Establishment of lung adenocarcinoma classification and risk model based on necroptosis-related genes

Abstract

Lung adenocarcinoma (LUAD) is the most widely known histological subtype of lung cancer. Its classification is significant for the characteristic evaluation of patients. The aim of this research is to assess the categorization of LUAD and its risk model based on necroptosis and to investigate its potential regulatory mechanisms for diagnosing and treating LUAD. According to the expression profile data along with the clinical information related to LUAD from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we constructed a consistency matrix through consistency clustering, and used the ConsensusClusterPlus as the measurement distance to cluster and subtype the samples, and performed gene set enrichment analysis and immune infiltration analysis. Least absolute shrinkage and selection operator (Lasso) regression was utilized for obtaining prognostic significant necroptosis phenotype-related genes. Finally, we measured each patient’s riskscore (RS) and build a risk model, and predicted the effect of immunotherapy for different groups of risk factors in the model. Three molecular subtypes of LUAD were obtained by cluster analysis of necroptosis-related genes in LUAD samples. Compared with C1, C3 had a better prognosis and higher immune cell infiltration. The prognosis of the C1 subtype was poor and had a high clinical grade. The proportion of Stage II, Stage III, and Stage IV was much more in comparison with that of the other two subtypes. TP53 gene had a high mutation frequency in the C1 subtype. Gene Set Enrichment Analysis (GSEA) indicated that the aberrant pathways in the C1 and C3 subtypes mainly included some cell cycle-related pathways. In addition, seven genes were identified as related genes of necroptosis phenotype affecting prognosis. High RS had a poor prognosis, while low RS had a good prognosis. The RS was verified to have a strong ability to predict survival. LUAD can be classified by the genes linked with cell necrosis and apoptosis. The difference among various types is helpful to deepen the understanding of LUAD. In addition, a risk model was constructed based. In conclusion, this study provides potential detection targets and treatment methods for LUAD from a new perspective.

Classification of Signature-Based Phenotypes of Aging-Related Genes to Identify Prognostic and Immune Characteristics in HCC

“…This algorithm calculated the relative abundance of 22 immune cell types in each LIHC sample. ESTIMATE is a tool for predicting stromal score [ 15 , 16 ], which refers to the presence of stroma in tumor tissue, with the immune score suggesting the infiltration of immune cells in tumor tissue and the estimated score indicating tumor purity. The CIBERSORT algorithms and ESTIMATE algorithm were adopted to clarify the cell composition of 22 immune cell types from HCC tissues based on genomic profiles.…”

Construction of a prognostic model for lung adenocarcinoma tumor endothelial cells and prediction of immunotherapy based on single-cell transcriptome and Bulk transcriptome

“…Tumor cells have signi cant metabolic characteristics, and alterations in apoptotic characteristics and signaling characteristics can affect the prognosis of patients. For example, Wang Z et al (Wang et al 2023)reported a metabolism-related prognostic signature that predicted the overall survival of LUAD patients, Xie Y(Xie et al 2024) reported a signaling-related prognostic signature that predicted the overall survival of LUAD patients, and Wu G (Wu et al 2022) reported an apoptosis-related prognostic feature that predicts overall survival in patients with LUAD.LUAD has a complex and heterogeneous tumor microenvironment, which includes blood vessels, cancer cells, immune cells, and stromal cells (Zhao et al 2022).TME plays an important role in the therapeutic process of patients with LUAD. It has been suggested that Immune checkpoint inhibitors(ICI) resistance may be driven by loss of antigen presentation occurring in antigen-presenting cells within the TME or in cancer cells and is often associated with acquired genetic mutations (Chowell et al 2018).…”

Identification of a necroptosis-related gene signature for making clinical predictions of the survival of patients with lung adenocarcinoma

“…In addition, CALM1 and PGLYRP1 might play crucial roles in the occurrence and development of LUAD. Previous studies ( Zhang et al, 2022 ; Wu et al, 2022 ) solely relied on bioinformatics analysis. However, we performed RT-qPCR experimental validation, which confirmed our findings from bioinformatics analysis, thus increasing the credibility of our results.…”