近年来,以免疫检查点抑制剂(Immune Checkpoint Inhibitors, ICIs)为代表的免疫治疗,极大地提高了多种肿瘤的治疗效果,但总体反应率较低。因ICIs的有效性绝对依赖于能够识别和杀伤肿瘤细胞的T淋巴细胞浸润,故ICIs只对“热肿瘤”有效,而...近年来,以免疫检查点抑制剂(Immune Checkpoint Inhibitors, ICIs)为代表的免疫治疗,极大地提高了多种肿瘤的治疗效果,但总体反应率较低。因ICIs的有效性绝对依赖于能够识别和杀伤肿瘤细胞的T淋巴细胞浸润,故ICIs只对“热肿瘤”有效,而对“冷肿瘤”无效。本文通过梳理相关文献,系统、全面地探讨了阻碍T淋巴细胞激活与浸润的常见机制,并总结了重塑肿瘤免疫微环境(Tumor Immune Microenvironment, TIME)提高ICIs治疗效果的方法与研究进展。在免疫治疗时代,如何最大程度地挖掘ICIs的治疗潜力已成为研究热点,值得多角度探讨。In recent years, immune therapy represented by immune checkpoint inhibitors (ICIs) has greatly improved the treatment outcomes for various tumors, but the overall response rate remains low. The effectiveness of ICIs is entirely dependent on the infiltration of T lymphocytes that can recognize and kill tumor cells, which is why ICIs are only effective against “hot tumors” and ineffective against “cold tumors”. This article systematically and comprehensively discusses the common mechanisms that hinder T lymphocyte activation and infiltration by reviewing relevant literature, and summarizes methods and research progress in reshaping the tumor immune microenvironment (TIME) to enhance the efficacy of ICIs. In the era of immunotherapy, how to maximize the therapeutic potential of ICIs has become a research hotspot worthy of exploration from multiple perspectives.展开更多
以内质网应激相关基因构建骨肉瘤患者的风险模型,探索其与肿瘤免疫微环境的关系。采用生物信息学分析法,训练集的转录组数据及临床数据下载于UCSC Xena数据库,验证集的相应数据下载于GEO数据库(GSE21257,GSE39058)。采用单因素COX回归...以内质网应激相关基因构建骨肉瘤患者的风险模型,探索其与肿瘤免疫微环境的关系。采用生物信息学分析法,训练集的转录组数据及临床数据下载于UCSC Xena数据库,验证集的相应数据下载于GEO数据库(GSE21257,GSE39058)。采用单因素COX回归分析、LASSO回归分析及多因素COX回归分析提取风险特征基因构建风险模型,使用决策曲线分析、受试者工作特征曲线分析验证模型的准确性,随后构建列线图进一步预测骨肉瘤患者预后;根据风险评分将患者分为高、低风险组,使用Kaplan-Meier生存曲线评估高、低风险组间的生存差异,对差异表达基因(Differentially expressed genes,DEGs)进行GO/KEGG联合富集分析、基因集富集分析(Gene set enrichment analysis,GSEA)及基因集变异分析(Gene set variation analysis,GSVA);采用ESTIMATE算法、微环境种群计数器(Microenvironment cell population counter,MCP counter)方法、单样本基因集富集分析(Single sample gene set enrichment analysis,ssGSEA)进行免疫分析;最终在验证集中验证上述结果。6个风险特征基因中VEGFA、PTGIS及SERPINH1与骨肉瘤患者的不良预后相关,而TMED10、MAPK10及TOR1B与与骨肉瘤患者的良好预后相关,高、低风险组患者之间具有显著生存差异;GO/KEGG联合富集分析、GSVA、GSEA结果表明DEGs与免疫状态相关;免疫分析显示高风险组具有更低的免疫评分及免疫景观;列线图进一步准确地预测了骨肉瘤患者的预后。内质网应激相关基因构建的风险模型能准确预测骨肉瘤患者预后,并与肿瘤免疫微环境相关。展开更多
文摘近年来,以免疫检查点抑制剂(Immune Checkpoint Inhibitors, ICIs)为代表的免疫治疗,极大地提高了多种肿瘤的治疗效果,但总体反应率较低。因ICIs的有效性绝对依赖于能够识别和杀伤肿瘤细胞的T淋巴细胞浸润,故ICIs只对“热肿瘤”有效,而对“冷肿瘤”无效。本文通过梳理相关文献,系统、全面地探讨了阻碍T淋巴细胞激活与浸润的常见机制,并总结了重塑肿瘤免疫微环境(Tumor Immune Microenvironment, TIME)提高ICIs治疗效果的方法与研究进展。在免疫治疗时代,如何最大程度地挖掘ICIs的治疗潜力已成为研究热点,值得多角度探讨。In recent years, immune therapy represented by immune checkpoint inhibitors (ICIs) has greatly improved the treatment outcomes for various tumors, but the overall response rate remains low. The effectiveness of ICIs is entirely dependent on the infiltration of T lymphocytes that can recognize and kill tumor cells, which is why ICIs are only effective against “hot tumors” and ineffective against “cold tumors”. This article systematically and comprehensively discusses the common mechanisms that hinder T lymphocyte activation and infiltration by reviewing relevant literature, and summarizes methods and research progress in reshaping the tumor immune microenvironment (TIME) to enhance the efficacy of ICIs. In the era of immunotherapy, how to maximize the therapeutic potential of ICIs has become a research hotspot worthy of exploration from multiple perspectives.
文摘以内质网应激相关基因构建骨肉瘤患者的风险模型,探索其与肿瘤免疫微环境的关系。采用生物信息学分析法,训练集的转录组数据及临床数据下载于UCSC Xena数据库,验证集的相应数据下载于GEO数据库(GSE21257,GSE39058)。采用单因素COX回归分析、LASSO回归分析及多因素COX回归分析提取风险特征基因构建风险模型,使用决策曲线分析、受试者工作特征曲线分析验证模型的准确性,随后构建列线图进一步预测骨肉瘤患者预后;根据风险评分将患者分为高、低风险组,使用Kaplan-Meier生存曲线评估高、低风险组间的生存差异,对差异表达基因(Differentially expressed genes,DEGs)进行GO/KEGG联合富集分析、基因集富集分析(Gene set enrichment analysis,GSEA)及基因集变异分析(Gene set variation analysis,GSVA);采用ESTIMATE算法、微环境种群计数器(Microenvironment cell population counter,MCP counter)方法、单样本基因集富集分析(Single sample gene set enrichment analysis,ssGSEA)进行免疫分析;最终在验证集中验证上述结果。6个风险特征基因中VEGFA、PTGIS及SERPINH1与骨肉瘤患者的不良预后相关,而TMED10、MAPK10及TOR1B与与骨肉瘤患者的良好预后相关,高、低风险组患者之间具有显著生存差异;GO/KEGG联合富集分析、GSVA、GSEA结果表明DEGs与免疫状态相关;免疫分析显示高风险组具有更低的免疫评分及免疫景观;列线图进一步准确地预测了骨肉瘤患者的预后。内质网应激相关基因构建的风险模型能准确预测骨肉瘤患者预后,并与肿瘤免疫微环境相关。
