Data_Sheet_7_Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer.xlsx

Pancreatic cancer (PaCa) is the seventh most fatal malignancy, with more than 90% mortality rate within the first year of diagnosis. Its treatment can be improved the identification of specific therapeutic targets and their relevant pathways. Therefore, the objective of this study is to identify cancer specific biomarkers, therapeutic targets, and their associated pathways involved in the PaCa progression. RNA-seq and microarray datasets were obtained from public repositories such as the European Bioinformatics Institute (EBI) and Gene Expression Omnibus (GEO) databases. Differential gene expression (DE) analysis of data was performed to identify significant differentially expressed genes (DEGs) in PaCa cells in comparison to the normal cells. Gene co-expression network analysis was performed to identify the modules co-expressed genes, which are strongly associated with PaCa and as well as the identification of hub genes in the modules. The key underlaying pathways were obtained from the enrichment analysis of hub genes and studied in the context of PaCa progression. The significant pathways, hub genes, and their expression profile were validated against The Cancer Genome Atlas (TCGA) data, and key biomarkers and therapeutic targets with hub genes were determined. Important hub genes identified included ITGA1, ITGA2, ITGB1, ITGB3, MET, LAMB1, VEGFA, PTK2, and TGFβ1. Enrichment analysis characterizes the involvement of hub genes in multiple pathways. Important ones that are determined are ECM–receptor interaction and focal adhesion pathways. The interaction of overexpressed surface proteins of these pathways with extracellular molecules initiates multiple signaling cascades including stress fiber and lamellipodia formation, PI3K-Akt, MAPK, JAK/STAT, and Wnt signaling pathways. Identified biomarkers may have a strong influence on the PaCa early stage development and progression. Further, analysis of these pathways and hub genes can help in the identification of putative therapeutic targets and development of effective therapies for PaCa.

胰腺癌（Pancreatic cancer, PaCa）是第七大致死性恶性肿瘤，确诊后第一年的死亡率超过90%。针对该疾病的治疗可通过识别特定治疗靶点及其相关调控通路得以优化。因此，本研究旨在识别胰腺癌进展过程中相关的癌症特异性生物标志物、治疗靶点及其调控通路。本研究从欧洲生物信息学研究所（European Bioinformatics Institute, EBI）、基因表达综合数据库（Gene Expression Omnibus, GEO）等公共数据库获取了RNA测序（RNA-seq）与基因微阵列数据集。对数据集进行差异基因表达（Differential gene expression, DE）分析，以筛选胰腺癌细胞与正常细胞间的显著差异表达基因（DEGs）。通过基因共表达网络分析，筛选与胰腺癌显著相关的共表达基因模块，并识别模块中的核心基因（hub genes）。通过对核心基因进行富集分析，获取其潜在的关键调控通路，并结合胰腺癌进展进程展开研究。通过癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据集对筛选得到的关键通路、核心基因及其表达谱进行验证，最终确定核心的生物标志物及治疗靶点。本次研究识别得到的关键核心基因包括ITGA1、ITGA2、ITGB1、ITGB3、MET、LAMB1、VEGFA、PTK2及TGFβ1。富集分析结果显示，核心基因参与多条信号通路，其中关键通路包括细胞外基质（extracellular matrix, ECM）-受体相互作用通路与黏着斑通路。上述通路中过表达的表面蛋白与细胞外分子的相互作用，可激活包括应力纤维与板状伪足形成、PI3K-Akt、MAPK、JAK/STAT及Wnt信号通路在内的多条信号级联反应。本次识别得到的生物标志物可能对胰腺癌的早期发生与进展具有重要调控作用。此外，对上述通路及核心基因的分析，可为胰腺癌潜在治疗靶点的筛选及高效治疗方案的开发提供理论依据。