A pyroptosis-related gene signature for prognosis and immune microenvironment of pancreatic cancer

Abstract

Pancreatic cancer is one of the most lethal tumors owing to its unspecific symptoms during the early stage and multiple treatment resistances. Pyroptosis, a newly discovered gasdermin-mediated cell death, facilitates anti- or pro-tumor effects in a variety of cancers, whereas the impact of pyroptosis in pancreatic cancer remains unclear. Therefore, we downloaded RNA expression and clinic data from the TCGA-PAAD cohort and were surprised to find that most pyroptosis-related genes (PRGs) are not only overexpressed in tumor tissue but also strongly associated with overall survival. For their remarkable prognostic value, cox regression analysis and lasso regression were used to establish a five-gene signature. All patients were divided into low- and high-risk groups based on the media value of the risk score, and we discovered that low-risk patients had better outcomes in both the testing and validation cohorts using time receiver operating characteristic (ROC), nomograms, survival, and decision analysis. More importantly, a higher somatic mutation burden and less immune cell infiltration were found in the high-risk group. Following that, we predicted tumor response to chemotherapy and immunotherapy in both low- and high-risk groups, which suggests patients with low risk were more likely to respond to both immunotherapy and chemotherapy. To summarize, our study established an effective model that can help clinicians better predict patients’ drug responses and outcomes, and we also present basic evidence for future pyroptosis related studies in pancreatic cancer.

Gene signature developed for predicting early relapse and survival in early-stage pancreatic cancer

“…The signature was further validated in an independent cohort with discrepant ethnicity. Integrating only four gene features, the AUC of the SVM signature for 1-year DFS (0.900 in the training cohort) was significantly larger than the AUCs of other patient and tumour characteristics, including TNM stage and differentiation, and single gene features, and was also greater than the AUCs of most of the previously developed signatures based on more gene features (0.661–0.750) 19–24 .…”

“…Cancer classification based on gene markers has enhanced prognosis prediction 13–17 . Unlike previous potentially similar studies 19–24 , the present analysis combined the machine learning and bioinformatics methods, which might have enhanced feature-selection efficiency and performance, to comprehensively integrate early-relapse-associated aberrant DNA events identified using WES of cancer samples from a Chinese cohort for prediction of prognosis in resected stage I–II PDAC. The signature was further validated in an independent cohort with discrepant ethnicity.…”

“…Integrating different types of cancer genetic events, including mutations and copy number variations (CNVs), may substantially improve the prediction of prognosis in resected stage I–II PDAC. While there have been some studies trying to develop integrative gene signatures for PC 19–24 , the signatures were mostly based on specific biological-function-associated genes at the RNA level and developed using publicly available datasets and applying bioinformatics or epidemiological methods only, and none of them has been applied in routine clinical practice yet. No comprehensive early-relapse-oriented signatures based on aberrant DNA events for PDAC seem to have yet been established using machine learning.…”

Construction and comprehensive analysis of a novel prognostic signature associated with pyroptosis molecular subtypes in patients with pancreatic adenocarcinoma

“…Similar to these studies, our study also carried out methods such as survival analysis, ROC and external cohort validation of PMSRPS, but it still has its advantages. First, after knowing the importance of subtyping based on molecular characteristics of cancer for improving clinical treatment decision-making, we did not directly use PRGs to establish a prognostic signature as many previous studies had done (84)(85)(86)(87)(88), but instead identified three stable pyroptosis molecular subtypes based on PRGs expression levels. By analysis, we found that different PRGclusters had different survival outcomes and immune cell infiltration manifestations.…”

Machine learning algorithm integrates bulk and single-cell transcriptome sequencing to reveal immune-related personalized therapy prediction features for pancreatic cancer

“…Otherwise, three conventional published signatures (Wang's signature [66], Tao's signature [67] and Dai's signature [68]) and two classical prognostic signatures (PAMG [69] and PurIST [70] signature) in PC were collected to compare the predictive accuracy of the IRS and these signatures. For three conventional signatures, we calculated the risk scores based on the genes and coefficients provided by the articles (Supplementary Table 9).…”