Identification of Immune Infiltration and Prognostic Biomarkers in Small Cell Lung Cancer Based on Bioinformatic Methods from 3 Studies

Abstract

Aims: This study aimed to investigate the correlation between gene expression and immune cell infiltration and the overall survival rate in tumor tissues, which may contribute to the therapy and prognosis of small cell lung cancer (SCLC) patients. Background: SCLC is the most aggressive type of lung neoplasm. There is no proper marker for the treatment and prediction of prognosis in SCLC. Objectives: Three gene expression profiles of SCLC patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified between normal lung samples and SCLC lung samples. Methods: Functional enrichment analysis of all DEGs was performed to explore the linkage among DEGs, the tumor immune microenvironment, and SCLC tumorigenesis. The common genes among the 3 groups in the Venn diagram and hub genes in protein-protein interaction (PPI) networks were considered potential key genes in SCLC patients. The TIMER (tumor immune estimation resource) database calculation and Kaplan–Meier survival curves were used to investigate the association between potential key genes and immune infiltrate prognosis of SCLC patients. Results: A total of 750 (top 250 from each study) differentially expressed genes (DEGs) were identified. CLDN18 and BRIP1 were significantly related to immune infiltration in the tumor microenvironment. SHCBP1 and KIF23 were related mostly to prognosis in SCLC patients. Conclusion: The present study may provide some potential biomarkers for the therapy and prognosis of SCLC.