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Salim F, Mizutani S, Shiba S, Takamaru H, Yamada M, Nakajima T, Yachida T, Soga T, Saito Y, Fukuda S, Yachida S, Yamada T. Fusobacterium species are distinctly associated with patients with Lynch syndrome colorectal cancer. iScience 2024; 27:110181. [PMID: 38993678 PMCID: PMC11237946 DOI: 10.1016/j.isci.2024.110181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/11/2024] [Accepted: 06/01/2024] [Indexed: 07/13/2024] Open
Abstract
Accumulating evidence demonstrates clear correlation between the gut microbiota and sporadic colorectal cancer (CRC). Despite this, there is limited understanding of the association between the gut microbiota and CRC in Lynch Syndrome (LS), a hereditary type of CRC. Here, we analyzed fecal shotgun metagenomic and targeted metabolomic of 71 Japanese LS subjects. A previously published Japanese sporadic CRC cohort, which includes non-LS controls, was utilized as a non-LS cohort (n = 437). LS subjects exhibited reduced microbial diversity and low-Faecalibacterium enterotypes compared to non-LS. Patients with LS-CRC had higher levels of Fusobacterium nucleatum and fap2. Differential fecal metabolites and functional genes suggest heightened degradation of lysine and arginine in LS-CRC. A comparison between LS and non-LS subjects prior to adenoma formation revealed distinct fecal metabolites of LS subjects. These findings suggest that the gut microbiota plays a more responsive role in CRC tumorigenesis in patients with LS than those without LS.
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Affiliation(s)
- Felix Salim
- School of Life Science and Technology, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan
| | - Sayaka Mizutani
- School of Life Science and Technology, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan
- Research Fellow of Japan Society for the Promotion of Science, Tokyo, Japan
| | - Satoshi Shiba
- Division of Cancer Genomics, National Cancer Center Research Institute, Chuo-ku, Tokyo 104-0045, Japan
| | - Hiroyuki Takamaru
- Endoscopy Division, National Cancer Center Hospital, Chuo-ku 104-0045, Tokyo, Japan
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, Chuo-ku 104-0045, Tokyo, Japan
| | - Takeshi Nakajima
- Endoscopy Division, National Cancer Center Hospital, Chuo-ku 104-0045, Tokyo, Japan
| | - Tatsuo Yachida
- Department of Gastroenterology & Neurology, Faculty of Medicine, Kagawa University, Miki-cho, Kagawa 761-0793, Japan
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Chuo-ku 104-0045, Tokyo, Japan
| | - Shinji Fukuda
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
- Gut Environmental Design Group, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Kanagawa 210-0821, Japan
- Transborder Medical Research Center, University of Tsukuba, Tsukuba, Ibaraki 305-8575, Japan
- Laboratory for Regenerative Microbiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo 113-8421, Japan
- Metagen, Inc., Tsuruoka, Yamagata 997-0052, Japan
- Metagen Theurapeutics, Inc., Tsuruoka, Yamagata 997-0052, Japan
| | - Shinichi Yachida
- Department of Cancer Genome Informatics, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Osaka 565-0871, Japan
| | - Takuji Yamada
- School of Life Science and Technology, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan
- Metagen, Inc., Tsuruoka, Yamagata 997-0052, Japan
- Metagen Theurapeutics, Inc., Tsuruoka, Yamagata 997-0052, Japan
- digzyme, Inc., Minato-ku, Tokyo 105-0004, Japan
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2
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Shi Y, Zhou M, Chang C, Jiang P, Wei K, Zhao J, Shan Y, Zheng Y, Zhao F, Lv X, Guo S, Wang F, He D. Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management. Front Immunol 2024; 15:1409555. [PMID: 38915408 PMCID: PMC11194317 DOI: 10.3389/fimmu.2024.1409555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 05/24/2024] [Indexed: 06/26/2024] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.
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Affiliation(s)
- Yiming Shi
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Mi Zhou
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Cen Chang
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Ping Jiang
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Kai Wei
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Jianan Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Yu Shan
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Yixin Zheng
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Fuyu Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Xinliang Lv
- Traditional Chinese Medicine Hospital of Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia Autonomous Region, China
| | - Shicheng Guo
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fubo Wang
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
- Department of Urology, Affiliated Tumor Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China
| | - Dongyi He
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
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3
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Wang H, Li X, Shi P, You X, Zhao G. Establishment and evaluation of on-chip intestinal barrier biosystems based on microfluidic techniques. Mater Today Bio 2024; 26:101079. [PMID: 38774450 PMCID: PMC11107260 DOI: 10.1016/j.mtbio.2024.101079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/17/2024] [Accepted: 05/01/2024] [Indexed: 05/24/2024] Open
Abstract
As a booming engineering technology, the microfluidic chip has been widely applied for replicating the complexity of human intestinal micro-physiological ecosystems in vitro. Biosensors, 3D imaging, and multi-omics have been applied to engineer more sophisticated intestinal barrier-on-chip platforms, allowing the improved monitoring of physiological processes and enhancing chip performance. In this review, we report cutting-edge advances in the microfluidic techniques applied for the establishment and evaluation of intestinal barrier platforms. We discuss different design principles and microfabrication strategies for the establishment of microfluidic gut barrier models in vitro. Further, we comprehensively cover the complex cell types (e.g., epithelium, intestinal organoids, endothelium, microbes, and immune cells) and controllable extracellular microenvironment parameters (e.g., oxygen gradient, peristalsis, bioflow, and gut-organ axis) used to recapitulate the main structural and functional complexity of gut barriers. We also present the current multidisciplinary technologies and indicators used for evaluating the morphological structure and barrier integrity of established gut barrier models in vitro. Finally, we highlight the challenges and future perspectives for accelerating the broader applications of these platforms in disease simulation, drug development, and personalized medicine. Hence, this review provides a comprehensive guide for the development and evaluation of microfluidic-based gut barrier platforms.
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Affiliation(s)
- Hui Wang
- Master Lab for Innovative Application of Nature Products, National Center of Technology Innovation for Synthetic Biology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences (CAS), Tianjin, 300308, China
| | - Xiangyang Li
- Henan Engineering Research Center of Food Microbiology, College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023, China
- Haihe Laboratory of Synthetic Biology, Tianjin, 300308, China
| | - Pengcheng Shi
- Henan Engineering Research Center of Food Microbiology, College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023, China
| | - Xiaoyan You
- Master Lab for Innovative Application of Nature Products, National Center of Technology Innovation for Synthetic Biology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences (CAS), Tianjin, 300308, China
- Henan Engineering Research Center of Food Microbiology, College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023, China
| | - Guoping Zhao
- Master Lab for Innovative Application of Nature Products, National Center of Technology Innovation for Synthetic Biology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences (CAS), Tianjin, 300308, China
- CAS-Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200032, China
- CAS-Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
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4
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Hagen M, Dass R, Westhues C, Blom J, Schultheiss SJ, Patz S. Interpretable machine learning decodes soil microbiome's response to drought stress. ENVIRONMENTAL MICROBIOME 2024; 19:35. [PMID: 38812054 PMCID: PMC11138018 DOI: 10.1186/s40793-024-00578-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 05/10/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Extreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil bacterial microbiota and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable machine learning holds immense potential for drought stress classification of soil based on marker taxa. RESULTS This study demonstrates that the 16S rRNA-based metagenomic approach of Differential Abundance Analysis methods and machine learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil bacterial micobiome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested. CONCLUSIONS In the detection of drought stress in soil bacterial microbiota, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.
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Affiliation(s)
- Michelle Hagen
- Computomics GmbH, Eisenbahnstraße 1, 72072, Tübingen, Baden-Württemberg, Germany
| | - Rupashree Dass
- Computomics GmbH, Eisenbahnstraße 1, 72072, Tübingen, Baden-Württemberg, Germany
| | - Cathy Westhues
- Computomics GmbH, Eisenbahnstraße 1, 72072, Tübingen, Baden-Württemberg, Germany
| | - Jochen Blom
- Bioinformatics and Systems Biology, Justus Liebig University Gießen, Heinrich-Buff-Ring 58, 35390, Gießen, Hesse, Germany
| | | | - Sascha Patz
- Computomics GmbH, Eisenbahnstraße 1, 72072, Tübingen, Baden-Württemberg, Germany.
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5
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Mallick H, Porwal A, Saha S, Basak P, Svetnik V, Paul E. An integrated Bayesian framework for multi-omics prediction and classification. Stat Med 2024; 43:983-1002. [PMID: 38146838 DOI: 10.1002/sim.9953] [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: 04/21/2022] [Revised: 10/06/2023] [Accepted: 10/24/2023] [Indexed: 12/27/2023]
Abstract
With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from cross-sectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by combining information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner.
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Affiliation(s)
- Himel Mallick
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, 10065, New York, USA
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA
| | - Anupreet Porwal
- Department of Statistics, University of Washington, Seattle, Washington, USA
| | - Satabdi Saha
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Piyali Basak
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
| | - Vladimir Svetnik
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
| | - Erina Paul
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
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6
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Novielli P, Romano D, Magarelli M, Bitonto PD, Diacono D, Chiatante A, Lopalco G, Sabella D, Venerito V, Filannino P, Bellotti R, De Angelis M, Iannone F, Tangaro S. Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification. Front Microbiol 2024; 15:1348974. [PMID: 38426064 PMCID: PMC10901987 DOI: 10.3389/fmicb.2024.1348974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
Background Colorectal cancer (CRC) is a type of tumor caused by the uncontrolled growth of cells in the mucosa lining the last part of the intestine. Emerging evidence underscores an association between CRC and gut microbiome dysbiosis. The high mortality rate of this cancer has made it necessary to develop new early diagnostic methods. Machine learning (ML) techniques can represent a solution to evaluate the interaction between intestinal microbiota and host physiology. Through explained artificial intelligence (XAI) it is possible to evaluate the individual contributions of microbial taxonomic markers for each subject. Our work also implements the Shapley Method Additive Explanations (SHAP) algorithm to identify for each subject which parameters are important in the context of CRC. Results The proposed study aimed to implement an explainable artificial intelligence framework using both gut microbiota data and demographic information from subjects to classify a cohort of control subjects from those with CRC. Our analysis revealed an association between gut microbiota and this disease. We compared three machine learning algorithms, and the Random Forest (RF) algorithm emerged as the best classifier, with a precision of 0.729 ± 0.038 and an area under the Precision-Recall curve of 0.668 ± 0.016. Additionally, SHAP analysis highlighted the most crucial variables in the model's decision-making, facilitating the identification of specific bacteria linked to CRC. Our results confirmed the role of certain bacteria, such as Fusobacterium, Peptostreptococcus, and Parvimonas, whose abundance appears notably associated with the disease, as well as bacteria whose presence is linked to a non-diseased state. Discussion These findings emphasizes the potential of leveraging gut microbiota data within an explainable AI framework for CRC classification. The significant association observed aligns with existing knowledge. The precision exhibited by the RF algorithm reinforces its suitability for such classification tasks. The SHAP analysis not only enhanced interpretability but identified specific bacteria crucial in CRC determination. This approach opens avenues for targeted interventions based on microbial signatures. Further exploration is warranted to deepen our understanding of the intricate interplay between microbiota and health, providing insights for refined diagnostic and therapeutic strategies.
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Affiliation(s)
- Pierfrancesco Novielli
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Donato Romano
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Michele Magarelli
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Pierpaolo Di Bitonto
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Domenico Diacono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Annalisa Chiatante
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Giuseppe Lopalco
- Dipartimento di Medicina di Precisione e Rigenerativa e Area Jonica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Daniele Sabella
- Dipartimento di Medicina di Precisione e Rigenerativa e Area Jonica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Vincenzo Venerito
- Dipartimento di Medicina di Precisione e Rigenerativa e Area Jonica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Pasquale Filannino
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Maria De Angelis
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Florenzo Iannone
- Dipartimento di Medicina di Precisione e Rigenerativa e Area Jonica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
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7
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Qin S, Sun S, Wang Y, Li C, Fu L, Wu M, Yan J, Li W, Lv J, Chen L. Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework. Sci Rep 2024; 14:527. [PMID: 38177198 PMCID: PMC10767103 DOI: 10.1038/s41598-023-51108-x] [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: 10/17/2023] [Accepted: 12/30/2023] [Indexed: 01/06/2024] Open
Abstract
Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational autoencoder (VAE) and multilayer perceptron (MLP) was proposed. And the Shapley Additive Explanations (SHAP) method was introduced to evaluate the contribution of feature genes to the classification decision, which helped us to develop a biologically meaningful biomarker potential scoring algorithm. Nineteen potential biomarkers for LUAD were identified, which were involved in the regulation of immune and metabolic functions in LUAD. A prognostic risk model for LUAD was constructed by the biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5 screened by Cox regression analysis, dividing the patients into high-risk and low-risk groups. The prognostic risk model was validated with external datasets. The low-risk group was characterized by enrichment of immune pathways and higher immune infiltration compared to the high-risk group. While, the high-risk group was accompanied by an increase in metabolic pathway activity. There were significant differences between the high- and low-risk groups in metabolic reprogramming of aerobic glycolysis, amino acids, and lipids, as well as in angiogenic activity, epithelial-mesenchymal transition, tumorigenic cytokines, and inflammatory response. Furthermore, high-risk patients were more sensitive to Afatinib, Gefitinib, and Gemcitabine as predicted by the pRRophetic algorithm. This study provides prognostic signatures capable of revealing the immune and metabolic landscapes for LUAD, and may shed light on the identification of other cancer biomarkers.
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Affiliation(s)
- Shimei Qin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Shibin Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Yahui Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Chao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Lei Fu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Ming Wu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Jinxing Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China.
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, China.
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8
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Gallos IK, Tryfonopoulos D, Shani G, Amditis A, Haick H, Dionysiou DD. Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions. Diagnostics (Basel) 2023; 13:3673. [PMID: 38132257 PMCID: PMC10743128 DOI: 10.3390/diagnostics13243673] [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: 11/10/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.
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Affiliation(s)
- Ioannis K. Gallos
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
| | - Dimitrios Tryfonopoulos
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
| | - Gidi Shani
- Laboratory for Nanomaterial-Based Devices, Technion—Israel Institute of Technology, Haifa 3200003, Israel; (G.S.); (H.H.)
| | - Angelos Amditis
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
| | - Hossam Haick
- Laboratory for Nanomaterial-Based Devices, Technion—Israel Institute of Technology, Haifa 3200003, Israel; (G.S.); (H.H.)
| | - Dimitra D. Dionysiou
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
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9
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Dong C, Guan Q, Xu W, Zhang X, Jin B, Yu S, Xu X, Xia Y. Disentangling the age-related manner in the associations between gut microbiome and women's health: a multi-cohort microbiome study. Gut Microbes 2023; 15:2290320. [PMID: 38059752 PMCID: PMC10730178 DOI: 10.1080/19490976.2023.2290320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 11/27/2023] [Indexed: 12/08/2023] Open
Abstract
Women's health encompasses life-course healthcare, and mounting evidence emphasizes the pivotal contribution of gut microbiota. Therefore, understanding the temporal dynamics of gut microbiota and how age influences disease-gut microbiota associations is essential for improving women's health. By analyzing metagenomic data from 3625 healthy women, we revealed significant effects of age on gut microbiota and age-dependent patterns in microbial features, such as relative abundance, Shannon index, and microbial network properties. Additionally, declining trends in the predictive accuracy of gut microbiota for age groups were shown using iterative sub-sampling based random forest (ISSRF) model. Age-specific species markers were also identified, many of which were shared across age groups. To investigate the influence of age on disease-gut microbiota associations, metagenomic data from 681 women with various disease conditions and 491 matched healthy controls were collected. A substantial proportion of species markers for inflammatory bowel disease (IBD), type 2 diabetes (T2D), atherosclerotic cardiovascular disease (ACVD), and impaired glucose tolerance (IGT) differed in relative abundance across age groups, and were also age-specific species markers. Besides, the microbiota-based probabilities of IBD and ACVD were positively correlated with age. Furthermore, the age specificity of disease-gut microbiota associations was explored using the ISSRF model. Associations between IBD and gut microbiota were age-specific, with reduced stability of disease species markers in childhood and adolescence, possibly due to decrease in the effect size between patients and controls. Our findings provided valuable insights into promoting healthy aging and developing personalized healthcare strategies for women.
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Affiliation(s)
- Chao Dong
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Quanquan Guan
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Wei Xu
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaochen Zhang
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Bowen Jin
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shumin Yu
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xiaoyu Xu
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yankai Xia
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
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10
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Acharjee A. Explainable AI for gut microbiome-based diagnostics: colorectal cancer as a case study. Diagnosis (Berl) 2023; 10:448-449. [PMID: 37328267 DOI: 10.1515/dx-2023-0062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 06/04/2023] [Indexed: 06/18/2023]
Affiliation(s)
- Animesh Acharjee
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Institute of Translational Medicine, University of Birmingham, Birmingham, UK
- MRC Health Data Research UK (HDR UK), Midlands Site, Birmingham, UK
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11
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D’Elia D, Truu J, Lahti L, Berland M, Papoutsoglou G, Ceci M, Zomer A, Lopes MB, Ibrahimi E, Gruca A, Nechyporenko A, Frohme M, Klammsteiner T, Pau ECDS, Marcos-Zambrano LJ, Hron K, Pio G, Simeon A, Suharoschi R, Moreno-Indias I, Temko A, Nedyalkova M, Apostol ES, Truică CO, Shigdel R, Telalović JH, Bongcam-Rudloff E, Przymus P, Jordamović NB, Falquet L, Tarazona S, Sampri A, Isola G, Pérez-Serrano D, Trajkovik V, Klucar L, Loncar-Turukalo T, Havulinna AS, Jansen C, Bertelsen RJ, Claesson MJ. Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action. Front Microbiol 2023; 14:1257002. [PMID: 37808321 PMCID: PMC10558209 DOI: 10.3389/fmicb.2023.1257002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/05/2023] [Indexed: 10/10/2023] Open
Abstract
The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
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Affiliation(s)
- Domenica D’Elia
- Department of Biomedical Sciences, National Research Council, Institute for Biomedical Technologies, Bari, Italy
| | - Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Magali Berland
- Université Paris-Saclay, INRAE, MetaGenoPolis, Jouy-en-Josas, France
| | - Georgios Papoutsoglou
- JADBio Gnosis DA S.A., Science and Technology Park of Crete, Heraklion, Greece
- Department of Computer Science, University of Crete, Heraklion, Greece
| | - Michelangelo Ceci
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
| | - Aldert Zomer
- Department of Biomolecular Health Sciences (Infectious Diseases and Immunology), Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
| | - Marta B. Lopes
- Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, Caparica, Portugal
- UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Caparica, Portugal
| | - Eliana Ibrahimi
- Department of Biology, University of Tirana, Tirana, Albania
| | - Aleksandra Gruca
- Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
| | - Alina Nechyporenko
- Systems Engineering Department, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
- Department of Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Germany
| | - Marcus Frohme
- Department of Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Germany
| | - Thomas Klammsteiner
- Department of Microbiology, Universität Innsbruck, Innsbruck, Austria
- Department of Ecology, Universität Innsbruck, Innsbruck, Austria
| | - Enrique Carrillo-de Santa Pau
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain
| | - Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain
| | - Karel Hron
- Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký University, Olomouc, Czechia
| | - Gianvito Pio
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
| | - Andrea Simeon
- BioSense Institute, University of Novi Sad, Novi Sad, Serbia
| | - Ramona Suharoschi
- Molecular Nutrition and Proteomics Research Laboratory, Department of Food Science, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Cluj-Napoca, Romania
| | - Isabel Moreno-Indias
- Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, the Biomedical Research Institute of Malaga and Platform in Nanomedicine (IBIMA-BIONAND Platform), University of Malaga, Malaga, Spain
| | - Andriy Temko
- Department of Electrical and Electronic Engineering, University College Cork, Cork, Ireland
| | | | - Elena-Simona Apostol
- Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Ciprian-Octavian Truică
- Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Jasminka Hasić Telalović
- Department of Computer Science, University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Erik Bongcam-Rudloff
- Swedish University of Agricultural Sciences, Department of Animal Breeding and Genetics, Uppsala, Sweden
| | | | - Naida Babić Jordamović
- Computational Biology, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
- Verlab Research Institute for BIomedical Engineering, Medical Devices and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina
| | - Laurent Falquet
- University of Fribourg and Swiss Institute of Bioinformatics, Fribourg, Switzerland
| | - Sonia Tarazona
- Department of Applied Statistics and Operations Research and Quality, Universitat Politècnica de València, València, Spain
| | - Alexia Sampri
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
| | - Gaetano Isola
- Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Catania, Italy
| | - David Pérez-Serrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain
| | | | - Lubos Klucar
- Institute of Molecular Biology, Slovak Academy of Sciences, Bratislava, Slovakia
| | | | - Aki S. Havulinna
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland
| | - Christian Jansen
- Biome Diagnostics GmbH, Vienna, Austria
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
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