直肠癌新辅助放化疗抵抗性的基因组学预测研究

目的 基于加权基因共表达网络(WGCNA)从分子水平寻找影响直肠癌放化疗抵抗的枢纽基因。方法 于基因表达数据库获得接受放化疗患者的全基因组表达数据GSE119409,分别构建放化疗病理完全缓解组与未获得病理完全缓解组的加权基因共表达网络。利用NetRep保守性评估方法综合分析网络模块各个节点基因连接度、基因显著性与网络位置属性,确定与直肠癌放化疗抵抗性密切相关的枢纽基因。结果 通过WGCNA方法共获得5个(black、blue、green、yellow、purple)与放化疗抵抗性密切相关的网络模块,筛选出5个(SLC22A14、SIDT2、CABP4、EPHB6、RAB11B)与直肠癌放化疗抵抗性相关的枢纽基因。结论 通过加权基因共表达网络分析方法共筛选出与直肠癌放化疗抵抗性相关的5个基因共表达网络模块及相应的5个枢纽基因,为寻找术前放化疗抵抗性评估分子标志物及潜在的治疗靶点提供了线索。

Genomic prediction of neoadjuvant chemoradiotherapy resistance in rectal cancer

Objective To search for the key genes influencing the resistance of rectal cancer to chemoradiotherapy based on the weighted gene co-expression network analysis (WGCNA). Methods The data were collected from gene expression omnibus. The whole genome expression data GSE119409 of patients receiving radiotherapy and chemotherapy were obtained by gene expression ominibus. The weighted gene co-expression networks of pathological complete response group and non-pathological complete response group were constructed respectively. NetRep conservative evaluation method was used to comprehensively analyze the three key network attributes of gene connectivity, gene significance and module membership of each node in the network module, and to determine the key genes closely related to the sensitivity of rectal cancer to radiotherapy and chemotherapy. Results Network modules including black, blue, green, yellow and purple were obtained by WGCNA, and five key genes including SLC22A14, SIDT2, CABP4, EPHB6 and RAB11B were screened out. Conclusions Five gene co-expression network modules and five key genes related to chemoradiotherapy resistance of rectal cancer were screened by weighted gene co-expression network analysis, which provided clues for finding molecular markers and potential therapeutic targets for neoadjuvant chemoradiotherapy resistance evaluation.