Identification and characterization of aging/senescence-induced genes in osteosarcoma and predicting clinical prognosis.
Identification and characterization of aging/senescence-induced genes in osteosarcoma and predicting clinical prognosis.
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DOI：
10.3389/fimmu.2022.997765
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发表时间：
2022
影响因子：
7.3
通讯作者：
Zhou, Hengxing
中科院分区：
文献类型：
--
作者：
Lv, Yigang;Wu, Liyuan;Jian, Huan;Zhang, Chi;Lou, Yongfu;Kang, Yi;Hou, Mengfan;Li, Zhen;Li, Xueying;Sun, Baofa;Zhou, Hengxing
关键词：
衰老是骨骼退行性疾病和肿瘤疾病进展的重要危险因素。骨肉瘤被认为是最常见的原发性骨间充质肿瘤，是一种5年生存率较低的世界性疾病。本研究探讨了衰老/衰老诱导基因（ASIGs）在骨肉瘤诊断、预后和治疗药物预测中的作用。使用TARGET （Therapeutically applied Research to Generate Effective therapies）、GEO （Gene Expression Omnibus）和TCGA （The Cancer Genome Atlas）收集骨肉瘤和癌旁组织的相关基因表达和临床数据。采用与预后相关的asg对患者进行聚类。采用ssGSEA、ESTIMATE和TIMER测定各亚组肿瘤免疫微环境（TIME）。对亚组间差异表达基因进行功能分析，包括基因本体（GO）、京都基因与基因组百科全书（KEGG）和基因集变异分析（GSVAs），以阐明功能状态。采用单变量Cox回归和最小绝对收缩和选择算子（LASSO）回归构建预后风险模型。我们使用SCISSOR来鉴定不同风险人群骨肉瘤单细胞数据中的相关细胞。采用CTRP和PRISM，根据TIDE评分和化疗药物敏感性预测免疫治疗的效果。根据预后差异表达的asg确定了三个分子亚组。各组免疫浸润水平差异有统计学意义。基于GO和KEGG分析，三个亚群之间的差异表达基因主要与免疫和衰老调控途径有关；GSVA在亚组中表现出多种Hallmark通路的显著差异。基于差异表达基因建立的ASIG风险评分可以预测患者的生存和免疫状态。结合临床特点，我们还开发了一种能准确预测预后的nomogram图。讨论了患者免疫激活谱与风险评分之间的相关性。通过对肿瘤微环境的单细胞分析，我们发现了不同的风险组相关细胞，它们在免疫信号通路上存在显著差异。基于风险评分评估免疫治疗疗效和化疗药物筛选。衰老相关预后基因可区分骨肉瘤分子亚群。我们的新衰老相关基因标记风险评分可用于预测骨肉瘤的免疫景观和预后。此外，风险评分与TIME相关，为骨肉瘤的免疫治疗和化疗提供参考。
Aging is an influential risk factor for progression of both degenerative and oncological diseases of the bone. Osteosarcoma, considered the most common primary mesenchymal tumor of the bone, is a worldwide disease with poor 5-year survival. This study investigated the role of aging-/senescence-induced genes (ASIGs) in contributing to osteosarcoma diagnosis, prognosis, and therapeutic agent prediction. Therapeutically Applicable Research to Generate Effective Treatments (TARGET), Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) were used to collect relevant gene expression and clinical data of osteosarcoma and paracancerous tissues. Patients were clustered by consensus using prognosis-related ASIGs. ssGSEA, ESTIMATE, and TIMER were used to determine the tumor immune microenvironment (TIME) of subgroups. Functional analysis of differentially expressed genes between subgroups, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set variation analyses (GSVAs), was performed to clarify functional status. Prognostic risk models were constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. SCISSOR was used to identify relevant cells in osteosarcoma single-cell data for different risk groups. The effect of immunotherapy was predicted based on TIDE scores and chemotherapy drug sensitivity using CTRP and PRISM. Three molecular subgroups were identified based on prognostic differentially expressed ASIGs. Immunological infiltration levels of the three groups differed significantly. Based on GO and KEGG analyses, differentially expressed genes between the three subgroups mainly relate to immune and aging regulation pathways; GSVA showed substantial variations in multiple Hallmark pathways among the subgroups. The ASIG risk score built based on differentially expressed genes can predict patient survival and immune status. We also developed a nomogram graph to accurately predict prognosis in combination with clinical characteristics. The correlation between the immune activation profile of patients and the risk score is discussed. Through single-cell analysis of the tumor microenvironment, we identified distinct risk-group-associated cells with significant differences in immune signaling pathways. Immunotherapeutic efficacy and chemotherapeutic agent screening were evaluated based on risk score. Aging-related prognostic genes can distinguish osteosarcoma molecular subgroups. Our novel aging-associated gene signature risk score can be used to predict the osteosarcoma immune landscape and prognosis. Moreover, the risk score correlates with the TIME and provides a reference for immunotherapy and chemotherapy in terms of osteosarcoma.
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影响因子：
5.5
作者：
Li H;Liu S;Li C;Xiao Z;Hu J;Zhao C
通讯作者：
Zhao C
影响因子：
6
作者：
Saul D;Kosinsky RL
通讯作者：
Kosinsky RL
影响因子：
3.7
作者：
HAYFLICK, L;MOORHEAD, PS
通讯作者：
MOORHEAD, PS
影响因子：
39.2
作者：
LI, FP;FRAUMENI, JF
通讯作者：
FRAUMENI, JF
影响因子：
7.8
作者：
Price, JS;Waters, JG;Clark, IM
通讯作者：
Clark, IM