ArticleGenes2023
Unlocking the Potential of the CA2, CA7, and ITM2C Gene Signatures for the Early Detection of Colorectal Cancer: A Comprehensive Analysis of RNA-Seq Data by Utilizing Machine Learning Algorithms.
Article in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- Bridging the Precancerous Gap in Colorectal Cancer Through AI-Enhanced Liquid Biopsy, Endoscopy, and Digital Pathology.Cancers · 2026Review
- Circ17399 RNA promotes melanoma progression through the miR-150-3p/ITM2C Axis and ALKBH5-mediated FOXM1 m6A modification.Journal of translational medicine · 2026Article
- Carbonic Anhydrase Inhibitors in Oncology.Sub-cellular biochemistry · 2026Review
- Machine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications.International journal of molecular sciences · 2025Review
- An Integrative Genomics Approach for the Discovery of Potential Clinically Actionable Diagnostic and Prognostic Biomarkers in Colorectal Cancer.Biomedicines · 2025Article
- Migration of Kupffer's vesicle-derived cells is essential for tail morphogenesis in zebrafish embryos.Development (Cambridge, England) · 2025Article
- SEMA6A overexpression inhibited tumor growth and metastasis in colorectal cancer.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2025Article
- USP3 promotes osteosarcoma progression via deubiquitinating EPHA2 and activating the PI3K/AKT signaling pathway.Cell death & disease · 2024Article
- Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma.Frontiers in immunology · 2024Article
- Identification of a Prognostic Model Based on NK Cell-Related Genes in Multiple Myeloma Using Single-Cell and Transcriptomic Data Analysis.Blood and lymphatic cancer : targets and therapy · 2024Article
- Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer affects the colon or rectum and is a common global health issue, with 1.1 million new cases occurring yearly. The study aimed to identify gene signatures for the early detection of CRC using machine learning (ML) algorithms utilizing gene expression data. The TCGA-CRC and GSE50760 datasets were pre-processed and subjected to feature selection using the LASSO method in combination with five ML algorithms: Adaboost, Random Forest (RF), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM). The important features were further analyzed for gene expression, correlation, and survival analyses. Validation of the external dataset GSE142279 was also performed. The RF model had the best classification accuracy for both datasets. A feature selection process resulted in the identification of 12 candidate genes, which were subsequently reduced to 3 (CA2, CA7, and ITM2C) through gene expression and correlation analyses. These three genes achieved 100% accuracy in an external dataset. The AUC values for these genes were 99.24%, 100%, and 99.5%, respectively. The survival analysis showed a significant logrank
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.