ArticleBMC bioinformatics2022
Integrative analysis of mutated genes and mutational processes reveals novel mutational biomarkers in colorectal cancer.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.Frontiers in public health · 2022Pooled it
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- Preoperative differentiation of borderline and malignant ovarian tumors using interpretable machine learning.Journal of ovarian research · 2026Article
- Epigenetic regulation of NR5A2 influences colorectal cancer cell stemness via a stemness-related transcription factor NANOG.Cell communication and signaling : CCS · 2025Article
- The Translational Research Program in Cancer Differences across Populations.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Article
- Folate intake and colorectal cancer risk according to genetic subtypes defined by targeted tumor sequencing.The American journal of clinical nutrition · 2024Article
- Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration.The British journal of radiology · 2023Review
- Molecular methods for colorectal cancer screening: Progress with next-generation sequencing evolution.World journal of gastrointestinal oncology · 2023Review
- A Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants.International journal of molecular sciences · 2023Article
- Somatic point mutations are enriched in non-coding RNAs with possible regulatory function in breast cancer.Communications biology · 2022Article
- Integrative analysis of mutated genes and mutational processes reveals novel mutational biomarkers in colorectal cancer.BMC bioinformatics · 2022Article
- Hybrid HCNN-KNN Model Enhances Age Estimation Accuracy in Orthopantomography.Frontiers in public health · 2022Article
- Investigation of the effects of overexpression of jumping translocation breakpoint (JTB) protein in MCF7 cells for potential use as a biomarker in breast cancer.American journal of cancer research · 2022Article
- Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer.Cancers · 2021Article
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8 authors.
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Abstract
backgroundColorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Recent studies have observed causative mutations in susceptible genes related to colorectal cancer in 10 to 15% of the patients. This highlights the importance of identifying mutations for early detection of this cancer for more effective treatments among high risk individuals. Mutation is considered as the key point in cancer research. Many studies have performed cancer subtyping based on the type of frequently mutated genes, or the proportion of mutational processes. However, to the best of our knowledge, combination of these features has never been used together for this task. This highlights the potential to introduce better and more inclusive subtype classification approaches using wider range of related features to enable biomarker discovery and thus inform drug development for CRC.
resultsIn this study, we develop a new pipeline based on a novel concept called 'gene-motif', which merges mutated gene information with tri-nucleotide motif of mutated sites, for colorectal cancer subtype identification. We apply our pipeline to the International Cancer Genome Consortium (ICGC) CRC samples and identify, for the first time, 3131 gene-motif combinations that are significantly mutated in 536 ICGC colorectal cancer samples. Using these features, we identify seven CRC subtypes with distinguishable phenotypes and biomarkers, including unique cancer related signaling pathways, in which for most of them targeted treatment options are currently available. Interestingly, we also identify several genes that are mutated in multiple subtypes but with unique sequence contexts.
conclusionOur results highlight the importance of considering both the mutation type and mutated genes in identification of cancer subtypes and cancer biomarkers. The new CRC subtypes presented in this study demonstrates distinguished phenotypic properties which can be effectively used to develop new treatments. By knowing the genes and phenotypes associated with the subtypes, a personalized treatment plan can be developed that considers the specific phenotypes associated with their genomic lesion.
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