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TRV-2026-0980Version 1 · Certified

Written 2026-09-04 06:06:03 UTC · current record

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TRUVACE RECORD VERSION
record: TRV-2026-0980
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-04T06:06:03.925726Z
status: published
lens: g_space
sector: health
headline: Integrative bioinformatic analysis delineates a mitochondrial-hematopoietic gene signature for diagnosis and immune characterization in myelodysplastic syndromes
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
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.
limitation: 
tag: Evidence-backed gain
key_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.
rundown: 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. Results Twenty-nine MH-related DEGs were identified, significantly enriched in MAPK and PI3K-Akt pathways.
sources:
- peer_reviewed | Hematology | https://doi.org/10.1080/16078454.2026.2724212 | 2026-09-02
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