A NOD-Like Receptor Signaling-Based Gene Signature Identified as a Novel Prognostic Biomarker for Predicting Overall Survival of Colorectal Cancer Patients

Background: Colorectal cancer (CRC) is the most frequently diagnosed gastrointestinal tract malignant tumor worldwide, which is closely associated with distant metastasis and poor prognosis. Due to high degree of heterogeneity, reliable prognostic biomarkers are urgently needed to guide the therapeutic intervention of CRC patients. Objective: The present study aimed to develop a NOD-like receptors (NLRs) signaling-based gene signature that can successfully predict the overall survival of CRC patients. Methods: Firstly, differentially expressed NLR signaling-related genes were identified between primary and metastatic human CRC samples. Genes with prognostic value were then screened through univariate Cox regression analysis. Next, the NLR signaling-based prognostic signature was constructed by LASSO-penalized Cox regression analysis, and its predictive ability was further confirmed in an independent cohort. Furthermore, functional studies including GO, GSEA, ssGSEA and chemotherapeutic response analyses were performed to explore the role of the NLR signaling-based signature in CRC pathogenesis and therapy. Results: The established prognostic signature that consisted of 7 NLR signaling-related genes can effectively stratify the high-risk and low-risk CRC patients in both training and validation cohorts. Moreover, the signature proved to be an independent indicator of overall survival in CRC patients. Functional annotation and chemotherapeutic response analyses showed that the signature was closely associated with immune status and chemotherapeutic sensitivity of CRC patients. Conclusion: The novel NLR signaling-based gene signature could serve as a potential tool for survival prediction and therapeutic evaluation, thereby contributing to the personalized prognostic management of CRC patients.

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Prognostic Nomogram Based on Circular RNA-Associated Competing Endogenous RNA Network for Patients with Lung Adenocarcinoma

Oxidative Medicine and Cellular Longevity ◽

10.1155/2021/9978206 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Yang Li ◽

Rongrong Sun ◽

Rui Li ◽

Yonggang Chen ◽

He Du

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Lung Adenocarcinoma ◽

Regulatory Mechanism ◽

Cox Regression ◽

Gene Signature ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Cerna Network

Evidence is increasingly indicating that circular RNAs (circRNAs) are closely involved in tumorigenesis and cancer progression. However, the function and application of circRNAs in lung adenocarcinoma (LUAD) are still unknown. In this study, we constructed a circRNA-associated competitive endogenous RNA (ceRNA) network to investigate the regulatory mechanism of LUAD procession and further constructed a prognostic signature to predict overall survival for LUAD patients. Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed mRNAs (DEmRNAs) were selected to construct the ceRNA network. Based on the TargetScan prediction tool and Pearson correlation coefficient, we constructed a circRNA-associated ceRNA network including 11 DEcircRNAs, 8 DEmiRNAs, and 49 DEmRNAs. GO and KEGG enrichment indicated that the ceRNA network might be involved in the regulation of GTPase activity and endothelial cell differentiation. After removing the discrete points, a PPI network containing 12 DEmRNAs was constructed. Univariate Cox regression analysis showed that three DEmRNAs were significantly associated with overall survival. Therefore, we constructed a three-gene prognostic signature for LUAD patients using the LASSO method in the TCGA-LUAD training cohort. By applying the signature, patients could be categorized into the high-risk or low-risk subgroups with significant survival differences (HR: 1.62, 95% CI: 1.12-2.35, log-rank p = 0.009 ). The prognostic performance was confirmed in an independent GEO cohort (GSE42127, HR: 2.59, 95% CI: 1.32-5.10, log-rank p = 0.004 ). Multivariate Cox regression analysis proved that the three-gene signature was an independent prognostic factor. Combining the three-gene signature with clinical characters, a nomogram was constructed. The primary and external verification C -indexes were 0.717 and 0.716, respectively. The calibration curves for the probability of 3- and 5-year OS showed significant agreement between nomogram predictions and actual observations. Our findings provided a deeper understanding of the circRNA-associated ceRNA regulatory mechanism in LUAD pathogenesis and further constructed a useful prognostic signature to guide personalized treatment of LUAD patients.

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A Novel Ferroptosis-related 6-Gene Signature for Overall Survival Prediction in Patients with Thyroid carcinoma

10.21203/rs.3.rs-391766/v1 ◽

2021 ◽

Author(s):

Zhixing Wang ◽

Fudan Qiu ◽

Peilin Shen

Keyword(s):

Overall Survival ◽

Thyroid Carcinoma ◽

Cox Regression ◽

Independent Predictor ◽

Enrichment Analysis ◽

Gene Signature ◽

Survival Prediction ◽

Prognostic Signature ◽

Predictive Capacity ◽

Cox Regression Analysis

Abstract Background: Ferroptosis is a new form of regulated cell death (RCD) that plays a crucial role in the genesis and prognosis of tumor. Nevertheless, the relationship between ferroptosis and the prognosis of thyroid carcinoma (THCA) remains unclear and needs to be explored. Methods: By analyzing data from the THCA cohort in the TCGA database, ferroptosis-related differentially expressed genes (DEGs) with prognostic value were identified, which were used to establish a prognostic signature based on Lasso-penalized Cox regression analysis. Then, the model was testified with Kaplan-Meier survival, Cox regression and receiver operating characteristic (ROC) analyses based on overall survival (OS). Finally, DEGs between the low-risk and high-risk groups were identified and used to conduct GO enrichment analysis, KEGG pathways analysis and immune infiltration analysis.Results: A 6-gene signature was constructed which including DPP4, GPX4, GSS, HMGCR, TFRC and PGD. The area under the curve (AUC) were 0.890 (1 year), 0.863 (2 years) and 0.883 (3 years) which validated the prominent predictive capacity of the model. Multivariate Cox regression certified the model as a prognostic-related independent predictor for OS.Conclusion: In this study, we established an innovative prognostic signature of 6 ferroptosis-related genes which can be as a prognostic-related independent predictor for OS in THCA, while the potential mechanisms was still unclear and needed further exploration.

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Identification of a 9-Gene Prognostic signature for breast cancer

10.21203/rs.3.rs-26949/v1 ◽

2020 ◽

Author(s):

Zelin Tian ◽

Jianing Tang ◽

Xing Liao ◽

Qian Yang ◽

Yumin Wu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Overall Survival ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Disease Free Survival ◽

Gene Signature ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Breast cancer (BRCA) is the most common cancer among women worldwide and results in the second leading cause of woman cancer death.Methods This study sought to develop a prognostic gene signature to predict the prognosis of patients with BRCA. Studies were performed using the genome-wide data of BRCA patients from the Gene Expression Omnibus dataset (GSE20685, GSE42568, GSE20711, GSE88770). Univariate COX regression analysis was used to determine the association between gene expression levels and overall survival(OS) in each dataset. Taking P value < 0.05 as the inclusion criterion, the common genes in all datasets were selected as prognostic genes, and a 9-gene prognostic signature was developed.Results The Kaplan-Meier survival curve was constructed using log-rank test to assess survival differences. The overall survival of patients in the low-risk group was significantly higher than that in the high-risk group. ROC analysis showed that this 9-gene signature showed good diagnostic efficiency both in overall survival(OS) and disease free survival(DFS). The 9-gene signature was further validated using GSE16446 dataset. In addition, multiple Cox regression analysis showed that this 9-gene signature was an independent risk factor. Finally, we established a nomogram that integrates conventional clinicopathological features and 9-gene signature. The analysis of the calibration plots showed that the nomogram has good performance.Conclusions This study has developed a reliable 9-gene prognostic signature, which is of great value in predicting the prognosis of BRCA and will help to make personalized treatment decisions for patients at different risk score.

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A Ferroptosis-Related Gene Signature Identified as a Novel Prognostic Biomarker for Colon Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.692426 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xin Qi ◽

Rui Wang ◽

Yuxin Lin ◽

Donghui Yan ◽

Jiachen Zuo ◽

...

Keyword(s):

Regression Analysis ◽

Functional Annotation ◽

Cox Regression ◽

Prognostic Biomarker ◽

Gene Signature ◽

Clinicopathological Features ◽

Survival Prediction ◽

Related Gene ◽

Prognostic Signature ◽

Cox Regression Analysis

BackgroundColon cancer (CC) is a common gastrointestinal malignant tumor with high heterogeneity in clinical behavior and response to treatment, making individualized survival prediction challenging. Ferroptosis is a newly discovered iron-dependent cell death that plays a critical role in cancer biology. Therefore, identifying a prognostic biomarker with ferroptosis-related genes provides a new strategy to guide precise clinical decision-making in CC patients.MethodsAlteration in the expression profile of ferroptosis-related genes was initially screened in GSE39582 dataset involving 585 CC patients. Univariate Cox regression analysis and LASSO-penalized Cox regression analysis were combined to further identify a novel ferroptosis-related gene signature for overall survival prediction. The prognostic performance of the signature was validated in the GSE17536 dataset by Kaplan-Meier survival curve and time-dependent ROC curve analyses. Functional annotation of the signature was explored by integrating GO and KEGG enrichment analysis, GSEA analysis and ssGSEA analysis. Furthermore, an outcome risk nomogram was constructed considering both the gene signature and the clinicopathological features.ResultsThe prognostic signature biomarker composed of 9 ferroptosis-related genes accurately discriminated high-risk and low-risk patients with CC in both the training and validation datasets. The signature was tightly linked to clinicopathological features and possessed powerful predictive ability for distinct clinical subgroups. Furthermore, the risk score was confirmed to be an independent prognostic factor for CC patients by multivariate Cox regression analysis (p < 0.05). Functional annotation analyses showed that the prognostic signature was closely correlated with pivotal cancer hallmarks, particularly cell cycle, transcriptional regulation, and immune-related functions. Moreover, a nomogram with the signature was also built to quantify outcome risk for each patient.ConclusionThe novel ferroptosis-related gene signature biomarker can be utilized for predicting individualized prognosis, optimizing survival risk assessment and facilitating personalized management of CC patients.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

10.21203/rs.3.rs-524410/v1 ◽

2021 ◽

Author(s):

Jixiang Cao ◽

Xi Chen ◽

Guang Lu ◽

Haowei Wang ◽

Xinyu Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Tumor Microenvironment ◽

Immune Cells ◽

Prognostic Model ◽

Cox Regression ◽

Gene Signature ◽

Risk Scores ◽

Tumor Infiltration ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

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A novel comprehensive immune-related gene signature as a promising survival predictor for head and neck squamous cell carcinoma

10.21203/rs.3.rs-26402/v1 ◽

2020 ◽

Author(s):

Ruihua Fang ◽

Lin Chen ◽

Jing Liao ◽

Jierong Luo ◽

Chenchen Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Head And Neck ◽

Cox Regression ◽

Gene Signature ◽

Related Gene ◽

Prognostic Signature ◽

Immune Microenvironment ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Gene

Abstract Background: Head and neck squamous cell carcinoma (HNSCC), the most frequent subtype of head and neck cancer, continues to have a poor prognosis with no improvement. Growing evidence has demonstrated that the immune system plays a crucial role in the development and progression of HNSCC. The goal of our study was to develop an immune-related signature for accurately predicting the survival of HNSCC patients. Methods: Gene expression profiles were established from a total of 546 HNSCC and normal tissues to establish a training set and 83 HNSCC tissues for a validation set. Differentially expressed prognostic immune genes were identified by univariate Cox regression analysis and a corresponding network of differentially expressed transcription factors (TFs) were identified using Cytoscape. The immune-related gene signature was established and validated by univariate Cox regression analysis, least absolute shrinkage and selector operation (LASSO), and multivariate Cox regression analyses. In addition, the prognostic value of the immune-related signature was analyzed by survival and Cox regression analysis. Finally, the correlation between the immune-related signature and the immune microenvironment was established.Results: In this study, the TF-mediated network revealed that Foxp3 plays a central role in the regulatory mechanism of most immune genes. A prognostic signature based on 10 immune-related genes, which divided patients into high and low risk groups, was developed and successfully validated using two independent databases. Our prognostic signature was significantly related to worse survival and predicted prognosis in patients with different clinicopathological factors. A nomogram including clinical characteristics was also constructed for accurate prediction. Furthermore, it was determined that our prognostic signature may act as an independent factor for predicting the survival of HNSCC patients. ROC analysis also revealed that our signature had superior predictive value compared with TNM stage. As for the immune microenvironment, our signature showed a positive correlation with activated mast cells and M0 macrophages, a negative correlation with Tregs, and immune checkpoint molecules PD-1 and CLTA-4. Conclusions: Our study established an immune-related gene signature, which not only provides a promising biomarker for survival prediction, but may be evaluated as an indicator for personalized immunotherapy in patients with HNSCC.

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Prognostic Value of an Immune-Related Gene Signature in Oral Squamous Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.776979 ◽

2021 ◽

Vol 11 ◽

Author(s):

Chao Zhu ◽

Liqun Gu ◽

Mianfeng Yao ◽

Jiang Li ◽

Changyun Fang

Keyword(s):

Overall Survival ◽

Squamous Cell Carcinoma ◽

Regression Analysis ◽

Oral Squamous Cell Carcinoma ◽

Risk Score ◽

Cox Regression ◽

Gene Signature ◽

Related Gene ◽

Cox Regression Analysis ◽

Immune Related Gene

The prognosis and immunotherapy response rates are unfavorable in patients with oral squamous cell carcinoma (OSCC). The tumor microenvironment is associated with tumor prognosis and progression, and the underlying mechanisms remain unclear. We obtained differentially expressed immune-related genes from OSCC mRNA data in The Cancer Genome Atlas (TCGA) database. Overall survival-related risk signature was constructed by univariate Cox regression analysis and LASSO Cox regression analysis. The prognostic performance was validated with receiver operating characteristic (ROC) analysis and Kaplan–Meier survival curves in the TCGA and Gene Expression Omnibus (GEO) datasets. The risk score was confirmed to be an independent prognostic factor and a nomogram was built to quantify the risk of outcome for each patient. Furthermore, a negative correlation was observed between the risk score and the infiltration rate of immune cells, as well as the expression of immunostimulatory and immunosuppressive molecules. Functional enrichment analysis between different risk score subtypes detected multiple immune-related biological processes, metabolic pathways, and cancer-related pathways. Thus, the immune-related gene signature can predict overall survival and contribute to the personalized management of OSCC patients.

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Identification and Validation of a Novel Metabolism‑Related Prognostic Signature in Hepatocellular Carcinoma

10.21203/rs.3.rs-50552/v1 ◽

2020 ◽

Author(s):

Zhihao Wang ◽

Kidane Siele Embaye ◽

Qing Yang ◽

Lingzhi Qin ◽

Chao Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Value ◽

Cox Regression ◽

Cancer Genome ◽

Regression Analyses ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Tcga Dataset

Abstract Background: Given that metabolic reprogramming has been recognized as an essential hallmark of cancer cells, this study sought to investigate the potential prognostic values of metabolism-related genes(MRGs) for hepatocellular carcinoma (HCC) diagnosis and treatment. Methods: The metabolism-related genes sequencing data of HCC samples with clinical information were obtained from the International Cancer Genome Consortium(ICGC) and The Cancer Genome Atlas (TCGA). The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate Cox regression analysis were performed to identify metabolism-related DEGs that related to overall survival(OS). A novel metabolism-related prognostic signature was developed using the least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analyses . Furthermore, the signature was validated in the TCGA dataset. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in HCC. Results: A total of 178 differentially expressed MRGs were detected between the ICGA dataset and the TCGA dataset. We found that 17 MRGs were most significantly associated with OS by using the univariate Cox proportional hazards regression analysis in HCC. Then, the Lasso and multivariate Cox regression analyses were applied to construct the novel metabolism-relevant prognostic signature, which consisted of six MRGs. The prognostic value of this prognostic model was further successfully validated in the TCGA dataset. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. Six MRGs (FLVCR1, MOGAT2, SLC5A11, RRM2, COX7B2, and SCN4A) showed high prognostic performance in predicting HCC outcomes, and were further associated with tumor TNM stage, gender, age, and pathological stage. Finally, the signature was found to be associated with various clinicopathological features. Conclusions: In summary, our data provided evidence that the metabolism-based signature could serve as a reliable prognostic and predictive tool for overall survival in patients with HCC.

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Tumor microenvironment related novel signature predict lung adenocarcinoma survival

PeerJ ◽

10.7717/peerj.10628 ◽

2021 ◽

Vol 9 ◽

pp. e10628

Author(s):

Juan Chen ◽

Rui Zhou

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Regression Analysis ◽

Tumor Microenvironment ◽

Prognostic Model ◽

Cox Regression ◽

Survival Prediction ◽

Clinical Parameters ◽

Ppi Network ◽

Cox Regression Analysis

Background Lung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer‐related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis. Method The information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters. Result A total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year. Conclusion A series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

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