Background: The circadian rhythm regulates important functions in the body, such as metabolism, the cell cycle, DNA repair, and immune balance. Disruption of this rhythm can contribute to the development of cancer. Circadian rhythm genes (CRGs) are attracting attention for their connection to various cancers. However, their roles in LUAD are not yet well understood. Additionally, our knowledge of how they function at both the bulk tissue and single-cell levels is limited. This gap hinders a complete understanding of how CRGs impact the development and outcomes of LUAD.Methods: We selected 554 CRGs from public databases. We then obtained transcriptome data from TCGA and GEO. A total of 101 machine learning algorithm combinations were tested using 10 algorithms and 10-fold cross-validation. The best-performing model was based on Stepwise Cox regression and SuperPC. This model was validated with additional datasets. We also examined the relationships between CRGs, immune features, tumor mutation burden (TMB), and the response to immunotherapy. Drug sensitivity was also assessed. Single-cell data identified the cell types with active CRGs. Next, we performed qRT-PCR and other basic experiments to validate the expression of ARNTL2 in LUAD tissues and cell lines. The results indicated that ARNTL2 may play a key role in lung adenocarcinoma.Results: The CRG-based model clearly distinguished LUAD patients based on their risk. High-risk patients exhibited low immune activity, high TMB, and poor predicted responses to immunotherapy. Single-cell data revealed strong CRG signals in epithelial and fibroblast cells. These cell groups also displayed different communication patterns. Laboratory experiments showed that ARNTL2 was highly expressed in LUAD. It promoted cell growth, movement, and invasion. This suggests that ARNTL2 may play a role in promoting cancer.Conclusions: This study developed a machine learning model based on CRGs. It can predict survival and immune status in LUAD patients. The research also identified ARNTL2 as a key gene that may contribute to cancer progression. These findings highlight the significance of the circadian rhythm in LUAD and provide new perspectives for diagnosis and treatment.
背景：昼夜节律调控着机体的重要功能，如新陈代谢、细胞周期、DNA修复和免疫平衡。该节律的紊乱可能促进癌症的发生。昼夜节律基因因其与多种癌症的关联而受到关注，但其在肺腺癌中的作用尚未明确。此外，我们对其在组织整体和单细胞水平的功能认知有限，这一知识缺口阻碍了对昼夜节律基因如何影响肺腺癌发生发展的全面理解。 方法：我们从公共数据库中筛选出554个昼夜节律基因，并获取TCGA和GEO的转录组数据。通过10种算法和10折交叉验证测试了101种机器学习算法组合，其中基于逐步Cox回归和SuperPC的模型表现最佳，并在额外数据集中得到验证。我们进一步分析了昼夜节律基因与免疫特征、肿瘤突变负荷及免疫治疗反应的关系，同时评估了药物敏感性。单细胞数据揭示了昼夜节律基因活跃的细胞类型。随后通过qRT-PCR等基础实验验证ARNTL2在肺腺癌组织和细胞系中的表达，结果表明ARNTL2可能在肺腺癌中发挥关键作用。 结果：基于昼夜节律基因的模型能清晰区分肺腺癌患者的风险分层。高风险患者表现出低免疫活性、高肿瘤突变负荷及较差的免疫治疗预测反应。单细胞数据显示上皮细胞和成纤维细胞中存在强烈的昼夜节律基因信号，这些细胞群体还表现出不同的通讯模式。实验证实ARNTL2在肺腺癌中高表达，能促进细胞增殖、迁移和侵袭，提示其可能发挥促癌作用。 结论：本研究构建的基于昼夜节律基因的机器学习模型可预测肺腺癌患者的生存和免疫状态，同时发现ARNTL2可能是促进癌症进展的关键基因。这些发现揭示了昼夜节律在肺腺癌中的重要意义，为诊疗提供了新视角。
肿瘤(癌症)患者之家