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. 2021 Apr 20:11:629333.
doi: 10.3389/fonc.2021.629333. eCollection 2021.

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

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

Bin Liu et al. Front Oncol. .

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.

Keywords: PD-L1; immune checkpoint; immunotherapy; non-small cell lung cancer; tumor microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
613 DEGs were identified in 11 datasets. (A) Heatmap showed the expression of genes between the tumor and control tissue samples; (B) Scatterplot showed the higher or lower DEGs between the tumor and control tissue samples; (C) The Go analyses of higher DEGs; (D) The KEGG analyses of higher DEGs; (E) The Go analyses of lower DEGs; (F) The KEGG analyses of lower DEGs.
Figure 2
Figure 2
166 tumor-special intersect genes were identified and 48 of them identified as potential immune-related genes. (A) 461 (203) and 351 (30) up-regulated (down-regulated) genes were identified using ESTIMATE in the control and tumor samples. ※ mean this part would be used in following step; (B) 151 up-regulated and 15 down-regulated ESTIMATE score genes (total 166 tumor-special intersect genes) were specifically expressed in the tumor samples; (C) The Go analyses of 151 up-regulated tumor-special intersect genes; (D) The KEGG analyses of 151 up-regulated tumor-special intersect genes; (E). The Go analyses of 15 down-regulated tumor-special intersect genes; (F) The KEGG analyses of 15 down-regulated tumor-special intersect genes; (G) 48 intersected genes, referred to potential immune-related genes, were identified between 613 DEGs and 166 tumor-special intersect genes.
Figure 3
Figure 3
Different distribution of TICs between the tumor and control tissue samples. (A) Heatmap showed the distribution state in tumor and control tissue samples; (B) The correlation among TICs in the tumor and control tissue samples; (C–R) 16 TICs which showed significant difference in the distribution between the tumor and control tissue samples.
Figure 4
Figure 4
Some pathways could be activated or repressed by each of the 9 differential immune-related genes.
Figure 5
Figure 5
OLR1 showed moderately strong correlations with 4 known immunotherapy biomarkers and its expression marked the TICs distribution. (A–D) OLR1 exhibited the highest correlation with PD-L1, CD8A, GZMB, and NOS2; (E) OLR1 expression could divide overall status of TICs distribution; (F) Violin plot showed the ratio differentiation of 22 kinds of TICs between higher 50% OLR1 expression tumor samples and lower 50% OLR1 expression tumor samples.
Figure 6
Figure 6
OLR1 may affect the clinical benefits to immunotherapy in NSCLC patients. (A) The comparison of responder number in top or bottom OLR1 expression samples. (B–E) OLR1 has strong positive correlation with some indicators of TIDE prediction.
Figure 7
Figure 7
The basic expression level of OLR1 in normal and part NSCLC cell lines.

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