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Health·G Space·Evidence-backed gain·Published 2026-09-04

Integrative bioinformatic analysis delineates a mitochondrial-hematopoietic gene signature for diagnosis and immune characterization in myelodysplastic syndromes

Abstract: Objective This study aimed to develop a mitochondrial and hematopoiesis-related differentially expressed genes (MH-related DEGs) signature for Myelodysplastic syndromes (MDS) diagnosis and to characterize its regulatory network and immune microenvironment. Methods MH-related DEGs were defined as the intersection of differentially expressed genes from three integrated microarray datasets (GSE145733, GSE19429, GSE81173) with a curated set of mitochondrial- and hematopoiesis-related genes from public databases. Fun…

TRV-2026-0980Peer-reviewedPermanent record — cite & verify
Integrative bioinformatic analysis delineates a mitochondrial-hematopoietic gene signature for diagnosis and immune characterization in myelodysplastic syndromes

Hospital Universitari Doctor Peset, València 06 by 19Tarrestnom65. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

The quick read

Objective This study aimed to develop a mitochondrial and hematopoiesis-related differentially expressed genes (MH-related DEGs) signature for Myelodysplastic syndromes (MDS) diagnosis and to characterize its regulatory network and immune microenvironment. Methods MH-related DEGs were defined as the intersection of differentially expressed genes from three integrated microarray datasets (GSE145733, GSE19429, GSE81173) with a curated set of mitochondrial- and hematopoiesis-related genes from public databases.

Functional enrichment, machine learning (logistic regression, support vector machine, LASSO), regulatory network (transcription factors, miRNA, RNA-binding proteins, and drug targets), immune infiltration characterization (ssGSEA), and RT-qPCR assessment were performed.

Main points
  • Objective This study aimed to develop a mitochondrial and hematopoiesis-related differentially expressed genes (MH-related DEGs) signature for Myelodysplastic syndromes (MDS) diagnosis and to characterize its regulatory network and immune microenvironment.
  • Methods MH-related DEGs were defined as the intersection of differentially expressed genes from three integrated microarray datasets (GSE145733, GSE19429, GSE81173) with a curated set of mitochondrial- and hematopoiesis-related genes from public databases.
  • Functional enrichment, machine learning (logistic regression, support vector machine, LASSO), regulatory network (transcription factors, miRNA, RNA-binding proteins, and drug targets), immune infiltration characterization (ssGSEA), and RT-qPCR assessment were performed.
Gain

A 17-gene diagnostic signature showed high accuracy for MDS detection (AUC = 0.963). Functional enrichment, machine learning (logistic regression, support vector machine, LASSO), regulatory network (transcription factors, miRNA, RNA-binding proteins, and drug targets), immune infiltration characterization (ssGSEA), and RT-qPCR assessment were performed.

The rundown

Functional enrichment, machine learning (logistic regression, support vector machine, LASSO), regulatory network (transcription factors, miRNA, RNA-binding proteins, and drug targets), immune infiltration characterization (ssGSEA), and RT-qPCR assessment were performed. Results Twenty-nine MH-related DEGs were identified, significantly enriched in MAPK and PI3K-Akt pathways.

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