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Zhang Z. The Initial COVID-19 Reliable Interactive DNA Methylation Markers and Biological Implications. BIOLOGY 2024; 13:245. [PMID: 38666857 PMCID: PMC11048280 DOI: 10.3390/biology13040245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/22/2024] [Accepted: 04/04/2024] [Indexed: 04/28/2024]
Abstract
Earlier research has established the existence of reliable interactive genomic biomarkers. However, reliable DNA methylation biomarkers, not to mention interactivity, have yet to be identified at the epigenetic level. This study, drawing from 865,859 methylation sites, discovered two miniature sets of Infinium MethylationEPIC sites, each having eight CpG sites (genes) to interact with each other and disease subtypes. They led to the nearly perfect (96.87-100% accuracy) prediction of COVID-19 patients from patients with other diseases or healthy controls. These CpG sites can jointly explain some post-COVID-19-related conditions. These CpG sites and the optimally performing genomic biomarkers reported in the literature become potential druggable targets. Among these CpG sites, cg16785077 (gene MX1), cg25932713 (gene PARP9), and cg22930808 (gene PARP9) at DNA methylation levels indicate that the initial SARS-CoV-2 virus may be better treated as a transcribed viral DNA into RNA virus, i.e., not as an RNA virus that has concerned scientists in the field. Such a discovery can significantly change the scientific thinking and knowledge of viruses.
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Affiliation(s)
- Zhengjun Zhang
- School of Computer, Data and Information Sciences, University of Wisconsin, Madison, WI 53706, USA
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Liu Y, Xu Y, Li X, Chen M, Wang X, Zhang N, Zhang H, Zhang Z. Towards precision oncology discovery: four less known genes and their unknown interactions as highest-performed biomarkers for colorectal cancer. NPJ Precis Oncol 2024; 8:13. [PMID: 38243058 PMCID: PMC10799029 DOI: 10.1038/s41698-024-00512-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 01/05/2024] [Indexed: 01/21/2024] Open
Abstract
The goal of this study was to use a new interpretable machine-learning framework based on max-logistic competing risk factor models to identify a parsimonious set of differentially expressed genes (DEGs) that play a pivotal role in the development of colorectal cancer (CRC). Transcriptome data from nine public datasets were analyzed, and a new Chinese cohort was collected to validate the findings. The study discovered a set of four critical DEGs - CXCL8, PSMC2, APP, and SLC20A1 - that exhibit the highest accuracy in detecting CRC in diverse populations and ethnicities. Notably, PSMC2 and CXCL8 appear to play a central role in CRC, and CXCL8 alone could potentially serve as an early-stage marker for CRC. This work represents a pioneering effort in applying the max-logistic competing risk factor model to identify critical genes for human malignancies, and the interpretability and reproducibility of the results across diverse populations suggests that the four DEGs identified can provide a comprehensive description of the transcriptomic features of CRC. The practical implications of this research include the potential for personalized risk assessment and precision diagnosis and tailored treatment plans for patients.
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Affiliation(s)
- Yongjun Liu
- Department of Laboratory Medicine and Pathology, University of Washington Medical Center, Seattle, WA, USA
| | - Yuqing Xu
- Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA
| | - Xiaoxing Li
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China.
| | - Mengke Chen
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Xueqin Wang
- Department of Statistics and Finance, University of Science and Technology of China, Hefei, China
| | - Ning Zhang
- Department of Gastroenterology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Heping Zhang
- Yale School of Public Health, Yale University, New Haven, CT, USA
| | - Zhengjun Zhang
- Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
- School of Economics and Management, and MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation, University of Chinese Academy of Sciences, Center for Forecasting Sciences, Chinese Academy of Sciences, Beijing, China.
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Zhang Z. Genomic Transcriptome Benefits and Potential Harms of COVID-19 Vaccines Indicated from Optimized Genomic Biomarkers. Vaccines (Basel) 2022; 10:1774. [PMID: 36366282 PMCID: PMC9692407 DOI: 10.3390/vaccines10111774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 09/28/2022] [Accepted: 10/20/2022] [Indexed: 11/07/2023] Open
Abstract
COVID-19 vaccines can be the tugboats for preventing SARS-CoV-2 infections when they are practical and, more importantly, without adverse effects. However, the reality is that they may result in short-term or long-term impacts on COVID-19-related diseases and even trigger the formation of new variants of SARS-CoV-2. Using published data, we use a set of optimized-performance COVID-19 genomic biomarkers (MND1, CDC6, ZNF282) to study the benefits and adverse effects of the BNT162b2 vaccine. We found that the vaccine lowered the expression values of genes MND1 and CDC6 while heightening the expression values of ZNF282 in individuals who are SARS-CoV-2 naïve, which is expected and satisfies the biological equivalence between the COVID-19 disease and the genomic signature patterns established in the literature. However, we also found that COVID-19-convalescent octogenarians responded reversely. The vaccine heightened the expression values of MND1 and CDC6. In addition, it lowered the expression values of ZNF282. Such adverse effects raise outstanding concerns about whether or not COVID-19-convalescent individuals should take the current vaccine or when they can take it. These findings are new at the genomic level and can provide insights into developing next-generation vaccines, antiviral drugs, and pandemic management guidance.
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Affiliation(s)
- Zhengjun Zhang
- Department of Statistics, School of Computer, Data & Information Sciences, University of Wisconsin, Madison, WI 53706, USA
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Zhang Z. Genomic Biomarker Heterogeneities between SARS-CoV-2 and COVID-19. Vaccines (Basel) 2022; 10:1657. [PMID: 36298522 PMCID: PMC9608907 DOI: 10.3390/vaccines10101657] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 09/27/2022] [Accepted: 09/29/2022] [Indexed: 11/06/2022] Open
Abstract
Genes functionally associated with SARS-CoV-2 infection and genes functionally related to the COVID-19 disease can be different, whose distinction will become the first essential step for successfully fighting against the COVID-19 pandemic. Unfortunately, this first step has not been completed in all biological and medical research. Using a newly developed max-competing logistic classifier, two genes, ATP6V1B2 and IFI27, stand out to be critical in the transcriptional response to SARS-CoV-2 infection with differential expressions derived from NP/OP swab PCR. This finding is evidenced by combining these two genes with another gene in predicting disease status to achieve better-indicating accuracy than existing classifiers with the same number of genes. In addition, combining these two genes with three other genes to form a five-gene classifier outperforms existing classifiers with ten or more genes. These two genes can be critical in fighting against the COVID-19 pandemic as a new focus and direction with their exceptional predicting accuracy. Comparing the functional effects of these genes with a five-gene classifier with 100% accuracy identified and tested from blood samples in our earlier work, the genes and their transcriptional response and functional effects on SARS-CoV-2 infection, and the genes and their functional signature patterns on COVID-19 antibodies, are significantly different. We will use a total of fourteen cohort studies (including breakthrough infections and omicron variants) with 1481 samples to justify our results. Such significant findings can help explore the causal and pathological links between SARS-CoV-2 infection and the COVID-19 disease, and fight against the disease with more targeted genes, vaccines, antiviral drugs, and therapies.
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Affiliation(s)
- Zhengjun Zhang
- Department of Statistics, School of Computer, Data & Information Sciences, University of Wisconsin, Madison, WI 53706, USA
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