Gut Microbiota in Patients with Non-Alcoholic Fatty Liver Disease without Type 2 Diabetes: Stratified by Body Mass Index.

The relationship between gut dysbiosis and body mass index (BMI) in non-diabetic patients with non-alcoholic fatty liver disease (NAFLD) is not adequately characterized. This study aimed to assess gut microbiota's signature in non-diabetic individuals with NAFLD stratified by BMI. The 16S ribosomal RNA sequencing was performed for gut microbiota composition in 100 patients with NAFLD and 16 healthy individuals. The differential abundance of bacterial composition between groups was analyzed using the DESeq2 method. The alpha diversity (Chao1, Shannon, and observed feature) and beta diversity of gut microbiota significantly differed between patients with NAFLD and healthy controls. However, significant differences in their diversities were not observed among subgroups of NAFLD. At the phylum level, there was no trend of an elevated Firmicutes/Bacteroidetes ratio according to BMI. At the genus level, patients with lean NAFLD displayed a significant enrichment of Escherichia-Shigella and the depletion of Lachnospira and Subdoligranulum compared to the non-lean subgroups. Combining these bacterial genera could discriminate lean from non-lean NAFLD with high diagnostic accuracy (AUC of 0.82). Non-diabetic patients with lean NAFLD had a significant difference in bacterial composition compared to non-lean individuals. Our results might provide evidence of gut microbiota signatures associated with the pathophysiology and potential targeting therapy in patients with lean NAFLD.

Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach.

Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer.