Life

Osteosarcoma is the most common primary malignant bone tumor, predominantly affecting adolescents and young adults. Survival outcomes vary widely, and previous studies have suggested differences in patient characteristics and disease patterns across populations; however, population-based evidence from Taiwan remains limited. In this nationwide retrospective cohort study, data from the Taiwan Cancer Registry were analyzed to evaluate overall survival (OS) and prognostic factors among patients diagnosed between 2013 and 2022. A total of 371 patients were included, with a mean follow-up of 4.6 years. Kaplan–Meier analyses demonstrated significant differences in overall survival according to tumor stage, primary tumor site, and histological grade (all log-rank p < 0.05). In multivariable Cox proportional hazards analyses, older age groups were associated with an increased risk of death, while high-stage disease (AJCC stage III–IV) was associated with a significantly higher risk of mortality compared with low-stage disease (HR = 1.94, 95% CI = 1.30–2.90). In contrast, low-grade histology (HR = 0.31, 95% CI = 0.14–0.71) and limb salvage surgery (HR = 0.38, 95% CI = 0.23–0.64) were associated with improved survival. These findings provide population-based evidence on prognostic factors for osteosarcoma in Taiwan and highlight the associations of age, disease stage, tumor biology, and surgical management with overall survival.

Identifying structurally similar ligand-binding sites in unrelated proteins can facilitate drug repurposing, reveal off-target effects, and deepen our understanding of protein function. A number of tools were developed for structural screening, but many of them suffer from limited sensitivity and scalability. Using a data bank of crystallized protein structures, we aimed to discover novel protein targets for a ligand by leveraging a known ligand-binding query protein with a resolved structure. Here, we present SLAM (Spacio-Linear Alignment of Macromolecules), a novel alignment-based algorithm that detects local 3D similarities between ligand-binding cavities or protein-exposed surfaces of query and target proteins. SLAM encodes spatial substructure neighborhoods into short linear sequences of physicochemically annotated atoms, then applies pairwise sequence alignment combined with distance-correlation scoring to identify high-fidelity structural matches. Benchmarking using the Kahraman-36 dataset demonstrated that SLAM outperforms the state-of-the-art ProBiS algorithm in true-positive rate for predicting ligand-docking compatibility. Furthermore, SLAM identifies candidate ligands that may inhibit functionally critical domains of CRISPR-Cas proteins and predicts novel binding partners of toxic per- and polyfluoroalkyl Substance (PFAS) compounds (PFOA, PFOS) with plausible mechanistic links to toxicity. In conclusion, SLAM is a robust computationally efficient and flexible structural screening tool capable of detecting subtle physicochemical compatibilities between protein surfaces, promising to accelerate target discovery in pharmacology and elucidate protein–ligand interactions in environmental toxicology.

Background/Objectives: Pancreatic cancer is one of the most lethal malignancies, with poor survival and few established modifiable risk factors. While Helicobacter pylori (H. pylori) infection is a known cause of gastric cancer, its role in pancreatic cancer remains unclear, with inconsistent observational evidence. Methods: We applied two-sample Mendelian randomisation (2SMR) to assess the causal effect of H. pylori infection on pancreatic cancer risk. Genetic instruments were derived from GWAS data on anti-H. pylori IgG levels in the ALSPAC cohort (n = 4638). Outcomes were pancreatic cancer cases from UK Biobank (936 cases, 400,294 controls) and a combined dataset including UK Biobank, FinnGen, and MVP (5979 cases, 1,234,860 controls). Inverse-variance weighted (IVW) MR was the primary method, supported by MR-Egger, weighted median/mode and MR-PRESSO, with sensitivity analyses for pleiotropy. Results: Twelve and eleven independent SNPs explained 7.5% and 6.9% of H. pylori variance (mean F-statistics 22.37 and 22.42). No significant causal association was observed. IVW ORs were 1.039 (95% CI: 0.846–1.440, p = 0.466) for UK Biobank and 1.077 (95% CI: 0.962–1.206, p = 0.197) for the combined dataset. All complementary methods yielded null results, with no strong evidence of pleiotropy. Conclusions: This 2SMR study found no evidence that H. pylori infection causally increases pancreatic cancer risk. Larger studies with refined exposure measures are warranted.