Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC.

IF 5 3区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
BioFactors Pub Date : 2024-10-31 DOI:10.1002/biof.2141
Xiaoli Wang, Yan Zhou, Xiaomin Lu, Lili Shao
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引用次数: 0

Abstract

Most patients with non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heightened risk of metastasis. Consequently, the timely identification of biomarkers associated with lymph node metastasis is essential for improving the clinical management of NSCLC patients. In this research, the WGCNA algorithm was utilized to pinpoint genes linked to lymph node metastasis in NSCLC. A cluster analysis was carried out to investigate how these genes correlate with the prognosis and the outcomes of immunotherapy for NSCLC patients. Following this, diagnostic and prognostic models were created and validated through various machine learning methodologies. The random forest technique highlighted the importance of ARHGAP11A, leading to an in-depth examination of its role in NSCLC. By analyzing 78 tissue chip samples from NSCLC patients, the study confirmed the association between ARHGAP11A expression, patient prognosis, and lymph node metastasis. Finally, the influence of ARHGAP11A on NSCLC cells was assessed through cell function experiments. This research utilized the WGCNA technique to identify 25 genes that are related to lymph node metastasis, clarifying their connections with tumor invasion, growth, and the activation of stemness pathways. Cluster analysis revealed significant associations between these genes and lymph node metastasis in NSCLC, especially concerning immunotherapy and targeted treatments. A diagnostic system that combines various machine learning approaches demonstrated strong efficacy in forecasting both the diagnosis and prognosis of NSCLC. Importantly, ARHGAP11A was identified as a key prognostic gene associated with lymph node metastasis in NSCLC. Molecular docking analyses suggested that ARHGAP11A has a strong affinity for targeted therapies within NSCLC. Additionally, immunohistochemical assessments confirmed that higher levels of ARHGAP11A expression correlate with unfavorable outcomes for NSCLC patients. Experiments on cells showed that reducing ARHGAP11A expression can hinder the proliferation, metastasis, and stemness traits of NSCLC cells. This investigation reveals the novel insight that ARHGAP11A may function as a potential biomarker connected to lymph node metastasis in NSCLC. Moreover, reducing the expression of ARHGAP11A has demonstrated the ability to diminish tumor stemness characteristics, presenting a promising opportunity for improving treatment strategies for this condition.

机器学习模型揭示了 ARHGAP11A 对 NSCLC 淋巴结转移和干细胞的影响。
大多数非小细胞肺癌(NSCLC)患者都是在疾病晚期确诊的,由于转移风险增加,治疗变得更加复杂。因此,及时发现与淋巴结转移相关的生物标志物对于改善 NSCLC 患者的临床治疗至关重要。本研究利用WGCNA算法来确定与NSCLC淋巴结转移相关的基因。通过聚类分析,研究了这些基因与 NSCLC 患者的预后和免疫疗法效果的相关性。随后,通过各种机器学习方法创建并验证了诊断和预后模型。随机森林技术突出了ARHGAP11A的重要性,从而对其在NSCLC中的作用进行了深入研究。通过分析78例NSCLC患者的组织芯片样本,研究证实了ARHGAP11A表达、患者预后和淋巴结转移之间的关联。最后,通过细胞功能实验评估了 ARHGAP11A 对 NSCLC 细胞的影响。这项研究利用WGCNA技术鉴定了25个与淋巴结转移相关的基因,明确了它们与肿瘤侵袭、生长和干性通路激活的关系。聚类分析揭示了这些基因与NSCLC淋巴结转移之间的重要关联,尤其是在免疫疗法和靶向治疗方面。一个结合了多种机器学习方法的诊断系统在预测NSCLC的诊断和预后方面表现出了强大的功效。重要的是,ARHGAP11A被确定为与NSCLC淋巴结转移相关的关键预后基因。分子对接分析表明,ARHGAP11A 与 NSCLC 靶向疗法有很强的亲和力。此外,免疫组化评估证实,ARHGAP11A表达水平越高,NSCLC患者的预后越差。细胞实验表明,减少ARHGAP11A的表达可以阻碍NSCLC细胞的增殖、转移和干性特征。这项研究揭示了一个新观点,即ARHGAP11A可能是与NSCLC淋巴结转移相关的潜在生物标志物。此外,减少ARHGAP11A的表达已被证明能够减少肿瘤干性特征,为改善该疾病的治疗策略提供了一个大有可为的机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BioFactors
BioFactors 生物-内分泌学与代谢
CiteScore
11.50
自引率
3.30%
发文量
96
审稿时长
6-12 weeks
期刊介绍: BioFactors, a journal of the International Union of Biochemistry and Molecular Biology, is devoted to the rapid publication of highly significant original research articles and reviews in experimental biology in health and disease. The word “biofactors” refers to the many compounds that regulate biological functions. Biological factors comprise many molecules produced or modified by living organisms, and present in many essential systems like the blood, the nervous or immunological systems. A non-exhaustive list of biological factors includes neurotransmitters, cytokines, chemokines, hormones, coagulation factors, transcription factors, signaling molecules, receptor ligands and many more. In the group of biofactors we can accommodate several classical molecules not synthetized in the body such as vitamins, micronutrients or essential trace elements. In keeping with this unified view of biochemistry, BioFactors publishes research dealing with the identification of new substances and the elucidation of their functions at the biophysical, biochemical, cellular and human level as well as studies revealing novel functions of already known biofactors. The journal encourages the submission of studies that use biochemistry, biophysics, cell and molecular biology and/or cell signaling approaches.
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