ArticlePeerJ2017
Risk analysis of colorectal cancer incidence by gene expression analysis.
Article in PeerJ, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Gene network profiling in muscle-invasive bladder cancer: A systematic review and meta-analysis.Urologic oncology · 2022Pooled it
- Meta-analysis of association between Arg326Gln (rs1503185) and Gln276Pro (rs1566734) polymorphisms of PTPRJ gene and cancer risk.Journal of applied genetics · 2019Pooled it
- Cellular zinc metabolism and zinc signaling: from biological functions to diseases and therapeutic targets.Signal transduction and targeted therapy · 2024Review
- Clinical plasma cells-related genes to aid therapy in colon cancer.BMC genomics · 2023Article
- A co-regulatory network ofAmerican journal of cancer research · 2023Article
- Ontologies and Knowledge Graphs in Oncology Research.Cancers · 2022Review
- A novel prognostic model based on urea cycle-related gene signature for colorectal cancer.Frontiers in surgery · 2022Article
- The correlation and role analysis of SLC30A1 and SLC30A10 in cervical carcinoma.Journal of Cancer · 2022Article
- A Dynamic Transcription Factor Signature Along the Colorectal Adenoma-Carcinoma Sequence in Patients With Co-Occurrent Adenoma and Carcinoma.Frontiers in oncology · 2021Article
- Overexpression of gene DEP domain containing 1 and its clinical prognostic significance in colorectal cancer.Journal of clinical laboratory analysis · 2020Article
- Comprehensive analysis of the expression of SLC30A family genes and prognosis in human gastric cancer.Scientific reports · 2020Article
- Crosstalk between DNA methylation and gene expression in colorectal cancer, a potential plasma biomarker for tracing this tumor.Scientific reports · 2020Article
- An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer.BioMed research international · 2020Article
- Integrative Gene Expression Profiling Analysis to Investigate Potential Prognostic Biomarkers for Colorectal Cancer.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- Identification of microRNA-451a as a Novel Circulating Biomarker for Colorectal Cancer Diagnosis.BioMed research international · 2020Article
- Identification of Potential Biomarkers with Diagnostic Value in Pituitary Adenomas Using Prediction Analysis for Microarrays Method.Journal of molecular neuroscience : MN · 2019Article
- Integrating proteomics and transcriptomics for the identification of potential targets in early colorectal cancer.International journal of oncology · 2019Article
- OMICfpp: a fuzzy approach for paired RNA-Seq counts.BMC genomics · 2019Article
- CLDN8 promotes colorectal cancer cell proliferation, migration, and invasion by activating MAPK/ERK signaling.Cancer management and research · 2019Article
- Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances.Journal of visualized experiments : JoVE · 2018Article
Corrections and comments
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Authors and funding
13 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColorectal cancer (CRC) is one of the leading cancers worldwide. Several studies have performed microarray data analyses for cancer classification and prognostic analyses. Microarray assays also enable the identification of gene signatures for molecular characterization and treatment prediction.
objectiveMicroarray gene expression data from the online Gene Expression Omnibus (GEO) database were used to to distinguish colorectal cancer from normal colon tissue samples.
methodsWe collected microarray data from the GEO database to establish colorectal cancer microarray gene expression datasets for a combined analysis. Using the Prediction Analysis for Microarrays (PAM) method and the GSEA MSigDB resource, we analyzed the 14,698 genes that were identified through an examination of their expression values between normal and tumor tissues.
resultsTen genes (
conclusionsSix genes were selected with 95% accuracy to effectively classify normal and colorectal cancer tissues. We hope that these results will provide the basis for new research projects in clinical practice that aim to rapidly assess colorectal cancer risk using microarray gene expression analysis.
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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.