Pyroptosis regulators exert crucial functions in prognosis, progression and immune microenvironment of pancreatic adenocarcinoma: a bioinformatic and in vitro research

Abstract

Pyroptosis is an inflammatory programmed cell death, showing potentials to be a novel anti-cancer approach. However, the roles of pyroptosis-related (PR) genes (PRGs) in pancreatic adenocarcinoma (PAAD) remain elusive. In the present study, we constructed a novel PR risk signature through the lasso regression analysis. The risk signature was greatly conducive to PAAD prognostic assessment. PR risk score was identified as an independent prognostic factor and could distinguish the prognostic differences of most clinical subgroups. Meanwhile, it could improve the traditional prognostic models based on TNM-staging. Next, its prognostic value was also tested in five validation cohorts. Using CIBERSORT, ESTIMATE, and ssGSEA algorithms, the effects of PR risk signature on tumor immune microenvironment (TIM) were explored. High PR risk suppressed antitumor immune through decreasing the infiltrating levels of CD8 T and NK cells. The genomic information and histological expression of risk PRGs were uncovered by USCA and HPA databases. Somatic mutation, methylation alteration, and homozygous CNV of eight PRGs barely occurred in PAAD samples. As for therapeutic correlation, PR risk score may not predict the efficacy of PD-1/L1 inhibitors and was weakly associated with multiple drug susceptibilities. Finally, the biofunctions of toll like receptor 3 (TLR3) in pancreatic cancer (PC) cells were investigated through qPCR, MTT, colony formation, and Transwell assays. Overexpression of TLR3 could promote the proliferation, migration, and invasion of PC cells. In conclusion, PRGs play crucial roles in prognosis, progression, and immune microenvironment of PAAD. TLR3 is expected to be a promising therapeutic target.

Cuproptosis-related gene index: A predictor for pancreatic cancer prognosis, immunotherapy efficacy, and chemosensitivity

“…Recently, with the progressions made in the in-depth understanding of cell death mechanisms, a considerable number of prognosis-predictive gene signatures have been proposed. To clarify whether CRGI behaves better than those signatures originating from other underlying cell death mechanisms, we retrieved gene signatures proposed for PAAD that were derived from autophagy-, ferroptosis-, and pyroptosis-related genes ( 60 – 62 ). Then, we performed time-dependent ROC curves across the TCGA and ICGC datasets for each signature.…”

A Novel Cuproptosis‐Associated Gene Signature to Predict Prognosis in Patients with Pancreatic Cancer

“…In addition, with the development of multiomics data, a large number of genetic features and risk models provide new insights for tumor diagnosis and prognosis prediction. Among them, a number of cell death-based disease models have shown considerable clinical value, such as autophagy, ferroptosis, and pyroptosis [ 21 , 22 ]. Considering the complexity of tumor biology, predictive models constructed from multiple genetic signatures are more accurate and reliable than single pathological features or single biomarkers.…”

“…The AUC values of the risk model in our study led to the predictive performance of overall survival at 1, 3, and 5 years, reaching 0.75, 0.84, and 0.86, respectively. In other cell death-related studies, the AUC values for overall survival at 1, 3, and 5 years were 0.537, 0.731, and 0.852 for autophagy [ 21 ]; 0.665, 0.738, and 0.871 for ferroptosis [ 23 ]; and 0.643, 0.705, and 0.807 for pyroptosis [ 22 ], respectively. Our risk model exhibits more robust and accurate predictive performance than models of other forms of cell death.…”

A Novel Immune-Related Gene Prognostic Index (IRGPI) in Pancreatic Adenocarcinoma (PAAD) and Its Implications in the Tumor Microenvironment

“…The general appearance of the tumor microenvironment was assessed by the R package “ESTIMATE” [ 33 ], while for the analysis of the abundance of various immune cell types, we curated relevant data from Tumor IMmune Estimation Resource 2.0 (TIMER 2.0) ( (accessed on 18 March 2022)) portal and processed it by the R package “immunedeconv” through 6 algorithms (i.e., TIMER, XCELL, MCPCOUNTER, CIBERSORT, EPIC, and QUANTISED) [ 33 , 34 , 35 , 36 ].…”