Calculus bovis inhibits M2 tumor-associated macrophage polarization via Wnt/ß-catenin pathway modulation to suppress liver cancer.

BACKGROUND: Calculus bovis (CB), used in traditional Chinese medicine, exhibits anti-tumor effects in various cancer models. It also constitutes an integral component of a compound formulation known as Pien Tze Huang, which is indicated for the treatment of liver cancer. However, its impact on the liver cancer tumor microenvironment, particularly on tumor-associated macrophages (TAMs), is not well understood. AIM: To elucidate the anti-liver cancer effect of CB by inhibiting M2-TAM polarization via Wnt/ß-catenin pathway modulation. METHODS: This study identified the active components of CB using UPLC-Q-TOF-MS, evaluated its anti-neoplastic effects in a nude mouse model, and elucidated the underlying mechanisms via network pharmacology, transcriptomics, and molecular docking. In vitro assays were used to investigate the effects of CB-containing serum on HepG2 cells and M2-TAMs, and Wnt pathway modulation was validated by real-time reverse transcriptase-polymerase chain reaction and Western blot analysis. RESULTS: This study identified 22 active components in CB, 11 of which were detected in the bloodstream. Preclinical investigations have demonstrated the ability of CB to effectively inhibit liver tumor growth. An integrated approach employing network pharmacology, transcriptomics, and molecular docking implicated the Wnt signaling pathway as a target of the antineoplastic activity of CB by suppressing M2-TAM polarization. In vitro and in vivo experiments further confirmed that CB significantly hinders M2-TAM polarization and suppresses Wnt/ß-catenin pathway activation. The inhibitory effect of CB on M2-TAMs was reversed when treated with the Wnt agonist SKL2001, confirming its pathway specificity. CONCLUSION: This study demonstrated that CB mediates inhibition of M2-TAM polarization through the Wnt/ß-catenin pathway, contributing to the suppression of liver cancer growth.

Plant-derived exosome-like nanoparticles for microRNA delivery in cancer treatment.

Plant-derived exosome-like nanoparticles (PELNs) are natural nanocarriers and effective delivery systems for plant microRNAs (miRNAs). These PELN-carrying plant miRNAs can regulate mammalian genes across species, thereby increasing the diversity of miRNAs in mammals and exerting multi-target effects that play a crucial role in diseases, particularly cancer. PELNs demonstrate exceptional stability, biocompatibility, and targeting capabilities that protect and facilitate the up-take and cross-kingdom communication of plant miRNAs in mammals. Primarily ingested and absorbed within the gastrointestinal tract of mammals, PELNs preferentially act on the intestine to regulate intestinal homeostasis through functional miRNA activity. The oncogenesis and progression of cancer are closely associated with disruptions in intestinal barriers, ecological imbalances, as well as secondary changes, such as abnormal inflammatory reactions caused by them. Therefore, it is imperative to investigate whether PELNs exert their anticancer effects by regulating mammalian intestinal homeostasis and inflammation. This review aims to elucidate the intrinsic crosstalk relationships and mechanisms of PELNs-mediated miRNAs in maintaining intestinal homeostasis, regulating inflammation and cancer treatment. Furthermore, serving as exceptional drug delivery systems for miRNAs molecules, PELNs offer broad prospects for future applications, including new drug research and development along with drug carrier selection within targeted drug delivery approaches for cancer therapy.

Small RNA sequencing highlights a potential regulatory network mediated by Gecko miRNA affecting the prognosis of hepatocellular carcinoma.

BACKGROUND: Gecko has been widely documented in Chinese scientific literature as an anti-tumor agent for various illnesses for thousands of years, and more recently, it has been examined for its anti-tumor effects on several cancers. The effect of Gecko microRNAs (miRNAs) on hepatocellular carcinoma (HCC) has not yet been reported. OBJECTIVES: This study was designed to identify miRNAs in Gecko through small RNA sequencing and utilize bioinformatics techniques to construct a potential regulatory network and explore the possible mechanisms of exogenous miRNAs involved in HCC. MATERIAL AND METHODS: RNA was extracted from Gecko tablets, and we screened the Gecko miRNA expression dataset after high-throughput sequencing. Bioinformatics analysis was used to identify novel Gecko and HCC survival-related miRNA-mRNA cross-species regulation networks. RESULTS: miR-100-5p, miR-99a-5p and miR-101-3p were identified as critical for the role of Geckos in HCC. Nine downstream mRNAs (EZH2, KPNA2, LMNB1, LRRC1, MRGBP, SMARCD1, STMN1, SUB1, and UBE2A) were identified as target genes for critical miRNAs. A miRNA-mRNA regulatory network was constructed, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed these key mRNAs might be associated with both the suppression and progression of HCC. The novel network significantly correlated with the abundance of multiple immune cells, as determined with immune infiltration analysis. CONCLUSIONS: These findings suggest that Gecko may inhibit progression and exert a therapeutic effect on HCC by targeting critical miRNA-mRNA networks for cross-species regulation. It also provides a reference for future research and development of traditional Chinese medicine (TCM).

Gear Fault Diagnosis and Life Prediction of Petroleum Drilling Equipment Based on SOM Neural Network.

In order to solve the problem that variable working conditions and fault types cannot be diagnosed in gear fault diagnosis of petroleum drilling equipment, four kinds of faults, namely, gear broken tooth, gear crack, gear pitting, and gear wear, are studied in this paper. Based on the SOM neural network algorithm, an intelligent diagnosis model of gear fault is proposed, and the PCA method is used to reduce data dimension and fuse features. The state index of life prediction is determined, and the remaining service life prediction of gear transmission system is predicted based on exponential degradation model. The results show that the accuracy of the SOM model for fault diagnosis is high, and the bearing in gearbox can be replaced or repaired in advance according to the residual life curve, so as to achieve the purpose of predictive maintenance.