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Tutanov O, Shefer A, Tsentalovich Y, Tamkovich S. Comparative Analysis of Molecular Functions and Biological Role of Proteins from Cell-Free DNA-Protein Complexes Circulating in Plasma of Healthy Females and Breast Cancer Patients. Int J Mol Sci 2023; 24:ijms24087279. [PMID: 37108441 PMCID: PMC10138639 DOI: 10.3390/ijms24087279] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/05/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023] Open
Abstract
Cell-free DNA (cfDNA) circulates in the bloodstream packed in membrane-coated structures (such as apoptotic bodies) or bound to proteins. To identify proteins involved in the formation of deoxyribonucleoprotein complexes circulating in the blood, native complexes were isolated using affinity chromatography with immobilized polyclonal anti-histone antibodies from plasma of healthy females (HFs) and breast cancer patients (BCPs). It was found that the nucleoprotein complexes (NPCs) from HF plasma samples contained shorter DNA fragments (~180 bp) than BCP NPCs. However, the share of DNA in the NPCs from cfDNA in blood plasma in HFs and BCPs did not differ significantly, as well as the share of NPC protein from blood plasma total protein. Proteins were separated by SDS-PAGE and identified by MALDI-TOF mass spectrometry. Bioinformatic analysis showed that in the presence of a malignant tumor, the proportion of proteins involved in ion channels, protein binding, transport, and signal transduction increased in the composition of blood-circulating NPCs. Moreover, 58 (35%) proteins are differentially expressed in a number of malignant neoplasms in the NPCs of BCPs. Identified NPC proteins from BCP blood can be recommended for further testing as breast cancer diagnostic/prognostic biomarkers or as being useful in developing gene-targeted therapy approaches.
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Affiliation(s)
- Oleg Tutanov
- V. Zelman Institute for Medicine and Psychology, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Aleksei Shefer
- V. Zelman Institute for Medicine and Psychology, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Yuri Tsentalovich
- International Tomography Center, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Svetlana Tamkovich
- V. Zelman Institute for Medicine and Psychology, Novosibirsk State University, 630090 Novosibirsk, Russia
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Noushmehr H, Herrgott G, Morosini NS, Castro AV. Noninvasive approaches to detect methylation-based markers to monitor gliomas. Neurooncol Adv 2022; 4:ii22-ii32. [PMID: 36380867 PMCID: PMC9650474 DOI: 10.1093/noajnl/vdac021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/07/2023] Open
Abstract
In this review, we summarize the current approaches used to detect glioma tissue-derived DNA methylation markers in liquid biopsy specimens with the aim to diagnose, prognosticate and potentially track treatment response and evolution of patients with gliomas.
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Affiliation(s)
- Houtan Noushmehr
- Department of Neurosurgery, Omics Laboratory, Henry Ford Health System, Detroit, Michigan, USA
| | - Grayson Herrgott
- Department of Neurosurgery, Omics Laboratory, Henry Ford Health System, Detroit, Michigan, USA
| | - Natalia S Morosini
- Department of Neurosurgery, Omics Laboratory, Henry Ford Health System, Detroit, Michigan, USA
| | - Ana Valeria Castro
- Department of Neurosurgery, Omics Laboratory, Henry Ford Health System, Detroit, Michigan, USA
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Sun X, Feng W, Cui P, Ruan R, Ma W, Han Z, Sun J, Pan Y, Zhu J, Zhong X, Li J, Ma M, Hu R, Lv M, Huang Q, Zhang W, Feng M, Zhuang X, Huang B, Zhou X. Detection and monitoring of HBV-related hepatocellular carcinoma from plasma cfDNA fragmentation profiles. Genomics 2022; 114:110502. [PMID: 36220554 DOI: 10.1016/j.ygeno.2022.110502] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 09/30/2022] [Accepted: 10/04/2022] [Indexed: 01/15/2023]
Abstract
Most hepatocellular carcinomas (HCCs) are associated with hepatitis B virus infection (HBV) in China. Early detection of HCC can significantly improve prognosis but is not yet fully clinically feasible. This study aims to develop methods for detecting HCC and studying the carcinogenesis of HBV using plasma cell-free DNA (cfDNA) whole-genome sequencing (WGS) data. Low coverage WGS was performed for 452 participants, including healthy individuals, hepatitis B patients, cirrhosis patients, and HCC patients. Then the sequencing data were processed using various machine learning models based on cfDNA fragmentation profiles for cancer detection. Our best model achieved a sensitivity of 87.10% and a specificity of 88.37%, and it showed an increased sensitivity with higher BCLC stages of HCC. Overall, this study proves the potential of a non-invasive assay based on cfDNA fragmentation profiles for the detection and prognosis of HCC and provides preliminary data on the carcinogenic mechanism of HBV.
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Affiliation(s)
- Xinfeng Sun
- Department of Liver Disease, the fourth Clinical Medical School, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China
| | - Wenxing Feng
- Department of Liver Disease, the fourth Clinical Medical School, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China
| | - Pin Cui
- Shenzhen Rapha Biotechnology Incorporate, Shenzhen 518118, China
| | - Ruyun Ruan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
| | - Wenfeng Ma
- Department of Liver Disease, the fourth Clinical Medical School, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China
| | - Zhiyi Han
- Department of Liver Disease, the fourth Clinical Medical School, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China
| | - Jialing Sun
- Department of Liver Disease, the fourth Clinical Medical School, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China
| | - Yuanke Pan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
| | - Jinxin Zhu
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
| | - Xin Zhong
- Department of Liver Disease, the fourth Clinical Medical School, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China
| | - Jing Li
- Department of Liver Disease, the fourth Clinical Medical School, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China
| | - Mengqing Ma
- Department of Liver Disease, the fourth Clinical Medical School, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China
| | - Rui Hu
- Department of Liver Disease, the fourth Clinical Medical School, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China
| | - Minling Lv
- Department of Liver Disease, the fourth Clinical Medical School, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China
| | - Qi Huang
- Department of Liver Disease, the fourth Clinical Medical School, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China
| | - Wei Zhang
- Department of Liver Disease, the fourth Clinical Medical School, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China
| | - Mingji Feng
- Shenzhen Rapha Biotechnology Incorporate, Shenzhen 518118, China
| | - Xintao Zhuang
- Shenzhen Rapha Biotechnology Incorporate, Shenzhen 518118, China
| | - Bingding Huang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China.
| | - Xiaozhou Zhou
- Department of Liver Disease, the fourth Clinical Medical School, Guangzhou University of Chinese Medicine, Shenzhen 518033, China; Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China.
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