PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed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. Preprints2021, 2021080366. https://doi.org/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. https://doi.org/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. Preprints2021, 2021080366. https://doi.org/10.20944/preprints202108.0366.v1
APA Style
Li, Z., Mao, K., Ding, B., & Xue, Q. (2021). Integrative Analysis of Multi-Omics Data Identified klrc3 as Key Nodes in a Gene Regulatory Network Related to Immune Phenotypes in Lung Adenocarcinoma. Preprints. https://doi.org/10.20944/preprints202108.0366.v1
Chicago/Turabian Style
Li, Z., Bo Ding and Qun Xue. 2021 "Integrative Analysis of Multi-Omics Data Identified klrc3 as Key Nodes in a Gene Regulatory Network Related to Immune Phenotypes in Lung Adenocarcinoma" Preprints. https://doi.org/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.
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
