Abstract
BackgroundLung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient’s outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients.MethodsThe expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model.ResultsA total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.
Highlights
Lung cancer is the deadliest malignant disease in the world with about two million new cases and 1.8 million deaths each year (Bray et al, 2018)
Extensive genomic studies have identified several high frequent genetic alternations in lung adenocarcinoma (LADC), such as EGFR, KRAS mutations and ALK rearrangements, which may be involved in the tumorigenesis and progress of LADC, and lead to the development of targeted drugs of EGFR tyrosine kinase inhibitor represented by gefitinib (Herbst, Morgensztern & Boshoff, 2018)
Function enrichment analysis of tumor microenvironment (TME)-related gene signature We identified pathways that were up- and down-regulated when the expression level of TME-related gene signature was changed by gene set enrichment analysis (GSEA 4.0) (Subramanian et al, 2005)
Summary
Lung cancer is the deadliest malignant disease in the world with about two million new cases and 1.8 million deaths each year (Bray et al, 2018). The NSCLC can be further classified into lung squamous cell carcinoma, lung adenocarcinoma (LADC) and large cell carcinoma, and LADC is the most common subtype of lung cancer (Pao & Girard, 2011; Sullivan, Minna & Shay, 2010). With the advance in surgery and chemoradiotherapy, as well as the introduction of targeted drugs and immunotherapy, great progress has been made in the treatment of lung cancer. The Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data (ESTIMATE) method has been successfully applied to the quantitative analysis of TME of. The differentially expressed TME-related genes between LADC and normal samples were identified using the LADC transcriptome expression data from The Cancer Genome Atlas (TCGA) database. A three-gene signature associated with LADC TME was constructed, which can be used to predict the OS of LADC patients
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