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Zhang L, Zhang X, Guan M, Zeng J, Yu F, Lai F. Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma. Inflamm Res 2024; 73:841-866. [PMID: 38507067 DOI: 10.1007/s00011-024-01871-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/03/2024] [Accepted: 03/05/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematically compiled ADCC-associated genes to create prognostic signatures. METHODS In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with various cancer types from diverse cohorts were included. R package ConsensusClusterPlus was utilized to classify patients into different subtypes. A number of machine-learning algorithms were used to construct the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IOBR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools were used to analyze the CNV and SNV data. The Oncopredict package was used to predict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to process single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associated cells. RESULTS Through unsupervised clustering, two distinct subtypes of LUAD were identified, each exhibiting distinct clinical characteristics. The ADCCRS, consisted of 16 genes, was constructed by integrated machine-learning methods. The prognostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in predicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS positively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME modulators. Cells representing high-ADCCRS phenotype exhibited more malignant features. LUAD patients with high ADCCRS levels exhibited sensitivity to chemotherapy and targeted therapy, while displaying resistance to immunotherapy. In pan-cancer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients. CONCLUSIONS ADCCRS offers a critical prognostic insight for patients with LUAD, shedding light on the tumor microenvironment and forecasting treatment responsiveness.
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Affiliation(s)
- Liangyu Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the Fitst Affiliated Hospiral, Fujian Medical University, Fuzhou, 350212, China
| | - Xun Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the Fitst Affiliated Hospiral, Fujian Medical University, Fuzhou, 350212, China
| | - Maohao Guan
- Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the Fitst Affiliated Hospiral, Fujian Medical University, Fuzhou, 350212, China
| | - Jianshen Zeng
- Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the Fitst Affiliated Hospiral, Fujian Medical University, Fuzhou, 350212, China
| | - Fengqiang Yu
- Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China.
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the Fitst Affiliated Hospiral, Fujian Medical University, Fuzhou, 350212, China.
| | - Fancai Lai
- Department of Thoracic Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China.
- Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the Fitst Affiliated Hospiral, Fujian Medical University, Fuzhou, 350212, China.
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Diao MN, Zhang XJ, Zhang YF. The critical roles of m6A RNA methylation in lung cancer: from mechanism to prognosis and therapy. Br J Cancer 2023; 129:8-23. [PMID: 36997662 PMCID: PMC10307841 DOI: 10.1038/s41416-023-02246-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 03/05/2023] [Accepted: 03/17/2023] [Indexed: 04/03/2023] Open
Abstract
Lung cancer, a highly malignant disease, greatly affects patients' quality of life. N6-methyladenosine (m6A) is one of the most common posttranscriptional modifications of various RNAs, including mRNAs and ncRNAs. Emerging studies have demonstrated that m6A participates in normal physiological processes and that its dysregulation is involved in many diseases, especially pulmonary tumorigenesis and progression. Among these, regulators including m6A writers, readers and erasers mediate m6A modification of lung cancer-related molecular RNAs to regulate their expression. Furthermore, the imbalance of this regulatory effect adversely affects signalling pathways related to lung cancer cell proliferation, invasion, metastasis and other biological behaviours. Based on the close association between m6A and lung cancer, various prognostic risk models have been established and novel drugs have been developed. Overall, this review comprehensively elaborates the mechanism of m6A regulation in the development of lung cancer, suggesting its potential for clinical application in the therapy and prognostic assessment of lung cancer.
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Affiliation(s)
- Mei-Ning Diao
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, 266021, China
| | - Xiao-Jing Zhang
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, 266021, China
| | - Yin-Feng Zhang
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, 266021, China.
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Huang Y, Zou Y, Tian Y, Yang Z, Hou Z, Li P, Liu F, Ling J, Wen Y. N6-methylandenosine-related immune genes correlate with prognosis and immune landscapes in gastric cancer. Front Oncol 2022; 12:1009881. [PMID: 36523987 PMCID: PMC9745091 DOI: 10.3389/fonc.2022.1009881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/16/2022] [Indexed: 11/10/2023] Open
Abstract
OBJECTIVES This study aimed to probe into the significance of N6-methyladenosine (m6A)-related immune genes (m6AIGs) in predicting prognoses and immune landscapes of patients with gastric cancer (GC). METHODS The clinical data and transcriptomic matrix of GC patients were acquired from The Cancer Genome Atlas database. The clinically meaningful m6AIGs were acquired by univariate Cox regression analysis. GC patients were stratified into different clusters via consensus clustering analysis and different risk subgroups via m6AIGs prognostic signature. The clinicopathological features and tumor microenvironment (TME) in the different clusters and different risk subgroups were explored. The predictive performance was evaluated using the KM method, ROC curves, and univariate and multivariate regression analyses. Moreover, we fabricated a nomogram based on risk scores and clinical risk characteristics. Biological functional analysis was performed based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. The connectivity map was used to screen out potential small molecule drugs for GC patients. RESULTS A total of 14 prognostic m6AIGs and two clusters based on 14 prognostic m6AIGs were identified. A prognostic signature based on 4 m6AIGs and a nomogram based on independent prognostic factors was constructed and validated. Different clusters and different risk subgroups were significantly correlated with TME scores, the distribution of immune cells, and the expression of immune checkpoint genes. Some malignant and immune biological processes and pathways were correlated with the patients with poor prognosis. Ten small molecular drugs with potential therapeutic effect were screened out. CONCLUSIONS This study revealed the prognostic role and significant values of m6AIGs in GC, which enhanced the understanding of m6AIGs and paved the way for developing predictive biomarkers and therapeutic targets for GC.
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Affiliation(s)
- Yuancheng Huang
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Yushan Zou
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Yanhua Tian
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Zehong Yang
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Zhengkun Hou
- Department of Gastroenterology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Peiwu Li
- Department of Gastroenterology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Fengbin Liu
- Department of Gastroenterology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- Department of Gastroenterology, Baiyun Branch of the First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Jiasheng Ling
- Department of Gastroenterology, Huizhou Hospital of Traditional Chinese Medicine, Huizhou, Guangdong, China
| | - Yi Wen
- Department of Gastroenterology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
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A Novel Risk Model for lncRNAs Associated with Oxidative Stress Predicts Prognosis of Bladder Cancer. JOURNAL OF ONCOLOGY 2022; 2022:8408328. [PMID: 36268283 PMCID: PMC9578793 DOI: 10.1155/2022/8408328] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/15/2022] [Accepted: 09/14/2022] [Indexed: 12/03/2022]
Abstract
Background Oxidative stress (OS) reactions are closely related to the development and progression of bladder cancer (BCa). This project aimed to identify new potential biomarkers to predict the prognosis of BCa and improve immunotherapy. Methods We downloaded transcriptomic information and clinical data on BCa from The Cancer Genome Atlas (TCGA). Screening for OS genes was statistically different between tumor and adjacent normal tissue. A coexpression analysis between lncRNAs and differentially expressed OS genes was performed to identify OS-related lncRNAs. Then, differentially expressed oxidative stress lncRNAs (DEOSlncRNAs) between tumors and normal tissues were identified. Univariate/multivariate Cox regression analysis was performed to select the lncRNAs for risk assessment. LASSO analysis was conducted to establish a prognostic model. The prognostic risk model could accurately predict BCa patient prognosis and reveal a close correlation with clinicopathological features. We analyzed the principal component analysis (PCA), immune microenvironment, and half-maximal inhibitory concentration (IC50) in the risk groups. Results We constructed a model containing eight DEOSlncRNAs (AC021321.1, AC068196.1, AC008750.1, SETBP1-DT, AL590617.2, THUMPD3-AS1, AC112721.1, and NR4A1AS). The prognostic risk model showed better results in predicting the prognosis of BCa patients and was strongly correlated with clinicopathological characteristics. We found great agreement between the calibration plots and prognostic predictions in this model. The areas under the receiver operating characteristic (ROC) curve (AUCs) at 1, 3, and 5 years were 0.792, 0.804, and 0.843, respectively. This model also showed good predictive ability regarding the tumor microenvironment and tumor mutation burden. In addition, the high-risk group was more sensitive to eight therapeutic agents, and the low-risk group was more responsive to five therapeutic agents. Sixteen immune checkpoints were significantly different between the two risk groups. Conclusion Our eight DEOSlncRNA risk models provide new insights into predicting prognosis and clinical progression in BCa patients.
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