Zhang Zhao, Zhu Zhijie, Fu Jun, Liu Xincheng, Mi Zhenzhou, Tao Huiren, Fan Hongbin
Department of Orthopaedics, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, China.
Department of Orthopaedics, Shenzhen University General Hospital, Shenzhen 518052, China.
Int Immunopharmacol. 2023 Feb;115:109684. doi: 10.1016/j.intimp.2023.109684. Epub 2023 Jan 9.
Osteosarcoma is highly aggressive and prone to metastasis, with a poor prognosis. Increasing evidence identified anoikis has a critical effect in tumor metastasis and invasion. However, the prognostic value of anoikis-related genes (ANRGs) in osteosarcoma and their role in the immune landscape of osteosarcoma remain unclear.
The RNA sequencing and clinical data of patients with osteosarcoma were extracted from the TARGET and GEO databases, and ANRGs were identified from the GeneCards database. Unsupervised clustering analysis was employed to identify anoikis-related patterns. The ESTIMATE, TIMER and ssGSEA algorithms were used to assess the immune microenvironment of different subtypes. A prognostic signature based on the identified ANRGs was constructed via univariate, LASSO and multivariate Cox regression analyses. KEGG, GO and GSEA were used for functional enrichment of genes associated with different risk subtypes. qPCR, WB and IHC were used to validate the expression of candidate genes.
Two anoikis-related patterns with distinct clinical features and immune statuses were identified based on prognosis-related ANRGs. Cluster 2 had more active immunogenicity and a better prognosis than Cluster 1. Subsequently, we developed and validated an anoikis prognostic signature demonstrating excellent predictive ability for the prognosis of osteosarcoma. Anoikis risk score was positively associated with osteosarcoma metastasis and was identified as an independent prognostic marker. Additionally, a nomogram was established to predict the 3- and 5-year survival probability of patients with osteosarcoma. Functional enrichment analysis revealed that immune dysregulation was correlated with poor prognosis. Besides, patients in the low-risk group had higher infiltration levels of immune cells and more active immune function than patients in the high-risk group. Drug sensitivity analysis revealed several chemotherapeutic agents for the treatment of different subtypes of osteosarcoma.
Our study demonstrated the role of ANRGs in osteosarcoma progression, providing insights into clinical decision making in osteosarcoma.
骨肉瘤具有高度侵袭性且易于转移，预后较差。越来越多的证据表明失巢凋亡在肿瘤转移和侵袭中起关键作用。然而，失巢凋亡相关基因（ANRGs）在骨肉瘤中的预后价值及其在骨肉瘤免疫格局中的作用仍不清楚。
从TARGET和GEO数据库中提取骨肉瘤患者的RNA测序和临床数据，并从GeneCards数据库中识别ANRGs。采用无监督聚类分析来识别失巢凋亡相关模式。使用ESTIMATE、TIMER和ssGSEA算法评估不同亚型的免疫微环境。通过单变量、LASSO和多变量Cox回归分析构建基于已识别ANRGs的预后特征。KEGG、GO和GSEA用于对与不同风险亚型相关的基因进行功能富集。使用qPCR、WB和IHC验证候选基因的表达。
基于与预后相关的ANRGs识别出两种具有不同临床特征和免疫状态的失巢凋亡相关模式。与簇1相比，簇2具有更活跃的免疫原性和更好的预后。随后，我们开发并验证了一种失巢凋亡预后特征，其对骨肉瘤的预后具有出色的预测能力。失巢凋亡风险评分与骨肉瘤转移呈正相关，并被确定为独立的预后标志物。此外，建立了一个列线图来预测骨肉瘤患者的3年和5年生存概率。功能富集分析表明免疫失调与预后不良相关。此外，低风险组患者的免疫细胞浸润水平高于高风险组患者，免疫功能更活跃。药物敏感性分析揭示了几种用于治疗不同亚型骨肉瘤的化疗药物。
我们的研究证明了ANRGs在骨肉瘤进展中的作用，为骨肉瘤的临床决策提供了见解。
