Department of Orthopedics Trauma and Microsurgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
Front Immunol. 2022 Apr 7;13:796606. doi: 10.3389/fimmu.2022.796606. eCollection 2022.
Tumor stemness has been reported to play important roles in cancers. However, a comprehensive analysis of tumor stemness remains to be performed to investigate the specific mechanisms and practical values of stemness in soft tissue sarcomas (STS). Here, we applied machine learning to muti-omic data of patients from TCGA-SARC and GSE21050 cohorts to reveal important roles of stemness in STS. We demonstrated limited roles of existing mRNAsi in clinical application. Therefore, based on stemness-related signatures (SRSs), we identified three stemness subtypes with distinct stemness, immune, and metabolic characteristics using consensus clustering. The low-stemness subtype had better prognosis, activated innate and adaptive immunity (e.g., infiltrating B, DC, Th1, CD8+ T, activated NK, gamma delta T cells, and M1 macrophages), more enrichment of metabolic pathways, more sites with higher methylation level, higher gene mutations, CNA burdens, and immunogenicity indicators. Furthermore, the 16 SRS-based stemness prognostic index (SPi) was developed, and we found that low-SPi patients with low stemness had better prognosis and other characteristics similar to those in the low-stemness subtype. Besides, low-stemness subtype and low-SPi patients could benefit from immunotherapy. The predictive value of SPi in immunotherapy was more accurate after the addition of MSI into SPi. MSISPi patients might be more sensitive to immunotherapy. In conclusion, we highlighted mechanisms and practical values of the stemness in STS. We also recommended the combination of MSI and SPi which is a promising tool to predict prognosis and achieve precise treatments of immunotherapy in STS.
肿瘤干性被报道在癌症中发挥重要作用。然而，为了研究干性在软组织肉瘤（STS）中的具体机制和实际价值，仍需要对肿瘤干性进行全面分析。在这里，我们应用机器学习对 TCGA-SARC 和 GSE21050 队列患者的多组学数据进行分析，以揭示干性在 STS 中的重要作用。我们证明了现有 mRNAsi 在临床应用中的作用有限。因此，基于干性相关特征（SRSs），我们使用共识聚类方法，根据干性、免疫和代谢特征，鉴定出三个具有不同干性特征的亚型。低干性亚型的预后较好，激活固有和适应性免疫（例如，浸润 B、DC、Th1、CD8+T、活化 NK、γδT 和 M1 巨噬细胞），代谢途径更丰富，高甲基化水平、更高基因突变、CNA 负担和免疫原性指标的位置更多。此外，我们开发了基于 16 个 SRS 的干性预后指数（SPi），并发现低 SPi 且低干性的患者具有更好的预后和其他与低干性亚型相似的特征。此外，低干性亚型和低 SPi 患者可能受益于免疫治疗。在将 MSI 添加到 SPi 中后，SPi 在免疫治疗中的预测价值更加准确。MSISPi 患者可能对免疫治疗更敏感。总之，我们强调了 STS 中干性的机制和实际价值。我们还建议将 MSI 和 SPi 结合起来，这是一种预测 STS 免疫治疗预后和实现精准治疗的有前途的工具。
