GEO Data Mining Identifies OLR1 as a Potential Biomarker in NSCLC Immunotherapy

Abstract

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. The tumor immune microenvironment (TME) in NSCLC is closely correlated to tumor initiation, progression, and prognosis. TME failure impedes the generation of an effective antitumor immune response. In this study, we attempted to explore TME and identify a potential biomarker for NSCLC immunotherapy. 48 potential immune-related genes were identified from 11 eligible Gene Expression Omnibus (GEO) data sets. We applied the CIBERSORT computational approach to quantify bulk gene expression profiles and thereby infer the proportions of 22 subsets of tumor-infiltrating immune cells (TICs); 16 kinds of TICs showed differential distributions between the tumor and control tissue samples. Multiple linear regression analysis was used to determine the correlation between TICs and 48 potential immune-related genes. Nine differential immune-related genes showed statistical significance. We analyzed the influence of nine differential immune-related genes on NSCLC immunotherapy, and OLR1 exhibited the strongest correlation with four well-recognized biomarkers (PD-L1, CD8A, GZMB, and NOS2) of immunotherapy. Differential expression of OLR1 showed its considerable potential to divide TICs distribution, as determined by non-linear dimensionality reduction analysis. In immunotherapy prediction analysis with the comparatively reliable tool TIDE, patients with higher OLR1 expression were predicted to have better immunotherapy outcomes, and OLR1 expression was potentially highly correlated with PD-L1 expression, the average of CD8A and CD8B, IFNG, and Merck18 expression, T cell dysfunction and exclusion potential, and other significant immunotherapy predictors. These findings contribute to the current understanding of TME with immunotherapy. OLR1 also shows potential as a predictor or a regulator in NSCLC immunotherapy.