ArticleAging2021
Transcriptome research identifies four hub genes related to primary myelofibrosis: a holistic research by weighted gene co-expression network analysis.
Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 12 citations in OpenAlex.
- Weighted Gene Co-expression Network Analysis and Machine Learning Validation for Identifying Major Genes Related to Sjogren's Syndrome.Biochemical genetics · 2025Article
- Primary myelofibrosis progression: a game of cellular telephone.Haematologica · 2025Article
- Research progress of HIF-1a on immunotherapy outcomes in immune vascular microenvironment.Frontiers in immunology · 2025Review
- Transcriptomics data integration and analysis to uncover hallmark genes in hypertrophic cardiomyopathy.American journal of translational research · 2024Article
- Applications of Artificial Intelligence in Philadelphia-Negative Myeloproliferative Neoplasms.Diagnostics (Basel, Switzerland) · 2023Review
- Deepening Our Understanding of the Factors Affecting Landscape of Myeloproliferative Neoplasms: What Do We Know about Them?Cancers · 2023Review
- Role of ferroptosis and immune infiltration in intervertebral disc degeneration: novel insights from bioinformatics analyses.Frontiers in cell and developmental biology · 2023Article
- Weighted gene co-expression network analysis identifies key hub genes and pathways in acute myeloid leukemia.Frontiers in genetics · 2023Article
- The Roles of Blood Lipid-Metabolism Genes in Immune Infiltration Could Promote the Development of IDD.Frontiers in cell and developmental biology · 2022Article
- High-Throughput Sequencing Reveals CXCR4 and IGF1 Behave Different Roles in Weightlessness Osteoporosis.Stem cells international · 2022Article
Corrections and comments
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Authors and funding
14 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to identify specific diagnostic as well as predictive targets of primary myelofibrosis (PMF).
methodsThe gene expression profiles of GSE26049 were obtained from Gene Expression Omnibus (GEO) dataset, WGCNA was constructed to identify the most related module of PMF. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) and Protein-Protein interaction (PPI) network were conducted to fully understand the detailed information of the interested green module. Machine learning, Principal component analysis (PCA), and expression pattern analysis including immunohistochemistry and immunofluorescence of genes and proteins were performed to validate the reliability of these hub genes.
resultsGreen module was strongly correlated with PMF disease after WGCNA analysis. 20 genes in green module were identified as hub genes responsible for the progression of PMF. GO, KEGG revealed that these hub genes were primarily enriched in erythrocyte differentiation, transcription factor binding, hemoglobin complex, transcription factor complex and cell cycle, etc. Among them, EPB42, CALR, SLC4A1 and MPL had the most correlations with PMF. Machine learning, Principal component analysis (PCA), and expression pattern analysis proved the results in this study.
conclusionsEPB42, CALR, SLC4A1 and MPL were significantly highly expressed in PMF samples. These four genes may be considered as candidate prognostic biomarkers and potential therapeutic targets for early stage of PMF. The effects are worth expected whether in the diagnosis at early stage or as therapeutic target.
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