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Ulcerative colitis (UC) is a common autoimmune disease worldwide. Circular RNA (circRNA) is a type of noncoding ribonucleic acids (ncRNAs). In addition to their roles in numerous biological processes, circRNAs are also linked to a vast range of diseases including UC. Although previous studies have examined many circRNAs, the physiological and pathological roles of the circRNA-associated competing endogenous RNA (ceRNA) network in UC remain unclear. Thus, we constructed a circRNA-miRNA-mRNA network based on the ceRNA hypothesis by analyzing data from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) database. Genes with higher degree values than others in the ceRNA network were selected as central nodes when constructing the corresponding core subnetworks. To fully understand the biological function of the ceRNA network, we entered all differentially expressed mRNAs (DEmRNAs) from the ceRNA network into the Database for Annotation and Integrated Discovery (DAVID), which was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We further entered DEmRNAs into the STRING database for protein-protein interaction (PPI) network analysis. The results elucidated that the ceRNA network comprised 403 circRNA nodes, 5 miRNA nodes, 138 mRNA nodes, and 559 edges. Three core ceRNA subnetworks centered on hsa-miR-342-3p, hsa-miR-199a-5p, and hsa-miR-142-3p were reconstructed in this study. GO and KEGG enrichment analyses identified 167 enriched GO categories and 14 enriched KEGG pathway terms. The core PPI network was composed of 15 core targets, of which CD44, HIF1A, and MMP2 were the most significant. In summary, 3 hub miRNAs (hsa-miR-342-3p, hsa-miR-199a-5p, hsa-miR-142-3p) and 3 hub genes (CD44, HIF1A, and MMP2) might play an important role in the development of UC. These hub nodes, first proposed here, might also be used as potential diagnostic markers and therapeutic targets.

Although data mining methods are extensively used in the rule analysis of famous old traditional Chinese medicine (TCM) experts’ prescriptions for the treatment of hypertension, most of them only mine the association between herbs and herbs, ignoring the importance of symptoms in the disease. This study collected 439 cases of hypertension treated by famous old TCM experts from the FangNet platform. Using the structure network algorithm, the symptom-herb network was constructed, which redefined the importance of herb in disease. Based on the network, 21 driver herbs, 76 herb pairs, and 41 symptom-herb associations were mined. Finally, the basic prescription composed of Gouteng (Uncariae Ramulus cum Uncis), Huanglian (Coptidis Rhizoma), Chuanxiong (Chuanxiong Rhizoma), Gegen (Puerariae Lobatae Radix), Danggui (Angelicae Sinensis Radix), and Huangqin (Scutellariae Radix) was found. These herbs are the most significant among all herbs, and they have a potential correlation with each other. To further verify the rationality of the data mining results, we adopted the network pharmacology method. Network pharmacological analysis shows that the five core targets in the basic prescription include IL6, VEGFA, TNF, TP53, and EGF, which link 10 significant active compounds and 7 important KEGG pathways. It was predicted that anti-inflammatory, antioxidant, vascular endothelial protection, emotion regulation, and ion channel intervention might be the main mechanisms of the basic prescription against hypertension. This study reveals the prescription rule of famous old TCM experts for treating hypertension from a new perspective, which provides a new approach to inherit the academic experience of famous old TCM experts and develop new drugs.

Object. To probe into the target and molecular mechanism of Jianpi Xiaoke (JPXK) prescription in the treatment of type 2 diabetes through high-throughput microRNA (miRNA) sequencing. Methods. Ten of the 31 SPF male Wistar rats were randomly taken as the control group; the remaining rats were fed a high-sugar and high-fat diet, combined with Streptozotocin (STZ, 35 mg/kg) that induced a type 2 diabetes model. The model rats were randomly divided into model groups (n = 11) and the JPXK group (n = 10). After 8 weeks of JPXK intervention, we detected the function of islet cells through HE staining and ELISA. High-pass sequencing technology was adopted to identify the differential expression of miRNA to explore the target of JPXK treatment, assess the relevant target genes, conduct functional analysis, and lastly verify the sequencing data by qRT-PCR. Results. After treatment, FPG, FINS, and HOMA-IR levels of the treatment group improved significantly compared with those of the control group ( P < 0.05 ). Among the miRNAs differentially expressed between the model group and the control group, there were 7 reversals after JPXK treatment, including miR-1-3p, miR-135a-5p, miR-181d-5p, miR-206-3p, miR-215, miR-3473, and miR-547-3p (log2FC ≥ 1 or ≤ −1, P < 0.05 ). Besides, the 1810 target genes associated with these 7 miRNAs were assessed by multiMiR. According to the results of the GO and KEGG analyses, they were associated with biological processes (e.g., glucose transport and fat cell formation), and it covered multiple signaling pathways, capable of regulating islet cell function (e.g., MAPK, PI3K-Akt, Ras, AMPK, and HIF-1 signaling pathways). The PCR verification results were consistent with the sequencing results. Conclusion. This discovery interpreted the potential therapeutic targets and signaling pathways of JPXK prescription against T2DM based on miRNA expression profiling. In conclusion, our research provided novel research insights into traditional Chinese medicine (TCM) treatment of diabetes.

Objective. Chromium-containing traditional Chinese medicine Tianmai Xiaoke tablet (TMXKT) is approved for treating newly diagnosed type 2 diabetes mellitus (T2DM) in China. This review aimed to compile the evidence from randomized clinical trials (RCTs) and quantify the effects of TMXKT on newly diagnosed T2DM. Methods. Seven online databases were investigated up to March 20, 2017. The meta-analysis included RCTs investigating the treatment of newly diagnosed T2DM, in which TMXKT combined with conventional therapy was compared with placebo or conventional therapy. The risk of bias was evaluated using the Cochrane Collaboration tool. The estimated mean difference (MD) and the standardized mean difference were within 95% confidence intervals (CI) with respect to the interstudy heterogeneity. The outcomes were measured using fasting blood glucose (FBG), 2-h postprandial blood glucose (2hPG), glycosylated hemoglobin A1c (HbA1c), and body mass index (BMI) levels. Results. TMXKT combined with conventional therapy lowered FBG level (MD=-0.68, 95% CI −0.90 to −0.45, P<0.00001), 2hPG (MD=-1.33, 95% CI −1.86 to −0.79, P<0.00001), HbA1c (MD = −0.46, 95% CI −0.57 to −0.36, P<0.00001), and BMI (MD = −0.77, 95% CI −1.12 to −0.41, P<0.00001). Conclusions. TMXKT combined with conventional therapy is beneficial for patients with newly diagnosed T2DM. However, the effectiveness and safety of TMXKT are uncertain because of the limited number of trials and low methodological quality. Therefore, practitioners should be cautious when applying TMXKT in daily practice. Also, well-designed clinical trials are needed in the future.