Significance of novel PANoptosis genes to predict prognosis and therapy effect in the lung adenocarcinoma

Abstract

Lung adenocarcinoma (LUAD) is the dominant histotype of non-small cell lung cancer. Panoptosis, a comprehensive form of programmed cell death, is central to carcinogenesis. In this study, the expression of PANoptosis-related genes (PRGs) and their impact on the development, prognosis, tumor microenvironment, and treatment response of patients with lung adenocarcinoma (LUAD) were systematically evaluated. PRGs were selected from The Cancer Genome Atlas database and Genecards dataset using differential expression analysis. The signature of included PRGs was identified using univariate Cox regression analysis and LASSO regression analysis. Additionally, a nomogram was developed that includes signature and clinical information. Kaplan-Meier survival analysis and receiver operating characteristic curves were used to assess the predictive validity of these risk models. Finally, functional analysis of the selected PRGs in signature and analysis of immune landscape were also performed. Preliminary identification of 10 genes related to PANoptosis has significant implications for prognosis. Subsequently, seven related genes were integrated to classify LUAD patients into different survival risk groups. The prognostic risk score generated from the signature and the TNM stage were as independent prognostic factors and were utilized in creating a nomogram plot. Both the features and the nomogram plot showed accurate performance in predicting the overall survival of LUAD patients. The PRGs were enriched in several biological functions and pathways, and stratified studies were conducted on the differences in immune landscape between high-risk and low-risk groups based on their characteristics. Ultimately, our evaluation focused on the differences in drug treatment efficacy between the high-risk and low-risk groups, providing a foundation for future research directions. Potential associations between PRGs and patient prognosis in LUAD have been identified in this study. Potential biomarkers for clinical application could be considered for the prognostic predictors identified in this study.

Keywords: Immune landscape; LUAD; PANoptosis; Prognosis; Tumor microenvironment.

Fig. 1

The analysis of PRGs in PANoptosis. (a) Waterfall plot of PANoptosis genes. (b) PPI network. (c) Differently expressed genes in LUAD. (d) Prognostic genes in LUAD.

Fig. 2

The result of PRGs cluster analysis. (a–d) Cluster analysis. (e) Survival analysis of different groups. (f) PCA analysis.

Fig. 3

Result of Functional Enrichment Analysis. (a) The infiltration of immune cells in different groups. (b–c) The differentially expressed pathways in different groups. (d–e) The results of GO and KEGG analysis.

Fig. 4

Establishment of risk scores and study of differences. (a–b) LASSO regression. (c) Relationship between risk and different clusters. (d) Relationship Between Cluster, Risk Score, and Survival Outcomes for Patients. (e) Relationship between patient prognosis and clinical characteristics. (f) Survival curves between different risk groups for training patients’ samples in TCGA and test patients’ samples in GEO, entire patients in TCGA. (g) Survival statuses of patients in different risk groups grouped by the signature in the training, validation, and entire set, respectively. (h) Expressions for the selected PRG in training, testing, and the entire set, respectively.

Fig. 5

Predicting efficacy and the creation of a Nomogram plot. (a–b) Forest plots for single-factor and multifactor analyses. (c–e) Predictive efficacy of risk scores and clinical traits. (f–g) Establishment of nomogram and calibration curve.

Fig. 6

Result of Immune analysis. (a) Differences in immune checkpoints between the two groups. (b) Relationship between high and low TMB and patient prognosis. (c) Differences in tumor mutational burden between high and low-risk groups. (d) Differences in tumor microenvironment between high and low-risk groups. (e–f) Differences in immune cell infiltration between high and low-risk groups. (g) Differences in TIDE score between high and low-risk groups. (h) Differences in patient prognostic outcomes between the two groups.

Fig. 7

Result of drug sensitivity analysis. Sensitivity of chemical drugs to different groups of patients.