A 5-Gene Signature Is Closely Related to Tumor Immune Microenvironment and Predicts the Prognosis of Patients with Non-Small Cell Lung Cancer.

Establishing prognostic gene signature to predict clinical outcomes and guide individualized adjuvant therapy is necessary. Here, we aim to establish the prognostic efficacy of a gene signature that is closely related to tumor immune microenvironment (TIME). There are 13,035 gene expression profiles from 130 tumor samples of the non-small cell lung cancer (NSCLC) in the data set GSE103584. A 5-gene signature was identified by using univariate survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) to build risk models. Then, we used the CIBERSORT method to quantify the relative levels of different immune cell types in complex gene expression mixtures. It was found that the ratio of dendritic cells (DCs) activated and mast cells (MCs) resting in the low-risk group was higher than that in the high-risk group, and the difference was statistically significant (P < 0.001 and P < 0.001 and P < 0.001 and P < 0.001 and P < 0.001 and P < 0.001 and P < 0.001 and P < 0.001 and P < 0.001 and P < 0.001 and P < 0.001 and P < 0.001 and. The 5-gene signature is a powerful and independent predictor that could predict the prognosis of NSCLC patients. In addition, our gene signature is correlated with TIME parameters, such as DCs activated and MCs resting. Our findings suggest that the 5-gene signature closely related to TIME could predict the prognosis of NSCLC patients and provide some reference for immunotherapy.

Li J ，Wang H ，Li Z ，Zhang C ，Zhang C ，Li C ，Yu H ，Wang H ... -  《-》

A signature of tumor immune microenvironment genes associated with the prognosis of non‑small cell lung cancer.

Li J ，Li X ，Zhang C ，Zhang C ，Wang H ... -  《-》

Identification and Validation of a Tumor Microenvironment-Related Gene Signature for Prognostic Prediction in Advanced-Stage Non-Small-Cell Lung Cancer.

The development of immunotherapy has greatly changed the advanced-stage non-small-cell lung cancer (NSCLC) treatment landscape. The complexity and heterogeneity of tumor microenvironment (TME) lead to discrepant immunotherapy effects among patients at the same pathologic stages. This study is aimed at exploring potential biomarkers of immunotherapy and accurately predicting the prognosis for advanced NSCLC patients. RNA-seq data and clinical information on stage III/IV NSCLC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). In TCGA-NSCLC with stage III/IV (n = 192), immune scores and stromal scores were calculated by using the ESTIMATE algorithms. Univariate, LASSO, and multivariate Cox regression analyses were performed to screen prognostic TME-related genes (TMERGs) and constructed a gene signature risk score model. It was validated in external dataset including GSE41271 (n = 91) and GSE81089 (n = 36). Additionally, a nomogram incorporating TMERG signature risk score and clinical characteristics was established. Further, we accessed the proportion of 22 types of tumor-infiltrating immune cells (TIIC) from the CIBERSORT website and analyzed the difference between two risk groups. OS of patients with high immune/stromal scores were higher (log-rank P = 0.044/log-rank P = 0.048). Multivariate Cox regression identified six prognostic TMERGs, including CD200, CHI3L2, CNTN1, CTSL, FYB1, and SLC52A1. We developed a six-gene risk score model, which was validated as an independent prognostic factor for OS (HR: 3.32, 95% CI: 2.16-5.09). Time-ROC curves showed useful discrimination for TCGA-NSCLC cohort (1-, 2-, and 3-year AUCs were 0.718, 0.761, and 0.750). The predictive robustness was validated in the external dataset. The C-index and 1-, 2-, and 3-year AUCs of nomogram were the largest, which demonstrated the nomogram had the greatest predictive accuracy and effectiveness and could be used for clinical guidance. Besides, the increased infiltration of T cells regulatory (Tregs) and macrophages M2 in the high-risk group suggested that chronic inflammation may reduce survival probability in patients with advanced NSCLC. We conducted a comprehensive analysis of the tumor microenvironment and identified the TMERG signature, which could predict prognosis accurately and provide a reference for the personalized immunotherapy for advanced NSCLC patients.

Zhang X ，Shi X ，Zhao H ，Jia X ，Yang Y ... -  《-》

Development and Validation of an Individualized Immune Prognostic Signature in Early-Stage Nonsquamous Non-Small Cell Lung Cancer.

Li B ，Cui Y ，Diehn M ，Li R ... -  《-》

Gene co-expression modules integrated with immunoscore predicts survival of non-small cell lung cancer.

This study aimed to deconvolve the levels of infiltrating immune cells in non-small cell lung cancer (NSCLC) and to identify specific gene co-expression modules associated with prognosis of NSCLC. CIBERSORT algorithm was employed to infer the relative abundance of 22 immune cell subtypes in 1751 NSCLC subjects. The patterns of immune infiltration were identified for NSCLC with different clinical and genomic features and were used to construct an immunoscore by LASSO regression associated with NSCLC survival. Weighted gene co-expression network analysis (WGCNA) was employed to identify specific modules related to immunoscore and NSCLC survival. An integrated prognostic model was constructed with immunoscore combined with the available clinical variables and the selected gene modules to predict the prognosis of NSCLC. We found distinct immune infiltration patterns for NSCLC with different genotype. EGFR-mutant NSCLC was characterized by enriched resting memory CD4+ T cell. An immunoscore was established based on the infiltration abundance of 17 selected immune cell subtypes. Patients with a low immunoscore had a prolonged survival and higher abundance of CD4+ T cell, resting dendritic cells and resting mast cells. The WGCNA analysis identified the gene modules significantly associated with immunoscore and the prognosis of NSCLC. The immunoscore was further incorporated with clinical parameters and selected gene modules to fit a predictive model which stratified patients into subgroups with significantly different survival. The distinct immune profiles are associated with differential overall survival of NSCLC and the integrated model can robustly predict the prognosis of NSCLC.

Li XT ，Zhang JT ，Yan HH ，Su J ，Cheng ML ，Sun QH ，Zhong WZ ，Wu YL ，Zhang DX ，Hou DJ ... -  《-》