The Colon Transcriptome Explorer (CoTrEx) 2.0: a Reference Web-Based Resource for Exploring Population-Based Normal Colon Gene Expression

Gene expression data is key for the functional annotation of single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS). Expression and splicing quantitative trait loci (e/sQTLs) in normal colon tissue, such as those from the University of Barcelona and University of Virginia RNA sequencing project (BarcUVa-Seq) and the Genotype-Tissue Expression project (GTEx), are required to gain biological insight of colon-related diseases risk loci. Moreover, transcriptome-wide association studies (TWAS) rely on reference gene expression imputation panels in the tissue of interest to nominate susceptibility genes. Also, it is of high interest to study the relationships between genes in a network framework. For facilitating these analyses, we have updated and expanded the scope of the Colon Transcriptome Explorer (CoTrEx) to the version 2.0. This web-based resource provides exhaustive visualization and analysis of transcriptome-wide gene expression profiles of normal colon tissue from BarcUVa-Seq and GTEx. In addition to the integration of new datasets, CoTrEx 2.0 provides additional e/sQTLs sets, as well as gene expression prediction models and regulatory and co-expression networks. It is freely available at https://barcuvaseq.org/cotrex/. Overall, it is of high interest for researchers aiming to investigate the genetic susceptibility to colon-related complex traits and diseases.

Genotype-Based Gene Expression in Colon Tissue—Prediction Accuracy and Relationship with the Prognosis of Colorectal Cancer Patients

Colorectal cancer (CRC) survival has environmental and inherited components. The expression of specific genes can be inferred based on individual genotypes—so called expression quantitative trait loci. In this study, we used the PrediXcan method to predict gene expression in normal colon tissue using individual genotype data from 91 CRC patients and examined the correlation ρ between predicted and measured gene expression levels. Out of 5434 predicted genes, 58% showed a negative ρ value and only 16% presented a ρ higher than 0.10. We subsequently investigated the association between genotype-based gene expression in colon tissue for genes with ρ > 0.10 and survival of 4436 CRC patients. We identified an inverse association between the predicted expression of ARID3B and CRC-specific survival for patients with a body mass index greater than or equal to 30 kg/m2 (HR (hazard ratio) = 0.66 for an expression higher vs. lower than the median, p = 0.005). This association was validated using genotype and clinical data from the UK Biobank (HR = 0.74, p = 0.04). In addition to the identification of ARID3B expression in normal colon tissue as a candidate prognostic biomarker for obese CRC patients, our study illustrates the challenges of genotype-based prediction of gene expression, and the advantage of reassessing the prediction accuracy in a subset of the study population using measured gene expression data.

Identification of histological features to predict MUC2 expression in colon cancer tissues

Colorectal cancer (CRC) is the third-most common form of cancer among Americans. Like normal colon tissue, CRC cells are sustained by a subpopulation of “stem cells” that possess the ability to self-renew and differentiate into more specialized cancer cell types. In normal colon tissue, the enterocytes, goblet cells and other epithelial cells in the mucosa region have distinct morphologies that distinguish them from the other cells in the lamina propria, muscularis mucosa, and submucosa. However, in a tumor, the morphology of the cancer cells varies dramatically. Cancer cells that express genes specific to goblet cells significantly differ in shape and size compared to their normal counterparts. Even though a large number of hematoxylin and eosin (H&E)-stained sections and the corresponding RNA sequencing (RNASeq) data from CRC are available from The Cancer Genome Atlas (TCGA), prediction of gene expression patterns from tissue histological features has not been attempted yet. In this manuscript, we identified histological features that are strongly associated with MUC2 expression patterns in a tumor. Specifically, we show that large nuclear area is associated with MUC2-high tumors (p < 0.001). This discovery provides insight into cancer biology and tumor histology and demonstrates that it may be possible to predict certain gene expressions from histological features.

Evaluation of different tissue de-paraffinization procedures for infrared spectral imaging

Differential distribution of paraffin in a normal colon tissue section after various de-Waxing procedures in comparison to a paraffinized tissue.