Shi Yang, Yao Min, Shen Shuijie, Wang Li, Yao Dengfu
Research Center of Clinical Medicine, Affiliated Hospital of Nantong University & Department of Medical Immunology, Medical School of Nantong University, Nantong 226001, China.
Department of Thoracic Surgery, First People's Hospital of Yancheng, Yancheng 224001, China.
Heliyon. 2024 Mar 21;10(7):e28292. doi: 10.1016/j.heliyon.2024.e28292. eCollection 2024 Apr 15.
Lung cancer still is one of the most common malignancy tumors in the world. However, the mechanisms of its occurrence and development have not been fully elucidated. Zinc finger protein family (ZNFs) is the largest transcription factor family in human genome. Recently, the more and more basic and clinical evidences have confirmed that ZNFs/Krüppel-like factors (KLFs) refer to a group of conserved zinc finger-containing transcription factors that are involved in lung cancer progression, with the functions of promotion, inhibition, dual roles and unknown classifications. Based on the recent literature, some of the oncogenic KLFs are promising molecular biomarkers for diagnosis, prognosis or therapeutic targets of lung cancer. Interestingly, a novel computational approach has been proposed by using machine learning on features calculated from primary sequences, the XGBoost-based model with accuracy of 96.4 % is efficient in identifying KLF proteins. This paper reviews the recent some progresses of the oncogenic KLFs with their potential values for diagnosis, prognosis and molecular target in lung cancer.
肺癌仍然是世界上最常见的恶性肿瘤之一。然而,其发生和发展的机制尚未完全阐明。锌指蛋白家族(ZNFs)是人类基因组中最大的转录因子家族。最近,越来越多的基础和临床证据证实,ZNFs/类Krüppel样因子(KLFs)是一组保守的含锌指转录因子,参与肺癌进展,具有促进、抑制、双重作用和未知分类的功能。基于最近的文献,一些致癌性KLFs有望成为肺癌诊断、预后或治疗靶点的分子生物标志物。有趣的是,一种新的计算方法已经被提出,即利用机器学习对从一级序列计算出的特征进行分析,基于XGBoost的模型识别KLF蛋白的准确率为96.4%,效率很高。本文综述了致癌性KLFs的最新研究进展及其在肺癌诊断、预后和分子靶点方面的潜在价值。