Li Haoran, Zhang Yingru, Feng Yuanyuan, Hu Xueqing, Bi Ling, Zhu Huirong, Wang Yan
Oncology Institute, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Department of Medical Oncology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Front Oncol. 2024 Jan 9;13:1322421. doi: 10.3389/fonc.2023.1322421. eCollection 2023.
Up to one-third of colorectal cancer (CRC) patients experience recurrence after radical surgery, and it is still very difficult to assess and predict the risk of recurrence. Angiogenesis is the key factor of recurrence as metastasis of CRC is closely related to copper metabolism. Expression profiling by microarray from two datasets in Gene Expression Omnibus (GEO) was selected for quality control, genome annotation, normalization, etc. The identified angiogenesis-derived and cuproptosis-related Long non-coding RNAs (lncRNAs) and clinical data were screened and used as predictors to construct a Cox regression model. The stability of the model was evaluated, and a nomogram was drawn. The samples were divided into high-risk and low-risk groups according to the linear prediction of the model, and a Kaplan-Meier survival analysis was performed. In this study, a model was established to predict the postoperative recurrence of colon cancer, which exhibits a high prediction accuracy. Furthermore, the negative correlation between cuproptosis and angiogenesis was validated in colorectal cancer cell lines and the expression of lncRNAs was examined.
高达三分之一的结直肠癌(CRC)患者在根治性手术后会出现复发,并且评估和预测复发风险仍然非常困难。血管生成是复发的关键因素,因为CRC的转移与铜代谢密切相关。从基因表达综合数据库(GEO)中的两个数据集中选择通过微阵列进行的表达谱分析,用于质量控制、基因组注释、标准化等。筛选出已鉴定的血管生成衍生和铜死亡相关的长链非编码RNA(lncRNA)以及临床数据,并将其用作预测因子来构建Cox回归模型。评估模型的稳定性,并绘制列线图。根据模型的线性预测将样本分为高风险组和低风险组,并进行Kaplan-Meier生存分析。在本研究中,建立了一个预测结肠癌术后复发的模型,该模型具有较高的预测准确性。此外,在结直肠癌细胞系中验证了铜死亡与血管生成之间的负相关性,并检测了lncRNA的表达。