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Background and Aim Obesity affects the gut microbiome, which in turn increases the risk for colorectal cancer. Several studies have shown the mechanisms by which some bacteria may influence the development of colorectal cancer; however, gut microbiome characteristics in obese patients with colorectal cancer remain unclear. Therefore, this study evaluated their gut microbiome profile and its relationship with metabolic markers. Methods The study assessed fecal samples from 36 consecutive patients with colorectal cancer and 38 controls without colorectal cancer. To identify microbiotic variations between patients with colorectal cancer and controls, as well as between nonobese and obese individuals, 16S rRNA gene amplicon sequencing was performed. Results Principal coordinate analysis showed significant differences in the overall structure of the microbiome among the study groups. The α‐diversity, assessed by the Chao1 index or Shannon index, was higher in patients with colorectal cancer versus controls. The relative abundance of the genera Enterococcus, Capnocytophaga, and Polaribacter was significantly altered in obese patients with colorectal cancer, whose serum low‐density lipoprotein concentrations were positively correlated with the abundance of the genus Enterococcus; among the most abundant species was Enterococcus faecalis, observed at lower levels in obese versus nonobese patients. Conclusions This study demonstrated several compositional alterations of the gut microbiome in patients with colorectal cancer and showed that a reduced presence of E. faecalis may be associated with obesity‐related colorectal cancer development. The gut microbiome may provide novel insights into the potential mechanisms in obesity‐related colorectal carcinogenesis., This study demonstrated that a reduced presence of Enterococcus faecalis may be associated with the obesity‐related colorectal cancer development. The gut microbiome may provide novel insights for the potential mechanisms in obesity‐related colorectal carcinogenesis.

The risk of developing metachronous gastric cancer (MGC) following curative endoscopic submucosal dissection (ESD) of early gastric cancer (EGC) remains even after eradicating Helicobacter pylori (HP) successfully. We screened initial EGC and adjacent non-cancerous mucosa ESD-resected specimens for somatic variants of 409 cancer-related genes, assessing their mutational burden (MB) to predict molecular markers for metachronous post-ESD development. We compared variants between ten patients diagnosed with MGC more than 3 years after ESD and ten age-matched patients who did not have MGC developments after successful HP eradication. We found no significant background differences between the two groups. In adjacent non-cancerous mucosa, the MB tended to be higher in the patients with metachronous developments than in the others. Somatic genomic alterations of RECQL4, JAK3, ARID1A, and MAGI1 genes were significantly associated with MGC development. The criteria including both the MB and their variants, which had potential significant values for predicting MGC. In conclusion, combined of assessing specific somatic variants and MB may be useful for predicting MGC development. This study included a limited number of subjects; however, our novel findings may encourage further exploration of the significance of the molecular features of EGC that predict MGC development, thereby promoting focused follow-up strategies and helping elucidate the mechanisms.

Alcohol consumption and smoking pose a significant risk for esophageal squamous cell neoplasia (ESCN) development in males; however, ESCN is often diagnosed in non-drinking and non-smoking females. The mechanisms underlying these differences remain elusive, and understanding them can potentially identify novel pathways involved in ESCN development. We performed short-read sequencing to identify somatic variants on a cancer panel targeting 409 genes using DNA extracted from the superficial squamous cell carcinoma (ESCC) tissues and adjacent non-neoplastic epithelium (NE), and immunohistochemical staining of the protein encoded by the target gene. All male patients (n = 117) were drinkers or smokers, whereas 45% of the female patients (n = 33) were not. Somatic variants were compared among three age-matched groups: 13 female ESCC patients with smoking and drinking habits (known-risk group, F-KR), 13 female ESCC patients without these habits (unknown-risk group, F-UR), and 27 males with ESCC and smoking and drinking habits (M-KR). In the NE, the frequencies of CDKN2A variants were significantly higher in F-UR than in F-KR and M-KR. In both ESCC and NE, p14ARF was significantly overexpressed in F-UR than in the other groups. In conclusion, CDKN2A might be important in ESCC development, independent of known risk factors.

In recent years, the development of diagnostics using artificial intelligence (AI) has been remarkable. AI algorithms can go beyond human reasoning and build diagnostic models from a number of complex combinations. Using next-generation sequencing technology, we identified hepatitis C virus (HCV) variants resistant to directing-acting antivirals (DAA) by whole genome sequencing of full-length HCV genomes, and applied these variants to various machine-learning algorithms to evaluate a preliminary predictive model. HCV genomic RNA was extracted from serum from 173 patients (109 with subsequent sustained virological response [SVR] and 64 without) before DAA treatment. HCV genomes from the 109 SVR and 64 non-SVR patients were randomly divided into a training data set (57 SVR and 29 non-SVR) and a validation-data set (52 SVR and 35 non-SVR). The training data set was subject to nine machine-learning algorithms selected to identify the optimized combination of functional variants in relation to SVR status following DAA therapy. Subsequently, the prediction model was tested by the validation-data set. The most accurate learning method was the support vector machine (SVM) algorithm (validation accuracy, 0.95; kappa statistic, 0.90; F-value, 0.94). The second-most accurate learning algorithm was Multi-layer perceptron. Unfortunately, Decision Tree, and Naive Bayes algorithms could not be fitted with our data set due to low accuracy (< 0.8). Conclusively, with an accuracy rate of 95.4% in the generalization performance evaluation, SVM was identified as the best algorithm. Analytical methods based on genomic analysis and the construction of a predictive model by machine-learning may be applicable to the selection of the optimal treatment for other viral infections and cancer.