might provide a deep insight into the potential mechanism and find proper treatment.\u003c/p\u003e \u003cp\u003eBased on the TCGA database, eight full metabolism-related lncRNA signature linked to the prognosis in LUAD patients was identified in the training set and verified well in the testing and entire collection. We compared the tumor microenvironment between high-risk and low-risk groups using our prognostic model. To further explore how these metabolism-related lncRNAs are involved in LUAD progression, we examined their relationship with LUAD immune signatures and tumor drug resistance. Functional enrichment analysis was also performed to search for potential LUAD progression mechanisms. Ultimately, we confirmed that these eight lncRNAs were differentially expressed between the LUAD cell lines (A549, H1975) and human standard bronchial epithelium cell line BEAS2B by a qRT-PCR test. The model based on eight metabolism-related lncRNAs helps predict prognosis in LUAD patients and promotes more individualized treatment.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData preparation and Preprocessing\u003c/h2\u003e \u003cp\u003eSequencing data of lncRNA, somatic mutation profiles, and clinical information on LUAD were downloaded from the TCGA database [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. 816 metabolism-associated genes were acquired from the GSEA database. The differentially expressed genes were obtained by using the \u0026ldquo;limma\u0026rdquo; R package ((FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2 fold change (FC)| \u0026ge;1). 2633 metabolism-related lncRNAs were identified based on Pearson's correlation analysis (coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.4 and \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). LUAD patients with short OS values (\u0026lt;\u0026thinsp;30days) and missing overall survival (OS) values were deleted. 490 samples were collected and divided into training and testing sets. The training set, including 246 samples, was used to construct the risk model. The testing set, including 244 samples, was used to validate the risk model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment and Assessment of Prognostic Model\u003c/h2\u003e \u003cp\u003eEight lncRNAs significantly associated with prognosis were identified by performing univariate Cox, LASSO, and multivariate regression analysis using the \"glmnet\" package in R. A metabolism-related risk model was constructed. The following formula calculates the risk score:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$Risk score={\\sum }_{k=1}^{n}Coef\\left(lncRNA\\right)*expr\\left({lncRNA}^{k}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe coef in the formula is the coefficient of correlation between lncRNAs and survival, and expr represents the expression of lncRNAs. Patients in the high and low-risk groups were defined according to the median risk score. The model was confirmed to independently predict the prognosis of LUAD by comparing it with other clinical factors using univariate Cox regression and multivariate Cox regression analyses. The nomogram was established to better predict the LUAD patients\u0026rsquo; survival using \u0026ldquo;rms\u0026rdquo; packages in R. Using the Harrell concordance index and a calibration curve, the nomogram was verified for accuracy. Kaplan- Meier survival analysis was applied to test the accuracy of the risk model by using the \"survival\" package. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE)research were further conducted to verify the risk model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of Tumor Immune Microenvironment\u003c/h2\u003e \u003cp\u003eThe infiltration status of immune cells among high- and low-risk groups was calculated using the CIBERSORT and ssGSEA algorithms. StromalScore, ImmuneScore, and ESTIMATE Score of patients were calculated using the \u0026ldquo;ESTIMATE\u0026rdquo; package to explore TME in LUAD patients further. The R package \u0026ldquo;maftools\u0026rdquo; was conducted to measure the tumor mutation burdens (TMBs).\u003c/p\u003e \u003cp\u003e \u003cb\u003eExploration of Clinical Treatment.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe differences in tumor immune dysfunction and exclusion (TIDE) between the two groups of LUAD patients were evaluated. We measured common anti-tumor drugs' median inhibitory concentration (IC50) to guide our clinical treatment using the R package \u0026ldquo;pRRophetic\u0026rdquo;. The data of the immune subtype was downloaded on TIMER(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.comp-genomics.org/\u003c/span\u003e\u003cspan address=\"http://timer.comp-genomics.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We also evaluated the expression levels among high-risk and low-risk groups on PD-1, PD-L1, and others ICI-related biomarkers expression.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional analysis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGO and KEGG enrichment analysis was conducted with the R package \u0026ldquo;cluster profile\u0026rdquo; and \u0026ldquo;enrich plot\u0026rdquo;. Gene set enrichment analysis was performed to further screen possible enrichment functional pathways using software GSEA 4.2.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gesa-msigdb.org/gsea/index,jsp\u003c/span\u003e\u003cspan address=\"http://www.gesa-msigdb.org/gsea/index,jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The software Cytoscape established the co-expression network between lncRNA and mRNA (version 3.6.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCell Line and Reagents\u003c/h2\u003e \u003cp\u003eHuman standard lung epithelial cell lines BEAS-2B and LUAD cell lines (NCI-H1975, A549) were received from the American Type Culture Collection (Manassas, VA, USA). All cells were developed in PRMI 1640 medium (Beijing, China), supplemented with 10% fetal bovine serum (10% FBS), and in a humidified environment at 37\u0026deg;C and 5% CO2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTotal RNA Extraction and Real-Time Quantitative PCR\u003c/h2\u003e \u003cp\u003eRNAiso was used to extract total cellular RNA. 1ug of RNA was used to reverse transcribe the cDNA of the 20ul system. Quantitative real-time PCR was performed according to a total of 25ul reaction system of 1ul F-primer, 1ul R-primer, 12.5ul TB green, 1ul cDNA, and 9.5ul DEPC water. GAPDH was used as an internal reference to standardize the relative expression of target genes. The 2-ΔΔCT method was adopted to determine the relative expression of target genes. The PCR primer sequences directly were synthesized (Sangon Biotech, China) and are shown in \u003cb\u003eSupplementary Table S8\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eR platform (version 4.1.2) and GraphPad Prism 8 are applied to process, analyze and present the data. When no special instructions were given for the above analysis methods, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003eIdentification of metabolism-related lncRNAs in LUAD patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe workflow is presented in\u003cstrong\u003e\u0026nbsp;Figure 1\u003c/strong\u003e. We screened 490 LUAD patients and 14056 lncRNAs for this analysis. \u003cstrong\u003eTable 1\u003c/strong\u003e shows the clinical details of samples in the training and testing sets. We analyzed and compared the expression levels of the 816 metabolism-associated genes among normal and tumor samples\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand identified 185 differentially expressed metabolism genes. Moreover, we calculated the corresponding lncRNA expression matrix of these genes. Of them, 43 were downregulated, and 142 were upregulated. The expression distribution of metabolism-associated differently expressed genes in LUAD was displayed in \u003cstrong\u003eFigure 2A-2C\u003c/strong\u003e. Analyses of the GO enrichment indicated that 185 differentially expressed genes participated in many critical physiological processes, especially in the alpha-amino acid metabolic and small molecule catabolic process, mitochondrial matrix, basolateral plasma membrane, anion transmembrane transporter activity, and active transmembrane transporter activity (\u003cstrong\u003eFigure 2D\u003c/strong\u003e). KEGG analysis discovered that 185 differentially expressed metabolic genes were associated with signaling pathways, including biosynthesis of amino acids, biosynthesis of cofactors, purine metabolism, and carbon metabolism (\u003cstrong\u003eFigure 2E\u003c/strong\u003e). Finally, the coexpression network between metabolism-related lncRNAs and differentially expressed metabolic genes was shown in the San-key diagram (\u003cstrong\u003eFigure 2F\u003c/strong\u003e), and 2633 metabolism-related lncRNAs were identified as metabolism-related lncRNAs. The correlation between metabolism-related lncRNAs and corresponding genes, such as ABCA8, PMM2, and lncRNAs, was shown in \u003cstrong\u003e(Supplementary Table S1\u003c/strong\u003e) and visualized in (\u003cstrong\u003eFigure 2G\u003c/strong\u003e).\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eSample details of the training group and the test group\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026lt;=65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e231(47.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e110(45.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e121(49.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026gt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e249(50.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e129(52.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e120(48.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eunknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e10(2.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e5(2.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e5(2.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eFEMALE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e262(53.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e130(53.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e132(53.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eMALE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e228(46.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e114(46.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e114(46.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eStage I-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e378(77.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e192(39.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e186(37.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.8448\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eStage III-IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e104(21.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e48(9.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e56(11.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eunknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e8(1.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e4(1.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e4(1.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eT1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e426(86.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e210(42.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e216(44.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.8287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eT3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e61(12.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e32(6.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e29(5.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eunknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e3(0.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e2(0.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e1(0.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e324(66.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e161(65.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e163(66.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.5842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e24(4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e10(4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e14(5.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eunknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e142(28.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e73(29.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e69(28.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e317(64.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e161(65.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e156(63.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.8888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eN1-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e162(33.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e77(15.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e85(17.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eunknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e11(2.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e6(2.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e5(2.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and Validation of Risk Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected 527 metabolism-related lncRNAs by univariate COX regression analysis (\u003cstrong\u003eFigure 3A\u003c/strong\u003e, \u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e). LASSO regression analysis was further applied, with the result that 15 lncRNAs showed a strong correlation with the OS of LUAD patients (\u003cstrong\u003eFigure 3B-3C\u003c/strong\u003e). Finally, we employed multivariate Cox regression analysis for screening the eight metabolism-related lncRNAs (\u003cstrong\u003eTable 2\u003c/strong\u003e) to construct the risk model (Figure 3D).\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eMultivariate cox regression analysis of 8 hub lncRNAs\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" style=\"border-collapse: collapse; margin: 0px auto;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.873889875666073%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.761989342806395%\"\u003e\n \u003cp\u003e\u003cstrong\u003ecoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR.95L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR.95H\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e\u003cstrong\u003epvalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.873889875666073%\"\u003e\n \u003cp\u003eAC068228.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.761989342806395%\"\u003e\n \u003cp\u003e1.04537938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e3.31067518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e2.069563114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e5.296079195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e5.91E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.873889875666073%\"\u003e\n \u003cp\u003eLINC02390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.761989342806395%\"\u003e\n \u003cp\u003e-2.765775643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.05374742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.010851902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.266200811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.000341925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.873889875666073%\"\u003e\n \u003cp\u003eAC123595.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.761989342806395%\"\u003e\n \u003cp\u003e-0.612033991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.305070349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.148148482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.62820703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.001275727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.873889875666073%\"\u003e\n \u003cp\u003eAC021016.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.761989342806395%\"\u003e\n \u003cp\u003e-0.536593072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.274268488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.144707684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.519828675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e7.32E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.873889875666073%\"\u003e\n \u003cp\u003eLINC00707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.761989342806395%\"\u003e\n \u003cp\u003e0.409269302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e1.955105801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e1.532233927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e2.494683498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e6.98E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.873889875666073%\"\u003e\n \u003cp\u003eAL132656.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.761989342806395%\"\u003e\n \u003cp\u003e-1.227239484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.125174908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.039862049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.393074569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.00037181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.873889875666073%\"\u003e\n \u003cp\u003eAL033397.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.761989342806395%\"\u003e\n \u003cp\u003e0.313575844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e1.47613924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e1.183856926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e1.840583105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e0.00054189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.873889875666073%\"\u003e\n \u003cp\u003eLINC00941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.761989342806395%\"\u003e\n \u003cp\u003e0.330708502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e1.778741168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e1.396296046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e2.265937908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.341030195381883%\"\u003e\n \u003cp\u003e3.12E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA risk score was obtained based on the following formula: risk score=\u0026nbsp;expression\u0026nbsp;of\u0026nbsp;AC068228.1\u0026times;（1.04537938048222）+\u0026nbsp;expression of\u0026nbsp;LINC02390\u0026times;（-2.76577564344421）+\u0026nbsp;expression of\u0026nbsp;AC123595.1\u0026times;（-0.612033991186885）+ AC021016.1\u0026times;（-0.53659307165381）+\u0026nbsp;expression of\u0026nbsp;LINC00707\u0026times;（0.40926930226994）+\u0026nbsp;expression of AL132656.2\u0026times;（\u0026nbsp;-1.22723948408621）+\u0026nbsp;expression of AL033397.2\u0026times;（0.3135758443623）+\u0026nbsp;expression of\u0026nbsp;LINC00941\u0026times;（\u0026nbsp;0.330708502412192）\u003c/p\u003e\n\u003cp\u003eWe defined these LUAD patients as two groups: low-risk groups and high-risk groups(based on the median value of the predictive risk scores). \u003cstrong\u003eFigure 3E\u0026nbsp;\u003c/strong\u003epresented the distributional patterns of risk scores among two subgroups in the training set. We summarized the survival parameters of these patients in \u003cstrong\u003eFigures3F.\u003c/strong\u003e The results suggested that as risk score increased, OS time decreased while mortality rise. \u003cstrong\u003eFigure 3G\u003c/strong\u003e illustrated the relative expression difference for the eight metabolism-related lncRNAs in the training set. Then, we performed a K-M analysis and found that the high-risk group held an inferior OS rate than that of the low-risk group (\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/strong\u003e) (\u003cstrong\u003eFigure 3H\u003c/strong\u003e). Additionally, we evaluated the model\u0026rsquo;s efficacy based on the testing set and the entire set. We summarized the risk scores, survival parameters, and expression of the eight metabolism-related lncRNAs in the abovementioned two sets and listed them in (\u003cstrong\u003eFigure 4A-4F).\u003c/strong\u003e K-M analyses presented the same outcomes based on the testing set and the entire set (\u003cstrong\u003eFigure 4G-4H\u003c/strong\u003e). All the above results supported the power of our risk model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent Prognostic Analysis and Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing univariate Cox and multivariate Cox regression analyses, we combined metabolism-related lncRNAs with clinical parameters to investigate whether the risk model could be used independent factor for predicting the survival of LUAD. As shown in the univariate cox regression analysis, the hazard ratio (HR) of the risk model was 1.04, and the 95% confidence interval (CI) was 1.029\u0026ndash;1.061 (p\u0026lt; 0.001) (\u003cstrong\u003eFigure 5A\u003c/strong\u003e). Multivariate cox regression analysis showed that the HR was 1.041 and CI was 1.024\u0026ndash;1.059 (p\u0026lt; 0.001) (\u003cstrong\u003eFigure 5B\u003c/strong\u003e). Therefore, the risk model might serve as a prognostic factor independent of other clinical parameters such as age, sex, pathological stage and so on.\u003c/p\u003e\n\u003cp\u003eFurthermore, we constructed a nomogram for better predicting the 1,3,5, -year OS of LUAD patients by combining the risk model with clinical factors, including gender, age, stage, TNM, and risk score (\u003cstrong\u003eFigure 5C\u003c/strong\u003e).\u0026nbsp;After that, the prediction accuracy of the nomogram was assessed. Observed survival rates are blue, while the optimized survival rates are shown in gray, indicating a good match between them (\u003cstrong\u003eFigure 5D\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessing the Risk Model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used receiver operating characteristic (ROC) curve analysis to verify the efficacy of the risk model.\u0026nbsp;The 1-, 3-, and 5-year AUC of entire set was 0.843, 0.816, and 0.814 (\u003cstrong\u003eFigure 6A\u003c/strong\u003e).\u0026nbsp;The AUC for the risk score was higher than the AUC for any other clinicopathological feature, fully demonstrating that the predictive risk model for LUAD is robust and highly reliable\u0026nbsp;(\u003cstrong\u003eFigure 6B\u003c/strong\u003e). The concordance index also suggested the accuracy of the risk model (\u003cstrong\u003eFigure 6C\u003c/strong\u003e). Then, we employed P-principal-component (PCA) and t-SNE analyses to assess the distribution between the high and low-risk groups in training and testing sets (\u003cstrong\u003eFigure 6D-6E\u003c/strong\u003e). The PCA and t-SNE analysis results confirmed that the metabolism-related lncRNAs model had grouping capabilities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBesides, we also use PCA analysis to verify further the ability of the risk model between two subgroups based on the entire gene expression profiles, 185 differentially expressed metabolic genes, 2633 metabolism-related lncRNAs, and risk model according to the eight metabolism-related lncRNAs (\u003cstrong\u003eFigure 6F-6I\u003c/strong\u003e). The results confirmed that the distributional patterns of the high-risk and low-risk groups were significantly different, indicating that the risk model was competent to distinguish the two groups with high accuracy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe evaluated the discrepancies between two subgroups based on the universal clinicopathologic characteristics. By further grouping patients of LUAD by gender, age, stage, and TNM, survival analysis found that LUAD low-risk patients also had better OS than high-risk patients (\u003cstrong\u003eFigure 7\u003c/strong\u003e). The above results indicated that the risk model maintained its powerful predictive ability among subgroups of different clinical features.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentifying the immune infiltration status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe explored the immune infiltration status among two subgroups using the CIBERSORT algorithm (\u003cstrong\u003eSupplementary Table S3\u003c/strong\u003e). The proportions of 22 immune cells in every patient were presented in (\u003cstrong\u003eFigures 8A-8B\u003c/strong\u003e). Furthermore, the results based on the ssGSEA algorithm revealed that some factors reflecting immune functions were upregulated in the low-risk subgroup (e.g., T_cell_co_stimulation, HLA, Type_II_IFN_Response) (\u003cstrong\u003eFigure 8C, Supplementary Table S4\u003c/strong\u003e). The infiltration of ads, B_cells, DCS, iDCs, Mast_cells, Neutrophils, TIL, and T_helper_cells, was higher in the low-risk group (\u003cstrong\u003eFigure 8D\u003c/strong\u003e). These results suggest that the low-risk group has a higher immune infiltration status, combined with a better OS in the low-risk group, which we reasoned might contribute to the antitumor effect. Similarly, LUAD patients in the low-risk group also showed remarkably higher stromal, immune, and ESTIMATE scores, suggesting that the high-risk group held dissimilar TME(\u003cstrong\u003eFigure 8E-G\u003c/strong\u003e). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the comparison illustrates that approximately 93.49% of samples exhibited genetic mutations in the high-risk samples, as well as 82.19% of samples exhibited mutations in the low-risk samples were displayed in (\u003cstrong\u003eFigure 9A-9B\u003c/strong\u003e). Additionally, TMB scores were calculated from the TCGA mutation data. They presented a higher TMB status in the high-risk groups, indicatied that high-risk patients might benefit more from immunotherapy\u0026nbsp;(\u003cstrong\u003eFigure 9C\u003c/strong\u003e). Therefore, we tested the correlation between the risk model based on metabolism-related lncRNAs and TMB (\u003cstrong\u003eFigure 9D\u003c/strong\u003e, \u003cstrong\u003eR=0.3, \u003cem\u003eP\u003c/em\u003e=1.6e-11\u003c/strong\u003e). The results showed that the metabolism-based classifier index was highly correlated with TMB. To investigate the impact of TMB state on prognosis in LUAD patients, we applied survival analysis based on high and low TMB groups. However, the survival curve of patients with high TMB was similar to patients with low TMB, indicating that the TMB failed to distinguish the survival in LUAD (\u003cstrong\u003eFigure 9E\u003c/strong\u003e). Additionally, we checked the efficacy of TMB scores related to risk scores based on the model to judge its predictive ability for predicting the OS outcomes (\u003cstrong\u003eFigure 9F\u003c/strong\u003e). Surprisingly, the model showed a significant predictive power for patients with LUAD. Besides, according to the immune subtype data in TIMER2.0, we tested whether a the-risk model based on the eight metabolism-related lncRNAs could identify the different immune subtypes (\u003cstrong\u003eFigure 9G\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSupplementary Table S5\u003c/strong\u003e). The result suggested that the risk model effectively distinguished the resistant subtype. Our findings might shield new light on understanding the molecular pathogenesis of LUAD from the perspective of metabolism-lncRNAs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Treatment and Drug sensitivity analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering the significant differences in the immune microenvironment between the low-risk and high-risk groups, we speculated that responses to drugs, chemotherapy, critical ICPs, and immunotherapy might differ between the two groups. Using the \u0026ldquo;pRRophetic\u0026rdquo; package, we then evaluated the therapeutic response using the IC50 values of 138 anti-tumor drug patients obtainable in the GDSC database to explore potential drugs targeting our risk model and improve treatments for patients with LUAD. The IC50 of A.443654, A770041, AMG.706, AUY922, AZ6828, and AZD.0530 were higher in the low-risk group, suggesting high risk patients may respond better to those drugs. Interestingly, the ABT.888, AP.24534, ATRA, and Axitinib showed a higher level in the high-risk group (\u003cstrong\u003eFigure 10A\u003c/strong\u003e), indicating that low-risk patients were more sensitive to these drugs. Besides, with ICIs have been applied in the treatment of LUAD and other cancers, we further explored the differences in ICI-related biomarkers expression among two subgroups. The results presented that the low-risk group had high PD1, CTLA4, TIGIT, PD\u0026minus;L1, and HAVCR2 expression (\u003cstrong\u003eFigure 10B\u003c/strong\u003e). Additionally, we counted the IC50 of common anti-lung cancer drugs in two subgroups. Patients in the low-risk groups were related with a higher IC50 of targeted therapy such as erlotinib and gefitinib and chemotherapeutics like cisplatin, paclitaxel, etoposide, which indicated that the risk model served as a promising predictor of anti- tumor drug sensitivity (\u003cstrong\u003eFigure 10C\u003c/strong\u003e). Besides, we analyzed the correction between 8 metabolism-related lncRNAs and drugs. For example, the correlation coefficient between Sulfatinib and LINC00707 was the highest (\u003cstrong\u003eCor=\u0026minus;0.433, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001\u003c/strong\u003e)(\u003cstrong\u003eFigure 10D\u003c/strong\u003e). As a result, we might be able to select the most appropriate drugs for LUAD patients. Besides, a high-risk group with lower TIDE scores may be more sensitive to immunotherapy than the high-risk group. So, our metabolism-based classifier index might serve as a powerful indicator for instructing clinical treatment (\u003cstrong\u003eFigure 10E\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo understand the potential biological process involved, we employed enrichment analysis to identify the signature of the eight metabolism lncRNAs (\u003cstrong\u003eSupplementary Table S6\u003c/strong\u003e). As shown in \u003cstrong\u003eFigure 11A\u003c/strong\u003e, GO analysis revealed that it mainly participates in the humoral immune response, human antimicrobial response, clathrin-coated endocytic vesicle, multivesicular body, receptor-ligand activity, and signaling receptor activator activity. According to KEGG analysis, the signature was connected with Hematopoietic cell lineage, Amoebiasis, Arachidonic acid metabolism, Pancreatic secretion, and so on (\u003cstrong\u003eFigure 11B-11C\u003c/strong\u003e). Further, we leveraged GSEA software to explore better the differences in biological functions in the KEGG pathways (\u003cstrong\u003eSupplementary Table S7\u003c/strong\u003e). The GSEA results illustrated that the high-risk group was enriched in the pathway such as cell cycle, DNA replication, homologous recombination, glycolysis gluconeogenesis, pyrimidine metabolism, and others (\u003cstrong\u003eFigure 11D\u003c/strong\u003e). In contrast, pathways such as allograft rejection, asthma, B/T cell receptor signaling pathway, and others were enriched in the low-risk group (\u003cstrong\u003eFigure 11E\u003c/strong\u003e). Besides, we presented metabolism-related differently expressed genes, eight metabolism-related lncRNAs, and risk types in the Sankey network (\u003cstrong\u003eFigure 11F\u003c/strong\u003e). Finally, with the help of Cytoscape, we presented an interaction network for visualizing the co-expression between the lncRNAs and mRNAs (\u003cstrong\u003eFigure 11G\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVerifying the expression Level of eight Prognostic lncRNAs in vitro\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed RT-qPCR to validate the expression Level of eight Prognostic lncRNAs using BEAS-2B and LUAD cells, including A549 and H1975 (Figure 12). three lncRNAs (LINC02390, AC021016.1, AC123595.1) exhibited significant downregulation in both LUAD cells. Given that the high expression of lncRNAs could represent a better survival (Figure 3D), they might function as tumor suppressor factors. TwolncRNAs (LINC00941, LINC00707) were upregulated in both LUAD cells. Similarly, and thus the high expression of the four lncRNAs could be representative of worse survival, suggesting that they may serve as risk factors. Interestingly, AL132656.2 was downregulated in A549 cells while upregulated in H1975 cells, so a deeper understanding of the specific mechanism is required. In addition, we also verified the differences in the expression of these eight critical lncRNAs in LUAD samples and standard tissue samples based on the TCGA database. The expression patterns of the remaining three lncRNAs were reversed. Except for LINC02390, the other lncRNA expression patterns were consistent with our risk model\u0026apos;s respective coef coefficients of lncRNAs.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLung adenocarcinoma is currently the most aggressive and lethal of all human cancers and itself is a complex disease with high heterogeneity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In addition, lung adenocarcinoma, as a disease with the poor therapeutic effects of radiotherapy and conventional chemotherapy, how to carry out the appropriate treatment for different stages of LUAD is also the most critical problem currently perplexing clinicians [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Because of these characteristics of lung adenocarcinoma, people are constantly pursuing a deeper understanding of this disease. With the deepening of research, the dimensions of people's insights into tumor occurrence and progression are gradually enriched. For example, the reprogramming of tumor cell metabolism is currently c key role in tumor development [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. From the discovery of the earliest Warburg effect [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] to the widespread downregulation of metabolic pathways such as AKT, mTOR, and hypoxia-inducible factors (HIFs) in cancer [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], when electron transport chain (ETC) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], pyruvate carboxylase (PC) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and the generation of reactive oxygen species (ROS) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] were considered to be a critical link in the regulation of tumor development, people's understanding of the role of changes in tumor cell metabolic characteristics in tumor progression were constantly being updated and deepened. Today, the focus of tumor metabolism-related research has gradually shifted from individual tumor cell research to in vivo experimental research. The tumor microenvironment (TME), which is currently considered to be the most closely related factor to tumor progression, has also been proved to be inseparable from the metabolic changes of tumor cells [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In addition, we employed genome-wide association studies (GWAS) to extract the information on tumor-related lncRNAs, and the \"protagonist\" of non-coding RNAs, lncRNAs, also demonstrate unique tumor progression regulation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. At the same time, we also noticed that lncRNAs could also be involved in the tumor progression by regulating the activity of key enzymes related to metabolism, such as phosphoglycerate kinase 1 (PGK1) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], or by changing the activation of classical metabolic signaling pathways, such as AKT/mTOR signaling pathway [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Based on the above evidence, we explored how metabolism-related lncRNAs interact with TME, tumor immunity, and other related features in LUAD and how these lncRNAs affect the LUAD process.\u003c/p\u003e \u003cp\u003eNormal tissue and LUAD samples were downloaded from the TCGA database based on the above research background. We obtained metabolic-related lncRNA datasets from the KEGG database. After using univariate, multivariate, and LASSO regression, we identified eight metabolism-related lncRNAs, which were AC068228.1, LINC02390, AC123595.1 AC021016.1, LINC00707, AL132656.2, AL033397.2, and LINC00941, and used them to build a LUAD prognosis prediction model. After multi-dimensional validation of the predictive model, we found that this model shows strong efficacy, which is reliable for prognostic prediction. To apply the model to the clinic, a nomogram was also constructed. We also explored the impact of these eight critical metabolism-related lncRNAs on LUAD progression, their correlations with immune signatures of LUAD, and sensitivity to chemotherapeutic agents. In addition, to further explore how LUAD patients with different risk levels differ in molecular processes, we employed functional enrichment analysis for analyzing genes with an other expressions between the two groups. Finally, to verify the actual face of the eight lncRNAs we predicted and screened in LUAD, we performed real-time quantitative PCR verification using the LUAD cell line. The results proved the accuracy and scientificity of our prediction to a certain extent.\u003c/p\u003e \u003cp\u003eOf the 8 selected lncRNAs, 5 (AC068228.1, LINC02390, AC021016.1, AL132656.2, and AL033397.2) were not studied. AC123595.1 was available for predicting the prognostic survival risk of LUAD patients in the studies of Jian-Ping Li [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and Yugang Guo [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], respectively. It was closely related to the tumor immunity and ferroptosis process of LUAD. At present, there are relatively many studies on LINC00707. This lncRNA has been proved to be upregulated in LUAD by multiple studies and shows strong correlations to the high invasiveness and high malignancy of LUAD [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The study by Hongde Zhang et al. pointed out that LINC00707 can mediate the resistance of LUAD to the first-line chemotherapy drug cisplatin (DDP) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Similarly, LINC00941 can also prompt the progression of many tumors by regulating VEGFA expression[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], enhancing endothelial-mesenchymal transition[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and changing the level of intercellular link proteins[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].In addition, the study by Ming Xu et al. pointed out that this lncRNA can enhance the fitness of tumor cells by activating the Hippo pathway and increasing the glycolytic activity of pancreatic ductal adenocarcinoma cells [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Notably, no study has yet explored how this lncRNA affects the metabolic processes of LUAD cells. Unexpectedly, we found that LINC00707 had a cof coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0 in our risk score model. From the perspective that the high-risk group exhibited poorer survival outcomes than that of the low-risk group, LINC00707 was considered as a risk factor for LUAD, or a tumor-promoting factor. The research of Jun Shao et al. obtained the same results as ours [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], which also confirms the credibility of our risk model.\u003c/p\u003e \u003cp\u003eHowever, we also noticed that the PCR results and the validation results in the TCGA database were inconsistent with our risk model coef scores. For example, the coef\u0026thinsp;\u0026gt;\u0026thinsp;0 of AL033397.2 in the risk model means that it should be a risk factor for the prognosis of LUAD, but in PCR, its low expression in LUAD cell lines relative to normal cells is a protective factor. Similarly, LINC02390 also contradict the risk model coef coefficient in the validation based on the TCGA database. In addition, we also found that the expression level of AL132656.2 was significantly different in the two cell lines of LUAD (higher in the H1975 cell line than in the standard cell line, and opposite in the A549 cell line). At the same time, AC068228.1 in the There was no difference in expression levels between regular cell lines and LUAD lines. The possible reasons we conjecture are as follows: 1. The lncRNAs in our study are matched based on metabolism-related genes. There may be some unknown interaction between them, which eventually leads to the difference between the PCR results and the coef coefficients. 2. Compared with the real LUAD, the source of the cell line we used for PCR verification is relatively single, and there is inherent heterogeneity between H1975 and A549 [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], so there may be two cells, such as AL132656.2. The expression levels vary significantly between strains. Therefore, perhaps we need to use more LUAD cell lines to verify the expression of these 8 lncRNAs\u0026rsquo; expression and add more in-depth basic experiments to illustrate.\u003c/p\u003e \u003cp\u003eInterestingly, in the functional enrichment analysis based on eight hub lncRNAs, we found that these genes are closely related to hematopoietic cell lineage and arachidonic acid metabolism. Cells derived from hematopoietic cells, including bone marrow and lymphocytes, are important in TEM and constitute an essential lineage of immune cell infiltration in TME [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. For example, Min Liu et al. pointed out in their investigation that the deletion of specific c-Maf in bone marrow cells (mainly on the surface of macrophages) inhibits the activation of M2 macrophages, qualifying the tumor cells of non-small cell lung cancer with resistance to PD-1 inhibitors, while also reducing the mutational load of the organism [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Correspondingly, in our ssGSEA analysis, we also observed differential infiltration of different immune cells, including dendritic cells, monocytes in high and low-risk groups, and differential activation of the resistant such as type 2 interferon response, immune checkpoints, etc. in different risk groups. This evidence point to potential targets for the treatment of LUAD. As an essential product of lipid metabolism, arachidonic acid is also essential in tumor progression. As early as a study in 1999, it was found that the release of arachidonic acid contributes to the DNA synthesis of LUAD cells [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In 2016, a heavy study by Zoe Hall et al. pointed out that arachidonic acid phospholipids can also be used as Signaling precursors in the LUAD downregulate the COX/5-LOX pathway to reduce the organism's tumor mutational burden [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. However, we also noticed no systematic study on the relationship between hematopoietic cells, arachidonic acid, LAUD, and lncRNA, which may be the direction for the development of new LUAD therapies in the future.\u003c/p\u003e \u003cp\u003eAdmittedly, there might be some limitations concerning this research. First, all of the analysis is mainly based on the samples and data of the TCGA database, and there may be deviations in data sources or incomplete data. Secondly, this study is based on bioinformatics technology. Although PCA was used to verify the eight key lncRNAs screened, more cell and basic experiments are still needed.\u003c/p\u003e \u003cp\u003eIn conclusion, a predictive model of lncRNAs related to metabolic processes in LUAD cells was established. Through joint analysis of LUAD samples from TCGA and metabolic-related lncRNA datasets from the KEGG database, we finally screened out eight key lncRNAs that significantly impacted the LUAD process and verified their expression levels by PCR and TCGA database. On this basis, we established a risk model based on these critical lncRNAs. After multi-dimensional verification, the model exhibits reliability and could well distinguish and predict the clinical outcomes of LUAD patients. We also explored the relationship between tumor metabolism and the immunity of LUAD, tumor cell function, and tumor clinical drug resistance based on these critical lncRNAs. The above studies aim to improve the understanding of LUAD and open up new ideas for guiding clinical treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study investigated the publicly available data, and no ethical approval was required. All methods were carried out in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been approved by all authors for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used during the study are available online (TCGA database, https://portal.gdc.cancer.gov/; TIDE: http://tide.dfci.harvard.edu/. TIMER: http://timer.comp-genomics.org/; GDSC: https://www.cancerrxgene.org/; GSEA: http://www.gsea-msigdb.org/gsea/index.jsp). PCR data can be obtained by contacting the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by the Natural Science Foundation of Tianjin (19JCZDJC35500).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXTS, XQH designed the study. LAS, XJS and ZP ([email protected]) analyzed the data, participated in data collection, and prepared the manuscript. PZ ([email protected]) and \u0026nbsp;ZSL helped the analysis with constructive discussions and completed the PCR verification. All authors critically revised the manuscript. Xinti Sun, Xingqi Huang, Linao Sun shared the first authorship, and contributed equally to this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the investigators who participated and provided data unselfishly in TCGA and GSEA databases.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e Bray, F., et al., \u003cem\u003eGlobal cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.\u003c/em\u003e CA Cancer J Clin, 2018. \u003cstrong\u003e68\u003c/strong\u003e(6): p. 394-424.\u003c/li\u003e\n \u003cli\u003e Gutschner, T. et al., \u003cem\u003eThe noncoding RNA MALAT1 is a critical regulator of the metastasis phenotype of lung cancer cells.\u003c/em\u003e Cancer Res, 2013. \u003cstrong\u003e73\u003c/strong\u003e(3): p. 1180-9.\u003c/li\u003e\n \u003cli\u003e Deben, C., et al., \u003cem\u003eTP53 and MDM2 genetic alterations in non-small cell lung cancer: Evaluating their prognostic and predictive value.\u003c/em\u003e Crit Rev Oncol Hematol, 2016. \u003cstrong\u003e99\u003c/strong\u003e: p. 63-73.\u003c/li\u003e\n \u003cli\u003e Yang, S., et al., \u003cem\u003eClinicopathologic characteristics and survival outcome in patients with advanced lung adenocarcinoma and KRAS mutation.\u003c/em\u003e J Cancer, 2018. \u003cstrong\u003e9\u003c/strong\u003e(16): p. 2930-2937.\u003c/li\u003e\n \u003cli\u003e Statello, L., et al., \u003cem\u003eGene regulation by long non-coding RNAs and its biological functions.\u003c/em\u003e Nat Rev Mol Cell Biol, 2021. \u003cstrong\u003e22\u003c/strong\u003e(2): p. 96-118.\u003c/li\u003e\n \u003cli\u003e Ponting, C.P., P.L. 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With the deepening of tumor research, the metabolic reprogramming of tumor cells and the regulatory role of lncRNA in tumor progression have gradually become prominent. This article aims to explore the relationship between the above three. In this study, based on LUAD samples downloaded from TCGA and metabolism-related genes downloaded from KEGG database, 8 key metabolism-related lncRNAs (AC068228.1, LINC02390, AC123595.1, AC021016.1, LINC00707, AL132656.2, AL033397.2 and LINC00941) were screened for the construction of prognostic risk models. After multi-dimensional validation, this risk model proved to have good reliability and validity and was closely related to the immune status and drug tolerance of LUAD patients. In addition, to better clarify the molecular mechanism by which these key lncRNAs affect the LUAD process, we performed functional enrichment analysis and found their close relationship with hematopoietic cell lineage, lipid metabolism, and human humoral immunity. To increase the credibility of this study, we verified the expression levels of these eight lncRNAs in BEAS-2B, A549 and H1975 cell lines, and the results of PCR experiments were in good agreement with our risk model. Overall, the above studies aim to improve the understanding of LUAD and open up new ideas for guiding clinical treatment.\u003c/p\u003e","manuscriptTitle":"Comprehensive analyses of metabolism-related lncRNA for LUAD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-10 14:08:30","doi":"10.21203/rs.3.rs-1593827/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":"84ac36fb-3617-4cd4-b7c3-fe48d97243eb","owner":[],"postedDate":"May 10th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-06-28T06:44:33+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-10 14:08:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1593827","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1593827","identity":"rs-1593827","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}