ArticleBMC cancer2020
Network-based identification of biomarkers for colon adenocarcinoma.
Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Development and validation of a logistic regression model for the diagnosis of colorectal cancer.Scientific reports · 2025Article
- Ornithine decarboxylase antizyme 2 (OAZ2) in human colon adenocarcinoma: a potent prognostic factor associated with immunity.Scientific reports · 2025Article
- Piperine inhibits the proliferation of colorectal adenocarcinoma by regulatingBiomolecules & biomedicine · 2025Article
- Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma.Heliyon · 2024Article
- Evaluation of single-sample network inference methods for precision oncology.NPJ systems biology and applications · 2024Article
- Developing a label propagation approach for cancer subtype classification problem.Turkish journal of biology = Turk biyoloji dergisi · 2022Article
- Focus on pattern recognition receptors to identify prognosis and immune microenvironment in colon cancer.Frontiers in oncology · 2022Article
- Genome Instability and Long Noncoding RNA Reveal Biomarkers for Immunotherapy and Prognosis and Novel Competing Endogenous RNA Mechanism in Colon Adenocarcinoma.Frontiers in cell and developmental biology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundAs one of the most common cancers with high mortality in the world, we are still facing a huge challenge in the prevention and treatment of colon cancer. With the rapid development of high throughput technologies, new biomarkers identification for colon cancer has been confronted with the new opportunities and challenges.
methodsWe firstly constructed functional networks for each sample of colon adenocarcinoma (COAD) by using a sample-specific network (SSN) method which can construct individual-specific networks based on gene expression profiles of a single sample. The functional genes and interactions were identified from the functional networks, respectively.
resultsClassification and subtyping were used to test the function of the functional genes and interactions. The results of classification showed that the functional genes could be used as diagnostic biomarkers. The subtypes displayed different mechanisms, which were shown by the functional and pathway enrichment analysis for the representative genes of each subtype. Besides, subtype-specific molecular patterns were also detected, such as subtype-specific clinical and mutation features. Finally, 12 functional genes and 13 functional edges could serve as prognosis biomarkers since they were associated with the survival rate of COAD.
conclusionsIn conclusion, the functional genes and interactions in the constructed functional network could be used as new biomarkers for COAD.
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