Identification and Validation of Prognostic Model for Pancreatic Ductal Adenocarcinoma Based on Necroptosis-Related Genes

Abstract

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors with a poor prognosis. Recently, necroptosis has been reported to participate in the progression of multiple tumors. However, few studies have revealed the relationship between necroptosis and PDAC, and the role of necroptosis in PDAC has not yet been clarified.Methods: The mRNA expression data and corresponding clinical information of PDAC patients were downloaded from the TCGA and GEO databases. The necroptosis-related genes (NRGs) were obtained from the CUSABIO website. Consensus clustering was performed to divide PDAC patients into two clusters. Univariate and LASSO Cox regression analyses were applied to screen the NRGs related to prognosis to construct the prognostic model. The predictive value of the prognostic model was evaluated by Kaplan-Meier survival analysis and ROC curve. Univariate and multivariate Cox regression analyses were used to evaluate whether the risk score could be used as an independent predictor of PDAC prognosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single-sample gene set enrichment analysis (ssGSEA) were used for functional enrichment analysis. Finally, using qRT-PCR examined NRGs mRNA expression in vitro.Results: Based on the TCGA database, a total of 22 differential expressed NRGs were identified, among which eight NRGs (CAPN2, CHMP4C, PLA2G4F, PYGB, BCL2, JAK3, PLA2G4C and STAT4) that may be related to prognosis were screened by univariate Cox regression analysis. And CAPN2, CHMP4C, PLA2G4C and STAT4 were further selected to construct the prognostic model. Kaplan-Meier survival analysis and ROC curve showed that there was a significant correlation between the risk model and prognosis. Univariate and multivariate Cox regression analyses showed that the risk score of the prognostic model could be used as an independent predictor. The model efficacy was further demonstrated in the GEO cohort. Functional analysis revealed that there were significant differences in immune status between high and low-risk groups. Finally, the qRT-PCR results revealed a similar dysregulation of NRGs in PDAC cell lines.Conclusion: This study successfully constructed and verified a prognostic model based on NRGs, which has a good predictive value for the prognosis of PDAC patients.

The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping

“…With the latter, we reveal the mechanisms and processes of genes that represent the samples best, as shown by Xie et al. who investigated the immune functionalities linked to different risk groups of PDAC patients ( 32 ). Functional enrichment sheds light on the sample characteristics that the PDAC signatures represent.…”

Genome, Metabolism, or Immunity: Which Is the Primary Decider of Pancreatic Cancer Fate through Non-Apoptotic Cell Death?

“…There is a complex interplay among the immune system, signaling pathways, and gene expression in PDAC, which holds immense potential for therapeutic interventions and prognostic assessments [61,62,65,66]. For instance, evaluating the function of LPS, a double-edged sword factor in PDAC, superbly shows the crosstalk between these factors.…”

“…The C1 and C2 subtypes demonstrated highly activated tumorigenic pathways, while the C3 subtype exhibited the poorest survival rates [65]. Xie et al conducted a study in which 22 different NRGs were evaluated regarding gene expression and risk prognosis of PDAC [61]. Four genes (CAPN, CHMP4C, PYGB, and PLA2G4F) were upregulated, and 18 genes (IFNA6, IFNA2, IFNA13, BCL2, TNF, CYBB, FASLG, JAK3, STAT4, TNFAIP3, PLA2G4C, TLR4, NLRP3, IFNGR1, STAT5A, TYK2, JAK1, and SLC25A6) were downregulated in PDAC tissues.…”

The crucial prognostic signaling pathways of pancreatic ductal adenocarcinoma were identified by single-cell and bulk RNA sequencing data

“…The development of bioinformatics and multi-omics databases provides convenience for exploring the expression patterns of malignant tumors and constructing prognostic and diagnostic models (Ma et al 2022). Using next-generation sequencing (NGS), gene mutations and alterations in molecular pathways can be identi ed and used to develop strategies to selectively kill cancer cells such as KRAS, NRG1, NTRK and related molecules in PDAC patients (Jones et al 2019; Waters and Der 2018; Xie et al 2022). Despite the promising predictive power observed in the aforementioned studies, these prognostic signatures are based on bulk RNA-seq, which cannot detect the exact cellular and molecular changes in tumor cells, as it mainly focuses on the "average" expression of all cells in a sample.…”