PDF(Pubmed)
Abstract：
Osteosarcoma (OSA) presents a clinical challenge and has a low 5-year survival rate. Currently, the lack of advanced stratification models makes personalized therapy difficult. This study aims to identify novel biomarkers to stratify high-risk OSA patients and guide treatment.
We combined 10 machine-learning algorithms into 101 combinations, from which the optimal model was established for predicting overall survival based on transcriptomic profiles for 254 samples. Alterations in transcriptomic, genomic and epigenomic landscapes were assessed to elucidate mechanisms driving poor prognosis. Single-cell RNA sequencing (scRNA-seq) unveiled genes overexpressed in OSA cells as potential therapeutic targets, one of which was validated via tissue staining, knockdown and pharmacological inhibition. We characterized changes in multiple phenotypes, including proliferation, colony formation, migration, invasion, apoptosis, chemosensitivity and in vivo tumourigenicity. RNA-seq and Western blotting elucidated the impact of squalene epoxidase (SQLE) suppression on signalling pathways.
The artificial intelligence-derived prognostic index (AIDPI), generated by our model, was an independent prognostic biomarker, outperforming clinicopathological factors and previously published signatures. Incorporating the AIDPI with clinical factors into a nomogram improved predictive accuracy. For user convenience, both the model and nomogram are accessible online. Patients in the high-AIDPI group exhibited chemoresistance, coupled with overexpression of MYC and SQLE, increased mTORC1 signalling, disrupted PI3K-Akt signalling, and diminished immune infiltration. ScRNA-seq revealed high expression of MYC and SQLE in OSA cells. Elevated SQLE expression correlated with chemoresistance and worse outcomes in OSA patients. Therapeutically, silencing SQLE suppressed OSA malignancy and enhanced chemosensitivity, mediated by cholesterol depletion and suppression of the FAK/PI3K/Akt/mTOR pathway. Furthermore, the SQLE-specific inhibitor FR194738 demonstrated anti-OSA effects in vivo and exhibited synergistic effects with chemotherapeutic agents.
AIDPI is a robust biomarker for identifying the high-risk subset of OSA patients. The SQLE protein emerges as a metabolic vulnerability in these patients, providing a target with translational potential.
We combined 10 machine-learning algorithms into 101 combinations, from which the optimal model was established for predicting overall survival based on transcriptomic profiles for 254 samples. Alterations in transcriptomic, genomic and epigenomic landscapes were assessed to elucidate mechanisms driving poor prognosis. Single-cell RNA sequencing (scRNA-seq) unveiled genes overexpressed in OSA cells as potential therapeutic targets, one of which was validated via tissue staining, knockdown and pharmacological inhibition. We characterized changes in multiple phenotypes, including proliferation, colony formation, migration, invasion, apoptosis, chemosensitivity and in vivo tumourigenicity. RNA-seq and Western blotting elucidated the impact of squalene epoxidase (SQLE) suppression on signalling pathways.
The artificial intelligence-derived prognostic index (AIDPI), generated by our model, was an independent prognostic biomarker, outperforming clinicopathological factors and previously published signatures. Incorporating the AIDPI with clinical factors into a nomogram improved predictive accuracy. For user convenience, both the model and nomogram are accessible online. Patients in the high-AIDPI group exhibited chemoresistance, coupled with overexpression of MYC and SQLE, increased mTORC1 signalling, disrupted PI3K-Akt signalling, and diminished immune infiltration. ScRNA-seq revealed high expression of MYC and SQLE in OSA cells. Elevated SQLE expression correlated with chemoresistance and worse outcomes in OSA patients. Therapeutically, silencing SQLE suppressed OSA malignancy and enhanced chemosensitivity, mediated by cholesterol depletion and suppression of the FAK/PI3K/Akt/mTOR pathway. Furthermore, the SQLE-specific inhibitor FR194738 demonstrated anti-OSA effects in vivo and exhibited synergistic effects with chemotherapeutic agents.
AIDPI is a robust biomarker for identifying the high-risk subset of OSA patients. The SQLE protein emerges as a metabolic vulnerability in these patients, providing a target with translational potential.
摘要：
背景：骨肉瘤（OSA）提出了临床挑战，并且5年生存率较低。目前,缺乏先进的分层模式使得个性化治疗变得困难.这项研究旨在确定新的生物标志物，以对高风险OSA患者进行分层并指导治疗。
方法：我们将10种机器学习算法组合成101种组合，根据254个样本的转录组概况，建立了预测总生存期的最佳模型。转录组学的改变，我们评估了基因组和表观基因组景观,以阐明导致不良预后的机制.单细胞RNA测序（scRNA-seq）揭示了在OSA细胞中过表达的基因作为潜在的治疗靶标，其中一个是通过组织染色验证的，击倒和药理抑制。我们表征了多种表型的变化，包括扩散，菌落形成,迁移,入侵,凋亡，化学敏感性和体内致瘤性。RNA-seq和Western印迹阐明了角鲨烯环氧酶（SQLE）抑制对信号通路的影响。
结果：人工智能衍生的预后指数（AIDPI），由我们的模型生成，是一个独立的预后生物标志物，优于临床病理因素和以前发表的签名。将AIDPI与临床因素结合到列线图中提高了预测准确性。为方便用户,模型和列线图都可以在线访问。高AIDPI组的患者表现出化疗耐药，再加上MYC和SQLE的过度表达，mTORC1信号增加，PI3K-Akt信号中断，减少了免疫浸润。ScRNA-seq揭示了MYC和SQLE在OSA细胞中的高表达。在OSA患者中，SQLE表达升高与化疗耐药和不良预后相关。治疗学上,沉默SQLE抑制OSA恶性肿瘤并增强化学敏感性，由胆固醇消耗和FAK/PI3K/Akt/mTOR通路的抑制介导。此外，SQLE特异性抑制剂FR194738在体内表现出抗OSA作用,并与化疗剂表现出协同作用.
结论：AIDPI是鉴定OSA患者高危亚组的可靠生物标志物。SQLE蛋白在这些患者中作为一种代谢脆弱性出现，提供具有翻译潜力的目标。
方法：我们将10种机器学习算法组合成101种组合，根据254个样本的转录组概况，建立了预测总生存期的最佳模型。转录组学的改变，我们评估了基因组和表观基因组景观,以阐明导致不良预后的机制.单细胞RNA测序（scRNA-seq）揭示了在OSA细胞中过表达的基因作为潜在的治疗靶标，其中一个是通过组织染色验证的，击倒和药理抑制。我们表征了多种表型的变化，包括扩散，菌落形成,迁移,入侵,凋亡，化学敏感性和体内致瘤性。RNA-seq和Western印迹阐明了角鲨烯环氧酶（SQLE）抑制对信号通路的影响。
结果：人工智能衍生的预后指数（AIDPI），由我们的模型生成，是一个独立的预后生物标志物，优于临床病理因素和以前发表的签名。将AIDPI与临床因素结合到列线图中提高了预测准确性。为方便用户,模型和列线图都可以在线访问。高AIDPI组的患者表现出化疗耐药，再加上MYC和SQLE的过度表达，mTORC1信号增加，PI3K-Akt信号中断，减少了免疫浸润。ScRNA-seq揭示了MYC和SQLE在OSA细胞中的高表达。在OSA患者中，SQLE表达升高与化疗耐药和不良预后相关。治疗学上,沉默SQLE抑制OSA恶性肿瘤并增强化学敏感性，由胆固醇消耗和FAK/PI3K/Akt/mTOR通路的抑制介导。此外，SQLE特异性抑制剂FR194738在体内表现出抗OSA作用,并与化疗剂表现出协同作用.
结论：AIDPI是鉴定OSA患者高危亚组的可靠生物标志物。SQLE蛋白在这些患者中作为一种代谢脆弱性出现，提供具有翻译潜力的目标。
