机器学习识别出两个自噬相关基因作为结直肠癌复发的标志物。
Machine learning identifies two autophagy-related genes as markers of recurrence in colorectal cancer.
作者信息
Wu Jianping, Liu Sulai, Chen Xiaoming, Xu Hongfei, Tang Yaoping
机构信息
Hunan University of Science and Engineering, Yongzhou, China.
Department of Hepatobiliary Surgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.
出版信息
J Int Med Res. 2020 Oct;48(10):300060520958808. doi: 10.1177/0300060520958808.
OBJECTIVE
Colorectal cancer (CRC) is the most common cancer worldwide. Patient outcomes following recurrence of CRC are very poor. Therefore, identifying the risk of CRC recurrence at an early stage would improve patient care. Accumulating evidence shows that autophagy plays an active role in tumorigenesis, recurrence, and metastasis.
METHODS
We used machine learning algorithms and two regression models, univariable Cox proportion and least absolute shrinkage and selection operator (LASSO), to identify 26 autophagy-related genes (ARGs) related to CRC recurrence.
RESULTS
By functional annotation, these ARGs were shown to be enriched in necroptosis and apoptosis pathways. Protein-protein interactions identified , , , , and as core genes in CRC autophagy. Of 26 ARGs, and were regarded as having the most significant predictive ability of CRC recurrence, with prediction accuracy of 71.1%.
CONCLUSION
These results shed light on prediction of CRC recurrence by ARGs. Stratification of patients into recurrence risk groups by testing ARGs would be a valuable tool for early detection of CRC recurrence.
目的
结直肠癌（CRC）是全球最常见的癌症。CRC复发后的患者预后非常差。因此，早期识别CRC复发风险将改善患者护理。越来越多的证据表明，自噬在肿瘤发生、复发和转移中起积极作用。
方法
我们使用机器学习算法和两个回归模型，即单变量Cox比例模型和最小绝对收缩和选择算子（LASSO），来识别与CRC复发相关的26个自噬相关基因（ARG）。
结果
通过功能注释，这些ARG被证明在坏死性凋亡和凋亡途径中富集。蛋白质-蛋白质相互作用确定 、 、 、 和 为CRC自噬中的核心基因。在26个ARG中， 和 被认为对CRC复发具有最显著的预测能力，预测准确率为71.1%。
结论
这些结果为通过ARG预测CRC复发提供了线索。通过检测ARG将患者分层为复发风险组将是早期检测CRC复发的有价值工具。
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