Cuproptosis-related gene signatures for predicting prognosis of lung adenocarcinoma

Abstract

Lung cancer (LC) is a common malignancy with high mortality rate, and lung adenocarcinoma (LUAD) is one of the common pathological types. Cuproptosis is a recently discovered new type of cell death dependent on mitochondria. However, the role of cuproptosis in LUAD is unknown. We obtained LUAD transcriptome data from the Cancer Genome Atlas (TCGA). Long-stranded non-coding RNA (LncRNAs) based on cuproptosis prognosis associated with LUAD were constructed for prognostic multi-LncRNA characterization. We divided TCGA-LUAD into training set and validation set to prove feasibility, and all samples were divided into high-risk group or low risk group. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to evaluate potential biological functions and explore the relationship between risk models and immunity. We identified 3 differentially expressed LncRNAs associated with LUAD prognosis and constructed prognostic model. Kaplan–Meier (K-M) analysis revealed prognostic model and LUAD prognosis. Our risk assessment model has a good reliability in predicting the prognosis of LUAD and was able to improve predictive ability of tumor mutational burdern. Single sample gene enrichment analysis (ssGSEA) revealed risk subgroups were associated with immune-related functions. The prognostic model based on cuproptosis lncRNA has important value in predicting the survival of LUAD patients.

Construction and validation of a cuproptosis-related lncRNA signature for the prediction of the prognosis of LUAD and LUSC

“…NSCLC is a common classification to explore the mechanism of lung cancer (mainly LUAD and LUSC). Recently, there are several bioinformatics studies revealed LUAD had a relation with cuproptosis and TIME 36 – 38 . However, there is no research to explore the correlation between cuproptosis, TIME, and NSCLC.…”

Crosstalk between copper homeostasis and cuproptosis reveals a lncRNA signature to prognosis prediction, immunotherapy personalization, and agent selection for patients with lung adenocarcinoma

“…In order to conclude whether our study is more robust than previous, we searched PubMed and initially eleven studies [37][38][39][40][41][42][43][44][45][46][47] were found (Table 3). However, Wei Ye et al and Ran Chen et al 's study did not provide the coefficient details of their signatures, which were excluded from our choices.…”