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Gene signature and connectivity mapping to assist with drug prediction for pancreatic ductal adenocarcinoma

基因 生物 基因表达谱 癌症研究 小桶 气体6 转录组 马拉特1 基因表达 计算生物学 遗传学 下调和上调 激酶 受体酪氨酸激酶 长非编码RNA
作者
Yao Xiao,Baoluhe Zhang,Jordan M. Cloyd,Gang Xu,Shunda Du,Yilei Mao,Timothy M. Pawlik
出处
期刊:Surgical Oncology-oxford [Elsevier]
卷期号:44: 101849-101849 被引量:1
标识
DOI:10.1016/j.suronc.2022.101849
摘要

The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) is highly variable and there is a paucity of effective treatment options for patients with PDAC. Genome-wide analyses may allow for potential drugs to be identified using differentially expressed genes, as well as constructing protein interaction networks and molecule-gene connectivity mapping.Microarray data of RNA expression profiling of PDAC and normal pancreas tissues were downloaded from the Gene Expression Omnibus (GEO). Functional and pathway enrichment information of the DEGs was obtained using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Corresponding homologous proteins were analyzed by protein-protein interaction analysis. Survival-related hub genes were screened and potential therapeutic drugs for PDAC were identified using the connectivity mapping (cMap).Of 18,229 PDAC genes assessed using RNA expression profiling from 118 PDAC tumor samples and 13 normal pancreatic tissue samples, 1502 and 744 genes were upregulated and downregulated, respectively, versus normal pancreas tissue. Protein-protein interaction analysis revealed 10 upregulated hub genes (ITGB1, ITGAV, SDC1, KRAS, CCNB2, COL1A2, AURKA, CDC20, COL1A1, COL3A1) and 10 downregulated hub genes (CPB1, CPA1, CPA2, CTRB2, CTRC, CELA3A, CELA2B, PRSS3, CELA2A, REG1A). The connectivity mapping score related to this hub gene list was used to generate the candidate drugs for PDAC treatment, which includes tyrosine kinase inhibitors (lucitanib, lapatinib, ceritinib and CYT-387), serine/threonine protein kinase inhibitors (roscovitine, BS-181, purvalanol-a, MK-2206 and palomid-529) and other small molecules.Using available genetic atlas data, potential drug candidates for treatment of PDAC were identified based on differentially expressed genes, protein interaction analysis and connectivity mapping. These results may help focus efforts on identifying targeted agents with potential therapeutic efficacy for evaluation in prospective clinical trials of patients with PDAC.
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