Establishment of a prognostic signature for lung adenocarcinoma by integration of 7 pyroptosis-related genes and cross-validation between the TCGA and GEO cohorts: A comprehensive bioinformatics analysis

Abstract

Pyroptosis-related genes (PRGs) have been reported to be associated with prognosis of lung adenocarcinoma (LUAD). Until now, the relationship of PRGs to the prognosis of LUAD patients and its underlying mechanisms have been poorly elucidated. Using The Cancer Genome Atlas (TCGA) LUAD cohort, a prior bioinformatics analysis constructed a prognostic signature incorporating 5 PRGs (NLRP7, NLRP1, NLRP2, NOD1, and CASP6) for predicting prognosis of LUAD patients. However, it has not been validated by the Gene Expression Omnibus (GEO) LUAD cohort yet. We implemented a modified bioinformatics analysis to, respectively, construct one prognostic signature with the TCGA cohort and with the GEO cohort and attempted to perform cross-validations by the GEO cohort and the TCGA cohort alternately in turn. Univariate and multivariate Cox regression analysis screened PRGs and constructed 2 prognostic signatures with the TCGA and GEO cohorts. All LUAD samples were classified into high- and low-risk groups according to the median risk score that was generated by regression formula. Kaplan-Meier survival analysis compared the overall survival rate between the 2 risk groups, and receiver operating characteristic curve analysis evaluated predictive performance of the 2 signatures. Additionally, risk score, combined with clinicopathological features, was subjected to multivariate Cox regression analysis, to evaluate independent prognostic value of the 2 signatures. Finally, the 2 signatures received cross-validations by the GEO and TCGA cohorts, alternately. The TCGA cohort yielded a 3-gene signature (PYCARD, NLRP1, and NLRC4), whereas the GEO cohort built a 7-gene signature (SCAF11, NOD1, NLRP2, NLRP1, GPX4, CASP8, and AIM2) for predicting the prognosis of LUAD patients. Multivariate analysis proved independent prognostic value of risk score in the TCGA cohort (hazard ratio, = 1.939,; P = 8.43 × 10−4) and the GEO cohort (hazard ratio, = 2.291,; P = 4.34 × 10−9). Cross-validations confirmed prognostic value for the 7-gene signature from the GEO cohort by the TCGA cohort but not for the 3-gene signature from the TCGA cohort by the GEO cohort. We develop and validate a 7-gene prognostic signature (SCAF11, NOD1, NLRP2, NLRP1, GPX4, CASP8, and AIM2) with independent prognostic value for patients with LUAD.

The NLRP1 inflammasome emerges as a promising therapeutic target and prognostic biomarker across multiple cancer types: A comprehensive pan-cancer analysis

“…Furthermore, a multi-omics analysis by Chen et al revealed that NLRP1 levels were increased in CHOL and LIHC compared to normal tissues [38]. Several additional multi-omics analyses exploring pyroptosisrelated genes in lung adenocarcinoma consistently showed downregulation of NLRP1 in LUAD tissues compared to normal tissues [39][40][41][42]. Consistent with previous studies, our ndings highlight the tumorsuppressive in uence of NLRP1 in the majority of TCGA cancer types.…”

“…Furthermore, the downregulation of NLRP1 is correlated with unfavorable DFS in CHOL. Several studies have explored the potential of NLRP1 as a prognostic biomarker in various cancers, including lung adenocarcinoma [39,40,44], gastric cancer [45], prostate cancer [43], pancreatic cancer [46,47], head and neck squamous cell carcinoma [48], and laryngeal carcinoma [49]. Shen et al con rmed that decreased expression of NLRP1 was signi cantly associated with a poor prognosis of LUAD [41].…”

Application of an angiogenesis-related genes risk model in lung adenocarcinoma prognosis and immunotherapy

“…The findings revealed that RS strongly predicts outcomes in LUAD patients with various clinical characteristics, particularly in early LUAD patients ( Figures 7A–F ). In order to demonstrate the predictive performance of our model, we compared it with other models based on the TCGA-LUAD database, such as the published gene prediction models of Duan et al (2021) , Zhang et al (2022) , Yang et al (2022) , Xu and Chen, (2021) , Gong et al (2022) . Our model has the greatest c-index of nomogram compared to previously published lung cancer models, as indicated by the findings.…”

NLRP2 in health and disease

“…Therefore, the authors hypothesised that NLRP2 could suppress other types of cancer through affecting NF-κB activity [76]. Furthermore, NLRP2 has been suggested as a potential biomarker for predicting overall survival by building different risk models in patients with head and neck squamous cell carcinoma, pancreatic, lung adenocarcinoma and glioblastoma [80][81][82][83][84][85][86]. NLRP2 can also be used as a marker of leukaemia and has a critical influence on the prognosis after stem cell transplantation [87,88].…”