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Tang C, Zhang S, Teymur A, Yang B, Nazir F, Cai Q, Saxena R, Olsen NJ, Mohan C, Wu T. V-Set Immunoglobulin Domain-Containing Protein 4 as a Novel Serum Biomarker of Lupus Nephritis and Renal Pathology Activity. Arthritis Rheumatol 2023; 75:1573-1585. [PMID: 37163449 PMCID: PMC10524163 DOI: 10.1002/art.42545] [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: 04/11/2023] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 05/12/2023]
Abstract
OBJECTIVE To discover novel serum biomarkers that have diagnostic or predictive value in lupus nephritis (LN). METHODS Using a quantitative protein microarray, we screened for high-abundant proteome expression in the serum of patients with LN compared to healthy controls. Top candidates from this screening were validated using a larger cohort of patients with LN compared to a disease control cohort (subjects with other chronic kidney diseases) and a healthy control cohort. Promising markers were then selected using a machine-learning model and further validated with a larger patient cohort. The corresponding autoantibodies and immune complexes containing these proteins were also examined. RESULTS In total, 13 proteins were found to be significantly elevated in LN patient serum in the screening, among which 8 proteins were validated by enzyme-linked immunosorbent assay using 81 serum samples from LN patients and control subjects. Three serum markers with LN diagnostic potential were identified using feature importance analysis and further validated using 155 serum samples from LN patients and control subjects. V-set immunoglobulin domain-containing protein 4 (VSIG4) appeared to be the most promising marker in distinguishing LN from healthy controls, with an area under the curve of 0.93. VSIG4 could also discriminate active LN from inactive LN. Furthermore, serum VSIG4 levels were positively correlated with all of the following clinical parameters: the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) score (Spearman's rank correlation rs = 0.42, P < 0.001), the renal domain score of the SLEDAI (rs = 0.46, P < 0.001), the urinary protein-to-creatinine ratio (rs = 0.56, P < 0.001), and the serum creatinine level (rs = 0.41, P < 0.001). Importantly, we found that serum VSIG4 levels tracked with LN disease activity longitudinally, and that serum VSIG4 levels reflected the renal pathology activity index (AI), particularly the AI components of crescent formation and hyaline deposits. CONCLUSION VSIG4 may be a promising novel serum biomarker and therapeutic target in patients with LN.
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Affiliation(s)
- Chenling Tang
- Department of Biomedical Engineering, University of Houston, Texas, USA
| | - Shu Zhang
- Department of Biomedical Engineering, University of Houston, Texas, USA
| | - Aygun Teymur
- Department of Biomedical Engineering, University of Houston, Texas, USA
| | - Bowen Yang
- Department of Biomedical Engineering, University of Houston, Texas, USA
| | - Fariz Nazir
- Department of Biomedical Engineering, University of Houston, Texas, USA
| | - Qi Cai
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ramesh Saxena
- Division of Nephrology, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Nancy J. Olsen
- Division of Rheumatology, Department of Medicine, Penn State Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Chandra Mohan
- Department of Biomedical Engineering, University of Houston, Texas, USA
| | - Tianfu Wu
- Department of Biomedical Engineering, University of Houston, Texas, USA
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Masson E, Zou WB, Pu N, Rebours V, Génin E, Wu H, Lin JH, Wang YC, Abrantes A, Aguilera Munoz L, Albouys J, Alric L, Amiot X, Archambeaud I, Audiau S, Bastide L, Baudon J, Bellaiche G, Bellon S, Bertrand V, Bideau K, Billiemaz K, Billioud C, Bonnefoy S, Borderon C, Bournet B, Breton E, Brugel M, Buscail L, Cadiot G, Camus M, Causse X, Chamouard P, Chaput U, Cholet F, Ciocan DM, Clavel C, Coffin B, Coimet-Berger L, Creveaux I, Culetto A, Daboussi O, Mestier LDE, Degand T, D'Engremont C, Denis B, Dermine S, Desgrippes R, D'Aubigny AD, Enaud R, Fabre A, Gargot D, Gelsi E, Gentilcore E, Gincul R, Ginglinger-Favre E, Giovannini M, Gomercic C, Gondran H, Grainville T, Grandval P, Grasset D, Grimaldi S, Grimbert S, Hagege H, Heissat S, Hentic O, Herber-Mayne A, Hervouet M, Hoibian S, Jacques J, Jais B, Kaassis M, Koch S, Lacaze E, Lacroute J, Lamireau T, Laurent L, Guillou XLE, Rhun MLE, Leblanc S, Levy P, Lievre A, Lorenzo D, Maire F, Marcel K, Matias C, Mauillon J, Morgant S, Moussata D, Muller N, Nambot S, Napoleon B, Olivier A, Pagenault M, Pelletier AL, Pennec O, Pinard F, Pioche M, Prost B, Queneherve L, Rebours V, Reboux N, Rekik S, Riachi G, Rohmer B, Roquelaure B, Hezode IR, Rostain F, Saurin JC, Servais L, Stan-Iuga R, Subtil C, Texier C, Thomassin L, Tougeron D, Tsakiris L, Valats JC, Vuitton L, Wallenhorst T, Wangerme M, Zanaldi H, Zerbib F. Classification of PRSS1 variants responsible for chronic pancreatitis: An expert perspective from the Franco-Chinese GREPAN study group. Pancreatology 2023; 23:491-506. [PMID: 37581535 DOI: 10.1016/j.pan.2023.04.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/06/2023] [Accepted: 04/13/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND PRSS1 was the first reported chronic pancreatitis (CP) gene. The existence of both gain-of-function (GoF) and gain-of-proteotoxicity (GoP) pathological PRSS1 variants, together with the fact that PRSS1 variants have been identified in CP subtypes spanning the range from monogenic to multifactorial, has made the classification of PRSS1 variants very challenging. METHODS All currently reported PRSS1 variants (derived primarily from two databases) were manually reviewed with respect to their clinical genetics, functional analysis and population allele frequency. They were classified by variant type and pathological mechanism within the framework of our recently proposed ACMG/AMP guidelines-based seven-category system. RESULTS The total number of distinct germline PRSS1 variants included for analysis was 100, comprising 3 copy number variants (CNVs), 12 5' and 3' variants, 19 intronic variants, 5 nonsense variants, 1 frameshift deletion variant, 6 synonymous variants, 1 in-frame duplication, 3 gene conversions and 50 missense variants. Based upon a combination of clinical genetic and functional analysis, population data and in silico analysis, we classified 26 variants (all 3 CNVs, the in-frame duplication, all 3 gene conversions and 19 missense) as "pathogenic", 3 variants (missense) as "likely pathogenic", 5 variants (four missense and one promoter) as "predisposing", 13 variants (all missense) as "unknown significance", 2 variants (missense) as "likely benign", and all remaining 51 variants as "benign". CONCLUSIONS We describe an expert classification of the 100 PRSS1 variants reported to date. The results have immediate implications for reclassifying many ClinVar-registered PRSS1 variants as well as providing optimal guidelines/standards for reporting PRSS1 variants.
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Affiliation(s)
- Emmanuelle Masson
- Univ Brest, Inserm, EFS, UMR 1078, GGB, F-29200, Brest, France; Service de Génétique Médicale et de Biologie de la Reproduction, CHRU Brest, F-29200, Brest, France
| | - Wen-Bin Zou
- Department of Gastroenterology, Changhai Hospital, The Secondary Military Medical University, Shanghai, China; Shanghai Institute of Pancreatic Diseases, Shanghai, China
| | - Na Pu
- Univ Brest, Inserm, EFS, UMR 1078, GGB, F-29200, Brest, France; Department of Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Vinciane Rebours
- Pancreatology and Digestive Oncology Department, Beaujon Hospital, APHP - Clichy, Université Paris Cité, Paris, France
| | - Emmanuelle Génin
- Univ Brest, Inserm, EFS, UMR 1078, GGB, F-29200, Brest, France; Service de Génétique Médicale et de Biologie de la Reproduction, CHRU Brest, F-29200, Brest, France
| | - Hao Wu
- Department of Gastroenterology, Changhai Hospital, The Secondary Military Medical University, Shanghai, China; Shanghai Institute of Pancreatic Diseases, Shanghai, China
| | - Jin-Huan Lin
- Department of Gastroenterology, Changhai Hospital, The Secondary Military Medical University, Shanghai, China; Shanghai Institute of Pancreatic Diseases, Shanghai, China
| | - Yuan-Chen Wang
- Department of Gastroenterology, Changhai Hospital, The Secondary Military Medical University, Shanghai, China; Shanghai Institute of Pancreatic Diseases, Shanghai, China
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- Hôpital d'instruction des armées Percy, Clamart, France
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Chen L, Zhou X, Zeng T, Pan X, Zhang YH, Huang T, Fang Z, Cai YD. Recognizing Pattern and Rule of Mutation Signatures Corresponding to Cancer Types. Front Cell Dev Biol 2021; 9:712931. [PMID: 34513841 PMCID: PMC8427289 DOI: 10.3389/fcell.2021.712931] [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: 05/21/2021] [Accepted: 07/02/2021] [Indexed: 11/20/2022] Open
Abstract
Cancer has been generally defined as a cluster of systematic malignant pathogenesis involving abnormal cell growth. Genetic mutations derived from environmental factors and inherited genetics trigger the initiation and progression of cancers. Although several well-known factors affect cancer, mutation features and rules that affect cancers are relatively unknown due to limited related studies. In this study, a computational investigation on mutation profiles of cancer samples in 27 types was given. These profiles were first analyzed by the Monte Carlo Feature Selection (MCFS) method. A feature list was thus obtained. Then, the incremental feature selection (IFS) method adopted such list to extract essential mutation features related to 27 cancer types, find out 207 mutation rules and construct efficient classifiers. The top 37 mutation features corresponding to different cancer types were discussed. All the qualitatively analyzed gene mutation features contribute to the distinction of different types of cancers, and most of such mutation rules are supported by recent literature. Therefore, our computational investigation could identify potential biomarkers and prediction rules for cancers in the mutation signature level.
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Affiliation(s)
- Lei Chen
- School of Life Sciences, Shanghai University, Shanghai, China.,College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Xianchao Zhou
- School of Life Sciences and Technology, ShanghaiTech University, Shanghai, China.,Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Zeng
- 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 Sciences, Shanghai, China
| | - Xiaoyong Pan
- Key Laboratory of System Control and Information Processing, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai, China
| | - Yu-Hang Zhang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - 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 Sciences, Shanghai, China.,Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Zhaoyuan Fang
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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Søreide K, Roalsø M, Aunan JR. Is There a Trojan Horse to Aggressive Pancreatic Cancer Biology? A Review of the Trypsin-PAR2 Axis to Proliferation, Early Invasion, and Metastasis. J Pancreat Cancer 2020; 6:12-20. [PMID: 32064449 PMCID: PMC7014313 DOI: 10.1089/pancan.2019.0014] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Purpose: Pancreatic cancer is one of the most lethal of solid tumors and is associated with aggressive cancer biology. The purpose is to review the role of trypsin and effect on molecular and cellular processes potentially explaining the aggressive biology in pancreatic cancer. Methods: A narrative literature review of studies investigating trypsin and its effect on protease systems in cancer, with special reference to pancreatic cancer biology. Results: Proteases, such as trypsin, provides a significant advantage to developing tumors through the ability to remodel the extracellular matrix, promote cell invasion and migration, and facilitate angiogenesis. Trypsin is a digestive enzyme produced by the exocrine pancreas that is also related to mechanisms of proliferation, invasion and metastasis. Several of these mechanisms may be co-regulated or influenced by activation of proteinase-activated receptor 2 (PAR-2). The current role in pancreatic cancer is not clear but emerging data suggest several potential mechanisms. Trypsin may act as a Trojan horse in the pancreatic gland, facilitating several molecular pathways from the onset, which leads to rapid progression of the disease. Pancreatic cancer cell lines containing PAR-2 proliferate upon exposure to trypsin, whereas cancer cell lines not containing PAR-2 fail to proliferate upon trypsin expression. Several mechanisms of action include a proinflammatory environment, signals inducing proliferation and migration, and direct and indirect evidence for mechanisms promoting invasion and metastasis. Novel techniques (such as organoid models) and increased understanding of mechanisms (such as the microbiome) may yield improved understanding into the role of trypsin in pancreatic carcinogenesis. Conclusion: Trypsin is naturally present in the pancreatic gland and may experience pathological activation intracellularly and in the neoplastic environment, which speeds up molecular mechanisms of proliferation, invasion, and metastasis. Further investigation of these processes will provide important insights into how pancreatic cancer evolves, and suggest new ways for treatment.
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Affiliation(s)
- Kjetil Søreide
- Gastrointestinal Translational Research Unit, Laboratory for Molecular Medicine, Stavanger University Hospital, Stavanger, Norway.,Department of Gastrointestinal Surgery, HPB Unit, Stavanger University Hospital, Stavanger, Norway.,Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Marcus Roalsø
- Gastrointestinal Translational Research Unit, Laboratory for Molecular Medicine, Stavanger University Hospital, Stavanger, Norway.,Department of Gastrointestinal Surgery, HPB Unit, Stavanger University Hospital, Stavanger, Norway.,Faculty of Health and Medicine, University of Stavanger, Stavanger, Norway
| | - Jan Rune Aunan
- Gastrointestinal Translational Research Unit, Laboratory for Molecular Medicine, Stavanger University Hospital, Stavanger, Norway.,Department of Gastrointestinal Surgery, HPB Unit, Stavanger University Hospital, Stavanger, Norway
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