Angiogenesis-relevant lncRNA signature for osteosarcoma: prospects for prediction of clinical outcomes and immunotherapeutic efficacy

Abstract

Abstract Objective: Angiogenesis is a highly adaptive and complex course, which is essential for tumor growth and metastases of osteosarcoma. Considering the regulatory functions of lncRNAs in angiogenesis, the current study attempted to propose an angiogenesis-relevant lncRNA signature for assisting precision medicine of osteosarcoma. Methods: Transcriptome data of TARGET osteosarcomas and TCGA sarcoma (SARC) were acquired from the GDC. An angiogenesis-relevant lncRNA signature was defined utilizing LASSO approach. Somatic mutation was analyzed via Maftools. Immunotherapy response was inferred according to T cell-inflamed score, TIDE score and immune checkpoints. Angiogenesis-relevant lncRNAs were experimentally verified in osteoblasts hFOB1.19 and osteosarcoma cells (MG-63, U2OS, SJSA-1, HOS) utilizing RT-qPCR. In LINC01060-knockout cells, transwell and immunoblotting were conducted to investigate the invasion and angiogenesis. Results: The angiogenesis-relevant lncRNA signature was established, and high-risk osteosarcomas presented worse overall survival and disease-free survival. It was proven that the risk score possessed the reliability and independency in prognosis prediction. Higher genetic mutation occurred in high-risk osteosarcomas. From higher expression of immune checkpoints, lower TIDE score and higher T cell-inflamed score, low-risk osteosarcomas were more likely to respond to immunotherapy. After experimental verification, AC004862.6, CYTOR, LINC01060, LINC02596, and LOC101928228 were up-regulated in osteosarcoma, and LINC01060-knockout MG-63 and U2OS cells presented impaired invasive capacity and reduced expression of angiogenic genes VEGFA, Angpt1, and Angpt2. Conclusion: The angiogenesis-relevant lncRNA signature is a possible predictor of survival and immunotherapeutic response in osteosarcoma, and integrated transcriptome analysis coupled with clinical sample verification can facilitate biomarker discovery and clinical translation.