CN114627969A - 基于补体相关基因的肉瘤患者预后预测模型和试剂盒中的应用 - Google Patents
基于补体相关基因的肉瘤患者预后预测模型和试剂盒中的应用 Download PDFInfo
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Abstract
本发明提供C1S表达和C1QBP表达在构建用于肉瘤患者预后预测模型中的应用及检测C1S表达和C1QBP表达在制备用于肉瘤患者预后预测试剂盒中的应用。本发明提供了一种基于肿瘤标本的C1S、C1QBP表达的预测肉瘤患者预后的方法,在肉瘤患者的预后分层中具有较大的帮助。该模型在TCGA的肉瘤公共队列中对1,3,5年预后预测的AUC达到0.714、0.665、0.678,在TARGET‑OS公共队列中对1,3,5年预后预测的AUC达到0.719、0.648、0.590,在GSE63157公共队列中对1,3,5年预后预测的AUC达到0.730、0.673、0.630,具有较为优越的预测性能。
Description
技术领域
本发明涉及生物医学领域,具体涉及基于补体相关基因的肉瘤患者预后预测模型和试剂盒中的应用。
背景技术
肉瘤是一组罕见肿瘤,其发病率约占恶性肿瘤的1%。根据组织来源的不同,肉瘤可分为骨肉瘤和软组织肉瘤,它们包括了100多种不同的组织学亚型。肉瘤内部差异较大,且总体对包括免疫治疗在内的全身性的治疗不敏感,因此对患者的临床管理较为困难。为提升医疗实践中肉瘤的诊疗行为,根据不同的患者预后更好的进行差异化管理,亟待开发一种对肉瘤患者进行预后分层和免疫治疗预测的方法。
补体是先天免疫系统的重要组成成分,具有抵抗外来病原体并维持免疫稳态的作用。补体系统可介导对细胞的杀伤作用。但随着相关研究的深入,越来越多研究证实了补体系统在肿瘤进展中有着促进作用。补体系统的激活可以导致肿瘤微环境的慢性炎症,使得肿瘤发生免疫逃逸。在先前的研究中(Nature Cancer 2021Feb;2(2):218-232.),补体的凝集素途径已被证明能够促进肉瘤的免疫抑制;且对TCGA的肉瘤队列分析(Nature ReviewCancer 2019 Dec;19(12):698-715)也得出结论,证明了补体基因表达与患者预后的显著相关性。因此,用补体相关基因构建患者的预后模型具有原理上的可行性。
发明内容
本发明的目的在于提供C1S表达和C1QBP表达在构建用于肉瘤患者预后预测模型中的应用及检测C1S表达和C1QBP表达在制备用于肉瘤患者预后预测试剂盒中的应用。
根据本发明的一实施方式,将C1S表达水平和C1QBP表达水平代入模型公式,计算每个样本的风险评分;以及根据所述风险评分对肉瘤患者预后进行预测;其中,所述模型公式为risk score=(0.568945965×C1QBP表达水平)+(-0.338438143×C1S表达水平)。
根据本发明的另一实施方式,所述C1S表达为所述C1S的RNA表达或蛋白表达;所述C1QBP表达为所述C1QBP的RNA表达或蛋白表达。
本发明提供了一种基于肿瘤标本的C1S、C1QBP表达的预测肉瘤患者预后的方法,在肉瘤患者的预后分层中具有较大的帮助。该模型在TCGA的肉瘤公共队列中对1,3,5年预后预测的AUC达到0.714、0.665、0.678,在TARGET-OS公共队列中对1,3,5年预后预测的AUC达到0.719、0.648、0.590,在GSE63157公共队列中对1,3,5年预后预测的AUC达到0.730、0.673、0.630,具有较为优越的预测性能。
附图说明
图1是模型在TCGA肉瘤队列的training组中的性能。
图2是模型在TCGA肉瘤队列的test组中的性能。
图3是模型在TCGA肉瘤队列中的性能。
图4是TCGA肉瘤队列中high risk组和low risk组的免疫检查点基因表达及ESTIMATE相关指标评分。
图5是模型在TARGET-OS队列中的性能。
图6是模型在GSE63157队列中的性能。
图7是模型在独立队列中的性能。
图8是独立队列中患者的代表性免疫组化图片。
具体实施方式
下面结合实施案例对本发明的技术方案进行完整清晰的描述,所描述的实施例是本发明一部分实施例,而不是全部的实例。基于本发明的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的其他实施例,都属于本发明保护的范围。
首先,从综述文献中总结了补体基因列表,包含了51个补体相关基因。为了筛选到与预后相关的补体基因,从TCGA下载了肉瘤队列患者的转录组数据和生存数据。将得到的数据进行单因素Cox的预后分析,得到与预后显著相关(P<0.05)的15个基因。将TCGA队列随机分为两个组,一个training组,一个test组。用上述15个基因进行预后建模,并用LASSO减少过拟合,最后得到一个2基因的模型(Risk score=(0.568945965×C1QBP)+(-0.338438143×C1S))。根据该模型得到的risk score,将training组患者按risk score的中位值分为高风险组和低风险组,进行生存曲线绘制(如图1所示),发现低风险组的患者预后显著好于高风险组。在test组患者中验证上述模型,低风险组的患者预后仍显著好于高风险组(如图2所示)。在TCGA的整个肉瘤队列中进行验证,低风险组的患者预后仍显著好于高风险组(如图3所示)。
在TCGA的整个肉瘤队列中,低风险组的患者PDL1,PDL2,TIGIT,TIM3表达较高风险组患者高;用ESTIMATE算法进行计算,发现低风险组的ESTIMATE score,immune score,stromal score更高,肿瘤纯度更低(如图4所示)。上述结果提示低风险组的患者响应免疫治疗的可能性更高。
进一步使用其他公共队列验证,其来源如下:(1)TARGET数据库中的骨肉瘤队列(TARGET-OS,https://ocg.cancer.gov/programs/target/data-matrix);(2)GEO数据库中的一个肉文肉瘤队列(GSE63157,https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63157),结果分别如图5和图6所示。利用算法分别确定它们的optimal cutoff,按optimal cutoff将患者分为高风险组和低风险组,发现在两个验证队列中低风险组的患者预后都显著好于高风险组。
经过公共队列验证,本预后模型可以预测肉瘤患者预后,且确定的高风险个体为死亡风险较高的肉瘤患者。
因此,本发明包括检测C1S和C1QBP的表达从而计算risk score并对患者的风险进行分级。其中确定表达时可检测RNA或蛋白,然后使用定量公式risk score=(0.568945965×C1QBP)+(-0.338438143×C1S)计算得到risk score。之后,使用ROC特征曲线和AUC来评估模型对预后预测的准确度。
为了有助于更加清楚的理解本发明的内容,结合具体的实施例详细介绍如下:
实施例1用基于肉瘤标本C1S和C1QBP的蛋白表达得到的risk score预测肉瘤患者预后
为了验证上述模型的实际应用效果,我们使用了独立的肉瘤队列。在中国医学科学院肿瘤医院回顾性收集了50例肉瘤患者的临床标本,并随访得到患者的生存信息。使用C1S和C1QBP的抗体对上述标本的石蜡切片进行免疫组化染色(如图8所示),然后使用H-score对上述两个指标进行表达定量。将H-score代入公式计算得到risk score,并利用前述算法确定本队列的optimal cutoff,按optimal cutoff将患者分为高风险组和低风险组。如图7所示,在本队列中,低风险组的患者预后仍显著好于高风险组。并且在此队列中,模型对1,3,5年预后预测的AUC达到0.776、0.681、0.708,仍具有较为优越的预测性能。
以上公开的本发明优选实施例只是用于帮助阐述本发明。优选实施例并没有详尽叙述所有的细节,也不限制该发明仅为所述的具体实施方式。显然,根据本说明书的内容,可作很多的修改和变化。本说明书选取并具体描述这些实施例,是为了更好地解释本发明的原理和实际应用,从而使所属技术领域技术人员能很好地理解和利用本发明。本发明仅受权利要求书及其全部范围和等效物的限制。
Claims (6)
1.C1S表达和C1QBP表达在构建用于肉瘤患者预后预测模型中的应用。
2.根据权利要求1所述的应用,其特征在于,
将C1S表达水平和C1QBP表达水平代入模型公式,计算每个样本的风险评分;以及
根据所述风险评分对肉瘤患者预后进行预测;
其中,所述模型公式为risk score=(0.568945965×C1QBP表达水平)+(-0.338438143×C1S表达水平)。
3.根据权利要求1所述的应用,其特征在于,所述C1S表达为所述C1S的RNA表达或蛋白表达;所述C1QBP表达为所述C1QBP的RNA表达或蛋白表达。
4.检测C1S表达和C1QBP表达在制备用于肉瘤患者预后预测试剂盒中的应用。
5.根据权利要求4所述的应用,其特征在于,将C1S表达水平和C1QBP表达水平代入模型公式,计算每个样本的风险评分;以及
根据所述风险评分对肉瘤患者预后进行预测;
其中,所述模型公式为risk score=(0.568945965×C1QBP表达水平)+(-0.338438143×C1S表达水平)。
6.根据权利要求4所述的应用,其特征在于,所述C1S表达为所述C1S的RNA表达或蛋白表达;所述C1QBP表达为所述C1QBP的RNA表达或蛋白表达。
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