ArticleDiscover oncology2025
Identification and validation of susceptibility modules and hub genes of adrenocortical carcinoma through WGCNA and machine learning.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Interpretable Machine Learning Model for Survival Prediction in Pediatric Adrenocortical Tumors.Journal of the Endocrine Society · 2026Article
- Artificial Intelligence and Machine Learning in Pediatric Endocrine Tumors: Opportunities, Pitfalls, and a Roadmap for Trustworthy Clinical Translation.Biomedicines · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeAdrenocortical carcinoma (ACC) is a rare and aggressive endocrine malignancy characterized by rapid progression, significantly impacting patients' quality of life. Analyzing gene co-expression modules offers valuable insights into the molecular mechanisms driving ACC progression. In this study, we applied Weighted Gene Co-Expression Network Analysis (WGCNA) to identify gene co-expression modules associated with ACC progression.
methodsBefore conducting WGCNA, differential gene expression and immune infiltration analyses were performed on the GSE90713 dataset (available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi ). Dynamic tree cutting was utilized to identify co-expression modules, which were subsequently analyzed to determine their correlations and associations with traits. A total of 21 co-expression modules were identified, with the yellow module demonstrating a strong correlation with the progression of ACC. Enrichment analysis was carried out on differentially expressed genes, the yellow module, cross-module interactions, and the final hub genes to identify the associated Biological Processes (BPs) and pathways relevant to ACC. Additionally, the CIBERSORT algorithm was employed to predict immune cell infiltration in ACC.
resultsThe enrichment analysis revealed that pathways associated with cell division, protein synthesis, and metabolism play significant roles in the progression of ACC. Additionally, CDK1, AURKA, CCNB2, BIRC5, CCNB1, TYMS, and TOP2A were identified as key regulatory hub genes. Survival analysis further demonstrated that elevated expression levels of these genes in ACC tissues are significantly correlated with lower overall survival rates in patients, underscoring their critical involvement in ACC development and progression.
conclusionThis study sheds light on the mechanisms underlying ACC progression and highlights potential therapeutic targets. By identifying specific immune cell subtypes associated with ACC, the findings may aid in developing immune modulation therapies aimed at preventing or treating ACC.
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