Integrative Analysis of Multi-Omics Data-Identified Key Genes With KLRC3 as the Core in a Gene Regulatory Network Related to Immune Phenotypes in Lung Adenocarcinoma

Kai Mao; Yunxi Zhao; Bo Ding; Peng Feng; Zhenqing Li; You Lang Zhou; Qun Xue

doi:10.3389/fgene.2022.810193

Integrative Analysis of Multi-Omics Data-Identified Key Genes With KLRC3 as the Core in a Gene Regulatory Network Related to Immune Phenotypes in Lung Adenocarcinoma

Front Genet. 2022 Mar 31:13:810193. doi: 10.3389/fgene.2022.810193. eCollection 2022.

Authors

Kai Mao^{1

2}, Yunxi Zhao³, Bo Ding^{1

2}, Peng Feng⁴, Zhenqing Li^{1

2}, You Lang Zhou², Qun Xue^{1

2}

Affiliations

¹ Cardiovascular Surgery Department, Affiliated Hospital of Nantong University, Nantong, China.
² Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, China.
³ Department of Neurology, Rugao People's Hospital, Nantong, China.
⁴ Neurosurgery, Shaanxi Provincial People's Hospital, Shaanxi, China.

Abstract

In a recent study, the PD-1 inhibitor has been widely used in clinical trials and shown to improve various cancers. However, PD-1/PD-L1 inhibitors showed a low response rate and were effective for only a small number of cancer patients. Thus, it is important to figure out the issue about the low response rate of immunotherapy. Here, we performed ssGSEA and unsupervised clustering analysis to identify three clusters (clusters A, B, and C) according to different immune cell infiltration status, prognosis, and biological action. Of them, cluster C showed a better survival rate, higher immune cell infiltration, and immunotherapy effect, with enrichment of a variety of immune active pathways including T and B cell signal receptors. In addition, it showed more significant features associated with immune subtypes C2 and C3. Furthermore, we used WGCNA analysis to confirm the cluster C-associated genes. The immune-activated module highly correlated with 111 genes in cluster C. To pick candidate genes in SD/PD and CR/PR patients, we used the least absolute shrinkage (LASSO) and SVM-RFE algorithms to identify the targets with better prognosis, activated immune-related pathways, and better immunotherapy. Finally, our analysis suggested that there were six genes with KLRC3 as the core which can efficiently improve immunotherapy responses with greater efficacy and better prognosis, and our study provided clues for further investigation about target genes associated with the higher response rate of immunotherapy.

Keywords: LASSO analysis and SVM-RFE; PD-1 inhibitor; TCGA; immune cell infiltration; lung adenocarcinoma.