Sun Xinrong, Wang Xiang, Feng Wenming, Guo Huihui, Tang Chengwu, Lu Yongliang, Xiang Xiaobin, Bao Ying
Department of Gastrointestinal Surgery, The First Affiliated Hospital, Huzhou Teachers College, The First People's Hospital of Huzhou, Huzhou, Zhejiang 313000, P.R. China.
Department of Medicine, Huzhou Teachers College, Huzhou, Zhejiang 313000, P.R. China.
Oncol Lett. 2017 Apr;13(4):2089-2096. doi: 10.3892/ol.2017.5691. Epub 2017 Feb 7.
The identification of novel survival predictors may help to improve the appropriate management of colorectal cancer (CRC). In the present study, two gene sets associated with irinotecan or oxaliplatin resistance in CRC cell lines were first identified and subsequently applied to the clinical CRC microarray dataset GSE14333. Subsequently, a 60-gene irinotecan resistance-associated signature and a 13-gene oxaliplatin resistance-associated signature were established, which were able to classify CRC patients into high- and low-risk subgroups with varied clinical outcomes [irinotecan-resistance gene signature: hazard ratio (HR)=0.4607, 95% confidence interval (CI)=0.3369-0.6300, P<0.0001; oxaliplatin-resistance gene signature: HR=0.6119, 95% CI=0.4547-0.8233, P=0.0008]. The performance of these two gene expression signatures in predicting outcome risk were also validated in two other independent CRC gene expression microarray datasets, GSE17536 (irinotecan-resistance gene signature: HR=0.5318, 95% CI=0.3359-0.8419, P=0.0079; oxaliplatin-resistance gene signature: HR=0.5383, 95% CI=0.3400-0.8521, P=0.0114) and GSE17537 (irinotecan-resistance gene signature: HR=0.2827, 95% CI=0.1173-0.6813, P=0.0088; oxaliplatin-resistance gene signature: HR=0.2378, 95% CI=0.09773-0.5784, P=0.0023). Furthermore, the combination of these two gene classifiers demonstrated a superior performance in CRC prognosis prediction than either used individually. Therefore, this study proposed novel gene classifier models for CRC prognosis prediction, which may be potentially useful to inform treatment decisions for patients with CRC in clinical settings.
鉴定新的生存预测指标可能有助于改善结直肠癌(CRC)的合理管理。在本研究中,首先在CRC细胞系中鉴定出两个与伊立替康或奥沙利铂耐药相关的基因集,随后将其应用于临床CRC微阵列数据集GSE14333。随后,建立了一个60基因的伊立替康耐药相关特征和一个13基因的奥沙利铂耐药相关特征,它们能够将CRC患者分为具有不同临床结局的高风险和低风险亚组[伊立替康耐药基因特征:风险比(HR)=0.4607,95%置信区间(CI)=0.3369-0.6300,P<0.0001;奥沙利铂耐药基因特征:HR=0.6119,95%CI=0.4547-0.8233,P=0.0008]。这两个基因表达特征在预测结局风险方面的性能也在另外两个独立的CRC基因表达微阵列数据集GSE17536(伊立替康耐药基因特征:HR=0.5318,95%CI=0.3359-0.8419,P=0.0079;奥沙利铂耐药基因特征:HR=0.5383,95%CI=0.3400-0.8521,P=0.0114)和GSE17537(伊立替康耐药基因特征:HR=0.2827,95%CI=0.1173-0.6813,P=0.0088;奥沙利铂耐药基因特征:HR=0.2378,95%CI=0.09773-0.5784,P=0.0023)中得到验证。此外,这两个基因分类器的组合在CRC预后预测中表现出比单独使用任何一个更好的性能。因此,本研究提出了用于CRC预后预测的新基因分类器模型,这可能对临床环境中CRC患者的治疗决策具有潜在帮助。