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. 2020 Jun 9:2020:7291586.
doi: 10.1155/2020/7291586. eCollection 2020.

Identification of PDL1-Related Biomarkers to Select Lung Adenocarcinoma Patients for PD1/PDL1 Inhibitors

Yanping Wu  1 , Lianjun Lin  1 , Xinmin Liu  1
Affiliations

Identification of PDL1-Related Biomarkers to Select Lung Adenocarcinoma Patients for PD1/PDL1 Inhibitors

Yanping Wu et al. Dis Markers. .

Abstract

PD1/PDL1 inhibitors have been adopted for the treatment of advanced non-small-cell lung cancer, and PDL1 expression has been investigated as a predictive biomarker for PD1/PDL1 inhibitor therapy. However, PDL1 lacks diagnostic accuracy in differentiating patients who are likely or unlikely to benefit. So, it is urgent and clinically significant to identify other associated predictive biomarkers for PD1/PDL1 inhibitor therapy. Our work was to identify PDL1-related biomarkers that could improve the patient selection for PD1/PDL1 inhibitor treatment. We obtained 500 genes coexpressed with PDL1 in lung adenocarcinoma from the TCGA database. Then, we identified 125 out of 500 genes differentially expressed in lung adenocarcinoma. A total of 39 genes were distinguished with prognostic value and associated with overall survival. Median survival time analysis based on gene expression level, protein-protein interaction analysis, GO and KEGG enrichment analyses, and significant GO and KEGG function consistency analyses were conducted to screen candidate biomarkers. Three candidate genes, BRCA1, BRIP1, and EREG, were identified to be functionally significantly coexpressed with PDL1. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune response and cell activation. Additionally, to find potential adjuvant therapeutic targets in PD1/PDL1 inhibitor treatment, we performed transcription factor prediction analysis. A group of negative differential expression but PDL1-related biomarkers has been identified, which might help to assess the clinical management of lung cancer patients. A combination of potential biomarkers and adjuvant therapeutic targets with PDL1 will predict the response to PD1/PDL1 inhibitors more accurately and help with the patient selection for more personalized immune checkpoint inhibitor treatment.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Differentially expressed genes in lung adenocarcinoma. A heat map is showing the differentially expressed mRNAs.
Figure 2
Figure 2
The map represents the protein-protein interaction network of PDL1 coexpressed genes. Nodes represent genes and lines connecting genes represent interactions.
Figure 3
Figure 3
Top 14 enrichment of GO terms and top 7 enrichment of KEGG pathways for 39 candidate genes.
Figure 4
Figure 4
Three candidate genes of significant prognostic value. Kaplan-Meier curves showing the relationship between the three mRNAs and overall survival. The cases were divided into the low- and high-expression groups by the median expression level of genes.
Figure 5
Figure 5
A mRNA-GO network mainly focus on thirteen GO terms. Blue nodes represent GO terms, red nodes represent genes, and lines represent interactions.
See this image and copyright information in PMC

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