Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses (‘gained’ genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.

在处理异质性数据集时，利用差异表达基因（Differentially Expressed Genes, DEGs）筛选疾病中可行的生物标志物往往是一项颇具挑战的任务。基因表达数据极易受到实验技术、样本制备流程以及标记方法的显著影响。用于检测基因表达的各类微阵列平台（microarray platforms）的激增，使得开发能够比对其结果的模型需求愈发迫切，尤其是当不同技术所产生的信号值差异悬殊时。整合型荟萃分析（meta-analysis）可显著提升差异表达基因检测的可靠性与稳健性。本研究旨在开发一种整合型方法，通过整合两种不同平台的基因表达数据，筛选潜在的癌症生物标志物。胰腺导管腺癌（Pancreatic Ductal Adenocarcinoma, PDAC）因诊断周期较晚，亟需发掘新型生物标志物，因此成为验证该技术的理想对象。本研究采用了来自两个不同数据集的基因表达数据，即Affymetrix与Illumina平台数据集（分别纳入18例和36例胰腺导管腺癌患者样本，同时包含18例健康对照样本）。随后，本研究提出了一种基于经验贝叶斯方法（ComBat）的荟萃分析方案，以实现上述数据集的整合。最终，通过统计编程语言R从整合后的数据中筛选出差异表达基因。经整合型荟萃分析后，在两个独立数据集的单独分析中，共有5个基因被共同鉴定出。此外，本研究还发现了28个未被单独分析报道的新基因（即“新增”基因）。其中部分新增基因已被证实与其他胃肠道肿瘤相关。本研究提出的整合型荟萃分析揭示了一批全新的差异表达基因，这些基因可能在胰腺导管腺癌的发生发展中发挥重要作用，或可作为该疾病诊断的潜在生物标志物。