Identification of Shared Signature Genes and Immune Microenvironment Subtypes for Heart Failure and Chronic Kidney

Xuefu Wang1, Jin Rao2, Xiangyu Chen2

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China.

PubMed

Insights

This study identifies five key genes as potential biomarkers for diagnosing heart failure (HF) and chronic kidney disease (CKD), revealing shared molecular mechanisms involving immune dysregulation and metabolic disorders.

Area of Science:

  • Cardiovascular Medicine
  • Nephrology
  • Molecular Biology

Background:

  • Heart failure (HF) and chronic kidney disease (CKD) exhibit a complex interrelationship.
  • Understanding the molecular mechanisms of this organ-to-organ interplay is crucial.
  • Identifying sensitive and specific biomarkers for both conditions is a significant clinical need.

Purpose of the Study:

  • To clarify the molecular mechanisms underlying the interplay between HF and CKD.
  • To identify sensitive and specific biomarkers for the simultaneous diagnosis of HF and CKD.
  • To explore molecular subtypes and immune characteristics of co-existing HF and CKD.

Main Methods:

  • Differential gene expression analysis of HF and CKD microarray datasets.
  • Machine learning for biomarker identification and validation using ROC curves and RT-PCR.
  • Consensus clustering for molecular subtyping and ssGSEA for immune cell infiltration analysis.

Main Results:

  • Identified 33 crosstalk genes linked to inflammatory, immune, and metabolic pathways.
  • Five hub genes (PHLDA1, ATP1A1, IFIT2, HLTF, MPP3) selected as optimal diagnostic biomarkers.
  • Discovered distinct immune and metabolic subtypes of HF and CKD, with significant immune dysregulation.

Conclusions:

  • Five identified crosstalk genes show potential as diagnostic biomarkers for HF and CKD.
  • Metabolite disorders and subsequent immune cell activation are key to the common pathogenesis of HF and CKD.
  • An ImmuneScore model accurately predicted molecular subtypes, aiding risk stratification.
Abstract