基于WGCNA和ssGSEA的胰腺癌预后模型构建

目的通过加权基因共表达网络分析(WGCNA)和单样本基因富集分析(ssGSEA)构建胰腺癌预后模型。方法获取胰腺癌组织转录组测序数据和患者预后信息,ssGSEA对各肿瘤组织17810条通路进行评分,利用WGCNA、单因素分析和Lasso回归筛选各通路参数,构建多因素Cox比例风险回归模型对胰腺癌患者的预后情况进行预测,并对模型进行评价。结果经ssGSEA发现,不同胰腺癌组织各通路评分不同,如GO RECEPTOR REGULATOR ACTIVITY、GO MONOSACCHARIDE BINDING等通路,提示不同患者各通路活性有差异,这可能是引起肿瘤向不同结局发展的重要原因。经WGCNA分析采用动态剪切树法合并表达相似的通路模块,最终得到7个通路模块。其中,Blue模块中包含的通路最多,含3098条通路。Red模块与胰腺癌患者生存状态有较高的正相关性(r=0.33,P<0.001),Black模块也与其生存有显著的相关性(r=0.11,P=0.03)。同时,Red模块(r=0.30,P<0.001)和Blue模块(r=0.24,P=0.002)与胰腺癌组织学分期有显著的正相关性;Blue模块(r=0.27,P<0.001)、Yellow模块(r=-0.17,P=0.03)、Black模块(r=0.30,P<0.001)和Magenta模块(r=0.15,P=0.03)与胰腺癌肿瘤分期有明显的相关性。使用单因素分析、Lasso回归筛选Red模块中与生存预后相关的通路,共得到11条与胰腺癌患者生存预后相关的通路。进一步利用多因素Cox回归建立多通路预后预测模型,最终得到由5条通路构成的胰腺癌预后预测模型,C指数为0.7,赤池信息准则(AIC)为776.49,且该模型具有良好的预测效能(AUC=0.742)。结论WGCNA和ssGSEA技术在胰腺癌预后模型的构建中有重要作用。

骨肉瘤铜死亡相关LncRNA预后模型构建与验证

目的:Cuproptosis是一种新发现的程序性细胞死亡形式,被认为在肿瘤治疗中起重要作用。长链非编码RNA(long non-coding RNA,lncRNA)参与调节细胞多种生理与病理活动。本研究旨在探讨铜死亡相关LncRNA在骨肉瘤中的预后意义。方法:从公共数据库UCSC Xena和GTEx数据库下载骨肉瘤样本与正常样本的基因表达谱以及相应的临床数据,从已发表的文献中获取铜死亡基因,采用共表达网络、最小绝对收缩和选择算法(LASSO)和Cox回归模型构建骨肉瘤铜死亡相关lncRNA预后模型并进行内部验证。采用受试者工作特征(ROC)曲线和列线图来评估模型的预测能力。单样本基因集富集分析(ssGSEA)探讨不同风险组与骨肉瘤免疫细胞与功能的相关性。结果:对收集到的19个铜死亡基因进行共表达分析,得到181个铜死亡相关lncRNA,差异分析及单因素Cox分析筛选出10个铜死亡预后相关的LncRNA。随后通过Lasso及多因素Cox回归筛选得到了3个铜死亡相关lncRNA(AC124798.1、AC090152.1、AC090559.1)构建预后模型。根据风险评分中位值将患者分为高、低风险组。低风险组总生存期、风险评分分布、生存状况结果优于高风险组,并在内部数据中得到了验证。单因素和多因素Cox回归分析显示,风险评分是一个独立预后因素。列线图与ROC曲线表明,该预后模型具有较好的预测能力。ssGSEA结果提示高风险组中免疫细胞及功能可能受到抑制。结论:3个铜死亡相关lncRNA可能有助于对骨肉瘤患者预后进行指导,为临床决策提供一定理论依据。

Construction and validation of prognostic model of Cuproptosis-related LncRNA in osteosarcoma

Cuproptosis is a newly discovered form of apoptotic process that is thought to play an important role in cancer therapy.Long non-coding RNA(lncRNA)is involved in regulating many physiological and pathological activities of cells.The aim of this study was to investigate the prognostic significance of Cuproptosis-associated lncRNAs in osteosarcoma.Methods:The Gene expression profiling of osteosarcoma samples versus normal samples and corresponding clinical data were downloaded from the public databases UCSC Xena and GTEx,and the cuproptosis gene was obtained from the published literature,the prognostic model of osteosarcoma cuproptosis-related lncRNA was constructed by using coexpression network,minimum absolute contraction and selection algorithm(LASSO)and Cox regression model.Receiver operating characteristic(ROC)curves and nomograms were used to assess the predictive power of the model.Single-sample gene set enrichment analysis(ssGSEA)was used to explore the relationship between osteosarcoma immune cells and function in different risk groups.Results:181 cuproptosis-related lncRNAs were obtained by co-expression analysis of 19 cuproptosis genes collected.Ten lncRNAs were screened out by differential analysis and single-factor Cox analysis.Three cuproptosis-related lncrnas(AC124798.1,AC090152.1,AC090559.1)were screened by Lasso and multivariate Cox regression to construct the prognostic model.Patients were divided into high and low risk groups based on the median risk score.The results of overall survival,risk score distribution and survival status in the lowrisk group were better than those in the high-risk group,and were verified in the internal data.Univariate and multivariate Cox regression analyses showed that risk score was an independent prognostic factor.Nomograms and ROC curves showed that the prognostic model had good predictive ability.The results of ssGSEA suggest that immune cells and function may be inhibited in the high-risk group.Conclusion:The 3 cuproptosis-related lncRNAs may be helpful to guide the prognosis of osteosarcoma patients and provide some theoretical basis for clinical decision.

基于生物信息学分析卵巢癌免疫细胞浸润程度及其与预后的关系

目的探讨免疫细胞浸润程度与卵巢癌生存预后的关系。方法通过生物信息学方法,从GEO数据库下载卵巢癌患者的组织芯片测序数据和临床分期、病理分级、生存状态、生存时间等信息。通过ssGSEA基因集的各项免疫评分,将卵巢癌患者分为高分组和低分组。通过生存曲线比较滤泡辅助性T细胞(Tfh)高、低评分组的生存预后情况。通过Cibersort软件计算肿瘤组织中各类免疫细胞浸润的比例,并进行相关性分析。结果Tfh评分与预后生存有关(P<0.05)。Tfh高分组的浆细胞、活化的NK细胞、M1型巨噬细胞浸润程度明显高于低分组(P<0.05),而M2型巨噬细胞的浸润程度则明显低于低分组(P<0.05)。结论卵巢癌中存在不同程度的免疫细胞浸润,Tfh浸润可能是影响卵巢癌患者预后的因素。