A novel hypoxia- and lactate metabolism-related prognostic signature to characterize the immune landscape and predict immunotherapy response in osteosarcoma

Abstract

Background: Immunotherapy has shown considerable promise in cancer treatment, yet only a minority of osteosarcoma patients derive benefits from this approach. Hypoxia and lactate metabolism are two predominant characteristics of the tumor microenvironment. These features are crucial for molding the immune landscape and thus have the potential to act as predictive indicators for immunotherapy response.

Methods: Prognostic modeled genes were identified through univariate and multivariate Cox regression as well as LASSO regression analyses. The tumor microenvironment was evaluated using ESTIMATE, CIBERSORT, and ImmuCellAI analyses. Tide prediction and expression of immune checkpoints, MHC molecules, chemokines, interleukins, interferons, receptors, and other cytokines were utilized to estimate immunotherapy efficacy. Single-cell analysis was performed to demonstrate the expression of modeled genes among various immune cell types. Experimental validation was carried out to verify the expression and functions of SFXN4 and SQOR.

Results: A potent signature was constructed with 8 genes related to hypoxia and lactate metabolism, including MAFF, COL5A2, FAM162A, SQOR, UQCRB, SFXN4, PFKFB2 and COX6A2. A nomogram incorporating risk scores and other clinical features demonstrated excellent predictive capacity. Osteosarcoma patients with high-risk scores exhibited poor prognosis and more "cold" tumor characteristics. According to the ESTIMATE algorithm, these patients displayed lower immune, stromal, and ESTIMATE scores, partially attributed to inadequate infiltration of key immunocytes. The Ciborsort analysis similarly indicated that high-risk individuals had diminished infiltration of critical anti-tumor immune cells such as Cytotoxic T cells, CD4+ T cells, and NK cells. The low expression levels of certain immune checkpoints, MHC molecules, chemokines, interleukins, interferons, receptors, and other cytokines in high-risk cases suggested their unsatisfactory responses to immune treatment. Tide prediction further demonstrated that fewer individuals classified as high risk may exhibit sensitivity to immune checkpoint inhibitor therapy. Notably, SFXN4 was found to be highly expressed in osteosarcoma tissues and cells; it promoted the growth, migration, and invasion of osteosarcoma cells, while SQOR had the opposite effect.

Conclusion: Our research has developed a robust hypoxia- and lactate metabolism-related gene signature, providing a solid theoretical foundation for prognosis prediction, classification of "cold" and "hot" tumors, accessing immunotherapy response, and directing personalized treatment for osteosarcoma.

Keywords: gene signature; hypoxia; immune checkpoint inhibitors; immunotherapy; lactate metabolism; osteosarcoma; single-cell sequencing; tumor microenvironment.