Global Methylome Scores Correlate with Histological Subtypes of Colorectal Carcinoma and Show Different Associations with Common Clinical and Molecular Features.

BACKGROUND: The typical methylation patterns associated with cancer are hypermethylation at gene promoters and global genome hypomethylation. Aberrant CpG island hypermethylation at promoter regions and global genome hypomethylation have not been associated with histological colorectal carcinomas (CRC) subsets. Using Illumina's 450 k Infinium Human Methylation beadchip, the methylome of 82 CRCs were analyzed, comprising different histological subtypes: 40 serrated adenocarcinomas (SAC), 32 conventional carcinomas (CC) and 10 CRCs showing histological and molecular features of microsatellite instability (hmMSI-H), and, additionally, 35 normal adjacent mucosae. Scores reflecting the overall methylation at 250 bp, 1 kb and 2 kb from the transcription starting site (TSS) were studied. RESULTS: SAC has an intermediate methylation pattern between CC and hmMSI-H for the three genome locations. In addition, the shift from promoter hypermethylation to genomic hypomethylation occurs at a small sequence between 250 bp and 1 Kb from the gene TSS, and an asymmetric distribution of methylation was observed between both sides of the CpG islands (N vs. S shores). CONCLUSION: These findings show that different histological subtypes of CRC have a particular global methylation pattern depending on sequence distance to TSS and highlight the so far underestimated importance of CpGs aberrantly hypomethylated in the clinical phenotype of CRCs.

Proteomics in Liver Transplantation: A Systematic Review.

Background: Although proteomics has been employed in the study of several models of liver injury, proteomic methods have only recently been applied not only to biomarker discovery and validation but also to improve understanding of the molecular mechanisms involved in transplantation. Methods: The study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and the guidelines for performing systematic literature reviews in bioinformatics (BiSLR). The PubMed, ScienceDirect, and Scopus databases were searched for publications through April 2020. Proteomics studies designed to understand liver transplant outcomes, including ischemia-reperfusion injury (IRI), rejection, or operational tolerance in human or rat samples that applied methodologies for differential expression analysis were considered. Results: The analysis included 22 studies after application of the inclusion and exclusion criteria. Among the 497 proteins annotated, 68 were shared between species and 10 were shared between sample sources. Among the types of studies analyzed, IRI and rejection shared a higher number of proteins. The most enriched pathway for liver biopsy samples, IRI, and rejection was metabolism, compared to cytokine-cytokine receptor interactions for tolerance. Conclusions: Proteomics is a promising technique to detect large numbers of proteins. However, our study shows that several technical issues such as the identification of proteoforms or the dynamic range of protein concentration in clinical samples hinder the successful identification of biomarkers in liver transplantation. In addition, there is a need to minimize the experimental variability between studies, increase the sample size and remove high-abundance plasma proteins.

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

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.

Tolerance Biomarkers in Liver Transplantation: Independent External Validation of the Predictive Strength of SENP6 and FEM1C Gene Expression.

BACKGROUND: Numerous studies have emphasized the genetic and phenotypic profiles of tolerant transplant patients. Moreover, different groups have defined several biomarkers, trying to distinguish patients who are going to be tolerant from those who are going to reject. However, most of these biomarkers have not been validated by other groups or even established for clinical practice. METHODS: We reanalyzed and stratified the predictive capacity of 20 previously described biomarkers for liver transplantation tolerance in a cohort of 17 liver transplant patients subjected to an independent, nonrandomized, prospective study of immunosuppression drug withdrawal. RESULTS: Only 4 of the 20 studied biomarkers (expression of SENP6, FEM1C, miR31, and miR95) showed a strong predictive capacity in the present study. miR31 and FEM1C presented an area under the ROC curve of 96.7%, followed by SENP1 with 93.3%. Finally, miR95 had an area under the ROC curve value <86.7%. CONCLUSIONS: Even though this independent analysis seems to confirm the predictive strength of SENP6 and FEM1C in liver transplantation tolerance, there are also risks in establishing biomarkers for clinical phenotypes without an understanding of how they are biologically relevant. Future collaborations between groups should be promoted so that the most promising biomarkers can be validated and implemented in daily clinical practice.