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<p>Although macrophages and lipid metabolism significantly influence the progression of various cancers, their precise roles in pancreatic cancer (PC) remain unclear. This study focuses on identifying and validating biomarkers associated with macrophage-related genes (MRGs) and lipid metabolism-related genes (LMRGs), providing new targets and strategies for therapeutic intervention. This research utilized datasets from TCGA-PAAD, GSE62452, and GSE57495. Candidate genes were identified by overlapping differentially expressed genes with MRGs from WGCNA and LMRGs. Regression analyses were performed to pinpoint potential biomarkers and construct a risk model, which underwent evaluation. A nomogram was subsequently developed and validated. Additional analyses, including functional enrichment, somatic mutation profiling, immune landscape assessment, and RT-qPCR, were performed to investigate the underlying biological mechanisms in PC. The study identified ADH1A, ACACB, CD36, CERS4, PDE3B, ALOX5, and CRAT as biomarkers for PC. RT-qPCR results revealed reduced expression of ADH1A, ACACB, CD36, CERS4, PDE3B, and CRAT in tumor samples compared to adjacent tissues, whereas ALOX5 expression was significantly elevated in tumor samples. A risk model utilizing these biomarkers classified PC patients into high- and low-risk cohorts, with high-risk patients showing lower survival probabilities. Subsequently, risk score and N stage were identified as independent prognostic factors, leading to the development of a nomogram. Notably, both risk cohorts showed significant enrichment in the “cell cycle” pathway. Furthermore, TP53 mutations were prevalent in both high-risk (76%) and low-risk (50%) cohorts. Correlation analysis indicated that PVRL2 (an immunosuppressive factor), CD276 (an immunoactivator), and CCL20 (a chemotactic factor) had the highest positive correlation with the risk score. In this study, ADH1A, ACACB, CD36, CERS4, PDE3B, ALOX5, and CRAT were identified as biomarkers for PC, with their expression levels validated in clinical samples. These findings offered a potential theoretical foundation for developing targeted treatments for PC.</p>

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Identification of prognostic and therapeutic biomarkers associated with macrophage and lipid metabolism in pancreatic cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月25日

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  • Lili Wu,
  • Feihong Liang,
  • Changgan Chen,
  • Yaxin Zhang,
  • Heguang Huang,
  • Yu Pan

摘要

Although macrophages and lipid metabolism significantly influence the progression of various cancers, their precise roles in pancreatic cancer (PC) remain unclear. This study focuses on identifying and validating biomarkers associated with macrophage-related genes (MRGs) and lipid metabolism-related genes (LMRGs), providing new targets and strategies for therapeutic intervention. This research utilized datasets from TCGA-PAAD, GSE62452, and GSE57495. Candidate genes were identified by overlapping differentially expressed genes with MRGs from WGCNA and LMRGs. Regression analyses were performed to pinpoint potential biomarkers and construct a risk model, which underwent evaluation. A nomogram was subsequently developed and validated. Additional analyses, including functional enrichment, somatic mutation profiling, immune landscape assessment, and RT-qPCR, were performed to investigate the underlying biological mechanisms in PC. The study identified ADH1A, ACACB, CD36, CERS4, PDE3B, ALOX5, and CRAT as biomarkers for PC. RT-qPCR results revealed reduced expression of ADH1A, ACACB, CD36, CERS4, PDE3B, and CRAT in tumor samples compared to adjacent tissues, whereas ALOX5 expression was significantly elevated in tumor samples. A risk model utilizing these biomarkers classified PC patients into high- and low-risk cohorts, with high-risk patients showing lower survival probabilities. Subsequently, risk score and N stage were identified as independent prognostic factors, leading to the development of a nomogram. Notably, both risk cohorts showed significant enrichment in the “cell cycle” pathway. Furthermore, TP53 mutations were prevalent in both high-risk (76%) and low-risk (50%) cohorts. Correlation analysis indicated that PVRL2 (an immunosuppressive factor), CD276 (an immunoactivator), and CCL20 (a chemotactic factor) had the highest positive correlation with the risk score. In this study, ADH1A, ACACB, CD36, CERS4, PDE3B, ALOX5, and CRAT were identified as biomarkers for PC, with their expression levels validated in clinical samples. These findings offered a potential theoretical foundation for developing targeted treatments for PC.