Abstract
Non-small cell lung cancer (NSCLC), which consists mainly of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), are the leading cause of cancer deaths worldwide. In this study, we performed a comprehensive analysis of the tumor microenvironmental and genetic factors to identify prognostic biomarkers for NSCLC. We evaluated the immune and stromal scores of patients with LUAD and LUSC using data from The Cancer Genome Atlas database with the ESTIMATE algorithm. Based on these scores, the differentially expressed genes were obtained and immune-related prognostic genes were identified. Functional analysis and protein-protein interaction network further revealed the immune-related biological processes in which these genes participated. Additionally, 22 subsets of tumor-infiltrating immune cells (TIICs) in the tumor microenvironment were analyzed with the CIBERSORT algorithm. Finally, we validated these valuable genes using an independent cohort from the Gene Expression Omnibus database. The associations of the immune and stromal scores with patients’ clinical characteristics and prognosis were positive in LUAD but negative in LUSC and the correlations of TIICs with clinical characteristics were clarified. Several differentially expressed genes were identified to be potential immune-related prognostic genes. This study comprehensively analyzed the tumor microenvironment and presented immune-related prognostic biomarkers for NSCLC.
Highlights
Lung cancer is acknowledged as being the leading cause of cancer deaths worldwide, with more than 1,600,000 new cases diagnosed yearly [1]
In this study, using abundant The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database-sourced cases of Non-small cell lung cancer (NSCLC) and the ESTIMATE algorithm [20], we explored the microenvironmental and genetic factors associated with the disease to identify immune-related prognostic biomarkers in its main subtypes including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC)
We conducted the bioinformatics analysis with TCGA to identify tumor microenvironment (TME)-related genes that could predict the prognosis of NSCLC patients
Summary
Lung cancer is acknowledged as being the leading cause of cancer deaths worldwide, with more than 1,600,000 new cases diagnosed yearly [1]. Fridman et al [15] summarized the association of T cells with cancer clinical outcomes and found that TILs, especially cytotoxic T cells, memory T cells, and T helper 1 cells, were positively associated with good clinical outcomes in several cancers, including melanoma, head and neck, breast, bladder, urothelial, ovarian, colorectal, renal, prostate, and lung cancers. The TME was reported to have an influence on the gene expression of tumor tissues and the clinical outcome [18,19,20,21,22]. These findings elucidated the relationship between the TME and cancer progression, raising potential methods to improve the management of malignant tumors
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