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Yang M, Meng Y, Hao W, Zhang J, Liu J, Wu L, Lin B, Liu Y, Zhang Y, Yu X, Wang X, Gong Y, Ge L, Fan Y, Xie C, Xu Y, Chang Q, Zhang Y, Qin X. A prognostic model for SARS-CoV-2 breakthrough infection: Analyzing a prospective cellular immunity cohort. Int Immunopharmacol 2024; 131:111829. [PMID: 38489974 DOI: 10.1016/j.intimp.2024.111829] [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: 12/08/2023] [Revised: 03/03/2024] [Accepted: 03/06/2024] [Indexed: 03/17/2024]
Abstract
BACKGROUND Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets among individuals who received COVID-19 vaccine boosters and subsequently experienced a SARS-CoV-2 breakthrough infection. METHODS Flow cytometry (FCM) was employed to investigate the early and longitudinal pattern changes of cellular immunity in patients with SARS-CoV-2 breakthrough infections following COVID-19 vaccine boosters. XGBoost (a machine learning algorithm) was employed to analyze cellular immunity prior to SARS-CoV-2 breakthrough, aiming to establish a prognostic model for SARS-CoV-2 breakthrough infections. RESULTS Following SARS-CoV-2 breakthrough infection, naïve T cells and TEMRA subsets increased while the percentage of TCM and TEM cells decreased. Naïve and non-switched memory B cells increased while switched and double-negative memory B cells decreased. The XGBoost model achieved an area under the curve (AUC) of 0.78, with an accuracy rate of 81.8 %, a sensitivity of 75 %, and specificity of 85.7 %. TNF-α, CD27-CD19+cells, and TEMRA subsets were identified as high predictors. An increase in TNF-α, cTfh, double-negative memory B cells, IL-6, IL-10, and IFN-γ prior to SARS-CoV-2 infection was associated with enduring clinical symptoms; conversely, an increase in CD3+ T cells, CD4+ T cells, and IL-2 was associated with clinical with non-enduring clinical symptoms. CONCLUSION SARS-CoV-2 breakthrough infection leads to disturbances in cellular immunity. Assessing cellular immunity prior to breakthrough infection serves as a valuable prognostic tool for SARS-CoV-2 infection, which facilitates clinical decision-making.
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Affiliation(s)
- Mei Yang
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Yuan Meng
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Wudi Hao
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Jin Zhang
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Jianhua Liu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Lina Wu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Baoxu Lin
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Yong Liu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Yue Zhang
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Xiaojun Yu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Xiaoqian Wang
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Yu Gong
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Lili Ge
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Yan Fan
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Conghong Xie
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Yiyun Xu
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Qing Chang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
| | - Yixiao Zhang
- Department of Urology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
| | - Xiaosong Qin
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China.
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Ren J, Gao Q, Zhou X, Chen L, Guo W, Feng K, Huang T, Cai YD. Identification of key gene expression associated with quality of life after recovery from COVID-19. Med Biol Eng Comput 2024; 62:1031-1048. [PMID: 38123886 DOI: 10.1007/s11517-023-02988-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023]
Abstract
Post-acute sequelae of COVID-19 (PASC) is a persistent complication of severe acute respiratory syndrome coronavirus 2 infection that includes symptoms, such as fatigue, cognitive impairment, and respiratory distress. These symptoms severely affect the quality of life of patients after their recovery from COVID-19. In this study, a group of machine learning algorithms analyzed the whole blood RNA-seq data from patients with different PASC levels. The purpose of this analysis was to identify the gene markers associated with PASC and the special expression patterns for different PASC levels. By comparing the quality of life of patients after the acute phase of COVID-19 and before the disease, samples in the dataset were divided into three groups, namely, "Better," "The Same," and "Worse." Each patient was represented by the expression levels of 58,929 genes. The machine learning-based workflow included six feature-ranking algorithms, incremental feature selection (IFS), and four classification algorithms. The feature ranking algorithms were in charge of assessing feature importance, whereas IFS with classification algorithms were used to extract essential genes and to construct efficient classifiers and classification rules. The expression of top genes in the results was associated with the immune response to viral infection, which is supported by the published literature. For example, patients with low CCDC18 expression and high CPED1 expression had good quality of life, whereas those with low CDC16 expression had poor quality of life.
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Affiliation(s)
- JingXin Ren
- School of Life Sciences, Shanghai University, Shanghai, 200444, China
| | - Qian Gao
- Department of Pharmacy, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - XianChao Zhou
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China
| | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200030, China
| | - KaiYan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, 510507, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, 200444, China.
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3
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Yang Y, Zhang Y, Ren J, Feng K, Li Z, Huang T, Cai Y. Identification of Colon Immune Cell Marker Genes Using Machine Learning Methods. Life (Basel) 2023; 13:1876. [PMID: 37763280 PMCID: PMC10532943 DOI: 10.3390/life13091876] [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: 07/30/2023] [Revised: 08/24/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Immune cell infiltration that occurs at the site of colon tumors influences the course of cancer. Different immune cell compositions in the microenvironment lead to different immune responses and different therapeutic effects. This study analyzed single-cell RNA sequencing data in a normal colon with the aim of screening genetic markers of 25 candidate immune cell types and revealing quantitative differences between them. The dataset contains 25 classes of immune cells, 41,650 cells in total, and each cell is expressed by 22,164 genes at the expression level. They were fed into a machine learning-based stream. The five feature ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, minimum redundancy maximum relevance, and random forest) were first used to analyze the importance of gene features, yielding five feature lists. Then, incremental feature selection and two classification algorithms (decision tree and random forest) were combined to filter the most important genetic markers from each list. For different immune cell subtypes, their marker genes, such as KLRB1 in CD4 T cells, RPL30 in B cell IGA plasma cells, and JCHAIN in IgG producing B cells, were identified. They were confirmed to be differentially expressed in different immune cells and involved in immune processes. In addition, quantitative rules were summarized by using the decision tree algorithm to distinguish candidate immune cell types. These results provide a reference for exploring the cell composition of the colon cancer microenvironment and for clinical immunotherapy.
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Affiliation(s)
- Yong Yang
- Qianwei Hospital of Jilin Province, Changchun 130012, China;
| | - Yuhang Zhang
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Jingxin Ren
- School of Life Sciences, Shanghai University, Shanghai 200444, China;
| | - Kaiyan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, China;
| | - Zhandong Li
- College of Biological and Food Engineering, Jilin Engineering Normal University, Changchun 130052, China;
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yudong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, China;
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Ren JX, Gao Q, Zhou XC, Chen L, Guo W, Feng KY, Lu L, Huang T, Cai YD. Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes. BIOLOGY 2023; 12:947. [PMID: 37508378 PMCID: PMC10376631 DOI: 10.3390/biology12070947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/20/2023] [Accepted: 06/29/2023] [Indexed: 07/30/2023]
Abstract
As COVID-19 develops, dynamic changes occur in the patient's immune system. Changes in molecular levels in different immune cells can reflect the course of COVID-19. This study aims to uncover the molecular characteristics of different immune cell subpopulations at different stages of COVID-19. We designed a machine learning workflow to analyze scRNA-seq data of three immune cell types (B, T, and myeloid cells) in four levels of COVID-19 severity/outcome. The datasets for three cell types included 403,700 B-cell, 634,595 T-cell, and 346,547 myeloid cell samples. Each cell subtype was divided into four groups, control, convalescence, progression mild/moderate, and progression severe/critical, and each immune cell contained 27,943 gene features. A feature analysis procedure was applied to the data of each cell type. Irrelevant features were first excluded according to their relevance to the target variable measured by mutual information. Then, four ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and max-relevance and min-redundancy) were adopted to analyze the remaining features, resulting in four feature lists. These lists were fed into the incremental feature selection, incorporating three classification algorithms (decision tree, k-nearest neighbor, and random forest) to extract key gene features and construct classifiers with superior performance. The results confirmed that genes such as PFN1, RPS26, and FTH1 played important roles in SARS-CoV-2 infection. These findings provide a useful reference for the understanding of the ongoing effect of COVID-19 development on the immune system.
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Affiliation(s)
- Jing-Xin Ren
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Qian Gao
- Department of Pharmacy, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Xiao-Chao Zhou
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai 200025, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai 200030, China
| | - Kai-Yan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, China
| | - Lin Lu
- Department of Radiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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5
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Li H, Ma Q, Ren J, Guo W, Feng K, Li Z, Huang T, Cai YD. Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods. Front Genet 2023; 14:1157305. [PMID: 37007947 PMCID: PMC10065150 DOI: 10.3389/fgene.2023.1157305] [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: 02/02/2023] [Accepted: 03/07/2023] [Indexed: 03/19/2023] Open
Abstract
Multiple types of COVID-19 vaccines have been shown to be highly effective in preventing SARS-CoV-2 infection and in reducing post-infection symptoms. Almost all of these vaccines induce systemic immune responses, but differences in immune responses induced by different vaccination regimens are evident. This study aimed to reveal the differences in immune gene expression levels of different target cells under different vaccine strategies after SARS-CoV-2 infection in hamsters. A machine learning based process was designed to analyze single-cell transcriptomic data of different cell types from the blood, lung, and nasal mucosa of hamsters infected with SARS-CoV-2, including B and T cells from the blood and nasal cavity, macrophages from the lung and nasal cavity, alveolar epithelial and lung endothelial cells. The cohort was divided into five groups: non-vaccinated (control), 2*adenovirus (two doses of adenovirus vaccine), 2*attenuated (two doses of attenuated virus vaccine), 2*mRNA (two doses of mRNA vaccine), and mRNA/attenuated (primed by mRNA vaccine, boosted by attenuated vaccine). All genes were ranked using five signature ranking methods (LASSO, LightGBM, Monte Carlo feature selection, mRMR, and permutation feature importance). Some key genes that contributed to the analysis of immune changes, such as RPS23, DDX5, PFN1 in immune cells, and IRF9 and MX1 in tissue cells, were screened. Afterward, the five feature sorting lists were fed into the feature incremental selection framework, which contained two classification algorithms (decision tree [DT] and random forest [RF]), to construct optimal classifiers and generate quantitative rules. Results showed that random forest classifiers could provide relative higher performance than decision tree classifiers, whereas the DT classifiers provided quantitative rules that indicated special gene expression levels under different vaccine strategies. These findings may help us to develop better protective vaccination programs and new vaccines.
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Affiliation(s)
- Hao Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Qinglan Ma
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Jingxin Ren
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Institutes for Biological Sciences (SIBS), Shanghai Jiao Tong University School of Medicine (SJTUSM), Chinese Academy of Sciences (CAS), Shanghai, China
| | - Kaiyan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, China
| | - Zhandong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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6
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Li J, Ren J, Liao H, Guo W, Feng K, Huang T, Cai YD. Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods. Front Microbiol 2023; 14:1138674. [PMID: 37007526 PMCID: PMC10063797 DOI: 10.3389/fmicb.2023.1138674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/01/2023] [Indexed: 03/19/2023] Open
Abstract
To date, COVID-19 remains a serious global public health problem. Vaccination against SARS-CoV-2 has been adopted by many countries as an effective coping strategy. The strength of the body’s immune response in the face of viral infection correlates with the number of vaccinations and the duration of vaccination. In this study, we aimed to identify specific genes that may trigger and control the immune response to COVID-19 under different vaccination scenarios. A machine learning-based approach was designed to analyze the blood transcriptomes of 161 individuals who were classified into six groups according to the dose and timing of inoculations, including I-D0, I-D2-4, I-D7 (day 0, days 2–4, and day 7 after the first dose of ChAdOx1, respectively) and II-D0, II-D1-4, II-D7-10 (day 0, days 1–4, and days 7–10 after the second dose of BNT162b2, respectively). Each sample was represented by the expression levels of 26,364 genes. The first dose was ChAdOx1, whereas the second dose was mainly BNT162b2 (Only four individuals received a second dose of ChAdOx1). The groups were deemed as labels and genes were considered as features. Several machine learning algorithms were employed to analyze such classification problem. In detail, five feature ranking algorithms (Lasso, LightGBM, MCFS, mRMR, and PFI) were first applied to evaluate the importance of each gene feature, resulting in five feature lists. Then, the lists were put into incremental feature selection method with four classification algorithms to extract essential genes, classification rules and build optimal classifiers. The essential genes, namely, NRF2, RPRD1B, NEU3, SMC5, and TPX2, have been previously associated with immune response. This study also summarized expression rules that describe different vaccination scenarios to help determine the molecular mechanism of vaccine-induced antiviral immunity.
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Affiliation(s)
- Jing Li
- School of Computer Science, Baicheng Normal University, Baicheng, Jilin, China
| | - JingXin Ren
- School of Life Sciences, Shanghai University, Shanghai, China
| | | | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, China
| | - KaiYan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, China
| | - Tao Huang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- *Correspondence: Tao Huang, ; Yu-Dong Cai,
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
- *Correspondence: Tao Huang, ; Yu-Dong Cai,
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Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features. Int J Mol Sci 2023; 24:ijms24054905. [PMID: 36902333 PMCID: PMC10002748 DOI: 10.3390/ijms24054905] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 02/24/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023] Open
Abstract
The persistent impact of the COVID-19 pandemic and heterogeneity in disease manifestations point to a need for innovative approaches to identify drivers of immune pathology and predict whether infected patients will present with mild/moderate or severe disease. We have developed a novel iterative machine learning pipeline that utilizes gene enrichment profiles from blood transcriptome data to stratify COVID-19 patients based on disease severity and differentiate severe COVID cases from other patients with acute hypoxic respiratory failure. The pattern of gene module enrichment in COVID-19 patients overall reflected broad cellular expansion and metabolic dysfunction, whereas increased neutrophils, activated B cells, T-cell lymphopenia, and proinflammatory cytokine production were specific to severe COVID patients. Using this pipeline, we also identified small blood gene signatures indicative of COVID-19 diagnosis and severity that could be used as biomarker panels in the clinical setting.
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Avila-Ponce de León U, Vazquez-Jimenez A, Cervera A, Resendis-González G, Neri-Rosario D, Resendis-Antonio O. Machine Learning and COVID-19: Lessons from SARS-CoV-2. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1412:311-335. [PMID: 37378775 DOI: 10.1007/978-3-031-28012-2_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.
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Affiliation(s)
- Ugo Avila-Ponce de León
- Programa de Doctorado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Aarón Vazquez-Jimenez
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Alejandra Cervera
- Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Galilea Resendis-González
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Daniel Neri-Rosario
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico
| | - Osbaldo Resendis-Antonio
- Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México, Mexico.
- Coordinación de la Investigación Científica - Red de Apoyo a la Investigación - Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico.
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Das B. An implementation of a hybrid method based on machine learning to identify biomarkers in the Covid-19 diagnosis using DNA sequences. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2022; 230:104680. [PMID: 36213553 PMCID: PMC9528020 DOI: 10.1016/j.chemolab.2022.104680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 09/20/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Although some people do not have any chronic disease or are not in the risky age group for Covid-19, they are more vulnerable to the coronavirus. As the reason for this situation, some experts focus on the immune system of the person, while others think that the genetic history of patients may play a role. It is critical to detect corona from DNA signals as early as possible to determine the relationship between Covid-19 and genes. Thus, the effect on the severe course of the disease of variations in the genes associated with the corona disease will be revealed. In this study, a novel intelligent computer approach is proposed to identify coronavirus from nucleotide signals for the first time. The proposed method presents a multilayered feature extraction structure to extract the most effective features using an Entropy-based mapping technique, Discrete Wavelet Transform (DWT), statistical feature extractor, and Singular Value Decomposition (SVD), together. Then 94 distinctive features are selected by the ReliefF technique. Support vector machine (SVM) and k nearest neighborhood (k-NN) are chosen as classifiers. The method achieved the highest classification accuracy rate of 98.84% with an SVM classifier to detect Covid-19 from DNA signals. The proposed method is ready to be tested with a different database in the diagnosis of Covid-19 using RNA or other signals.
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Affiliation(s)
- Bihter Das
- Department of Software Engineering, Technology Faculty, Firat University, 23119, Elazig, Turkey
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Aramini B, Masciale V, Samarelli AV, Tonelli R, Cerri S, Clini E, Stella F, Dominici M. Biological effects of COVID-19 on lung cancer: Can we drive our decisions. Front Oncol 2022; 12:1029830. [PMID: 36300087 PMCID: PMC9589049 DOI: 10.3389/fonc.2022.1029830] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 09/23/2022] [Indexed: 11/13/2022] Open
Abstract
COVID-19 infection caused by SARS-CoV-2 is considered catastrophic because it affects multiple organs, particularly those of the respiratory tract. Although the consequences of this infection are not fully clear, it causes damage to the lungs, the cardiovascular and nervous systems, and other organs, subsequently inducing organ failure. In particular, the effects of SARS-CoV-2-induced inflammation on cancer cells and the tumor microenvironment need to be investigated. COVID-19 may alter the tumor microenvironment, promoting cancer cell proliferation and dormant cancer cell (DCC) reawakening. DCCs reawakened upon infection with SARS-CoV-2 can populate the premetastatic niche in the lungs and other organs, leading to tumor dissemination. DCC reawakening and consequent neutrophil and monocyte/macrophage activation with an uncontrolled cascade of pro-inflammatory cytokines are the most severe clinical effects of COVID-19. Moreover, neutrophil extracellular traps have been demonstrated to activate the dissemination of premetastatic cells into the lungs. Further studies are warranted to better define the roles of COVID-19 in inflammation as well as in tumor development and tumor cell metastasis; the results of these studies will aid in the development of further targeted therapies, both for cancer prevention and the treatment of patients with COVID-19.
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Affiliation(s)
- Beatrice Aramini
- Division of Thoracic Surgery, Department of Experimental, Diagnostic and Specialty Medicine—DIMES of the Alma Mater Studiorum, University of Bologna, G.B. Morgagni—L. Pierantoni Hospital, Forlì, Italy
- *Correspondence: Beatrice Aramini,
| | - Valentina Masciale
- Division of Oncology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Laboratory of Cell Therapy, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena and Reggio Emilia, Modena, Italy
| | - Anna Valeria Samarelli
- Laboratory of Cell Therapy, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena and Reggio Emilia, Modena, Italy
- Respiratory Disease Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena and Reggio Emilia, Modena, Italy
| | - Roberto Tonelli
- Respiratory Disease Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena and Reggio Emilia, Modena, Italy
| | - Stefania Cerri
- Respiratory Disease Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena and Reggio Emilia, Modena, Italy
| | - Enrico Clini
- Respiratory Disease Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena and Reggio Emilia, Modena, Italy
| | - Franco Stella
- Division of Thoracic Surgery, Department of Experimental, Diagnostic and Specialty Medicine—DIMES of the Alma Mater Studiorum, University of Bologna, G.B. Morgagni—L. Pierantoni Hospital, Forlì, Italy
| | - Massimo Dominici
- Division of Oncology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Laboratory of Cell Therapy, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena and Reggio Emilia, Modena, Italy
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