RESUMO
Osteosarcoma is one of the most common malignant bone tumors with high chemoresistance and poor prognosis, exhibiting abnormal gene regulation and epigenetic events. Methotrexate (MTX) is often used as a primary agent in neoadjuvant chemotherapy for osteosarcoma; However, the high dosage of methotrexate and strong drug resistance limit its therapeutic efficacy and application prospects. Studies have shown that abnormal expression and dysfunction of some coding or non-coding RNAs (e.g., DNA methylation and microRNA) affect key features of osteosarcoma progression, such as proliferation, migration, invasion, and drug resistance. Comprehensive multi-omics analysis is critical to understand its chemoresistant and pathogenic mechanisms. Currently, the network analysis-based non-negative matrix factorization (netNMF) method is widely used for multi-omics data fusion analysis. However, the effects of data noise and inflexible settings of regularization parameters affect its performance, while integrating and processing different types of genetic data is also a challenge. In this study, we introduced a novel adaptive total variation netNMF (ATV-netNMF) method to identify feature modules and characteristic genes by integrating methylation and gene expression data, which can adaptively choose an anisotropic smoothing scheme to denoise or preserve feature details based on the gradient information of the data by introducing an adaptive total variation constraint in netNMF. By comparing with other similar methods, the results showed that the proposed method could extract multi-omics fusion features more effectively. Furthermore, by combining the mRNA and miRNA data of methotrexate (MTX) resistance with the extracted feature genes, four genes, Carboxypeptidase E (CPE), LIM, SH3 protein 1 (LASP1), Pyruvate Dehydrogenase Kinase 1 (PDK1) and Serine beta-lactamase-like protein (LACTB) were finally identified. The results showed that the gene signature could reliably predict the prognostic status and immune status of osteosarcoma patients.
RESUMO
Background: COPD patients living in Tibet are exposed to specific environments and different risk factors and probably have different characteristics of COPD from those living in flatlands. We aimed to describe the distinction between stable COPD patients permanently residing at the Tibet plateau and those in flatlands. Methods: We conducted an observational cross-sectional study that enrolled stable COPD patients from Tibet Autonomous Region People's Hospital (Plateau Group) and Peking University Third Hospital (Flatland Group), respectively. Their demographic information, clinical features, spirometry test, blood routine and high-resolution chest CT were collected and evaluated. Results: A total of 182 stable COPD patients (82 from plateau and 100 from flatland) were consecutively enrolled. Compared to those in flatlands, patients in plateau had a higher proportion of females, more biomass fuel use and less tobacco exposure. CAT score and frequency of exacerbation in the past year were higher in plateau patients. The blood eosinophil count was lower in plateau patients, with fewer patients having an eosinophil count ≥300/µL. On CT examination, the proportions of previous pulmonary tuberculosis and bronchiectasis were higher in plateau patients, but emphysema was less common and milder. The ratio of diameters of pulmonary artery to aorta ≥1 was more often in plateau patients. Conclusion: Patients with COPD living at Tibet Plateau had a heavier respiratory burden, lower blood eosinophil count, less emphysema but more bronchiectasis and pulmonary hypertension. Biomass exposure and previous tuberculosis were more common in these patients.