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Objective <p>This study aimed to identify independent prognostic factors for advanced unresectable pancreatic ductal adenocarcinoma (PDAC) and construct a nomogram-based prediction model. The efficacy of different chemotherapy regimens was evaluated based on metabolic risk levels.</p> Methods <p>Clinical data from 276 patients with unresectable PDAC treated between 2020 and 2022 were retrospectively analyzed. Cox proportional hazards regression identified prognostic factors, and survival analysis was performed using Kaplan-Meier methods. A nomogram was developed, and ROC analysis assessed its predictive performance. Two-way ANOVA evaluated chemotherapy efficacy, and TCGA transcriptomic data explored metabolic correlations.</p> Results <p>Metabolic syndrome (MetS) and distant metastasis were independent prognostic factors. Patients with MetS had significantly shorter survival. The nomogram showed good discrimination (AUC: 0.815 training, 0.793 validation). Patients without MetS had better outcomes with FOLFIRINOX or GS regimens. Transcriptomic analysis revealed metabolic pathways linked to PDAC progression.</p> Conclusions <p>MetS and distant metastasis significantly impact PDAC prognosis. Patients without MetS benefit more from specific chemotherapy regimens. Our predictive model may aid personalized treatment strategies.</p>

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Metabolic syndrome as a prognostic factor in advanced pancreatic cancer: a predictive model and chemotherapy evaluation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月16日

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  • Jingchang Zhang,
  • Jianbiao Li,
  • Huiting Deng,
  • Yongfu Zhao,
  • Ye Zhang

摘要

Objective

This study aimed to identify independent prognostic factors for advanced unresectable pancreatic ductal adenocarcinoma (PDAC) and construct a nomogram-based prediction model. The efficacy of different chemotherapy regimens was evaluated based on metabolic risk levels.

Methods

Clinical data from 276 patients with unresectable PDAC treated between 2020 and 2022 were retrospectively analyzed. Cox proportional hazards regression identified prognostic factors, and survival analysis was performed using Kaplan-Meier methods. A nomogram was developed, and ROC analysis assessed its predictive performance. Two-way ANOVA evaluated chemotherapy efficacy, and TCGA transcriptomic data explored metabolic correlations.

Results

Metabolic syndrome (MetS) and distant metastasis were independent prognostic factors. Patients with MetS had significantly shorter survival. The nomogram showed good discrimination (AUC: 0.815 training, 0.793 validation). Patients without MetS had better outcomes with FOLFIRINOX or GS regimens. Transcriptomic analysis revealed metabolic pathways linked to PDAC progression.

Conclusions

MetS and distant metastasis significantly impact PDAC prognosis. Patients without MetS benefit more from specific chemotherapy regimens. Our predictive model may aid personalized treatment strategies.