ArticleScientific reports2024
Machine learning-based screening and validation of liver metastasis-specific genes in colorectal cancer.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Multimodal prediction of metachronous liver metastasis in stage I-III colorectal cancer patients: multicenter cohort study employing machine learning.Abdominal radiology (New York) · 2026Article
- Article
- Construction of chronic inflammation and mitochondrial energy metabolism-associated predictive and therapeutic models for lung adenocarcinoma patients.Discover oncology · 2026Article
- Multi-omics fusion network for prediction of early recurrence in colorectal liver metastases.NPJ precision oncology · 2026Article
- CytokineProfile: An Integrated Web Tool for Cytokine Profiling Analysis.Computational and structural biotechnology journal · 2026Article
- Exploring the causal role and mechanism of galanin in glioblastoma: integration of mendelian randomization, network analysis, molecular docking and experimental validation.Frontiers in pharmacology · 2026Article
- EMCN is associated with vascular-immune crosstalk and represents a potential biomarker in lung adenocarcinoma.Frontiers in molecular biosciences · 2026Article
- Predicting liver metastasis in colorectal cancer patients using routine biochemical tests enhanced by machine learning.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- A Novel Prognostic Signature Composed of Autophagy and Liver Metastasis in Colorectal Cancer: Comprehensive Analysis of Bulk and Single-Cell Transcriptomic Data.ImmunoTargets and therapy · 2026Article
- DAPK1 identified as a novel biomarker for colorectal cancer liver metastasis.Cancer cell international · 2025Article
- Study on ensemble model with weight allocation based on improved dung beetle optimization algorithm for screening colorectal cancer using laboratory test indicators.Journal of gastrointestinal oncology · 2025Article
- Identifying serum lipidomic signatures related to prognosis in first-episode schizophrenia.BMC psychiatry · 2025Article
- Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology.BMC gastroenterology · 2025Article
- Clinical Validation of a Machine Learning-Based Biomarker Signature to Predict Response to Cytotoxic Chemotherapy Alone or Combined with Targeted Therapy in Metastatic Colorectal Cancer Patients: A Study Protocol and Review.Life (Basel, Switzerland) · 2025Article
- Molecular Complexity of Colorectal Cancer: Pathways, Biomarkers, and Therapeutic Strategies.Cancer management and research · 2024Review
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Authors and funding
13 authors.
Funding
Abstract
Colorectal liver metastasis (CRLM) is challenging in the clinical treatment of colorectal cancer. Limited research has been conducted on how CRLM develops. RNA sequencing data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Four machine learning algorithms were used to screen the hub CRLM-specific genes, including Least Absolute Shrinkage and Selection Operator (Lasso), Random forest, SVM-RFE, and XGboost. The model for identifying CRLM was developed using stepwise logistic regression and was validated using internal and independent datasets. The prognostic value of hub CRLM-specific genes was assessed using the Lasso-Cox method. The in vitro experiments were performed using SW620 cells. The CRLM identification model was developed based on four CRLM-specific genes (SPP1, ZG16, P2RY14, and PRKAR2B), and the model efficacy was validated using GSE41258 and three external cohorts. Five CRLM-specific prognostic hub genes, SPP1, ZG16, P2RY14, CYP2E1, and C5, were identified using the Lasso-Cox algorithm, and a risk score was constructed. The risk score was validated using the GSE39582 cohort. Three genes have both efficacy in identifying CRLM and prognostic value: ZG16, P2RY14, and SPP1. Immune infiltration and enrichment analyses demonstrated that SPP1 was associated with M2 macrophage polarization and extracellular matrix remodeling. In vitro experiments indicated that SPP1 may act as a cancer-promoting factor. The hub CRLM-specific gene SPP1 can help determine the diagnosis, prognosis, and immune infiltration of patients with CRLM.
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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.