Classification of Lung Adenocarcinoma Based on Immune Checkpoint and Screening of Related Genes

Abstract

Aims. Lung adenocarcinoma (LUAD) cells could escape from the monitoring of immune cells and metastasize rapidly through immune escape. Therefore, we aimed to develop a method to predict the prognosis of LUAD patients based on immune checkpoints and their associated genes, thus providing guidance for LUAD treatment. Methods. Gene sequencing data were downloaded from the Cancer Genome Atlas (TCGA) and analyzed by R software and R Bioconductor software package. Based on immune checkpoint genes, kmdist clustering in ConsensusClusterPlus R software package was utilized to classify LUAD. CIBERSORT was used to quantify the abundance of immune cells in LUAD samples. LM22 signature was performed to distinguish 22 phenotypes of human infiltrating immune cells. Gene set variation analysis (GSVA) was performed on immune checkpoint cluster and immune checkpoint score using GSVA R software package. The risk score was calculated by LASSO regression coefficient. Gene Ontology (GO), Hallmark, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. PROC was performed to generate the ROC curve and calculate the area under the curve (AUC). Results. According to the immune checkpoint, LUAD was classified into clusters 1 and 2. Survival rate, immune infiltration patterns, TMB, and immune score were significantly different between the two clusters. Functional prediction showed that the functions of cluster 1 focused on apoptosis, JAK/STAT signaling pathway, TNF-α/NFκB signaling pathway, and STAT5 signaling pathway. The risk score model was constructed based on nine genes associated with immune checkpoints. Survival analysis and ROC analysis showed that patients with high-risk score had poor prognosis. The risk score was significantly correlated with cancer status (with tumor), male proportion, status, tobacco intake, and cancer stage. With the increase of the risk score, the enrichment of 22 biological functions increased, such as p53 signaling pathway. The signature was verified in IMvigor immunotherapy dataset with excellent diagnostic accuracy. Conclusion. We established a nine-gene signature based on immune checkpoints, which may contribute to the diagnosis, prognosis, and clinical treatment of LUAD.

Establishment of a Prognostic Model of Lung Adenocarcinoma Based on Tumor Heterogeneity

“…Chiba et al found that CLEC7A could promote natural killer cells to secrete cytotoxic factors and thus fight lung cancer through cell-to-cell contact ( Chiba et al, 2014 ). In this study, CLEC7A had a positive effect on the prognosis of LUAD patients, which was consistent with the findings of previous studies ( Zou et al, 2020 ; Zhou et al, 2021b ; Lin et al, 2021 ).…”

Identification of Tumor Antigens and Immune Subtypes in Lung Adenocarcinoma for mRNA Vaccine Development

“…The most relevant characteristic gene modules of this population were found by WGCNA, and four biomarkers that could be used as vaccination responses were found in these modules. Although previous studies have described LUAD immune subtypes (Wu et al, 2021;Zhao et al, 2021;Zhou et al, 2021), in the present study, we associated immune subtypes and mRNA vaccination for the first time. Drug sensitivity analysis of different immune subtypes showed significant differences in drug sensitivity among different subtypes, not only providing a reference for treatment strategies for populations not suitable for vaccination but also revealing the importance of individualized treatment.…”

Unraveling the Expression Patterns of Immune Checkpoints Identifies New Subtypes and Emerging Therapeutic Indicators in Lung Adenocarcinoma

“…The study of Guo et al reported a 10 immune-related gene model that could facilitate the management of immunotherapy in LUAD [ 34 ]. In addition, Zhou et al also established a nine-gene signature based on immune checkpoints to aid prognostic analysis in LUAD [ 35 ]. The aforementioned studies were confined to several-gene model, while the antitumor immune response effect was influenced by complex components of TIME.…”