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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.

SUBMITTER: Liu B

PROVIDER: S-EPMC8095246 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Introduction: Recurrent implantation failure (RIF) is a distressing problem in assisted reproductive technology (ART). Immunity plays a vital role in recurrent implantation failure (RIF) occurrence and development, but its underlying mechanism still needs to be fully elucidated. Through bioinformatics analysis, this study aims to identify the RIF-associated immune cell types and immune-related genes. Methods: The differentially expressed genes (DEGs) were screened based on RIF-associated Gene Expression Omnibus (GEO) datasets. Then, the enrichment analysis and protein-protein interaction (PPI) analysis were conducted with the DEGs. The RIF-associated immune cell types were clarified by combining single sample gene set enrichment analysis (ssGSEA) and CIBERSORT. Differentially expressed immune cell types-related modules were identified by weighted gene co-expression network analysis (WGCNA) and local maximal quasi-clique merger (lmQCM) analysis. The overlapping genes between DEGs and genes contained by modules mentioned above were delineated as candidate hub genes and validated in another two external datasets. Finally, the microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) that interacted with hub genes were predicted, and the competing endogenous RNA (ceRNA) regulatory network was structured. Results: In the present study, we collected 324 DEGs between RIF and the control group, which functions were mainly enriched in immune-related signaling pathways. Regarding differential cell types, the RIF group had a higher proportion of activated memory CD4 T cells and a lower proportion of γδ T cells in the endometrial tissue. Finally, three immune-related hub genes (ALOX5AP, SLC7A7, and PTGS2) were identified and verified to effectively discriminate RIF from control individuals with a specificity rate of 90.8% and a sensitivity rate of 90.8%. In addition, we constructed a key ceRNA network that is expected to mediate molecular mechanisms in RIF. Conclusion: Our study identified the intricate correlation between immune cell types and RIF and provided new immune-related hub genes that offer promising diagnostic and therapeutic targets for RIF.

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

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