Identification of a gene signature closely related to immunosuppressive tumor microenvironment predicting prognosis of patients in EGFR mutant lung adenocarcinoma

Abstract Background: Lung adenocarcinomas (LUAD) harboring epidermal growth factor receptor (EGFR) mutations generally are unable to benefit from immune checkpoint inhibitors (ICIs), due to an immunosuppressive tumor microenvironment (TME) and a lower tumor mutation burden (TMB). Currently, there has been no gene signature that can comprehensively evaluate the TME and predict the prognosis of EGFR mutant LUAD patients. Methods: Using the cancer genome atlas (TCGA) database of EGFR mutant LUAD based on the immune score derived from the ESTIMATE algorithm, we screened the differential immune-related genes with prognostic value and compared the TMB profiles. Gene ontology (GO) and Kyoto encyclopedia of gene and genomic (KEGG) enrichment analysis were used to analyze the potential functions. The least absolute shrinkage and selectionator operator (LASSO) cox regression model was applied to identify a gene signature and constructed risk model. Kaplan-Meier survival and receiver operating characteristic (ROC) analysis were used to evaluate the prognostic value of the gene signature. CIBERSORT was used to evaluate the abundance of immune cells infiltration.Results: We screened 396 the differential immune-related genes based on immune score, whose potential functions were significantly related to T cell differentiation, immune response, cell cycle and cell proliferation. The disparities of TMB profile could be found between the high and low immune score group. Then, we identified a three-gene signature, including B and T lymphocyte attenuator (BTLA), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) and centromere protein E (CENPE). The three-gene signature could well identify at-risk patients of EGFR-mutant LUAD patients in the training and validating set, and the high-risk patients were related to shorter overall survival (OS) (p=0.0053 and p=0.035). The immune activity of B cells and macrophages were higher in the low-risk group, in contrast the immune activity of Natural Killer (NK) cells and T cells were higher in the high-risk group. Conclusions: The three-gene signature closely related to immunosuppressive TME could predict risk prognosis of patients in EGFR mutant LUAD.

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Identification of a Gene Signature Closely Related to Immunosuppressive Tumour Microenvironment Predicting Prognosis of Patients in EGFR Mutant Lung Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.732841 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jia Li ◽

Huahua Li ◽

Chenyue Zhang ◽

Chenxing Zhang ◽

Lifeng Jiang ◽

...

Keyword(s):

Cell Cycle ◽

Immune Response ◽

High Risk ◽

Risk Group ◽

Tumour Microenvironment ◽

Gene Signature ◽

Pathway Enrichment Analysis ◽

Immune Activity ◽

Egfr Mutant ◽

Immune Score

Lung adenocarcinomas (LUADs) harbouring epidermal growth factor receptor (EGFR) mutations are generally unable to benefit from immune checkpoint inhibitors (ICIs) due to an immunosuppressive tumour microenvironment (TME) and a lower tumour mutation burden. Currently, no gene signature can comprehensively evaluate the TME and predict the prognosis of patients with EGFR-mutant LUAD. Using the Cancer Genome Atlas database of EGFR-mutant LUAD based on the immune score derived from the ESTIMATE algorithm, we divided 80 patients with EGFR-mutant LUAD samples into high and low immune score groups with different immune microenvironments. Subsequently, we screened 396 differentially expressed immune-related genes with prognostic value. The top Gene Ontology terms were significantly enriched in biological functions related to T cell differentiation, immune response, cell cycle, and cell proliferation, which are closely related to the immune microenvironment of tumours. In addition, the KEGG pathway enrichment analysis mainly focused on cell cycle, cell adhesion molecules, and cytokine-cytokine receptor interaction, which also had a relationship with the immune response. Subsequently, we identified a three-gene signature including BTLA, BUB1B, and CENPE using the LASSO Cox regression model. The three-gene signature could accurately identify patients at risk of EGFR-mutant LUAD in the training and validation sets and high-risk patients from both the sets exhibited significantly shorter overall survival (p=0.0053 and p=0.035, respectively). CIBERSORT was used to evaluate the abundance of immune cell infiltration in the EGFR-mutant LUAD microenvironment. The immune activity of B cells and macrophages was higher in the low-risk group, while the immune activity of natural killer cells and T cells was higher in the high-risk group. Thus, the three-gene signature closely related to immunosuppressive TME could predict the risk and prognosis in patients with EGFR-mutant LUAD.

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Development of a Toll-Like Receptor-Based Gene Signature That Can Predict Prognosis, Tumor Microenvironment, and Chemotherapy Response for Hepatocellular Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.729789 ◽

2021 ◽

Vol 8 ◽

Author(s):

Lixia Liu ◽

Bin Liu ◽

Jie Yu ◽

Dongyun Zhang ◽

Jianhong Shi ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Tumor Microenvironment ◽

Immune Checkpoint ◽

Immune Cell ◽

Gene Signature ◽

Chemotherapy Response ◽

Toll Like Receptor ◽

Risk Patients ◽

Immune Score

Objective: Emerging evidence highlights the implications of the toll-like receptor (TLR) signaling pathway in the pathogenesis and therapeutic regimens of hepatocellular carcinoma (HCC). Herein, a prognostic TLR-based gene signature was conducted for HCC.Methods: HCC-specific TLRs were screened in the TCGA cohort. A LASSO model was constructed based on prognosis-related HCC-specific TLRs. The predictive efficacy, sensitivity, and independency of this signature was then evaluated and externally verified in the ICGC, GSE14520, and GSE76427 cohorts. The associations between this signature and tumor microenvironment (stromal/immune score, immune checkpoint expression, and immune cell infiltrations) and chemotherapy response were assessed in HCC specimens. The expression of TLRs in this signature was verified in HCC and normal liver tissues by Western blot. Following si-MAP2K2 transfection, colony formation and apoptosis of Huh7 and HepG2 cells were examined.Results: Herein, we identified 60 HCC-specific TLRs. A TLR-based gene signature (MAP2K2, IRAK1, RAC1, TRAF3, MAP3K7, and SPP1) was conducted for HCC prognosis. High-risk patients exhibited undesirable outcomes. ROC curves confirmed the well prediction performance of this signature. Multivariate Cox regression analysis demonstrated that the signature was an independent prognostic indicator. Also, high-risk HCC was characterized by an increased immune score, immune checkpoint expression, and immune cell infiltration. Meanwhile, high-risk patients displayed higher sensitivity to gemcitabine and cisplatin. The dysregulation of TLRs in the signature was confirmed in HCC. MAP2K2 knockdown weakened colony formation and elevated apoptosis of Huh7 and HepG2 cells.Conclusion: Collectively, this TLR-based gene signature might assist clinicians to select personalized therapy programs for HCC patients.

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Gene signature for prognosis in comparison of pancreatic cancer patients with diabetes and non-diabetes

PeerJ ◽

10.7717/peerj.10297 ◽

2020 ◽

Vol 8 ◽

pp. e10297

Author(s):

Mingjun Yang ◽

Boni Song ◽

Juxiang Liu ◽

Zhitong Bing ◽

Yonggang Wang ◽

...

Keyword(s):

Pancreatic Cancer ◽

High Risk ◽

Prognostic Value ◽

Prognostic Biomarker ◽

Risk Group ◽

Gene Signature ◽

Low Risk ◽

Risk Genes ◽

Genetic Biomarkers ◽

Patients With Diabetes

Background Pancreatic cancer (PC) has much weaker prognosis, which can be divided into diabetes and non-diabetes. PC patients with diabetes mellitus will have more opportunities for physical examination due to diabetes, while pancreatic cancer patients without diabetes tend to have higher risk. Identification of prognostic markers for diabetic and non-diabetic pancreatic cancer can improve the prognosis of patients with both types of pancreatic cancer. Methods Both types of PC patients perform differently at the clinical and molecular levels. The Cancer Genome Atlas (TCGA) is employed in this study. The gene expression of the PC with diabetes and non-diabetes is used for predicting their prognosis by LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression. Furthermore, the results are validated by exchanging gene biomarker with each other and verified by the independent Gene Expression Omnibus (GEO) and the International Cancer Genome Consortium (ICGC). The prognostic index (PI) is generated by a combination of genetic biomarkers that are used to rank the patient’s risk ratio. Survival analysis is applied to test significant difference between high-risk group and low-risk group. Results An integrated gene prognostic biomarker consisted by 14 low-risk genes and six high-risk genes in PC with non-diabetes. Meanwhile, and another integrated gene prognostic biomarker consisted by five low-risk genes and three high-risk genes in PC with diabetes. Therefore, the prognostic value of gene biomarker in PC with non-diabetes and diabetes are all greater than clinical traits (HR = 1.102, P-value < 0.0001; HR = 1.212, P-value < 0.0001). Gene signature in PC with non-diabetes was validated in two independent datasets. Conclusions The conclusion of this study indicated that the prognostic value of genetic biomarkers in PCs with non-diabetes and diabetes. The gene signature was validated in two independent databases. Therefore, this study is expected to provide a novel gene biomarker for predicting prognosis of PC with non-diabetes and diabetes and improving clinical decision.

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A four-gene prognostic signature for predicting the overall survival of patients with lung adenocarcinoma

PeerJ ◽

10.7717/peerj.11911 ◽

2021 ◽

Vol 9 ◽

pp. e11911

Author(s):

Lei Liu ◽

Huayu He ◽

Yue Peng ◽

Zhenlin Yang ◽

Shugeng Gao

Keyword(s):

Overall Survival ◽

High Risk ◽

Lung Adenocarcinoma ◽

Prognostic Model ◽

Risk Group ◽

Gene Signature ◽

Tnm Staging ◽

High Risk Group ◽

Prognostic Signature ◽

Risk Patients

Background The prognosis of patients for lung adenocarcinoma (LUAD) is known to vary widely; the 5-year overall survival rate is just 63% even for the pathological IA stage. Thus, in order to identify high-risk patients and facilitate clinical decision making, it is vital that we identify new prognostic markers that can be used alongside TNM staging to facilitate risk stratification. Methods We used mRNA expression from The Cancer Genome Atlas (TCGA) cohort to identify a prognostic gene signature and combined this with clinical data to develop a predictive model for the prognosis of patients for lung adenocarcinoma. Kaplan-Meier curves, Lasso regression, and Cox regression, were used to identify specific prognostic genes. The model was assessed via the area under the receiver operating characteristic curve (AUC-ROC) and validated in an independent dataset (GSE50081) from the Gene Expression Omnibus (GEO). Results Our analyses identified a four-gene prognostic signature (CENPH, MYLIP, PITX3, and TRAF3IP3) that was associated with the overall survival of patients with T1-4N0-2M0 in the TCGA dataset. Multivariate regression suggested that the total risk score for the four genes represented an independent prognostic factor for the TCGA and GEO cohorts; the hazard ratio (HR) (high risk group vs low risk group) were 2.34 (p < 0.001) and 2.10 (p = 0.017). Immune infiltration estimations, as determined by an online tool (TIMER2.0) showed that CD4+ T cells were in relative abundance in the high risk group compared to the low risk group in both of the two cohorts (both p < 0.001). We established a composite prognostic model for predicting OS, combined with risk-grouping and clinical factors. The AUCs for 1-, 3-, 5- year OS in the training set were 0.750, 0.737, and 0.719; and were 0.645, 0.766, and 0.725 in the validation set. The calibration curves showed a good match between the predicted probabilities and the actual probabilities. Conclusions We identified a four-gene predictive signature which represents an independent prognostic factor and can be used to identify high-risk patients from different TNM stages of LUAD. A new prognostic model that combines a prognostic gene signature with clinical features exhibited better discriminatory ability for OS than traditional TNM staging.

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Immune Score-based Molecular Subtypes and Signature Associated with Clinical Outcome in Hepatoblastoma

Hepatitis Monthly ◽

10.5812/hepatmon.118268 ◽

2021 ◽

Vol 21 (9) ◽

Author(s):

Yongping Huang ◽

Jinlong Yan ◽

Ruiqi Liu ◽

Guang Tang ◽

Qi Dong ◽

...

Keyword(s):

T Cells ◽

Scoring System ◽

Prognostic Value ◽

Molecular Subtypes ◽

Risk Group ◽

Molecular Subtype ◽

Gene Signature ◽

Risk Scoring ◽

Immune Score ◽

Risk Scoring System

Background: This study aimed to identify genes related to the immune score of hepatoblastoma, examine the characteristics of the immune microenvironment of hepatoblastoma, and construct a risk scoring system for predicting the prognosis of hepatoblastoma. Methods: Through using the gene chip data of patients with hepatoblastoma with survival data in the ArrayExpress and GEO databases, the immune score of hepatoblastoma was calculated by the ESITIMATE algorithm, and the prognostic value of immune score in patients with hepatoblastoma was studied by the survival analysis. Genes related to the immune score were identified by the WGCNA algorithm. According to these genes, patients with hepatoblastoma were clustered unsupervised. Finally, the risk scoring system was constructed according to the immune score-related genes. Results: The immune score calculated by the ESTIMATE algorithm had a good prognostic value in patients with hepatoblastoma. Patients with high immune scores had better OS than those with low immune scores (P < 0.001). A total of 146 immune score-related genes were identified by WGCNA analysis, and univariate COX regression analysis indicated that 59 of the genes had prognostic value. According to the unsupervised clustering results of the 146 immune score-related genes, patients with hepatoblastoma could be divided into two subtypes with different prognoses, namely molecular subtype 1 and subtype 2, with molecular subtype 1 having a better prognosis. The immunocyte infiltration analysis results showed that the difference between the two subtypes was mainly in activated CD4 T cells, activated dendritic cells, CD56 bright natural killer cells, the macrophage, and regulatory T cells. According to the immune score-related genes, a risk scoring system was constructed based on a five-gene signature. After the cut-off value was determined, patients with hepatoblastoma were divided into a high-risk group and a low-risk group. The prognosis of the two groups was different. Conclusions: The immune score has a good prognostic value in patients with hepatoblastoma. Based on the different expression patterns of immune score-related genes, hepatoblastoma can be divided into two different prognostic molecular subtypes, showing different immunocyte infiltration patterns. The established risk scoring system based on a five-gene signature has a good predictive value in patients with hepatoblastoma.

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Identification of Immune-Related Genes for Establishment of Prognostic Index in Hepatocellular Carcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.760079 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yinfang Li ◽

Ling Zou ◽

Xuejun Liu ◽

Judong Luo ◽

Hui Liu

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Prognostic Index ◽

Hub Genes ◽

Cox Regression Analysis ◽

Risk Patients ◽

Immune Related Genes

Background: Immune checkpoint inhibitor (ICI) therapy has been proved to be a promising therapy to many types of solid tumors. However, effective biomarker for estimating the response to ICI therapy and prognosis of hepatocellular carcinoma (HCC) patients remains underexplored. The aim of this study is to build a novel immune-related prognostic index based on transcriptomic profiles.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to identify immune-related hub genes that are differentially expressed in HCC cohorts. Next, univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to detect hub genes associated to overall survival (OS). To validate the immune-related prognostic index, univariate and multivariate Cox regression analysis were performed. CIBERSORT and ESTIMATE were used to explore the tumor microenvironment and immune infiltration level.Results: The differential expression analysis detected a total of 148 immune-related genes, among which 25 genes were identified to be markedly related to overall survival in HCC patients. LASSO analysis yielded 10 genes used to construct the immune-related gene prognostic index (IRGPI), by which a risk score is computed to estimate low vs. high risk indicating the response to ICI therapy and prognosis. Further analysis confirmed that this immune-related prognostic index is an effective indicator to immune infiltration level, response to ICI treatment and OS. The IRGPI low-risk patients had better overall survival (OS) than IRGPI high-risk patients on two independent cohorts. Moreover, we found that IRGPI high-risk group was correlated with high TP53 mutation rate, immune-suppressing tumor microenvironment, and these patients acquired less benefit from ICI therapy. In contrast, IRGPI-low risk group was associated with low TP53 and PIK3CA mutation rate, high infiltration of naive B cells and T cells, and these patients gained relatively more benefit from ICI therapy.

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A Novel Nine-Gene Signature Associated With Immune Infiltration for Predicting Prognosis in Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.730732 ◽

2021 ◽

Vol 12 ◽

Author(s):

Rongqiang Liu ◽

ZeKun Jiang ◽

Weihao Kong ◽

Shiyang Zheng ◽

Tianxing Dai ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Liver Cancer ◽

Prognostic Value ◽

Risk Group ◽

Prognostic Significance ◽

Group Versus ◽

Gene Signature ◽

High Risk Group ◽

Data Sets

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, and its prognosis remains unsatisfactory. The identification of new and effective markers is helpful for better predicting the prognosis of patients with HCC and for conducting individualized management. The oncogene Aurora kinase A (AURKA) is involved in a variety of tumors; however, its role in liver cancer is poorly understood. The aim of this study was to establish AURKA-related gene signatures for predicting the prognosis of patients with HCC.Methods: We first analyzed the expression of AURKA in liver cancer and its prognostic significance in different data sets. Subsequently, we selected genes with prognostic value related to AURKA and constructed a gene signature based on them. The predictive ability of the gene signature was tested using the HCC cohort development and verification data sets. A nomogram was constructed by integrating the risk score and clinicopathological characteristics. Finally, the influence of the gene signature on the immune microenvironment in HCC was comprehensively analyzed.Results: We found that AURKA was highly expressed in HCC, and it exhibited prognostic value. We selected eight AURKA-related genes with prognostic value through the protein-protein interaction network and successfully constructed a gene signature. The nine-gene signature could effectively stratify the risk of patients with HCC and demonstrated a good ability in predicting survival. The nomogram showed good discrimination and consistency of risk scores. In addition, the high-risk group showed a higher percentage of immune cell infiltration (i.e., macrophages, myeloid dendritic cells, neutrophils, and CD4+T cells). Moreover, the immune checkpoints SIGLEC15, TIGIT, CD274, HAVCR2, and PDCD1LG2 were also higher in the high-risk group versus the low-risk group.Conclusions: This gene signature may be useful prognostic markers and therapeutic targets in patients with HCC.

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A signature of tumor DNA repair genes associated with the prognosis of surgically-resected lung adenocarcinoma

PeerJ ◽

10.7717/peerj.10418 ◽

2020 ◽

Vol 8 ◽

pp. e10418

Author(s):

Xiongtao Yang ◽

Guohui Wang ◽

Runchuan Gu ◽

Xiaohong Xu ◽

Guangying Zhu

Keyword(s):

Lung Cancer ◽

Dna Repair ◽

High Risk ◽

Risk Score ◽

Prognostic Value ◽

Risk Group ◽

Gene Signature ◽

Dna Repair Genes ◽

Prognostic Signature ◽

Repair Genes

Background Lung cancer has the highest morbidity and mortality of cancers worldwide. Lung adenocarcinoma (LUAD) is the most common pathological subtype of lung cancer and surgery is its most common treatment. The dysregulated expression of DNA repair genes is found in a variety of cancers and has been shown to affect the origin and progression of these diseases. However, the function of DNA repair genes in surgically-treated LUAD is unclear. Methods We sought to determine the association between the signature of DNA repair genes for patients with surgical LUAD and their overall prognosis. We obtained gene expression data and corresponding clinical information of LUAD from The Cancer Genome Atlas (TCGA) database. The differently expressed DNA repair genes of surgically-treated LUAD and normal tissues were identified using the Wilcoxon rank-sum test. We used uni- and multivariate Cox regression analyses to shrink the aberrantly expressed genes, which were then used to construct the prognostic signature and the risk score formula associated with the independent prognosis of surgically-treated LUAD. We used Kaplan–Meier and Cox hazard ratio analyses to confirm the diagnostic and prognostic roles. Two validation sets (GSE31210 and GSE37745) were downloaded from the Gene Expression Omnibus (GEO) and were used to externally verify the prognostic value of the signature. OSluca online database verifies the hazard ratio for the DNA repair genes by which the signature was constructed. We investigated the correlation between the signature of the DNA repair genes and the clinical parameters. The potential molecular mechanisms and pathways of the prognostic signature were explored using Gene Set Enrichment Analysis (GSEA). Results We determined the prognostic signature based on six DNA repair genes (PLK1, FOXM1, PTTG1, CCNO, HIST3H2A, and BLM) and calculated the risk score based on this formula. Patients with surgically-treated LUAD were divided into high-risk and low-risk groups according to the median risk score. The high-risk group showed poorer overall survival than the low-risk group; the signature was used as an independent prognostic indicator and had a greater prognostic value in surgically-treated LUAD. The prognostic value was replicated in GSE31210 and GSE37745. OSluca online database analysis shows that six DNA repair genes were associated with poor prognosis in most lung cancer datasets. The prognostic signature risk score correlated with the pathological stage and smoking status in surgically-treated LUAD. The GSEA of the risk signature in high-risk patients showed pathways associated with the cell cycle, oocyte meiosis, mismatch repair, homologous recombination, and nucleotide excision repair. Conclusions A six-DNA repair gene signature was determined using TCGA data mining and GEO data verification. The gene signature may serve as a novel prognostic biomarker and therapeutic target for surgically-treated LUAD.

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Prognostic Value of an Immunohistochemical Signature in Patients With Bladder Cancer Undergoing Radical Cystectomy

Frontiers in Oncology ◽

10.3389/fonc.2021.641385 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jie Wu ◽

Jun-Miao Wen ◽

Yu-Chen Wang ◽

Wen-Jie Luo ◽

Qi-Feng Wang ◽

...

Keyword(s):

Bladder Cancer ◽

High Risk ◽

Regression Model ◽

Radical Cystectomy ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Training Cohort ◽

Risk Patients

BackgroundThis study aimed to assess the prognostic value of various diagnostic immunohistochemical (IHC) markers and develop an IHC-based classifier to predict the disease-free survival (DFS) of patients with bladder cancer undergoing radical cystectomy.MethodsIHC was performed on tumor specimens from 366 patients with transitional cell bladder cancer. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to develop a multi-marker classifier for predicting DFS of patients with bladder cancer. The Kaplan–Meier estimate was performed to assess DFS, and unadjusted and adjusted Cox regression models were used to identify independent risk factors to predict DFS of patients with bladder cancer.ResultsBased on the LASSO Cox regression model, nine prognostic markers were identified in the training cohort. Patients were stratified into low- and high-risk groups using the IHC-based classifier. In the training cohort, the 10-year DFS was significantly better in low-risk patients (71%) compared with high-risk patients (18%) (p < 0.001); in the validation cohort, the 10-year DFS was 86% for the low-risk group and 20% for the high-risk group (p < 0.001). Multivariable Cox regression analyses showed that the high-risk group based on the classifier was associated with poorer DFS adjusted by clinicopathological characteristics. Finally, a nomogram comprising the classifier and clinicopathological factors was developed for clinical application.ConclusionThe nine-IHC-based classifier is a reliable prognostic tool, which can eventually guide clinical decision making regarding treatment strategy and follow-up scheduling of bladder cancer.

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Identification of an immune prognostic 11-gene signature for lung adenocarcinoma

PeerJ ◽

10.7717/peerj.10749 ◽

2021 ◽

Vol 9 ◽

pp. e10749

Author(s):

Tao Yang ◽

Lizheng Hao ◽

Renyun Cui ◽

Huanyu Liu ◽

Jian Chen ◽

...

Keyword(s):

High Risk ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Risk Group ◽

Stage Iv ◽

Gene Signature ◽

High Risk Group ◽

Stage I ◽

Local Progression ◽

Immune Score

Background The immunological tumour microenvironment (TME) has occupied a very important position in the beginning and progression of non-small cell lung cancer (NSCLC). Prognosis of lung adenocarcinoma (LUAD) remains poor for the local progression and widely metastases at the time of clinical diagnosis. Our objective is to identify a potential signature model to improve prognosis of LUAD. Methods With the aim to identify a novel immune prognostic signature associated with overall survival (OS), we analysed LUADs extracted from The Cancer Genome Atlas (TCGA). Immune scores and stromal scores of TCGA-LUAD were downloaded from Estimation of STromal and Immune cells in MAlignant Tumour tissues Expression using data (ESTIMATE). LASSO COX regression was applied to build the prediction model. Then, the prognostic gene signature was validated in the GSE68465 dataset. Results The data from TCGA datasets showed patients in stage I and stage II had higher stromal scores than patients in stage IV (P < 0.05), and for immune score patients in stage I were higher than patients in stage III and stage IV (P < 0.05). The improved overall survivals were observed in high stromal score and immune score groups. Patients in the high-risk group exhibited the inferior OS (P = 2.501e − 05). By validating the 397 LUAD patients from GSE68465, we observed a better OS in the low-risk group compared to the high-risk group, which is consistent with the results from the TCGA cohort. Nomogram results showed that practical and predicted survival coincided very well, especially for 3-year survival. Conclusion We obtained an 11 immune score related gene signature model as an independent element to effectively classify LUADs into different risk groups, which might provide a support for precision treatments. Moreover, immune score may play a potential valuable sole for estimating OS in LUADs.

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