A 13-Gene Metabolic Prognostic Signature Is Associated With Clinical and Immune Features in Stomach Adenocarcinoma

Patients with advanced stomach adenocarcinoma (STAD) commonly show high mortality and poor prognosis. Increasing evidence has suggested that basic metabolic changes may promote the growth and aggressiveness of STAD; therefore, identification of metabolic prognostic signatures in STAD would be meaningful. An integrative analysis was performed with 407 samples from The Cancer Genome Atlas (TCGA) and 433 samples from Gene Expression Omnibus (GEO) to develop a metabolic prognostic signature associated with clinical and immune features in STAD using Cox regression analysis and least absolute shrinkage and selection operator (LASSO). The different proportions of immune cells and differentially expressed immune-related genes (DEIRGs) between high- and low-risk score groups based on the metabolic prognostic signature were evaluated to describe the association of cancer metabolism and immune response in STAD. A total of 883 metabolism-related genes in both TCGA and GEO databases were analyzed to obtain 184 differentially expressed metabolism-related genes (DEMRGs) between tumor and normal tissues. A 13-gene metabolic signature (GSTA2, POLD3, GLA, GGT5, DCK, CKMT2, ASAH1, OPLAH, ME1, ACYP1, NNMT, POLR1A, and RDH12) was constructed for prognostic prediction of STAD. Sixteen survival-related DEMRGs were significantly related to the overall survival of STAD and the immune landscape in the tumor microenvironment. Univariate and multiple Cox regression analyses and the nomogram proved that a metabolism-based prognostic risk score (MPRS) could be an independent risk factor. More importantly, the results were mutually verified using TCGA and GEO data. This study provided a metabolism-related gene signature for prognostic prediction of STAD and explored the association between metabolism and the immune microenvironment for future research, thereby furthering the understanding of the crosstalk between different molecular mechanisms in human STAD. Some prognosis-related metabolic pathways have been revealed, and the survival of STAD patients could be predicted by a risk model based on these pathways, which could serve as prognostic markers in clinical practice.

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Identification of an autophagy-related gene signature for survival prediction in patients with cervical cancer

Journal of Ovarian Research ◽

10.1186/s13048-020-00730-8 ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Hengyu Chen ◽

Qingchun Deng ◽

Wenwen Wang ◽

Huishan Tao ◽

Ying Gao

Keyword(s):

Cervical Cancer ◽

High Risk ◽

Risk Score ◽

Squamous Cell ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Survival Prediction ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Cervical cancer is one of the most common female malignancy that occurs worldwide and is reported to cause over 300,000 deaths in 2018. Autophagy controls the survival and death of cancerous cells by regulating the degradation process of cytoplasm and cellular organelle. In the present study, the differentially expressed autophagy-related genes (ARGs) between healthy and cancerous cervical tissues (squamous cell neoplasms) were obtained using data from GTEx and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology (GO) as well as the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Next, we conducted univariate Cox regression assay and obtained 12 ARGs that were associated with the prognosis of cervical cancer patients. We carried out a multivariate Cox regression analysis and developed six ARG-related prognostic signature for the survival prediction of patients with squamous cell cervical cancer (Risk score = − 0.63*ATG3–0.42*BCL2 + 0.85*CD46–0.38*IFNG+ 0.23*NAMPT+ 0.82*TM9SF1). Following the calculation of risk score using the signature, the patients were divided into high and low-risk groups according to the median value. Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis (P < 0.001). The value for area under the curves corresponding to the receiver operating characteristic (ROC) was 0.740. As observed, the expression of IFNG was negatively associated with lymph node metastasis (P = 0.026), while a high-risk score was significantly associated with increased age (P = 0.008). To further validate the prognostic signature, we carried out a permutation test and confirmed the performance of the risk score. In conclusion, our study developed six ARG-related prognostic signature for patients with squamous cell cervical cancer, which might help in improving the prognostic predictions of such patients.

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Development of a prognostic signature based on six immune related lncRNAs for hepatocellular carcinoma.

10.21203/rs.3.rs-16348/v1 ◽

2020 ◽

Author(s):

Ze-bing Song ◽

Guo-pei Zhang ◽

shaoqiang li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumor in the world which prognosis is poor. Therefore, a precise biomarker is needed to guide treatment and improve prognosis. More and more studies have shown that lncRNAs and immune response are closely related to the prognosis of hepatocellular carcinoma. The aim of this study was to establish a prognostic signature based on immune related lncRNAs for HCC.Methods: Univariate cox regression analysis was performed to identify immune related lncRNAs, which had negative correlation with overall survival (OS) of 370 HCC patients from The Cancer Genome Atlas (TCGA). A prognostic signature based on OS related lncRNAs was identified by using multivariate cox regression analysis. Gene set enrichment analysis (GSEA) and a competing endogenous RNA (ceRNA) network were performed to clarify the potential mechanism of lncRNAs included in prognostic signature. Results: A prognostic signature based on OS related lncRNAs (AC145207.5, AL365203.2, AC009779.2, ZFPM2-AS1, PCAT6, LINC00942) showed moderately in prognosis prediction, and related with pathologic stage (Stage I&II VS Stage III&IV), distant metastasis status (M0 VS M1) and tumor stage (T1-2 VS T3-4). CeRNA network constructed 15 aixs among differentially expressed immune related genes, lncRNAs included in prognostic signature and differentially expressed miRNA. GSEA indicated that these lncRNAs were involved in cancer-related pathways. Conclusion: We constructed a prognostic signature based on immune related lncRNAs which can predict prognosis and guide therapies for HCC.

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Identification of Prognostic Immune-Related Genes in Pancreatic Adenocarcinoma and Establishment of a Prognostic Nomogram: A Bioinformatic Study

BioMed Research International ◽

10.1155/2020/1346045 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Guolin Wu ◽

Zhenfeng Deng ◽

Zongrui Jin ◽

Jilong Wang ◽

Banghao Xu ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Adenocarcinoma ◽

Risk Score ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Evaluation ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Immune Related Genes

Background. The prognosis of pancreatic adenocarcinoma (PAAD) is extremely poor and has not been improved. Thus, an effective method to assess the prognosis of patients must be established to improve their survival rate. Method. This study investigated immune-related genes that could be used as potential therapeutic targets for PAAD. Level 3 gene expression data from the PAAD cohort and the relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. For validation, other PAAD datasets (DSE62452) were downloaded from the Gene Expression Omnibus (GEO) database. The PAAD datasets from TCGA and GEO were used to screen immune-related genes through the Molecular Signatures Database using gene set enrichment analysis. Then, the overlapping immune-related genes of the two datasets were identified. Coexpression networks of the immune-related genes were constructed. Results. A signature of three immune-related genes (CKLF, ERAP2, and EREG) was identified in patients with PAAD. The signature could be used to divide the patients with PAAD into high- and low-risk groups based on their median risk score. Multivariate Cox regression analysis was performed to determine the independent prognostic factors of PAAD. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to assess the prediction accuracy of the prognostic signature. Last, a nomogram was established to assess the individualized prognosis prediction model based on the clinical characteristics and risk score of the TCGA PAAD dataset. The accuracy of the prognostic signature was further evaluated through functional evaluation and principal component analysis. Conclusions. The results indicated that the signature of three immune-related genes had excellent predictive value for PAAD. These findings might help improve personalized treatment and medical decisions.

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Establishment and validation of a prognostic signature for lung adenocarcinoma based on metabolism‐related genes

Cancer Cell International ◽

10.1186/s12935-021-01915-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zhihao Wang ◽

Kidane Siele Embaye ◽

Qing Yang ◽

Lingzhi Qin ◽

Chao Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Cancer Biology ◽

Prognostic Value ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Calibration Plot ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Given that dysregulated metabolism has been recently identified as a hallmark of cancer biology, this study aims to establish and validate a prognostic signature of lung adenocarcinoma (LUAD) based on metabolism-related genes (MRGs). Methods The gene sequencing data of LUAD samples with clinical information and the metabolism-related gene set were obtained from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB), respectively. The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate cox regression analysis was performed to identify MRGs that related to overall survival (OS). A prognostic signature was developed by multivariate Cox regression analysis. Furthermore, the signature was validated in the GSE31210 dataset. In addition, a nomogram that combined the prognostic signature was created for predicting the 1-, 3- and 5-year OS of LUAD. The accuracy of the nomogram prediction was evaluated using a calibration plot. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in LUAD. Results A total of 116 differentially expressed MRGs were detected in the TCGA dataset. We found that 12 MRGs were most significantly associated with OS by using the univariate regression analysis in LUAD. Then, multivariate Cox regression analyses were applied to construct the prognostic signature, which consisted of six MRGs-aldolase A (ALDOA), catalase (CAT), ectonucleoside triphosphate diphosphohydrolase-2 (ENTPD2), glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1), lactate dehydrogenase A (LDHA), and thymidylate synthetase (TYMS). The prognostic value of this signature was further successfully validated in the GSE31210 dataset. Furthermore, the calibration curve of the prognostic nomogram demonstrated good agreement between the predicted and observed survival rates for each of OS. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. The high-risk group patients have higher levels of immune checkpoint molecules and are therefore more sensitive to immunotherapy. Finally, we confirmed six MRGs protein and mRNA expression in six lung cancer cell lines and firstly found that ENTPD2 might played an important role on LUAD cells colon formation and migration. Conclusions We established a prognostic signature based on MRGs for LUAD and validated the performance of the model, which may provide a promising tool for the diagnosis, individualized immuno-/chemotherapeutic strategies and prognosis in patients with LUAD.

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A Prognostic Model Based on the Immune-Related lncRNAs in Colorectal Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.658736 ◽

2021 ◽

Vol 12 ◽

Author(s):

Fengxia Qin ◽

Houxi Xu ◽

Guoli Wei ◽

Yi Ji ◽

Jialin Yu ◽

...

Keyword(s):

Colorectal Cancer ◽

Risk Score ◽

Cox Regression ◽

Prognostic Index ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Indicators ◽

Cox Regression Analysis ◽

Model Based ◽

Cancer Tissues

BackgroundColorectal cancer (CRC) is one of the most common malignant tumors with a poor prognosis. At present, the pathogenesis is not completely clear. Therefore, finding reliable prognostic indicators for CRC is of important clinical significance. In this study, bioinformatics methods were used to screen the prognostic immune-related lncRNAs of CRC, and a prognostic risk scoring model based on immune-related lncRNAs signatures were constructed to provide a basis for prognostic evaluation and immunotherapy of CRC patients.MethodsThe clinical information and RNA-seq data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database. The information of immune-related lncRNA was downloaded from the immunology database and analysis portal. The differentially expressed immune-related lncRNAs (IRLs) were screened by the edgeR package of R software. The prognostic value of IRLs was studied. Based on Cox regression analysis, a prognostic index (IRLPI) based on IRLs was established, and the relationship between the risk score and the clinicopathological characteristics of CRC was analyzed to determine the effectiveness of the risk score model as an independent prognostic factor.ResultsA total of 240 differentially expressed IRLs were identified between normal colorectal cancer tissues and normal colorectal cancer tissues, in which 8 were significantly associated with the survival of CRC patients (P < 0.05), including LINC00461, LINC01055, ELFN1-AS1, LMO7-AS1, CYP4A22-AS1, AC079612.1, LINC01351, and MIR31HG. And most of the lncRNAs related to survival were risk factors for the prognosis of CRC. The index established based on the 7 survival-related IRLs found to be highly accurate in monitoring CRC prognosis. Besides, IRLPI was significantly correlated with a variety of pathological factors and immune cell infiltration.ConclusionEight immune-related lncRNAs closely related to the prognosis of CRC patients were identified from the TCGA database. At the same time, an independent IRLPI was constructed, which may be helpful for clinicians to assess the prognosis of patients with CRC and to formulate individualized treatment plans.

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Prognostic lncRNAs, miRNAs, and mRNAs Form a Competing Endogenous RNA Network in CESC

10.21203/rs.3.rs-352332/v1 ◽

2021 ◽

Author(s):

Lang Li ◽

Qiusheng Guo ◽

Gaochen Lan ◽

Fei Liu ◽

Wenwu Wang ◽

...

Keyword(s):

Prognostic Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Rank Test ◽

Prognostic Signature ◽

Competing Endogenous Rna ◽

Genetic Characteristics ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumorigenesis involves a combination of multiple genetic alteration processes. Constructing a survival-associated competing endogenous RNA (ceRNA) network and a multi-mRNA-based prognostic signature model can help us better understand the complexity and genetic characteristics of CESC.Methods: The RNA-seq data and clinical information of CESC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs, lncRNAs and miRNAs were identified by edgeR package. Constructing prognostic model used the differentially expressed RNAs. The Kaplan-Meier method and log-rank test were performed to assess survival rates. The relationships between overall survival (OS) and clinical parameters were evaluated by Cox regression analysis. A survival-associated ceRNA network was constructed by multiMiR package and miRcode database. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) were used to identify the functional role of the ceRNA network in the prognosis of CESC.Results: Differentially expressed 298 mRNAs, 8 miRNAs, and 29 lncRNAs were significantly associated with the prognosis of CESC. The prognostic signature model based on 4 mRNAs (OPN3, DAAM2, HENMT1, and CAVIN3) was constructed. The prognostic ability was 0.726 for this model. Patients in the high-risk group were significantly associated with worse OS. The KEGG pathways were significantly enriched in the TGF-β and Cell adhesion molecules signaling pathways.Conclusion: This study identified several potential prognostic biomarkers to construct a multi-mRNA-based prognostic model for CESC.

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Establishment and Validation of a Prognostic Signature for Lung Adenocarcinoma Based on Metabolism-Related Genes

10.21203/rs.3.rs-62222/v1 ◽

2020 ◽

Author(s):

Zhihao Wang ◽

Kidane Siele Embaye ◽

Qing Yang ◽

Lingzhi Qin ◽

Chao Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Cancer Biology ◽

Prognostic Value ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Calibration Plot ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background: Given that dysregulated metabolism has been recently identified as a hallmark of cancer biology, this study aims to establish and validate a prognostic signature of lung adenocarcinoma (LUAD) based on metabolism-related genes(MRGs). Methods: The gene sequencing data of LUAD samples with clinical information and the metabolism-related gene set were obtained from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB), respectively. The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate Cox regression analysis were performed to identify MRGs that related to overall survival(OS). A prognostic signature was developed by multivariate Cox regression analysis. Furthermore, the signature was validated in the GSE31210 dataset. In addition, a nomogram that combined the prognostic signature was created for predicting the 1-, 3- and 5-year OS of LUAD.The accuracy of the nomogram prediction was evaluated using a calibration plot. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in LUAD. Results: A total of 116 differentially expressed MRGs were detected in the TCGA dataset. We found that 12 MRGs were most significantly associated with OS by using the univariate regression analysis in LUAD. Then, multivariate Cox regression analyses were applied to construct the prognostic signature, which consisted of six MRGs(ALDOA, CAT, ENTPD2, GNPNAT1, LDHA, and TYMS). The prognostic value of this signature was further successfully validated in the GSE31210 dataset. Furthermore, the calibration curve of the prognostic nomogram demonstrated good agreement between the predicted and observed survival rates for each of OS. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. Finally, the signature was found to be associated with various clinicopathological features. Conclusions: We established a prognostic signature based on MRGs for LUAD and validated the performance of the model, which may provide a promising tool for the diagnosis and prognosis in patients with LUAD.

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A Prognostic Signature Constructed by CTHRC1 and LRFN4 in Stomach Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.646818 ◽

2021 ◽

Vol 12 ◽

Author(s):

Songling Han ◽

Wei Zhu ◽

Weili Yang ◽

Qijie Guan ◽

Chao Chen ◽

...

Keyword(s):

Gene Expression ◽

Real Time ◽

Real Time Pcr ◽

Molecular Mechanisms ◽

Cox Regression ◽

Differentially Expressed ◽

Prognostic Signature ◽

Quantitative Real Time Pcr ◽

Stomach Adenocarcinoma ◽

Geo Database

BackgroundStomach adenocarcinoma (STAD) is the most common histological type of stomach cancer, which causes a considerable number of deaths worldwide. This study aimed to identify its potential biomarkers with the notion of revealing the underlying molecular mechanisms.MethodsGene expression profile microarray data were downloaded from the Gene Expression Omnibus (GEO) database. The “limma” R package was used to screen the differentially expressed genes (DEGs) between STAD and matched normal tissues. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for function enrichment analyses of DEGs. The STAD dataset from The Cancer Genome Atlas (TCGA) database was used to identify a prognostic gene signature, which was verified in another STAD dataset from the GEO database. CIBERSORT algorithm was used to characterize the 22 human immune cell compositions. The expression of LRFN4 and CTHRC1 in tissues was determined by quantitative real-time PCR from the patients recruited to the present study.ResultsThree public datasets including 90 STAD patients and 43 healthy controls were analyzed, from which 44 genes were differentially expressed in all three datasets. These genes were implicated in biological processes including cell adhesion, wound healing, and extracellular matrix organization. Five out of 44 genes showed significant survival differences. Among them, CTHRC1 and LRFN4 were selected for construction of prognostic signature by univariate Cox regression and stepwise multivariate Cox regression in the TCGA-STAD dataset. The fidelity of the signature was evaluated in another independent dataset and showed a good classification effect. The infiltration levels of multiple immune cells between high-risk and low-risk groups had significant differences, as well as two immune checkpoints. TIM-3 and PD-L2 were highly correlated with the risk score. Multiple signaling pathways differed between the two groups of patients. At the same time, the expression level of LRFN4 and CTHRC1 in tissues analyzed by quantitative real-time PCR were consistent with the in silico findings.ConclusionThe present study constructed the prognostic signature by expression of CTHRC1 and LRFN4 for the first time via comprehensive bioinformatics analysis, which provided the potential therapeutic targets of STAD for clinical treatment.

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A 16-CpG-based Prognostic Signature for Colorectal Cancer

10.21203/rs.3.rs-28805/v1 ◽

2020 ◽

Author(s):

Shuo Chen ◽

Yan Wang ◽

Lin Zhang ◽

Mingyue Xu ◽

Boxue Wang ◽

...

Keyword(s):

Colorectal Cancer ◽

Prediction Model ◽

Risk Score ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Cancer Genome Atlas ◽

Prognostic Prediction ◽

Survival Risk ◽

Differential Methylation Analysis

Abstract Background: To develop a CpG-based prognostic prediction model to provide survival risk prediction for colorectal cancer. Differential methylation analysis was performed on 309 colorectal cancer and 38 adjacent cancer specimens from the Cancer Genome Atlas (TCGA). Results: 2113 hypermethylation sites as well as 723 hypomethylation sites were screened out and 16 related CpG methylation loci were further identified. The risk score was calculated based on the methylation sites identified and utilized as an independent prognostic variable for multivariate Cox regression prediction model, which was further optimized by the independent prognostic factors (including stage and risk score). Conclusion: This study has identified several potential prognostic biomarkers and established a CpG-based prognostic prediction model for colorectal cancer, which provides a valuable reference for future clinical research.

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Establishment and Validation of a Prognostic Signature for Lung Adenocarcinoma Based on Metabolism-related Genes

10.21203/rs.3.rs-113480/v1 ◽

2020 ◽

Author(s):

Zhihao Wang ◽

Kidane Siele Embaye ◽

Qing Yang ◽

Lingzhi Qin ◽

Chao Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Cancer Biology ◽

Prognostic Value ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Calibration Plot ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background: Given that dysregulated metabolism has been recently identified as a hallmark of cancer biology, this study aims to establish and validate a prognostic signature of lung adenocarcinoma (LUAD) based on metabolism-related genes(MRGs).Methods: The gene sequencing data of LUAD samples with clinical information and the metabolism-related gene set were obtained from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB), respectively. The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate Cox regression analysis were performed to identify MRGs that related to overall survival(OS). A prognostic signature was developed by multivariate Cox regression analysis. Furthermore, the signature was validated in the GSE31210 dataset. In addition, a nomogram that combined the prognostic signature was created for predicting the 1-, 3- and 5-year OS of LUAD.The accuracy of the nomogram prediction was evaluated using a calibration plot. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in LUAD.Results: A total of 116 differentially expressed MRGs were detected in the TCGA dataset. We found that 12 MRGs were most significantly associated with OS by using the univariate regression analysis in LUAD. Then, multivariate Cox regression analyses were applied to construct the prognostic signature, which consisted of six MRGs (ALDOA, CAT, ENTPD2, GNPNAT1, LDHA, and TYMS). The prognostic value of this signature was further successfully validated in the GSE31210 dataset. Furthermore, the calibration curve of the prognostic nomogram demonstrated good agreement between the predicted and observed survival rates for each of OS. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. The signature was found to be associated with various clinicopathological features. Finally, we confirmed six MRGs protein and mRNA expression in six lung cancer cells and firstly found that ENTPD2 might played an important role on lung adenocarcinoma cell lines clone formation and migration.Conclusions: We established a prognostic signature based on MRGs for LUAD and validated the performance of the model, which may provide a promising tool for the diagnosis and prognosis in patients with LUAD.

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