Bian Yiying, Shi Jixiang, Chen Ziyun, Fang Ji, Chen Weidong, Zou Yutong, Yao Hao, Tu Jian, Liao Yan, Xie Xianbiao, Shen Jingnan
Department of Musculoskeletal Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
Heliyon. 2024 Aug 10;10(16):e35719. doi: 10.1016/j.heliyon.2024.e35719. eCollection 2024 Aug 30.
Osteosarcoma is a bone-derived malignancy that often leads to lung metastasis and death.
The RNA-seq data of TARGET-osteosarcoma were collected from TARGET database. GSE16088 and GSE12865 datasets of osteosarcoma x from Gene Expression Database (GEO) were donwloaded. ConsensusClusterPlus was used for molecular subtype classification. Univariate Cox and Lasso regression was employed to develop a risk model. To analyze the regulatory effects of model feature genes on the malignant phenotype of osteosarcoma cell lines, qRT-PCR, Transwell and wound healing assays were performed. The abundance of immune cell infiltration was assessed using MCP-Counter, Gene Set Enrichment Analysis (GSEA), and ESTIMATE. The Tumor Immune Dysfunction and Exclusion (TIDE) software was employed to evaluate immunotherapy and response to conventional chemotherapy drugs.
Three clusters (C1, C2 and C3) were classified using 39 necroptosis score-associated genes. In general, C1 and C2 showed better prognosis outcome and lower death rate than C3. Specifically, C2 could benefit more from immunotherapy, while C3 was more sensitive to traditional medicines, and C1 had higher immune cell infiltration. Next, an 8-gene signature and a risk score model were developed, with a low risk score indicating better survival and immune cell infiltration. ROC analysis showed that 1-, 3-, and 5-year overall survival of osteosarcoma could be correctly predicted by the risk score model. Cellular experiments revealed that the model feature gene IFITM3 promoted the osteosarcoma cell migration and invasion. Furthermore, the overall survival of osteosarcoma patients from TARGET and validation datasets can be accurately evaluated using the nomogram model.
Our prognostic model developed using necroptosis genes could facilitate the prognostic prediction for patients suffering from osteosarcoma, offering potential osteosarcoma targets.
骨肉瘤是一种起源于骨的恶性肿瘤，常导致肺转移和死亡。
从TARGET数据库收集TARGET-骨肉瘤的RNA测序数据。从基因表达数据库（GEO）下载骨肉瘤的GSE16088和GSE12865数据集。使用ConsensusClusterPlus进行分子亚型分类。采用单因素Cox和Lasso回归建立风险模型。为分析模型特征基因对骨肉瘤细胞系恶性表型的调控作用，进行了qRT-PCR、Transwell和伤口愈合实验。使用MCP-Counter、基因集富集分析（GSEA）和ESTIMATE评估免疫细胞浸润的丰度。采用肿瘤免疫功能障碍和排除（TIDE）软件评估免疫治疗及对传统化疗药物的反应。
使用39个与坏死性凋亡评分相关的基因将其分为三个簇（C1、C2和C3）。总体而言，C1和C2的预后结果较好，死亡率低于C3。具体来说，C2从免疫治疗中获益更多，而C3对传统药物更敏感，C1的免疫细胞浸润更高。接下来，建立了一个8基因特征和风险评分模型，低风险评分表明生存和免疫细胞浸润情况更好。ROC分析表明，风险评分模型能够正确预测骨肉瘤1年、3年和5年的总生存率。细胞实验表明，模型特征基因IFITM3促进骨肉瘤细胞迁移和侵袭。此外，使用列线图模型可以准确评估TARGET和验证数据集中骨肉瘤患者的总生存率。
我们使用坏死性凋亡基因建立的预后模型有助于骨肉瘤患者的预后预测，提供潜在的骨肉瘤治疗靶点。
