ArticleCancer cell international2020
Random-forest algorithm based biomarkers in predicting prognosis in the patients with hepatocellular carcinoma.
Article in Cancer cell international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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44 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.
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- Centromere Protein I, a Cell Cycle Checkpoint Gene, Accelerates Tumor Progression via the Hippo Pathway and Mediates Immune Escape in Hepatocellular Carcinoma.Journal of clinical and translational hepatology · 2025Article
- Liquid-liquid phase separation-related genes in the immune microenvironment and diagnosis of focal segmental glomerulosclerosis and minimal change disease.Renal failure · 2025Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- PBK as a novel biomarker performed excellent diagnostic and prognostic value in HCC associated with immune infiltration and methylation.Journal of molecular histology · 2025Article
- Prevalence and main factors influencing anxiety symptoms in Chinese older adults with cataracts: a national cross-sectional survey.Frontiers in psychiatry · 2025Article
- A Machine Learning Model for Predicting Prognosis in HCC Patients With Diabetes After TACE.Journal of hepatocellular carcinoma · 2025Article
- Depressive symptoms and its influencing factors of older people with cataracts in China: a national cross-sectional survey.Frontiers in public health · 2025Article
- The Comprehensive Analysis of Weighted Gene Co-Expression Network Analysis and Machine Learning Revealed Diagnostic Biomarkers for Breast Implant Illness Complicated with Breast Cancer.Breast cancer (Dove Medical Press) · 2025Article
- Determinants of life satisfaction in older adults with diabetes in China: a national cross-sectional study.Frontiers in public health · 2025Article
- Key influencing factors analysis on life satisfaction among Chinese older adults with hypertension: a National Cross-Sectional Survey.Frontiers in public health · 2025Article
- The multifaceted functions of SPC25 in cancer: from molecular pathways to targeted therapy.Frontiers in medicine · 2025Review
- Association of microtubule-based processes gene expression with immune microenvironment and its predictive value for drug response in oestrogen receptor-positive breast cancer.Frontiers in immunology · 2025Article
- Integrative machine learning and Mendelian randomization identify causal laboratory biomarkers for coronary artery lesions in Kawasaki disease: a prospective study.Frontiers in genetics · 2025Article
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12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHepatocellular carcinoma (HCC) one of the most common digestive system tumors, threatens the tens of thousands of people with high morbidity and mortality world widely. The purpose of our study was to investigate the related genes of HCC and discover their potential abilities to predict the prognosis of the patients.
methodsWe obtained RNA sequencing data of HCC from The Cancer Genome Atlas (TCGA) database and performed analysis on protein coding genes. Differentially expressed genes (DEGs) were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were conducted to discover biological functions of DEGs. Protein and protein interaction (PPI) was performed to investigate hub genes. In addition, a method of supervised machine learning, recursive feature elimination (RFE) based on random forest (RF) classifier, was used to screen for significant biomarkers. And the basic experiment was conducted by lab, we constructe a clinical patients' database, and obtained the data and results of immunohistochemistry.
resultsWe identified five biomarkers with significantly high expression to predict survival risk of the HCC patients. These prognostic biomarkers included SPC25, NUF2, MCM2, BLM and AURKA. We also defined a risk score model with these biomarkers to identify the patients who is in high risk. In our single-center experiment, 95 pairs of clinical samples were used to explore the expression levels of NUF2 and BLM in HCC. Immunohistochemical staining results showed that NUF2 and BLM were significantly up-regulated in immunohistochemical staining. High expression levels of NUF2 and BLM indicated poor prognosis.
conclusionOur investigation provided novel prognostic biomarkers and model in HCC and aimed to improve the understanding of HCC. In the results obtained, we also conducted a part of experiments to verify the theory described earlier, The experimental results did verify our theory.
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