ArticleFrontiers in genetics2021
A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Role of intratumoral heterogeneity in metastatic progression and drug resistance.Discover oncology · 2025Review
- Identification of Diagnostic and Prognostic Subnetwork Biomarkers for Women with Breast Cancer Using Integrative Genomic and Network-Based Analysis.International journal of molecular sciences · 2024Article
- Article
- A renaissance for YES in cancer.Oncogene · 2023Review
- Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases.Pharmaceutics · 2023Review
- Global Transcriptional Profiling of Granulosa Cells from Polycystic Ovary Syndrome Patients: Comparative Analyses of Patients with or without History of Ovarian Hyperstimulation Syndrome Reveals Distinct Biomarkers and Pathways.Journal of clinical medicine · 2022Article
- Article
- Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods.Scientific reports · 2022Article
- Integrated Analysis of Transcriptomic and Genomic Data Reveals Blood Biomarkers With Diagnostic and Prognostic Potential in Non-small Cell Lung Cancer.Frontiers in molecular biosciences · 2022Article
- Transcriptomic data analysis coupled with copy number aberrations reveals a blood-based 17-gene signature for diagnosis and prognosis of patients with colorectal cancer.Frontiers in genetics · 2022Article
- The RNA interactome in the Hallmarks of Cancer.Wiley interdisciplinary reviews. RNAReview
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including
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