Alteration of the esophageal microbiota in Barrett's esophagus and esophageal adenocarcinoma

Postmenopausal osteoporosis (PMOP) is a major global public health concern and older women are more susceptible to experiencing fragility fractures. Our study investigated the associations between circulating proteins with bone mineral density (BMD) in postmenopausal women with or without low BMD (osteoporosis and osteopenia) using a tandem mass tag (TMT) labeling proteomic experiment and parallel reaction monitoring testing. Across all plasma samples, we quantitatively measured 1,092 proteins, and the OP and normal control (NC) samples were differentiated by principal component analysis and a partial least squares-discrimination analysis model based on the protein profiling data. The differentially abundant proteins between the low BMD and NC samples mostly exhibited binding, molecular function regulator, transporter and molecular transducer activity, and were involved in metabolic and cellular processes, stimulus response, biological regulation, immune system processes and so forth. TMT analysis and RRM validation indicated that the expression of protein Lysozyme C (P61626) was negatively related to BMD, while the expression of proteins Glucosidase (A0A024R592) and Protein disulfideisomerase A5 (Q14554) was positively related to BMD values. Collectively, our results suggest that postmenopausal women with low BMD have a different proteomic profile or signature. Protein alterations may play an important role in the pathogenesis of PMOP, and they may act as novel biomarkers and targets of therapeutic agents for this disease.

The aim of this study was to identify critical gene pathways that are associated with lung cancer metastasis to the brain. The RNA-Seq approach was used to establish the expression profiles of a primary lung cancer, adjacent benign tissue, and metastatic brain tumor from a single patient. The expression profiles of these three types of tissues were compared to define differentially expressed genes, followed by serial-cluster analysis, gene ontology analysis, pathway analysis, and knowledge-driven network analysis. Reverse transcription–polymerase chain reaction (RT-PCR) was used to validate the expression of essential candidate genes in tissues from ten additional patients. Differential gene expression among these three types of tissues was classified into multiple clusters according to the patterns of their alterations. Further bioinformatic analysis of these expression profile data showed that the network of the signal transduction pathways related to actin cytoskeleton reorganization, cell migration, and adhesion was associated with lung cancer metastasis to the brain. The expression of ACTN4 (actinin, alpha 4), a cytoskeleton protein gene essential for cytoskeleton organization and cell motility, was significantly elevated in the metastatic brain tumor but not in the primary lung cancer tissue. The signaling pathways involved in the regulation of cytoskeleton reorganization, cell motility, and focal adhesion play a role in the process of lung cancer metastasis to the brain. The contribution of ACTN4 to the process of lung cancer metastasis to the brain could be mainly through regulation of actin cytoskeleton reorganization, cell motility, and focal adhesion.

Barrett's esophagus (BE) is defined as a metaplasia condition in the distal esophagus, in which the native squamous epithelium lining is replaced by a columnar epithelium with or without intestinal metaplasia. It is commonly accepted that BE is a precancerous lesion for esophageal adenocarcinoma. The aim of this study was to investigate the aberrant microRNAs (miRNAs) and differentially expressed genes (DEGs) associated with BE based on online microarray datasets. One miRNA and five gene expression profiling datasets were retrieved from the Gene Expression Omnibus Database. Aberrant microRNAs and DEGs were obtained using R/Bioconductor statistical analysis language and software. 23 dysregulated miRNAs and 632 DEGs demonstrating consistent expression tendencies in the five gene microarrays were identified in BE. Moreover, 1962 target genes of aberrant miRNAs were predicted using three bioinformatic tools, namely TargetScan, RNA22-HSA and miRDB. Ultimately, 93 target DEGs were obtained, after which functional annotation was performed on DAVID Bioinformatics Resources. Among Gene Ontology (GO) biological processes, digestive tract development and epithelial cell differentiation have demonstrated significant associations with BE pathogenesis. In addition, analysis of the KEGG pathways has revealed associations with cancer. To enable further study, one miRNA-target DEGs regulatory network was constructed using Cytoscape. 6 target DEGs demonstrated higher-degree distributions in the network, and ROC analysis indicated that FNDC3B may be the best potential biomarker for BE diagnosis. The data presented herein may provide new perspectives for exploring BE pathogenesis and may offer hits with regard to potential biomarkers in BE diagnosis, prediction and therapeutic evaluation.

Gastrointestinal cancers, including esophageal, gastric, colorectal, liver, gallbladder, cholangiocarcinoma, and pancreatic cancers, pose a significant global health challenge due to their high mortality rates and poor prognosis, particularly when diagnosed at advanced stages. These malignancies, characterized by diverse clinical presentations and etiologies, require innovative approaches for improved management. Bayesian networks (BN) have emerged as a powerful tool in this field, offering the ability to manage uncertainty, integrate heterogeneous data sources, and support clinical decision-making. This review explores the application of BN in addressing critical challenges in gastrointestinal cancers, including the identification of risk factors, early detection, treatment optimization, and prognosis prediction. By integrating genetic predispositions, lifestyle factors, and clinical data, BN hold the potential to enhance survival rates and improve quality of life through personalized treatment strategies. Despite their promise, the widespread adoption of BN is hindered by challenges such as data quality limitations, computational complexities, and the need for greater clinical acceptance. The review concludes with future research directions, emphasizing the development of advanced BN algorithms, the integration of multi-omics data, and strategies to ensure clinical applicability, aiming to fully realize the potential of BN in personalized medicine for gastrointestinal cancers.