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Pozzi C, Levi R, Braga D, Carli F, Darwich A, Spadoni I, Oresta B, Dioguardi CC, Peano C, Ubaldi L, Angelotti G, Bottazzi B, Garlanda C, Desai A, Voza A, Azzolini E, Cecconi M, Mantovani A, Penna G, Barbieri R, Politi LS, Rescigno M. A 'Multiomic' Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in Coronavirus Disease 2019. Gastro Hep Adv 2022; 1:194-209. [PMID: 35174369 PMCID: PMC8818445 DOI: 10.1016/j.gastha.2021.12.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 12/06/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND AIMS The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the health care systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized (inpatients) or discharged (outpatients). Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration, and discharged patients often returned to the hospital for aggravation of their condition. Here, we have developed a new combined approach of omics to identify factors that could distinguish coronavirus disease 19 (COVID-19) inpatients from outpatients. METHODS Saliva and blood samples were collected over the course of two observational cohort studies. By using machine learning approaches, we compared salivary metabolome of 50 COVID-19 patients with that of 270 healthy individuals having previously been exposed or not to SARS-CoV-2. We then correlated the salivary metabolites that allowed separating COVID-19 inpatients from outpatients with serum biomarkers and salivary microbiota taxa differentially represented in the two groups of patients. RESULTS We identified nine salivary metabolites that allowed assessing the need of hospitalization. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrrolidineacetic acid) and one serum protein, chitinase 3-like-1 (CHI3L1), were sufficient to separate inpatients from outpatients completely and correlated with modulated microbiota taxa. In particular, we found Corynebacterium 1 to be overrepresented in inpatients, whereas Actinomycetaceae F0332, Candidatus Saccharimonas, and Haemophilus were all underrepresented in the hospitalized population. CONCLUSION This is a proof of concept that a combined omic analysis can be used to stratify patients independently from COVID-19.
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Key Words
- AUC, area under the curve
- CHI3L1
- CHI3L1, chitinase 3-like-1
- CI, confidence interval
- COVID-19
- COVID-19, coronavirus disease 19
- DT, decision tree
- ELISA, enzyme-linked immunosorbent assay
- ESI, electrospray ionization
- FDR, false discovery rate
- IgG, immunoglobulin G
- LR, logistic regression
- Metabolome
- Microbiota
- PCA, principal component analysis
- PTX3, pentraxin 3
- RFE, recursive feature elimination
- SVM, support vector machine
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Affiliation(s)
- Chiara Pozzi
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Riccardo Levi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Daniele Braga
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Francesco Carli
- Department of Informatics, Università degli Studi di Torino, Torino, Piemonte, Italy
| | - Abbass Darwich
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Ilaria Spadoni
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Bianca Oresta
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Carola Conca Dioguardi
- Institute of Genetic and Biomedical Research, UoS of Milan, National Research Council, Rozzano, Milan, Italy
| | - Clelia Peano
- Institute of Genetic and Biomedical Research, UoS of Milan, National Research Council, Rozzano, Milan, Italy
| | - Leonardo Ubaldi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | | | | | - Cecilia Garlanda
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy,Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Antonio Desai
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy,Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Antonio Voza
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy,Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Elena Azzolini
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy,Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Maurizio Cecconi
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy,Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | | | - Alberto Mantovani
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy,Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy,The William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Giuseppe Penna
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Riccardo Barbieri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Letterio S. Politi
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy,Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Maria Rescigno
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy,Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy,Correspondence: Address correspondence to: Prof. Maria Rescigno, PhD, Humanitas University Pieve Emanuele, Milan, Italy
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Albaradei S, Thafar M, Alsaedi A, Van Neste C, Gojobori T, Essack M, Gao X. Machine learning and deep learning methods that use omics data for metastasis prediction. Comput Struct Biotechnol J 2021; 19:5008-5018. [PMID: 34589181 PMCID: PMC8450182 DOI: 10.1016/j.csbj.2021.09.001] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 08/16/2021] [Accepted: 09/02/2021] [Indexed: 12/14/2022] Open
Abstract
Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.
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Key Words
- AE, autoencoder
- ANN, Artificial Neural Network
- AUC, area under the curve
- Acc, Accuracy
- Artificial intelligence
- BC, Betweenness centrality
- BH, Benjamini-Hochberg
- BioGRID, Biological General Repository for Interaction Datasets
- CCP, compound covariate predictor
- CEA, Carcinoembryonic antigen
- CNN, convolution neural networks
- CV, cross-validation
- Cancer
- DBN, deep belief network
- DDBN, discriminative deep belief network
- DEGs, differentially expressed genes
- DIP, Database of Interacting Proteins
- DNN, Deep neural network
- DT, Decision Tree
- Deep learning
- EMT, epithelial-mesenchymal transition
- FC, fully connected
- GA, Genetic Algorithm
- GANs, generative adversarial networks
- GEO, Gene Expression Omnibus
- HCC, hepatocellular carcinoma
- HPRD, Human Protein Reference Database
- KNN, K-nearest neighbor
- L-SVM, linear SVM
- LIMMA, linear models for microarray data
- LOOCV, Leave-one-out cross-validation
- LR, Logistic Regression
- MCCV, Monte Carlo cross-validation
- MLP, multilayer perceptron
- Machine learning
- Metastasis
- NPV, negative predictive value
- PCA, Principal component analysis
- PPI, protein-protein interaction
- PPV, positive predictive value
- RC, ridge classifier
- RF, Random Forest
- RFE, recursive feature elimination
- RMA, robust multi‐array average
- RNN, recurrent neural networks
- SGD, stochastic gradient descent
- SMOTE, synthetic minority over-sampling technique
- SVM, Support Vector Machine
- Se, sensitivity
- Sp, specificity
- TCGA, The Cancer Genome Atlas
- k-CV, k-fold cross validation
- mRMR, minimum redundancy maximum relevance
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Affiliation(s)
- Somayah Albaradei
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- King Abdulaziz University, Faculty of Computing and Information Technology, Jeddah, Saudi Arabia
| | - Maha Thafar
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Taif University, Collage of Computers and Information Technology, Taif, Saudi Arabia
| | - Asim Alsaedi
- King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia
- King Abdulaziz Medical City, Jeddah, Saudi Arabia
| | - Christophe Van Neste
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Takashi Gojobori
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Magbubah Essack
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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