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

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 <i>n</i> = 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. <i>TPX2</i>, <i>BUB1</i>, and <i>ESPL1</i> 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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Publication Details

Journal
International Journal of Molecular Sciences
Published
2024-11-18
DOI
https://doi.org/10.3390/ijms252212356
Citations
3
Primary Topic
Endometrial and Cervical Cancer Treatments
Type
article
Field-Weighted Citation Impact
1.75

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article

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

Zoë Clark, Eric Stahlberg, A. J. Clark, Rajesh Singh et al.
3 citations
International Journal of Molecular Sciences
Endometrial and Cervical Cancer Treatments
1.75
article

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

Zoë Clark, Eric Stahlberg, A. J. Clark, Rajesh Singh, Regina Leonis, James W. Lillard
article en
3 citations

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 <i>n</i> = 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. <i>TPX2</i>, <i>BUB1</i>, and <i>ESPL1</i> 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.

International Journal of Molecular SciencesVol. 25(22)
Clayton State University (US), Frederick National Laboratory for Cancer Research (US), Morehouse School of Medicine (US)
Frederick National Laboratory for Cancer Research
Good health and well-being
Openalex Percentile: Top 14%
Endometrial and Cervical Cancer Treatments
1.75
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Gene Co-Expression Network Analysis Associated with Endometrial Cancer Tumorigenesis and Survival Outcomes — Zoë Clark, Eric Stahlberg, et al. · International Journal of Molecular Sciences (2024) | TGRS Research Map | TGRS