ArticlePloS one2025
Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
The trial behind it
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
3 citing papers in PubMed.
- VEGFC as a prognostic cytokine biomarker linking lymph node metastasis to immune suppression in breast cancer.European cytokine network · 2026Article
- Molecular Investigation of Product Nkabinde in HIV Therapy: A Network Pharmacology and Molecular Docking Approach.International journal of molecular sciences · 2026Article
- CENPs in hepatocellular carcinoma: a comprehensive review of expression patterns, clinical correlations, and functional mechanisms.American journal of cancer research · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existing studies used genetic datasets, taken from various platforms, but focused only on common differentially expressed genes (DEGs) across platforms. Consequently, these studies may missed some important genes in the investigation of HCC. To solve these problems, we have taken datasets from multiple platforms and designed a statistical and machine learning-based system to determine platform-independent key genes (KGs) for HCC patients. DEGs were determined from each dataset using limma. Individual combined DEGs (icDEGs) were identified from each platform and then determined grand combined DEGs (gcDEGs) from icDEGs of all platforms. Differentially expressed discriminative genes (DEDGs) was determined based on the classification accuracy using Support vector machine. We constructed PPI network on DEDGs and identified hub genes using MCC. This study determined the optimal modules using the MCODE scores of the PPI network and selected their gene combinations. We combined all genes, obtained from previous studies to form metadata, known as meta-hub genes. Finally, six KGs (CDC20, TOP2A, CENPF, DLGAP5, UBE2C, and RACGAP1) were selected by intersecting the overlapping hub genes, meta-hub genes, and hub module genes. The discriminative power of six KGs and their prognostic potentiality were evaluated using AUC and survival analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.