ArticleScientific reports2021
Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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28 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- A nomogram model based on the number of examined lymph nodes-related signature to predict prognosis and guide clinical therapy in gastric cancer.Frontiers in immunology · 2022Pooled it
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- ORG-317 Repurposing as a Potential Agonist Targeting TMEM236 in Colorectal Cancer Treatment: Insights from Molecular Dynamics Simulation, Principal Component Analysis, and Free Energy Landscape Study.Current medicinal chemistry · 2026Article
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- Transcriptional patterns of cancer-related genes in primary and metastatic tumours revealed by machine learning.BMC biology · 2025Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- A Narrative Review of Prognostic Gene Signatures in Oral Squamous Cell Carcinoma Using LASSO Cox Regression.Biomedicines · 2025Review
- The diagnostic and prognostic value ofBioImpacts : BI · 2025Article
- Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset.Briefings in bioinformatics · 2024Article
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- New evidence: Metformin unsuitable as routine adjuvant for breast cancer: a drug-target mendelian randomization analysis.BMC cancer · 2024Article
- Exploring the role of interleukin 11 in cancer progression, patient survival, and therapeutic insights.Molecular biology reports · 2024Article
- Identification of potential biomarkers and therapeutic targets related to post-traumatic stress disorder due to traumatic brain injury.European journal of medical research · 2024Article
- Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach.Bioinformatics and biology insights · 2024Article
- Patterns of Gene Expression Profiles Associated with Colorectal Cancer in Colorectal Mucosa by Using Machine Learning Methods.Combinatorial chemistry & high throughput screening · 2024Article
- Identification of ribosome biogenesis genes and subgroups in ischaemic stroke.Frontiers in immunology · 2024Article
- Identification of ZMYND19 as a novel biomarker of colorectal cancer: RNA-sequencing and machine learning analysis.Journal of cell communication and signaling · 2023Article
- Transcriptomic research in atherosclerosis: Unravelling plaque phenotype and overcoming methodological challenges.Journal of molecular and cellular cardiology plus · 2023Review
- The Prognostic Value ofCancers · 2023Article
- Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) is a common cause of cancer-related deaths worldwide. The CRC mRNA gene expression dataset containing 644 CRC tumor and 51 normal samples from the cancer genome atlas (TCGA) was pre-processed to identify the significant differentially expressed genes (DEGs). Feature selection techniques Least absolute shrinkage and selection operator (LASSO) and Relief were used along with class balancing for obtaining features (genes) of high importance. The classification of the CRC dataset was done by ML algorithms namely, random forest (RF), K-nearest neighbour (KNN), and artificial neural networks (ANN). The significant DEGs were 2933, having 1832 upregulated and 1101 downregulated genes. The CRC gene expression dataset had 23,186 features. LASSO had performed better than Relief for classifying tumor and normal samples through ML algorithms namely RF, KNN, and ANN with an accuracy of 100%, while Relief had given 79.5%, 85.05%, and 100% respectively. Common features between LASSO and DEGs were 38, from them only 5 common genes namely, VSTM2A, NR5A2, TMEM236, GDLN, and ETFDH had shown statistically significant survival analysis. Functional review and analysis of the selected genes helped in downsizing the 5 genes to 2, which are VSTM2A and TMEM236. Differential expression of TMEM236 was statistically significant and was markedly reduced in the dataset which solicits appreciation for assessment as a novel biomarker for CRC diagnosis.
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