Development of a Machine Learning-Based Autophagy-Related lncRNA Signature to Improve Prognosis Prediction in Osteosarcoma Patients

BackgroundOsteosarcoma is a frequent bone malignancy in children and young adults. Despite the availability of some prognostic biomarkers, most of them fail to accurately predict prognosis in osteosarcoma patients. In this study, we used bioinformatics tools and machine learning algorithms to establish an autophagy-related long non-coding RNA (lncRNA) signature to predict the prognosis of osteosarcoma patients.MethodsWe obtained expression and clinical data from osteosarcoma patients in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We acquired an autophagy gene list from the Human Autophagy Database (HADb) and identified autophagy-related lncRNAs by co-expression analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the autophagy-related lncRNAs were conducted. Univariate and multivariate Cox regression analyses were performed to assess the prognostic value of the autophagy-related lncRNA signature and validate the relationship between the signature and osteosarcoma patient survival in an independent cohort. We also investigated the relationship between the signature and immune cell infiltration.ResultsWe initially identified 69 autophagy-related lncRNAs, 13 of which were significant predictors of overall survival in osteosarcoma patients. Kaplan-Meier analyses revealed that the 13 autophagy-related lncRNAs could stratify patients based on their outcomes. Receiver operating characteristic curve analyses confirmed the superior prognostic value of the lncRNA signature compared to clinically used prognostic biomarkers. Importantly, the autophagy-related lncRNA signature predicted patient prognosis independently of clinicopathological characteristics. Furthermore, we found that the expression levels of the autophagy-related lncRNA signature were significantly associated with the infiltration levels of different immune cell subsets, including T cells, NK cells, and dendritic cells.ConclusionThe autophagy-related lncRNA signature established here is an independent and robust predictor of osteosarcoma patient survival. Our findings also suggest that the expression of these 13 autophagy-related lncRNAs may promote osteosarcoma progression by regulating immune cell infiltration in the tumor microenvironment.

An mTOR and VEGFR inhibitor combination arrests a doxorubicin resistant lung metastatic osteosarcoma in a PDOX mouse model

In order to identify more effective therapy for recalcitrant osteosarcoma, we evaluated the efficacy of an mTOR-VEGFR inhibitor combination on tumor growth in a unique osteosarcoma patient-derived orthotopic xenograft (PDOX) mouse model derived from the lung metastasis of an osteosarcoma patient who failed doxorubicin therapy. We also determined the efficacy of this inhibitor combination on angiogenesis using an in vivo Gelfoam fluorescence angiogenesis mouse model implanted with osteosarcoma patient-derived cells (OS-PDCs). PDOX models were randomly divided into five groups of seven nude mice. Group 1, control; Group 2, doxorubicin (DOX); Group 3, everolimus (EVE, an mTOR and VEGF inhibitor); Group 4, pazopanib (PAZ, a VEGFR inhibitor); Group 5, EVE-PAZ combination. Tumor volume and body weight were monitored 2 times a week. The in vivo Gelfoam fluorescence angiogenesis assay was performed with implanted OS-PDCs. The nude mice with implanted Gelfoam and OSPDCs also were divided into the four therapeutic groups and vessel length was monitored once a week. The EVE-PAZ combination suppressed tumor growth in the osteosarcoma PDOX model and decreased the vessel length ratio in the in vivo Gelfoam fluorescent angiogenesis model, compared with all other groups (p < 0.05). There was no significant body-weight loss in any group. Only the EVE-PAZ combination caused tumor necrosis. The present study demonstrates that a combination of an mTOR-VEGF inhibitor and a VEGFR inhibitor was effective for a DOX-resistant lung-metastatic osteosarcoma PDOX mouse model, at least in part due to strong anti-angiogenesis efficacy of the combination.

OSTEOSARCOMA HAS NOT BECOME ATTENTION TO SOCIETY PROFILE OF OSTEOSARCOMA PATIENTS AT DR. SOETOMO GENERAL HOSPITAL SURABAYA “A RETROSPECTIVE STUDY”

Background: Osteosarcoma is the most common bone neoplasm found in the community but evaluation osteosarcoma cases in RSUD Dr. Soetomo has not been updated since 1995.Purpose: This study o osteosarcoma patient characteristic as well as therapy at Dr. Soetomo General Hospital in 2007 to 2016. It expects to show survival rates of osteosarcoma patient, so it can be a reference for searching the problems in the treatment of osteosarcoma cases and helps to decide treatment for osteosarcoma.Research Methods: Descriptive retrospective study, conducted on osteosarcoma patients at Dr. Soetomo General Hospital during 2007-2016 periods. Data were obtained from Ortho tumor patient database, and contacting them by phone or home visit.Results: Osteosarcoma patients was found mostly in 2015, while the least in 2008, with trend increasing by time. Majority of the patients came with advanced stage. Osteosarcoma treated mostly by amputation, either with or without chemotherapy. The survival rate in the first, second, or the fifth year was found lower than other references. Most common cause of mortality was the metastasis.Conclusion: Awareness of the society about the cancer sign of cancer and desire to use medical treatment as a priority is still low. This causes a low early detection rate of osteosarcoma and a high rate of metastatic cases because of inappropriate early treatment. Further socialization and increased awareness of medics about the suspicion of osteosarcoma are needed to improve the success rate of treatment as well as the survival rate.