Zhang Lu, Wang Qiang, Han Yali, Huang Yingsa, Chen Tianhui, Guo Xiangqian
Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng 475004, China.
Department of Cancer Prevention/Zhejiang Cancer Institute, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China.
J Proteomics. 2023 Feb 20;273:104810. doi: 10.1016/j.jprot.2022.104810. Epub 2022 Dec 29.
Prognostic biomarker, as a feasible and objective indicator, is valuable in the assessment of cancer risk. With the development of high-throughput sequencing technology, the screening of prognostic biomarkers has become easy, but it is difficult to screen prognostic markers based on proteomic data. In this study we developed a tool named Online consensus Survival analysis web server based on Proteome of Pan-cancers, abbreviated as OSppc, to evaluate the prognostic values of protein biomarkers. >8000 cancer cases with proteomic data, transcriptomic data and clinical follow-up information were collected from TCGA and CPTAC. 14,038 proteins (including proteins and their phosphorylated forms) analyzed by reverse-phase protein arrays and mass spectrometry in 33 types of cancers were collected. In OSppc, three analysis modules are provided, including Survival Analysis, Differential Analysis and Correlation Analysis. Survival analysis module exhibits HR with 95% CI and KM curves with log-rank p value of protein and mRNA levels of input genes. Differential analysis module shows the box plots of protein expression levels in different tissues. Correlation analysis module provides scatter plot with pearson's and spearman's correlation coefficient of the protein and its corresponding mRNA. OSppc can be accessed at http://bioinfo.henu.edu.cn/Protein/OSppc.html. SIGNIFICANCE: OSppc can analyze the association between protein, mRNA and prognosis, the correlation between proteome data and gene expression profiles, the differential expression of proteome data between subgroups such as normal and cancer as well. OSppc is registration-free and very valuable to evaluate the prognostic potency of protein of interests. OSppc is very valuable for researchers and clinicians to screen, develop and validate potential protein prognostic biomarkers in pan-cancers, and offers the opportunities to investigate the clinical important functional genes and therapeutic targets of cancers.
预后生物标志物作为一种可行且客观的指标,在癌症风险评估中具有重要价值。随着高通量测序技术的发展,预后生物标志物的筛选变得容易,但基于蛋白质组学数据筛选预后标志物却很困难。在本研究中,我们开发了一种基于泛癌蛋白质组的在线共识生存分析网络服务器工具,简称为OSppc,用于评估蛋白质生物标志物的预后价值。我们从TCGA和CPTAC收集了8000多例具有蛋白质组学数据、转录组学数据和临床随访信息的癌症病例。收集了通过反相蛋白质阵列和质谱分析的33种癌症中的14038种蛋白质(包括蛋白质及其磷酸化形式)。在OSppc中,提供了三个分析模块,包括生存分析、差异分析和相关性分析。生存分析模块展示输入基因的蛋白质和mRNA水平的带有95%置信区间的风险比(HR)以及带有对数秩p值的Kaplan-Meier(KM)曲线。差异分析模块显示不同组织中蛋白质表达水平的箱线图。相关性分析模块提供蛋白质及其相应mRNA的带有皮尔逊和斯皮尔曼相关系数的散点图。可通过http://bioinfo.henu.edu.cn/Protein/OSppc.html访问OSppc。意义:OSppc可以分析蛋白质、mRNA与预后之间的关联、蛋白质组数据与基因表达谱之间的相关性,以及正常和癌症等亚组之间蛋白质组数据的差异表达。OSppc无需注册,对于评估感兴趣蛋白质的预后效力非常有价值。OSppc对于研究人员和临床医生在泛癌中筛选、开发和验证潜在的蛋白质预后生物标志物非常有价值,并为研究癌症的临床重要功能基因和治疗靶点提供了机会。