SynthesisDisease markers2014
Gene expression profiling of colorectal tumors and normal mucosa by microarrays meta-analysis using prediction analysis of microarray, artificial neural network, classification, and regression trees.
Synthesis in Disease markers, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- Guanylate cyclase-C Signaling Axis as a theragnostic target in colorectal cancer: a systematic review of literature.Frontiers in oncology · 2023Pooled it
- Exploration of the shared gene signatures and potential molecular mechanisms between Crohn's disease and psoriasis.Clinical rheumatology · 2026Article
- The TCN1 Paradox: Unraveling the Dual Role of Transcobalamin I in Colorectal Cancer-From Metabolic Regulation to Oncogenic Signaling.Cancer management and research · 2026Review
- Identification of CWH43 as a novel prognostic biomarker and therapeutic target in clear cell renal cell carcinoma by a multi-omics approach and correlation with autophagy progression.Discover oncology · 2025Article
- Gene Coexpression and miRNA Regulation: A Path to Early Intervention in Colorectal Cancer.Human gene therapy · 2024Article
- CWH43 Is a Novel Tumor Suppressor Gene with Negative Regulation of TTK in Colorectal Cancer.International journal of molecular sciences · 2023Article
- Article
- TCN1 Deficiency Inhibits the Malignancy of Colorectal Cancer Cells by Regulating the ITGB4 Pathway.Gut and liver · 2023Article
- 5-mRNA-based prognostic signature of survival in lung adenocarcinoma.World journal of clinical oncology · 2023Article
- A co-regulatory network ofAmerican journal of cancer research · 2023Article
- Identifying the tumor location-associated candidate genes in development of new drugs for colorectal cancer using machine-learning-based approach.Medical & biological engineering & computing · 2022Article
- Diagnostic and Therapeutic Perspectives Associated to Cobalamin-Dependent Metabolism and Transcobalamins' Synthesis in Solid Cancers.Nutrients · 2022Review
- Article
- ETV4 plays a role on the primary events during the adenoma-adenocarcinoma progression in colorectal cancer.BMC cancer · 2021Article
- Increased Expression ofJournal of personalized medicine · 2021Article
- Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis.PeerJ · 2021Article
- Genome wide meta-analysis of cDNA datasets reveals new target gene signatures of colorectal cancer based on systems biology approach.Journal of biological research (Thessalonike, Greece) · 2020Article
- Clinicopathological Analysis and Prognostic Assessment of Transcobalamin I (TCN1) in Patients with Colorectal Tumors.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- Identification of Potential Biomarkers with Diagnostic Value in Pituitary Adenomas Using Prediction Analysis for Microarrays Method.Journal of molecular neuroscience : MN · 2019Article
- A reliable method for colorectal cancer prediction based on feature selection and support vector machine.Medical & biological engineering & computing · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroarray technology shows great potential but previous studies were limited by small number of samples in the colorectal cancer (CRC) research. The aims of this study are to investigate gene expression profile of CRCs by pooling cDNA microarrays using PAM, ANN, and decision trees (CART and C5.0).
methodsPooled 16 datasets contained 88 normal mucosal tissues and 1186 CRCs. PAM was performed to identify significant expressed genes in CRCs and models of PAM, ANN, CART, and C5.0 were constructed for screening candidate genes via ranking gene order of significances.
resultsThe first screening identified 55 genes. The test accuracy of each model was over 0.97 averagely. Less than eight genes achieve excellent classification accuracy. Combining the results of four models, we found the top eight differential genes in CRCs; suppressor genes, CA7, SPIB, GUCA2B, AQP8, IL6R and CWH43; oncogenes, SPP1 and TCN1. Genes of higher significances showed lower variation in rank ordering by different methods.
conclusionWe adopted a two-tier genetic screen, which not only reduced the number of candidate genes but also yielded good accuracy (nearly 100%). This method can be applied to future studies. Among the top eight genes, CA7, TCN1, and CWH43 have not been reported to be related to CRC.
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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.