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Recent studies have indicated that RRM2 plays a crucial part in the tumor immune microenvironment. According to the expression of RRM2, we evaluated immune cell infiltration, immunotherapy biomarkers, and the expression of immune checkpoint molecules in four lung adenocarcinoma (LUAD) datasets. We employed the Tumor Immune Dysfunction and Exclusion (TIDE) and CIBERSORTx algorithms to examine the patterns of immune cell distribution and evaluate the responses to anti-programmed death protein-1/programmed death ligand-1 (PD-1/PD-L1) therapy in three publicly available LUAD datasets. These findings were corroborated using a validation group comprising patients who received treatment with PD-1/PD-L1 inhibitors. Additionally, we conducted experiments using LUAD cell lines to investigate how RRM2 affects the expression of PD-L1. In comparison to the low RRM2 group, the high RRM2 group exhibited a high interferon gamma signature, high T-cell-inflamed signature, high CD274 expression, high CD8+ T cell levels, low cancer-associated fibroblasts, and low M2 macrophages, according to TIDE analysis in the three LUAD datasets. Analysis of the three LUAD datasets using CIBERSORTx confirmed a positive correlation between RRM2 and CD8+ T cells, and this finding was validated by immunohistochemistry in a separate validation set. In the three LUAD datasets without PD-1/PD-L1 inhibitor treatment, higher RRM2 expression was associated with a poorer prognosis. However, in the LUAD dataset treated with PD-1/PD-L1 inhibitors, higher RRM2 expression was associated with better prognosis. In the three datasets, the high-RRM2 group exhibited higher expression of inhibitory immune checkpoint molecules. In a LUAD cell line study, we discovered that RRM2 regulates PD-L1 expression through the ANXA1/AKT pathway. The expression of RRM2 shows promise as a predictive biomarker for PD-1/PD-L1 inhibitors in LUAD patients, and it may represent a new target to overcome resistance to PD-L1/PD-1 therapies.
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Comput Biol Chem
September 2025
Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macao Special Administrative Region of China. Electronic address:
With the advancements of next-generation sequencing, publicly available pharmacogenomic datasets from cancer cell lines provide a handle for developing predictive models of drug responses and identifying associated biomarkers. However, many currently available predictive models are often just used as black boxes, lacking meaningful biological interpretations. In this study, we made use of open-source drug response data from cancer cell lines, in conjunction with KEGG pathway information, to develop sparse neural networks, K-net, enabling the prediction of drug response in EGFR signaling pathways and the identification of key biomarkers.
View Article and Find Full Text PDFLung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially.
View Article and Find Full Text PDFJ Appl Stat
January 2025
Department of Statistics and Data Science, School of Economics, Xiamen University, Xiamen, People's Republic of China.
Practical scenarios often present instances where the types of responses are different between multi-source different datasets, reflecting distinct attributes or characteristics. In this paper, an integrative rank-based regression is proposed to facilitate information sharing among varied datasets with multi-type responses. Taking advantage of the rank-based regression, our proposed approach adeptly tackles differences in the magnitude of loss functions.
View Article and Find Full Text PDFOncol Lett
November 2025
Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin 300052, P.R. China.
Lung adenocarcinoma (LUAD) remains one of the most prevalent and lethal cancers globally, making it critical to understand the mechanisms driving its progression and improve prognosis. Moreover, cuproptosis and mitochondrial dysfunction may be involved in lung cancer. Therefore, the present study aimed to identify mitochondrial genes associated with cuproptosis to develop a prognostic model for patients with LUAD, with the potential to predict survival outcomes and responses to treatment.
View Article and Find Full Text PDFHereditas
August 2025
Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Jinzhou Medical University, No. 2 Section 5 Guta Road, Renmin District, Jinzhou, 121000, Liaoning, China.
Background: Lung adenocarcinoma (LUAD) stands as a prevalent malignancy, yet its pathology remains incompletely comprehended.
Methods: In this comprehensive study, we explored the roles of the pituitary tumor-transforming gene (PTTG) family, including PTTG1, PTTG2, and the pseudogene PTTG3P in lung adenocarcinoma (LUAD). Employing a multi-faceted approach, we conducted in-depth analyses using clinical samples and expression datasets.