The objectives of the present study were to identify the aberrant expression of microRNA (miRNA) in colorectal carcinoma (CRC) tissues from published miRNA profiling studies and to investigate the effects of the identified miRNA inhibitor and mimic miR‑96‑5p on CRC cell migration and invasion. The altered expression of the regulators of cytoskeleton mRNA in miR‑96‑5p inhibitor‑transfected cells was determined. The miR‑96‑5p expression level in five CRC cell lines, HCT11, CaCo2, HT29, SW480 and SW620, and 26 archived paraffin‑embedded CRC tissues were also investigated by reverse‑transcriptase quantitative polymerase chain reaction (RT‑qPCR). Cell viability in response to the miR‑96‑5p inhibitor and mimic transfections was determined by an MTT assay. A Matrigel invasion assay was conducted to select the invasive subpopulation designated SW480‑7, by using the parental cell line SW480. The effects of miR‑96‑5p mimic‑ or inhibitor‑transfected SW480‑7 cells on cell migration and invasion were evaluated using the Transwell and Matrigel assays, and the change in expression of the regulators of cytoskeleton mRNAs was identified by Cytoskeleton Regulators RT2‑Profiler PCR array followed by validation with RT‑qPCR. CRC tissues exhibited a significant increase in miR‑96‑5p expression, compared with their matched normal adjacent tissues, indicating an oncogenic role for miR‑96‑5p. The results demonstrated that the miR‑96‑5p inhibitor decreased the migration of SW480‑7 cells, but had no effect on invasion. This may be due to the promotion of cell invasion by Matrigel, which counteracts the blockade of cell invasion by the miR‑96‑5p inhibitor. The miR‑96‑5p mimic enhanced SW480‑7 cell migration and invasion, as expected. It was determined that there was a >2.5 fold increase in the expression of genes involved in cytoskeleton regulation, myosin light chain kinase 2, pleckstrin homology like domain family B member 2, cyclin A1, IQ motif containing GTPase activating protein 2, Brain‑specific angiogenesisinhibitor 1‑associated protein 2 and microtubule‑actin crosslinking factor 1, in miR‑96‑5p inhibitor‑transfected cells, indicating that they are negative regulators of cell migration. In conclusion, the miR‑96‑5p inhibitor blocked cell migration but not invasion, and the latter may be due to the counteraction of Matrigel, which has been demonstrated to stimulate cell invasion.

Nutritional disorders and muscle wasting associated with liver disease are key determinants of poor prognosis in patients with chronic liver disease. The formation of these conditions involves multiple factors, including impaired energy metabolism, enhanced protein degradation, and gut microbiota imbalance. In recent years, with the deepening of microbiome research, the concept of the “gut-liver-muscle axis” has gradually emerged to explain the more systematic interaction between gut microbiota, liver metabolism, and skeletal muscle homeostasis. Gut dysbiosis can promote liver inflammation and metabolic disorders through various pathways, further weakening muscle energy utilization and protein synthesis, ultimately leading to malnutrition and sarcopenia. This review systematically explores the crucial role of gut microbiota in liver disease-related malnutrition and muscle wasting, elucidates its potential mechanisms in influencing host metabolism and nutritional status through the “gut-liver-muscle axis,” and discusses the prospects of microbiome interventions in improving nutritional outcomes in liver disease.

Objectives: Gastric tumors are one of the most common digestive system diseases, and their accurate diagnosis largely depends on the expertise of medical professionals in interpreting imaging data. As medical imaging technology has advanced, the volume of imaging data for gastric tumors has rapidly increased. This wealth of data provides valuable diagnostic information but also poses challenges for experts in diagnosis and analysis. To address these challenges, a more accurate and efficient diagnostic model is urgently needed. Methods: In this study, we propose a gastric tumor classification model that combines deep learning and radiomic features to differentiate gastrointestinal stromal tumors from other gastric tumor types. This retrospective study collected 4,806 endoscopic ultrasound images from 219 patients. Deep learning and radiomic features were extracted, and feature selection was performed using t-tests and the LASSO method. Additionally, clinical features were recorded. Single-parameter models were constructed based on these three categories of features. Following this, a gastrointestinal tumor classification model was developed by integrating the predictions from the single-parameter models. Results: The model achieved an area under the receiver operating characteristic curve of 0.95, with sensitivities, specificities, positive predictive values, negative predictive values, and accuracies of 100.00%, 82.86%, 60.00%, 100.00%, and 86.36%, respectively. Conclusions: The experimental results indicate that the gastric tumor classification model has demonstrated strong performance in classifying gastric tumors using endoscopic ultrasound images, offering valuable diagnostic support for clinicians.