ArticleScientific reports2015
Identification of a multi-cancer gene expression biomarker for cancer clinical outcomes using a network-based algorithm.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
51 citing papers in PubMed, 107 citations in OpenAlex.
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- The endocytic receptor protein LRP-1 modulate P-glycoprotein mediated drug resistance in MCF-7 cells.PloS one · 2023Article
- A novel focal adhesion-related risk model predicts prognosis of bladder cancer -- a bioinformatic study based on TCGA and GEO database.BMC cancer · 2022Article
- The Future of Biomarkers in Veterinary Medicine: Emerging Approaches and Associated Challenges.Animals : an open access journal from MDPI · 2022Review
- Gene and protein interaction network analysis in the epithelial-mesenchymal transition of Hertwig's Epithelial Root Sheath reveals periodontal regenerative drug targets - An in silico study.Saudi journal of biological sciences · 2022Article
- Screening of Prognostic Markers for Hepatocellular Carcinoma Patients Based on Multichip Combined Analysis.Computational and mathematical methods in medicine · 2022Article
- A Three-mRNA Signature Associated with Pyrimidine Metabolism for Prognosis of BRCA.BioMed research international · 2022Article
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- Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data.Scientific reports · 2021Article
- Expression Profile and Prognostic Value of Wnt Signaling Pathway Molecules in Colorectal Cancer.Biomedicines · 2021Article
- DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data.Genome medicine · 2021Article
- Screening Hub Genes of Hepatocellular Carcinoma Based on Public Databases.Computational and mathematical methods in medicine · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Cancer types are commonly classified by histopathology and more recently through molecular characteristics such as gene expression, mutations, copy number variations, and epigenetic alterations. These molecular characterizations have led to the proposal of prognostic biomarkers for many cancer types. Nevertheless, most of these biomarkers have been proposed for a specific cancer type or even specific subtypes. Although more challenging, it is useful to identify biomarkers that can be applied for multiple types of cancer. Here, we have used a network-based exploration approach to identify a multi-cancer gene expression biomarker highly connected by ESR1, PRKACA, LRP1, JUN and SMAD2 that can be predictive of clinical outcome in 12 types of cancer from The Cancer Genome Atlas (TCGA) repository. The gene signature of this biomarker is highly supported by cancer literature, biological terms, and prognostic power in other cancer types. Additionally, the signature does not seem to be highly associated with specific mutations or copy number alterations. Comparisons with cancer-type specific and other multi-cancer biomarkers in TCGA and other datasets showed that the performance of the proposed multi-cancer biomarker is superior, making the proposed approach and multi-cancer biomarker potentially useful in research and clinical settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.