Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Hunan Key Laboratory of Tumor Models and Individualized Medicine of The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Front Immunol. 2024 Jan 9;14:1321616. doi: 10.3389/fimmu.2023.1321616. eCollection 2023.
Soft tissue sarcoma (STS) is a highly heterogeneous musculoskeletal tumor with a significant impact on human health due to its high incidence and malignancy. Long non-coding RNA (lncRNA) and Neutrophil Extracellular Traps (NETs) have crucial roles in tumors. Herein, we aimed to develop a novel NETsLnc-related signature using machine learning algorithms for clinical decision-making in STS.
We applied 96 combined frameworks based on 10 different machine learning algorithms to develop a consensus signature for prognosis and therapy response prediction. Clinical characteristics, univariate and multivariate analysis, and receiver operating characteristic curve (ROC) analysis were used to evaluate the predictive performance of our models. Additionally, we explored the biological behavior, genomic patterns, and immune landscape of distinct NETsLnc groups. For patients with different NETsLnc scores, we provided information on immunotherapy responses, chemotherapy, and potential therapeutic agents to enhance the precision medicine of STS. Finally, the gene expression was validated through real-time quantitative PCR (RT-qPCR).
Using the weighted gene co-expression network analysis (WGCNA) algorithm, we identified NETsLncs. Subsequently, we constructed a prognostic NETsLnc signature with the highest mean c-index by combining machine learning algorithms. The NETsLnc-related features showed excellent and stable performance for survival prediction in STS. Patients in the low NETsLnc group, associated with improved prognosis, exhibited enhanced immune activity, immune infiltration, and tended toward an immunothermal phenotype with a potential immunotherapy response. Conversely, patients with a high NETsLnc score showed more frequent genomic alterations and demonstrated a better response to vincristine treatment. Furthermore, RT-qPCR confirmed abnormal expression of several signature lncRNAs in STS.
In conclusion, the NETsLnc signature shows promise as a powerful approach for predicting the prognosis of STS. which not only deepens our understanding of STS but also opens avenues for more targeted and effective treatment strategies.
软组织肉瘤（STS）是一种高度异质性的肌肉骨骼肿瘤，由于其发病率和恶性程度高，对人类健康有重大影响。长链非编码 RNA（lncRNA）和中性粒细胞胞外陷阱（NETs）在肿瘤中具有关键作用。在此，我们旨在使用机器学习算法为 STS 的临床决策开发一种新的 NETsLnc 相关特征。
我们应用了 96 个基于 10 种不同机器学习算法的组合框架，以开发用于预后和治疗反应预测的共识特征。临床特征、单变量和多变量分析以及接收者操作特征曲线（ROC）分析用于评估我们模型的预测性能。此外，我们还探索了不同 NETsLnc 组的生物学行为、基因组模式和免疫景观。对于不同 NETsLnc 评分的患者，我们提供了免疫治疗反应、化疗和潜在治疗药物的信息，以增强 STS 的精准医学。最后，通过实时定量 PCR（RT-qPCR）验证基因表达。
我们使用加权基因共表达网络分析（WGCNA）算法鉴定了 NETsLncs。随后，我们通过结合机器学习算法构建了具有最高平均 c-指数的预后 NETsLnc 特征。NETsLnc 相关特征在 STS 的生存预测中表现出优异且稳定的性能。NETsLnc 评分较低的患者，与改善的预后相关，表现出增强的免疫活性、免疫浸润，并倾向于具有潜在免疫治疗反应的免疫热表型。相反，NETsLnc 评分较高的患者表现出更频繁的基因组改变，并对长春新碱治疗有更好的反应。此外，RT-qPCR 证实了 STS 中几个特征 lncRNA 的异常表达。
NETsLnc 特征有望成为预测 STS 预后的有力方法。这不仅加深了我们对 STS 的理解，也为更有针对性和有效的治疗策略开辟了道路。
