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CENPA 调控肺腺癌肿瘤干细胞干性。

CENPA regulates tumor stemness in lung adenocarcinoma.

机构信息

Institute of Biology and Medicine, College of Life and Health Sciences, Wuhan University of Science and Technology, Hubei 430081, P.R. China.

Department of Medical Laboratory, Central Hospital of Wuhan, Tong Ji Medical College, Hua Zhong University of Science and Technology, Hubei 430014, P.R. China.

出版信息

Aging (Albany NY). 2022 Jul 11;14(13):5537-5553. doi: 10.18632/aging.204167.

DOI:10.18632/aging.204167
PMID:35816352
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9320546/
Abstract

Lung adenocarcinoma is a malignant and fatal respiratory disease. However, due to its complex pathogenesis and poorly effective therapeutic options, accurate early diagnosis and prognosis remain elusive. Now, there is increasing evidence that tumor stem cells are involved in tumorigenesis, metastasis, relapse, resistance to chemotherapy and radiotherapy and are one of the reasons why tumors cannot be cured. The mRNA expression based-stemness index (mRNAsi) is a parameter obtained by Malta and his colleagues applying innovative one-class logistic regression machine learning algorithm (OCLR) on mRNA expression in normal stem cells and their progeny. It is a valid evaluation parameter and is currently employed to evaluate the degree of differentiation of a certain tumor. In this study, we first used WGCNA and the software Cytoscape to obtain key modules and hub genes. We then applied LASSO regression analysis to calculate the genes in the key module to obtain a six-gene risk model. Moreover, the accuracy of this model was validated. Finally, we took the intersection of hub genes and risk genes and validated CENPA as both a tumor stemness regulator and a tumor prognostic factor in lung cancer.

摘要

肺腺癌是一种恶性且致命的呼吸系统疾病。然而，由于其复杂的发病机制和治疗效果不佳的治疗选择，准确的早期诊断和预后仍然难以实现。现在，越来越多的证据表明肿瘤干细胞参与肿瘤的发生、转移、复发、对化疗和放疗的耐药性，是肿瘤无法治愈的原因之一。基于 mRNA 表达的干细胞指数（mRNAsi）是由 Malta 及其同事在正常干细胞及其后代的 mRNA 表达中应用创新的单类逻辑回归机器学习算法（OCLR）获得的一个参数。它是一个有效的评估参数，目前用于评估某种肿瘤的分化程度。在这项研究中，我们首先使用 WGCNA 和 Cytoscape 软件获得关键模块和枢纽基因。然后，我们应用 LASSO 回归分析计算关键模块中的基因，以获得一个六基因风险模型。此外，还验证了该模型的准确性。最后，我们取枢纽基因和风险基因的交集，并验证 CENPA 既是肺癌中的肿瘤干细胞调节因子，也是肿瘤预后因子。

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