Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Integrative Analysis of Multi-Omics Data Identified klrc3 as Key Nodes in a Gene Regulatory Network Related to Immune Phenotypes in Lung Adenocarcinoma

Version 1 : Received: 17 August 2021 / Approved: 17 August 2021 / Online: 17 August 2021 (15:02:28 CEST)
Version 2 : Received: 17 August 2021 / Approved: 25 August 2021 / Online: 25 August 2021 (09:22:41 CEST)

How to cite: Li, Z.; Mao, K.; Ding, B.; Xue, Q. Integrative Analysis of Multi-Omics Data Identified klrc3 as Key Nodes in a Gene Regulatory Network Related to Immune Phenotypes in Lung Adenocarcinoma. Preprints 2021, 2021080366 (doi: 10.20944/preprints202108.0366.v1). Li, Z.; Mao, K.; Ding, B.; Xue, Q. Integrative Analysis of Multi-Omics Data Identified klrc3 as Key Nodes in a Gene Regulatory Network Related to Immune Phenotypes in Lung Adenocarcinoma. Preprints 2021, 2021080366 (doi: 10.20944/preprints202108.0366.v1).

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 showed to be effective for a small number of cancer patients. Thus, it is important to identify key genes, which can enhance the PD-1/PD-L1 response for promoting immunotherapy. Here, we used ssGSEA and unsupervised clustering analysis to identify three clusters to show different immune cell infiltration status, prognosis, and biological action. The cluster C showed a better survival rate, high immune cells infiltration, and immunotherapy effect enriched in a variety of immune active pathways, including T and B cell signal receptors. Besides, it showed more immune subtypes C2 and C3. Further, we used WGCNA analysis to confirm the cluster C correlated genes. The red module highly correlated with cluster C for 111 genes which were enriched in a variety of immune-related pathways. To pick candidate genes in SD/PD and CR/PR patients, we used the Least Absolute Shrinkage and SVM-RFE algorithms. In conclusion, our LASSO analysis and SVM-RFE based research identified targets with better prognosis, activated immune-related pathways, and better immunotherapy. The KLRC3 was identified as the key gene which can efficiently respond to immunotherapy with greater efficacy and better prognosis.

Keywords

Lung adenocarcinoma; PD-1 inhibitor; LASSO analysis and SVM-RFE; Immune cell infiltration; TCGA

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