RESUMEN
Introduction: Osteosarcoma (OS), the primary malignant bone tumor, has a low survival rate for recurrent patients. Latest reports indicated that cancer-associated fibroblasts (CAFs) were the main component of tumor microenvironment, and would generate a variable role in the progression of tumors. However, the role of CAFs is still few known in osteosarcoma. Methods: The processed RNA-seq data and the corresponding clinical and molecular information were retrieved from the Cancer Genome Atlas Program (TCGA) database and processed data of tumor tissue was obtained from Gene Expression Omnibus (GEO) database. Xcell method was used in data processing, and Gene set variation analysis (GSVA) was used to calculates enrichment scores. Nomogram was constructed to evaluate prognostic power of the predictive model. And the construction of risk scores and assessment of prognostic predictive were based on the LASSO model. Results: This study classified Cancer Genome Atlas (TCGA) cohort into high and low CAFs infiltrate phenotype with different CAFs infiltration enrichment scores. Then TOP 9 genes were screened as prognostic signatures among 2,488 differentially expressed genes between the two groups. Key prognostic molecules were CGREF1, CORT and RHBDL2 and the risk score formula is: Risk-score = CGREF1*0.004 + CORT*0.004 + RHBDL2*0.002. The signatures were validated to be independent prognostic factors to predict tumor prognosis with single-factor COX and multi-factor COX regression analyses and Norton chart. The risk score expression of risk score model genes could predict the drug resistance, and significant differences could be found between the high and low scoring groups for 17-AAG, AZD6244, PD-0325901 and Sorafenib. Discussion: To sum up, this article validated the prediction role of CAF infiltration in the prognosis of OS, which might shed light on the treatment of OS.
RESUMEN
Integrating medical instruments with medical information systems becomes more and more important in healthcare industry. To make medical instruments without standard communication interface possess the capability of interoperating and sharing information with medical information systems, we developed a medical instrument integration gateway based on Integrating the Healthcare Enterprise Patient Care Device (IHE PCD) integration profiles in this research. The core component is an integration engine which is implemented according to integration profiles and Health Level Seven (HL7) messages defined in IHE PCD. Working with instrument specific Javascripts, the engine transforms medical instrument data into HL7 ORU message. This research enables medical instruments to interoperate and exchange medical data with information systems in a standardized way, and is valuable for medical instrument integration, especially for traditional instruments.
