ArticleFrontiers in genetics2019
Identification of Prognostic Genes in Leiomyosarcoma by Gene Co-Expression Network Analysis.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Chaperonin in health and disease.Molecular biomedicine · 2026Review
- Weighted gene co-expression network analysis of the association between upregulated AMD1, EN1 and VGLL1 and the progression and poor prognosis of breast cancer.Experimental and therapeutic medicine · 2021Article
- Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma.Scientific reports · 2021Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
aimsLeiomyosarcoma (LMS) is a tumor derived from malignant mesenchymal tissue associated with poor prognosis. Determining potential prognostic markers for LMS can provide clues for early diagnosis, recurrence, and treatment.
methodsRNA sequence data and clinical features of 103 LMS were obtained from the Cancer Genome Atlas (TCGA) database. Application Weighted Gene Co-Expression Network Analysis (WGCNA) was used to construct a free-scale gene co-expression network, to study the interrelationship between its potential modules and clinical features, and to identify hub genes in the module. The hub gene function was verified by an external database.
resultsTwenty-four co-expression modules were constructed using WGCNA. A dark red co-expression module was found to be significantly associated with disease recurrence. Functional enrichment analysis and GEPIA and ONCOMINE database analyses demonstrated that hub genes CDK4, CCT2, and MGAT1 may play an important role in LMS recurrence.
conclusionOur study constructed an LMS co-expressing gene module and identified prognostic markers for LMS recurrence detection and treatment.
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