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. 2019 Oct 31;6(1):255.
doi: 10.1038/s41597-019-0198-z.

ColPortal, an integrative multiomic platform for analysing epigenetic interactions in colorectal cancer

Angel Esteban-Gil  1 , Fernando Pérez-Sanz  2 , José García-Solano  3   4   5 , Begoña Alburquerque-González  4 , María Antonia Parreño-González  2 , María Del Carmen Legaz-García  2 , Jesualdo Tomás Fernández-Breis  6 , Edith Rodriguez-Braun  7 , Paola Pimentel  7 , Anne Tuomisto  8 , Markus Mäkinen  8 , Ondrej Slaby  9 , Pablo Conesa-Zamora  10   11   12
Affiliations

ColPortal, an integrative multiomic platform for analysing epigenetic interactions in colorectal cancer

Angel Esteban-Gil et al. Sci Data. .

Abstract

Colorectal cancer (CRC) is the third leading cause of cancer mortality worldwide. Different pathological pathways and molecular drivers have been described and some of the associated markers are used to select effective anti-neoplastic therapy. 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. Joint analysis of clinical and omics data can help clarify such relations. We present ColPortal, a platform that integrates transcriptomic, microtranscriptomic, methylomic and microbiota data of patients with colorectal cancer. 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. The current cohort consists of Caucasian patients from Europe. For each patient, demographic information, location, histology, tumor staging, tissue prognostic factors, molecular biomarker status and clinical outcomes are integrated with omics data. ColPortal allows one to perform multiomics analyses for groups of patients selected by their clinical data.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
ColPortal Technological Architecture.
Fig. 2
Fig. 2
MCA analysis of clinical cases classified by disease status (normal, polyp, tumoral), using the variables age, sex, general localization and T, N and M staging.
Fig. 3
Fig. 3
Differential gene expression for genes filtered by human phenotypes related with cancer, colon, rectal and rectum and with highest absolute value for log of fold change in the 26 preloaded clinical cases.
Fig. 4
Fig. 4
Methylation pattern for the gene EPCAM.
Fig. 5
Fig. 5
Methylation pattern (normal vs tumoral) for the gene EPCAM.
Fig. 6
Fig. 6
Differential miRNA expression for the miRNA hsa-miR-221.
Fig. 7
Fig. 7
Correlation plot between Microbiome and Methylome.
Fig. 8
Fig. 8
PCA and Hierarchical Clustering between Microbiome and Methylome.
See this image and copyright information in PMC

Dataset use reported in

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