Linking Targeted Pancreatic Cancer Genes With Metabolic Disorders: A Cross-Species Translational Pathway

Sep 22, 2026

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer often diagnosed late and treated with limited options. This study examines how PDAC-associated genes intersect with metabolic disorder pathways. Bulk RNA-Seq data from human and murine adipose tissue were integrated with single-cell RNA-Seq from advanced-stage PDAC. Key genes were assessed across datasets using unsupervised clustering, KEGG pathway enrichment, and STRING protein–protein interaction networks, with experimental validation via ΔCT qPCR.

LanguageEnglish

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Pages15

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Abstract

Introduction

Methods

Results

Cancer Medicine | RESEARCH ARTICLE OPEN ACCESS
Linking Targeted Pancreatic Cancer Genes With Metabolic Disorders: A Cross-Species Translational Pathway
Dipanwita Nath1 | Caitlin Ditchfield2 | Joshua Price2,3 | Shivan Sivakumar4 | Simon W. Jones2,3 | Animesh Acharjee1,5,6
1Department of Cancer and Genomic Sciences, School of Medical Sciences, College of Medicine and Health, University of Birmingham, Birmingham,
UK | 2Department of Inflammation and Ageing, MRC-Versus Arthritis Centre for Musculoskeletal Ageing Research, University of Birmingham, Birmingham, UK | 3NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK | 4Department of Immunology and Immunotherapy, School of Infection, Inflammation and Immunology, College of Medicine and Health, Birmingham, UK | 5MRC Health Data Research UK (HDR), Birmingham, UK | 6Centre for Health Data Research, University of Birmingham, Birmingham, UK
Correspondence: Animesh Acharjee ([a.acharjee@bham.ac.uk](a.acharjee@bham.ac.uk))
Received: 1 September 2025 | Revised: 17 January 2026 | Accepted: 24 March 2026
Keywords: diabetes | metabolic inflammation | pancreatic ductal adenocarcinoma (PDAC) | single-cell RNA-seq | translational oncology | unsupervised clustering | | --- | | ABSTRACT
Introduction: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies because of its typically late diagnosis and limited treatment options, with surgical resection being the primary intervention. Emerging studies have consistently reported associations between PDAC and metabolic dysfunctions, including obesity, chronic inflammation, and diabetes. In this study, we investigated the molecular interplay between PDAC-associated genes and metabolic disorder pathways. Methods: We analysed publicly available bulk RNA-Seq datasets from human and murine adipose tissues, complemented by single-cell RNA-Seq data from advanced-stage PDAC. A set of key genes, ITGAM, PECAM1, CCL5, STAT1, STAT2, and CD44, was examined for expression patterns across datasets. Unsupervised clustering techniques were applied to single-cell data to identify transcriptionally distinct populations. Functional analyses were conducted using KEGG pathway enrichment and STRING-based protein–protein interaction networks. To experimentally validate transcriptomic findings, we performed ΔCTbased quantitative PCR (qPCR) on human adipose tissue samples.
Results: Gene expression analyses revealed significantly high expression of PDAC-associated markers in both obese human and mouse models. Specific single-cell clusters demonstrated transcriptional profiles linked to metabolic dysregulation in | | | | Abbreviations: AGE, advanced glycation end-products; AIF1, allograft inflammatory factor 1; AKT, AKT serine/threonine kinase (Protein Kinase B); BMI, Body Mass Index; CCL3, C-C motif chemokine ligand 3; CCL5, C-C motif chemokine ligand 5; CD163, cluster of differentiation 163; CD44, cell surface adhesion receptor; CRP, C-reactive protein; DEG, differentially expressed genes; DESeq2, differential gene expression analysis based onon the basis of the negative binomial distribution; DNA, deoxyribonucleic acid; ER, endoplasmic reticulum; EREG, epiregulin; FFA, free fatty acids; GEO, gene expression omnibus; GO, gene ontology; HCAR2, hydroxy carboxylic acid receptor 2; HLA-DPA1, major histocompatibility complex, Class II, DP Alpha 1; HLA-DPB1, major histocompatibility complex, Class II, DP Beta 1; HLA-DQA1, major histocompatibility complex, Class II, DQ Alpha 1; HLA-DQB1, major histocompatibility complex, Class II, DQ Beta 1; HLA-DRA, major histocompatibility complex, Class II, DR Alpha; IAPP, islet amyloid polypeptide; IGF, insulin-like growth factor; IGF-1R, IGF-1 receptor; IGFBP, IGF-binding proteins; IL1A, interleukin 1 Alpha; IL1B, interleukin 1 Beta; IL-6, interleukin-6; IRS, insulin receptor substrate; ITGAM, integin subunit alpha M; KEGG, kyoto encyclopediaencyclopaedia

FAQs

What relationship does the study investigate between PDAC and metabolic disorders?

It investigates the molecular interplay between PDAC-associated genes and metabolic disorder pathways, highlighting how metabolic dysfunctions connect with PDAC biology.

Which datasets and data types are used in the analysis?

The study integrates publicly available bulk RNA-Seq datasets from human and murine adipose tissues and complements them with single-cell RNA-Seq data from advanced-stage PDAC.

How are the findings analyzed and validated experimentally?

Unsupervised clustering identifies transcriptionally distinct single-cell populations, and KEGG enrichment plus STRING protein–protein interaction networks support functional interpretation. ΔCT-based quantitative PCR (qPCR) on human adipose tissue samples validates transcriptomic results.

Sep 22, 2026

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