TCM-Intigrated Cancer Center of Southern Medical University

Heterotopic ossification (HO) within tendons leads to debilitating tissue dysfunction, primarily arising from the abnormal osteogenic differentiation of tendon-derived stem cells (TDSCs). Despite its clinical prevalence, effective pharmacological treatments remain elusive. Farnesol, a natural isoprenoid with potent anti-inflammatory properties, represents a novel but unexplored candidate for musculoskeletal repair. This study aimed to evaluate the therapeutic efficacy of Farnesol in attenuating tendon HO and to elucidate the specific molecular mechanisms driving its effects. The anti-osteogenic effects of Farnesol were assessed using invitro TDSC differentiation assays and an invivo tendon injury model. Integrated RNA sequencing and network pharmacology analysis were employed to identify potential molecular targets. The mechanism was rigorously validated through rescue experiments using a specific GSTP1 inhibitor. Farnesol treatment significantly inhibited osteogenic differentiation invitro and attenuated ectopic bone formation invivo. Mechanistic screening identified the GSTP1/MAPK signaling axis as a critical regulatory pathway. Crucially, the administration of a GSTP1 inhibitor reversed the suppressive effects of Farnesol on osteogenesis, confirming GSTP1 as the primary mediator. Farnesol effectively inhibits the progression of post-traumatic tendon HO by targeting the GSTP1/MAPK pathway. These findings provide the first evidence of Farnesol's therapeutic potential in preventing pathological ossification.

Danggui Niantong granules (DGNTG), a traditional Chinese medicine, serve as an effective therapeutic agent for the treatment of rheumatoid arthritis (RA). However, the comprehensive molecular mechanisms of DGNTG in RA remain unclear. Increasing evidence highlights the significant role of competing endogenous RNAs (ceRNA) in diagnosing and treating various diseases. The study aims to explore the molecular mechanism of DGNTG in the treatment of RA through the ceRNA network. The MH7Acells were divided into control group and DGNTG group (2 and 4mg/mL).The proliferation, migration and invasion ability of MH7A were accessed using MTT assay, cloning formation, wound-healing assay transwell assay and Western blotting. Subsequently, ceRNA microarray analyses were performed and a circRNA-miRNA-mRNA ternary transcription network was established. The data were validated through qPCR. DGNTG inhibited proliferation, suppressed migration and invasion in MH7A (P < 0.05, 0.01 or 0.001), with the most pronounced effects observed in the DGNTG (4mg/mL) group. Subsequently, we identified 301 differentially expressed mRNAs and 507 differentially expressed circRNAs (FC ≤ 0.5 or ≥2, P < 0.05). Bioinformatics analyses indicated that DGNTG may exert therapeutic effects through multiple pathways. Furthermore, we constructed a circRNA-miRNA-mRNA network and conducted additional bioinformatics analysis on this network. In addition, we developed a representative ceRNA network and analyzed the correlations among its components. This study presents evidence that DGNTG exerts anti-RA effects through the inhibition of synoviocyte proliferation, migration and invasion. Additionally, It provided a valuable resource for elucidating the mechanism of action of DGNTG by constructing a competing endogenous RNA network based on transcriptomic data obtained from DGNTG-treated MH7Acells.

The selective induction of nucleolar stress in cancer cells has become a potential anticancer therapy. However, precisely regulating the key molecules involved in nucleolar stress remains a challenging topic in current research. ENY2 transcription and export complex 2 subunit (ENY2) is a transcription-associated nuclear protein that is upregulated in several cancers. However, its specific function and mechanistic role in oncogenesis remain poorly characterized and require further exploration. ENY2 was identified by screening ChIP-seq and public databases. Its role in tumor development was confirmed through in vivo and in vitro experiments. RNA sequencing, polysome profiling, agarose gel electrophoresis, and immunofluorescence suggested ENY2's involvement in ribosome biogenesis. Interacting proteins were identified by confocal microscopy, co-IP, and molecular docking, then validated by western blotting and ubiquitination assays. Finally, drug resistance experiments evaluated ENY2's clinical potential. We discovered that the overexpression of ENY2 significantly enhances tumor growth and cell cycle progression both in vitro and in vivo. Conversely, depletion of ENY2 facilitating the release of NPM1 into the nucleoplasm, thereby impeding ribosomal subunit export and inducing nucleolar stress. Additionally, the released NPM1 interacts with MDM2 within the nucleus to stabilize p53 protein levels, consequently inhibiting tumor growth. Notably, knockdown of ENY2 in p53-mutant cancer cell lines exhibits an augmented binding affinity and silencing efficacy of RISC towards target mRNA molecules, ultimately suppressing tumor proliferation through a p53-independent manner. This study elucidated a previously unrecognized role of ENY2 in tumor growth, clarified the NPM1/MDM2/ p53-dependent mechanism of ENY2-mediated tumor cell growth suppression. We also provided a novel p53-independent RISC-IL11 nucleolar stress response pathway, which may provide a new target for the treatment of breast cancer.

Traditional methods recognize images by extracting their features. However, extracting image features poses certain difficulties, especially for high-dimensional images and those with complex backgrounds. To address this issue,a recognition method based on Convolutional Neural Networks(CNN) is proposed. Under the improved LeNet5 framework, sample images are directly used as input parameters, and the model is trained through a Convolutional Neural Networks to obtain output results, thus eliminating the need to extract complex features. Experimental verification shows that the above method can accurately recognize images. The method proposed in this paper does not require the extraction of complex features, making it highly valuable in image processing.