What does this research mean for the field?

A 17-gene mitochondrial and hematopoiesis-related signature, featuring FGF2 as a key predictor, provides high diagnostic accuracy for Myelodysplastic syndromes and identifies distinct immune infiltration clusters. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To establish a mitochondrial- and hematopoiesis-related gene signature for diagnosing myelodysplastic syndromes (MDS) and to explore its regulatory networks and immune microenvironment associations.

September 5, 2026HematologyOpen Access

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

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Authors

XLXianchuan Liu HZHong Zhang XLXiangzhu Li

Discussion

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Overview

Bioinformatic analysis identifies a 17-gene mitochondrial-hematopoietic diagnostic signature in myelodysplastic syndromes, indicating distinct immune subtypes and potential therapeutic targets.

Key Points

To establish a mitochondrial- and hematopoiesis-related gene signature for diagnosing myelodysplastic syndromes (MDS) and to explore its regulatory networks and immune microenvironment associations.
Integrated differentially expressed genes across three microarray cohorts (GSE145733, GSE19429, GSE81173) with curated mitochondrial- and hematopoiesis-related gene databases.
Utilized functional enrichment, machine learning models (LASSO, logistic regression, support vector machines), ssGSEA immune infiltration profiling, and RT-qPCR validation.
Identified 29 overlapping differentially expressed genes enriched in MAPK and PI3K-Akt pathways, yielding a 17-gene diagnostic signature with an AUC of 0.963.
Observed that FGF2 served as the highest-weighted diagnostic predictor, showing significant upregulation confirmed by RT-qPCR (p < 0.05) and regulatory connections to RUNX1 and miR-15/16 families.
Identified two immune clusters: Cluster 1 linked CREB1 to effector memory CD4 T cells (r = 0.461) and Cluster 2 linked KITLG to T follicular helper cells (r = 0.546).

Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd4536b95aff0620ebf8c https://doi.org/10.1080/16078454.2026.2724212

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September 5, 2026HematologyOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

XLXianchuan Liu HZHong Zhang XLXiangzhu Li

Discussion

Member takes

Overview

Bioinformatic analysis identifies a 17-gene mitochondrial-hematopoietic diagnostic signature in myelodysplastic syndromes, indicating distinct immune subtypes and potential therapeutic targets.

Key Points

To establish a mitochondrial- and hematopoiesis-related gene signature for diagnosing myelodysplastic syndromes (MDS) and to explore its regulatory networks and immune microenvironment associations.
Integrated differentially expressed genes across three microarray cohorts (GSE145733, GSE19429, GSE81173) with curated mitochondrial- and hematopoiesis-related gene databases.
Utilized functional enrichment, machine learning models (LASSO, logistic regression, support vector machines), ssGSEA immune infiltration profiling, and RT-qPCR validation.
Identified 29 overlapping differentially expressed genes enriched in MAPK and PI3K-Akt pathways, yielding a 17-gene diagnostic signature with an AUC of 0.963.
Observed that FGF2 served as the highest-weighted diagnostic predictor, showing significant upregulation confirmed by RT-qPCR (p < 0.05) and regulatory connections to RUNX1 and miR-15/16 families.
Identified two immune clusters: Cluster 1 linked CREB1 to effector memory CD4 T cells (r = 0.461) and Cluster 2 linked KITLG to T follicular helper cells (r = 0.546).

Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd4536b95aff0620ebf8c https://doi.org/10.1080/16078454.2026.2724212

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