ArticleScientific data2021
A merged microarray meta-dataset for transcriptionally profiling colorectal neoplasm formation and progression.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Colorectal cancer-derived FGF19 is a metabolically active serum biomarker that exerts enteroendocrine effects on mouse liver.Molecular oncology · 2026Article
- Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer.iScience · 2025Article
- Unveiling expression patterns, mechanisms, and therapeutic opportunities of transmembrane protein 106C: From pan-cancers to hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
- Inhibition of Proteasome LMP2 Activity SuppressesOncology research · 2025Article
- TGFβ-Responsive Stromal Activation Occurs Early in Serrated Colorectal Carcinogenesis.International journal of molecular sciences · 2024Article
- Multikinase inhibitors modulate non-constitutive proteasome expression in colorectal cancer cells.Frontiers in molecular biosciences · 2024Article
- A Shortcut from Genome to Drug: The Employment of Bioinformatic Tools to Find New Targets for Gastric Cancer Treatment.Pharmaceutics · 2023Article
- Transcriptomic and functional analyses reveal a tumour-promoting role for the IL-36 receptor in colon cancer and crosstalk between IL-36 signalling and the IL-17/ IL-23 axis.British journal of cancer · 2023Article
- Bottom-Up Approach to the Discovery of Clinically Relevant Biomarker Genes: The Case of Colorectal Cancer.Cancers · 2022Article
- In silico identification of novel biomarkers for key players in transition from normal colon tissue to adenomatous polyps.PloS one · 2022Article
- A merged microarray meta-dataset for transcriptionally profiling colorectal neoplasm formation and progression.Scientific data · 2021Article
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Authors and funding
7 authors.
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Abstract
Transcriptional profiling of pre- and post-malignant colorectal cancer (CRC) lesions enable temporal monitoring of molecular events underlying neoplastic progression. However, the most widely used transcriptomic dataset for CRC, TCGA-COAD, is devoid of adenoma samples, which increases reliance on an assortment of disparate microarray studies and hinders consensus building. To address this, we developed a microarray meta-dataset comprising 231 healthy, 132 adenoma, and 342 CRC tissue samples from twelve independent studies. Utilizing a stringent analytic framework, select datasets were downloaded from the Gene Expression Omnibus, normalized by frozen robust multiarray averaging and subsequently merged. Batch effects were then identified and removed by empirical Bayes estimation (ComBat). Finally, the meta-dataset was filtered for low variant probes, enabling downstream differential expression as well as quantitative and functional validation through cross-platform correlation and enrichment analyses, respectively. Overall, our meta-dataset provides a robust tool for investigating colorectal adenoma formation and malignant transformation at the transcriptional level with a pipeline that is modular and readily adaptable for similar analyses in other cancer types.
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