Determining the familial risk distribution of colorectal cancer: a data mining approach

Whole exome sequencing identifies novel germline variants of SLC15A4 gene as potentially cancer predisposing in familial colorectal cancer

About 15% of colorectal cancer (CRC) patients have first-degree relatives affected by the same malignancy. However, for most families the cause of familial aggregation of CRC is unknown. To identify novel high-to-moderate-penetrance germline variants underlying CRC susceptibility, we performed whole exome sequencing (WES) on four CRC cases and two unaffected members of a Polish family without any mutation in known CRC predisposition genes. After WES, we used our in-house developed Familial Cancer Variant Prioritization Pipeline and identified two novel variants in the solute carrier family 15 member 4 (SLC15A4) gene. The heterozygous missense variant, p. Y444C, was predicted to affect the phylogenetically conserved PTR2/POT domain and to have a deleterious effect on the function of the encoded peptide/histidine transporter. The other variant was located in the upstream region of the same gene (GRCh37.p13, 12_129308531_C_T; 43 bp upstream of transcription start site, ENST00000266771.5) and it was annotated to affect the promoter region of SLC15A4 as well as binding sites of 17 different transcription factors. Our findings of two distinct variants in the same gene may indicate a synergistic up-regulation of SLC15A4 as the underlying genetic cause and implicate this gene for the first time in genetic inheritance of familial CRC.

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

Predictive Factors of Advanced Colonic Adenomas and Cancer Using Data Mining

BACKGROUND

Colorectal cancer is the third common cancer in Iran. In this study we aimed to identify factors associated with the prevalence of advanced colonic neoplasms among a high-risk population.

METHODS

Participants were 474 first degree relatives of patients with colon cancer who underwent a screening colonoscopy at Digestive Disease Research Institute, Shariati Hospital affiliated to Tehran University of Medical Sciences. Features examined in this study were age, sex, body mass index, Aspirin use, smoking, and relationship type with patients with cancer in family. Also, patient’s age at the time of cancer diagnosis, number and sex of the patients with colon cancer in the family were assessed. Data analysis was performed by data mining methods using K-Medoid clustering and decision tree C4.5.

RESULTS

Results showed that female sex of the patients with colon cancer and their young age (< 60 years old) at the time of cancer diagnosis were important predictive factors for the prevalence of colorectal advanced neoplasms among their family members.

CONCLUSION

Data mining methods were found to be applicable in recognizing predictive factors of colorectal advanced neoplasms in each cluster and tree.