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Wu X, Li Z, Wang ZQ, Xu X. The neurological and non-neurological roles of the primary microcephaly-associated protein ASPM. Front Neurosci 2023; 17:1242448. [PMID: 37599996 PMCID: PMC10436222 DOI: 10.3389/fnins.2023.1242448] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 07/24/2023] [Indexed: 08/22/2023] Open
Abstract
Primary microcephaly (MCPH), is a neurological disorder characterized by small brain size that results in numerous developmental problems, including intellectual disability, motor and speech delays, and seizures. Hitherto, over 30 MCPH causing genes (MCPHs) have been identified. Among these MCPHs, MCPH5, which encodes abnormal spindle-like microcephaly-associated protein (ASPM), is the most frequently mutated gene. ASPM regulates mitotic events, cell proliferation, replication stress response, DNA repair, and tumorigenesis. Moreover, using a data mining approach, we have confirmed that high levels of expression of ASPM correlate with poor prognosis in several types of tumors. Here, we summarize the neurological and non-neurological functions of ASPM and provide insight into its implications for the diagnosis and treatment of MCPH and cancer.
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Affiliation(s)
- Xingxuan Wu
- Guangdong Key Laboratory for Genome Stability and Disease Prevention and Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, Guangdong, China
- Shenzhen University-Friedrich Schiller Universität Jena Joint PhD Program in Biomedical Sciences, Shenzhen University School of Medicine, Shenzhen, Guangdong, China
- Laboratory of Genome Stability, Leibniz Institute on Aging-Fritz Lipmann Institute, Jena, Germany
| | - Zheng Li
- Guangdong Key Laboratory for Genome Stability and Disease Prevention and Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, Guangdong, China
| | - Zhao-Qi Wang
- Shenzhen University-Friedrich Schiller Universität Jena Joint PhD Program in Biomedical Sciences, Shenzhen University School of Medicine, Shenzhen, Guangdong, China
- Laboratory of Genome Stability, Leibniz Institute on Aging-Fritz Lipmann Institute, Jena, Germany
| | - Xingzhi Xu
- Guangdong Key Laboratory for Genome Stability and Disease Prevention and Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, Guangdong, China
- Shenzhen University-Friedrich Schiller Universität Jena Joint PhD Program in Biomedical Sciences, Shenzhen University School of Medicine, Shenzhen, Guangdong, China
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Clinical Value of EZH2 in Hepatocellular Carcinoma and Its Potential for Target Therapy. Medicina (B Aires) 2022; 58:medicina58020155. [PMID: 35208478 PMCID: PMC8877936 DOI: 10.3390/medicina58020155] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/15/2022] [Accepted: 01/17/2022] [Indexed: 12/22/2022] Open
Abstract
Background and objectives: EZH2 is overexpressed in hepatocellular carcinoma (HCC) and is correlated with poor prognosis. However, its clinical significance and molecular mechanism have not been studied in HCC. In this study, clinical and prognostic values of EZH2 was studied using Total Cancer Genome Atlas (TCGA) data and then, these data were confirmed in Huh1 and HepG2 cell lines. Materials and Methods: We used the TCGA database from cBioPortal. In addition, we analyzed EZH2 mRNA levels in HCC cell lines and its correlation with STAT3 and EZH2. Results: According to TCGA, EZH2 had a prognostic value in various cancers, especially in HCC. Furthermore, EZH2 in HCC was correlated with N stage (p = 0.045) and alpha-fetoprotein (AFP) > 20 ng/mL (p < 0.01). However, a negative association between EZH2 and age (p = 0.027) was found. The overall survival result of HCC was significantly poorer in patients with high EZH2 expression. In addition, the recurrence rate was also significantly higher in patients with high expression of EZH2 than those with low expression (χ2 = 16.10, p < 0.001). EZH2 expression was negatively correlated with STAT3 expression among EZH2-associated genes (R = −0.163, p = 0.002). EZH2 expression level was down-regulated to 50% or less compared to the control group treated negative siRNA. MTT assays showed that EZH2-siRNA affected on the viability of HCC cell line significantly. Conclusions: In conclusion, the overexpression of EZH2 was an independent biomarker for poor outcomes of HCC. However, more in vivo studies are required to identify the downstream target genes in HCC to improve our understanding of the biological role of EZH2 in HCC.
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Identification of Key Exosome Gene Signature in Mediating Coronary Heart Disease by Weighted Gene Correlation Network Analysis. BIOMED RESEARCH INTERNATIONAL 2021; 2021:3440498. [PMID: 34692829 PMCID: PMC8536412 DOI: 10.1155/2021/3440498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/07/2021] [Accepted: 09/18/2021] [Indexed: 12/04/2022]
Abstract
Background Coronary heart disease (CHD) is the most prevalent disease with an unelucidated pathogenetic mechanism and is mediated by complex molecular interactions of exosomes. Here, we aimed to identify differentially expressed exosome genes for the disease development and prognosis of CHD. Method Six CHD samples and 32 normal samples were downloaded from the exoRbase database to identify the candidate genes in the CHD. The differentially expressed genes (DEGs) were identified. And then, weighted gene correlation network analysis (WGCNA) was used to investigate the modules in coexpressed genes between CHD samples and normal samples. DEGs and the module of the WGCNA were intersected to obtain the most relevant exosome genes. After that, the function enrichment analyses and protein-protein interaction network (PPI) were performed for the particular module using STRING and Cytoscape software. Finally, the CIBERSORT algorithm was used to analyze the immune infiltration of exosome genes between CHD samples and normal samples. Result We obtain a total of 715 overlapping exosome genes located at the intersection of the DEGs and key modules. The Gene Ontology enrichment of DEGs in the blue module included inflammatory response, neutrophil degranulation, and activation of CHD. In addition, protein-protein networks were constructed, and hub genes were identified, such as LYZ, CAMP, HP, ORM1, and LTF. The immune infiltration profiles varied significantly between normal controls and CHD. Finally, we found that mast cells activated and eosinophils had a positive correlation. B cell memory had a significant negative correlation with B cell naive. Besides, neutrophils and mast cells were significantly increased in CHD patients. Conclusion The underlying mechanism may be related to neutrophil degranulation and the immune response. The hub genes and the difference in immune infiltration identified in the present study may provide new insights into the diagnostic and provide candidate targets for CHD.
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Tian D, Yu Y, Zhang L, Sun J, Jiang W. A Five-Gene-Based Prognostic Signature for Hepatocellular Carcinoma. Front Med (Lausanne) 2021; 8:681388. [PMID: 34568357 PMCID: PMC8455941 DOI: 10.3389/fmed.2021.681388] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 08/05/2021] [Indexed: 12/13/2022] Open
Abstract
Objective: This study intends to identify potential prognostic marker genes associated with the prognosis of patients suffering from hepatocellular carcinoma (HCC) based on TCGA and GEO analysis. Methods: TCGA-LIHC cohort was downloaded and the data related to HCC were extracted from The Cancer Genome Atlas (TCGA) database and subjected to differential analysis. HCC-related gene expression datasets were retrieved from the GEO database, followed by differential analysis. After intersection of the results of TCGA and GEO databases, gene interaction analysis was performed to obtain the core genes. To identify the genes related to the prognosis of HCC patients, we conducted univariate and multivariate Cox analyses. Results: Based on differential analysis of TCGA database, 854 genes were differentially expressed in HCC, any of which might link to the occurrence and progression of HCC. Meanwhile, joint analysis of HCC-related gene expression datasets in the GEO database screened 214 genes. Five core genes CDC20, TOP2A, RRM2, UBE2C and AOX1 were significantly associated with the prognosis of HCC patients and the risk model based on these five genes effectively predicted the prognosis of HCC patients. Conclusion: Collectively, our data suggest that CDC20, TOP2A, RRM2, UBE2C and AOX1 may be the key genes affecting the prognosis of patients with HCC. The five-gene signature could accurately predict the prognosis of HCC patients.
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Affiliation(s)
- Dazhi Tian
- Department of Liver Transplantation, Tianjin First Central Hospital, Tianjin, China
| | - Yang Yu
- Department of Liver Transplantation, Tianjin First Central Hospital, Tianjin, China
| | - Li Zhang
- Department of Liver Transplantation, Tianjin First Central Hospital, Tianjin, China
| | - Jisan Sun
- Department of Liver Transplantation, Tianjin First Central Hospital, Tianjin, China
| | - Wentao Jiang
- Department of Liver Transplantation, Tianjin First Central Hospital, Tianjin, China
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Wu C, Qi X, Qiu Z, Deng G, Zhong L. Low expression of KIF20A suppresses cell proliferation, promotes chemosensitivity and is associated with better prognosis in HCC. Aging (Albany NY) 2021; 13:22148-22163. [PMID: 34491228 PMCID: PMC8507281 DOI: 10.18632/aging.203494] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 06/18/2021] [Indexed: 12/29/2022]
Abstract
This study analysed the microarray datasets from Gene Expression Omnibus (GEO) database, and aimed to identify novel potential hub genes associated with the progression of HCC via bioinformatics analysis and experimental validation. The common differentially expressed genes (DEGs) from five GEO datasets were screened using GEO2R tool. The expression and survival analysis of hub genes in HCC were performed using Gene Expression Profiling Interactive Analysis, UALCAN and Kaplan-Meier plotter tools. In vitro functional assays were used to determine the caspase-3, -9, cell proliferation and chemo-sensitivity of HCC cells. A total of 177 common DEGs were identified between normal liver and HCC tissues among these datasets. Functional enrichment and PPI network analysis identified 22 hub genes from the common DEGs. The mRNA expression of 22 hub genes was all significantly up-regulated in HCC tissues compared to that in normal liver tissues. Further survival analysis showed that 10 hub genes predicted poor prognosis of patients with HCC. More importantly, the in vitro functional studies demonstrated that KIF20A knockdown suppressed the HCC cell proliferation and promoted the chemosensitivity of HCC cells to cisplatin and sorafenib. In conclusion, the present study identified a total of 177 common DEGs among 5 GEO microarray datasets and found that 10 hub genes could predict the poor prognosis of patients with HCC using the comprehensive bioinformatics analysis. Furthermore, KIF20A silence suppressed cell proliferation and enhanced chemosensitivity in HCC cells. Further studies may be required to determine the mechanistic role of these hub genes in HCC progression.
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Affiliation(s)
- Chuanxing Wu
- Department of General Surgery, Shanghai General Hospital Affiliated to Shanghai Jiaotong University, Shanghai, China
| | - Xiaosheng Qi
- Department of General Surgery, Shanghai General Hospital Affiliated to Shanghai Jiaotong University, Shanghai, China
| | - Zhengjun Qiu
- Department of General Surgery, Shanghai General Hospital Affiliated to Shanghai Jiaotong University, Shanghai, China
| | - Guilong Deng
- Department of General Surgery, Shanghai General Hospital Affiliated to Shanghai Jiaotong University, Shanghai, China
| | - Lin Zhong
- Department of General Surgery, Shanghai General Hospital Affiliated to Shanghai Jiaotong University, Shanghai, China
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Song H, Ding N, Li S, Liao J, Xie A, Yu Y, Zhang C, Ni C. Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis. Front Genet 2020; 11:895. [PMID: 33133125 PMCID: PMC7561391 DOI: 10.3389/fgene.2020.00895] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 07/20/2020] [Indexed: 12/11/2022] Open
Abstract
Background Bioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority. Methods We employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA–mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model. Results Two key modules showed significant association with clinical traits. In combination with protein–protein interaction analysis, 29 hub genes were identified. Among these genes, 19 from one module showed a pattern of upregulation in HCC and were associated with the tumor node metastasis stage, and 10 from the other module displayed the opposite trend. Survival analyses indicated that all these genes were significantly related to patient prognosis. Based on the miRNA-mRNA regulatory network, 29 genes strongly linked to tumor activity were identified. Notably, five of the novel risk genes, ABAT, DAO, PCK2, SLC27A2, and HAO1, have rarely been reported in previous studies. Gene set enrichment analysis for each gene revealed regulatory roles in proliferation and prognosis of HCC. Least absolute shrinkage and selection operator regression analysis further validated DAO, PCK2, and HAO1 as prognostic factors in an external HCC dataset. Conclusion Analysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.
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Affiliation(s)
- Hao Song
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.,Department of Intervention Therapy, The Fourth Medical Center of PLA General Hospital, Beijing, China
| | - Na Ding
- Department of Computational Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shang Li
- Department of Computational Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jianlong Liao
- Department of Computational Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Aimin Xie
- Department of Computational Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Youtao Yu
- Department of Intervention Therapy, The Fourth Medical Center of PLA General Hospital, Beijing, China
| | - Chunlong Zhang
- Department of Computational Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Caifang Ni
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
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