Development and validation of biomarkers related to PANoptosis in osteoarthritis.

OBJECTIVE: Osteoarthritis (OA) is a high-incidence disease of the orthopedic system. However, studies on the molecular mechanisms of OA and pyroptosis, apoptosis, and necroptosis (PANoptosis) at the transcriptome level remain scarce. Therefore, this study purposed to detect biomarkers in OA and explore their relationship to the immune microenvironment. MATERIALS AND METHODS: OA-related expression data was sourced from the Gene Expression Omnibus (GEO) database. Subsequently, differentially expressed analysis and a Venn diagram were performed to obtain differentially expressed PANoptosis-related genes (DEPGs). Furthermore, the least absolute shrinkage and selection operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest (RF) were implemented to screen diagnostic genes. Receiver operating characteristic (ROC) curves were performed to verify the diagnostic ability of the diagnostic genes. Next, immune infiltration analysis was performed to find the relationships between differential immune cells (OA vs. normal) and diagnostic genes. Finally, drug prediction analysis was also carried out, and the expression of diagnostic genes was verified in external datasets. RESULTS: A total of 62 DEPGs were identified, which were enriched for regulating apoptotic signaling pathways, tumor necrosis factor (TNF) signaling pathways, and other related pathways. Three feature genes, nuclear factor-kappa-B inhibitor-alpha (NFKBIA), RING finger protein 34 (RNF34), and serine incorporator 3 (SERINC3) were obtained by intersecting genes obtained by the LASSO regression algorithm, SVM algorithm, and RF algorithm and showed excellent diagnostic efficacy with the Area under the curve (AUC) values of individual genes were all greater than 0.7, indicating that the model was more effective. Immuno-infiltration analysis showed that RNF34 was positively correlated with CD56dim natural killer cells, type 17 helper T cells, and NFKBIA was positively correlated with plasmacytoid dendritic cells. Additionally, 12 drugs were predicted by NFKBIA, such as gambogic acid and dioscin. In addition, NFKBIA and SERINC3 were significantly downregulated, and RNF34 was upregulated in OA samples. CONCLUSIONS: Three genes (NFKBIA, RNF34, and SERINC3) related to PANoptosis, were obtained by bioinformatics analysis, which would provide a new direction for the diagnosis and treatment of OA.

Exploration at the network pharmacology level of possible targeting mechanisms of Smilacis Glabrae Rhixoma for the treatment of osteoporosis.

OBJECTIVE: The aim of this study was to evaluate the therapeutic effect of Smilacis Glabrae Rhixoma (SGR) on osteoporosis at the level of network pharmacology, and to find new targets and mechanisms of SGR in the treatment of osteoporosis, to better find new drugs and their clinical applications. MATERIALS AND METHODS: In the original network pharmacology mode, we used an improved mode, such as screening the ingredients and targets of SGR through tools such as GEO database, Autodock Vina, and GROMACS. Through molecular docking, we conducted further screening for the targets acting on the effective ingredients of SGR, and finally we performed molecular dynamics simulation and consulted a large amount of related literature for the validation of the results. RESULTS: By screening and validating the data, we finally confirmed that there were mainly 10 active ingredients in SGR, which were isoeruboside b, smilagenin, diosgenin, stigmasterol, beta-sitosterol, sodium taurocholate, sitogluside, 4,7-dihydroxy-5-methoxy-6-methyl-8-formyl-flavan, simiglaside B, and simiglaside E, and mainly acted on eleven targets. These targets mainly exert therapeutic effects on osteoporosis by regulating 20 signaling pathways including Th17 cell differentiation, HIF-1 signaling pathway, apoptosis, inflammatory bowel disease, and osteoclast differentiation. CONCLUSIONS: Our study successfully explains the effective mechanism by which SGR ameliorates osteoporosis while predicting the potential targets NFKB1 and CTSK of SGR for the treatment of osteoporosis, which provides a novel basis for investigating the mechanism of action of new Traditional Chinese medicines (TCMs) at the network pharmacology level and a great support for subsequent studies on osteoporosis.