Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts. Harvard Medical School, Boston, Massachusetts.
Harvard Medical School, Boston, Massachusetts. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts.
Clin Cancer Res. 2015 Nov 1;21(21):4960-9. doi: 10.1158/1078-0432.CCR-14-3173. Epub 2015 May 5.
To generate a comprehensive "Secretome" of proteins potentially found in the blood and derive a virtual Affymetrix array. To validate the utility of this database for the discovery of novel serum-based biomarkers using ovarian cancer transcriptomic data.
The secretome was constructed by aggregating the data from databases of known secreted proteins, transmembrane or membrane proteins, signal peptides, G-protein coupled receptors, or proteins existing in the extracellular region, and the virtual array was generated by mapping them to Affymetrix probeset identifiers. Whole-genome microarray data from ovarian cancer, normal ovarian surface epithelium, and fallopian tube epithelium were used to identify transcripts upregulated in ovarian cancer.
We established the secretome from eight public databases and a virtual array consisting of 16,521 Affymetrix U133 Plus 2.0 probesets. Using ovarian cancer transcriptomic data, we identified candidate blood-based biomarkers for ovarian cancer and performed bioinformatic validation by demonstrating rediscovery of known biomarkers including CA125 and HE4. Two novel top biomarkers (FGF18 and GPR172A) were validated in serum samples from an independent patient cohort.
We present the secretome, comprising the most comprehensive resource available for protein products that are potentially found in the blood. The associated virtual array can be used to translate gene-expression data into cancer biomarker discovery. A list of blood-based biomarkers for ovarian cancer detection is reported and includes CA125 and HE4. FGF18 and GPR172A were identified and validated by ELISA as being differentially expressed in the serum of ovarian cancer patients compared with controls.
生成一个可能在血液中发现的蛋白质的综合“分泌组”，并衍生出一个虚拟的 Affymetrix 阵列。利用卵巢癌转录组数据验证该数据库在发现新的血清生物标志物方面的效用。
通过将已知分泌蛋白、跨膜或膜蛋白、信号肽、G 蛋白偶联受体或存在于细胞外区的蛋白的数据库中的数据聚合来构建分泌组，通过将其映射到 Affymetrix 探针集标识符来生成虚拟阵列。使用卵巢癌、正常卵巢表面上皮和输卵管上皮的全基因组微阵列数据来鉴定卵巢癌中上调的转录本。
我们从八个公共数据库中建立了分泌组和一个由 16521 个 Affymetrix U133 Plus 2.0 探针组成的虚拟阵列。使用卵巢癌转录组数据，我们鉴定了候选的血液生物标志物用于卵巢癌，并通过证明包括 CA125 和 HE4 在内的已知生物标志物的重新发现进行了生物信息学验证。在来自独立患者队列的血清样本中验证了两个新的顶级生物标志物（FGF18 和 GPR172A）。
我们提出了分泌组，它包含了在血液中可能发现的蛋白质产物的最全面的资源。相关的虚拟阵列可用于将基因表达数据转化为癌症生物标志物的发现。报告了一组用于卵巢癌检测的基于血液的生物标志物，其中包括 CA125 和 HE4。通过 ELISA 鉴定和验证 FGF18 和 GPR172A 在卵巢癌患者血清中与对照相比表达差异。
