基于生物信息学筛选甲状腺乳头状癌相关预后基因

目的:通过生物信息学方法分析甲状腺乳头状癌预后相关基因，为后续实验提供理论基础，以便进一步研究甲状腺乳头状癌发生的可能机制。方法:利用生物信息学方法从癌症基因组图谱(the cancer genome atlas, TCGA)数据库中下载甲状腺乳头状癌和癌旁组织样本的RNA测序数据，通过差异分析获取差异基因；利用基因本体论(gene ontology, GO)和京都基因和基因组百科全书(Kyoto Encyclopedia of Genes Genomes, KEGG)进行差异基因生物学功能和通路富集分析；通过蛋白交互网络分析法(protein-protein interaction, PPI)筛选出关键基因；通过生存分析研究关键基因对甲状腺乳头状癌患者生存预后的影响。结果:在TCGA数据库中共筛选出甲状腺乳头状癌患者237例，其中甲状腺乳头状癌组织212例，甲状腺癌旁组织25例。通过差异基因分析后共筛选出715个差异基因；通路富集分析显示，神经信号转导活性、逆行内源性大麻素信号为主要的信号通路；通过PPI法共筛选出18个关键基因，包含9个上调基因和9个下调基因；通过生存分析发现高表达...

Bioinformatic screening of papillary thyroid carcinoma related prognostic genes

Objective: To analyze prognostic genes of papillary thyroid carcinoma with bioinformatics and to provide theoretical basis for subsequent experiments, so to further study the possible mechanism of papillary thyroid carcinoma. Methods: RNA sequencing data on papillary thyroid carcinoma and para-carcinoma tissue samples were downloaded from The Cancer Genome Atlas (TCGA) database with the bioinformatics method, and differential genes were obtained by differential analysis. Biological function and pathway enrichment analysis on different genes were analyzed using Gene Ontology (GO) database and the Kyoto Encyclopedia of Genes (KEGG) database. Key genes were screened by protein-protein interaction (PPI). The influence of key genes on survival and prognosis of patients with papillary thyroid carcinoma was studied with survival analysis. Results: A total of 237 cases of papillary thyroid carcinoma was screened from TCGA database, including 212 cases of papillary thyroid carcinoma tissue and 25 cases of para-thyroid carcinoma tissue. A total of 715 differential genes were screened by differential gene analysis. Pathway enrichment analysis showed that signal transduction activity and retrograde endocannabinoid signaling were the main pathways. A total of 18 key genes were screened with PPI method, including 9 up-regulated genes and 9 down-regulated genes. Survival analysis showed that patients with high expression of OPRK1 had a poor prognosis. Conclusion: The expression level of OPRK1 is significantly correlated with the survival time of patients. However, the specific mechanism needs to be further studied.