Prediction of Prognosis, Efficacy of Lung Adenocarcinoma by Machine Learning Model Based on Immune and Metabolic Related Genes

Prediction of Prognosis, Efficacy of Lung Adenocarcinoma by Machine Learning Model Based on Immune and Metabolic Related Genes | 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 Prediction of Prognosis, Efficacy of Lung Adenocarcinoma by Machine Learning Model Based on Immune and Metabolic Related Genes Cong Xue, Yi-Zhi Dai, Gui-Long Li, Yi Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4700280/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background The aim of this study is to integrate immune and metabolism-related genes in order to construct a predictive model for predicting the prognosis and treatment response of LUAD(lung adenocarcinoma) patients, aiming to address the challenges posed by this highly lethal and heterogeneous disease. Material and Methods Using TCGA-LUAD as the training subset, differential gene expression analysis, batch survival analysis, Lasso regression analysis, univariate and multivariate Cox regression analysis were performed to construct prognostic related gene models. GEO queue as validation subsets, is used to validate build RiskScore. Then, we explore the RiskScore and mutation status, immune cell infiltration, the relationship between immune therapy and chemotherapy, and build the model of the nomogram. Results The RiskScore has been determined to be composed of seven gene. In the high-risk group defined by this score, both early-stage and advanced-stage LUAD patients exhibit a decreased overall survival rate. The mutation status of patients as well as immune cell infiltration show associations with the RiskScore value obtained from these genes' expression levels. Furthermore, there exist variations in response to immunotherapy as well as sensitivity to commonly used chemotherapy drugs among different individuals. Lastly, when using a column line plot model based on the calculated RiskScore values, we obtain a concordance index (C-index) was 0 .716 (95% CI: 0.671–0.762), and time-dependent ROC predicted probabilities of 1-, 3- and 5-year survival for LUAD patients were 0.752、0.725 and 0.654, respectively. Conclusion In summary, by combining immune- and metabolism-related genes, we successfully con-structed a novel model for predicting prognosis and treatment response in LUAD patients. Lung Adenocarcinoma Immunity Metabolism Prognostic Immunotherapy Chemotherapy 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 Figure 13 Background Lung adenocarcinoma is the predominant histological subtype of lung cancer, resulting in millions of fatalities annually[ 1 ]. Despite the significant advancements in lung cancer survival achieved through chemotherapy and immune checkpoint inhibitors, not all patients derive benefits from immunotherapy due to the extensive heterogeneity observed in lung cancer genomes. Consequently, there exists an urgent imperative for novel methodologies to identify potential candidates who would benefit from personalized treatment approaches. Hence, the development of innovative genetic models holds immense significance for both diagnosis and prognosis of lung adenocarcinoma. Metabolism is a fundamental process of life, which produces energy, redox equivalents, and macromolecules and metabolites required for various life activities. Studies have found that tumors still require anaerobic glycolysis to obtain energy when metabolic substrates are sufficient, a process known as the Warburg Effect[ 2 ].Subsequently, it was found that metabolic abnormalities in tumors are not limited to the Warburg effect, but also abnormalities in central metabolic pathways. This heterogeneity is known as tumor metabolic reprogramming[ 3 ]. Recent studies have shown that metabolic reprogramming in tumors is not only a phenotypic feature, but also can be used to counteract the body's anti-tumor response[ 3 , 4 ]. Immunity refers to the process of recognizing and eliminating "non-self" components. Various immune cell populations in the body work together to maintain the homeostasis of the immune system, and through this process, cancer cells are monitored and eliminated. The immune system can regulate its function according to different signals in the environment to start or turn off specific immune responses. Immunotherapy for cancer can kill tumor cells by activating the patient's anti-tumor immune response[ 5 ]. Such as PD-1 or PD-L1, Although immunotherapy has many advantages compared with traditional chemotherapy alone, the response to treatment in each patient will vary greatly due to tumor heterogeneity and different degrees of immune infiltration. Common biomarkers such as PD-L1 expression cannot accurately predict the sensitivity of patients to treatment[ 6 , 7 ]. Not only does the metabolic reprogramming of cancer confer the characteristics of rapid tumor proliferation, but also certain metabolites are involved in the regulation of anti-tumor immune response[ 8 – 10 ]. This regulation affects tumor progression and drug resistance by altering the intensity of immune response[ 3 , 11 , 12 ]. In addition, it seems that we can improve the sensitivity of tumors to drugs by regulating their abnormal metabolism[ 13 , 14 ]. Many studies have shown that there is an interactive relationship between tumor metabolic reprogramming and immune response[ 13 , 15 , 16 ]. Current studies have focused on either immune or metabolic factors, whereas we developed a risk model that integrates both metabolic and immune-related genes to predict the prognosis of lung adenocarcinoma. The model not only accurately predicts the prognosis of lung adenocarcinoma, but also evaluates the efficacy of chemotherapy and immune checkpoint inhibitors. Furthermore, we combined the prognostic model with nomogram based on clinicopathological factors (age, gender, T, N, M stage) and found that it performed well in 1-, 3-, and 5-year survival rates, suggesting that our prognostic model has high clinical value. Material and Methods Data acquisition and pre-processing The transcriptome profiles, mutation data and corresponding clinical information of LUAD patients in the present study were retrieved and downloaded from the TCGA portal ( https://portal.gdc.cancer.gov/ , up to November 10, 2023) and GEO database ( https://www.ncbi.nlm.nih.gov/geo/ , up to November 10, 2023 Table 1). the ComBat method was employed to correct the batch effects of raw sequencing data sets from different platforms for ensuring comparability among all samples. Some cases with survival of less than 3 months or incomplete clinical data were then filtered out. In addition, 483 immune-related genes were obtained from IMMPORT database( https://immport.niaid.nih.gov/home ). Metabolically associated Genes were screened from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database( https://www.genome.jp/kegg/ ). 977 Metabolically associated Genes were extracted from the KEGG database. ( http://www.broadinstitute.org/gsea/msigdb ). NMF(Non-negative matrix factorization) consensus clustering analysis Non-negative matrix factorization (NMF) algorithm is a non-negative factorization of a matrix under the condition that all the elements of the matrix are non-negative, so as to find out the relationships and interactions between them. Elements with similar characteristics are grouped into one group and elements with different characteristics are grouped into another group. Prior to performing the NMF clustering algorithm, the “limma” package with the thresholds of p 1 was utilized to screen out the differentially expressed HP-related genes. Afterwards, on the basis of these genes, the NMF clustering method was implemented to sort TCGA. The number of clusters (K) from 2 to 8 were tested by running ten iterations per K, and the optimal K number was eventually determined in accordance with silhouette, consensus, as well as cophenetic. Then, Kaplan-Meier (K-M) curve was carried out to estimate the differences of OS between distinct subtypes. Moreover, the ESTIMATE algorithms were performed to elucidate the immune infiltration landscape of different clusters. Construction and validation of the risk model To quantify immune-related correlation patterns for individual tumors, we divided the patients into training and validation groups in a ratio of 7:3, and univariate Cox proportional hazard regression was conducted to identify immune-related prognostic markers. We further applied significant factors to Least Absolute Shrinkage and Selective Operator (LASSO) and univariate Cox proportional hazard regression analyses to construct the risk model. The risk score was calculated based on the coefficients of the candidate genes. According to the median risk score, patients were divided into low- and high-risk groups. ,Then Kaplan-Meier (K-M) curve was carried out to estimate the differences of OS between distinct subgroup. To improve the accuracy and practicability of the clinical predictive model, we constructed a nomogram model that included the following parameters: risk score, TNM stage, age, and sex. A calibration curve of the nomogram model was established to assess the consistency between the predicted and observed results. TMB(Tumor Mutational Burden) calculation and prognostic analysis somatic mutation information was extracted with a Perl script (23), and the TMB score of each sample was calculated through dividing the number of variants by exon length (38 million). For prognostic analysis and clinical correlation analysis, R was utilized to merge the patients’ TMB scores with corresponding clinical information. Kaplan–Meier (K-M) analysis was conducted to compare the difference in overall survival (OS) with the log-rank test for statistical significance. Prediction of ICB(Immune checkpoint blockade) therapy response Predict the effectiveness of patient immunotherapy using TIDE ( http://tide.dfci.harvard.edu/ ). Obtain immune checkpoint scores for patients in the TCGA cohort using TICA ( https://www.cancerimagingarchive.net/) ." Additionally, 298 urothelial carcinoma patients with both transcriptomic data and treatment response to immunotherapy from the IMvigor210 cohort were used for speculating the immunotherapy response of the signature. Drug response prediction By logging in to the Gene Set Cancer Analysis (GSCA) ( http://bioinfo.life.hust.edu.cn/GSCA/#/drug ) database, the association between the sensitivity of drugs derived from the Genomics of Drug Sensitivity in Cancer (GDSC) database and the mRNA expression of hub genes was explored[ 17 ]. At the same time, the chemical structural formulas of GDSC agents that exhibited a significant positive or negative correlation with the expression of these hub genes were collected by querying the MedChemExpress website ( https://www.medchemexpress.cn/ ). Results 1、Classification and characterization of lung adenocarcinoma The overview of the process used in this study was shown in Fig. 1 . After extraction from KEGG and MMPORT databases, a total of 947 genes related to metabolic pathways and 483 genes related to immunity were obtained. In the TCGA LUAD cohort (n = 502), 343 pairs of gene expression differences were found in contrast to adjacent normal tissues. The GEO-LUAD cohort was merged using the combat package and principal component analysis (PCA) was applied to evaluate the merging effect (Fig. 2 ). Through GEO cohort validation, 272 genes were identified that were expressed in both GEO and TCGA cohorts. Using these 272 pairs of genes for Non-negative matrix factorization (NMF) algorithm, the TCGA cohort was divided into two subtypes (Fig. 3 A). According to the results of survival analysis, they were named high-risk group and low-risk group, and it was found that there were statistically significant differences in disease-free survival and survival between these two types (Fig. 3 B, C). We used the ESTIMATE algorithm to estimate the population abundance of immune cells and stromal cell populations infiltrating the tissues in both subtypes (Fig. 3 D). The ESTIMATE evaluation results showed that C1 subtype had a higher score compared to C2 subtype, indicating a more significant level of immune cell infiltration in C1 subtype (Fig. 3 E). 2、Establishment and validation of prognostic models In order to further explore the value of differential genes, The TCGA-LUAD cohort was randomly divided into a training group and a testing group in a 7:3 ratio. we combined univariate Cox regression, LASSO regression (Fig. 4 A, B) and multivariate Cox regression (Fig. 4 C), used to select 9 genes to construct an immune prognostic risk model. Risk scores were obtained by combining expression levels and regression coefficients (RiskScore = CAT exp × -0.166751936312374 + CCL20 exp ×0.0563504470190809 + GPI exp ×0.133661988590366 + INSL4 exp * 0.126270665159998 + NT5Eexp * 0.105058801123867 + GSTA3exp × -0.064669483229661) + GNPNAT1 exp × 0.136377362257813). The training set and test set patients were divided into low-risk group and high-risk group based on median risk score, and the robustness of the prognostic risk model was evaluated. In both TCGA cohort (Fig. 5 A, B, C) and GEO cohort (Figure D), patients in the high-risk group had shorter survival time than those in the low-risk group ROC curves showed that RiskScore had the largest area under AUC curve among all clinical features (Fig. 5 E). In TCGA cohort, the areas under AUC curve at 1 year, 3 years, and 5 years were: 0.752, 0.755, and 0.654 respectively (Fig. 5 F). According to the RiskScore, calculate the scores of each sample and then divide the GEO cohort and TCGA cohort into two groups based on the median risk score (Fig. 6 A, B). Compare the gene expression patterns in different risk groups between the two cohort. The results show that regardless of whether it is in different risk groups in TCGA or GEO cohort similar abnormal gene expressions are observed in both (Fig. 6 C, D). Additionally, there is a higher distribution of death cases in the high-risk group (Fig. 6 E, F). In addition, after dividing the TCGA cohort into two groups based on patient staging (I-II stage and III-IV stage), we found that the high-risk group exhibited shorter survival (Fig. 6 G, H). 3. Construction of a Nomogram After univariate Cox regression and Multivariate Cox analysis, we found that the risk score is an independent risk factor and used it to construct a column chart for prediction (Fig. 7 A, B). In the column chart, scores for various factors such as age, stage, and risk score were calculated, and the total score can be used as a predictive tool (Fig. 7 C). At the same time, a calibration curve was plotted to measure the consistency between actual observed prognosis values and predicted values from the column chart (Fig. 7 D). The performance of the column chart was evaluated using ROC curves with AUC values of 0.715 at 1-year survival period, 0.735 at 3-year survival period, and 0.739 at 5-year survival period (Fig. 7 E). 4. Clinical differences between high-risk and low-risk groups After analysis, it was found that there is a correlation between the genes used to construct the prognostic model and the risk scores (Fig. 8 A), and there are also differences in gene expression between high-risk and low-risk groups (Fig. 8 B-H). However, when dividing the high and low expression groups based on the median expression of each gene, we observed survival differences between the high and low expression groups of CAT\CCL20\GPI\GNPNAT1 in TCGA cohort (Fig. 9 A-D). The analysis of the correlation between RiskScore and tumor mutation showed that risk score was positively correlated with tumor (Fig. 10 A), and there were differences in TMB between high and low risk groups (Fig. 10 B). Further investigation into the relationship between TMB and survival time found that the high mutation group had a shorter survival time (Fig. 10 C). According to the division of risk groups, the TCGA cohort was divided into four groups: high-risk high-TMB group, high-risk low-TMB group, low-risk high-TMB group, and low-risk low-TMB group. The results showed that the low-risk high-TMB group had the longest survival time, followed by the low-risk low-TMB group and the high-risk high-TMB group (Fig. 10 D). In terms of gene mutations, the analysis of the top 10 genes TP53, TTN, MUC16, CSMD3, RYR2, LRP1B, ZFHX4, USH2A, KRAS and IRP2 showed that the mutation rates of each gene in the high-risk group were significantly higher than those in the low-risk group (Fig. 10 E, F). To further investigate immune infiltration, we employed the MCPcounter method. We calculated the high- and low-risk groups, risk scores, and the population abundance of stromal cell populations. The results showed a correlation between B-cell lineage, T-cell, myeloid cell, endothelial cell, and fibroblast with riskscore (Fig. 11 A). In the high- and low-risk groups, the abundance of myeloid cells, endothelial cells, fibroblasts, and neutrophils varied (Fig. 11 B-E). 4、Potential drug sensitivity analysis We used TIDE and TICA models to predict clinical differences between high-risk and low-risk groups, and used IMvigor210 as an external validation set. According to TIDE scores, high-risk groups showed higher TIDE score and Worse treatment response than low-risk groups (Fig. 12 A). According to TICA scores, there was no statistically significant difference in AIPS scores in PD1 positive groups, but in PD1 negative groups, regardless of CALT4 expression, low-risk groups had higher scores (Fig. 12 B). External validation on the IMvigor210 cohort showed that patients classified as high-risk groups had shorter survival and weaker immunotherapy response (Fig. 12 C, D). The oncoPredict was used to conduct drug sensitivity analysis on high- and low-risk groups, specifically for common chemotherapy regimens of lung adenocarcinoma. The results showed that there were varying degrees of response differences among commonly used chemotherapy drugs such as paclitaxel(Fig. 13 A), docetaxel(Fig. 13 B), cisplatin(Fig. 13 C), gemcitabine(Fig. 13 D), and vinorelbine(Fig. 13 E) in the high- and low-risk groups. These drugs are commonly used chemotherapy drugs for lung adenocarcinoma and showed higher scores in the low-risk group. Discussion In this study, we combined immune and metabolism-related genes and identified two molecular subtypes through MNF non-negative matrix factorization. Subsequently, we established gene prognosis models related to immunity or metabolism using univariate Cox regression analysis, multivariate Cox regression analysis, and LASSO Cox regression analysis. Patients were divided into high- and low-risk groups based on the median risk score, and differences in overall survival were observed among these groups as well as within each subtype. Based on these results, we further plotted a prognostic nomogram incorporating patient characteristics such as gender, age, T stage, N stage, and M stage which demonstrated good predictive efficacy for patient prognosis. Additionally, CAT, CCL20, GPI and GNPNAT1 showed a close correlation with survival time. These findings suggest that immune- and metabolism-related genes play important roles in the occurrence and development of LUAD. The CAT gene encodes catalas. Disrupting the ROS balance in tumor tissue can disturb its homeostasis and inhibit tumor growth. Meanwhile, delivering catalase to the lesion through different carriers can enhance the tumor treatment response[ 18 , 19 ]. CCL20 is the ligand for CCR6[ 20 ]. Activation of the CCL20/CCR6/ERK signaling pathway through autocrine or paracrine mechanisms promotes cell proliferation and migration[ 21 ]. Related studies have confirmed the high expression of CCL20 and CCR6 in lung cancer tissues, which may promote tumor cell proliferation and migration through autocrine or paracrine mechanisms[ 22 ]. and its elevated levels may be associated with poor prognosis.[ 23 , 24 ]GPI plays an important role in glycolysis and gluconeogenesis. Abnormal expression of GPI has been associated with metastasis and poor prognosis in colorectal cancer, renal cell carcinoma, breast cancer, and endometrial cancer[ 25 , 26 ]. Additionally, decreased expression of GPI may lead to increased oxidative stress sensitivity, resulting in age-related death associated with oxidative stress[ 27 ]. GNPNAT1 is a key enzyme in the biosynthesis pathway of uridine diphosphate-N-acetylglucosamine.glutamine plays a crucial role in tumor tissues and its end product uridine diphosphate-N-acetylglucosamine has critical functions[ 28 ]. Enhancing the expression of this gene can promote malignant phenotypes of cancer cells and is associated with platinum resistance[ 29 ] and Kras mutations[ 30 ].In vitro experiments have also confirmed the relationship between its expression levels in lung cancer cells and prognosis[ 31 , 32 ]. INSL4 belongs to the insulin superfamily and was first identified in the human placenta[ 33 ]. It has been found to have a promoting effect on cancer[ 34 ]. but due to its specificity as a gene in primates, there have not been any in vivo experiments conducted to validate its mechanism. NT5E encoding (CD73) catalyzes the dephosphorylation of nucleoside 5'-monophosphate, converting it into adenosine. Relevant studies have found that overexpression of this gene is associated with the collaborative action of immune cells in the immune microenvironment of lung cancer[ 35 ], affecting immune evasion mechanisms. The protein encoded by GSTA3 is GST. The ability of cells to transform toxic chemicals and endogenous substances is correlated with the expression level of GST[ 36 ]. We also examined the mutation status of TCGA samples and discovered that the high-risk group exhibited a greater frequency of mutations. Specifically, TP53 displayed an elevated mutation rate within this high-risk cohort, which is correlated with poorer prognosis[ 37 , 38 ]. Within this same high-risk group, 10 genes demonstrated a mutation rate surpassing 30%, whereas only 5 genes did so within the low-risk group. Furthermore, survival analysis indicated that patients with increased tumor mutational burden (TMB) experienced prolonged overall survival time. This could potentially be attributed to the fact that heightened TMB encompasses more neoantigens and possesses enhanced immunogenicity, thereby leading to improved treatment response[ 39 ]. Chemotherapy and immunotherapy are important treatment strategies for LUAD. We used the oncoPredict R package to predict the responsiveness of high- and low-risk groups to common chemotherapy drugs. The results showed that compared to the high-risk group, the low-risk group performed better in terms of sensitivity scores for commonly used chemotherapy drugs such as vinorelbine, docetaxel, paclitaxel, and cisplatin. Additionally, we utilized the TCIA\TIDE model to predict the effectiveness of immunotherapy and found that the low-risk group exhibited a better immune response. By using IMvigor210 as an external validation dataset, we also discovered that this prognostic model could effectively predict immunotherapy outcomes in bladder cancer This study is based on the TCGA and GEO databases, and it classifies lung adenocarcinoma based on two key features: immunity and metabolism. A risk scoring system consisting of seven genes was identified. In the high-risk group, patients had shorter overall survival and poor response to chemotherapy and immunotherapy. This model has certain significance in guiding clinical prognosis assessment and treatment response prediction. However, it should be noted that this model still has limitations. For example, there may be differences between different sequencing platforms that could lead to errors. Additionally, the pathway mechanisms involved in this study have not been experimentally validated, which may reduce the credibility of the results. Although this study is retrospective in design, it does establish new prognostic models and treatment prediction models. In summary, by combining immune- and metabolism-related genes, we successfully constructed a novel model for predicting prognosis and treatment response in lung adenocarcinoma patients. Conclusion In summary, by combining immune- and metabolism-related genes, we successfully con-structed a novel model for predicting prognosis and treatment response in LUAD patients. Declarations Ethics approval and consent to participate No clinical patient study was conducted for this article, so no ethical statement is needed. The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Funding Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Availability of data and materials RNA-Seq data were deposited into the Gene Expression Omnibus database under accession number GSE11969 / GSE13213 / GSE41271/ GSE30219/ GSE31210/ GSE37745/ GSE42127/ GSE50081/ GSE72094/ GSE115002 are available at the following URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11969. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE13213. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE41271. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE30219. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE31210. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE37745. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE42127. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE50081. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115002 TCGA-LUAD cohort were deposited into the The Cancer Genome Atlas. data were available at the following URL: https://portal.gdc.cancer.gov/analysis_page?app=Downloads References Thai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS. 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Table 1 Table 1 GEO cohort patients with LUAD Age Sex Stage Status GSE11969 n=94 ＞70=18 ≤70=76 Female=43 Male=51 I~II=67 III~IV=27 Dead=43 Alive=51 GSE13213 n=117 ＞70=20 ≤70=107 Female=57 Male=60 I~II=92 III~IV =25 Dead=49 Alive=68 GSE41271 n=183 ＞70=62 ≤70=121 Female=90 Male=93 I~II=130 III~IV =53 Dead=71 Alive=112 GSE30219 n=85 ＞70=17 ≤70=68 Female=19 Male=66 I~II=84 III~IV =1 Dead=45 Alive=40 GSE31210 n=226 ＞70=5 ≤70=221 Female=121 Male=105 I~II=226 Dead=35 Alive=191 GSE37745 n=105 ＞70=81 ≤70=24 Female=60 Male=45 I~II=88 III=17 Dead=76 Alive=29 GSE42127 n=133 ＞70=81 ≤70=52 Female=65 Male=68 I~II=111 III=22 Dead=43 Alive=90 GSE50081 n=127 ＞70=35 ≤70=52 Female=62 Male=65 I~II=127 Dead=51 Alive=76 GSE72094 n=387 ＞70=189 ≤70=198 Female=216 Male=171 I~II=315 III~IV =72 Dead=107 Alive=280 GSE115002 n=52 ＞70=5 ≤70=47 Female=26 Male=26 / Dead=38 Alive=14 TCGA-LUAD n=465 ＞70=146 ≤70=309 Female=26 Male=26 I~II=370 III~IV =95 Dead=291 Alive=174 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Aug, 2024 Reviews received at journal 08 Aug, 2024 Reviewers agreed at journal 08 Aug, 2024 Reviews received at journal 02 Aug, 2024 Reviewers agreed at journal 25 Jul, 2024 Reviewers invited by journal 18 Jul, 2024 Editor assigned by journal 15 Jul, 2024 Submission checks completed at journal 13 Jul, 2024 First submitted to journal 07 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4700280","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336164619,"identity":"3f5dce3a-7eaa-4f6c-bdb4-b520f974f0dc","order_by":0,"name":"Cong Xue","email":"","orcid":"","institution":"Zhangzhou Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Xue","suffix":""},{"id":336164620,"identity":"f70468f2-2f9e-45eb-9064-d8f9ea232e21","order_by":1,"name":"Yi-Zhi Dai","email":"","orcid":"","institution":"Zhangzhou Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi-Zhi","middleName":"","lastName":"Dai","suffix":""},{"id":336164621,"identity":"9137dccc-429a-4d13-b463-5f245253f6fc","order_by":2,"name":"Gui-Long Li","email":"","orcid":"","institution":"Zhangzhou Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Gui-Long","middleName":"","lastName":"Li","suffix":""},{"id":336164622,"identity":"30343410-83d7-4001-b986-21b1a412abf5","order_by":3,"name":"Yi Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYDCCA0CcYGDDw8/fQIqWBxVpMpIzDpCghfHBmcM2Bg0JROrgu338mURi23keA4YDjB8+5hChRfJcQhpQy20ec+YGZsmZ24jQYnCG4RhYi2XDATZmXuK0MLYBtZzjMTiQQLQWZjaJhDMHSNAieYaN2SKhIplHcsbBZuL8wneG/eHNHwZ29vz8zQc/fCRGCxCwSEBoxgbi1AMB8weilY6CUTAKRsHIBABU6DhNTmNa8gAAAABJRU5ErkJggg==","orcid":"","institution":"Zhangzhou Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-07-07 12:55:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4700280/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4700280/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62217869,"identity":"7c19c9f9-2f97-4773-9ac5-48b5aefc5bf3","added_by":"auto","created_at":"2024-08-11 11:57:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":177641,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of this study\u003c/p\u003e","description":"","filename":"FIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/adbc50c1830adce32484a515.png"},{"id":62217870,"identity":"b55d2b9f-7f78-4249-b694-f7cda71b0b97","added_by":"auto","created_at":"2024-08-11 11:57:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":674393,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) dimensionality reduction before merging GEO-LUAD Cohorts（A）Principal Component Analysis (PCA) dimensionality reduction before merging GEO-LUAD Cohorts（B）\u003c/p\u003e","description":"","filename":"FIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/58fb5b28eaec62170b275dab.png"},{"id":62218653,"identity":"8c43ba16-fbf2-41c1-a417-4cb3e0adcaa2","added_by":"auto","created_at":"2024-08-11 12:05:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":504448,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of a NMF subtype based on Immune and Metabolic Related Genes in the TCGA-LUAD cohort. (A) NMF consensus clustering for k = 2. (B) Kaplan–Meier analysis of overall survival (OS) for Cluster C1 and C2. (C) ESTIMATE for Cluster C1 and C2.\u003c/p\u003e","description":"","filename":"FIGURE3.png","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/544436b43ec8c72a1c0679f0.png"},{"id":62219328,"identity":"8c10d14a-978e-4e7a-bd2a-591955280a81","added_by":"auto","created_at":"2024-08-11 12:13:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":198685,"visible":true,"origin":"","legend":"\u003cp\u003eScreening for prognostic genes in LUAD using the TCGA-LUAD cohort.Lasso regression (A,B), multivariate Cox regression analyses (C)\u003c/p\u003e","description":"","filename":"FIGURE4.png","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/05eee2611038d36a68847d76.png"},{"id":62218655,"identity":"cd6c32f8-8bef-428b-a018-6b5edc2cc198","added_by":"auto","created_at":"2024-08-11 12:05:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":374692,"visible":true,"origin":"","legend":"\u003cp\u003eWe were performed to screen candidate genes. Kaplan–Meier curves demonstrated the overall survival of LUAD patients in the RiskScore-high and -low groups of TCGA-LUAD train cohort（A）TCGA-LUAD test cohort（B）TCGA-LUAD cohort（C） and e GEO-LUAD cohort (D). The ROC curves to validate the accuracy of the risk model in predicting the clinical outcomes of patients with LUAD (E, F) All of statistical tests were performed using Cox regression analysis.\u003c/p\u003e","description":"","filename":"FIGURE5.png","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/619f5afdb225e2a148b53c57.png"},{"id":62217872,"identity":"04594dde-2b9a-49b9-865d-1300df7f0156","added_by":"auto","created_at":"2024-08-11 11:57:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":454358,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic value of RiskScore. According to the median risk score, TCGA-LUAD cohort was divided into high-risk group and low-risk group (A) GEO-LUAD cohort was divided into high-risk group and low-risk group(B) Gene expression profiles associated with high and low-risk groups in the TCGA-LUAD cohort prognostic model（C）. Gene expression profiles associated with high and low-risk groups in the GEO-LUAD cohort prognostic model（D）. The scatter plots are for the TCGA-LUAD cohort and the GEO-LUAD cohort, respectively. (E, F) Disparities in survival rates persist among high and low-risk subgroups within both the early-stage（G）and Advanced stage(H) cohorts.\u003c/p\u003e","description":"","filename":"FIGURE6.png","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/2b2bf06290947f0c2ffe6ce1.png"},{"id":62217877,"identity":"6c008748-91e7-4cfd-a896-74d25541de02","added_by":"auto","created_at":"2024-08-11 11:57:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":312991,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic efficiency of the Nomogram model. (A and B) Forest plots of the (A) univariate and (B) multivariate Cox regression analyses in TCGA LUAD cohorts. A nomogram model constructed based on the TCGA-LUAD cohort was used to assess the 1-, 3-, and 5-year overall survival of LUAD patients. (C) The calibration plots for the internal validation of the nomogram predicting 1-, 3- and 5-year (D) Time-dependent ROC curve demonstrated the ability of the model to predict overall survival at 1-, 3- and 5-year in LUAD patients(E).\u003c/p\u003e","description":"","filename":"FIGURE7.png","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/55eec90d1ed4f5b6ccbf6660.png"},{"id":62219329,"identity":"8271d7b3-08cb-4dbc-9336-abe9b2605537","added_by":"auto","created_at":"2024-08-11 12:13:33","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":268847,"visible":true,"origin":"","legend":"\u003cp\u003eModel of gene expression and riskscore relationships in the TCGA-LUAD cohort (A) Differential expression of prognostic genes between high and low risk groups in TCGA-LUAD cohort(B-H)\u003c/p\u003e","description":"","filename":"FIGURE8.png","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/47881c44ecdf5ee776c4193d.png"},{"id":62217881,"identity":"4717a587-d9b2-4aad-9284-e004540c1a5f","added_by":"auto","created_at":"2024-08-11 11:57:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":454279,"visible":true,"origin":"","legend":"\u003cp\u003eCAT\\CCL20\\GPI\\GNPNAT1 expression and its relationship with overall survival in the TCGA-LUAD cohort(A-D)\u003c/p\u003e","description":"","filename":"FIGURE9.png","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/7c41f69d5317688dd26697ed.png"},{"id":62219327,"identity":"c0737f84-889f-417a-b7df-f43b289737b2","added_by":"auto","created_at":"2024-08-11 12:13:33","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":661073,"visible":true,"origin":"","legend":"\u003cp\u003eThe tumor mutation burden characteristics in low- and high-risk group. Correlation analysis between riskscore and TMB (A) The difference in TMB between two groups. (B) Survival difference between high- and low-TMB. (C) Survival analysis of TMB along with riskscore. (D): Mutational landscape in the high-risk group. (E) The difference in TMB between two groups. (F) Abbreviation TMB: Tumor mutational burden.\u003c/p\u003e","description":"","filename":"FIGURE10.png","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/8be16e5096490357ec0088c6.png"},{"id":62217875,"identity":"f362fe9a-0e5d-409b-86f2-338b80a8fbff","added_by":"auto","created_at":"2024-08-11 11:57:33","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":348700,"visible":true,"origin":"","legend":"\u003cp\u003eImmuno-infiltration analysis and riskscore relationships in the TCGA-LUAD cohort(A) The difference in Immuno-infiltration between low- and high-risk group in TCGA-LUAD(B)\u003c/p\u003e","description":"","filename":"FIGURE11.png","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/820b41c1e0b074be2617f03e.png"},{"id":62217879,"identity":"0b93ba49-54f9-4ff0-b1bf-4da07fb2f67c","added_by":"auto","created_at":"2024-08-11 11:57:33","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":289892,"visible":true,"origin":"","legend":"\u003cp\u003eImmunotherapy response of low- and high-risk TCGA-LUAD cohort. Difference of the TIDE(A，B). TCIA score of the low- and high-risk group in the of CTLA4- PD1-, CTLA4- PD1+, CTLA+ PD1-, and CTLA+ PD1+ subgroups. (B)Kaplan–Meier curves for Prognostic Model in the IMvigor210. (C) Differences in riskscore between patients with CR/PR and individuals with SD/PD after treatment with ICIs in the IMvigor210(D).\u003c/p\u003e","description":"","filename":"FIGURE12.png","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/920f366a25e10617b126cc23.png"},{"id":62218657,"identity":"924f90ef-3441-4a5f-81ee-e257f664d438","added_by":"auto","created_at":"2024-08-11 12:05:33","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":115858,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of chemotherapeutic drugs in high-risk and low-risk groups.. (A–E ) The boxplot shows drugs with different IC50 values between the high and low risk groups\u003c/p\u003e","description":"","filename":"FIGURE13.png","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/10d24184f64b311c91303d7d.png"},{"id":62220463,"identity":"73335a14-8063-43a4-a953-4c5855d760af","added_by":"auto","created_at":"2024-08-11 12:21:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5500674,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4700280/v1/2bfc99f7-4a5f-4197-9ee2-c173ac1187e6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of Prognosis, Efficacy of Lung Adenocarcinoma by Machine Learning Model Based on Immune and Metabolic Related Genes","fulltext":[{"header":"Background","content":"\u003cp\u003eLung adenocarcinoma is the predominant histological subtype of lung cancer, resulting in millions of fatalities annually[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite the significant advancements in lung cancer survival achieved through chemotherapy and immune checkpoint inhibitors, not all patients derive benefits from immunotherapy due to the extensive heterogeneity observed in lung cancer genomes. Consequently, there exists an urgent imperative for novel methodologies to identify potential candidates who would benefit from personalized treatment approaches. Hence, the development of innovative genetic models holds immense significance for both diagnosis and prognosis of lung adenocarcinoma.\u003c/p\u003e \u003cp\u003eMetabolism is a fundamental process of life, which produces energy, redox equivalents, and macromolecules and metabolites required for various life activities. Studies have found that tumors still require anaerobic glycolysis to obtain energy when metabolic substrates are sufficient, a process known as the Warburg Effect[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].Subsequently, it was found that metabolic abnormalities in tumors are not limited to the Warburg effect, but also abnormalities in central metabolic pathways. This heterogeneity is known as tumor metabolic reprogramming[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recent studies have shown that metabolic reprogramming in tumors is not only a phenotypic feature, but also can be used to counteract the body's anti-tumor response[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImmunity refers to the process of recognizing and eliminating \"non-self\" components. Various immune cell populations in the body work together to maintain the homeostasis of the immune system, and through this process, cancer cells are monitored and eliminated. The immune system can regulate its function according to different signals in the environment to start or turn off specific immune responses. Immunotherapy for cancer can kill tumor cells by activating the patient's anti-tumor immune response[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Such as PD-1 or PD-L1, Although immunotherapy has many advantages compared with traditional chemotherapy alone, the response to treatment in each patient will vary greatly due to tumor heterogeneity and different degrees of immune infiltration. Common biomarkers such as PD-L1 expression cannot accurately predict the sensitivity of patients to treatment[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNot only does the metabolic reprogramming of cancer confer the characteristics of rapid tumor proliferation, but also certain metabolites are involved in the regulation of anti-tumor immune response[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This regulation affects tumor progression and drug resistance by altering the intensity of immune response[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition, it seems that we can improve the sensitivity of tumors to drugs by regulating their abnormal metabolism[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Many studies have shown that there is an interactive relationship between tumor metabolic reprogramming and immune response[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent studies have focused on either immune or metabolic factors, whereas we developed a risk model that integrates both metabolic and immune-related genes to predict the prognosis of lung adenocarcinoma. The model not only accurately predicts the prognosis of lung adenocarcinoma, but also evaluates the efficacy of chemotherapy and immune checkpoint inhibitors. Furthermore, we combined the prognostic model with nomogram based on clinicopathological factors (age, gender, T, N, M stage) and found that it performed well in 1-, 3-, and 5-year survival rates, suggesting that our prognostic model has high clinical value.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eData acquisition and pre-processing\u003c/p\u003e \u003cp\u003eThe transcriptome profiles, mutation data and corresponding clinical information of LUAD patients in the present study were retrieved and downloaded from the TCGA portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, up to November 10, 2023) and GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, up to November 10, 2023 Table\u0026nbsp;1). the ComBat method was employed to correct the batch effects of raw sequencing data sets from different platforms for ensuring comparability among all samples. Some cases with survival of less than 3 months or incomplete clinical data were then filtered out. In addition, 483 immune-related genes were obtained from IMMPORT database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://immport.niaid.nih.gov/home\u003c/span\u003e\u003cspan address=\"https://immport.niaid.nih.gov/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Metabolically associated Genes were screened from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). 977 Metabolically associated Genes were extracted from the KEGG database. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.broadinstitute.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"http://www.broadinstitute.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e \u003cp\u003eNMF(Non-negative matrix factorization) consensus clustering analysis\u003c/p\u003e \u003cp\u003eNon-negative matrix factorization (NMF) algorithm is a non-negative factorization of a matrix under the condition that all the elements of the matrix are non-negative, so as to find out the relationships and interactions between them. Elements with similar characteristics are grouped into one group and elements with different characteristics are grouped into another group. Prior to performing the NMF clustering algorithm, the \u0026ldquo;limma\u0026rdquo; package with the thresholds of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |logFC| (fold change)\u0026thinsp;\u0026gt;\u0026thinsp;1 was utilized to screen out the differentially expressed HP-related genes. Afterwards, on the basis of these genes, the NMF clustering method was implemented to sort TCGA. The number of clusters (K) from 2 to 8 were tested by running ten iterations per K, and the optimal K number was eventually determined in accordance with silhouette, consensus, as well as cophenetic. Then, Kaplan-Meier (K-M) curve was carried out to estimate the differences of OS between distinct subtypes. Moreover, the ESTIMATE\u003c/p\u003e \u003cp\u003ealgorithms were performed to elucidate the immune infiltration landscape of different clusters.\u003c/p\u003e \u003cp\u003eConstruction and validation of the risk model\u003c/p\u003e \u003cp\u003eTo quantify immune-related correlation patterns for individual tumors, we divided the patients into training and validation groups in a ratio of 7:3, and univariate Cox proportional hazard regression was conducted to identify immune-related prognostic markers. We further applied significant factors to Least Absolute Shrinkage and Selective Operator (LASSO) and univariate Cox proportional hazard regression analyses to construct the risk model. The risk score was calculated based on the coefficients of the candidate genes. According to the median risk score, patients were divided into low- and high-risk groups. ,Then Kaplan-Meier (K-M) curve was carried out to estimate the differences of OS between distinct subgroup. To improve the accuracy and practicability of the clinical predictive model, we constructed a nomogram model that included the following parameters: risk score, TNM stage, age, and sex. A calibration curve of the nomogram model was established to assess the consistency between the predicted and observed results.\u003c/p\u003e \u003cp\u003eTMB(Tumor Mutational Burden) calculation and prognostic analysis\u003c/p\u003e \u003cp\u003esomatic mutation information was extracted with a Perl script (23), and the TMB score of each sample was calculated through dividing the number of variants by exon length (38\u0026nbsp;million). For prognostic analysis and clinical correlation analysis, R was utilized to merge the patients\u0026rsquo; TMB scores with corresponding clinical information. Kaplan\u0026ndash;Meier (K-M) analysis was conducted to compare the difference in overall survival (OS) with the log-rank test for statistical significance.\u003c/p\u003e \u003cp\u003ePrediction of ICB(Immune checkpoint blockade) therapy response\u003c/p\u003e \u003cp\u003ePredict the effectiveness of patient immunotherapy using TIDE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Obtain immune checkpoint scores for patients in the TCGA cohort using TICA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerimagingarchive.net/)\u003c/span\u003e\u003cspan address=\"https://www.cancerimagingarchive.net/)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\" Additionally, 298 urothelial carcinoma patients with both transcriptomic data and treatment response to immunotherapy from the IMvigor210 cohort were used for speculating the immunotherapy response of the signature.\u003c/p\u003e \u003cp\u003eDrug response prediction\u003c/p\u003e \u003cp\u003eBy logging in to the Gene Set Cancer Analysis (GSCA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.life.hust.edu.cn/GSCA/#/drug\u003c/span\u003e\u003cspan address=\"http://bioinfo.life.hust.edu.cn/GSCA/#/drug\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database, the association between the sensitivity of drugs derived from the Genomics of Drug Sensitivity in Cancer (GDSC) database and the mRNA expression of hub genes was explored[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. At the same time, the chemical structural formulas of GDSC agents that exhibited a significant positive or negative correlation with the expression of these hub genes were collected by querying the MedChemExpress website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.medchemexpress.cn/\u003c/span\u003e\u003cspan address=\"https://www.medchemexpress.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e1、Classification and characterization of lung adenocarcinoma\u003c/p\u003e \u003cp\u003eThe overview of the process used in this study was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. After extraction from KEGG and MMPORT databases, a total of 947 genes related to metabolic pathways and 483 genes related to immunity were obtained. In the TCGA LUAD cohort (n\u0026thinsp;=\u0026thinsp;502), 343 pairs of gene expression differences were found in contrast to adjacent normal tissues. The GEO-LUAD cohort was merged using the combat package and principal component analysis (PCA) was applied to evaluate the merging effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Through GEO cohort validation, 272 genes were identified that were expressed in both GEO and TCGA cohorts. Using these 272 pairs of genes for Non-negative matrix factorization (NMF) algorithm, the TCGA cohort was divided into two subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). According to the results of survival analysis, they were named high-risk group and low-risk group, and it was found that there were statistically significant differences in disease-free survival and survival between these two types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe used the ESTIMATE algorithm to estimate the population abundance of immune cells and stromal cell populations infiltrating the tissues in both subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The ESTIMATE evaluation results showed that C1 subtype had a higher score compared to C2 subtype, indicating a more significant level of immune cell infiltration in C1 subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e2、Establishment and validation of prognostic models\u003c/p\u003e \u003cp\u003eIn order to further explore the value of differential genes, The TCGA-LUAD cohort was randomly divided into a training group and a testing group in a 7:3 ratio. we combined univariate Cox regression, LASSO regression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B) and multivariate Cox regression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), used to select 9 genes to construct an immune prognostic risk model. Risk scores were obtained by combining expression levels and regression coefficients (RiskScore\u0026thinsp;=\u0026thinsp;CAT exp \u0026times; -0.166751936312374\u0026thinsp;+\u0026thinsp;CCL20 exp \u0026times;0.0563504470190809\u0026thinsp;+\u0026thinsp;GPI exp \u0026times;0.133661988590366\u0026thinsp;+\u0026thinsp;INSL4 exp * 0.126270665159998\u0026thinsp;+\u0026thinsp;NT5Eexp * 0.105058801123867\u0026thinsp;+\u0026thinsp;GSTA3exp \u0026times; -0.064669483229661)\u0026thinsp;+\u0026thinsp;GNPNAT1 exp \u0026times; 0.136377362257813). The training set and test set patients were divided into low-risk group and high-risk group based on median risk score, and the robustness of the prognostic risk model was evaluated. In both TCGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B, C) and GEO cohort (Figure D), patients in the high-risk group had shorter survival time than those in the low-risk group ROC curves showed that RiskScore had the largest area under AUC curve among all clinical features (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). In TCGA cohort, the areas under AUC curve at 1 year, 3 years, and 5 years were: 0.752, 0.755, and 0.654 respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the RiskScore, calculate the scores of each sample and then divide the GEO cohort and TCGA cohort into two groups based on the median risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). Compare the gene expression patterns in different risk groups between the two cohort. The results show that regardless of whether it is in different risk groups in TCGA or GEO cohort similar abnormal gene expressions are observed in both (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, D). Additionally, there is a higher distribution of death cases in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, F). In addition, after dividing the TCGA cohort into two groups based on patient staging (I-II stage and III-IV stage), we found that the high-risk group exhibited shorter survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG, H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3. Construction of a Nomogram\u003c/p\u003e \u003cp\u003eAfter univariate Cox regression and Multivariate Cox analysis, we found that the risk score is an independent risk factor and used it to construct a column chart for prediction (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B). In the column chart, scores for various factors such as age, stage, and risk score were calculated, and the total score can be used as a predictive tool (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). At the same time, a calibration curve was plotted to measure the consistency between actual observed prognosis values and predicted values from the column chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). The performance of the column chart was evaluated using ROC curves with AUC values of 0.715 at 1-year survival period, 0.735 at 3-year survival period, and 0.739 at 5-year survival period (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e4. Clinical differences between high-risk and low-risk groups\u003c/p\u003e \u003cp\u003eAfter analysis, it was found that there is a correlation between the genes used to construct the prognostic model and the risk scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA), and there are also differences in gene expression between high-risk and low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB-H). However, when dividing the high and low expression groups based on the median expression of each gene, we observed survival differences between the high and low expression groups of CAT\\CCL20\\GPI\\GNPNAT1 in TCGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of the correlation between RiskScore and tumor mutation showed that risk score was positively correlated with tumor (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA), and there were differences in TMB between high and low risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). Further investigation into the relationship between TMB and survival time found that the high mutation group had a shorter survival time (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). According to the division of risk groups, the TCGA cohort was divided into four groups: high-risk high-TMB group, high-risk low-TMB group, low-risk high-TMB group, and low-risk low-TMB group. The results showed that the low-risk high-TMB group had the longest survival time, followed by the low-risk low-TMB group and the high-risk high-TMB group (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). In terms of gene mutations, the analysis of the top 10 genes TP53, TTN, MUC16, CSMD3, RYR2, LRP1B, ZFHX4, USH2A, KRAS and IRP2 showed that the mutation rates of each gene in the high-risk group were significantly higher than those in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further investigate immune infiltration, we employed the MCPcounter method. We calculated the high- and low-risk groups, risk scores, and the population abundance of stromal cell populations. The results showed a correlation between B-cell lineage, T-cell, myeloid cell, endothelial cell, and fibroblast with riskscore (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA). In the high- and low-risk groups, the abundance of myeloid cells, endothelial cells, fibroblasts, and neutrophils varied (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB-E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e4、Potential drug sensitivity analysis\u003c/p\u003e \u003cp\u003eWe used TIDE and TICA models to predict clinical differences between high-risk and low-risk groups, and used IMvigor210 as an external validation set. According to TIDE scores, high-risk groups showed higher TIDE score and Worse treatment response than low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA). According to TICA scores, there was no statistically significant difference in AIPS scores in PD1 positive groups, but in PD1 negative groups, regardless of CALT4 expression, low-risk groups had higher scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB). External validation on the IMvigor210 cohort showed that patients classified as high-risk groups had shorter survival and weaker immunotherapy response (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe oncoPredict was used to conduct drug sensitivity analysis on high- and low-risk groups, specifically for common chemotherapy regimens of lung adenocarcinoma. The results showed that there were varying degrees of response differences among commonly used chemotherapy drugs such as paclitaxel(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA), docetaxel(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eB), cisplatin(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eC), gemcitabine(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eD), and vinorelbine(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eE) in the high- and low-risk groups. These drugs are commonly used chemotherapy drugs for lung adenocarcinoma and showed higher scores in the low-risk group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we combined immune and metabolism-related genes and identified two molecular subtypes through MNF non-negative matrix factorization. Subsequently, we established gene prognosis models related to immunity or metabolism using univariate Cox regression analysis, multivariate Cox regression analysis, and LASSO Cox regression analysis. Patients were divided into high- and low-risk groups based on the median risk score, and differences in overall survival were observed among these groups as well as within each subtype. Based on these results, we further plotted a prognostic nomogram incorporating patient characteristics such as gender, age, T stage, N stage, and M stage which demonstrated good predictive efficacy for patient prognosis. Additionally, CAT, CCL20, GPI and GNPNAT1 showed a close correlation with survival time. These findings suggest that immune- and metabolism-related genes play important roles in the occurrence and development of LUAD.\u003c/p\u003e \u003cp\u003eThe CAT gene encodes catalas. Disrupting the ROS balance in tumor tissue can disturb its homeostasis and inhibit tumor growth. Meanwhile, delivering catalase to the lesion through different carriers can enhance the tumor treatment response[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. CCL20 is the ligand for CCR6[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Activation of the CCL20/CCR6/ERK signaling pathway through autocrine or paracrine mechanisms promotes cell proliferation and migration[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Related studies have confirmed the high expression of CCL20 and CCR6 in lung cancer tissues, which may promote tumor cell proliferation and migration through autocrine or paracrine mechanisms[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. and its elevated levels may be associated with poor prognosis.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]GPI plays an important role in glycolysis and gluconeogenesis. Abnormal expression of GPI has been associated with metastasis and poor prognosis in colorectal cancer, renal cell carcinoma, breast cancer, and endometrial cancer[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, decreased expression of GPI may lead to increased oxidative stress sensitivity, resulting in age-related death associated with oxidative stress[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. GNPNAT1 is a key enzyme in the biosynthesis pathway of uridine diphosphate-N-acetylglucosamine.glutamine plays a crucial role in tumor tissues and its end product uridine diphosphate-N-acetylglucosamine has critical functions[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Enhancing the expression of this gene can promote malignant phenotypes of cancer cells and is associated with platinum resistance[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and Kras mutations[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].In vitro experiments have also confirmed the relationship between its expression levels in lung cancer cells and prognosis[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eINSL4 belongs to the insulin superfamily and was first identified in the human placenta[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. It has been found to have a promoting effect on cancer[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. but due to its specificity as a gene in primates, there have not been any in vivo experiments conducted to validate its mechanism. NT5E encoding (CD73) catalyzes the dephosphorylation of nucleoside 5'-monophosphate, converting it into adenosine. Relevant studies have found that overexpression of this gene is associated with the collaborative action of immune cells in the immune microenvironment of lung cancer[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], affecting immune evasion mechanisms. The protein encoded by GSTA3 is GST. The ability of cells to transform toxic chemicals and endogenous substances is correlated with the expression level of GST[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe also examined the mutation status of TCGA samples and discovered that the high-risk group exhibited a greater frequency of mutations. Specifically, TP53 displayed an elevated mutation rate within this high-risk cohort, which is correlated with poorer prognosis[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Within this same high-risk group, 10 genes demonstrated a mutation rate surpassing 30%, whereas only 5 genes did so within the low-risk group. Furthermore, survival analysis indicated that patients with increased tumor mutational burden (TMB) experienced prolonged overall survival time. This could potentially be attributed to the fact that heightened TMB encompasses more neoantigens and possesses enhanced immunogenicity, thereby leading to improved treatment response[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChemotherapy and immunotherapy are important treatment strategies for LUAD. We used the oncoPredict R package to predict the responsiveness of high- and low-risk groups to common chemotherapy drugs. The results showed that compared to the high-risk group, the low-risk group performed better in terms of sensitivity scores for commonly used chemotherapy drugs such as vinorelbine, docetaxel, paclitaxel, and cisplatin. Additionally, we utilized the TCIA\\TIDE model to predict the effectiveness of immunotherapy and found that the low-risk group exhibited a better immune response. By using IMvigor210 as an external validation dataset, we also discovered that this prognostic model could effectively predict immunotherapy outcomes in bladder cancer\u003c/p\u003e \u003cp\u003eThis study is based on the TCGA and GEO databases, and it classifies lung adenocarcinoma based on two key features: immunity and metabolism. A risk scoring system consisting of seven genes was identified. In the high-risk group, patients had shorter overall survival and poor response to chemotherapy and immunotherapy. This model has certain significance in guiding clinical prognosis assessment and treatment response prediction. However, it should be noted that this model still has limitations. For example, there may be differences between different sequencing platforms that could lead to errors. Additionally, the pathway mechanisms involved in this study have not been experimentally validated, which may reduce the credibility of the results. Although this study is retrospective in design, it does establish new prognostic models and treatment prediction models.\u003c/p\u003e \u003cp\u003eIn summary, by combining immune- and metabolism-related genes, we successfully constructed a novel model for predicting prognosis and treatment response in lung adenocarcinoma patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, by combining immune- and metabolism-related genes, we successfully con-structed a novel model for predicting prognosis and treatment response in LUAD patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNo clinical patient study was conducted for this article, so no ethical statement is needed. The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Consent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Availability of data and materials\u003c/p\u003e\n\u003cp\u003eRNA-Seq data were deposited into the Gene Expression Omnibus database under accession number GSE11969 / GSE13213 / GSE41271/ GSE30219/ GSE31210/ GSE37745/ GSE42127/ GSE50081/ GSE72094/ GSE115002 \u0026nbsp;are available at the following URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11969. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE13213. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE41271. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE30219. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE31210. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE37745. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE42127. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE50081.\u003c/p\u003e\n\u003cp\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115002\u003c/p\u003e\n\u003cp\u003eTCGA-LUAD cohort were deposited into the The Cancer Genome Atlas. data were available at the following URL: https://portal.gdc.cancer.gov/analysis_page?app=Downloads\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS. 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Semin Cancer Biol. 2022. 86(Pt 3): 542-565.\u003c/li\u003e\n\u003cli\u003ePatel CH, Leone RD, Horton MR, Powell JD. Targeting metabolism to regulate immune responses in autoimmunity and cancer. Nat Rev Drug Discov. 2019. 18(9): 669-688.\u003c/li\u003e\n\u003cli\u003eSedlak JC, Yilmaz \u0026Ouml;H, Roper J. Metabolism and Colorectal Cancer. Annu Rev Pathol. 2023. 18: 467-492.\u003c/li\u003e\n\u003cli\u003eZhu M, Zeng Q, Fan T, et al. Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis. Front Immunol. 2022. 13: 783495.\u003c/li\u003e\n\u003cli\u003eLiu CJ, Hu FF, Xia MX, Han L, Zhang Q, Guo AY. GSCALite: a web server for gene set cancer analysis. Bioinformatics. 2018. 34(21): 3771-3772.\u003c/li\u003e\n\u003cli\u003eWu T, Liu Y, Cao Y, Liu Z. Engineering Macrophage Exosome Disguised Biodegradable Nanoplatform for Enhanced Sonodynamic Therapy of Glioblastoma. Adv Mater. 2022. 34(15): e2110364.\u003c/li\u003e\n\u003cli\u003eChen Q, Chen J, Yang Z, et al. Nanoparticle-Enhanced Radiotherapy to Trigger Robust Cancer Immunotherapy. Adv Mater. 2019. 31(10): e1802228.\u003c/li\u003e\n\u003cli\u003eBaba M, Imai T, Nishimura M, et al. Identification of CCR6, the specific receptor for a novel lymphocyte-directed CC chemokine LARC. J Biol Chem. 1997. 272(23): 14893-8.\u003c/li\u003e\n\u003cli\u003eKadomoto S, Izumi K, Mizokami A. The CCL20-CCR6 Axis in Cancer Progression. Int J Mol Sci. 2020. 21(15): 5186.\u003c/li\u003e\n\u003cli\u003eWang B, Shi L, Sun X, Wang L, Wang X, Chen C. Production of CCL20 from lung cancer cells induces the cell migration and proliferation through PI3K pathway. J Cell Mol Med. 2016. 20(5): 920-9.\u003c/li\u003e\n\u003cli\u003eFan T, Li S, Xiao C, et al. CCL20 promotes lung adenocarcinoma progression by driving epithelial-mesenchymal transition. Int J Biol Sci. 2022. 18(11): 4275-4288.\u003c/li\u003e\n\u003cli\u003eWei W, Zhao X, Zhu J, et al. lncRNA‑u50535 promotes the progression of lung cancer by activating CCL20/ERK signaling. Oncol Rep. 2019. 42(5): 1946-1956.\u003c/li\u003e\n\u003cli\u003eLuo CT, Liao W, Dadi S, Toure A, Li MO. Graded Foxo1 activity in Treg cells differentiates tumour immunity from spontaneous autoimmunity. Nature. 2016. 529(7587): 532-6.\u003c/li\u003e\n\u003cli\u003eWu ST, Liu B, Ai ZZ, et al. Esculetin Inhibits Cancer Cell Glycolysis by Binding Tumor PGK2, GPD2, and GPI. Front Pharmacol. 2020. 11: 379.\u003c/li\u003e\n\u003cli\u003eFunasaka T, Hu H, Hogan V, Raz A. Down-regulation of phosphoglucose isomerase/autocrine motility factor expression sensitizes human fibrosarcoma cells to oxidative stress leading to cellular senescence. J Biol Chem. 2007. 282(50): 36362-9.\u003c/li\u003e\n\u003cli\u003eAkella NM, Ciraku L, Reginato MJ. Fueling the fire: emerging role of the hexosamine biosynthetic pathway in cancer. BMC Biol. 2019. 17(1): 52.\u003c/li\u003e\n\u003cli\u003eLuanpitpong S, Angsutararux P, Samart P, Chanthra N, Chanvorachote P, Issaragrisil S. Hyper-O-GlcNAcylation induces cisplatin resistance via regulation of p53 and c-Myc in human lung carcinoma. Sci Rep. 2017. 7(1): 10607.\u003c/li\u003e\n\u003cli\u003eTaparra K, Wang H, Malek R, et al. O-GlcNAcylation is required for mutant KRAS-induced lung tumorigenesis. J Clin Invest. 2018. 128(11): 4924-4937.\u003c/li\u003e\n\u003cli\u003eFeng Y, Li N, Ren Y. GNPNAT1 Predicts Poor Prognosis and Cancer Development in Non-Small Cell Lung Cancer. Cancer Manag Res. 2022. 14: 2419-2428.\u003c/li\u003e\n\u003cli\u003eLiu W, Jiang K, Wang J, Mei T, Zhao M, Huang D. Upregulation of GNPNAT1 Predicts Poor Prognosis and Correlates With Immune Infiltration in Lung Adenocarcinoma. Front Mol Biosci. 2021. 8: 605754.\u003c/li\u003e\n\u003cli\u003eChassin D, Laurent A, Janneau JL, Berger R, Bellet D. Cloning of a new member of the insulin gene superfamily (INSL4) expressed in human placenta. Genomics. 1995. 29(2): 465-70.\u003c/li\u003e\n\u003cli\u003eKlonisch T, Bialek J, Radestock Y, Hoang-Vu C, Hombach-Klonisch S. Relaxin-like ligand-receptor systems are autocrine/paracrine effectors in tumor cells and modulate cancer progression and tissue invasiveness. Adv Exp Med Biol. 2007. 612: 104-18.\u003c/li\u003e\n\u003cli\u003eZhang H, Cao Y, Tang J, Wang R. CD73 (NT5E) Promotes the Proliferation and Metastasis of Lung Adenocarcinoma through the EGFR/AKT/mTOR Pathway. Biomed Res Int. 2022. 2022: 9944847.\u003c/li\u003e\n\u003cli\u003eDi Pietro G, Magno LA, Rios-Santos F. Glutathione S-transferases: an overview in cancer research. Expert Opin Drug Metab Toxicol. 2010. 6(2): 153-70.\u003c/li\u003e\n\u003cli\u003eLi VD, Li KH, Li JT. TP53 mutations as potential prognostic markers for specific cancers: analysis of data from The Cancer Genome Atlas and the International Agency for Research on Cancer TP53 Database. J Cancer Res Clin Oncol. 2019. 145(3): 625-636.\u003c/li\u003e\n\u003cli\u003eDong ZY, Zhong WZ, Zhang XC, et al. Potential Predictive Value of TP53 and KRAS Mutation Status for Response to PD-1 Blockade Immunotherapy in Lung Adenocarcinoma. Clin Cancer Res. 2017. 23(12): 3012-3024.\u003c/li\u003e\n\u003cli\u003eJardim DL, Goodman A, de Melo Gagliato D, Kurzrock R. The Challenges of Tumor Mutational Burden as an Immunotherapy Biomarker. Cancer Cell. 2021. 39(2): 154-173.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.592964824120603%\" valign=\"top\"\u003e\n \u003cp\u003eGEO cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.927973199329983%\" valign=\"top\"\u003e\n \u003cp\u003epatients with LUAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42043551088777%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.912897822445562%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.592964824120603%\" valign=\"top\"\u003e\n \u003cp\u003eGSE11969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.927973199329983%\" valign=\"top\"\u003e\n \u003cp\u003en=94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42043551088777%\" valign=\"top\"\u003e\n \u003cp\u003e＞70=18\u003c/p\u003e\n \u003cp\u003e\u0026le;70=76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.912897822445562%\" valign=\"top\"\u003e\n \u003cp\u003eFemale=43\u003c/p\u003e\n \u003cp\u003eMale=51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eI~II=67\u003c/p\u003e\n \u003cp\u003eIII~IV=27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eDead=43\u003c/p\u003e\n \u003cp\u003eAlive=51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.592964824120603%\" valign=\"top\"\u003e\n \u003cp\u003eGSE13213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.927973199329983%\" valign=\"top\"\u003e\n \u003cp\u003en=117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42043551088777%\" valign=\"top\"\u003e\n \u003cp\u003e＞70=20\u003c/p\u003e\n \u003cp\u003e\u0026le;70=107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.912897822445562%\" valign=\"top\"\u003e\n \u003cp\u003eFemale=57\u003c/p\u003e\n \u003cp\u003eMale=60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eI~II=92\u003c/p\u003e\n \u003cp\u003eIII~IV =25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eDead=49\u003c/p\u003e\n \u003cp\u003eAlive=68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.592964824120603%\" valign=\"top\"\u003e\n \u003cp\u003eGSE41271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.927973199329983%\" valign=\"top\"\u003e\n \u003cp\u003en=183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42043551088777%\" valign=\"top\"\u003e\n \u003cp\u003e＞70=62\u003c/p\u003e\n \u003cp\u003e\u0026le;70=121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.912897822445562%\" valign=\"top\"\u003e\n \u003cp\u003eFemale=90\u003c/p\u003e\n \u003cp\u003eMale=93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eI~II=130\u003c/p\u003e\n \u003cp\u003eIII~IV =53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eDead=71\u003c/p\u003e\n \u003cp\u003eAlive=112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.592964824120603%\" valign=\"top\"\u003e\n \u003cp\u003eGSE30219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.927973199329983%\" valign=\"top\"\u003e\n \u003cp\u003en=85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42043551088777%\" valign=\"top\"\u003e\n \u003cp\u003e＞70=17\u003c/p\u003e\n \u003cp\u003e\u0026le;70=68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.912897822445562%\" valign=\"top\"\u003e\n \u003cp\u003eFemale=19\u003c/p\u003e\n \u003cp\u003eMale=66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eI~II=84\u003c/p\u003e\n \u003cp\u003eIII~IV =1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eDead=45\u003c/p\u003e\n \u003cp\u003eAlive=40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.592964824120603%\" valign=\"top\"\u003e\n \u003cp\u003eGSE31210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.927973199329983%\" valign=\"top\"\u003e\n \u003cp\u003en=226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42043551088777%\" valign=\"top\"\u003e\n \u003cp\u003e＞70=5\u003c/p\u003e\n \u003cp\u003e\u0026le;70=221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.912897822445562%\" valign=\"top\"\u003e\n \u003cp\u003eFemale=121\u003c/p\u003e\n \u003cp\u003eMale=105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eI~II=226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eDead=35\u003c/p\u003e\n \u003cp\u003eAlive=191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.592964824120603%\" valign=\"top\"\u003e\n \u003cp\u003eGSE37745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.927973199329983%\" valign=\"top\"\u003e\n \u003cp\u003en=105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42043551088777%\" valign=\"top\"\u003e\n \u003cp\u003e＞70=81\u003c/p\u003e\n \u003cp\u003e\u0026le;70=24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.912897822445562%\" valign=\"top\"\u003e\n \u003cp\u003eFemale=60\u003c/p\u003e\n \u003cp\u003eMale=45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eI~II=88\u003c/p\u003e\n \u003cp\u003eIII=17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eDead=76\u003c/p\u003e\n \u003cp\u003eAlive=29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.592964824120603%\" valign=\"top\"\u003e\n \u003cp\u003eGSE42127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.927973199329983%\" valign=\"top\"\u003e\n \u003cp\u003en=133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42043551088777%\" valign=\"top\"\u003e\n \u003cp\u003e＞70=81\u003c/p\u003e\n \u003cp\u003e\u0026le;70=52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.912897822445562%\" valign=\"top\"\u003e\n \u003cp\u003eFemale=65\u003c/p\u003e\n \u003cp\u003eMale=68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eI~II=111\u003c/p\u003e\n \u003cp\u003eIII=22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eDead=43\u003c/p\u003e\n \u003cp\u003eAlive=90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.592964824120603%\" valign=\"top\"\u003e\n \u003cp\u003eGSE50081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.927973199329983%\" valign=\"top\"\u003e\n \u003cp\u003en=127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42043551088777%\" valign=\"top\"\u003e\n \u003cp\u003e＞70=35\u003c/p\u003e\n \u003cp\u003e\u0026le;70=52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.912897822445562%\" valign=\"top\"\u003e\n \u003cp\u003eFemale=62\u003c/p\u003e\n \u003cp\u003eMale=65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eI~II=127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eDead=51\u003c/p\u003e\n \u003cp\u003eAlive=76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.592964824120603%\" valign=\"top\"\u003e\n \u003cp\u003eGSE72094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.927973199329983%\" valign=\"top\"\u003e\n \u003cp\u003en=387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42043551088777%\" valign=\"top\"\u003e\n \u003cp\u003e＞70=189\u003c/p\u003e\n \u003cp\u003e\u0026le;70=198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.912897822445562%\" valign=\"top\"\u003e\n \u003cp\u003eFemale=216\u003c/p\u003e\n \u003cp\u003eMale=171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eI~II=315\u003c/p\u003e\n \u003cp\u003eIII~IV =72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eDead=107\u003c/p\u003e\n \u003cp\u003eAlive=280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.592964824120603%\" valign=\"top\"\u003e\n \u003cp\u003eGSE115002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.927973199329983%\" valign=\"top\"\u003e\n \u003cp\u003en=52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42043551088777%\" valign=\"top\"\u003e\n \u003cp\u003e＞70=5\u003c/p\u003e\n \u003cp\u003e\u0026le;70=47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.912897822445562%\" valign=\"top\"\u003e\n \u003cp\u003eFemale=26\u003c/p\u003e\n \u003cp\u003eMale=26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eDead=38\u003c/p\u003e\n \u003cp\u003eAlive=14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.592964824120603%\" valign=\"top\"\u003e\n \u003cp\u003eTCGA-LUAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.927973199329983%\" valign=\"top\"\u003e\n \u003cp\u003en=465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42043551088777%\" valign=\"top\"\u003e\n \u003cp\u003e＞70=146\u003c/p\u003e\n \u003cp\u003e\u0026le;70=309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.912897822445562%\" valign=\"top\"\u003e\n \u003cp\u003eFemale=26\u003c/p\u003e\n \u003cp\u003eMale=26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eI~II=370\u003c/p\u003e\n \u003cp\u003eIII~IV =95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.57286432160804%\" valign=\"top\"\u003e\n \u003cp\u003eDead=291\u003c/p\u003e\n \u003cp\u003eAlive=174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lung Adenocarcinoma, Immunity, Metabolism, Prognostic, Immunotherapy, Chemotherapy","lastPublishedDoi":"10.21203/rs.3.rs-4700280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4700280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe aim of this study is to integrate immune and metabolism-related genes in order to construct a predictive model for predicting the prognosis and treatment response of LUAD(lung adenocarcinoma) patients, aiming to address the challenges posed by this highly lethal and heterogeneous disease.\u003c/p\u003e\u003ch2\u003eMaterial and Methods\u003c/h2\u003e \u003cp\u003eUsing TCGA-LUAD as the training subset, differential gene expression analysis, batch survival analysis, Lasso regression analysis, univariate and multivariate Cox regression analysis were performed to construct prognostic related gene models. GEO queue as validation subsets, is used to validate build RiskScore. Then, we explore the RiskScore and mutation status, immune cell infiltration, the relationship between immune therapy and chemotherapy, and build the model of the nomogram.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe RiskScore has been determined to be composed of seven gene. In the high-risk group defined by this score, both early-stage and advanced-stage LUAD patients exhibit a decreased overall survival rate. The mutation status of patients as well as immune cell infiltration show associations with the RiskScore value obtained from these genes' expression levels. Furthermore, there exist variations in response to immunotherapy as well as sensitivity to commonly used chemotherapy drugs among different individuals. Lastly, when using a column line plot model based on the calculated RiskScore values, we obtain a concordance index (C-index) was 0 .716 (95% CI: 0.671\u0026ndash;0.762), and time-dependent ROC predicted probabilities of 1-, 3- and 5-year survival for LUAD patients were 0.752、0.725 and 0.654, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn summary, by combining immune- and metabolism-related genes, we successfully con-structed a novel model for predicting prognosis and treatment response in LUAD patients.\u003c/p\u003e","manuscriptTitle":"Prediction of Prognosis, Efficacy of Lung Adenocarcinoma by Machine Learning Model Based on Immune and Metabolic Related Genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-11 11:57:28","doi":"10.21203/rs.3.rs-4700280/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-12T08:52:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-08T13:02:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248828069386181270287705114712953329149","date":"2024-08-08T08:34:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-02T05:15:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123913572573336485467926959358314554249","date":"2024-07-25T14:54:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-18T10:43:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-15T12:30:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-13T05:11:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2024-07-07T12:53:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2bbc1ec5-5b40-46dc-bf40-3b2cd97408eb","owner":[],"postedDate":"August 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T05:38:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-11 11:57:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4700280","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4700280","identity":"rs-4700280","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}