SynthesisEBioMedicine2018
RankProd Combined with Genetic Algorithm Optimized Artificial Neural Network Establishes a Diagnostic and Prognostic Prediction Model that Revealed C1QTNF3 as a Biomarker for Prostate Cancer.
Synthesis in EBioMedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it, 80 citations in OpenAlex.
- Assessment of Prostate and Bladder Cancer Genomic Biomarkers Using Artificial Intelligence: a Systematic Review.Current urology reports · 2024Pooled it
- Transcriptomics and network analysis highlight potential pathways in the pathogenesis of pterygium.Scientific reports · 2022Pooled it
- Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration.Annals of medicine · 2025Article
- Biobanks and biomarkers: Their current and future role in biomedical research.World journal of methodology · 2024Review
- From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.Health information science and systems · 2024Review
- Development and validation of a novel combinational index of liquid biopsy biomarker for longitudinal lung cancer patient management.The journal of liquid biopsy · 2024Article
- Artificial intelligence methods available for cancer research.Frontiers of medicine · 2024Review
- Systematic review and integrated analysis of prognostic gene signatures for prostate cancer patients.Discover oncology · 2023Article
- Fatty-Acid-Binding Proteins: From Lipid Transporters to Disease Biomarkers.Biomolecules · 2023Review
- Review
- Eye Melanoma Diagnosis System using Statistical Texture Feature Extraction and Soft Computing Techniques.Journal of biomedical physics & engineering · 2023Article
- Pediatric Crohn's disease diagnosis aidFrontiers in pediatrics · 2023Article
- Fatty acid binding protein 5 regulates docetaxel sensitivity in taxane-resistant prostate cancer cells.PloS one · 2023Article
- Article
- LTBP2 inhibits prostate cancer progression and metastasis via the PI3K/AKT signaling pathway.Experimental and therapeutic medicine · 2022Article
- Identification of hub genes and biological pathways in glioma via integrated bioinformatics analysis.The Journal of international medical research · 2022Article
- Establishment of an Artificial Neural Network Model Using Immune-Infiltration Related Factors for Endometrial Receptivity Assessment.Vaccines · 2022Article
- Cancer glycomics offers potential biomarkers and therapeutic targets in the framework of 3P medicine.Frontiers in endocrinology · 2022Review
- Assessing the Potential Prognostic and Immunological Role of TK1 in Prostate Cancer.Frontiers in genetics · 2022Article
- Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome.Frontiers in immunology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer (PCa) is the most commonly diagnosed cancer in males in the Western world. Although prostate-specific antigen (PSA) has been widely used as a biomarker for PCa diagnosis, its results can be controversial. Therefore, new biomarkers are needed to enhance the clinical management of PCa. From publicly available microarray data, differentially expressed genes (DEGs) were identified by meta-analysis with RankProd. Genetic algorithm optimized artificial neural network (GA-ANN) was introduced to establish a diagnostic prediction model and to filter candidate genes. The diagnostic and prognostic capability of the prediction model and candidate genes were investigated in both GEO and TCGA datasets. Candidate genes were further validated by qPCR, Western Blot and Tissue microarray. By RankProd meta-analyses, 2306 significantly up- and 1311 down-regulated probes were found in 133 cases and 30 controls microarray data. The overall accuracy rate of the PCa diagnostic prediction model, consisting of a 15-gene signature, reached up to 100% in both the training and test dataset. The prediction model also showed good results for the diagnosis (AUC = 0.953) and prognosis (AUC of 5 years overall survival time = 0.808) of PCa in the TCGA database. The expression levels of three genes, FABP5, C1QTNF3 and LPHN3, were validated by qPCR. C1QTNF3 high expression was further validated in PCa tissue by Western Blot and Tissue microarray. In the GEO datasets, C1QTNF3 was a good predictor for the diagnosis of PCa (GSE6956: AUC = 0.791; GSE8218: AUC = 0.868; GSE26910: AUC = 0.972). In the TCGA database, C1QTNF3 was significantly associated with PCa patient recurrence free survival (P < .001, AUC = 0.57). In this study, we have developed a diagnostic and prognostic prediction model for PCa. C1QTNF3 was revealed as a promising biomarker for PCa. This approach can be applied to other high-throughput data from different platforms for the discovery of oncogenes or biomarkers in different kinds of diseases.
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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.