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<p>Ferroptosis plays a role in tumorigenesis by affecting lipid peroxidation and metabolic pathways; however, its prognostic or therapeutic relevance in pancreatic adenocarcinoma (PAAD) remains poorly understood. In this study, we developed a prognostic ferroptosis-related gene (FRG)-based risk model using cohorts of The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), proposing plausible therapeutics. Differentially expressed FRGs between tumors from TCGA-PAAD and normal pancreatic tissues from Genotype-Tissue Expression were analyzed to construct a prognostic risk model using univariate and multivariate Cox regression and LASSO analyses. A model incorporating AURKA, CAV1, and PML gene expression effectively distinguished survival differences between high- and low-risk groups among TCGA-PAAD patients, with validation in two ICGC cohorts. The high-risk group was enriched in gene sets involving mTOR, MAPK, and E2F signaling. The immune and stromal cells infiltration score did not differ between the groups. Analysis of PRISM datasets using our risk model to classify pancreatic cell lines suggested the dasatinib’s efficacy in the high-risk group, which was experimentally confirmed in four cell lines with a high- or low-risk signature. In conclusion, this study proposed a robust FRG-based prognostic model that may help stratify PAAD patients with poor prognoses and select potential therapeutic avenues.</p>

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Identification of ferroptosis-related signature predicting prognosis and therapeutic responses in pancreatic cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月2日

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  • Ting Ting Chung,
  • Zanyue Piao,
  • Seung Jin Lee

摘要

Ferroptosis plays a role in tumorigenesis by affecting lipid peroxidation and metabolic pathways; however, its prognostic or therapeutic relevance in pancreatic adenocarcinoma (PAAD) remains poorly understood. In this study, we developed a prognostic ferroptosis-related gene (FRG)-based risk model using cohorts of The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), proposing plausible therapeutics. Differentially expressed FRGs between tumors from TCGA-PAAD and normal pancreatic tissues from Genotype-Tissue Expression were analyzed to construct a prognostic risk model using univariate and multivariate Cox regression and LASSO analyses. A model incorporating AURKA, CAV1, and PML gene expression effectively distinguished survival differences between high- and low-risk groups among TCGA-PAAD patients, with validation in two ICGC cohorts. The high-risk group was enriched in gene sets involving mTOR, MAPK, and E2F signaling. The immune and stromal cells infiltration score did not differ between the groups. Analysis of PRISM datasets using our risk model to classify pancreatic cell lines suggested the dasatinib’s efficacy in the high-risk group, which was experimentally confirmed in four cell lines with a high- or low-risk signature. In conclusion, this study proposed a robust FRG-based prognostic model that may help stratify PAAD patients with poor prognoses and select potential therapeutic avenues.