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Gene Co-Expression Network Analysis Associated with Endometrial Cancer Tumorigenesis and Survival Outcomes

  1. Author:
    Clark, Alexis J [ORCID]
    Singh, Rajesh [ORCID]
    Leonis, Regina L [ORCID]
    Stahlberg, Eric A
    Clark, Zachary S
    Lillard, James W
  2. Author Address

    Department of Microbiology, Biochemistry and Immunology, Morehouse School of Medicine, Atlanta, GA 30310, USA., Department of Obstetrics & Gynecology, Morehouse School of Medicine, Atlanta, GA 30310, USA., Cancer Data Science Initiatives, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA., Department of Information Technology, Clayton State University, Morrow, GA 30260, USA.,
    1. Year: 2024
    2. Date: Nov 18
    3. Epub Date: 2024 11 18
  1. Journal: International Journal of Molecular Sciences
    1. 25
    2. 22
  2. Type of Article: Article
  3. Article Number: 12356
  1. Abstract:

    Endometrial cancer (EC) presents a substantial health challenge, with increasing incidence and mortality rates. Despite advances in diagnosis and treatment, understanding the molecular underpinnings of EC progression remains unknown. In this study, we conducted a comprehensive investigation utilizing The Cancer Genome Atlas (TCGA-UCEC n = 588) data to analyze gene co-expression patterns, elucidate biological process pathways, and identify potential prognostic and diagnostic biomarkers for EC, using weighted gene co-expression network analysis (WGCNA), differential gene expression, survival analysis, and functional analysis, respectively. We determined that the Green module (M5) was significantly correlated with patient survival. Functional analysis of the genes in module M5 indicates involvement in cell cycle regulation, mitotic spindle assembly, and intercellular signaling. TPX2, BUB1, and ESPL1 were among the top differentially expressed genes in the Green module, suggesting their involvement in critical pathways that contribute to disease progression and patient survival outcomes. The biological and clinical assessments of our findings provide an understanding of the molecular landscape of EC and identified several potential prognostic markers for patient risk stratification and treatment selection.

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External Sources

  1. DOI: 10.3390/ijms252212356
  2. PMID: 39596419
  3. PMCID: PMC11594472
  4. PII : ijms252212356

Library Notes

  1. Fiscal Year: FY2024-2025
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