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Mori KM, McElroy JP, Weng DY, Chung S, Fadda P, Reisinger SA, Ying KL, Brasky TM, Wewers MD, Freudenheim JL, Shields PG, Song MA. Lung mitochondrial DNA copy number, inflammatory biomarkers, gene transcription and gene methylation in vapers and smokers. EBioMedicine 2022; 85:104301. [PMID: 36215783 PMCID: PMC9561685 DOI: 10.1016/j.ebiom.2022.104301] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/31/2022] [Accepted: 09/21/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Mitochondrial DNA copy number (mtCN) maintains cellular function and homeostasis, and is linked to nuclear DNA methylation and gene expression. Increased mtCN in the blood is associated with smoking and respiratory disease, but has received little attention for target organ effects for smoking or electronic cigarette (EC) use. METHODS Bronchoscopy biospecimens from healthy EC users, smokers (SM), and never-smokers (NS) were assessed for associations of mtCN with mtDNA point mutations, immune responses, nuclear DNA methylation and gene expression using linear regression. Ingenuity pathway analysis was used for enriched pathways. GEO and TCGA respiratory disease datasets were used to explore the involvement of mtCN-associated signatures. FINDINGS mtCN was higher in SM than NS, but EC was not statistically different from either. Overall there was a negative association of mtCN with a point mutation in the D-loop but no difference within groups. Positive associations of mtCN with IL-2 and IL-4 were found in EC only. mtCN was significantly associated with 71,487 CpGs and 321 transcripts. 263 CpGs were correlated with nearby transcripts for genes enriched in the immune system. EC-specific mtCN-associated-CpGs and genes were differentially expressed in respiratory diseases compared to controls, including genes involved in cellular movement, inflammation, metabolism, and airway hyperresponsiveness. INTERPRETATION Smoking may elicit a lung toxic effect through mtCN. While the impact of EC is less clear, EC-specific associations of mtCN with nuclear biomarkers suggest exposure may not be harmless. Further research is needed to understand the role of smoking and EC-related mtCN on lung disease risks. FUNDING The National Cancer Institute, the National Heart, Lung, and Blood Institute, the Food and Drug Administration Center for Tobacco Products, the National Center For Advancing Translational Sciences, and Pelotonia Intramural Research Funds.
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Affiliation(s)
- Kellie M Mori
- Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH, United States
| | - Joseph P McElroy
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH, United States
| | - Daniel Y Weng
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH, United States
| | - Sangwoon Chung
- Pulmonary and Critical Care Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH, United States
| | - Paolo Fadda
- Genomics Shared Resource, The Ohio State University and James Cancer Hospital, Columbus, OH, United States
| | - Sarah A Reisinger
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH, United States
| | - Kevin L Ying
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH, United States
| | - Theodore M Brasky
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH, United States
| | - Mark D Wewers
- Pulmonary and Critical Care Medicine, Department of Internal Medicine, The Ohio State University, Columbus, OH, United States
| | - Jo L Freudenheim
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, United States
| | - Peter G Shields
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH, United States.
| | - Min-Ae Song
- Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH, United States.
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Wang X, Li W, Zhang K, Sun J, Yang J, Zhang A, Xu L. A Novel Local Tumor Progression Prediction Method for Multimode Ablation Treatment. IEEE Trans Biomed Eng 2021; 69:1386-1397. [PMID: 34591754 DOI: 10.1109/tbme.2021.3116607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE The multimode ablation of liver cancer, which uses radio-frequency heating after a pre-freezing process to treat the tumor, has shown significantly improved therapeutic effects and enhanced anti-tumor immune response. Unlike open surgery, the ablated lesions remain in the body after treatment, so it is critical to assess the immediate outcome and to monitor disease status over time. Here we propose a novel tumor progression prediction method for simultaneous postoperative evaluation and prognosis analysis. METHODS We propose to leverage the intraoperative therapeutic information extracted from thermal dose distribution. For tumors with specific sensitivity reflected in medical images, different thermal doses implicitly indicate the degree of instant damage and long-term inhibition excited under specific ablation energy. We further propose a survival analysis framework for the multimode ablation treatment. It extracts carefully designed features from clinical, preoperative, intraoperative, and postoperative data, then uses random survival forest for feature selection and deep neural networks for survival prediction. RESULTS We evaluated the proposed methods using clinical data. The results show that our method outperforms the state-of-the-art survival analysis methods with a C-index of 0.8550.090. The thermal dose information contributes significantly to the prediction accuracy by taking up 21.7% of the overall feature importance. CONCLUSION The proposed methods have been demonstrated to be a powerful tool in tumor progression prediction of multimode ablation therapy. SIGNIFICANCE This kind of data-driven prognosis analysis may benefit personalized medicine and simplify the follow-up process.
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dos Santos FRC, Guardia GDA, dos Santos FF, Ohara D, Galante PAF. Reboot: a straightforward approach to identify genes and splicing isoforms associated with cancer patient prognosis. NAR Cancer 2021; 3:zcab024. [PMID: 34316711 PMCID: PMC8210018 DOI: 10.1093/narcan/zcab024] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/26/2021] [Accepted: 06/03/2021] [Indexed: 12/20/2022] Open
Abstract
Nowadays, the massive amount of data generated by modern sequencing technologies provides an unprecedented opportunity to find genes associated with cancer patient prognosis, connecting basic and translational research. However, treating high dimensionality of gene expression data and integrating it with clinical variables are major challenges to perform these analyses. Here, we present Reboot, an integrative approach to find and validate genes and transcripts (splicing isoforms) associated with cancer patient prognosis from high dimensional expression datasets. Reboot innovates by using a multivariate strategy with penalized Cox regression (LASSO method) combined with a bootstrap approach, in addition to statistical tests and plots to support the findings. Applying Reboot on data from 154 glioblastoma patients, we identified a three-gene signature (IKBIP, OSMR, PODNL1) whose increased derived risk score was significantly associated with worse patients' prognosis. Similarly, Reboot was able to find a seven-splicing isoforms signature related to worse overall survival in 177 pancreatic adenocarcinoma patients with elevated risk scores after uni- and multivariate analyses. In summary, Reboot is an efficient, intuitive and straightforward way of finding genes or splicing isoforms signatures relevant to patient prognosis, which can democratize this kind of analysis and shed light on still under-investigated cancer-related genes and splicing isoforms.
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Affiliation(s)
- Felipe R C dos Santos
- Centro de Oncologia Molecular, Hospital Sirio-Libanes, Sao Paulo, SP 01308-060, Brazil
- Programa Interunidades em Bioinformatica, Universidade de São Paulo, Sao Paulo, SP 05508-090, Brazil
| | - Gabriela D A Guardia
- Centro de Oncologia Molecular, Hospital Sirio-Libanes, Sao Paulo, SP 01308-060, Brazil
| | - Filipe F dos Santos
- Centro de Oncologia Molecular, Hospital Sirio-Libanes, Sao Paulo, SP 01308-060, Brazil
- Departamento de Bioquimica, Universidade de Sao Paulo, SP 05508-000, Brazil
| | - Daniel T Ohara
- Centro de Oncologia Molecular, Hospital Sirio-Libanes, Sao Paulo, SP 01308-060, Brazil
| | - Pedro A F Galante
- Centro de Oncologia Molecular, Hospital Sirio-Libanes, Sao Paulo, SP 01308-060, Brazil
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