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Cicirò Y, Ragusa D, Sala A. Expression of the checkpoint kinase BUB1 is a predictor of response to cancer therapies. Sci Rep 2024; 14:4461. [PMID: 38396175 PMCID: PMC10891059 DOI: 10.1038/s41598-024-55080-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 02/19/2024] [Indexed: 02/25/2024] Open
Abstract
The identification of clinically-relevant biomarkers is of upmost importance for the management of cancer, from diagnosis to treatment choices. We performed a pan-cancer analysis of the mitotic checkpoint budding uninhibited by benzimidazole 1 gene BUB1, in the attempt to ascertain its diagnostic and prognostic values, specifically in the context of drug response. BUB1 was found to be overexpressed in the majority of cancers, and particularly elevated in clinically aggressive molecular subtypes. Its expression was correlated with clinico-phenotypic features, notably tumour staging, size, invasion, hypoxia, and stemness. In terms of prognostic value, the expression of BUB1 bore differential clinical outcomes depending on the treatment administered in TCGA cancer cohorts, suggesting sensitivity or resistance, depending on the expression levels. We also integrated in vitro drug sensitivity data from public projects based on correlation between drug efficacy and BUB1 expression to produce a list of candidate compounds with differential responses according to BUB1 levels. Gene Ontology enrichment analyses revealed that BUB1 overexpression in cancer is associated with biological processes related to mitosis and chromosome segregation machinery, reflecting the mechanisms of action of drugs with a differential effect based on BUB1 expression.
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Affiliation(s)
- Ylenia Cicirò
- Centre for Inflammation Research and Translational Medicine (CIRTM), Brunel University London, Uxbridge, UB8 3PH, UK
| | - Denise Ragusa
- Centre for Genome Engineering and Maintenance (CenGEM), Brunel University London, Uxbridge, UB8 3PH, UK.
| | - Arturo Sala
- Centre for Inflammation Research and Translational Medicine (CIRTM), Brunel University London, Uxbridge, UB8 3PH, UK.
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Yang R, Lu Y, Yin N, Faiola F. Transcriptomic Integration Analyses Uncover Common Bisphenol A Effects Across Species and Tissues Primarily Mediated by Disruption of JUN/FOS, EGFR, ER, PPARG, and P53 Pathways. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:19156-19168. [PMID: 37978927 DOI: 10.1021/acs.est.3c02016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Bisphenol A (BPA) is a common endocrine disruptor widely used in the production of electronic, sports, and medical equipment, as well as consumer products like milk bottles, dental sealants, and thermal paper. Despite its widespread use, current assessments of BPA exposure risks remain limited due to the lack of comprehensive cross-species comparative analyses. To address this gap, we conducted a study aimed at identifying genes and fundamental molecular processes consistently affected by BPA in various species and tissues, employing an effective data integration method and bioinformatic analyses. Our findings revealed that exposure to BPA led to significant changes in processes like lipid metabolism, proliferation, and apoptosis in the tissues/cells of mammals, fish, and nematodes. These processes were found to be commonly affected in adipose, liver, mammary, uterus, testes, and ovary tissues. Additionally, through an in-depth analysis of signaling pathways influenced by BPA in different species and tissues, we observed that the JUN/FOS, EGFR, ER, PPARG, and P53 pathways, along with their downstream key transcription factors and kinases, were all impacted by BPA. Our study provides compelling evidence that BPA indeed induces similar toxic effects across different species and tissues. Furthermore, our investigation sheds light on the underlying molecular mechanisms responsible for these toxic effects. By uncovering these mechanisms, we gain valuable insights into the potential health implications associated with BPA exposure, highlighting the importance of comprehensive assessments and awareness of this widespread endocrine disruptor.
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Affiliation(s)
- Renjun Yang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuanping Lu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Nuoya Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Francesco Faiola
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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Li D, Lin X, Li J, Liu X, Zhang F, Tang W, Zhang S, Dong L, Xue R. Eleven metabolism‑related genes composed of Stard5 predict prognosis and contribute to EMT phenotype in HCC. Cancer Cell Int 2023; 23:277. [PMID: 37978523 PMCID: PMC10656919 DOI: 10.1186/s12935-023-03097-0] [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: 06/19/2023] [Accepted: 10/11/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, with a high mortality and poor survival rate. Abnormal tumor metabolism is considered a hallmark of HCC and is a potential therapeutic target. This study aimed to identify metabolism-related biomarkers to evaluate the prognosis of patients with HCC. METHOD The Cancer Genome Atlas (TCGA) database was used to explore differential metabolic pathways based on high and low epithelial-mesenchymal transition (EMT) groupings. Genes in differential metabolic pathways were obtained for HCC metabolism-related molecular subtype analysis. Differentially expressed genes (DEGs) from the three subtypes were subjected to Lasso Cox regression analysis to construct prognostic risk models. Stard5 expression in HCC patients was detected by western blot and immunohistochemistry (IHC), and the role of Stard5 in the metastasis of HCC was investigated by cytological experiments. RESULTS Unsupervised clustering analysis based on metabolism-related genes revealed three subtypes in HCC with differential prognosis. A risk prognostic model was constructed based on 11 genes (STARD5, FTCD, SCN4A, ADH4, CFHR3, CYP2C9, CCL14, GADD45G, SOX11, SCIN, and SLC2A1) obtained by LASSO Cox regression analysis of the three subtypes of DEGs. We validated that the model had a good predictive power. In addition, we found that the high-risk group had a poor prognosis, higher proportion of Tregs, and responded poorly to chemotherapy. We also found that Stard5 expression was markedly decreased in HCC tissues, which was associated with poor prognosis and EMT. Knockdown of Stard5 contributed to the invasion and migration of HCC cells. Overexpression of Stard5 inhibited EMT in HCC cells. CONCLUSION We developed a new model based on 11 metabolism-related genes, which predicted the prognosis and response to chemotherapy or immunotherapy for HCC. Notably, we demonstrated for the first time that Stard5 acted as a tumor suppressor by inhibiting metastasis in HCC.
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Affiliation(s)
- Dongping Li
- Department of Gastroenterology and Hepatology, Shanghai Institute of Liver Diseases, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Xiahui Lin
- Department of Gastroenterology and Hepatology, Shanghai Institute of Liver Diseases, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Jiale Li
- Key Laboratory of Glycoconjugate Research Ministry of Public Health, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China
| | - Xinyi Liu
- Department of Gastroenterology and Hepatology, Shanghai Institute of Liver Diseases, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Feng Zhang
- Department of Gastroenterology and Hepatology, Shanghai Institute of Liver Diseases, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Wenqing Tang
- Department of Gastroenterology and Hepatology, Shanghai Institute of Liver Diseases, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Si Zhang
- Key Laboratory of Glycoconjugate Research Ministry of Public Health, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China
| | - Ling Dong
- Department of Gastroenterology and Hepatology, Shanghai Institute of Liver Diseases, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| | - Ruyi Xue
- Department of Gastroenterology and Hepatology, Shanghai Institute of Liver Diseases, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
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Li S, Hao L, Hu X, Li L. A systematic study on the treatment of hepatitis B-related hepatocellular carcinoma with drugs based on bioinformatics and key target reverse network pharmacology and experimental verification. Infect Agent Cancer 2023; 18:41. [PMID: 37393234 DOI: 10.1186/s13027-023-00520-z] [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: 05/20/2023] [Accepted: 06/20/2023] [Indexed: 07/03/2023] Open
Abstract
BACKGROUND Chronic hepatitis B virus (HBV) infection is the major etiology of hepatocellular carcinoma (HCC). However, the mechanism of hepatitis B-related hepatocellular carcinoma (HBV-related HCC) is still unclear. Therefore, understanding the pathogenesis and searching for drugs to treat HBV-related HCC was an effective strategy to treat this disease. PURPOSE Bioinformatics was used to predict the potential targets of HBV-related HCC. The reverse network pharmacology of key targets was used to analyze the clinical drugs, traditional Chinese medicine (TCM) and small molecules of TCM in the treatment of HBV-related HCC. METHODS In this study, three microarray datasets totally containing 330 tumoral samples and 297 normal samples were selected from the GEO database. These microarray datasets were used to screen DEGs. And the expression profile and survival of 6 key genes were analyzed. In addition, Comparative Toxicogenomics Database and Coremine Medical database were used to enrich clinical drugs and TCM of HBV-related HCC by the 6 key targets. Then the obtained TCM were classified based on the Chinese Pharmacopoeia. Among these top 6 key genes, CDK1 and CCNB1 had the most connection nodes and the highest degree and were the most significantly expressed. In general, CDK1 and CCNB1 tend to form a complex, which is conducive to cell mitosis. Hence, this study mainly studied CDK1 and CCNB1. HERB database was used to predict small molecules TCM. The inhibition effect of quercetin, celastrol and cantharidin on HepG2.2.15 cells and Hep3B cells was verified by CCK8 experiment. The effects of quercetin, celastrol and cantharidin on CDK1 and CCNB1 of HepG2.2.15 cells and Hep3B cells were determined by Western Blot. RESULTS In short, 272 DEGs (53 upregulated and 219 downregulated) were identified. Among these DEGs, 6 key genes with high degree were identified, which were AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS. Kaplan-Meier plotter analysis showed that higher expression levels of AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS were associated with poor OS. According to the first 6 key targets, a variety of drugs and TCM were identified. These results showed that clinical drugs included targeted drugs, such as sorafenib, palbociclib and Dasatinib. and chemotherapy drugs, such as cisplatin and doxorubicin. TCM, such as the TCM flavor was mainly warm and bitter, and the main meridians were liver and lung. Small molecules of TCM included flavonoids, terpenoids, alkaloids and glycosides, such as quercetin, celastrol, cantharidin, hesperidin, silymarin, casticin, berberine and ursolic acid, which have great potential in anti-HBV-related HCC. For molecular docking of chemical components, the molecules with higher scores were flavonoids, alkaloids, etc. Three representative types of TCM small molecules were verified respectively, and it was found that quercetin, celastrol and cantharidin inhibited the proliferation of HepG2.2.15 cells and Hep3B cells along concentration gradient. Quercetin, celastrol and cantharidin decreased CDK1 expression in HepG2.2.15 and Hep3B cells, but for CCNB1, only cantharidin decreased CCNB1 expression in the two strains of cells. CONCLUSION In conclusion, AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS could be potential targets for the diagnosis and prognosis of HBV-related HCC. Clinical drugs include chemotherapeutic and targeted drug, traditional Chinese medicine is mainly bitter and warm TCM. Small molecular of TCM including flavonoids, terpenoids and glycosides and alkaloids, which have great potential in anti-HBV-related HCC. This study provides potential therapeutic targets and novel strategies for the treatment of HBV-related HCC.
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Affiliation(s)
- Shenghao Li
- Chengdu University of Traditional Chinese Medicine, No. 37 Shi-er-qiao Road, Chengdu, 610075, Sichuan, People's Republic of China
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610072, Sichuan, People's Republic of China
| | - Liyuan Hao
- Chengdu University of Traditional Chinese Medicine, No. 37 Shi-er-qiao Road, Chengdu, 610075, Sichuan, People's Republic of China
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610072, Sichuan, People's Republic of China
| | - Xiaoyu Hu
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610072, Sichuan, People's Republic of China.
| | - Luya Li
- Department of Pharmacy Department, The Fourth Hospital of Hebei Medical University, NO.12, Jian Kang Road, Shijiazhuang, 050010, Hebei, People's Republic of China
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Tang FF, Liu L, Tian XT, Li N, Peng YX, Qian CM, Jia TT, Liu JJ, Gao WH, Xu YF. Network pharmacological analysis of corosolic acid reveals P4HA2 inhibits hepatocellular carcinoma progression. BMC Complement Med Ther 2023; 23:171. [PMID: 37248456 DOI: 10.1186/s12906-023-04008-6] [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: 01/05/2023] [Accepted: 05/22/2023] [Indexed: 05/31/2023] Open
Abstract
BACKGROUND Corosolic acid is a pentacyclic triterpene acid with hypoglycemic, anti-inflammatory, and anti-cancer effects. However, its potential targets in hepatocellular carcinoma (HCC) are unknown, hindering clinical utilization. METHODS Differentially expressed proteins of the Bel-7404 cell line were identified with tandem mass tag analysis and differentially expressed genes (DEGs) of an HCC TCGA dataset using bioinformatics. Gene functions and pathways were inferred using the DAVID database. Online databases were used to establish P4HA2 expression in HCC (GEPIA2) and its relationship with patient survival (UALCAN and The Human Protein Atlas), the association between P4HA2 expression and immune cell infiltration (TIMER2), and DNA methylation of the P4HA2 gene (MethSurv). Cell proliferation, cell cycle, and cell death were assessed with PI and SYTOX-Green staining, CCK-8, and colony formation assays. Protein expression levels were detected by Western blotting. RESULTS A total of 44 differentially expressed proteins and 4498 DEGs were identified. Four genes whose proteins were also found in the differential protein profile but with opposing expressions were selected as candidate targets. The candidate gene prolyl 4-hydroxylase subunit alpha 2 (P4HA2) was recognized as the only potential target due to its high expression in public datasets, association with poor patient survival, and relation to immune cell infiltration in HCC tissues. Moreover, the DNA methylation status in 4 CpG islands of the P4HA2 gene correlated with a poor prognosis. Furthermore, corosolic acid treatment inhibited the proliferation of HCC cell lines Bel-7404 and HepG2 in a dose-dependent manner, caused G2/M phase cell cycle arrest, and promoted cell death. In addition, the treatment reduced P4HA2 protein levels. CONCLUSION Our results indicate that P4HA2 is a potential target of corosolic acid. Thus, they contribute to understanding molecular changes in HCC after corosolic acid treatment and facilitate finding new treatment regimens.
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Affiliation(s)
- Fei-Feng Tang
- Department of Pharmacy, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200071, People's Republic of China
| | - Long Liu
- Department of Traditional Chinese Medicine, Tianyou Hospital of Tongji University, Shanghai, 200331, People's Republic of China
| | - Xiao-Ting Tian
- Shanghai Chest Hospital, Shanghai Institute of Thoracic Oncology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Ning Li
- Central Laboratory, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200071, People's Republic of China
| | - Ying-Xiu Peng
- Department of Pharmacy, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200071, People's Republic of China
| | - Chun-Mei Qian
- Central Laboratory, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200071, People's Republic of China
| | - Ting-Ting Jia
- Department of Pharmacy, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200071, People's Republic of China
| | - Jing-Jin Liu
- Department of Pharmacy, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200071, People's Republic of China
| | - Wen-Hui Gao
- Department of Pharmacy, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200071, People's Republic of China
| | - Yan-Feng Xu
- Department of Pharmacy, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200071, People's Republic of China.
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Hasan MAM, Maniruzzaman M, Shin J. Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning. Sci Rep 2023; 13:3771. [PMID: 36882493 PMCID: PMC9992474 DOI: 10.1038/s41598-023-30851-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 03/02/2023] [Indexed: 03/09/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.
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Affiliation(s)
- Md Al Mehedi Hasan
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan.,Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh
| | - Md Maniruzzaman
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan.,Statistics Discipline, Khulna University, Khulna, 9208, Bangladesh
| | - Jungpil Shin
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan.
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Identification of Prognostic Biomarkers for Suppressing Tumorigenesis and Metastasis of Hepatocellular Carcinoma through Transcriptome Analysis. Diagnostics (Basel) 2023; 13:diagnostics13050965. [PMID: 36900109 PMCID: PMC10001411 DOI: 10.3390/diagnostics13050965] [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: 12/15/2022] [Accepted: 02/16/2023] [Indexed: 03/06/2023] Open
Abstract
Cancer is one of the deadliest diseases developed through tumorigenesis and could be fatal if it reaches the metastatic phase. The novelty of the present investigation is to explore the prognostic biomarkers in hepatocellular carcinoma (HCC) that could develop glioblastoma multiforme (GBM) due to metastasis. The analysis was conducted using RNA-seq datasets for both HCC (PRJNA494560 and PRJNA347513) and GBM (PRJNA494560 and PRJNA414787) from Gene Expression Omnibus (GEO). This study identified 13 hub genes found to be overexpressed in both GBM and HCC. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability, leading to improper chromosome segregation, causing aneuploidy. A 13-gene predictive model was obtained and validated using a KM plot. These hub genes could be prognostic biomarkers and potential therapeutic targets, inhibition of which could suppress tumorigenesis and metastasis.
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Zhang HY, Zong RQ, Wu FX, Li YR. Bioinformatics Analysis Identifies ASCL1 as the Key Transcription Factor in Hepatocellular Carcinoma Progression. DISEASE MARKERS 2023; 2023:3560340. [PMID: 36755802 PMCID: PMC9902118 DOI: 10.1155/2023/3560340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/12/2022] [Accepted: 11/25/2022] [Indexed: 01/31/2023]
Abstract
Methods Differentially transcription factors (DETFs) were identified from differentially expressed genes (DEGs) in GSE62232 and transcription factors. Then, they were analyzed by regulatory networks, prognostic risk model, and overall survival analyses to identify the key DETF. Combined with the regulatory networks and binding site analysis, the target mRNA of key DETF was determined, and its prognostic value in HCC was evaluated by survival, clinical characteristics analyses, and experiments. Finally, the expressions and functions of the key DETF on the DEmRNAs were investigated in HCC cells. Results Through multiple bioinformatics analyses, ASCL1 was identified as the key DETF, and SLC6A13 was predicted to be its target mRNA with the common binding site of CCAGCAACTGGCC, both downregulated in HCC. In survival analysis, high SLC6A13 was related to better HCC prognosis, and SLC6A13 was differentially expressed in HCC patients with clinical characteristics. Furthermore, cell experiments showed the mRNA expressions of ASCL1 and SLC6A13 were both reduced in HCC, and their overexpressions suppressed the growth, invasion, and migration of HCC cells. Besides, over-ASCL1 could upregulate SLC6A13 expression in HCC cells. Conclusion This study identifies two suppressor genes in HCC progression, ASCL1 and SLC6A13, and the key transcription factor ASCL1 suppresses HCC progression by targeting SLC6A13 mRNA. They are both potential treatment targets and prognostic biomarkers for HCC patients, which provides new clues for HCC research.
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Affiliation(s)
- Hong-yan Zhang
- Department of Intensive Care Medicine, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Rui-qing Zong
- Department of Intensive Care Medicine, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Fei-xiang Wu
- Department of Intensive Care Medicine, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Yi-ran Li
- Department of Intensive Care Medicine, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Naval Medical University, Shanghai, China
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Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics. BIOMED RESEARCH INTERNATIONAL 2023; 2023:2152432. [PMID: 36714024 PMCID: PMC9876670 DOI: 10.1155/2023/2152432] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/31/2022] [Accepted: 11/17/2022] [Indexed: 01/19/2023]
Abstract
Objective To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment. Methods Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan-Meier online plotter tool. Results CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate. Conclusion CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.
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Qiao S, Zhang W, Su Y, Jiang Y. Integrated bioinformatics analysis of IFITM1 as a prognostic biomarker and investigation of its immunological role in prostate adenocarcinoma. Front Oncol 2022; 12:1037535. [PMID: 36591519 PMCID: PMC9795034 DOI: 10.3389/fonc.2022.1037535] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 11/23/2022] [Indexed: 12/15/2022] Open
Abstract
Introduction Prostate adenocarcinoma (PRAD) is a highly aggressive malignancy with high mortality and poor prognosis, and its potential mechanism remains unclear. Our study aimed to identify novel markers for the prognosis of PRAD using bioinformatics technology. Methods The GSE32571 dataset was downloaded from the GEO database, and analyzed via the limma R package to identify differentially expressed genes (DEGs) and differentially expressed immune score-related genes (DEISRGs). The immune-related genes (IRGs) were further obtained by overlapping DEISRGs and DEGs, and the core gene was identified via survival analysis. Furthermore, the expression level, prognostic value, and potential functions of the core gene were evaluated via multiple bioinformatics databases. Results A total of 301 IRGs were identified from the GSE32571 dataset, and IFITM1 was a down-regulated gene in several types of cancer, including PRAD. Besides, low expression of IFITM1 was associated with a poor prognosis in PRAD. GSEA indicated that the vital pathways of IFITM1-associated genes were mainly enriched in primary immunodeficiency, Th17 cell differentiation, Th1, and Th2 cell differentiation, natural killer cell-mediated cytotoxicity, myeloid dendritic cell activation, regulation of leukocyte activation, etc. Furthermore, IFITM1 was closely correlated with 22 types of tumor-infiltrating immune cells. Discussion IFITM1 was a prognostic biomarker for PRAD patients, and it can be acted as a potential immune therapy target in PRAD.
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Micronome Revealed miR-205-5p as Key Regulator of VEGFA During Cancer Related Angiogenesis in Hepatocellular Carcinoma. Mol Biotechnol 2022:10.1007/s12033-022-00619-5. [DOI: 10.1007/s12033-022-00619-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022]
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12
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Chen M, Wu GB, Xie ZW, Shi DL, Luo M. A novel diagnostic four-gene signature for hepatocellular carcinoma based on artificial neural network: Development, validation, and drug screening. Front Genet 2022; 13:942166. [PMID: 36246599 PMCID: PMC9554094 DOI: 10.3389/fgene.2022.942166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 09/02/2022] [Indexed: 11/30/2022] Open
Abstract
Background: Hepatocellular carcinoma (HCC) is one of the most common cancers with high mortality in the world. HCC screening and diagnostic models are becoming effective strategies to reduce mortality and improve the overall survival (OS) of patients. Here, we expected to establish an effective novel diagnostic model based on new genes and explore potential drugs for HCC therapy. Methods: The gene expression data of HCC and normal samples (GSE14811, GSE60502, GSE84402, GSE101685, GSE102079, GSE113996, and GSE45436) were downloaded from the Gene Expression Omnibus (GEO) dataset. Bioinformatics analysis was performed to distinguish two differentially expressed genes (DEGs), diagnostic candidate genes, and functional enrichment pathways. QRT-PCR was used to validate the expression of diagnostic candidate genes. A diagnostic model based on candidate genes was established by an artificial neural network (ANN). Drug sensitivity analysis was used to explore potential drugs for HCC. CCK-8 assay was used to detect the viability of HepG2 under various presentative chemotherapy drugs. Results: There were 82 DEGs in cancer tissues compared to normal tissue. Protein–protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and infiltrating immune cell analysis were administered and analyzed. Diagnostic-related genes of MT1M, SPINK1, AKR1B10, and SLCO1B3 were selected from DEGs and used to construct a diagnostic model. The receiver operating characteristic (ROC) curves were 0.910 and 0.953 in the training and testing cohorts, respectively. Potential drugs, including vemurafenib, LOXO-101, dabrafenib, selumetinib, Arry-162, and NMS-E628, were found as well. Vemurafenib, dabrafenib, and selumetinib were observed to significantly affect HepG2 cell viability. Conclusion: The diagnostic model based on the four diagnostic-related genes by the ANN could provide predictive significance for diagnosis of HCC patients, which would be worthy of clinical application. Also, potential chemotherapy drugs might be effective for HCC therapy.
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Affiliation(s)
- Min Chen
- Department of General Surgery, Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guang-Bo Wu
- Department of General Surgery, Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhi-Wen Xie
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dan-Li Shi
- Department of General Surgery, Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Dan-Li Shi, ; Meng Luo,
| | - Meng Luo
- Department of General Surgery, Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Dan-Li Shi, ; Meng Luo,
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Identification and Validation of a Potential Stemness-Associated Biomarker in Hepatocellular Carcinoma. Stem Cells Int 2022; 2022:1534593. [PMID: 35859724 PMCID: PMC9293570 DOI: 10.1155/2022/1534593] [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: 03/28/2022] [Accepted: 06/11/2022] [Indexed: 11/17/2022] Open
Abstract
Background Cancer stem cells (CSCs) are typically related to metastasis, recurrence, and drug resistance in malignant tumors. However, the biomarker and mechanism of CSCs need further exploration. This study is aimed at comprehensively depicting the stemness characteristics and identify a potential stemness-associated biomarker in hepatocellular carcinoma (HCC). Methods The data of HCC patients from The Cancer Genome Atlas (TCGA) were collected and divided based on the mRNA expression-based stemness index (mRNAsi) in this study. Weighted gene coexpression network analysis (WGCNA) and the protein-protein interaction (PPI) network were performed, and the genes were screened through the Cytoscape software. Then, we constructed a prognostic expression signature using the multivariable Cox analysis and verified using the GEO and ICGC databases. Even more importantly, we used the three-dimensional (3D) fibrin gel to enrich the tumor-repopulating cells (TRCs) to validate the expression of the signature in CSCs by quantitative RT-PCR. Results mRNAsi was significantly elevated in tumor and high-mRNAsi score was associated with poor overall survival in HCC. The positive stemness-associated (blue) module with 737 genes were screened based on WGCNA, and Budding uninhibited by benzimidazoles 1 (BUB1) was identified as the hub gene highly related to stemness in HCC. Then, the prognostic value and stemness characteristics were well validated in the ICGC and GSE14520 cohorts. Further analysis showed the expression of BUB1 was elevated in TRCs. Conclusion BUB1, as a potential stemness-associated biomarker, could serve as a therapeutic CSCs-target and predicted the clinical outcomes of patients with HCC.
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Chen S, Zhao Z, Wang X, Zhang Q, Lyu L, Tang B. The Predictive Competing Endogenous RNA Regulatory Networks and Potential Prognostic and Immunological Roles of Cyclin A2 in Pan-Cancer Analysis. Front Mol Biosci 2022; 9:809509. [PMID: 35480884 PMCID: PMC9035520 DOI: 10.3389/fmolb.2022.809509] [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: 11/05/2021] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
Although accumulating evidence has verified the relationship between CCNA2 and cancers, no pan-cancer analysis about the function and the upstream molecular mechanism of CCNA2 is available. For the first time, we analyzed potential oncogenic roles of CCNA2 in 33 cancer types via The Cancer Genome Atlas (TCGA) database. Overexpression of CCNA2 is widespread in almost all cancer types, and it is related to poor prognosis and advanced pathological stages in most cases. Moreover, we conducted upstream miRNAs and lncRNAs of CCNA2 to establish upstream regulatory networks in kidney renal clear cell carcinoma (LINC00997/miR-27b-3p/CCNA2), liver hepatocellular carcinoma (SNHG16, GUSBP11, FGD5-AS1, LINC00630, CD27-AS1, LINC00997/miR-22-3p/CCNA2, miR-29b-3p/CCNA2, miR-29c-3p/CCNA2, and miR-204-5p/CCNA2), and lung adenocarcinoma (miRNA-218-5p/CCNA2 and miR-204-5p/CCNA2) by expression analysis, survival analysis, and correlation analysis. The CCNA2 expression is positively correlated with Th2 cell infiltration and negatively correlated with CD4+ central memory and effector memory T-cell infiltration in different cancer types. Furthermore, CCNA2 is positively associated with expressions of immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT) in most cancer types. Our first CCNA2 pan-cancer study contributes to understanding the prognostic and immunological roles and potential upstream molecular mechanisms of CCNA2 in different cancers.
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Affiliation(s)
- Shenyong Chen
- Department of Pathology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Zhijia Zhao
- Department of Hematology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xiaobo Wang
- Department of Hematology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Qi Zhang
- Department of Hematology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Li Lyu
- Department of Pathology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Bo Tang
- Department of Hematology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Bo Tang,
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Kakar MU, Mehboob MZ, Akram M, Shah M, Shakir Y, Ijaz HW, Aziz U, Ullah Z, Ahmad S, Ali S, Yin Y. Identification of Differentially Expressed Genes Associated with the Prognosis and Diagnosis of Hepatocellular Carcinoma by Integrated Bioinformatics Analysis. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4237633. [PMID: 36317111 PMCID: PMC9617698 DOI: 10.1155/2022/4237633] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 09/29/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVE The goal of this study was to understand the possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis. METHODS GEO contains datasets of gene expression, miRNA, and methylation patterns of diseased and healthy/control patients. The GSE62232 dataset was selected by employing the server Gene Expression Omnibus. A total of 91 samples were collected, including 81 HCC and 10 healthy samples as control. GSE62232 was analysed through GEO2R, and Functional Enrichment Analysis was performed to extract rational information from a set of DEGs. The Protein-Protein Relationship Networking search method has been used for extracting the interacting genes. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analysed using GEPIA to estimate the effect of their differential expression on cancer progression. RESULTS We identified the top 10 hub genes through CytoHubba plugin. These included BUB1, BUB1B, CCNB1, CCNA2, CCNB2, CDC20, CDK1 and MAD2L1, NCAPG, and NDC80. NCAPG and NDC80 reported for the first time in this study while the remaining from a recently reported literature. The pathogenesis of HCC may be directly linked with the aforementioned genes. In this analysis, we found critical genes for HCC that showed recommendations for future prognostic and predictive biomarkers studies that could promote selective molecular therapy for HCC.
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Affiliation(s)
- Mohib Ullah Kakar
- Beijing Key Laboratory for Separation and Analysis in Biomedicine and Pharmaceutical, School of life Sciences, Beijing Institute of Technology (BIT), Beijing 100081, China
- Faculty of Marine Sciences, Lasbela University of Agriculture, Water and Marine Sciences (LUAWMS), Uthal, Balochistan, Pakistan
| | - Muhammad Zubair Mehboob
- CAS Centre for Excellence in Biotic Interaction, College of Life Sciences, University of Chinese Academy of Science, Beijing 100049, China
- Department of Biochemistry and Biotechnology, University of Gujrat, Gujrat 50700, Pakistan
| | - Muhammad Akram
- School of Science, Department of Life sciences, University of Management and Technology, Johar Town, Lahore 54770, Pakistan
| | - Muddaser Shah
- Department of Botany, Abdul Wali Khan University, Mardan 23200, Pakistan
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat Al-Mauz, P.O. Box 33, Nizwa 616, Oman
| | - Yasmeen Shakir
- Department of Biochemistry, Hazara University, Mansehra, Pakistan
| | - Hafza Wajeeha Ijaz
- CAS Centre for Excellence in Biotic Interaction, College of Life Sciences, University of Chinese Academy of Science, Beijing 100049, China
| | - Ubair Aziz
- Research Centre of Molecular Simulation, National University of Science and Technology, Islamabad, Pakistan
| | - Zahid Ullah
- School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
| | - Sajjad Ahmad
- Faculty of Veterinary and Animal Sciences, Lasbela University of Agriculture, Water and Marine Sciences, LUAWMS, Uthal, 90150 Balochistan, Pakistan
| | - Sikandar Ali
- Dow Institute for Advanced Biological and Animal Research, Dow University of Health Sciences, Ojha Campus, Karachi, Pakistan
| | - Yongxiang Yin
- Department of Pathology, Wuxi Maternity and Child Health Hospital Affiliated to Nanjing Medical University, Wuxi, China
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Chu JL, Bi SH, He Y, Ma RY, Wan XY, Wang ZH, Zhang L, Zheng MZ, Yang ZQ, Du LW, Maimaiti Y, Biekedawulaiti G, Duolikun M, Chen HY, Chen L, Li LL, Tie L, Lin J. 5-Hydroxymethylcytosine profiles in plasma cell-free DNA reflect molecular characteristics of diabetic kidney disease. Front Endocrinol (Lausanne) 2022; 13:910907. [PMID: 35966076 PMCID: PMC9372268 DOI: 10.3389/fendo.2022.910907] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/06/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Diabetic kidney disease (DKD), one of the main complications of diabetes mellitus (DM), has become a frequent cause of end-stage renal disease. A clinically convenient, non-invasive approach for monitoring the development of DKD would benefit the overall life quality of patients with DM and contribute to lower medical burdens through promoting preventive interventions. METHODS We utilized 5hmC-Seal to profile genome-wide 5-hydroxymethylcytosines in plasma cell-free DNA (cfDNA). Candidate genes were identified by intersecting the differentially hydroxymethylated genes and differentially expressed genes from the GSE30528 and GSE30529. Then, a protein interaction network was constructed for the candidate genes, and the hub genes were identified by the MCODE and cytoHubba algorithm. The correlation analysis between the hydroxymethylation level of the hub genes and estimated glomerular filtration rate (eGFR) was carried out. Finally, we demonstrated differences in expression levels of the protein was verified by constructing a mouse model of DKD. In addition, we constructed a network of interactions between drugs and hub genes using the Comparative Toxicogenomics Database. RESULTS This study found that there were significant differences in the overall distribution of 5hmC in plasma of patients with DKD, and an alteration of hydroxymethylation levels in genomic regions involved in inflammatory pathways which participate in the immune response. The final 5 hub genes, including (CTNNB1, MYD88, CD28, VCAM1, CD44) were confirmed. Further analysis indicated that this 5-gene signature showed a good capacity to distinguish between DKD and DM, and was found that protein levels were increased in renal tissue of DKD mice. Correlation analysis indicated that the hydroxymethylation level of 5 hub genes were nagatively correlated with eGFR. Toxicogenomics analysis showed that a variety of drugs for the treatment of DKD can reduce the expression levels of 4 hub genes (CD44, MYD88, VCAM1, CTNNB1). CONCLUSIONS The 5hmC-Seal assay was successfully applied to the plasma cfDNA samples from a cohort of DM patients with or without DKD. Altered 5hmC signatures indicate that 5hmC-Seal has the potential to be a non-invasive epigenetic tool for monitoring the development of DKD and it provides new insight for the future molecularly targeted anti-inflammation therapeutic strategies of DKD.
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Affiliation(s)
- Jin-Lin Chu
- College of Pharmacy, Xinjiang Medical University Key Laboratory of Active Components of Xinjiang Natural Medicine and Drug Release Technology, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, China
| | - Shu-Hong Bi
- Department of Nephrology, Peking University Third Hospital, Beijing, China
| | - Yao He
- Department of Pharmacology, School of Basic Medical Sciences, Peking University and Beijing Key Laboratory of Tumor Systems Biology, Peking University, Beijing, China
| | - Rui-Yao Ma
- College of Pharmacy, Xinjiang Medical University Key Laboratory of Active Components of Xinjiang Natural Medicine and Drug Release Technology, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, China
| | - Xing-Yu Wan
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Innovation Center for Genomics, Peking University, Beijing, China
| | - Zi-Hao Wang
- Beijing Institute of Pharmacology and Toxicology, Beijing, China
| | - Lei Zhang
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Innovation Center for Genomics, Peking University, Beijing, China
| | - Meng-Zhu Zheng
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Innovation Center for Genomics, Peking University, Beijing, China
| | - Zhan-Qun Yang
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Innovation Center for Genomics, Peking University, Beijing, China
| | - Ling-Wei Du
- School of Food Science and Engineering, Hainan University, Haikou, China
| | - Yiminiguli Maimaiti
- College of Pharmacy, Xinjiang Medical University Key Laboratory of Active Components of Xinjiang Natural Medicine and Drug Release Technology, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, China
| | - Gulinazi Biekedawulaiti
- College of Pharmacy, Xinjiang Medical University Key Laboratory of Active Components of Xinjiang Natural Medicine and Drug Release Technology, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, China
| | - Maimaitiyasen Duolikun
- College of Pharmacy, Xinjiang Medical University Key Laboratory of Active Components of Xinjiang Natural Medicine and Drug Release Technology, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, China
| | - Hang-Yu Chen
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Innovation Center for Genomics, Peking University, Beijing, China
| | - Long Chen
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Innovation Center for Genomics, Peking University, Beijing, China
| | - Lin-Lin Li
- College of Pharmacy, Xinjiang Medical University Key Laboratory of Active Components of Xinjiang Natural Medicine and Drug Release Technology, Urumqi, China
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, China
- *Correspondence: Lin-Lin Li, ; Lu Tie, ; Jian Lin,
| | - Lu Tie
- Department of Pharmacology, School of Basic Medical Sciences, Peking University and Beijing Key Laboratory of Tumor Systems Biology, Peking University, Beijing, China
- *Correspondence: Lin-Lin Li, ; Lu Tie, ; Jian Lin,
| | - Jian Lin
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Innovation Center for Genomics, Peking University, Beijing, China
- *Correspondence: Lin-Lin Li, ; Lu Tie, ; Jian Lin,
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Ye D, Liu Y, Li G, Sun B, Peng J, Xu Q. A New Risk Score Based on Eight Hepatocellular Carcinoma- Immune Gene Expression Can Predict the Prognosis of the Patients. Front Oncol 2021; 11:766072. [PMID: 34868990 PMCID: PMC8639602 DOI: 10.3389/fonc.2021.766072] [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: 08/28/2021] [Accepted: 11/01/2021] [Indexed: 11/16/2022] Open
Abstract
Background Hepatocellular carcinoma (HCC) is one of the malignant tumors with high morbidity and mortality worldwide. Immunotherapy has emerged as an increasingly important cancer treatment modality. However, the potential relationship between immune genes and HCC still needs to be explored. The purpose of this study is to construct a new prognostic risk signature to predict the prognosis of HCC patients based on the expression of immune-related genes (IRGs) and explore its potential mechanism. Methods We analyzed the gene expression data of 332 HCC patient samples and 46 adjacent normal tissues samples (Solid Tissue Normal including cirrhotic tissue) in The Cancer Genome Atlas (TCGA) database and clinical characteristics. We analyzed the gene expression data, identified differentially expressed IRGs in HCC tissues, filtered IRGs with prognostic value to construct an IRG signature, and classified patients into high and low gene expression groups based on the expression of IRGs in their tumor tissues. We also investigated the potential molecular mechanisms of IRGs through a bioinformatics approach using Protein-Protein Interaction (PPI) network, Kyoto Encyclopedia of Genes and Genomes (KEGG) database analysis and Gene Ontology (GO) database analysis. Differentially expressed IRGs associated with significant clinical outcomes (SIRGs) were identified by univariate Cox regression analysis. An immune-related risk score model (IRRSM) was established based on Lasso Cox regression analysis and multivariate Cox regression analysis. Based on the IRRSM, the immune score of the patients was calculated, and the patients were divided into high-risk and low-risk patients according to the median score, and the differences in survival between the two groups were compared. Then, the correlation analysis between the IRRSM and clinical characteristics was performed, and the IRRSM was validated using the International Cancer Genome Consortium (ICGC) database. Results The IRRSM was eventually constructed and confirmed to be an independent prognostic model for HCC patients. The IRRSM was shown to be positively correlated with the infiltration of four types of immune cells. Conclusion Our results showed that some SIRGs have potential value for predicting the prognosis and clinical outcomes of HCC patients. IRGs affect the prognosis of HCC patients by regulating the tumor immune microenvironment (TIME). This study provides a new insight for immune research and treatment strategies in HCC patients.
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Affiliation(s)
- Dingde Ye
- Nanjing Drum Tower Hospital, Medicine School of Southeast University, Nanjing, China
| | - Yaping Liu
- School of Life Science and Technology, Southeast University, Nanjing, China
| | - Guoqiang Li
- Department of General Surgery, Affiliated Drum Tower Hospital, Medical School, Nanjing University, Nanjing, China
| | - Beicheng Sun
- Department of General Surgery, Affiliated Drum Tower Hospital, Medical School, Nanjing University, Nanjing, China
| | - Jin Peng
- Department of General Surgery, Affiliated Drum Tower Hospital, Medical School, Nanjing University, Nanjing, China
| | - Qingxiang Xu
- Nanjing Drum Tower Hospital, Medicine School of Southeast University, Nanjing, China.,Department of General Surgery, Affiliated Drum Tower Hospital, Medical School, Nanjing University, Nanjing, China
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Wang E, Li Y, Ming R, Wei J, Du P, Zhou P, Zong S, Xiao H. The Prognostic Value and Immune Landscapes of a m 6A/m 5C/m 1A-Related LncRNAs Signature in Head and Neck Squamous Cell Carcinoma. Front Cell Dev Biol 2021; 9:718974. [PMID: 34917609 PMCID: PMC8670092 DOI: 10.3389/fcell.2021.718974] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 11/05/2021] [Indexed: 12/17/2022] Open
Abstract
Background: N6-methyladenosine (m6A), 5-methylcytosine (m5C) and N1-methyladenosine (m1A) are the main RNA methylation modifications involved in the progression of cancer. However, it is still unclear whether m6A/m5C/m1A-related long non-coding RNAs (lncRNAs) affect the prognosis of head and neck squamous cell carcinoma (HNSCC). Methods: We summarized 52 m6A/m5C/m1A-related genes, downloaded 44 normal samples and 501 HNSCC tumor samples with RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) database, and then searched for m6A/m5C/m1A-related genes co-expressed lncRNAs. We adopt the least absolute shrinkage and selection operator (LASSO) Cox regression to obtain m6A/m5C/m1A-related lncRNAs to construct a prognostic signature of HNSCC. Results: This prognostic signature is based on six m6A/m5C/m1A-related lncRNAs (AL035587.1, AC009121.3, AF131215.5, FMR1-IT1, AC106820.5, PTOV1-AS2). It was found that the high-risk subgroup has worse overall survival (OS) than the low-risk subgroup. Moreover, the results showed that most immune checkpoint genes were significantly different between the two risk groups (p < 0.05). Immunity microenvironment analysis showed that the contents of NK cell resting, macrophages M2, and neutrophils in samples of low-risk group were significantly lower than those of high-risk group (p < 0.05), while the contents of B cells navie, plasma cells, and T cells regulatory (Tregs) were on the contrary (p < 0.05). In addition, patients with high tumor mutational burden (TMB) had the worse overall survival than those with low tumor mutational burden. Conclusion: Our study elucidated how m6A/m5C/m1A-related lncRNAs are related to the prognosis, immune microenvironment, and TMB of HNSCC. In the future, these m6A/m5C/m1A-related lncRNAs may become a new choice for immunotherapy of HNSCC.
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Affiliation(s)
- Enhao Wang
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Li
- Department of Stomatology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ruijie Ming
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiahui Wei
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peiyu Du
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peng Zhou
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shimin Zong
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongjun Xiao
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Chang YW, Zhu WJ, Gu W, Sun J, Li ZQ, Wei XE. Neohesperidin promotes the osteogenic differentiation of bone mesenchymal stem cells by activating the Wnt/β-catenin signaling pathway. J Orthop Surg Res 2021; 16:334. [PMID: 34020675 PMCID: PMC8139099 DOI: 10.1186/s13018-021-02468-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 05/06/2021] [Indexed: 12/12/2022] Open
Abstract
Background Osteoporosis is a common disease in aging populations. However, osteoporosis treatment is still challenging. Here, we aimed to investigate the role of neohesperidin (NEO) in osteoporosis progression and the potential mechanism. Methods Bone mesenchymal stem cells (BMSCs) were isolated and treated with different concentrations of NEO (0, 10, 30, 100 M). Cell proliferation was analyzed by cell count kit-8 (CCK-8) assay. RNA-sequencing was performed on the isolated BMSCs with control and NEO treatment. Differentially expressed genes were obtained by R software. Alkaline phosphatase (ALP) staining and Alizarin red staining (ARS) were performed to assess the osteogenic capacity of the NEO. qRT-PCR was used to detect the expression of osteoblast markers. Western blot was used to evaluate the protein levels in BMSCs. Results NEO treatment significantly improved hBMSC proliferation at different time points, particularly when cells were incubated with 30 M NEO (P < 0.05). NEO dose-dependently increased the ALP activity and calcium deposition than the control group (P < 0.05). A total of 855 differentially expressed genes were identified according to the significance criteria of log2 (fold change) > 1 and adj P < 0.05. DKK1 partially reversed the promotion effects of NEO on osteogenic differentiation of BMSCs. NEO increased levels of the -catenin protein in BMSCs. Conclusion NEO plays a positive role in promoting osteogenic differentiation of BMSCs, which was related with activation of Wnt/-catenin pathway.
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Affiliation(s)
- Yue-Wen Chang
- Department of Orthopedics, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, No. 185, Puan Road, Huangpu District, Shanghai, 200021, China.
| | - Wen-Jun Zhu
- Department of Orthopedics, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, No. 185, Puan Road, Huangpu District, Shanghai, 200021, China
| | - Wei Gu
- Department of Orthopedics, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, No. 185, Puan Road, Huangpu District, Shanghai, 200021, China
| | - Jun Sun
- Department of Orthopedics, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, No. 185, Puan Road, Huangpu District, Shanghai, 200021, China
| | - Zhi-Qiang Li
- Department of Orthopedics, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, No. 185, Puan Road, Huangpu District, Shanghai, 200021, China
| | - Xiao-En Wei
- Department of Orthopedics, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, No. 185, Puan Road, Huangpu District, Shanghai, 200021, China
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