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Mini-chromosome maintenance protein 6 (MCM6), a member of the DNA replication initiation complex, is considered a potential prognostic marker for multiple tumors. However, the biological role of MCM6 has not been reported in pan-cancer. In present study, a pan-cancer analysis of MCM6 was performed using multiple databases and online websites. The relationship between MCM6 and DNA methylation, prognosis, immune infiltration and immunotherapy response was investigated. Weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) Cox regression models were performed to construct prognostic risk signature for lung adenocarcinoma (LUAD) based on MCM6-related cell cycle genes (MrCCGs). Meanwhile, the biological function of MCM6 in lung adenocarcinoma was further verified through in vivo and in vitro experiments. MCM6 is highly expressed and is a prognostic risk factor in most tumors. MCM6 expression is significantly associated with the infiltration of immune cells (especially MDSCs) in a variety of tumors. The risk signature based on MrCCGs can reliably predict the prognosis of LUAD (AUC = 0.739). Immunohistochemical staining showed that the expression of MCM6 is higher in lung adenocarcinoma tissues compared with para-cancer tissues and is associated with the poor prognosis of lung adenocarcinoma patients. In vitro, MCM6 knockdown inhibited proliferation, invasion, and migration of A549 and H1299 cells, and blocked the G1 phase of the A549 cell cycle. In vivo, knockdown of MCM6 inhibited the growth of xenograft tumors in nude mice. The study suggests that MCM6 may be a potential prognostic and immunological biomarker in many cancers.
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http://dx.doi.org/10.1038/s41598-025-13598-9 | DOI Listing |
Cisplatin resistance significantly limits the efficacy of chemotherapy in non-small cell lung cancer, necessitating the development of new strategies to overcome this barrier. This in vitro study aimed to elucidate the mechanism by which β-Ele reverses cisplatin resistance in lung adenocarcinoma cells via the LINC00511-mediated glycolysis and Wnt/β-catenin signaling pathways. The cisplatin-resistant human lung adenocarcinoma cell line (A549/DDP), with either LINC00511 overexpression or knockdown, was established through plasmid transfection.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Purpose: Bronchiolar adenoma (BA) is a rare benign pulmonary neoplasm originating from the bronchial mucosal epithelium and mimics lung adenocarcinoma (LAC) both radiographically and microscopically. This study aimed to develop a nomogram for distinguishing BA from LAC by integrating clinical characteristics and artificial intelligence (AI)-derived histogram parameters across two medical centers.
Methods: This retrospective study included 215 patients with diagnoses confirmed by postoperative pathology from two medical centers.
J Thorac Oncol
August 2025
Department of Radiation Medicine, Markey Cancer Center, University of Kentucky, Lexington, Kentucky.
Introduction: Cigarette smoking negatively affects lung cancer prognosis. Incorporating smoking history into stage-stratified survival analyses may improve prognostication.
Methods: Using the International Association for the Study of Lung Cancer ninth edition NSCLC database, we evaluated the association between smoking status at diagnosis and overall survival (OS) using Kaplan-Meier plots and multivariate Cox proportional hazard regression models adjusted for age, region, sex, histologic type, performance status, and TNM stage.
Brief Bioinform
August 2025
Department of Respiratory Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xincheng District, Xi'an 710004, China.
Accurate tumor mutation burden (TMB) quantification is critical for immunotherapy stratification, yet remains challenging due to variability across sequencing platforms, tumor heterogeneity, and variant calling pipelines. Here, we introduce TMBquant, an explainable AI-powered caller designed to optimize TMB estimation through dynamic feature selection, ensemble learning, and automated strategy adaptation. Built upon the H2O AutoML framework, TMBquant integrates variant features, minimizes classification errors, and enhances both accuracy and stability across diverse datasets.
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