Artificial intelligence assisted preoperative planning and 3D-printing guiding frame for percutaneous screw reconstruction in periacetabular metastatic cancer patients.

Background: The percutaneous screw reconstruction technique, known as the "Tripod Technique," has demonstrated favorable clinical outcomes in the management of metastatic periacetabular lesions, as evidenced by our prior investigations and corroborated by independent studies. Nevertheless, there is a steep learning curve in handling this technique, with possible complications such as intraarticular screw placement. Methods: Preoperative pelvic CT scans were acquired before surgery and utilized for the guiding frame design. A convolutional neural network model was trained with annotated data to identify the starting point and trajectory of each potential screw. A model boundary intersection detection technology was used to determine the optimal diameter and length of each screw. A non-rigid registration technology was matched with a prefabricated model of the body surface to design personalized anchoring skin pads. Finally, a polylactic acid-based guiding frame for intraoperative was custom-made with a 3D printer. Results: 12 patients underwent a guiding frame-assisted Tripod procedure for treatment of periacetabular metastatic lesions. An intraoperative CT scan was performed in all cases to confirm screw trajectories. Among 36 screws that were implanted, 26 screws were implanted as designed. The remaining ten screws drifted, but all remained within the intra-osseous conduit without any complications. The mean surgical time was 1.22 h with the guiding frame compared with 2.3 h without the guiding frame. Following the surgical procedure, a noteworthy enhancement in pain management, as evidenced by a reduction in scores on the visual analog scale (p < 0.01), and an improvement in functional status, as assessed through the Eastern Cooperative Oncology Group score (p < 0.01), were observed when compared to the patient's pre-operative condition. Conclusion: This proof-of-concept investigation demonstrates that the amalgamation of AI-assisted surgical planning and additive manufacturing can improve surgical accuracy and shorten surgical duration. While access to this technology is currently constrained during its early stages of development, it is anticipated that these limitations will diminish as the potential of AI and additive manufacturing in facilitating complex orthopedic procedures becomes more evident, leading to a surge in interest and adoption of this approach.

Novel candidate metastasis-associated genes for synovial sarcoma.

Synovial sarcoma (SS) is an aggressive soft tissue sarcoma with poor prognosis due to late recurrence and metastasis. Metastasis is an important prognostic factor of SS. This study aimed to identify the core genes and mechanisms associated with SS metastasis. Microarray data for GSE40021 and GSE40018 were obtained from the Gene Expression Omnibus database. 186 differentially expressed genes (DEGs) were identified. The biological functions and signalling pathways closely associated with SS metastasis included extracellular matrix (ECM) organization and ECM-receptor interaction. Gene set enrichment analysis showed that the terms cell cycle, DNA replication, homologous recombination and mismatch repair were significantly enriched in the metastasis group. Weighted gene co-expression network analysis identified the most relevant module and 133 hub genes, and 31 crossover genes were identified by combining DEGs. Subsequently, four characteristic genes, EXO1, NCAPG, POLQ and UHRF1, were identified as potential biomarkers associated with SS metastasis using the least absolute shrinkage and selection operator algorithm and validation dataset verification analysis. Immunohistochemistry results from our cohort of 49 patients revealed visible differences in the expression of characteristic genes between the non-metastatic and metastatic groups. Survival analysis indicated that high expression of characteristic genes predicted poor prognosis. Our data revealed that primary SS samples from patients who developed metastasis showed activated homologous recombination and mismatch repair compared to samples from patients without metastasis. Furthermore, EXO1, NCAPG, POLQ and UHRF1 were identified as potential candidate metastasis-associated genes. This study provides further research insights and helps explore the mechanisms of SS metastasis.

Targeted inhibition of SCFSKP2 confers anti-tumor activities resulting in a survival benefit in osteosarcoma.

Osteosarcoma(OS) is a highly aggressive bone cancer for which treatment has remained essentially unchanged for decades. Although OS is characterized by extensive genomic heterogeneity and instability, RB1 and TP53 have been shown to be the most commonly inactivated tumor suppressors in OS. We previously generated a mouse model with a double knockout (DKO) of Rb1 and Trp53 within cells of the osteoblastic lineage, which largely recapitulates human OS with nearly complete penetrance. SKP2 is a repression target of pRb and serves as a substrate recruiting subunit of the SCFSKP2 complex. In addition, SKP2 plays a central role in regulating the cell cycle by ubiquitinating and promoting the degradation of p27. We previously reported the DKOAA transgenic model, which harbored a knock-in mutation in p27 that impaired its binding to SKP2. Here, we generated a novel p53-Rb1-SKP2 triple-knockout model (TKO) to examine SKP2 function and its potential as a therapeutic target in OS. First, we observed that OS tumorigenesis was significantly delayed in TKO mice and their overall survival was markedly improved. In addition, the loss of SKP2 also promoted an apoptotic microenvironment and reduced the stemness of DKO tumors. Furthermore, we found that small-molecule inhibitors of SKP2 exhibited anti-tumor activities in vivo and in OS organoids as well as synergistic effects when combined with a standard chemotherapeutic agent. Taken together, our results suggest that SKP2 inhibitors may reduce the stemness plasticity of OS and should be leveraged as next-generation adjuvants in this cancer.