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A merged microarray meta-dataset for transcriptionally profiling colorectal neoplasm formation and progression.

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.

SUBMITTER: Rohr M

PROVIDER: S-EPMC8358057 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Transcriptional profiling of pre-malignant and malignant colorectal cancer lesions provides a means for temporally monitoring key molecular events underlying neoplastic progression. Unfortunately, the most widely used central dataset for colorectal cancer samples from The Cancer Genome Atlas (TCGA) does not contain adenoma samples, putting a greater reliance of in silico analyses and pre-clinical modelling on a handful of independent microarray experiments. Due to the differences in sample acquisition, preparation, downstream analysis and other parameters, results are often incongruent, hindering consensus building. Here, we developed a microarray meta-dataset consisting of 231 normal, 132 adenoma, and 342 colon cancer tissue samples (705 samples total) sourced from 12 independent microarray studies all using the Affymetrix HG U133 Plus 2.0 (GPL570) chip platform including GSE4183, GSE8671,GSE9348, GSE15960, GSE20916, GSE21510, GSE22598, GSE23194, GSE23878, GSE32323, GSE33113, and GSE37364. Individual datasets were pre-processed and normalized by frozen robust multiarray averaging (fRMA) before merging by matching probe sets. Batch effects were subsequently identified by Principal Component Analysis (PCA) and removed using ComBat. In addition, low variant probes were filtered from the meta-dataset before downstream analysis. Finally, biological signatures corresponding to cancer and adenoma samples were both quantitatively and functionally validated. Quantitative validation was performed by correlation analysis of LogFC values with the TCGA-COAD or other external GEO microarray datasets, respectively. Functional validation was carried out through predictive analyses using Ingenuity Pathway Analysis (IPA) and Gene Set Enrichment Analysis (GSEA). Overall, our meta-dataset provides a powerful tool for studying transcriptome-wide changes which occur during early dysplasia and malignant transformation of adenomas as well as colorectal cancer in general.

Dataset Information

A merged microarray meta-dataset for transcriptionally profiling colorectal neoplasm formation and progression.

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