ArticleScientific reports2020
An integrated approach to biomarker discovery reveals gene signatures highly predictive of cancer progression.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- RRM2 as a biomarker and therapeutic target in letrozole resistant estrogen receptor positive breast cancer.Scientific reports · 2026Article
- Nested co-expression network analysis identifies compact gene clusters in a black box.Bioinformatics (Oxford, England) · 2026Article
- Integrative machine learning analysis suggests novel molecular targets for liver cancer diagnosis and therapy.Discover oncology · 2026Article
- Soluble Major Histocompatibility Complex I-Related Chain A (sMICA)*008 Levels Associate with Smoking, Presence of Chronic Obstructive Pulmonary Disease, and Prevalence of Lung Cancer.Journal of clinical medicine · 2026Article
- REDAC: RNA-seq expression data analysis chatbot.Bioinformatics advances · 2026Article
- The Redox-Adhesion-Exosome (RAX) Hub in Cancer: Lipid Peroxidation-Driven EMT Plasticity and Ferroptosis Defense with HNE/MDA Signaling and Lipidomic Perspectives.Antioxidants (Basel, Switzerland) · 2025Review
- Recognition of molecular clusters and a novel prognostic signature based on natural killer cell-related genes in skin cutaneous melanoma.World journal of surgical oncology · 2025Article
- Precision Medicine for Cancer and Health Equity in Latin America: Generating Understanding for Policy and Health System Shaping.International journal of environmental research and public health · 2025Review
- Machine learning reveals glycolytic key gene in gastric cancer prognosis.Scientific reports · 2025Article
- Comprehensive Bioinformatics Analysis of Glycosylation-Related Genes and Potential Therapeutic Targets in Colorectal Cancer.International journal of molecular sciences · 2025Article
- An unfolded protein response (UPR)-signature regulated by the NFKB-miR-29b/c axis fosters tumor aggressiveness and poor survival in bladder cancer.Frontiers in molecular biosciences · 2025Article
- Epigenetic dysregulation-induced metabolic reprogramming fuels tumor progression in bladder cancer.Frontiers in molecular biosciences · 2025Article
- Multi-bioinformatics revealed potential biomarkers and repurposed drugs for gastric adenocarcinoma-related gastric intestinal metaplasia.NPJ systems biology and applications · 2024Article
- Selective regulation of chemosensitivity in glioblastoma by phosphatidylinositol 3-kinase beta.iScience · 2024Article
- Use of tryptic peptide MALDI mass spectrometry imaging to identify the spatial proteomic landscape of colorectal cancer liver metastases.Clinical and experimental medicine · 2024Article
- A comprehensive review of 3D cancer models for drug screening and translational research.Cancer innovation · 2024Review
- Article
- Identification of NCAPG as an Essential Gene for Neuroblastoma Employing CRISPR-Cas9 Screening Database and Experimental Verification.International journal of molecular sciences · 2023Article
- A Systems Biology Approach to Identify Novel Biomarkers in Progression from Crohn's Disease to Colorectal Cancer.Asian Pacific journal of cancer prevention : APJCP · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Current cancer biomarkers present variability in their predictive power and demonstrate limited clinical efficacy, possibly due to the lack of functional relevance of biomarker genes to cancer progression. To address this challenge, a biomarker discovery pipeline was developed to integrate gene expression profiles from The Cancer Genome Atlas and essential survival gene datasets from The Cancer Dependency Map, the latter of which catalogs genes driving cancer progression. By applying this pipeline to lung adenocarcinoma, lung squamous cell carcinoma, and glioblastoma, genes highly associated with cancer progression were identified and designated as progression gene signatures (PGSs). Analysis of area under the receiver operating characteristics curve revealed that PGSs predicted patient survival more accurately than previously identified cancer biomarkers. Moreover, PGSs stratified patients with high risk for progressive disease indicated by worse prognostic outcomes, increased frequency of cancer progression, and poor responses to chemotherapy. The robust performance of these PGSs were recapitulated in four independent microarray datasets from Gene Expression Omnibus and were further verified in six freshly dissected tumors from glioblastoma patients. Our results demonstrate the power of an integrated approach to cancer biomarker discovery and the possibility of implementing PGSs into clinical biomarker tests.
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