Characterization of Novel Murine and Human PDAC Cell Models: Identifying the Role of Intestine Specific Homeobox Gene ISX in Hypoxia and Disease Progression

Interaction between retinol intake and ISX rs5755368 polymorphism in colorectal cancer risk: a case-control study in a Korean population

This study aimed to examine whether the ISX rs5755368 genotypes are associated with the effect of dietary retinol consumption on CRC risk. We recruited 923 CRC patients and 1846 controls to identify the association between dietary retinol and CRC risk. Dietary retinol intake was assessed using a semiquantitative food frequency questionnaire. Genotype data were available for 1419 patients (600 cases and 819 controls) of the total study population. Genotyping was performed using an Illumina MEGA Expanded Array. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using unconditional logistic regression models. Retinol intake was inversely associated with CRC (OR = 0.49; 95% CI = 0.37-0.63). Participants with AA genotype showed lower CRC risk than subjects carrying the G allele (AG + GG) (OR = 0.76; 95% CI = 0.58-0.99). A 68% reduced risk of CRC was related to subjects who had the highest retinol intake and carrying AA genotype compared to the risk of participants consumed the lowest retinol intake and carrying the G allele (OR = 0.32; 95% CI = 0.20-0.53; P interaction = 0.026). Retinol intake could be a protective factor for CRC risk while this association could be strengthened among individuals carrying the homozygous AA genotype.

REL-NPMI: Exploring genotype and phenotype relationship of pancreatitis based on improved normalized point-by-point mutual information

Pancreatitis is a relatively serious disease caused by the self-digestion of trypsin in the pancreas. The generation of diseases is closely related to gene and phenotype information. Generally, gene-phenotype relations are mainly obtained through clinical experiments, but the cost is huge. With the amount of published biomedical literature increasing exponentially, it carries a wealth of disease-related gene and phenotype information. This study provided an effective way to obtain disease-related gene and phenotype information. To our best knowledge, this work first attempted to explore relationships between genotype and phenotype about the pancreatitis from the computational perspective. It mined 6152 genes and 76,753 pairs of genotype and phenotype extracted from the biomedical literature about pancreatitis using text mining. Based on the above 76,753 pairs, the study proposed an improved normalized point-wise mutual information (REL-NPMI) model to optimize gene-phenotype relations related to pancreatitis, and obtained 12,562 gene-phenotype pairs which may be related to pancreatitis. The extracted top 20 results were validated and evaluated. The experimental results show that the method is promising for exploring pancreatitis' molecular mechanism, thus it provides a computational way for studying pancreatitis' disease pathogenesis. Data resources and the Pancreatitis Gene-Phenotype Association Database are available at http://114.116.4.45:8081/and resources are also available at https://github.com/polipoptbe8023/REL-NPMI.git.

Computing microRNA-gene interaction networks in pan-cancer using miRDriver

DNA copy number aberrated regions in cancer are known to harbor cancer driver genes and the short non-coding RNA molecules, i.e., microRNAs. In this study, we integrated the multi-omics datasets such as copy number aberration, DNA methylation, gene and microRNA expression to identify the signature microRNA-gene associations from frequently aberrated DNA regions across pan-cancer utilizing a LASSO-based regression approach. We studied 7294 patient samples associated with eighteen different cancer types from The Cancer Genome Atlas (TCGA) database and identified several cancer-specific and common microRNA-gene interactions enriched in experimentally validated microRNA-target interactions. We highlighted several oncogenic and tumor suppressor microRNAs that were cancer-specific and common in several cancer types. Our method substantially outperformed the five state-of-art methods in selecting significantly known microRNA-gene interactions in multiple cancer types. Several microRNAs and genes were found to be associated with tumor survival and progression. Selected target genes were found to be significantly enriched in cancer-related pathways, cancer hallmark and Gene Ontology (GO) terms. Furthermore, subtype-specific potential gene signatures were discovered in multiple cancer types.