A risk signature based on endoplasmic reticulum stress-associated genes predicts prognosis and immunity in pancreatic cancer

Haofei Chen; Ning Xu; Jia Xu; Cheng Zhang; Xin Li; Hao Xu; Weixiong Zhu; Jinze Li; Daoming Liang; Wence Zhou

doi:10.3389/fmolb.2023.1298077

A risk signature based on endoplasmic reticulum stress-associated genes predicts prognosis and immunity in pancreatic cancer

Front Mol Biosci. 2023 Nov 29:10:1298077. doi: 10.3389/fmolb.2023.1298077. eCollection 2023.

Authors

Haofei Chen^#^{1

2}, Ning Xu^#³, Jia Xu^#⁴, Cheng Zhang^#³, Xin Li², Hao Xu⁵, Weixiong Zhu¹, Jinze Li⁶, Daoming Liang³, Wence Zhou^{1

2}

Affiliations

¹ The Second Clinical Medical School, Lanzhou University, Lanzhou, China.
² Department of General Surgery, Lanzhou University Second Hospital, Lanzhou, China.
³ The Second Affiliated Hospital of Kunming Medical University, Kunming, China.
⁴ Wuhan Blood Center, Wuhan, China.
⁵ Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China.
⁶ Department of Gastrointestinal Surgery, The Third People's Hospital of Hubei Province, Wuhan, China.

^# Contributed equally.

Abstract

Introduction: The involvement of endoplasmic reticulum (ER) stress in cancer biology is increasingly recognized, yet its role in pancreatic cancer (PC) remains unclear. This study aims to elucidate the impact of ER stress on prognosis and biological characteristics in PC patients. Methods: A bioinformatic analysis was conducted using RNA-seq data and clinicopathological information from PC patients in the TCGA and ICGC databases. The ER stress-associated gene sets were extracted from MSigDB. ER stress-associated genes closely linked with overall survival (OS) of PC patients were identified via log-rank test and univariate Cox analysis, and further narrowed by LASSO method. A risk signature associated with ER stress was formulated using multivariate Cox regression and assessed through Kaplan-Meier curves, receiver operating characteristic (ROC) analyses, and Harrell's concordance index. External validation was performed with the ICGC cohort. The single-sample gene-set enrichment analysis (ssGSEA) algorithm appraised the immune cell infiltration landscape. Results: Worse OS in PC patients with high-risk signature score was observed. Multivariate analysis underscored our ER stress-associated signature as a valuable and independent predictor of prognosis. Importantly, these results based on TCGA were further validated in ICGC dataset. In addition, our risk signature was closely associated with homeostasis, protein secretion, and immune regulation in PC patients. In particular, PC microenvironment in the high-risk cluster exhibited a more immunosuppressive status. At last, we established a nomogram model by incorporating the risk signature and clinicopathological parameters, which behaves better in predicting prognosis of PC patients. Discussion: This comprehensive molecular analysis presents a new predictive model for the prognosis of PC patients, highlighting ER stress as a potential therapeutic target. Besides, the findings indicate that ER stress can have effect modulating PC immune responses.

Keywords: ER stress; bioinformatics; immune microenvironment; pancreatic cancer; prognosis.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (grant number 82260555); Medical Innovation and Development Project of Lanzhou University (grant number lzuyxcx-2022-177) and Major Science and Technology Projects of Gansu Province (grant number 22ZD6FA021-4).