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Tomoto M, Mineharu Y, Sato N, Tamada Y, Nogami-Itoh M, Kuroda M, Adachi J, Takeda Y, Mizuguchi K, Kumanogoh A, Natsume-Kitatani Y, Okuno Y. Idiopathic pulmonary fibrosis-specific Bayesian network integrating extracellular vesicle proteome and clinical information. Sci Rep 2024; 14:1315. [PMID: 38225283 PMCID: PMC10789725 DOI: 10.1038/s41598-023-50905-8] [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: 08/17/2023] [Accepted: 12/27/2023] [Indexed: 01/17/2024] Open
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive disease characterized by severe lung fibrosis and a poor prognosis. Although the biomolecules related to IPF have been extensively studied, molecular mechanisms of the pathogenesis and their association with serum biomarkers and clinical findings have not been fully elucidated. We constructed a Bayesian network using multimodal data consisting of a proteome dataset from serum extracellular vesicles, laboratory examinations, and clinical findings from 206 patients with IPF and 36 controls. Differential protein expression analysis was also performed by edgeR and incorporated into the constructed network. We have successfully visualized the relationship between biomolecules and clinical findings with this approach. The IPF-specific network included modules associated with TGF-β signaling (TGFB1 and LRC32), fibrosis-related (A2MG and PZP), myofibroblast and inflammation (LRP1 and ITIH4), complement-related (SAA1 and SAA2), as well as serum markers, and clinical symptoms (KL-6, SP-D and fine crackles). Notably, it identified SAA2 associated with lymphocyte counts and PSPB connected with the serum markers KL-6 and SP-D, along with fine crackles as clinical manifestations. These results contribute to the elucidation of the pathogenesis of IPF and potential therapeutic targets.
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Affiliation(s)
- Mei Tomoto
- Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yohei Mineharu
- Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
- Department of Artificial Intelligence in Healthcare and Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Noriaki Sato
- Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokane-Dai, Minato-Ku, Tokyo, 108-8639, Japan
| | - Yoshinori Tamada
- Innovation Center for Health Promotion, Hirosaki University, 5 Zaifu-Cho Hirosaki City, Aomori, 036-8562, Japan
| | - Mari Nogami-Itoh
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17, Senrioka-Shinmachi, Settsu City, Osaka, 566-0002, Japan
| | - Masataka Kuroda
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17, Senrioka-Shinmachi, Settsu City, Osaka, 566-0002, Japan
- Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 1000, Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa, 227-0033, Japan
| | - Jun Adachi
- Laboratory of Proteomics for Drug Discovery, Center for Drug Design Research, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki, Osaka, 567-0085, Japan
| | - Yoshito Takeda
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, 2-2 Yamada-Oka, Suita City, Osaka, 565-0871, Japan
| | - Kenji Mizuguchi
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17, Senrioka-Shinmachi, Settsu City, Osaka, 566-0002, Japan
- Institute for Protein Research, Osaka University, 3-2 Yamada-Oka, Suita City, Osaka, 565-0871, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, 2-2 Yamada-Oka, Suita City, Osaka, 565-0871, Japan
| | - Yayoi Natsume-Kitatani
- Innovation Center for Health Promotion, Hirosaki University, 5 Zaifu-Cho Hirosaki City, Aomori, 036-8562, Japan.
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17, Senrioka-Shinmachi, Settsu City, Osaka, 566-0002, Japan.
- Institute of Advanced Medical Sciences, Tokushima University, 3-18-15, Kuramoto-Cho, Tokushima City, Tokushima, 770-8503, Japan.
| | - Yasushi Okuno
- Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
- Department of Artificial Intelligence in Healthcare and Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
- Biomedical Computational Intelligence Unit, HPC- and AI-Driven Drug Development Platform Division, RIKEN Center for Computational Science, 7-1-26, Minatojima-Minami-Machi, Chuo-Ku, Kobe, Hyogo, 650-0047, Japan.
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Tang L, Xiang Y, Zhou J, Li T, Jia T, Du G. miR-186 regulates epithelial-mesenchymal transformation to promote nasopharyngeal carcinoma metastasis by targeting ZEB1. Braz J Otorhinolaryngol 2024; 90:101358. [PMID: 37989078 PMCID: PMC10679499 DOI: 10.1016/j.bjorl.2023.101358] [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: 08/20/2023] [Revised: 10/12/2023] [Accepted: 10/20/2023] [Indexed: 11/23/2023] Open
Abstract
OBJECTIVES Nasopharyngeal carcinoma (NPC) is an aggressive epithelial cancer. The expression of miR-186 is decreased in a variety of malignancies and can promote the invasion and metastasis of cancer cells. This study aimed to explore the role and possible mechanism of miR-186 in the metastasis and epithelial-mesenchymal transformation (EMT) of NPC. METHODS The expression of miR-186 in NPC tissues and cells was detected by RT-PCR. Then, miR-186 mimic was used to transfect NPC cell lines C666-1 and CNE-2, and cell activity, invasion and migration were detected by CCK8, transwell and scratch assay, respectively. The expression of EMT-related proteins was analyzed by western blotting analysis. The binding relationship between miR-186 and target gene Zinc Finger E-Box Binding Homeobox 1 (ZEB1) was confirmed by double luciferase assay. RESULTS The expression of miR-186 in NPC was significantly decreased, and transfection of miR-186 mimic could significantly inhibit the cell activity, invasion, and migration, and regulate the protein expressions of E-cadherin, N-cadherin and vimentin in C666-1 and CNE-2 cells. Further experiments confirmed that miR-186 could directly target ZEB1 and negatively regulate its expression. In addition, ZEB1 has been confirmed to be highly expressed in NPC, and inhibition of ZEB1 could inhibit the activity, invasion, metastasis and EMT of NPC cells. And co-transfection of miR-186 mimic and si-ZEB1 could further inhibit the proliferation and metastasis of NPC. CONCLUSION miR-186 may inhibit the proliferation, metastasis and EMT of NPC by targeting ZEB1, and the miR-186/ZEB1 axis plays an important role in NPC.
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Affiliation(s)
- Liangke Tang
- Affiliated Hospital of North Sichuan Medical College, Department of Oncology, Nanchong, China; North Sichuan Medical College, Nanchong, China
| | - Yalang Xiang
- Affiliated Hospital of North Sichuan Medical College, Department of Oncology, Nanchong, China; North Sichuan Medical College, Nanchong, China
| | - Jing Zhou
- Affiliated Hospital of North Sichuan Medical College, Department of Neurology, Nanchong, China
| | - Tao Li
- Department of Oncology, People's Hospital of Nanbu County, Nanchong, China
| | - Tingting Jia
- Affiliated Hospital of North Sichuan Medical College, Department of Oncology, Nanchong, China
| | - Guobo Du
- Affiliated Hospital of North Sichuan Medical College, Department of Oncology, Nanchong, China; The First Affiliated Hospital of Jinan University, Tianhe, China.
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Maiorino E, Loscalzo J. Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping. Arterioscler Thromb Vasc Biol 2023; 43:1111-1123. [PMID: 37226730 PMCID: PMC10330619 DOI: 10.1161/atvbaha.122.318892] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/10/2023] [Indexed: 05/26/2023]
Abstract
The complex landscape of cardiovascular diseases encompasses a wide range of related pathologies arising from diverse molecular mechanisms and exhibiting heterogeneous phenotypes. This variety of manifestations poses significant challenges in the development of treatment strategies. The increasing availability of precise phenotypic and multiomics data of cardiovascular disease patient populations has spurred the development of a variety of computational disease subtyping techniques to identify distinct subgroups with unique underlying pathogeneses. In this review, we outline the essential components of computational approaches to select, integrate, and cluster omics and clinical data in the context of cardiovascular disease research. We delve into the challenges faced during different stages of the analysis, including feature selection and extraction, data integration, and clustering algorithms. Next, we highlight representative applications of subtyping pipelines in heart failure and coronary artery disease. Finally, we discuss the current challenges and future directions in the development of robust subtyping approaches that can be implemented in clinical workflows, ultimately contributing to the ongoing evolution of precision medicine in health care.
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Affiliation(s)
- Enrico Maiorino
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
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HSSG: Identification of Cancer Subtypes Based on Heterogeneity Score of A Single Gene. Cells 2022; 11:cells11152456. [PMID: 35954300 PMCID: PMC9368717 DOI: 10.3390/cells11152456] [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: 06/16/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 11/17/2022] Open
Abstract
Cancer is a highly heterogeneous disease, which leads to the fact that even the same cancer can be further classified into different subtypes according to its pathology. With the multi-omics data widely used in cancer subtypes identification, effective feature selection is essential for accurately identifying cancer subtypes. However, the feature selection in the existing cancer subtypes identification methods has the problem that the most helpful features cannot be selected from a biomolecular perspective, and the relationship between the selected features cannot be reflected. To solve this problem, we propose a method for feature selection to identify cancer subtypes based on the heterogeneity score of a single gene: HSSG. In the proposed method, the sample-similarity network of a single gene is constructed, and pseudo-F statistics calculates the heterogeneity score for cancer subtypes identification of each gene. Finally, we construct gene-gene networks using genes with higher heterogeneity scores and mine essential genes from the networks. From the seven TCGA data sets for three experiments, including cancer subtypes identification in single-omics data, the performance in feature selection of multi-omics data, and the effectiveness and stability of the selected features, HSSG achieves good performance in all. This indicates that HSSG can effectively select features for subtypes identification.
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Gong Y, Liu X, Sahu A, Reddy AV, Wang H. Exploration of hub genes, lipid metabolism, and the immune microenvironment in stomach carcinoma and cholangiocarcinoma. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:834. [PMID: 36034995 PMCID: PMC9403925 DOI: 10.21037/atm-22-3530] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/02/2022] [Indexed: 01/11/2023]
Abstract
Background Gastric cancer (GC) is the 5th most common cause of cancer in the world and the 3rd largest cause of cancer-related death. It is usually associated with a variety of cancers, of which cholangiocarcinoma (CCA) combined with GC accounts for about 1.6%. This study sought to examine the hub genes and role of lipid metabolism in the development and diagnosis of GC and CCA. Methods To screen potential hub genes, The Cancer Genome Atlas (TCGA) data sets, including the GC (STAD, dataset of GC) and CCA (CHOL, dataset of CCA) data sets, were used to conduct a differentially expressed gene (DEG) analysis and an enrichment analysis of the DEGs. A weighted-gene co-expression network analysis (WGCNA) was conducted to identify the significant gene module and then find the hub genes in the module. To verify the 4 hub genes, we conducted a differentiation analysis of the 4 genes in GC and CCA and found that there were differences. A survival analysis of the hub genes was performed and mutations were mapped. Additionally, tumor immune microenvironment (TIME) and immune analyses were performed to evaluate how lipid metabolism affects the development of GC with CCA. Results The principal component analysis showed that both GC and CCA had distinct up-regulated and down-regulated genes, which are involved in a variety of metabolic processes. Upon WGCNA, the turquoise and blue modules were meaningful, and the hub genes were identified from these 2 modules. Four hub genes were identified: amyloid beta precursor protein binding family B member 1 (APBB1), Homo sapiens armadillo repeat containing X-linked 1 (ARMCX1), DAZ interacting zinc finger protein 1 (DZIP1), and methionine sulfoxide reductase B3 (MSRB3). In survival analysis, increased expression of the 4 hub genes was associated with inferior survival outcomes, with variations in all 4 genes. Additionally, we demonstrated that genes related to lipid metabolism had an effect on immune function. Conclusions APBB1, ARMCX1, DZIP1, and MSRB3 affect the development of GC and CCA and can be used as biomarkers. The expression of lipid metabolism genes is related to the TIME of patients with GC and CCA.
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Affiliation(s)
- Yuda Gong
- Department of General Surgery, Fudan University Zhongshan Hospital, Shanghai, China
| | - Xuan Liu
- Department of General Surgery, Fudan University Zhongshan Hospital, Shanghai, China
| | - Arvind Sahu
- Department of Oncology, Goulburn Valley Health, Shepparton, Victoria, Australia
| | - Abhinav V Reddy
- Department of Radiation Oncology & Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Sidney Kimmel Cancer Center, Baltimore, MD, USA
| | - Haiyu Wang
- Department of General Surgery, Fudan University Zhongshan Hospital, Shanghai, China
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