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Lehnertz K. Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems. CHAOS (WOODBURY, N.Y.) 2024; 34:072102. [PMID: 38985967 DOI: 10.1063/5.0214733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 06/21/2024] [Indexed: 07/12/2024]
Abstract
Real-world non-autonomous systems are open, out-of-equilibrium systems that evolve in and are driven by temporally varying environments. Such systems can show multiple timescale and transient dynamics together with transitions to very different and, at times, even disastrous dynamical regimes. Since such critical transitions disrupt the systems' intended or desired functionality, it is crucial to understand the underlying mechanisms, to identify precursors of such transitions, and to reliably detect them in time series of suitable system observables to enable forecasts. This review critically assesses the various steps of investigation involved in time-series-analysis-based detection of critical transitions in real-world non-autonomous systems: from the data recording to evaluating the reliability of offline and online detections. It will highlight pros and cons to stimulate further developments, which would be necessary to advance understanding and forecasting nonlinear behavior such as critical transitions in complex systems.
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Lyu C, Chen L, Liu X. Detecting tipping points of complex diseases by network information entropy. Brief Bioinform 2024; 25:bbae311. [PMID: 38960408 PMCID: PMC11221888 DOI: 10.1093/bib/bbae311] [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: 02/22/2024] [Revised: 05/30/2024] [Accepted: 06/14/2024] [Indexed: 07/05/2024] Open
Abstract
The progression of complex diseases often involves abrupt and non-linear changes characterized by sudden shifts that trigger critical transformations. Identifying these critical states or tipping points is crucial for understanding disease progression and developing effective interventions. To address this challenge, we have developed a model-free method named Network Information Entropy of Edges (NIEE). Leveraging dynamic network biomarkers, sample-specific networks, and information entropy theories, NIEE can detect critical states or tipping points in diverse data types, including bulk, single-sample expression data. By applying NIEE to real disease datasets, we successfully identified critical predisease stages and tipping points before disease onset. Our findings underscore NIEE's potential to enhance comprehension of complex disease development.
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Affiliation(s)
- Chengshang Lyu
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 1 Xiangshan Branch Alley, Xihu District, Hangzhou 310024, China
- Department of Biomedical Sciences, City University of Hong Kong, 31 To Yuen Street, Kowloon Tong, Kowloon, Hong Kong 999077, China
| | - Lingxi Chen
- Department of Biomedical Sciences, City University of Hong Kong, 31 To Yuen Street, Kowloon Tong, Kowloon, Hong Kong 999077, China
| | - Xiaoping Liu
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 1 Xiangshan Branch Alley, Xihu District, Hangzhou 310024, China
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Liao Y, Xu Y, Mo Z, Zhu T, Dong H, Zhou W, Xia Q. Interleukin-25 as a Potential Biomarker in Lung Metastasis of Hepatocellular Carcinoma with HBV History in Chinese Patients: A Single Center, Case-control Study. Int J Med Sci 2024; 21:1337-1343. [PMID: 38818476 PMCID: PMC11134585 DOI: 10.7150/ijms.90642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 04/22/2024] [Indexed: 06/01/2024] Open
Abstract
Background: Interleukin-25 (IL-25) has been proved to play a role in the pathogenesis and metastasis of Hepatocellular carcinoma (HCC), but the relationship between the level of IL-25 and the metastasis and prognosis of HCC is still not clear. This study aimed to investigate the expression of IL-25 and other potential biochemical indicators among healthy people, HBV-associated HCC patients without lung metastasis and HBV-associated HCC patients with lung metastasis. Methods: From September 2019 to November 2021, 33 HCC patients without lung metastasis, 37 HCC patients with lung metastasis and 29 healthy controls were included in the study. IL-25 and other commonly used biochemical markers were measured to establish predictors of overall survival (OS) and progression-free survival (PFS) after treatment. Results: The serum level of IL-25 was increased in HCC patients than healthy controls (p < 0.001) and HCC patients with lung metastasis had higher IL-25 level than HCC patients without metastasis (p = 0.035). Lung metastasis also indicated higher death rate (p < 0.001) by chi-square test, higher GGT level (p = 0.024) and higher AFP level (p = 0.049) by non-parametric test. Kaplan-Meier analysis demonstrated that IL-25 was negatively associated with PFS (p = 0.024). Multivariate Cox-regression analysis indicated IL-25 (p = 0.030) and GGT (p = 0.020) to be independent predictors of poorer PFS, while IL-25 showed no significant association with OS. Conclusion: The level of IL-25 was significantly associated with disease progression and lung metastasis of HBV-associated HCC. The high expression of IL-25 predicted high recurrence rate and death probability of HCC patients after treatment. Therefore, IL-25 may be an effective predictor of prognosis in HCC.
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Affiliation(s)
- Yuan Liao
- Department of Laboratory Medicine, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - Yixin Xu
- School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Ziying Mo
- Department of Laboratory Medicine, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - Tianyi Zhu
- School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Huimin Dong
- Department of Laboratory Medicine, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - Wenying Zhou
- Department of Laboratory Medicine, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - Qing Xia
- Department of Oncology, State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
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Niu J, Chen Y, Chai HC, Sasidharan S. Exploring MiR-484 Regulation by Polyalthia longifolia: A Promising Biomarker and Therapeutic Target in Cervical Cancer through Integrated Bioinformatics and an In Vitro Analysis. Biomedicines 2024; 12:909. [PMID: 38672263 PMCID: PMC11047986 DOI: 10.3390/biomedicines12040909] [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: 03/11/2024] [Revised: 04/05/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND MiR-484, implicated in various carcinomas, holds promise as a prognostic marker, yet its relevance to cervical cancer (CC) remains unclear. Our prior study demonstrated the Polyalthia longifolia downregulation of miR-484, inhibiting HeLa cells. This study investigates miR-484's potential as a biomarker and therapeutic target in CC through integrated bioinformatics and an in vitro analysis. METHODS MiR-484 levels were analyzed across cancers, including CC, from The Cancer Genome Atlas. The limma R package identified differentially expressed genes (DEGs) between high- and low-miR-484 CC cohorts. We assessed biological functions, tumor microenvironment (TME), immunotherapy, stemness, hypoxia, RNA methylation, and chemosensitivity differences. Prognostic genes relevant to miR-484 were identified through Cox regression and Kaplan-Meier analyses, and a prognostic model was captured via multivariate Cox regression. Single-cell RNA sequencing determined cell populations related to prognostic genes. qRT-PCR validated key genes, and the miR-484 effect on CC proliferation was assessed via an MTT assay. RESULTS MiR-484 was upregulated in most tumors, including CC, with DEGs enriched in skin development, PI3K signaling, and immune processes. High miR-484 expression correlated with specific immune cell infiltration, hypoxia, and drug sensitivity. Prognostic genes identified were predominantly epidermal and stratified patients with CC into risk groups, with the low-risk group showing enhanced survival and immunotherapeutic responses. qRT-PCR confirmed FGFR3 upregulation in CC cells, and an miR-484 mimic reversed the P. longifolia inhibitory effect on HeLa proliferation. CONCLUSION MiR-484 plays a crucial role in the CC progression and prognosis, suggesting its potential as a biomarker for targeted therapy.
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Affiliation(s)
- Jiaojiao Niu
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia;
- School of Biological Engineering, Xinxiang University, Xinxiang 453003, China
| | - Yeng Chen
- Department of Oral & Craniofacial Sciences, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Hwa Chia Chai
- Department of Biomedical Science, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Sreenivasan Sasidharan
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia;
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Ren J, Yao X, Yang M, Cheng S, Wu D, Xu K, Li R, Zhang H, Zhang D. Kinesin Family Member-18A (KIF18A) Promotes Cell Proliferation and Metastasis in Hepatocellular Carcinoma. Dig Dis Sci 2024; 69:1274-1286. [PMID: 38446308 PMCID: PMC11026273 DOI: 10.1007/s10620-024-08321-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/26/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND & AIMS Kinesin family member 18A (KIF18A) is notable for its aberrant expression across various cancer types and its pivotal role is driving cancer progression. In this study, we aim to investigate the intricate molecular mechanisms underlying the impact of KIF18A on the progression of HCC. METHODS Western blotting assays, a quantitative real-time PCR and immunohistochemical analyses were performed to quantitatively assess KIF18A expression in HCC tissues. We then performed genetic manipulations within HCC cells by silencing endogenous KIF18A using short hairpin RNA (shRNA) and introducing exogenous plasmids to overexpress KIF18A. We monitored cell progression, analyzed cell cycle and cell apoptosis and assessed cell migration and invasion both in vitro and in vivo. Moreover, we conducted RNA-sequencing to explore KIF18A-related signaling pathways utilizing Reactome and KEGG enrichment methods and validated these critical mediators in these pathways. RESULTS Analysis of the TCGA-LIHC database revealed pronounced overexpression of KIF18A in HCC tissues, the finding was subsequently confirmed through the analysis of clinical samples obtained from HCC patients. Notably, silencing KIF18A in cells led to an obvious inhibition of cell proliferation, migration and invasion in vitro. Furthermore, in subcutaneous and orthotopic xenograft models, suppression of KIF18A sgnificantly redudce tumor weight and the number of lung metastatic nodules. Mechanistically, KIF18A appears to facilitate cell proliferation by upregulating MAD2 and CDK1/CyclinB1 expression levels, with the activation of SMAD2/3 signaling contributing to KIF18A-driven metastasis. CONCLUSION Our study elucidates the molecular mechanism by which KIF18A mediates proliferation and metastasis in HCC cells, offering new insights into potential therapeutic targets.
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Affiliation(s)
- Jihua Ren
- The Key Laboratory of Molecular Biology of Infectious Diseases Designated by the Chinese Ministry of Education, Chongqing Medical University, Chongqing, 400016, China
| | - Xinyan Yao
- The Key Laboratory of Molecular Biology of Infectious Diseases Designated by the Chinese Ministry of Education, Chongqing Medical University, Chongqing, 400016, China
| | - Minli Yang
- The Key Laboratory of Molecular Biology of Infectious Diseases Designated by the Chinese Ministry of Education, Chongqing Medical University, Chongqing, 400016, China
| | - Shengtao Cheng
- The Key Laboratory of Molecular Biology of Infectious Diseases Designated by the Chinese Ministry of Education, Chongqing Medical University, Chongqing, 400016, China
| | - Daiqing Wu
- The Key Laboratory of Molecular Biology of Infectious Diseases Designated by the Chinese Ministry of Education, Chongqing Medical University, Chongqing, 400016, China
| | - Kexin Xu
- The Key Laboratory of Molecular Biology of Infectious Diseases Designated by the Chinese Ministry of Education, Chongqing Medical University, Chongqing, 400016, China
| | - Ranran Li
- The Key Laboratory of Molecular Biology of Infectious Diseases Designated by the Chinese Ministry of Education, Chongqing Medical University, Chongqing, 400016, China
| | - Han Zhang
- The Key Laboratory of Molecular Biology of Infectious Diseases Designated by the Chinese Ministry of Education, Chongqing Medical University, Chongqing, 400016, China
| | - Dapeng Zhang
- The Key Laboratory of Molecular Biology of Infectious Diseases Designated by the Chinese Ministry of Education, Chongqing Medical University, Chongqing, 400016, China.
- , Room 706, Chongyi Building, 1 Yixue Yuan Road, Yuzhong District, Chongqing, 400016, China.
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Wang J, Guan X, Shang N, Wu D, Liu Z, Guan Z, Zhang Z, Jin Z, Wei X, Liu X, Song M, Zhu W, Dai G. Dysfunction of CCT3-associated network signals for the critical state during progression of hepatocellular carcinoma. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167054. [PMID: 38360074 DOI: 10.1016/j.bbadis.2024.167054] [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: 08/24/2023] [Revised: 01/25/2024] [Accepted: 01/30/2024] [Indexed: 02/17/2024]
Abstract
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and is a serious threat to human health; thus, early diagnosis and adequate treatment are essential. However, there are still great challenges in identifying the tipping point and detecting early warning signals of early HCC. In this study, we aimed to identify the tipping point (critical state) of and key molecules involved in hepatocarcinogenesis based on time series transcriptome expression data of HCC patients. The phase from veHCC (very early HCC) to eHCC (early HCC) was identified as the critical state in HCC progression, with 143 genes identified as key candidate molecules by combining the DDRTree (dimensionality reduction via graph structure learning) and DNB (dynamic network biomarker) methods. Then, we ranked the candidate genes to verify their mRNA levels using the diethylnitrosamine (DEN)-induced HCC mouse model and identified five early warning signals, namely, CCT3, DSTYK, EIF3E, IARS2 and TXNRD1; these signals can be regarded as the potential early warning signals for the critical state of HCC. We identified CCT3 as an independent prognostic factor for HCC, and functions of CCT3 involving in the "MYCtargets_V1" and "E2F-Targets" are closely related to the progression of HCC. The predictive method combining the DDRTree and DNB methods can not only identify the key critical state before cancer but also determine candidate molecules of critical state, thus providing new insight into the early diagnosis and preemptive treatment of HCC.
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Affiliation(s)
- Jianwei Wang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 45001, China; School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Xiaowen Guan
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Ning Shang
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Di Wu
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Zihan Liu
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Zhenzhen Guan
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Zhizi Zhang
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Zhongzhen Jin
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Xiaoyi Wei
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Xiaoran Liu
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Mingzhu Song
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Weijun Zhu
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 45001, China.
| | - Guifu Dai
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China.
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Yonezawa S, Haruki T, Koizumi K, Taketani A, Oshima Y, Oku M, Wada A, Sato T, Masuda N, Tahara J, Fujisawa N, Koshiyama S, Kadowaki M, Kitajima I, Saito S. Establishing Monoclonal Gammopathy of Undetermined Significance as an Independent Pre-Disease State of Multiple Myeloma Using Raman Spectroscopy, Dynamical Network Biomarker Theory, and Energy Landscape Analysis. Int J Mol Sci 2024; 25:1570. [PMID: 38338848 PMCID: PMC10855579 DOI: 10.3390/ijms25031570] [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: 11/30/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
Multiple myeloma (MM) is a cancer of plasma cells. Normal (NL) cells are considered to pass through a precancerous state, such as monoclonal gammopathy of undetermined significance (MGUS), before transitioning to MM. In the present study, we acquired Raman spectra at three stages-834 NL, 711 MGUS, and 970 MM spectra-and applied the dynamical network biomarker (DNB) theory to these spectra. The DNB analysis identified MGUS as the unstable pre-disease state of MM and extracted Raman shifts at 1149 and 1527-1530 cm-1 as DNB variables. The distribution of DNB scores for each patient showed a significant difference between the mean values for MGUS and MM patients. Furthermore, an energy landscape (EL) analysis showed that the NL and MM stages were likely to become stable states. Raman spectroscopy, the DNB theory, and, complementarily, the EL analysis will be applicable to the identification of the pre-disease state in clinical samples.
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Affiliation(s)
- Shota Yonezawa
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Graduate School of Science and Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Takayuki Haruki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Sustainable Design, University of Toyama, Toyama 930-8555, Japan
| | - Keiichi Koizumi
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Akinori Taketani
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Yusuke Oshima
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Makito Oku
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Akinori Wada
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Tsutomu Sato
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY 14260-2900, USA
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, NY 14260-2200, USA
| | - Jun Tahara
- Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Noritaka Fujisawa
- Graduate School of Science and Engineering, University of Toyama, Toyama 930-8555, Japan
| | - Shota Koshiyama
- Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Makoto Kadowaki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Isao Kitajima
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Shigeru Saito
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
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Panahi S, Do Y, Hastings A, Lai YC. Rate-induced tipping in complex high-dimensional ecological networks. Proc Natl Acad Sci U S A 2023; 120:e2308820120. [PMID: 38091288 PMCID: PMC10743502 DOI: 10.1073/pnas.2308820120] [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: 05/25/2023] [Accepted: 11/15/2023] [Indexed: 12/24/2023] Open
Abstract
In an ecosystem, environmental changes as a result of natural and human processes can cause some key parameters of the system to change with time. Depending on how fast such a parameter changes, a tipping point can occur. Existing works on rate-induced tipping, or R-tipping, offered a theoretical way to study this phenomenon but from a local dynamical point of view, revealing, e.g., the existence of a critical rate for some specific initial condition above which a tipping point will occur. As ecosystems are subject to constant disturbances and can drift away from their equilibrium point, it is necessary to study R-tipping from a global perspective in terms of the initial conditions in the entire relevant phase space region. In particular, we introduce the notion of the probability of R-tipping defined for initial conditions taken from the whole relevant phase space. Using a number of real-world, complex mutualistic networks as a paradigm, we find a scaling law between this probability and the rate of parameter change and provide a geometric theory to explain the law. The real-world implication is that even a slow parameter change can lead to a system collapse with catastrophic consequences. In fact, to mitigate the environmental changes by merely slowing down the parameter drift may not always be effective: Only when the rate of parameter change is reduced to practically zero would the tipping be avoided. Our global dynamics approach offers a more complete and physically meaningful way to understand the important phenomenon of R-tipping.
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Affiliation(s)
- Shirin Panahi
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ85287
| | - Younghae Do
- Department of Mathematics, Nonlinear Dynamics Mathematical Application Center, Kyungpook National University, Daegu41566, Republic of Korea
| | - Alan Hastings
- Department of Environmental Science and Policy, University of California, Davis, CA95616
- Santa Fe Institute, Santa Fe, NM87501
| | - Ying-Cheng Lai
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ85287
- Department of Physics, Arizona State University, Tempe, AZ85287
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Huang X, Su B, Li M, Zhou Y, He X. Multiomics characterization of fatty acid metabolism for the clinical management of hepatocellular carcinoma. Sci Rep 2023; 13:22472. [PMID: 38110715 PMCID: PMC10728109 DOI: 10.1038/s41598-023-50156-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/15/2023] [Indexed: 12/20/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a prevalent malignancy and there is a lack of effective biomarkers for HCC diagnosis. Living organisms are complex, and different omics molecules interact with each other to implement various biological functions. Genomics and metabolomics, which are the top and bottom of systems biology, play an important role in HCC clinical management. Fatty acid metabolism is associated with malignancy, prognosis, and immune phenotype in cancer, which is a potential hallmark in malignant tumors. In this study, the genes and metabolites related to fatty acid metabolism were thoroughly investigated by a dynamic network construction algorithm named EWS-DDA for the early diagnosis and prognosis of HCC. Three gene ratios and eight metabolite ratios were identified by EWS-DDA as potential biomarkers for HCC clinical management. Further analysis using biological analysis, statistical analysis and document validation in the discovery and validation sets suggested that the selected potential biomarkers had great clinical prognostic value and helped to achieve effective early diagnosis of HCC. Experimental results suggested that in-depth evaluation of fatty acid metabolism from different omics viewpoints can facilitate the further understanding of pathological alterations associated with HCC characteristics, improving the performance of early diagnosis and clinical prognosis.
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Affiliation(s)
- Xin Huang
- School of Artificial Intelligence, Anshan Normal University, Pingan Street, Anshan, 114007, Liaoning, China.
- Biomedical Engineering Postdoctoral Research Station, Dalian University of Technology, Dalian, Liaoning, China.
- Postdoctoral Workstation of Dalian Yongjia Electronic Technology Co., Ltd, Dalian, Liaoning, China.
| | - Benzhe Su
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, China
| | - Mengjun Li
- School of Artificial Intelligence, Anshan Normal University, Pingan Street, Anshan, 114007, Liaoning, China
| | - Yang Zhou
- Ningbo Institute of Innovation for Combined Medicine and Engineering, Ningbo Medical Center Li Huili Hospital, Ningbo, Zhejiang, China
| | - Xinyu He
- School of Computer and Information Technology, Liaoning Normal University, Dalian, Liaoning, China
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10
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Zhang L, Du F, Jin Q, Sun L, Wang B, Tan Z, Meng X, Huang B, Zhan Y, Su W, Song R, Wu C, Chen L, Chen X, Ding X. Identification and Characterization of CD8 + CD27 + CXCR3 - T Cell Dysregulation and Progression-Associated Biomarkers in Systemic Lupus Erythematosus. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2300123. [PMID: 37875396 PMCID: PMC10724430 DOI: 10.1002/advs.202300123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 08/04/2023] [Indexed: 10/26/2023]
Abstract
Systemic Lupus Erythematosus (SLE) etiopathogenesis highlights the contributions of overproduction of CD4+ T cells and loss of immune tolerance. However, the involvement of CD8+ T cells in SLE pathology and disease progression remains unclear. Here, the comprehensive immune cell dysregulation in total 263 clinical peripheral blood samples composed of active SLE (aSLE), remission SLE (rSLE) and healthy controls (HCs) is investigated via mass cytometry, flow cytometry and single-cell RNA sequencing. This is observed that CD8+ CD27+ CXCR3- T cells are increased in rSLE compare to aSLE. Meanwhile, the effector function of CD8+ CD27+ CXCR3- T cells are overactive in aSLE compare to HCs and rSLE, and are positively associated with clinical SLE activity. In addition, the response of peripheral blood mononuclear cells (PBMCs) is monitored to interleukin-2 stimulation in aSLE and rSLE to construct dynamic network biomarker (DNB) model. It is demonstrated that DNB score-related parameters can faithfully predict the remission of aSLE and the flares of rSLE. The abundance and functional dysregulation of CD8+ CD27+ CXCR3- T cells can be potential biomarkers for SLE prognosis and concomitant diagnosis. The DNB score with accurate prediction to SLE disease progression can provide clinical treatment suggestions especially for drug dosage determination.
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Affiliation(s)
- Lulu Zhang
- Department of RheumatologyShanghai Jiao Tong University School of Medicine Affiliated Renji Hospital and School of Biomedical EngineeringShanghai200030China
- State Key Laboratory of Oncogenes and Related GenesInstitute for Personalized MedicineSchool of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200001China
| | - Fang Du
- Department of RheumatologyShanghai Jiao Tong University School of Medicine Affiliated Renji Hospital and School of Biomedical EngineeringShanghai200030China
- State Key Laboratory of Oncogenes and Related GenesInstitute for Personalized MedicineSchool of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200001China
| | - Qiqi Jin
- Key Laboratory of Systems BiologyCenter for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyChinese Academy of SciencesShanghai200031China
- University of Chinese Academy of SciencesBeijing100049China
- School of Life Science and TechnologyShanghaiTech UniversityShanghai201210China
| | - Li Sun
- Department of Rheumatology and ImmunologyThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China
| | - Boqian Wang
- Department of RheumatologyShanghai Jiao Tong University School of Medicine Affiliated Renji Hospital and School of Biomedical EngineeringShanghai200030China
- State Key Laboratory of Oncogenes and Related GenesInstitute for Personalized MedicineSchool of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200001China
| | - Ziyang Tan
- Science for Life LaboratoryDepartment of Women's and Children's HealthKarolinska InstitutetSolna17121Sweden
| | - Xinyu Meng
- State Key Laboratory of Oncogenes and Related GenesInstitute for Personalized MedicineSchool of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200001China
| | - Baozhen Huang
- Department of Chemical PathologyLi Ka Shing Institute of Health SciencesFaculty of MedicineThe Chinese University of Hong KongHong Kong999077China
| | - Yifan Zhan
- Drug DiscoveryShanghai Huaota Biopharmaceutical Co. Ltd.Shanghai200131China
| | - Wenqiong Su
- State Key Laboratory of Oncogenes and Related GenesInstitute for Personalized MedicineSchool of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200001China
| | - Rui Song
- Department of RheumatologyShanghai Jiao Tong University School of Medicine Affiliated Renji Hospital and School of Biomedical EngineeringShanghai200030China
- Nantong First People's HospitalAffiliated Hospital 2 of Nantong UniversityNantong Hospital of Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
| | - Chunmei Wu
- Department of RheumatologyShanghai Jiao Tong University School of Medicine Affiliated Renji Hospital and School of Biomedical EngineeringShanghai200030China
| | - Luonan Chen
- Key Laboratory of Systems BiologyCenter for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyChinese Academy of SciencesShanghai200031China
- School of Life Science and TechnologyShanghaiTech UniversityShanghai201210China
- Key Laboratory of Systems Health Science of Zhejiang ProvinceSchool of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou310024China
| | - Xiaoxiang Chen
- Department of RheumatologyShanghai Jiao Tong University School of Medicine Affiliated Renji Hospital and School of Biomedical EngineeringShanghai200030China
| | - Xianting Ding
- Department of RheumatologyShanghai Jiao Tong University School of Medicine Affiliated Renji Hospital and School of Biomedical EngineeringShanghai200030China
- State Key Laboratory of Oncogenes and Related GenesInstitute for Personalized MedicineSchool of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200001China
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11
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Su C, Cheng CY, Rong Z, Yang JC, Li ZM, Yao JY, Liu A, Yang L, Zhao MG. Repurposing fluphenazine as an autophagy modulator for treating liver cancer. Heliyon 2023; 9:e22605. [PMID: 38107270 PMCID: PMC10724577 DOI: 10.1016/j.heliyon.2023.e22605] [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: 05/17/2023] [Revised: 11/08/2023] [Accepted: 11/15/2023] [Indexed: 12/19/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system with a low early diagnosis rate. Owing to the side effects, tolerance, and patient contraindications of existing therapies, effective drug treatments for HCC remain a major clinical challenge. However, using approved or investigational drugs not initially intended for cancer therapy is a promising strategy for resolving this problem because their safety have been tested in clinic. Therefore, this study evaluated differentially expressed genes between liver cancer and normal tissues in a cohort of patients with HCC from The Cancer Genome Atlas and applied them to query a connectivity map to identify candidate anti-HCC drugs. As a result, fluphenazine was identified as a candidate for anti-HCC therapy in vitro and in vivo. Fluphenazine suppressed HCC cell proliferation and migration and induced cell cycle arrest and apoptosis, possibly owing to disrupted lysosomal function, blocking autophagy flux. Additionally, in vivo studies demonstrated that fluphenazine suppresses HCC subcutaneous xenografts growth without causing severe side effects. Strikingly, fluphenazine could be used as an analgesic to alleviate oxaliplatin-induced pain as well as pain related anxiety-like behavior. Therefore, fluphenazine could be a novel liver cancer treatment candidate.
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Affiliation(s)
- Chang Su
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
- Shaanxi Provincial Corps, Chinese People's Armed Police Force, Xi'an, China
| | - Cai-yan Cheng
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China
| | - Zheng Rong
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
| | - Jing-cheng Yang
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
| | - Zhi-mei Li
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China
| | - Jing-yue Yao
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
| | - An Liu
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
| | - Le Yang
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
| | - Ming-gao Zhao
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China
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12
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Su B, Wang W, Lin X, Liu S, Huang X. Identifying the potential miRNA biomarkers based on multi-view networks and reinforcement learning for diseases. Brief Bioinform 2023; 25:bbad427. [PMID: 38018913 PMCID: PMC10753537 DOI: 10.1093/bib/bbad427] [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: 07/01/2023] [Revised: 09/24/2023] [Accepted: 10/30/2023] [Indexed: 11/30/2023] Open
Abstract
MicroRNAs (miRNAs) play important roles in the occurrence and development of diseases. However, it is still challenging to identify the effective miRNA biomarkers for improving the disease diagnosis and prognosis. In this study, we proposed the miRNA data analysis method based on multi-view miRNA networks and reinforcement learning, miRMarker, to define the potential miRNA disease biomarkers. miRMarker constructs the cooperative regulation network and functional similarity network based on the expression data and known miRNA-disease relations, respectively. The cooperative regulation of miRNAs was evaluated by measuring the changes of relative expression. Natural language processing was introduced for calculating the miRNA functional similarity. Then, miRMarker integrates the multi-view miRNA networks and defines the informative miRNA modules through a reinforcement learning strategy. We compared miRMarker with eight efficient data analysis methods on nine transcriptomics datasets to show its superiority in disease sample discrimination. The comparison results suggested that miRMarker outperformed other data analysis methods in receiver operating characteristic analysis. Furthermore, the defined miRNA modules of miRMarker on colorectal cancer data not only show the excellent performance of cancer sample discrimination but also play significant roles in the cancer-related pathway disturbances. The experimental results indicate that miRMarker can build the robust miRNA interaction network by integrating the multi-view networks. Besides, exploring the miRNA interaction network using reinforcement learning favors defining the important miRNA modules. In summary, miRMarker can be a hopeful tool in biomarker identification for human diseases.
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Affiliation(s)
- Benzhe Su
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Weiwei Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Xiaohui Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Shenglan Liu
- School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Xin Huang
- School of Mathematics and Information Science, Anshan Normal University, Anshan 114007, Liaoning, China
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13
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Sukko N, Kalapanulak S, Saithong T. Trehalose metabolism coordinates transcriptional regulatory control and metabolic requirements to trigger the onset of cassava storage root initiation. Sci Rep 2023; 13:19973. [PMID: 37968317 PMCID: PMC10651926 DOI: 10.1038/s41598-023-47095-8] [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: 07/12/2023] [Accepted: 11/09/2023] [Indexed: 11/17/2023] Open
Abstract
Cassava storage roots (SR) are an important source of food energy and raw material for a wide range of applications. Understanding SR initiation and the associated regulation is critical to boosting tuber yield in cassava. Decades of transcriptome studies have identified key regulators relevant to SR formation, transcriptional regulation and sugar metabolism. However, there remain uncertainties over the roles of the regulators in modulating the onset of SR development owing to the limitation of the widely applied differential gene expression analysis. Here, we aimed to investigate the regulation underlying the transition from fibrous (FR) to SR based on Dynamic Network Biomarker (DNB) analysis. Gene expression analysis during cassava root initiation showed the transition period to SR happened in FR during 8 weeks after planting (FR8). Ninety-nine DNB genes associated with SR initiation and development were identified. Interestingly, the role of trehalose metabolism, especially trehalase1 (TRE1), in modulating metabolites abundance and coordinating regulatory signaling and carbon substrate availability via the connection of transcriptional regulation and sugar metabolism was highlighted. The results agree with the associated DNB characters of TRE1 reported in other transcriptome studies of cassava SR initiation and Attre1 loss of function in literature. The findings help fill the knowledge gap regarding the regulation underlying cassava SR initiation.
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Affiliation(s)
- Nattavat Sukko
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand
| | - Saowalak Kalapanulak
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
- School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
| | - Treenut Saithong
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
- School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut's University of Technology Thonburi (Bang Khun Thian), Bangkok, 10150, Thailand.
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14
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Li J, Ma S, Pei H, Jiang J, Zou Q, Lv Z. Review of T cell proliferation regulatory factors in treatment and prognostic prediction for solid tumors. Heliyon 2023; 9:e21329. [PMID: 37954355 PMCID: PMC10637962 DOI: 10.1016/j.heliyon.2023.e21329] [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: 09/14/2023] [Revised: 10/15/2023] [Accepted: 10/19/2023] [Indexed: 11/14/2023] Open
Abstract
T cell proliferation regulators (Tcprs), which are positive regulators that promote T cell function, have made great contributions to the development of therapies to improve T cell function. CAR (chimeric antigen receptor) -T cell therapy, a type of adoptive cell transfer therapy that targets tumor cells and enhances immune lethality, has led to significant progress in the treatment of hematologic tumors. However, the applications of CAR-T in solid tumor treatment remain limited. Therefore, in this review, we focus on the development of Tcprs for solid tumor therapy and prognostic prediction. We summarize potential strategies for targeting different Tcprs to enhance T cell proliferation and activation and inhibition of cancer progression, thereby improving the antitumor activity and persistence of CAR-T. In summary, we propose means of enhancing CAR-T cells by expressing different Tcprs, which may lead to the development of a new generation of cell therapies.
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Affiliation(s)
- Jiayu Li
- Student Innovation Competition Team, College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
- College of Life Science, Sichuan University, Chengdu 610065, China
| | - Shuhan Ma
- Student Innovation Competition Team, College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Hongdi Pei
- Student Innovation Competition Team, College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Jici Jiang
- Student Innovation Competition Team, College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Zhibin Lv
- Student Innovation Competition Team, College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
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15
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Jain P, Pillai M, Duddu AS, Somarelli JA, Goyal Y, Jolly MK. Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity. Semin Cancer Biol 2023; 96:48-63. [PMID: 37788736 DOI: 10.1016/j.semcancer.2023.09.007] [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: 04/19/2023] [Revised: 09/24/2023] [Accepted: 09/28/2023] [Indexed: 10/05/2023]
Abstract
Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.
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Affiliation(s)
- Paras Jain
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India
| | - Maalavika Pillai
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India; Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL 60611, USA
| | | | - Jason A Somarelli
- Department of Medicine, Duke Cancer Institute, Duke University, Durham, NC 27710, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL 60611, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Mohit Kumar Jolly
- Department of Bioengineering, Indian Institute of Science, Bangalore 560012, India.
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16
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Zhang Z, Sun W, Wen L, Liu Y, Guo X, Liu Y, Yao C, Xue Q, Sun Z, Wang Z, Zhang Y. Dynamic gene regulatory networks improving spike fertility through regulation of floret primordia fate in wheat. PLANT, CELL & ENVIRONMENT 2023; 46:3628-3643. [PMID: 37485926 DOI: 10.1111/pce.14672] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 07/09/2023] [Accepted: 07/11/2023] [Indexed: 07/25/2023]
Abstract
The developmental process of spike is critical for spike fertility through affecting floret primordia fate in wheat; however, the genetic regulation of this dynamic and complex developmental process remains unclear. Here, we conducted a high temporal-resolution analysis of spike transcriptomes and monitored the number and morphology of floret primordia within spike. The development of all floret primordia in a spike was clearly separated into three distinct phases: differentiation, pre-dimorphism and dimorphism. Notably, we identified that floret primordia with meiosis ability at the pre-dimorphism phase usually develop into fertile floret primordia in the next dimorphism phase. Compared to control, increasing plant space treatment achieved the maximum increasement range (i.e., 50%) in number of fertile florets by accelerating spike development. The process of spike fertility improvement was directed by a continuous and dynamic regulatory network involved in transcription factor and genes interaction. This was based on the coordination of genes related to heat shock protein and jasmonic acid biosynthesis during differentiation phase, and genes related to lignin, anthocyanin and chlorophyll biosynthesis during dimorphism phase. The multi-dimensional association with high temporal-resolution approach reported here allows rapid identification of genetic resource for future breeding studies to realise the maximum spike fertility potential in more cereal crops.
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Affiliation(s)
- Zhen Zhang
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
- College of Biological Sciences, China Agricultural University, Beijing, China
| | - Wan Sun
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Liangyun Wen
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Yaqun Liu
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Xiaolei Guo
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Ying Liu
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Chunsheng Yao
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Qingwu Xue
- Texas A&M AgriLife Research and Extension Center at Amarillo, Amarillo, Texas, USA
| | - Zhencai Sun
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
- Engineering Technology Research Center for Agriculture in Low Plain Areas, Hebei Province, China
| | - Zhimin Wang
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
- Engineering Technology Research Center for Agriculture in Low Plain Areas, Hebei Province, China
| | - Yinghua Zhang
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
- Engineering Technology Research Center for Agriculture in Low Plain Areas, Hebei Province, China
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17
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Lu J, Lai J, Xiao K, Peng S, Zhang Y, Xia Q, Liu S, Cheng L, Zhang Q, Chen Y, Chen X, Lin T. A clinically practical model for the preoperative prediction of lymph node metastasis in bladder cancer: a multicohort study. Br J Cancer 2023; 129:1166-1175. [PMID: 37542107 PMCID: PMC10539530 DOI: 10.1038/s41416-023-02383-y] [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: 03/05/2023] [Revised: 07/20/2023] [Accepted: 07/26/2023] [Indexed: 08/06/2023] Open
Abstract
BACKGROUND The aim of this study was to construct a clinically practical model to precisely predict lymph node (LN) metastasis in bladder cancer patients. METHODS Four independent cohorts were included. The least absolute shrinkage and selection operator regression with multivariate logistic regression were applied. The diagnostic efficacy of LN score and CT/MRI was compared by accuracy, sensitivity, specificity, and area under curve (AUC). RESULTS A total of 606 patients were included to develop a basic prediction model. After multistep gene selection, the LN metastasis prediction model was constructed with 5 genes. The model can accurately predict LN metastasis with an AUC of 0.781. For clinically practical use, we transformed the model into a Fast LN Scoring System using the SYSMH cohort (n = 105). High LN score patients exhibited a 72.2% LN metastasis rate, while low LN score patients showed a 3.4% LN metastasis rate. The LN score achieved a superior accuracy than CT/MRI (0.882 vs. 0.727). Application of LN score can correct the diagnosis of 88% (22/25) patients who were misdiagnosed by CT/MRI. DISCUSSION The clinically practical LN score can precisely, rapidly, and conveniently predict LN status, which will assist preoperative diagnosis for LN metastasis and guide precise therapy.
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Affiliation(s)
- Junlin Lu
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510120, Guangzhou, Guangdong, P. R. China
| | - Jiajian Lai
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510120, Guangzhou, Guangdong, P. R. China
| | - Kanghua Xiao
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510120, Guangzhou, Guangdong, P. R. China
| | - Shengmeng Peng
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510120, Guangzhou, Guangdong, P. R. China
| | - Yangjie Zhang
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510120, Guangzhou, Guangdong, P. R. China
| | - Qidong Xia
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, P. R. China
| | - Sen Liu
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510120, Guangzhou, Guangdong, P. R. China
| | - Liang Cheng
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510120, Guangzhou, Guangdong, P. R. China
| | - Qiang Zhang
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510120, Guangzhou, Guangdong, P. R. China
| | - Yuelong Chen
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510120, Guangzhou, Guangdong, P. R. China
| | - Xu Chen
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, Guangdong, P. R. China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510120, Guangzhou, Guangdong, P. R. China.
- Guangdong Provincial Clinical Research Center for Urological Diseases, 510120, Guangzhou, Guangdong, P. R. China.
| | - Tianxin Lin
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, Guangdong, P. R. China.
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510120, Guangzhou, Guangdong, P. R. China.
- Guangdong Provincial Clinical Research Center for Urological Diseases, 510120, Guangzhou, Guangdong, P. R. China.
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18
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Akagi K, Koizumi K, Kadowaki M, Kitajima I, Saito S. New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory. Cells 2023; 12:2297. [PMID: 37759519 PMCID: PMC10528308 DOI: 10.3390/cells12182297] [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: 09/12/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
Aging is the slowest process in a living organism. During this process, mortality rate increases exponentially due to the accumulation of damage at the cellular level. Cellular senescence is a well-established hallmark of aging, as well as a promising target for preventing aging and age-related diseases. However, mapping the senescent cells in tissues is extremely challenging, as their low abundance, lack of specific markers, and variability arise from heterogeneity. Hence, methodologies for identifying or predicting the development of senescent cells are necessary for achieving healthy aging. A new wave of bioinformatic methodologies based on mathematics/physics theories have been proposed to be applied to aging biology, which is altering the way we approach our understand of aging. Here, we discuss the dynamical network biomarkers (DNB) theory, which allows for the prediction of state transition in complex systems such as living organisms, as well as usage of Raman spectroscopy that offers a non-invasive and label-free imaging, and provide a perspective on potential applications for the study of aging.
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Affiliation(s)
- Kazutaka Akagi
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Keiichi Koizumi
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
- Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Makoto Kadowaki
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Isao Kitajima
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
| | - Shigeru Saito
- Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan
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19
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Jia W, Liang S, Lin W, Li S, Yuan J, Jin M, Nie S, Zhang Y, Zhai X, Zhou L, Ling C, Cheng B, Ling C. Hypoxia-induced exosomes facilitate lung pre-metastatic niche formation in hepatocellular carcinoma through the miR-4508-RFX1-IL17A-p38 MAPK-NF-κB pathway. Int J Biol Sci 2023; 19:4744-4762. [PMID: 37781522 PMCID: PMC10539707 DOI: 10.7150/ijbs.86767] [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: 06/03/2023] [Accepted: 08/26/2023] [Indexed: 10/03/2023] Open
Abstract
Background: Hypoxia plays an important role in the lung metastasis of hepatocellular carcinoma (HCC). However, the process by which hypoxia promotes the formation of a pre-metastatic niche (PMN) and its underlying mechanism remain unclear. Methods: Exosomes derived from normoxic and hypoxic HCC cells were collected to induce fibroblast activation in vitro and PMN formation in vivo. The micro RNA (miR) profiles of the exosomes were sequenced to identify differentially expressed miRNAs. Gain- and loss-of-function analyses were performed to investigate miR-4508 function. Dual-luciferase, western blotting, and real-time reverse transcription-PCR analyses were used to identify the direct targets of miR-4508 and its downstream signaling pathways. To demonstrate the roles of hypoxic tumor-derived exosomes (H-TDEs) and miR-4508 in the lung metastasis of liver cancer, H22 tumor cells were injected through the tail vein of mice. Blood plasma-derived exosomes from patients with HCC who underwent transarterial chemoembolization (TACE) were applied to determine clinical correlations. Results: We demonstrated that H-TDEs activated lung fibroblasts and facilitated PMN formation, thereby promoting lung metastasis in mice. Screening for upregulated exosomal miRNAs revealed that miR-4508 and its target, regulatory factor X1 (RFX1), were involved in H-TDE-induced lung PMN formation. Moreover, miR-4508 was significantly upregulated in plasma exosomes derived from patients with HCC after TACE. We confirmed that the p38 MAPK-NF-κB signaling pathway is involved in RFX1 knockdown-induced fibroblast activation and PMN formation. In addition, IL17A, a downstream target of RFX1, was identified as a link between RFX1 knockdown and p38 MAPK activation in fibroblasts. Conclusion: Hypoxia enhances the release of TDEs enriched with miR-4508, thereby promoting lung PMN formation by targeting the RFX1-IL17A-p38 MAPK-NF-κB pathway. These findings highlight a novel mechanism underlying hypoxia-induced pulmonary metastasis of HCC.
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Affiliation(s)
- Wentao Jia
- Oncology Department of Traditional Chinese Medicine, the First Affiliated Hospital of Naval Medical University, Shanghai 200433, China
- Faculty of Traditional Chinese Medicine, Naval Medical University, Shanghai, 200043, China
| | - Shufang Liang
- Oncology Department of Traditional Chinese Medicine, the First Affiliated Hospital of Naval Medical University, Shanghai 200433, China
| | - Wanfu Lin
- Faculty of Traditional Chinese Medicine, Naval Medical University, Shanghai, 200043, China
| | - Shu Li
- Department of Gastroenterology, Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201900, China
| | - Jiaying Yuan
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Naval Medical University, Shanghai 200433, China
| | - Mingming Jin
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
| | - Shuchang Nie
- Oncology Department of Traditional Chinese Medicine, the First Affiliated Hospital of Naval Medical University, Shanghai 200433, China
- Faculty of Traditional Chinese Medicine, Naval Medical University, Shanghai, 200043, China
| | - Ya'ni Zhang
- Oncology Department of Traditional Chinese Medicine, the First Affiliated Hospital of Naval Medical University, Shanghai 200433, China
- Faculty of Traditional Chinese Medicine, Naval Medical University, Shanghai, 200043, China
| | - Xiaofeng Zhai
- Oncology Department of Traditional Chinese Medicine, the First Affiliated Hospital of Naval Medical University, Shanghai 200433, China
- Faculty of Traditional Chinese Medicine, Naval Medical University, Shanghai, 200043, China
| | - Liping Zhou
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology (Ministry of Education), School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Changquan Ling
- Oncology Department of Traditional Chinese Medicine, the First Affiliated Hospital of Naval Medical University, Shanghai 200433, China
- Faculty of Traditional Chinese Medicine, Naval Medical University, Shanghai, 200043, China
| | - Binbin Cheng
- Oncology Department of Traditional Chinese Medicine, the First Affiliated Hospital of Naval Medical University, Shanghai 200433, China
- Faculty of Traditional Chinese Medicine, Naval Medical University, Shanghai, 200043, China
| | - Chen Ling
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology (Ministry of Education), School of Life Sciences, Fudan University, Shanghai, 200438, China
- Department of Clinical Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
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20
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Su B, Wang X, Ouyang Y, Lin X. DA-SRN: Omics data analysis based on the sample network optimization for complex diseases. Comput Biol Med 2023; 164:107252. [PMID: 37454504 DOI: 10.1016/j.compbiomed.2023.107252] [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: 03/03/2023] [Revised: 05/30/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023]
Abstract
Effective biomarker identification and accurate sample label prediction are still challenging for complex diseases. Patient similarity network (PSN) analysis is a powerful tool in disease omics data analysis. The topology of PSN can reflect the discriminative ability of the corresponding feature space on which the sample network is built. In this study, a novel omics data analysis method based on the sample reference network (DA-SRN) is proposed to identify the potential biomarkers and predict the sample categories. DA-SRN defines the informative features and the sample reference network in optimizing the network structure by genetic algorithm. It labels the samples based on the graph neural network, the reference network and the selected informative features. DA-SRN was compared with nine efficient omics data analysis methods on the genomics, metabolomics and transcriptomics datasets to show its validation. The comparison results showed that it outperformed the other methods in area under receiver operating characteristic curve (AUROC), sensitivity, specificity and area under precision-recall curve (AUPRC) in most cases. Besides, the important metabolites identified by DA-SRN for the type 2 diabetes (T2D) metabolomics data were further examined. The pathway analysis revealed the close relationships between the identified metabolites and the critical metabolic pathways related to the occurrence and development of T2D. The experimental results illustrate that DA-SRN can extract the valuable information from the complex omics data by analyzing the sample relationship, and is promising in biomarker identification and sample discrimination for complex diseases.
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Affiliation(s)
- Benzhe Su
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China.
| | - Xiaoxiao Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China.
| | - Yang Ouyang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, Liaoning, China.
| | - Xiaohui Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China.
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21
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Su C, Yang JC, Rong Z, Li F, Luo LX, Liu G, Cheng CY, Zhao MG, Yang L. Identification of CCDC115 as an adverse prognostic biomarker in liver cancer based on bioinformatics and experimental analyses. Heliyon 2023; 9:e19233. [PMID: 37674842 PMCID: PMC10477456 DOI: 10.1016/j.heliyon.2023.e19233] [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: 03/27/2023] [Revised: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 09/08/2023] Open
Abstract
Liver hepatocellular carcinoma (LIHC) is a major malignant tumor of the digestive system with a high incidence rate and poor early diagnosis. Coiled-coil domain-containing protein 115 (CCDC115), an accessory component of vacuolar-ATPase with dramatically abnormal expression, is associated with survival outcomes of cancer patients. However, the role of CCDC115 in LIHC remains unclear. In this study, we aimed to determine the functional role of CCDC115 in LIHC by examining CCDC115 expression, and its influence on LIHC prognosis. Through extensive statistical analyses, using LIHC patient databases, we observed that CCDC115 expression significantly increased in tumor tissues of LIHC patients. In addition, CCDC115 expression correlated with the poor prognosis. Additionally, CCDC115 was found to be involved in several cancer-related pathways, specifically the PI3K-Akt pathway. The expression of CCDC115 was positively correlated with human leukocyte antigen molecules as well as with immune checkpoint molecules in LIHC patients. We performed in vitro experiments and confirmed that the expression of CCDC115 significantly affects the proliferation potential, metastasis and sorafenib resistance of liver cancer cells, as well as some key protein expression in PI3K-Akt pathway. These results indicate that CCDC115 could serve as a diagnostic and prognostic biomarker of LIHC, and targeting CCDC115 may provide a potential strategy to enhance the efficacy of liver cancer therapy.
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Affiliation(s)
- Chang Su
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
- Shaanxi Provincial Corps, Chinese People's Armed Police Force, Xi'an, China
| | - Jing-cheng Yang
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
| | - Zheng Rong
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
| | - Fei Li
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
| | - Lan-xin Luo
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
| | - Guan Liu
- Department of General Surgery, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
| | - Cai-yan Cheng
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China
| | - Ming-gao Zhao
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
| | - Le Yang
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Military Medical University, Xi'an, China
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22
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Zhang Q, Yang M, Zhang P, Wu B, Wei X, Li S. Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods. Cancer Biol Med 2023; 21:j.issn.2095-3941.2023.0129. [PMID: 37589244 PMCID: PMC11033716 DOI: 10.20892/j.issn.2095-3941.2023.0129] [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: 04/17/2023] [Accepted: 06/26/2023] [Indexed: 08/18/2023] Open
Abstract
Gastric cancer (GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development; therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.
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Affiliation(s)
- Qian Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Mingran Yang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Bowen Wu
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaosen Wei
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
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23
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Chen F, Qin J, Wu P, Gao W, Sun G. Glucose-Responsive Antioxidant Hydrogel Accelerates Diabetic Wound Healing. Adv Healthc Mater 2023; 12:e2300074. [PMID: 37021750 DOI: 10.1002/adhm.202300074] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/23/2023] [Indexed: 04/07/2023]
Abstract
Diabetic complications can be ameliorated by inhibiting excessive oxidative stress with antioxidants. To enhance therapeutic intervention, it is crucial to develop intelligent scaffolds for efficient delivery of antioxidants to diabetic wounds. This study introduces reversible boronic bonds to create an intelligent antioxidant hydrogel scaffold. This study modifies gelatin methacryloyl (GelMA) with 4-carboxyphenyboronic acid (CPBA) to synthesize a derivative of GelMA (GelMA-CPBA), and then photo cross-links GelMA-CPBA with (-)-epigallocatechin-3-gallate (EGCG) to form GelMA-CPBA/EGCG (GMPE) hydrogel. The GMPE hydrogel responds to changes in glucose levels, and more EGCG is released as glucose level increases due to the dissociation of boronic ester bonds. The GMPE hydrogel shows good biocompatibility and biodegradability, and its mechanical property is similar to that of the skin tissue. Both in vitro and in vivo results demonstrate that the GMPE hydrogel scaffolds effectively eliminate reactive oxygen species (ROS), reduce the inflammation, and promote angiogenesis, thereby improve collagen deposition and tissue remodeling during diabetic wound healing. This strategy offers new insight into glucose-responsive scaffolds, and this responsive antioxidan hydrogel scaffold holds great potential for the treatment of chronic diabetic wounds.
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Affiliation(s)
- Fang Chen
- Hebei Provincial Key Laboratory of Skeletal Metabolic Physiology of Chronic Kidney Disease, Central Laboratory, Affiliated Hospital of Hebei University, College of Clinical Medicine, Hebei University, Baoding, 071000, China
| | - Jianghui Qin
- College of Chemistry and Materials Science, Institute of Life Science and Green Development, Hebei University, Baoding, 071002, China
| | - Pingli Wu
- College of Chemistry and Materials Science, Institute of Life Science and Green Development, Hebei University, Baoding, 071002, China
| | - Wenshan Gao
- Hebei Provincial Key Laboratory of Skeletal Metabolic Physiology of Chronic Kidney Disease, Central Laboratory, Affiliated Hospital of Hebei University, College of Clinical Medicine, Hebei University, Baoding, 071000, China
| | - Guoming Sun
- College of Chemistry and Materials Science, Institute of Life Science and Green Development, Hebei University, Baoding, 071002, China
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24
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Han Y, Akhtar J, Liu G, Li C, Wang G. Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning. Comput Struct Biotechnol J 2023; 21:3478-3489. [PMID: 38213892 PMCID: PMC10782000 DOI: 10.1016/j.csbj.2023.07.002] [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: 04/12/2023] [Revised: 06/19/2023] [Accepted: 07/01/2023] [Indexed: 01/13/2024] Open
Abstract
Background Early detection of complex diseases like hepatocellular carcinoma remains challenging due to their network-driven pathology. Dynamic network biomarkers (DNB) based on monitoring changes in molecular correlations may enable earlier predictions. However, DNB analysis often overlooks disease heterogeneity. Methods We integrated DNB analysis with graph convolutional neural networks (GCN) to identify critical transitions during hepatocellular carcinoma development in a mouse model. A DNB-GCN model was constructed using transcriptomic data and gene expression levels as node features. Results DNB analysis identified a critical transition point at 7 weeks of age despite histological examinations being unable to detect cancerous changes at that time point. The DNB-GCN model achieved 100% accuracy in classifying healthy and cancerous mice, and was able to accurately predict the health status of newly introduced mice. Conclusion The integration of DNB analysis and GCN demonstrates potential for the early detection of complex diseases by capturing network structures and molecular features that conventional biomarker discovery methods overlook. The approach warrants further development and validation.
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Affiliation(s)
- Yukun Han
- Institute of Modern Biology, Nanjing University, Nanjing 210023, China
- Guangdong Provincial Key Laboratory of Computational Science and Material Design, Shenzhen 518055, China
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Javed Akhtar
- Biomedical Science and Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Center for Endocrinology and Metabolic Diseases, Second Affiliated Hospital, The Chinese University of Hong Kong, Shenzhen 518172, China
- Guangdong Provincial Key Laboratory of Computational Science and Material Design, Shenzhen 518055, China
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Guozhen Liu
- Biomedical Science and Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Chenzhong Li
- Biomedical Science and Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Guanyu Wang
- Biomedical Science and Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Center for Endocrinology and Metabolic Diseases, Second Affiliated Hospital, The Chinese University of Hong Kong, Shenzhen 518172, China
- Guangdong Provincial Key Laboratory of Computational Science and Material Design, Shenzhen 518055, China
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
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25
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Craig AJ, Silveira MAD. The role of ETV4 in HCC: How transcription factors can inform immunotherapy combination treatments. J Hepatol 2023; 79:19-21. [PMID: 37088313 DOI: 10.1016/j.jhep.2023.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 03/30/2023] [Accepted: 04/10/2023] [Indexed: 04/25/2023]
Affiliation(s)
- Amanda J Craig
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.
| | - Maruhen A D Silveira
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
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26
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Chen P, Li Z, Hong Z, Zheng H, Zeng R. Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning. BIOPHYSICS REPORTS 2023; 9:57-66. [PMID: 37753058 PMCID: PMC10518520 DOI: 10.52601/bpr.2023.230005] [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: 03/22/2023] [Accepted: 04/26/2023] [Indexed: 09/28/2023] Open
Abstract
Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasets-based semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles: (1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning; (2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality; (3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas (TCGA) and the Gene Expression Comprehensive Database (GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.
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Affiliation(s)
- Peng Chen
- School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Zhenlei Li
- School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Zhaolin Hong
- School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Haoran Zheng
- School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
- Anhui Key Laboratory of Software Engineering in Computing and Communication, University of Science and Technology of China, Hefei 230026, China
- Department of Systems Biology, University of Science and Technology of China, Hefei 230026, China
| | - Rong Zeng
- CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
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27
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Jirsa V, Wang H, Triebkorn P, Hashemi M, Jha J, Gonzalez-Martinez J, Guye M, Makhalova J, Bartolomei F. Personalised virtual brain models in epilepsy. Lancet Neurol 2023; 22:443-454. [PMID: 36972720 DOI: 10.1016/s1474-4422(23)00008-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 12/20/2022] [Accepted: 01/04/2023] [Indexed: 03/29/2023]
Abstract
Individuals with drug-resistant focal epilepsy are candidates for surgical treatment as a curative option. Before surgery can take place, the patient must have a presurgical evaluation to establish whether and how surgical treatment might stop their seizures without causing neurological deficits. Virtual brains are a new digital modelling technology that map the brain network of a person with epilepsy, using data derived from MRI. This technique produces a computer simulation of seizures and brain imaging signals, such as those that would be recorded with intracranial EEG. When combined with machine learning, virtual brains can be used to estimate the extent and organisation of the epileptogenic zone (ie, the brain regions related to seizure generation and the spatiotemporal dynamics during seizure onset). Virtual brains could, in the future, be used for clinical decision making, to improve precision in localisation of seizure activity, and for surgical planning, but at the moment these models have some limitations, such as low spatial resolution. As evidence accumulates in support of the predictive power of personalised virtual brain models, and as methods are tested in clinical trials, virtual brains might inform clinical practice in the near future.
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Affiliation(s)
- Viktor Jirsa
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France.
| | - Huifang Wang
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | - Paul Triebkorn
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | - Meysam Hashemi
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | - Jayant Jha
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | | | - Maxime Guye
- Centre National de la Recherche Scientifique, Center for Magnetic Resonance in Biology and Medicine, Aix Marseille Université, Marseille, France; Centre d'Exploration Métabolique par Résonance Magnétique, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France
| | - Julia Makhalova
- Centre National de la Recherche Scientifique, Center for Magnetic Resonance in Biology and Medicine, Aix Marseille Université, Marseille, France; Centre d'Exploration Métabolique par Résonance Magnétique, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France; Epileptology and Clinical Neurophysiology Department, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France
| | - Fabrice Bartolomei
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France; Epileptology and Clinical Neurophysiology Department, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France
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28
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Zhong Z, Li J, Zhong J, Huang Y, Hu J, Zhang P, Zhang B, Jin Y, Luo W, Liu R, Zhang Y, Ling F. MAPKAPK2, a potential dynamic network biomarker of α-synuclein prior to its aggregation in PD patients. NPJ Parkinsons Dis 2023; 9:41. [PMID: 36927756 PMCID: PMC10020541 DOI: 10.1038/s41531-023-00479-z] [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: 08/15/2022] [Accepted: 02/22/2023] [Indexed: 03/18/2023] Open
Abstract
One of the important pathological features of Parkinson's disease (PD) is the pathological aggregation of α-synuclein (α-Syn) in the substantia nigra. Preventing the aggregation of α-Syn has become a potential strategy for treating PD. However, the molecular mechanism of α-Syn aggregation is unclear. In this study, using the dynamic network biomarker (DNB) method, we first identified the critical time point when α-Syn undergoes pathological aggregation based on a SH-SY5Y cell model and found that DNB genes encode transcription factors that regulated target genes that were differentially expressed. Interestingly, we found that these DNB genes and their neighbouring genes were significantly enriched in the cellular senescence pathway and thus proposed that the DNB genes HSF1 and MAPKAPK2 regulate the expression of the neighbouring gene SERPINE1. Notably, in Gene Expression Omnibus (GEO) data obtained from substantia nigra, prefrontal cortex and peripheral blood samples, the expression level of MAPKAPK2 was significantly higher in PD patients than in healthy people, suggesting that MAPKAPK2 has potential as an early diagnostic biomarker of diseases related to pathological aggregation of α-Syn, such as PD. These findings provide new insights into the mechanisms underlying the pathological aggregation of α-Syn.
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Affiliation(s)
- Zhenggang Zhong
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Jiabao Li
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Jiayuan Zhong
- School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China
| | - Yilin Huang
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Jiaqi Hu
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Piao Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Baowen Zhang
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Yabin Jin
- The First People's Hospital of Foshan, Sun Yat-sen University, Foshan, China
| | - Wei Luo
- The First People's Hospital of Foshan, Sun Yat-sen University, Foshan, China.
| | - Rui Liu
- School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China.
| | - Yuhu Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Fei Ling
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China.
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29
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Jiang F, Yang L, Jiao X. Dynamic network biomarker to determine the critical point of breast cancer stage progression. Breast Cancer 2023; 30:453-465. [PMID: 36807044 DOI: 10.1007/s12282-023-01438-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 02/11/2023] [Indexed: 02/21/2023]
Abstract
BACKGROUND The discovery of early warning signs and biomarkers in patients with early breast cancer is crucial for the prevention and treatment of breast cancer. Dynamic Network Biomarker (DNB) is an approach based on nonlinear dynamics theory, which we exploited to identify a set of DNB members and their key genes as early warning signals during breast cancer staging progression. METHODS First, based on the gene expression profile of breast cancer in the TCGA database, the DNB algorithm was used to calculate the composite index (CI) of each gene cluster in the process of breast cancer anatomical staging. Then we calculated gene modules associated with the clinical phenotype stage based on weighted gene co-expression network analysis (WGCNA), combined with DNB membership to identify key genes in the network. RESULTS We identified a set of gene clusters with the highest CI in Stage II as DNBs, whose roles in related pathways indicate the emergence of a tipping point and impact on breast cancer development. In addition, analysis of the key gene GPRIN1 showed that high expression of GPRIN1 predicts poor prognosis, and related immune analysis showed that GPRIN1 is involved in the development of breast cancer through immune aspects. CONCLUSION The discovery of DNBs and the key gene GPRIN1 can provide potential biomarkers and therapeutic targets for breast cancer.
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Affiliation(s)
- Fa Jiang
- College of Biomedical Engineering, Taiyuan University of Technology, Jinzhong, 030600, China
| | - Lifeng Yang
- College of Information and Computer, Taiyuan University of Technology, Jinzhong, 030600, China
| | - Xiong Jiao
- College of Biomedical Engineering, Taiyuan University of Technology, Jinzhong, 030600, China.
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30
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Wang HE, Woodman M, Triebkorn P, Lemarechal JD, Jha J, Dollomaja B, Vattikonda AN, Sip V, Medina Villalon S, Hashemi M, Guye M, Makhalova J, Bartolomei F, Jirsa V. Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy. Sci Transl Med 2023; 15:eabp8982. [PMID: 36696482 DOI: 10.1126/scitranslmed.abp8982] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Precise estimates of epileptogenic zone networks (EZNs) are crucial for planning intervention strategies to treat drug-resistant focal epilepsy. Here, we present the virtual epileptic patient (VEP), a workflow that uses personalized brain models and machine learning methods to estimate EZNs and to aid surgical strategies. The structural scaffold of the patient-specific whole-brain network model is constructed from anatomical T1 and diffusion-weighted magnetic resonance imaging. Each network node is equipped with a mathematical dynamical model to simulate seizure activity. Bayesian inference methods sample and optimize key parameters of the personalized model using functional stereoelectroencephalography recordings of patients' seizures. These key parameters together with their personalized model determine a given patient's EZN. Personalized models were further used to predict the outcome of surgical intervention using virtual surgeries. We evaluated the VEP workflow retrospectively using 53 patients with drug-resistant focal epilepsy. VEPs reproduced the clinically defined EZNs with a precision of 0.6, where the physical distance between epileptogenic regions identified by VEP and the clinically defined EZNs was small. Compared with the resected brain regions of 25 patients who underwent surgery, VEP showed lower false discovery rates in seizure-free patients (mean, 0.028) than in non-seizure-free patients (mean, 0.407). VEP is now being evaluated in an ongoing clinical trial (EPINOV) with an expected 356 prospective patients with epilepsy.
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Affiliation(s)
- Huifang E Wang
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Marmaduke Woodman
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Paul Triebkorn
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Jean-Didier Lemarechal
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.,Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Centre MEG-EEG and Experimental Neurosurgery team, Paris F-75013, France
| | - Jayant Jha
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Borana Dollomaja
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Anirudh Nihalani Vattikonda
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Viktor Sip
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Samuel Medina Villalon
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.,APHM, Epileptology and Clinical Neurophysiology Department, Timone Hospital, Marseille 13005, France
| | - Meysam Hashemi
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Maxime Guye
- Aix-Marseille Université, CNRS, CRMBM, Marseille 13005, France.,APHM, Timone University Hospital, CEMEREM, Marseille 13005, France
| | - Julia Makhalova
- APHM, Epileptology and Clinical Neurophysiology Department, Timone Hospital, Marseille 13005, France.,Aix-Marseille Université, CNRS, CRMBM, Marseille 13005, France.,APHM, Timone University Hospital, CEMEREM, Marseille 13005, France
| | - Fabrice Bartolomei
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.,APHM, Epileptology and Clinical Neurophysiology Department, Timone Hospital, Marseille 13005, France
| | - Viktor Jirsa
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
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Huang X, Han C, Zhong J, Hu J, Jin Y, Zhang Q, Luo W, Liu R, Ling F. Low expression of the dynamic network markers FOS/JUN in pre-deteriorated epithelial cells is associated with the progression of colorectal adenoma to carcinoma. J Transl Med 2023; 21:45. [PMID: 36698183 PMCID: PMC9875500 DOI: 10.1186/s12967-023-03890-5] [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: 11/24/2022] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Deterioration of normal intestinal epithelial cells is crucial for colorectal tumorigenesis. However, the process of epithelial cell deterioration and molecular networks that contribute to this process remain unclear. METHODS Single-cell data and clinical information were downloaded from the Gene Expression Omnibus (GEO) database. We used the recently proposed dynamic network biomarker (DNB) method to identify the critical stage of epithelial cell deterioration. Data analysis and visualization were performed using R and Cytoscape software. In addition, Single-Cell rEgulatory Network Inference and Clustering (SCENIC) analysis was used to identify potential transcription factors, and CellChat analysis was conducted to evaluate possible interactions among cell populations. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set variation analysis (GSVA) analyses were also performed. RESULTS The trajectory of epithelial cell deterioration in adenoma to carcinoma progression was delineated, and the subpopulation of pre-deteriorated epithelial cells during colorectal cancer (CRC) initialization was identified at the single-cell level. Additionally, FOS/JUN were identified as biomarkers for pre-deteriorated epithelial cell subpopulations in CRC. Notably, FOS/JUN triggered low expression of P53-regulated downstream pro-apoptotic genes and high expression of anti-apoptotic genes through suppression of P53 expression, which in turn inhibited P53-induced apoptosis. Furthermore, malignant epithelial cells contributed to the progression of pre-deteriorated epithelial cells through the GDF signaling pathway. CONCLUSIONS We demonstrated the trajectory of epithelial cell deterioration and used DNB to characterize pre-deteriorated epithelial cells at the single-cell level. The expression of DNB-neighboring genes and cellular communication were triggered by DNB genes, which may be involved in epithelial cell deterioration. The DNB genes FOS/JUN provide new insights into early intervention in CRC.
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Affiliation(s)
- Xiaoqi Huang
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
| | - Chongyin Han
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
| | - Jiayuan Zhong
- grid.79703.3a0000 0004 1764 3838School of Mathematics, South China University of Technology, Guangzhou, 510641 China
| | - Jiaqi Hu
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
| | - Yabin Jin
- grid.452881.20000 0004 0604 5998Institute of Clinical Research, The First People’s Hospital of Foshan, Foshan, 528000 China
| | - Qinqin Zhang
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
| | - Wei Luo
- grid.452881.20000 0004 0604 5998Institute of Clinical Research, The First People’s Hospital of Foshan, Foshan, 528000 China
| | - Rui Liu
- grid.79703.3a0000 0004 1764 3838School of Mathematics, South China University of Technology, Guangzhou, 510641 China ,grid.513189.7Pazhou Lab, Guangzhou, 510330 China
| | - Fei Ling
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
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Guo W, Jin P, Li R, Huang L, Liu Z, Li H, Zhou T, Fang B, Xia L. Dynamic network biomarker identifies cdkn1a-mediated bone mineralization in the triggering phase of osteoporosis. Exp Mol Med 2023; 55:81-94. [PMID: 36599933 PMCID: PMC9898265 DOI: 10.1038/s12276-022-00915-9] [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: 06/28/2022] [Revised: 10/17/2022] [Accepted: 11/02/2022] [Indexed: 01/06/2023] Open
Abstract
The identification of predictive markers to determine the triggering phase prior to the onset of osteoporosis is essential to mitigate further irrevocable deterioration. To determine the early warning signs before osteoporosis, we used the dynamic network biomarker (DNB) approach to analyze time-series gene expression data in a zebrafish osteoporosis model, which revealed that cyclin-dependent kinase inhibitor 1 A (cdkn1a) is a core DNB. We found that cdkn1a negatively regulates osteogenesis, as evidenced by loss-of-function and gain-of-function studies. Specifically, CRISPR/Cas9-mediated cdkn1a knockout in zebrafish significantly altered skeletal development and increased bone mineralization, whereas inducible cdkn1a expression significantly contributed to osteoclast differentiation. We also found several mechanistic clues that cdkn1a participates in osteoclast differentiation by regulating its upstream signaling cascades. To summarize, in this study, we provided new insights into the dynamic nature of osteoporosis and identified cdkn1a as an early-warning signal of osteoporosis onset.
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Affiliation(s)
- Weiming Guo
- grid.16821.3c0000 0004 0368 8293Department of Orthodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology, Shanghai, 200001 China
| | - Peng Jin
- grid.16821.3c0000 0004 0368 8293Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200001 China
| | - Ruomei Li
- grid.16821.3c0000 0004 0368 8293Department of Orthodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology, Shanghai, 200001 China
| | - Lu Huang
- grid.16821.3c0000 0004 0368 8293Department of Plastic and Reconstructive Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200001 China
| | - Zhen Liu
- grid.16821.3c0000 0004 0368 8293Department of Orthodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology, Shanghai, 200001 China
| | - Hairui Li
- grid.16821.3c0000 0004 0368 8293Department of Orthodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology, Shanghai, 200001 China
| | - Ting Zhou
- Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology, Shanghai, 200001, China.
| | - Bing Fang
- Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology, Shanghai, 200001, China.
| | - Lunguo Xia
- Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology, Shanghai, 200001, China.
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Dynamic network biomarker factors orchestrate cell-fate determination at tipping points during hESC differentiation. Innovation (N Y) 2022; 4:100364. [PMID: 36632190 PMCID: PMC9827382 DOI: 10.1016/j.xinn.2022.100364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
The generation of ectoderm, mesoderm, and endoderm layers is the most critical biological process during the gastrulation of embryo development. Such a differentiation process in human embryonic stem cells (hESCs) is an inherently nonlinear multi-stage dynamical process which contain multiple tipping points playing crucial roles in the cell-fate decision. However, the tipping points of the process are largely unknown, letting alone the understanding of the molecular regulation on these critical events. Here by designing a module-based dynamic network biomarker (M-DNB) model, we quantitatively pinpointed two tipping points of the differentiation of hESCs toward definitive endoderm, which leads to the identification of M-DNB factors (FOS, HSF1, MYCN, TP53, and MYC) of this process. We demonstrate that before the tipping points, M-DNB factors are able to maintain the cell states and orchestrate cell-fate determination during hESC (ES)-to-ME and ME-to-DE differentiation processes, which not only leads to better understanding of endodermal specification of hESCs but also reveals the power of the M-DNB model to identify critical transition points with their key factors in diverse biological processes, including cell differentiation and transdifferentiation dynamics.
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34
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Liu N, Zhu S, Deng Y, Xie M, Zhao M, Sun T, Yu C, Zhong Y, Guo R, Cheng K, Chang D, Zhu P. Construction of multifunctional hydrogel with metal-polyphenol capsules for infected full-thickness skin wound healing. Bioact Mater 2022; 24:69-80. [PMID: 36582352 PMCID: PMC9772805 DOI: 10.1016/j.bioactmat.2022.12.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/09/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Damaged skin cannot prevent harmful bacteria from invading tissues, causing infected wounds or even severe tissue damage. In this study, we developed a controlled-release antibacterial composite hydrogel system that can promote wound angiogenesis and inhibit inflammation by sustained releasing Cu-Epigallocatechin-3-gallate (Cu-EGCG) nano-capsules. The prepared SilMA/HAMA/Cu-EGCG hydrogel showed an obvious inhibitory effect on Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). It could also promote the proliferation and migration of L929 fibroblasts. In vivo full-thickness infected wound healing experiments confirmed the angiogenesis and inflammation regulating effect. Accelerate collagen deposition and wound healing speed were also observed in the SilMA/HAMA/Cu-EGCG hydrogel treated group. The findings of this study show the great potential of this controlled-release antibacterial composite hydrogel in the application of chronic wound healing.
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Affiliation(s)
- Nanbo Liu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510100, China,Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou, Guangdong, 510100, China
| | - Shuoji Zhu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510100, China,University of Tokyo, Tokyo, 113-8666, Japan,Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou, Guangdong, 510100, China
| | - Yuzhi Deng
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510100, China,Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, 524001, China,Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou, Guangdong, 510100, China
| | - Ming Xie
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510100, China,Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou, Guangdong, 510100, China
| | - Mingyi Zhao
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510100, China,Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou, Guangdong, 510100, China
| | - Tucheng Sun
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510100, China,Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou, Guangdong, 510100, China
| | - Changjiang Yu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510100, China,Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou, Guangdong, 510100, China
| | - Ying Zhong
- Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, 524001, China
| | - Rui Guo
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Guangdong Provincial Engineering and Technological Research Centre for Drug Carrier Development, Department of Biomedical Engineering, Jinan University, Guangzhou, 510632, China,Corresponding author.
| | - Keluo Cheng
- Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, 524001, China,Corresponding author.
| | - Dehua Chang
- University of Tokyo Hospital Department of Cell Therapy in Regenerative Medicine, Tokyo, 113-8666, Japan,Corresponding author.
| | - Ping Zhu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510100, China,Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, 524001, China,Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, Guangzhou, Guangdong, 510100, China,Corresponding author. Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510100, China.
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ARHGEF37 overexpression promotes extravasation and metastasis of hepatocellular carcinoma via directly activating Cdc42. J Exp Clin Cancer Res 2022; 41:230. [PMID: 35869555 PMCID: PMC9308268 DOI: 10.1186/s13046-022-02441-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 07/15/2022] [Indexed: 11/24/2022] Open
Abstract
Background The extravasation capability of hepatocellular carcinoma (HCC) cells plays a vital role in distant metastasis. However, the underlying mechanism of extravasation in HCC lung metastasis remains largely unclear. Methods The expression of ARHGEF37 in human HCC specimens and HCC cell lines was examined by quantitative RT-PCR, western blot, and immunohistochemistry (IHC) analyses. The biological roles and mechanisms of ARHGEF37/Cdc42 in promoting lung metastasis were investigated in vitro and in vivo using cell lines, patient samples, xenograft models. Results In the current study, we found that Rho guanine nucleotide exchange factor 37 (ARHGEF37) was upregulated in human HCC samples and was associated with tumor invasiveness, pulmonary metastasis and poor prognosis. Overexpressing ARHGEF37 significantly enhanced the extravasation and metastatic capability of HCC cells via facilitating tumor cell adhesion to endothelial cells and trans-endothelial migration. Mechanistically, ARHGEF37 directly interacted with and activated Cdc42 to promote the invadopodia formation in HCC cells, which consequently disrupted the interaction between endothelial cells and pericytes. Importantly, treatment with ZCL278, a specific inhibitor of Cdc42, dramatically inhibited the attachment of ARHGEF37-overexpressing HCC cells to endothelial cells, and the adherence and extravasation in the lung alveoli, resulting in suppression of lung metastasis in mice. Conclusion Our findings provide a new insight into the underlying mechanisms on the ARHGEF37 overexpression-mediated extravasation and pulmonary metastasis of HCC cells, and provided a potential therapeutic target for the prevention and treatment of HCC pulmonary metastasis. Supplementary Information The online version contains supplementary material available at 10.1186/s13046-022-02441-y.
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Chen L, Yu YN, Liu J, Chen YY, Wang B, Qi YF, Guan S, Liu X, Li B, Zhang YY, Hu Y, Wang Z. Modular networks and genomic variation during progression from stable angina pectoris through ischemic cardiomyopathy to chronic heart failure. Mol Med 2022; 28:140. [DOI: 10.1186/s10020-022-00569-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 11/04/2022] [Indexed: 11/28/2022] Open
Abstract
Abstract
Background
Analyzing disease–disease relationships plays an important role for understanding etiology, disease classification, and drug repositioning. However, as cardiovascular diseases with causative links, the molecular relationship among stable angina pectoris (SAP), ischemic cardiomyopathy (ICM) and chronic heart failure (CHF) is not clear.
Methods
In this study, by integrating the multi-database data, we constructed paired disease progression modules (PDPMs) to identified relationship among SAP, ICM and CHF based on module reconstruction pairs (MRPs) of K-value calculation (a Euclidean distance optimization by integrating module topology parameters and their weights) methods. Finally, enrichment analysis, literature validation and structural variation (SV) were performed to verify the relationship between the three diseases in PDPMs.
Results
Total 16 PDPMs were found with K > 0.3777 among SAP, ICM and CHF, in which 6 pairs in SAP–ICM, 5 pairs for both ICM–CHF and SAP–CHF. SAP–ICM was the most closely related by having the smallest average K-value (K = 0.3899) while the maximum is SAP–CHF (K = 0.4006). According to the function of the validation gene, inflammatory response were through each stage of SAP–ICM–CHF, while SAP–ICM was uniquely involved in fibrosis, and genes were related in affecting the upstream of PI3K–Akt signaling pathway. 4 of the 11 genes (FLT1, KDR, ANGPT2 and PGF) in SAP–ICM–CHF related to angiogenesis in HIF-1 signaling pathway. Furthermore, we identified 62.96% SVs were protein deletion in SAP–ICM–CHF, and 53.85% SVs were defined as protein replication in SAP–ICM, while ICM–CHF genes were mainly affected by protein deletion.
Conclusion
The PDPMs analysis approach combined with genomic structural variation provides a new avenue for determining target associations contributing to disease progression and reveals that inflammation and angiogenesis may be important links among SAP, ICM and CHF progression.
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Application of the Dynamical Network Biomarker Theory to Raman Spectra. Biomolecules 2022; 12:biom12121730. [PMID: 36551158 PMCID: PMC9776035 DOI: 10.3390/biom12121730] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/14/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022] Open
Abstract
The dynamical network biomarker (DNB) theory detects the early warning signals of state transitions utilizing fluctuations in and correlations between variables in complex systems. Although the DNB theory has been applied to gene expression in several diseases, destructive testing by microarrays is a critical issue. Therefore, other biological information obtained by non-destructive testing is desirable; one such piece of information is Raman spectra measured by Raman spectroscopy. Raman spectroscopy is a powerful tool in life sciences and many other fields that enable the label-free non-invasive imaging of live cells and tissues along with detailed molecular fingerprints. Naïve and activated T cells have recently been successfully distinguished from each other using Raman spectroscopy without labeling. In the present study, we applied the DNB theory to Raman spectra of T cell activation as a model case. The dataset consisted of Raman spectra of the T cell activation process observed at 0 (naïve T cells), 2, 6, 12, 24 and 48 h (fully activated T cells). In the DNB analysis, the F-test and hierarchical clustering were used to detect the transition state and identify DNB Raman shifts. We successfully detected the transition state at 6 h and related DNB Raman shifts during the T cell activation process. The present results suggest novel applications of the DNB theory to Raman spectra ranging from fundamental research on cellular mechanisms to clinical examinations.
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Chen X, Wang Y, Liu H, Zhang J, Wang J, Jin X, Ma Y. CSP I-plus modified rEndostatin inhibits hepatocellular carcinoma metastasis via down-regulation of VEGFA and integrinβ1. BMC Cancer 2022; 22:1200. [PMID: 36419008 PMCID: PMC9682839 DOI: 10.1186/s12885-022-10318-8] [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: 06/27/2022] [Accepted: 11/15/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND In our previous study, N end of the Circumsporozoite protein (CSP I-plus) modified recombinant human Endostatin (rEndostatin, endostar) (rES-CSP) was constructed, which had antiangiogenic capability and bound to hepatocellular carcinoma in vivo and in vitro. In this study, the inhibition of rES-CSP on hepatocellular carcinoma metastasis was verified in vivo and in vitro, and its possible mechanism was explored. METHODS Firstly, the impact of rES-CSP on the migration, adhesion of hepatoma cell HCCLM3 was identified by wound healing, transwell, and on metastasis of orthotopic xenograft model was identified in nude mouse. Then the expression of metastasis-associated molecules (MMP2, E-cadherin, integrinβ1) and angiogenesis-related factors (VEGFA) in vitro and in vivo were detected by real-time PCR, western blotting, immunohistochemistry. RESULTS Finally, we found that rES-CSP could inhibit the migration and invasion of HCCLM3, and decrease tumor metastasis and growth in nude mouse orthotopic xenograft models. The tumor inhibiting rates of rES-CSP and Endostar were 42.46 ± 5.39% and 11.1 ± 1.88%. The lung metastasis rates of the control, Endostar and rES-CSP were 71, 50, and 42.8%, respectively. Compared with Endostar, rES-CSP significantly down-regulated the expression of VEGFA and integrinβ1. Heparin, a competitive inhibitor of CSP I-plus, which can be bind to the highly-sulfated heparan sulfate proteoglycans (HSPGs) over-expressed in liver and hepatocellular carcinoma, alleviated the down-regulation of VEGFA and integrinβ1. CONCLUSIONS These indicate that rES-CSP may play a role in inhibiting tumor growth and metastasis by down-regulating the angiogenic factor VEGF and the metastasis-related molecules or by interfering with HSPGs-mediated tumor metastasis.
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Affiliation(s)
- Xueqin Chen
- grid.411847.f0000 0004 1804 4300Guangdong Provincial Key Laboratory of Pharmaceutical Bioactive Substances, School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, No. 280, East Waihuan Road, Higher Education Mega Center, Guangzhou, 510006 China
| | - Yan Wang
- grid.411847.f0000 0004 1804 4300Zhongshan Campus Laboratory Center, Guangdong Pharmaceutical University, Guangzhou, 510006 China
| | - Hancong Liu
- grid.411847.f0000 0004 1804 4300Guangdong Provincial Key Laboratory of Pharmaceutical Bioactive Substances, School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, No. 280, East Waihuan Road, Higher Education Mega Center, Guangzhou, 510006 China
| | - Jingjing Zhang
- grid.411847.f0000 0004 1804 4300Guangdong Provincial Key Laboratory of Pharmaceutical Bioactive Substances, School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, No. 280, East Waihuan Road, Higher Education Mega Center, Guangzhou, 510006 China
| | - Jie Wang
- grid.411847.f0000 0004 1804 4300Guangdong Provincial Key Laboratory of Pharmaceutical Bioactive Substances, School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, No. 280, East Waihuan Road, Higher Education Mega Center, Guangzhou, 510006 China
| | - Xiaobao Jin
- grid.411847.f0000 0004 1804 4300Guangdong Provincial Key Laboratory of Pharmaceutical Bioactive Substances, School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, No. 280, East Waihuan Road, Higher Education Mega Center, Guangzhou, 510006 China
| | - Yan Ma
- grid.411847.f0000 0004 1804 4300Guangdong Provincial Key Laboratory of Pharmaceutical Bioactive Substances, School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, No. 280, East Waihuan Road, Higher Education Mega Center, Guangzhou, 510006 China
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Shan Z, Ding L, Zhu C, Sun R, Hong W. Medical care of rare and undiagnosed diseases: Prospects and challenges. FUNDAMENTAL RESEARCH 2022; 2:851-858. [PMID: 38933390 PMCID: PMC11197738 DOI: 10.1016/j.fmre.2022.08.018] [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: 06/28/2022] [Revised: 08/18/2022] [Accepted: 08/23/2022] [Indexed: 11/25/2022] Open
Abstract
Rare and undiagnosed diseases tend to be diverse, misdiagnosed, and difficult to diagnose. In some cases, the disease is progressive and life-threatening. Yet, to date, an estimated 95% of rare diseases have no approved therapy. Therefore, rare and undiagnosed diseases are considered the ultimate challenges for understanding human diseases. Here, we review the research progress, research frontiers, and important scientific issues related to rare and undiagnosed diseases. We mainly focus on five topics: (1) the identification and functional analysis of disease-causing genes; (2) the construction of cells, organoids, and animal models for mechanism validation; (3) subtyping and diagnosis; (4) treatment and drug screening based on causative genes and mutations; and (5) new technologies and methods for studying rare and undiagnosed diseases. In this review, we briefly update and discuss the pathogenic mechanisms and precision medicine for rare and undiagnosed diseases.
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Affiliation(s)
- Zhiyan Shan
- Department of Health Sciences, National Natural Science Foundation of China, Beijing 100085, China
- Department of Histology and Embryology, Harbin Medical University, Harbin 150081, China
| | - Lijun Ding
- Department of Health Sciences, National Natural Science Foundation of China, Beijing 100085, China
- Center for Reproductive Medicine and Obstetrics and Gynecology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, Jiangsu 210008, China
| | - Caiyun Zhu
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing 100005, China
| | - Ruijuan Sun
- Department of Health Sciences, National Natural Science Foundation of China, Beijing 100085, China
| | - Wei Hong
- Department of Health Sciences, National Natural Science Foundation of China, Beijing 100085, China
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Wang S, Sun Y, Zeng T, Wu Y, Ding L, Zhang X, Zhang L, Huang X, Li H, Yang X, Ni Y, Hu Q. Impact of preanalytical freezing delay time on the stability of metabolites in oral squamous cell carcinoma tissue samples. Metabolomics 2022; 18:82. [PMID: 36282338 DOI: 10.1007/s11306-022-01943-2] [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: 09/05/2022] [Accepted: 10/11/2022] [Indexed: 12/29/2022]
Abstract
INTRODUCTION Metabolite stability is critical for tissue metabolomics. However, changes in metabolites in tissues over time from the operating room to the laboratory remain underexplored. OBJECTIVES In this study, we evaluated the effect of postoperative freezing delay time on the stability of metabolites in normal and oral squamous cell carcinoma (OSCC) tissues. METHODS Tumor and paired normal tissues from five OSCC patients were collected after surgical resection, and samples was sequentially quenched in liquid nitrogen at 30, 40, 50, 60, 70, 80, 90 and 120 min (80 samples). Untargeted metabolic analysis by liquid chromatography-mass spectrometry/mass spectrometry in positive and negative ion modes was used to identify metabolic changes associated with delayed freezing time. The trends of metabolite changes at 30-120 and 30-60 min of delayed freezing were analyzed. RESULTS 190 metabolites in 36 chemical classes were detected. After delayed freezing for 120 min, approximately 20% of the metabolites changed significantly in normal and tumor tissues, and differences in the metabolites were found in normal and tumor tissues. After a delay of 60 min, 29 metabolites had changed significantly in normal tissues, and 84 metabolites had changed significantly in tumor tissues. In addition, we constructed three tissue freezing schemes based on the observed variation trends in the metabolites. CONCLUSION Delayed freezing of tissue samples has a certain impact on the stability of metabolites. For metabolites with significant changes, we suggest that the freezing time of tissues be reasonably selected according to the freezing schemes and the actual clinical situation.
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Affiliation(s)
- Shuai Wang
- Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
- Central Laboratory of Stomatology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Yawei Sun
- Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Tao Zeng
- State Key Lab of Pharmaceutical Biotechnology, College of Life Sciences, Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Yan Wu
- Department of Oral Pathology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
- Central Laboratory of Stomatology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Liang Ding
- Central Laboratory of Stomatology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Xiaoxin Zhang
- Central Laboratory of Stomatology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Lei Zhang
- Department of Oral Pathology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Xiaofeng Huang
- Department of Oral Pathology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Huiling Li
- Department of Oral Pathology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Xihu Yang
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu, China
| | - Yanhong Ni
- Central Laboratory of Stomatology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China.
| | - Qingang Hu
- Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China.
- Central Laboratory of Stomatology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China.
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Sun Z, Chen G, Wang L, Sang Q, Xu G, Zhang N. APEX1 promotes the oncogenicity of hepatocellular carcinoma via regulation of MAP2K6. Aging (Albany NY) 2022; 14:7959-7971. [PMID: 36205565 PMCID: PMC9596212 DOI: 10.18632/aging.204325] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 09/17/2022] [Indexed: 12/24/2022]
Abstract
Objective: Apurinic/apyrimidinic endonuclease 1 (APEX1), a key enzyme responsible for DNA base excision repair, has been linked to development and progression of cancers. In this work, we aimed to explore the role of APEX1 in hepatocellular carcinoma (HCC) and elucidate its molecular mechanism. Methods: The expression of APEX1 in HCC tissues and matched adjacent normal tissues (n = 80 cases) was evaluated by immunohistochemistry. Web-based tools UALCAN and the Kaplan-Meier plotter were used to analyze the Cancer Genome Atlas database to compare expression of APEX1 mRNA to 5-year overall survival. APEX1 was stably silenced in two HCC cell lines, Hep 3B and Bel-7402, with shRNA technology. An in vivo tumorigenesis model was established by subcutaneously injecting sh-APEX1-transfected Bel-7402 cells into mice, and tumor growth was determined. We performed high-throughput transcriptome sequencing in sh-APEX1-treated HCC cells to identify the key KEGG signaling pathways induced by silencing of APEX1. Results: APEX1 was significantly upregulated and predicted poor clinical overall survival in HCC patients. Silencing APEX1 inhibited the proliferation of HCC cells in vivo and in vitro, and it repressed invasion and migration and increased apoptosis and the percentage of cells in G1. Differentially expressed genes upon APEX1 silencing included genes involved in TNF signaling. A positive correlation between the expression of APEX1 and MAP2K6 was noted, and overexpressing MAP2K6 overcame cancer-related phenotypes associated with APEX1 silencing. Conclusion: APEX1 enhances the malignant properties of HCC via MAP2K6. APEX1 may represent a valuable prognostic biomarker and therapeutic target in HCC.
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Affiliation(s)
- Zhipeng Sun
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Institute for Precision Medicine, Tsinghua University, Beijing, China
| | - Guangyang Chen
- Oncology Surgery Department, Beijing Shijitan Hospital, Capital Medical University, Peking University Ninth School of Clinical Medicine, Beijing, China
| | - Liang Wang
- Oncology Surgery Department, Beijing Shijitan Hospital, Capital Medical University, Peking University Ninth School of Clinical Medicine, Beijing, China
| | - Qing Sang
- Oncology Surgery Department, Beijing Shijitan Hospital, Capital Medical University, Peking University Ninth School of Clinical Medicine, Beijing, China
| | - Guangzhong Xu
- Oncology Surgery Department, Beijing Shijitan Hospital, Capital Medical University, Peking University Ninth School of Clinical Medicine, Beijing, China
| | - Nengwei Zhang
- Oncology Surgery Department, Beijing Shijitan Hospital, Capital Medical University, Peking University Ninth School of Clinical Medicine, Beijing, China
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Chang W, Luo Q, Wu X, Nan Y, Zhao P, Zhang L, Luo A, Jiao W, Zhu Q, Fu Y, Liu Z. OTUB2 exerts tumor-suppressive roles via STAT1-mediated CALML3 activation and increased phosphatidylserine synthesis. Cell Rep 2022; 41:111561. [DOI: 10.1016/j.celrep.2022.111561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 01/17/2022] [Accepted: 10/04/2022] [Indexed: 12/09/2022] Open
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Qi J, Sheng Q, Zhou Y, Hua J, Xiao S, Jin S. scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information. Cell Biosci 2022; 12:142. [PMID: 36056412 PMCID: PMC9440561 DOI: 10.1186/s13578-022-00886-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/17/2022] [Indexed: 11/17/2022] Open
Abstract
Background Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to capture transcriptomes at single-cell resolution. However, dropout events distort the gene expression levels and underlying biological signals, misleading the downstream analysis of scRNA-seq data. Results We develop a statistical model-based multidimensional imputation algorithm, scMTD, that identifies local cell neighbors and specific gene co-expression networks based on the pseudo-time of cells, leveraging information on cell-level, gene-level, and transcriptome dynamic to recover scRNA-seq data. Compared with the state-of-the-art imputation methods through several real-data-based analytical experiments, scMTD effectively recovers biological signals of transcriptomes and consistently outperforms the other algorithms in improving FISH validation, trajectory inference, differential expression analysis, clustering analysis, and identification of cell types. Conclusions scMTD maintains the gene expression characteristics, enhances the clustering of cell subpopulations, assists the study of gene expression dynamics, contributes to the discovery of rare cell types, and applies to both UMI-based and non-UMI-based data. Overall, scMTD’s reliability, applicability, and scalability make it a promising imputation approach for scRNA-seq data. Supplementary Information The online version contains supplementary material available at 10.1186/s13578-022-00886-4.
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Proverbio D, Montanari AN, Skupin A, Gonçalves J. Buffering variability in cell regulation motifs close to criticality. Phys Rev E 2022; 106:L032402. [PMID: 36266798 DOI: 10.1103/physreve.106.l032402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Bistable biological regulatory systems need to cope with stochastic noise to fine tune their function close to bifurcation points. Here, we study stability properties of this regime in generic systems to demonstrate that cooperative interactions buffer system variability, hampering noise-induced regime shifts. Our analysis also shows that, in the considered cooperativity range, impending regime shifts can be generically detected by statistical early warning signals from distributional data. Our generic framework, based on minimal models, can be used to extract robustness and variability properties of more complex models and empirical data close to criticality.
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Affiliation(s)
- Daniele Proverbio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, EX4 4QL, Exeter, United Kingdom
| | - Arthur N Montanari
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
- Department of Physics and Material Science, University of Luxembourg, 162a Avenue de la Faiencerie, 1511 Luxembourg, Luxembourg
- Department of Neuroscience, University of California San Diego, 9500 Gilman Drive, La Jolla, California, United States
| | - Jorge Gonçalves
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
- Department of Plant Sciences, University of Cambridge, CB2 3EA, Cambridge, United Kingdom
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45
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Meng Y, Lai YC, Grebogi C. The fundamental benefits of multiplexity in ecological networks. J R Soc Interface 2022; 19:20220438. [PMID: 36167085 PMCID: PMC9514891 DOI: 10.1098/rsif.2022.0438] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 09/01/2022] [Indexed: 11/12/2022] Open
Abstract
A tipping point presents perhaps the single most significant threat to an ecological system as it can lead to abrupt species extinction on a massive scale. Climate changes leading to the species decay parameter drifts can drive various ecological systems towards a tipping point. We investigate the tipping-point dynamics in multi-layer ecological networks supported by mutualism. We unveil a natural mechanism by which the occurrence of tipping points can be delayed by multiplexity that broadly describes the diversity of the species abundances, the complexity of the interspecific relationships, and the topology of linkages in ecological networks. For a double-layer system of pollinators and plants, coupling between the network layers occurs when there is dispersal of pollinator species. Multiplexity emerges as the dispersing species establish their presence in the destination layer and have a simultaneous presence in both. We demonstrate that the new mutualistic links induced by the dispersing species with the residence species have fundamental benefits to the well-being of the ecosystem in delaying the tipping point and facilitating species recovery. Articulating and implementing control mechanisms to induce multiplexity can thus help sustain certain types of ecosystems that are in danger of extinction as the result of environmental changes.
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Affiliation(s)
- Yu Meng
- Institute for Complex Systems and Mathematical Biology, School of Natural and Computing Sciences, King’s College, University of Aberdeen, AB24 3UE, UK
- Max Planck Institute for the Physics of Complex Systems, Nöthnitzer Straße 38, Dresden 01187, Germany
- Center for Systems Biology Dresden, Pfotenhauerstraße 108, Dresden 01307, Germany
| | - Ying-Cheng Lai
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287, USA
- Department of Physics, Arizona State University, Tempe, AZ 85287, USA
| | - Celso Grebogi
- Institute for Complex Systems and Mathematical Biology, School of Natural and Computing Sciences, King’s College, University of Aberdeen, AB24 3UE, UK
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46
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Jin Q, Zuo C, Cui H, Li L, Yang Y, Dai H, Chen L. Single-cell entropy network detects the activity of immune cells based on ribosomal protein genes. Comput Struct Biotechnol J 2022; 20:3556-3566. [PMID: 35860411 PMCID: PMC9287362 DOI: 10.1016/j.csbj.2022.06.056] [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: 03/05/2022] [Revised: 06/25/2022] [Accepted: 06/26/2022] [Indexed: 11/16/2022] Open
Abstract
We developed a new computational method, Single-Cell Entropy Network (SCEN) to analyze single-cell RNA-seq data, which used the information of gene-gene associations to discover new heterogeneity of immune cells as well as identify existing cell types. Based on SCEN, we defined association-entropy (AE) for each cell and each gene through single-cell gene co-expression networks to measure the strength of association between each gene and all other genes at a single-cell resolution. Analyses of public datasets indicated that the AE of ribosomal protein genes (RP genes) varied greatly even in the same cell type of immune cells and the average AE of RP genes of immune cells in each person was significantly associated with the healthy/disease state of this person. Based on existing research and theory, we inferred that the AE of RP genes represented the heterogeneity of ribosomes and reflected the activity of immune cells. We believe SCEN can provide more biological insights into the heterogeneity and diversity of immune cells, especially the change of immune cells in the diseases.
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Affiliation(s)
- Qiqi Jin
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chunman Zuo
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Haoyue Cui
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lin Li
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yiwen Yang
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hao Dai
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.,Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai, Guangdong 519031, China.,Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
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Li X, Xiang J, Wu FX, Li M. A Dual Ranking Algorithm Based on the Multiplex Network for Heterogeneous Complex Disease Analysis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1993-2002. [PMID: 33577455 DOI: 10.1109/tcbb.2021.3059046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Identifying biomarkers of heterogeneous complex diseases has always been one of the focuses in medical research. In previous studies, the powerful network propagation methods have been applied to finding marker genes related to specific diseases, but existing methods are mostly based on a single network, which may be greatly affected by the incompleteness of the network and the ignorance of a large amount of information about physical and functional interactions between biological components. Other methods that directly integrate multiple types of interactions into an aggregate network have the risks that different types of data may conflict with each other and the characteristics and topologies of each individual network are lost. Meanwhile, biomarkers used in clinical trials should have the characteristics of small quantity and strong discriminate ability. In this study, we developed a multiplex network-based dual ranking framework (DualRank) for heterogeneous complex disease analysis. We applied the proposed method to heterogeneous complex diseases for diagnosis, prognosis, and classification. The results showed that DualRank outperformed competing methods and could identify biomarkers with the small quantity, great prediction performance (average AUC = 0.818) and biological interpretability.
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48
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Shi J, Aihara K, Li T, Chen L. Energy landscape decomposition for cell differentiation with proliferation effect. Natl Sci Rev 2022; 9:nwac116. [PMID: 35992240 PMCID: PMC9385468 DOI: 10.1093/nsr/nwac116] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 05/22/2022] [Accepted: 05/25/2022] [Indexed: 11/16/2022] Open
Abstract
Complex interactions between genes determine the development and differentiation of cells. We establish a landscape theory for cell differentiation with proliferation effect, in which the developmental process is modeled as a stochastic dynamical system with a birth-death term. We find that two different energy landscapes, denoted U and V, collectively contribute to the establishment of non-equilibrium steady differentiation. The potential U is known as the energy landscape leading to the steady distribution, whose metastable states stand for cell types, while V indicates the differentiation direction from pluripotent to differentiated cells. This interpretation of cell differentiation is different from the previous landscape theory without the proliferation effect. We propose feasible numerical methods and a mean-field approximation for constructing landscapes U and V. Successful applications to typical biological models demonstrate the energy landscape decomposition's validity and reveal biological insights into the considered processes.
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Affiliation(s)
- Jifan Shi
- Research Institute of Intelligent Complex Systems, Fudan University , Shanghai 200433, China
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study , The University of Tokyo, Tokyo 113-0033 , Japan
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study , The University of Tokyo, Tokyo 113-0033 , Japan
| | - Tiejun Li
- LMAM and School of Mathematical Sciences, Peking University , Beijing 100871, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences , Shanghai 200031, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Hangzhou 310024, China
- School of Life Science and Technology, ShanghaiTech University , Shanghai 201210, China
- Guangdong Institute of Intelligence Science and Technology , Zhuhai 519031, China
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49
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Yang XH, Goldstein A, Sun Y, Wang Z, Wei M, Moskowitz IP, Cunningham JM. Detecting critical transition signals from single-cell transcriptomes to infer lineage-determining transcription factors. Nucleic Acids Res 2022; 50:e91. [PMID: 35640613 PMCID: PMC9458468 DOI: 10.1093/nar/gkac452] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 04/02/2022] [Accepted: 05/13/2022] [Indexed: 12/29/2022] Open
Abstract
Analyzing single-cell transcriptomes promises to decipher the plasticity, heterogeneity, and rapid switches in developmental cellular state transitions. Such analyses require the identification of gene markers for semi-stable transition states. However, there are nontrivial challenges such as unexplainable stochasticity, variable population sizes, and alternative trajectory constructions. By advancing current tipping-point theory-based models with feature selection, network decomposition, accurate estimation of correlations, and optimization, we developed BioTIP to overcome these challenges. BioTIP identifies a small group of genes, called critical transition signal (CTS), to characterize regulated stochasticity during semi-stable transitions. Although methods rooted in different theories converged at the same transition events in two benchmark datasets, BioTIP is unique in inferring lineage-determining transcription factors governing critical transition. Applying BioTIP to mouse gastrulation data, we identify multiple CTSs from one dataset and validated their significance in another independent dataset. We detect the established regulator Etv2 whose expression change drives the haemato-endothelial bifurcation, and its targets together in CTS across three datasets. After comparing to three current methods using six datasets, we show that BioTIP is accurate, user-friendly, independent of pseudo-temporal trajectory, and captures significantly interconnected and reproducible CTSs. We expect BioTIP to provide great insight into dynamic regulations of lineage-determining factors.
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Affiliation(s)
- Xinan H Yang
- Department of Pediatrics, The University of Chicago, Chicago, IL, USA
| | - Andrew Goldstein
- Department of Statistics, The University of Chicago, Chicago IL, USA
| | - Yuxi Sun
- Department of Pediatrics, The University of Chicago, Chicago, IL, USA
| | - Zhezhen Wang
- Department of Pediatrics, The University of Chicago, Chicago, IL, USA
| | - Megan Wei
- Johns Hopkins University, Baltimore, MD, USA
| | - Ivan P Moskowitz
- Department of Pediatrics, The University of Chicago, Chicago, IL, USA
| | - John M Cunningham
- Department of Pediatrics, The University of Chicago, Chicago, IL, USA
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50
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Kołat D, Kałuzińska Ż, Bednarek AK, Płuciennik E. Determination of WWOX Function in Modulating Cellular Pathways Activated by AP-2α and AP-2γ Transcription Factors in Bladder Cancer. Cells 2022; 11:cells11091382. [PMID: 35563688 PMCID: PMC9106060 DOI: 10.3390/cells11091382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/16/2022] [Accepted: 04/18/2022] [Indexed: 02/07/2023] Open
Abstract
Following the invention of high-throughput sequencing, cancer research focused on investigating disease-related alterations, often inadvertently omitting tumor heterogeneity. This research was intended to limit the impact of heterogeneity on conclusions related to WWOX/AP-2α/AP-2γ in bladder cancer which differently influenced carcinogenesis. The study examined the signaling pathways regulated by WWOX-dependent AP-2 targets in cell lines as biological replicates using high-throughput sequencing. RT-112, HT-1376 and CAL-29 cell lines were subjected to two stable lentiviral transductions. Following CAGE-seq and differential expression analysis, the most important genes were identified and functionally annotated. Western blot was performed to validate the selected observations. The role of genes in biological processes was assessed and networks were visualized. Ultimately, principal component analysis was performed. The studied genes were found to be implicated in MAPK, Wnt, Ras, PI3K-Akt or Rap1 signaling. Data from pathways were collected, explaining the differences/similarities between phenotypes. FGFR3, STAT6, EFNA1, GSK3B, PIK3CB and SOS1 were successfully validated at the protein level. Afterwards, a definitive network was built using 173 genes. Principal component analysis revealed that the various expression of these genes explains the phenotypes. In conclusion, the current study certified that the signaling pathways regulated by WWOX and AP-2α have more in common than that regulated by AP-2γ. This is because WWOX acts as an EMT inhibitor, AP-2γ as an EMT enhancer while AP-2α as a MET inducer. Therefore, the relevance of AP-2γ in targeted therapy is now more evident. Some of the differently regulated genes can find application in bladder cancer treatment.
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