# Bayesian Optimization–Enhanced Machine Learning for Osteosarcoma Risk Stratification Based on Sphingolipid Metabolism

## TL;DR

This study uses machine learning to identify genes linked to sphingolipid metabolism that predict osteosarcoma patient outcomes and highlights TERT as a key gene affecting cancer progression.

## Contribution

A novel machine learning pipeline (SNEX) is developed for osteosarcoma risk stratification using sphingolipid metabolism genes, achieving high prognostic accuracy.

## Key findings

- The SNEX model achieved a perfect C-index of 1.000 and high AUCs for 1, 3, and 5-year survival predictions.
- TERT was identified as the most significant sphingolipid metabolism gene, with high expression linked to more aggressive cancer traits.
- Inhibiting TERT reduced osteosarcoma cell proliferation, invasion, and migration in experiments.

## Abstract

Background: Heterogenized sphingolipid metabolism (SM) drives osteosarcoma tumorigenesis and its tumor-promoting microenvironment. State-of-the-art bioinformatic tools, such as machine learning, are essential for dissecting the prognostic value of SM by investigating its molecular and cellular mechanisms.

Methods: A tailored machine learning pipeline was established by integrating Cox regression, 5-fold cross-validation, Elastic Net, eXtreme Gradient Boosting (XGBoost), and Bayesian optimization (for hyperparameters tuning) to foster an SM Elastic Net-XGBoost (SNEX) prognostic model, interpreted by the Shapley additive explanations (SHAP) algorithm. The alterations in molecular pathways and immune microenvironment–driven unfavorable prognosis of SNEX-identified high-risk osteosarcoma were further investigated. The SNEX predicted results have also been clinically and experimentally validated.

Results: We identified 22 critical SM prognostic genes for Bayesian-optimized SNEX. This model provided outstanding estimates of the prognoses of osteosarcoma patients (C-index of 1.000). Its robustness was confirmed in the independent test set with a high area under the curve (AUC) of 0.875 at 1 year, 0.930 at 3 years, and 0.930 at 5 years. SNEX also significantly outperformed all previous genetic prognostic signatures with a significantly higher net benefit of decision curves and higher AUCs. ACTA2 was the most pivotal gene critical to the negative prediction of SNEX, while BNIP3 was for positive prediction. Mechanistically, SNEX-identified high-risk osteosarcoma suffered unfavorable prognoses due to dysregulation of many critical metabolic/inflammatory/immune biologic processes and immunosuppressive microenvironment, with reduced infiltration of 14 types of immune cells (macrophages, CD8+ T cells, NK cells, etc.). Notably, SNEX highlighted TERT as the most remarkable SM prognostic gene. Clinical osteosarcomas with high expression of TERT exhibited more significant malignant characteristics than others, as evidenced by their higher proliferation efficiency. In addition, all the experiments in vitro and in vivo validated that inhibiting TERT abundance reduces the proliferation, invasion, and migration capabilities of osteosarcoma cells.

Conclusions: This study is a first-hand report employing a tailored machine-learning pipeline for dissecting the prognostic value and roles of SM in osteosarcoma. The present study fostered a SNEX for risk-stratification with outstanding accuracy and offered deep insights into SM-mediated pathways and microenvironment dysregulation in osteosarcoma.