Integrated multiple microarray studies by robust rank aggregation to identify immune-associated biomarkers in Crohn's disease based on three machine learning methods.

Crohn's disease (CD) is a complex autoimmune disorder presumed to be driven by complex interactions of genetic, immune, microbial and even environmental factors. Intrinsic molecular mechanisms in CD, however, remain poorly understood. The identification of novel biomarkers in CD cases based on larger samples through machine learning approaches may inform the diagnosis and treatment of diseases. A comprehensive analysis was conducted on all CD datasets of Gene Expression Omnibus (GEO); our team then used the robust rank aggregation (RRA) method to identify differentially expressed genes (DEGs) between controls and CD patients. PPI (protein‒protein interaction) network and functional enrichment analyses were performed to investigate the potential functions of the DEGs, with molecular complex detection (MCODE) identifying some important functional modules from the PPI network. Three machine learning algorithms, support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and least absolute shrinkage and selection operator (LASSO), were applied to determine characteristic genes, which were verified by ROC curve analysis and immunohistochemistry (IHC) using clinical samples. Univariable and multivariable logistic regression were used to establish a machine learning score for diagnosis. Single-sample GSEA (ssGSEA) was performed to examine the correlation between immune infiltration and biomarkers. In total, 5 datasets met the inclusion criteria: GSE75214, GSE95095, GSE126124, GSE179285, and GSE186582. Based on RRA integrated analysis, 203 significant DEGs were identified (120 upregulated genes and 83 downregulated genes), and MCODE revealed some important functional modules in the PPI network. Machine learning identified LCN2, REG1A, AQP9, CCL2, GIP, PROK2, DEFA5, CXCL9, and NAMPT; AQP9, PROK2, LCN2, and NAMPT were further verified by ROC curves and IHC in the external cohort. The final machine learning score was defined as [Expression level of AQP9 × (2.644)] + [Expression level of LCN2 × (0.958)] + [Expression level of NAMPT × (1.115)]. ssGSEA showed markedly elevated levels of dendritic cells and innate immune cells, such as macrophages and NK cells, in CD, consistent with the gene enrichment results that the DEGs are mainly involved in the IL-17 signaling pathway and humoral immune response. The selected biomarkers analyzed by the RRA method and machine learning are highly reliable. These findings improve our understanding of the molecular mechanisms of CD pathogenesis.

The Value of Endoscopic Ultrasonography in the Endoscopic Resection of Gastrointestinal Stromal Tumors.

OBJECTIVE: This study aimed to explore the clinical value of endoscopic ultrasonography (EUS) in the endoscopic resection of gastrointestinal stromal tumors (GISTs). METHODS: A retrospective study of 92 patients who were confirmed to have GISTs by endoscopic resection after EUS examination was conducted. The preoperative features of the EUS examination, ultrasound diagnosis, endoscopic resection methods, surgical procedures, complications, and complete degree of lesion resection were recorded. And 16 patients who were diagnosed by endoscopy and EUS and confirmed by surgical operation were included and analyzed in the subsequent part of the investigation (gastroscopy and EUS image analysis, EUS image and risk classification). RESULTS: The preoperative diagnosis rate of EUS and postoperative pathological diagnosis of GISTs was 78.7% (85/108), and the presence of a non-homogeneous echo and liquid anechoic zone in GISTs often indicated higher risk (P < 0.05). There was a positive correlation between tumor size and risk (P < 0.05). CONCLUSION: The endoscopic resection of GISTs is feasible and safe. EUS is of great significance for the diagnosis and risk assessment of GISTs and can assist in the endoscopic resection of GISTs.