LINC00960 affects osteosarcoma treatment and prognosis by regulating the tumor immune microenvironment

Abstract

Background: Osteosarcoma (OS), the commonest primary malignant bone tumor, is mainly seen in children and teenagers. LINC00960, a newly discovered long intergenic non-protein coding RNA, has been shown to be important in certain cancers. The objective of this study was to assess LINC00960's prognostic and therapeutic value and analyze its mechanism of action in osteosarcoma. Methods: With the transcriptome information of 85 osteosarcomas from the TARGET database, the Cox regression analyses, K-M curve, and ROC curve, were conducted for survival and prognostic analysis. The functional analysis was conducted using GO, KEGG, GSEA, and GSVA. The ESTIMATE, ssGSEA, MCP-counter, ImmuCellAI algorithms, and immune checkpoint correlation analysis were performed for immune-related analysis. The single-cell RNA sequencing data of 6 osteosarcoma patients was obtained from the Gene Expression Omnibus database. The Tumor Immune Dysfunction and Exclusion algorithm and the “pRRophetic” R package were performed to predict the response to immunotherapy and chemotherapy. Results: LINC00960 overexpression is associated with osteosarcoma metastasis and poor prognosis. Based on the LINC00960 expression, the nomogram prediction model was created, which showed good accuracy and precision to predict the overall survival of osteosarcoma. Single-cell and immune-related analysis showed that LINC00960 is mainly highly expressed in the tumor-exhausted CD8 T cells in osteosarcoma. In osteosarcoma, the expression of LIC00960 was favorably connected with immune checkpoint-related genes and negatively correlated with immune infiltration. TIDE analysis indicated that low LINC00960 expression patients might have a better response to immunotherapy. Drug sensitivity analysis showed that high LINC00960 expression patients might have better responses to Bleomycin and Doxorubicin. Conclusion: LINC00960 has the potential to be a novel biomarker for predicting overall survival in osteosarcoma patients and to guide more individualized treatment and clinical decision-making.

Keywords

• Osteosarcoma
• Prediction model
• Tumor immune microenvironment
• Therapy
• Prognosis
• Single-cell analysis