ColPortal allows one to perform multiomics analyses for groups of patients selected by their clinical data.Ĭolorectal cancer (CRC) is the second most commonly diagnosed cancer in Europe and the third leading cause of death worldwide. For each patient, demographic information, location, histology, tumor staging, tissue prognostic factors, molecular biomarker status and clinical outcomes are integrated with omics data. The current cohort consists of Caucasian patients from Europe. ![]() ColPortal also includes detailed information of histological features and digital histological slides from the study cases, since histology is a morphological manifestation of a complex molecular change. We present ColPortal, a platform that integrates transcriptomic, microtranscriptomic, methylomic and microbiota data of patients with colorectal cancer. Joint analysis of clinical and omics data can help clarify such relations. More recent evidence points to a causal role of microbiota and altered microRNA expression in CRC carcinogenesis, but their relationship with pathological drivers or molecular phenotypes is not clearly established. Different pathological pathways and molecular drivers have been described and some of the associated markers are used to select effective anti-neoplastic therapy. The following R packages have been used: limma 24, tidyverse 25, FactoMineR 22, factoextra 23, corrplot 29, minfi 26, 19 27, IlluminaHumanMethylation450kmanifest 28, cluster 30 and ape 31.Ĭolorectal cancer (CRC) is the third leading cause of cancer mortality worldwide. ![]() The result is a graph with the relations between categories.Īll scripts are developed in R 59 and they are used as templates, which are instantiated using the values defined by the user. This performs a multiple correspondence analysis with the selected variables. The results are a DM gene table and the methylation values (beta values) of each sample. This script performs a differential methylation (DM) analysis using raw data from Illumina microarrays. The color distinguishes the different groups of samples. This script generates a plot resulting from a principal components analysis (PCA) using the methylation values of the selected genes in the filtered samples, and the selected genus(s) (or families, or species). It is also necessary to know the number of microbiome variables (# numgenus#) and the number of genes (# numGenes#). An extra variable called “class” contains the labels of the classes. # m i c r o b i o m a G e n e s − M e t h y l a t e d # is a table whose variables are the microbiome data and the methylation levels of the genes for the selected samples. This script generates a correlation plot between microbial abundance and methylation data from selected DM genes obtained from samples previously filtered. Likewise, a second table with the normalized gene expression values per sample is generated. As a result, a table of DE miRNA is generated. This script reads preprocessed and normalized miRNA expression data and makes a differential expression (DE) analysis between the samples defined in parameter # U R L #. A second table with the normalized gene expression values per sample is also generated. As a result, a table of DE genes is generated. This script reads preprocessed and normalized expression data and makes a differential expression (DE) analysis between the samples defined in the parameter # U R L #. These are the scripts 58 used in the different analyses: Differences in expression profiling between tumoral colorectal carcinomas subsets and normal tissue and polyps Gene Expression Omnibus. ![]() Methyome profiling in serrated carcinoma. ![]() The Creative Commons Public Domain Dedication waiver applies to the metadata files associated with this article. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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