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He J, Yi J, Ji L, Dai L, Chen Y, Xue W. ECHDC2 inhibits the proliferation of gastric cancer cells by binding with NEDD4 to degrade MCCC2 and reduce aerobic glycolysis. Mol Med 2024; 30:69. [PMID: 38783226 PMCID: PMC11118108 DOI: 10.1186/s10020-024-00832-9] [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: 01/04/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND The Enoyl-CoA hydratase/isomerase family plays a crucial role in the metabolism of tumors, being crucial for maintaining the energy balance and biosynthetic needs of cancer cells. However, the enzymes within this family that are pivotal in gastric cancer (GC) remain unclear. METHODS We employed bioinformatics techniques to identify key Enoyl-CoA hydratase/isomerase in GC. The expression of ECHDC2 and its clinical significance were validated through tissue microarray analysis. The role of ECHDC2 in GC was further assessed using colony formation assays, CCK8 assay, EDU assay, Glucose and lactic acid assay, and subcutaneous tumor experiments in nude mice. The mechanism of action of ECHDC2 was validated through Western blotting, Co-immunoprecipitation, and immunofluorescence experiments. RESULTS Our analysis of multiple datasets indicates that low expression of ECHDC2 in GC is significantly associated with poor prognosis. Overexpression of ECHDC2 notably inhibits aerobic glycolysis and proliferation of GC cells both in vivo and in vitro. Further experiments revealed that overexpression of ECHDC2 suppresses the P38 MAPK pathway by inhibiting the protein level of MCCC2, thereby restraining glycolysis and proliferation in GC cells. Ultimately, it was discovered that ECHDC2 promotes the ubiquitination and subsequent degradation of MCCC2 protein by binding with NEDD4. CONCLUSIONS These findings underscore the pivotal role of the ECHDC2 in regulating aerobic glycolysis and proliferation in GC cells, suggesting ECHDC2 as a potential therapeutic target in GC.
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Affiliation(s)
- Jiancheng He
- Department of Gastrointestinal Surgery, Affliated Hospital of Nantong University, Medical School of Nantong University, 20 Xisi Street, Nantong, 226001, China
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China
- Nantong Key Laboratory of Gastrointestinal Oncology, Nantong, 226001, China
| | - Jianfeng Yi
- Department of Gastrointestinal Surgery, Affliated Hospital of Nantong University, Medical School of Nantong University, 20 Xisi Street, Nantong, 226001, China
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China
- Nantong Key Laboratory of Gastrointestinal Oncology, Nantong, 226001, China
| | - Li Ji
- Department of Gastrointestinal Surgery, Affliated Hospital of Nantong University, Medical School of Nantong University, 20 Xisi Street, Nantong, 226001, China
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China
- Nantong Key Laboratory of Gastrointestinal Oncology, Nantong, 226001, China
| | - Lingchen Dai
- Department of Gastrointestinal Surgery, Affliated Hospital of Nantong University, Medical School of Nantong University, 20 Xisi Street, Nantong, 226001, China
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China
- Nantong Key Laboratory of Gastrointestinal Oncology, Nantong, 226001, China
| | - Yu Chen
- Department of Gastrointestinal Surgery, Affliated Hospital of Nantong University, Medical School of Nantong University, 20 Xisi Street, Nantong, 226001, China.
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China.
- Nantong Key Laboratory of Gastrointestinal Oncology, Nantong, 226001, China.
| | - Wanjiang Xue
- Department of Gastrointestinal Surgery, Affliated Hospital of Nantong University, Medical School of Nantong University, 20 Xisi Street, Nantong, 226001, China.
- Nantong Key Laboratory of Gastrointestinal Oncology, Nantong, 226001, China.
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2
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Mi N, Li Z, Zhang X, Gao Y, Wang Y, Liu S, Wang S. Identification of potential immunotherapeutic targets and prognostic biomarkers in Graves' disease using weighted gene co-expression network analysis. Heliyon 2024; 10:e27175. [PMID: 38468967 PMCID: PMC10926144 DOI: 10.1016/j.heliyon.2024.e27175] [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: 07/21/2023] [Revised: 12/11/2023] [Accepted: 02/26/2024] [Indexed: 03/13/2024] Open
Abstract
Graves' disease (GD) is an autoimmune disorder characterized by hyperthyroidism resulting from autoantibody-induced stimulation of the thyroid gland. Despite recent advancements in understanding GD's pathogenesis, the molecular processes driving disease progression and treatment response remain poorly understood. In this study, we aimed to identify crucial immunogenic factors associated with GD prognosis and immunotherapeutic response. To achieve this, we implemented a comprehensive screening strategy that combined computational immunogenicity-potential scoring with multi-parametric cluster analysis to assess the immunomodulatory genes in GD-related subtypes involving stromal and immune cells. Utilizing weighted gene co-expression network analysis (WGCNA), we identified co-expressed gene modules linked to cellular senescence and immune infiltration in CD4+ and CD8+ GD samples. Additionally, gene set enrichment analysis enabled the identification of hallmark pathways distinguishing high- and low-immune subtypes. Our WGCNA analysis revealed 21 gene co-expression modules comprising 1,541 genes associated with immune infiltration components in various stages of GD, including T cells, M1 and M2 macrophages, NK cells, and Tregs. These genes primarily participated in T cell proliferation through purinergic signaling pathways, particularly neuroactive ligand-receptor interactions, and DNA binding transcription factor activity. Three genes, namely PRSS1, HCRTR1, and P2RY4, exhibited robustness in GD patients across multiple stages and were involved in immune cell infiltration during the late stage of GD (p < 0.05). Importantly, HCRTR1 and P2RY4 emerged as potential prognostic signatures for predicting overall survival in high-immunocore GD patients (p < 0.05). Overall, our study provides novel insights into the molecular mechanisms driving GD progression and highlights potential key immunogens for further investigation. These findings underscore the significance of immune infiltration-related cellular senescence in GD therapy and present promising targets for the development of new immunotherapeutic strategies.
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Affiliation(s)
- Nianrong Mi
- Department of General Practice, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Zhe Li
- Department of Health Management Center, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Xueling Zhang
- Department of Integrated Chinese and Western Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Yingjing Gao
- Department of Endocrinology, Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Yanan Wang
- Department of Endocrinology, Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Siyan Liu
- Department of Endocrinology, Shandong First Medical University, Jinan, Shandong Province, 250013, China
| | - Shaolian Wang
- Department of Integrated Chinese and Western Medicine, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250013, China
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Doan LMT, Angione C, Occhipinti A. Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer. Methods Mol Biol 2023; 2553:325-393. [PMID: 36227551 DOI: 10.1007/978-1-0716-2617-7_16] [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] [Indexed: 06/16/2023]
Abstract
Breast cancer is one of the most common cancers in women worldwide, which causes an enormous number of deaths annually. However, early diagnosis of breast cancer can improve survival outcomes enabling simpler and more cost-effective treatments. The recent increase in data availability provides unprecedented opportunities to apply data-driven and machine learning methods to identify early-detection prognostic factors capable of predicting the expected survival and potential sensitivity to treatment of patients, with the final aim of enhancing clinical outcomes. This tutorial presents a protocol for applying machine learning models in survival analysis for both clinical and transcriptomic data. We show that integrating clinical and mRNA expression data is essential to explain the multiple biological processes driving cancer progression. Our results reveal that machine-learning-based models such as random survival forests, gradient boosted survival model, and survival support vector machine can outperform the traditional statistical methods, i.e., Cox proportional hazard model. The highest C-index among the machine learning models was recorded when using survival support vector machine, with a value 0.688, whereas the C-index recorded using the Cox model was 0.677. Shapley Additive Explanation (SHAP) values were also applied to identify the feature importance of the models and their impact on the prediction outcomes.
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Affiliation(s)
- Le Minh Thao Doan
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK
- Centre for Digital Innovation, Teesside University, Middlesbrough, UK
- Healthcare Innovation Centre, Teesside University, Middlesbrough, UK
- National Horizons Centre, Teesside University, Darlington, UK
| | - Annalisa Occhipinti
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK.
- Centre for Digital Innovation, Teesside University, Middlesbrough, UK.
- National Horizons Centre, Teesside University, Darlington, UK.
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4
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Han Y, Zhou Y, Li H, Gong Z, Liu Z, Wang H, Wang B, Ye X, Liu Y. Identification of diagnostic mRNA biomarkers in whole blood for ankylosing spondylitis using WGCNA and machine learning feature selection. Front Immunol 2022; 13:956027. [PMID: 36172367 PMCID: PMC9510835 DOI: 10.3389/fimmu.2022.956027] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 08/24/2022] [Indexed: 11/28/2022] Open
Abstract
Ankylosing spondylitis (AS) is a common inflammatory spondyloarthritis affecting the spine and sacroiliac joint that finally results in sclerosis of the axial skeleton. Aside from human leukocyte antigen B27, transcriptomic biomarkers in blood for AS diagnosis still remain unknown. Hence, this study aimed to investigate credible AS-specific mRNA biomarkers from the whole blood of AS patients by analyzing an mRNA expression profile (GSE73754) downloaded Gene Expression Omnibus, which includes AS and healthy control blood samples. Weighted gene co-expression network analysis was performed and revealed three mRNA modules associated with AS. By performing gene set enrichment analysis, the functional annotations of these modules revealed immune biological processes that occur in AS. Several feature mRNAs were identified by analyzing the hubs of the protein-protein interaction network, which was based on the intersection between differentially expressed mRNAs and mRNA modules. A machine learning-based feature selection method, SVM-RFE, was used to further screen out 13 key feature mRNAs. After verifying by qPCR, IL17RA, Sqstm1, Picalm, Eif4e, Srrt, Lrrfip1, Synj1 and Cxcr6 were found to be significant for AS diagnosis. Among them, Cxcr6, IL17RA and Lrrfip1 were correlated with severity of AS symptoms. In conclusion, our findings provide a framework for identifying the key mRNAs in whole blood of AS that is conducive for the development of novel diagnostic markers for AS.
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Affiliation(s)
- Yaguang Han
- Department of Orthopaedics, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Yiqin Zhou
- Department of Orthopaedics, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Haobo Li
- Department of Orthopaedics, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Zhenyu Gong
- Department of Neurosurgery, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Ziye Liu
- Department of Orthopaedics, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Huan Wang
- Department of Orthopaedics, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Bo Wang
- Department of Orthopaedics, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- *Correspondence: Bo Wang, ; Xiaojian Ye, ; Yi Liu,
| | - Xiaojian Ye
- Department of Orthopaedics, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Bo Wang, ; Xiaojian Ye, ; Yi Liu,
| | - Yi Liu
- Department of Orthopaedics, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Department of Orthopaedics, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Bo Wang, ; Xiaojian Ye, ; Yi Liu,
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5
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Li L, Yu S, Chen J, Quan M, Gao Y, Li Y. miR-15a and miR-20b sensitize hepatocellular carcinoma cells to sorafenib through repressing CDC37L1 and consequent PPIA downregulation. Cell Death Dis 2022; 8:297. [PMID: 35760798 PMCID: PMC9237098 DOI: 10.1038/s41420-022-01094-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 06/15/2022] [Accepted: 06/17/2022] [Indexed: 11/29/2022]
Abstract
Sorafenib is a classical targeted drug for the treatment of advanced hepatocellular carcinoma (HCC), but intrinsic resistance severely limited its therapeutic effects. In the present study, we aimed to identify crucial genes in HCC cells that affect sorafenib resistance by a CRISPR/Cas9 genome-scale screening. The results indicated that the deficiency of miR-15a and miR-20b contributed to sorafenib resistance, whereas exogenous expression of miR-15a and miR-20b enhanced sorafenib sensitivity of HCC cells by cell viability, colony formation, and flow cytometry analyses. Further analyses revealed that cell division cycle 37 like 1 (CDC37L1) as a common target of miR-15a and 20b, was negatively regulated by the two miRNAs and could enhance sorafenib resistance of HCC cells in vitro and in vivo. Mechanistically, CDC37L1, as a cochaperone, effectively increased the expression of peptidylprolyl isomerase A (PPIA) through strengthening the binding between heat shock protein 90 (HSP90) and PPIA. The results from immunohistochemical staining of a HCC tissue microarray revealed a positive association between CDC37L1 and PPIA expression, and high expression of CDC37L1 and PPIA predicted worse prognosis of HCC patients after sorafenib therapy. Taken together, our findings reveal crucial roles of miR-15a, miR-20b, CDC37L1, and PPIA in sorafenib response of HCC cells. These factors may serve as therapeutic targets and predict prognosis for HCC treated with sorafenib.
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Lin Z, Xu Q, Song X, Zeng Y, Zeng L, Zhao L, Xu J, Miao D, Chen Z, Yu F. Comprehensive Analysis Identified Mutation-Gene Signature Impacts the Prognosis Through Immune Function in Hepatocellular Carcinoma. Front Oncol 2022; 12:748557. [PMID: 35311113 PMCID: PMC8931204 DOI: 10.3389/fonc.2022.748557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 02/04/2022] [Indexed: 12/20/2022] Open
Abstract
BackgroundHepatocellular carcinoma (HCC) is a life-threatening and refractory malignancy with poor outcome. Genetic mutations are the hallmark of cancer. Thus far, there is no comprehensive prognostic model constructed by mutation-gene transcriptome in HCC. The prognostic value of mutation-gene signature in HCC remains elusive.MethodsRNA expression profiles and the corresponding clinical information were recruited from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to establish gene signature. Kaplan–Meier curve and time-dependent receiver operating characteristic curve were implemented to evaluate the prognostic value. The Wilcoxon test was performed to analyze the expression of immune checkpoint genes, cell cycle genes, and tumor drug resistance genes in different risk groups. Finally, quantitative real-time PCR (qRT-RCR) and immunohistochemistry (IHC) were performed to validate the mRNA and protein expression between HCC and adjacent nontumorous tissues in an independent cohort.ResultsA prognostic model consisting of five mutated genes was established by LASSO Cox regression analysis. The prognostic model classified patients into high- and low-risk groups. Compared with the low‐risk group, patients in the high‐risk group had significantly worse survival results. The prognostic model can accurately predict the overall survival of HCC patients and predict overall survival more accurately when combined with stage. Furthermore, the immune checkpoint genes, cell cycle genes, and tumor drug resistance genes were higher expressed in the high-risk group compared in the low-risk group. In addition, the expression level of prognostic signature genes was validated in an independent sample cohort, which was consistent with RNA sequencing expression in the TCGA database.ConclusionThe prediction model of HCC constructed using mutation-related genes is of great significance for clinical decision making and the personalized treatment of patients with HCC.
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Affiliation(s)
- Zhuo Lin
- Laboratory Animal Centre, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qian Xu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xian Song
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yuan Zeng
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Liuwei Zeng
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Luying Zhao
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jun Xu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Dan Miao
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhuoyan Chen
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- *Correspondence: Fujun Yu, ; Zhuoyan Chen,
| | - Fujun Yu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- *Correspondence: Fujun Yu, ; Zhuoyan Chen,
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7
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He W, Yang G, Liu S, Maghsoudloo M, Shasaltaneh MD, Kaboli PJ, Zhang C, Zhang J, Entezari M, Imani S, Wen Q. Comparative mRNA/micro-RNA co-expression network drives melanomagenesis by promoting epithelial-mesenchymal transition and vasculogenic mimicry signaling. Transl Oncol 2021; 14:101237. [PMID: 34626953 PMCID: PMC8512639 DOI: 10.1016/j.tranon.2021.101237] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 09/30/2021] [Indexed: 12/17/2022] Open
Abstract
This study aimed to identify a novel disease-associated differentially co-expressed mRNA-microRNA (miRNA) that is associated with vasculogenic mimicry (VM) and epithelial-to-mesenchymal transition (EMT) network at different stages of melanoma. By applying weighted gene co-expression network analysis, we constructed a VM+EMT biological network with the available microarray dataset downloaded from a public database. Quantitative real-time PCR, immunohistochemical staining, and CD31-periodic acid solution dual staining were performed to confirm the expression of genes associated with EMT and VM formation in subjects with malignant melanoma (n = 18) and primary melanoma (n = 13) and in healthy subjects (n = 10). Our findings suggested that phosphatidylserine-specific phospholipase A1-alpha (PLA1A) and dermokine (DMKN) genes function as oncogenes that trigger VM and EMT processes during melanomagenesis on interaction with miR-370, miR-563, and miR-770-5p. PLA1A and DMKN genes can be considered potential VM+EMT network-based diagnostic biomarkers for distinguishing between melanoma patients. We postulate that a network with altered PLA1A/miR-563 and DMNK/miR-770-5p/miR-370 may contribute to melanomagenesis by triggering the EMT signaling pathway and VM formation. This study provides a potentially valuable approach for the early diagnosis and prognosis of melanoma progression.
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Affiliation(s)
- WenFeng He
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Gang Yang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China; Department of Oncology, Anyue Hospital of Traditional Chinese Medicine, Second Ziyang Hospital of Traditional Chinese Medicine, Ziyang, Sichuan, China
| | - Shuya Liu
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China; Department of Oncology, Chengdu Jinniu District People's Hospital, Chengdu, Sichuan, China
| | - Mazaher Maghsoudloo
- Laboratory of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran; Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | | | - Parham Jabbarzadeh Kaboli
- Graduate Institute of Biomedical Sciences, Research Center for Cancer Biology, and Center for Molecular Medicine, China Medical University, Taichung, Taiwan
| | - Cuiwei Zhang
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - JingHeng Zhang
- Oncology Department, Luzhou People's Hospital, Luzhou, Sichuan, China
| | - Maliheh Entezari
- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Farhikhtegan Medical Convergence sciences Research Center, Farhikhtegan Hospital Tehran Medical sciences, Islamic Azad University, Tehran, Iran
| | - Saber Imani
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
| | - QingLian Wen
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
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Fang Y, Chen G, Chen F, Hu E, Dong X, Li Z, He L, Sun Y, Qiu L, Xu H, Cai Z, Liu X. Accurate transcriptome assembly by Nanopore RNA sequencing reveals novel functional transcripts in hepatocellular carcinoma. Cancer Sci 2021; 112:3555-3568. [PMID: 34255396 PMCID: PMC8409408 DOI: 10.1111/cas.15058] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 06/30/2021] [Accepted: 07/10/2021] [Indexed: 01/13/2023] Open
Abstract
The long reads of Nanopore sequencing permit accurate transcript assembly and ease in discovering novel transcripts with potentially important functions in cancers. The wide adoption of Nanopore sequencing for transcript quantification, however, is largely limited by high costs. To address this issue, we developed a bioinformatics software, NovelQuant, that can specifically quantify long-read-assembled novel transcripts with short-read sequencing data. Nanopore Direct RNA Sequencing was carried out on three hepatocellular carcinoma (HCC) patients' tumor, matched portal vein tumor thrombus, and peritumor to reconstruct the HCC transcriptome. Then, based on the reconstructed transcriptome, NovelQuant was applied on Illumina RNA sequencing data of 59 HCC patients' tumor and paired peritumor to quantify novel transcripts. Our further analysis revealed 361 novel transcripts dysregulated in HCC and that 101 of them were significantly associated with prognosis. There were 19 novel prognostic transcripts predicted to be long noncoding RNAs (lncRNAs), and some of them had regulatory targets that were reported to be associated with HCC. Additionally, 42 novel prognostic transcripts were predicted to be protein-coding mRNAs, and many of them could be involved in xenobiotic metabolism. Moreover, the tumor-suppressive roles of two representative novel prognostic transcripts, CDO1-novel (lncRNA) and CYP2A6-novel (protein-coding mRNA), were further functionally validated during HCC progression. Overall, the current study shows a possibility of combining long- and short-read sequencing to explore functionally important novel transcripts in HCC with accuracy and cost-efficiency, which expands the pool of molecular biomarkers that could enhance our understanding of the molecular mechanisms of HCC.
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Affiliation(s)
- Yuanchang Fang
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.,The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China.,Mengchao Med-X Center, Fuzhou University, Fuzhou, China
| | - Geng Chen
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.,The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China.,Mengchao Med-X Center, Fuzhou University, Fuzhou, China
| | - Feng Chen
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.,The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China.,Mengchao Med-X Center, Fuzhou University, Fuzhou, China
| | - En Hu
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.,The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China.,Mengchao Med-X Center, Fuzhou University, Fuzhou, China
| | - Xiuqing Dong
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.,The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China.,Mengchao Med-X Center, Fuzhou University, Fuzhou, China
| | - Zhenli Li
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.,The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China.,Mengchao Med-X Center, Fuzhou University, Fuzhou, China
| | - Lei He
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.,The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China.,Mengchao Med-X Center, Fuzhou University, Fuzhou, China
| | - Yupeng Sun
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.,The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China.,Mengchao Med-X Center, Fuzhou University, Fuzhou, China
| | - Liman Qiu
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.,The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China.,Mengchao Med-X Center, Fuzhou University, Fuzhou, China
| | - Haipo Xu
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.,The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China.,Mengchao Med-X Center, Fuzhou University, Fuzhou, China
| | - Zhixiong Cai
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.,The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China.,Mengchao Med-X Center, Fuzhou University, Fuzhou, China
| | - Xiaolong Liu
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.,The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China.,Mengchao Med-X Center, Fuzhou University, Fuzhou, China
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Chen T, Hua W, Xu B, Chen H, Xie M, Sun X, Ge X. Robust rank aggregation and cibersort algorithm applied to the identification of key genes in head and neck squamous cell cancer. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:4491-4507. [PMID: 34198450 DOI: 10.3934/mbe.2021228] [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: 06/13/2023]
Abstract
OBJECTIVE Although multiple hub genes have been identified in head and neck squamous cell cancer (HNSCC) in recent years, because of the limited sample size and inconsistent bioinformatics analysis methods, the results are not reliable. Therefore, it is urgent to use reliable algorithms to find new prognostic markers of HNSCC. METHOD The Robust Rank Aggregation (RRA) method was used to integrate 8 microarray datasets of HNSCC downloaded from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs). Later, Gene Ontology (GO) functional annotation together with Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was carried out to discover functions of those discovered DEGs. According to the KEGG results, those discovered DEGs showed tight association with the occurrence and development of HNSCC. Then cibersort algorithm was used to analyze the infiltration of immune cells of HNSCC and we found that the main infiltrated immune cells were B cells, dendritic cells and macrophages. A protein-protein interaction (PPI) network was established; moreover, key modules were also constructed to select 5 hub genes from the whole network using cytoHubba. 3 hub genes showed significant relationship with prognosis for TCGA-derived HNSCC patients. RESULT The potent DEGs along with hub genes were selected by the combined bioinformatic approach. AURKA, BIRC5 and UBE2C genes may be the potential prognostic biomarker and therapeutic targets of HNSCC. CONCLUSIONS The Robust Rank Aggregation method and cibersort algorithm method can accurately predict the potential prognostic biomarker and therapeutic targets of HNSCC through multiple GEO datasets.
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Affiliation(s)
- Tingting Chen
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, China
- Department of Oncology, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu 225000, China
| | - Wei Hua
- Department of Oncology, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu 225000, China
| | - Bing Xu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, China
| | - Hui Chen
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, China
| | - Minhao Xie
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu 210000, China
| | - Xinchen Sun
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, China
| | - Xiaolin Ge
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, China
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10
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Li L, Tao X, Li Y, Gao Y, Li Q. CDC37L1 acts as a suppressor of migration and proliferation in gastric cancer by down-regulating CDK6. J Cancer 2021; 12:3145-3153. [PMID: 33976724 PMCID: PMC8100790 DOI: 10.7150/jca.56097] [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: 11/08/2020] [Accepted: 03/17/2021] [Indexed: 11/18/2022] Open
Abstract
The co-chaperone protein CDC37 (Cell division cycle 37) is well known to regulate multiple protein kinases and involved in tumor progression. However to date, little is known about its analogue CDC37L1 (Cell division cycle 37 like 1) in tumorigenesis. This study aimed to explore the expression and function of CDC37L1 in gastric cancer (GC). The immunohistochemical staining in a tissue microarray showed a weak expression of CDC37L1 in high grade GC tissues compared with low grade tissues. Consistently, data from online database analysis demonstrated that CDC37L1 level was decreased in stage 4 patients and low expression of CDC37L1 indicated a poor prognosis. Functional studies revealed that CDC37L1 could inhibit GC cell proliferation and migration in CCK8, EdU incorporation, colony formation and transwell assays. In the meantime, CDC37L1 also inhibited the tumorigenicity of GC cells in nude mice. Mechanistically, we found that CDC37L1 had an impact on CDK6 protein expression by western blotting. Palbociclib, a specific CDK4/6 inhibitor, was discovered to block the rapid growth phenotype of GC cells induced by CDC37L1 silencing. Taken together, these findings unveiled a tumor-suppressive role of CDC37L1 in GC, and CDK6 may act as a downstream effector in this process.
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Affiliation(s)
- Li Li
- Department of Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China
| | - Xinyi Tao
- Department of Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China
- Department of Medical Experimental Techniques, Jinzhou Medical University, Jinzhou 121001, China
| | - Yandong Li
- Department of Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China
| | - Yong Gao
- Department of Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China
| | - Qinchuan Li
- Department of Thoracic Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China
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11
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Sharma A, Colonna G. System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration. Mol Diagn Ther 2021; 25:9-27. [PMID: 33475988 PMCID: PMC7847983 DOI: 10.1007/s40291-020-00505-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/01/2020] [Indexed: 12/17/2022]
Abstract
Biomedical institutions rely on data evaluation and are turning into data factories. Big-data storage centers, supercomputing systems, and increased algorithmic efficiency allow us to analyze the ever-increasing amount of data generated every day in biomedical research centers. In network science, the principal intrinsic problem is how to integrate the data and information from different experiments on genes or proteins. Data curation is an essential process in annotating new functional data to known genes or proteins, undertaken by a biobank curator, which is then reflected in the calculated networks. We provide an example of how protein-protein networks today have space-time limits. The next step is the integration of data and information from different biobanks. Omics data and networks are essential parts of this step but also have flawed protocols and errors. Consider data from patients with cancer: from biopsy procedures to experimental tests, to archiving methods and computational algorithms, these are continuously handled so require critical and continuous "updates" to obtain reproducible, reliable, and correct results. We show, as a second example, how all this distorts studies in cellular hepatocellular carcinoma. It is not unlikely that these flawed data have been polluting biobanks for some time before stringent conditions for the veracity of data were implemented in Big data. Therefore, all this could contribute to errors in future medical decisions.
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Affiliation(s)
- Ankush Sharma
- Department of Biosciences, University of Oslo, Oslo, Norway.
- Department of Informatics, University of Oslo, Oslo, Norway.
- Institute of Cancer Research, Institute of Clinical medicine, University of Oslo, Oslo, Norway.
| | - Giovanni Colonna
- Medical Informatics, AOU-Vanvitelli, Università della Campania, Naples, Italy
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12
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Vittrant B, Leclercq M, Martin-Magniette ML, Collins C, Bergeron A, Fradet Y, Droit A. Identification of a Transcriptomic Prognostic Signature by Machine Learning Using a Combination of Small Cohorts of Prostate Cancer. Front Genet 2020; 11:550894. [PMID: 33324443 PMCID: PMC7723980 DOI: 10.3389/fgene.2020.550894] [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: 06/05/2020] [Accepted: 10/29/2020] [Indexed: 01/31/2023] Open
Abstract
Determining which treatment to provide to men with prostate cancer (PCa) is a major challenge for clinicians. Currently, the clinical risk-stratification for PCa is based on clinico-pathological variables such as Gleason grade, stage and prostate specific antigen (PSA) levels. But transcriptomic data have the potential to enable the development of more precise approaches to predict evolution of the disease. However, high quality RNA sequencing (RNA-seq) datasets along with clinical data with long follow-up allowing discovery of biochemical recurrence (BCR) biomarkers are small and rare. In this study, we propose a machine learning approach that is robust to batch effect and enables the discovery of highly predictive signatures despite using small datasets. Gene expression data were extracted from three RNA-Seq datasets cumulating a total of 171 PCa patients. Data were re-analyzed using a unique pipeline to ensure uniformity. Using a machine learning approach, a total of 14 classifiers were tested with various parameters to identify the best model and gene signature to predict BCR. Using a random forest model, we have identified a signature composed of only three genes (JUN, HES4, PPDPF) predicting BCR with better accuracy [74.2%, balanced error rate (BER) = 27%] than the clinico-pathological variables (69.2%, BER = 32%) currently in use to predict PCa evolution. This score is in the range of the studies that predicted BCR in single-cohort with a higher number of patients. We showed that it is possible to merge and analyze different small and heterogeneous datasets altogether to obtain a better signature than if they were analyzed individually, thus reducing the need for very large cohorts. This study demonstrates the feasibility to regroup different small datasets in one larger to identify a predictive genomic signature that would benefit PCa patients.
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Affiliation(s)
- Benjamin Vittrant
- Centre de Recherche du CHU de Québec - Université Laval, Québec, QC, Canada.,Département de Médecine Moléculaire, Université Laval, QC, Canada
| | - Mickael Leclercq
- Centre de Recherche du CHU de Québec - Université Laval, Québec, QC, Canada.,Département de Médecine Moléculaire, Université Laval, QC, Canada
| | - Marie-Laure Martin-Magniette
- Universities of Paris Saclay, Paris, Evry, CNRS, INRAE, Institute of Plant Sciences Paris Saclay (IPS2), 91192, GIf sur Yvette, France.,UMR MIA-Paris, AgroParisTech, INRA, Université Paris-Saclay, Paris, France
| | - Colin Collins
- Vancouver Prostate Cancer Centre, Vancouver, BC, Canada.,Department of Urologic Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - Alain Bergeron
- Centre de Recherche du CHU de Québec - Université Laval, Québec, QC, Canada.,Département de Chirurgie, Oncology Axis, Université Laval, Québec, QC, Canada
| | - Yves Fradet
- Centre de Recherche du CHU de Québec - Université Laval, Québec, QC, Canada.,Département de Chirurgie, Oncology Axis, Université Laval, Québec, QC, Canada
| | - Arnaud Droit
- Centre de Recherche du CHU de Québec - Université Laval, Québec, QC, Canada.,Département de Médecine Moléculaire, Université Laval, QC, Canada
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13
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Bai Z, Li H, Li C, Sheng C, Zhao X. Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients. Medicine (Baltimore) 2020; 99:e21503. [PMID: 33019382 PMCID: PMC7535691 DOI: 10.1097/md.0000000000021503] [Citation(s) in RCA: 4] [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] [Indexed: 12/13/2022] Open
Abstract
Hepatitis B virus (HBV) infection is a leading cause of hepatocellular carcinoma (HCC), but HBV-HCC related prognosis signature remains rarely investigated. This study was to identify an integrated long non-coding RNAs-messenger RNAs (lncRNA-mRNA) signature for prediction of overall survival (OS) and explore their underlying functions.One RNA-sequencing dataset (training set, n = 95) and one microarray dataset E-TABM-36 (validation set, n = 44) were collected. Least absolute shrinkage and selection operator analysis was performed to identify an lncRNA-mRNA prognosis signature. The OS difference of patients in the high-risk and low-risk risk groups was evaluated by Kaplan-Meier curve. Area under the receiver operating characteristic curve (AUC), Harrell concordance index (C-index) calculation, and multivariate analyses with clinical characteristics were used to determine the prognostic ability. Furthermore, a coexpression network was constructed to interpret the functions.Nine signature genes (3 lncRNAs and 6 mRNAs) were selected to generate the risk score model. Patients belonging to the high-risk group showed a significantly shorter survival than those of the low-risk group. The prediction accuracy of the risk score for 5-year OS was 0.936 and 0.905 for the training set and validation set, respectively. Also, this risk score was independent of various clinical variables for the prognosis prediction. Incorporation of the risk score remarkably increased the predictive power of the routine clinical prognostic factors (vascular invasion status, tumor recurrence status) (AUC = 0.942 vs 0.628; C-index = 0.7997 vs 0.6908). Furthermore, LncRNA insulin-like growth factor 2 antisense RNA (IGF2-AS) and long intergenic non-protein coding RNA 342 (LINC00342) were predicted to exert tumor suppression effects by regulating homeobox D1 (HOXD1) and secreted frizzled related protein 5 (SFRP5), respectively; while lncRNA rhophilin Rho GTPase binding protein 1 antisense RNA 1 (RHPN1-AS1) may possess carcinogenic potential by promoting the transcription of chromobox 2 (CBX2), cell division cycle 20 (CDC20), matrix metallopeptidase 12 (MMP12), stratifin (SFN), tripartite motif containing 16 (TRIM16), and uroplakin 3A (UPK3A). These mRNAs may be associated with cell proliferation or apoptosis related pathways.This study may provide a novel, effective prognostic biomarker, and some therapeutic targets for HBV-HCC patients.
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14
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Zhang B, Tang B, Gao J, Li J, Kong L, Qin L. A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients. J Transl Med 2020; 18:342. [PMID: 32887635 PMCID: PMC7487492 DOI: 10.1186/s12967-020-02492-9] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 08/20/2020] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Hypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aim to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism. METHODS Differentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival (OS) were identified using Cox regression and LASSO analysis. Then the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. The Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature. CIBERSORT was used for estimating the fractions of immune cell types. RESULTS A total of 397 hypoxia-related DEGs in HCC were detected and three genes (PDSS1, CDCA8 and SLC7A11) among them were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response. Meanwhile, the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1. CONCLUSIONS Altogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.
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Affiliation(s)
- Baohui Zhang
- Department of Physiology, School of Life Science, China Medical University, No. 77 Puhe Road, Shenyang North New AreaLiaoning Province, Shenyang, 110122, People's Republic of China
| | - Bufu Tang
- Department of Radiology, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, 310058, China
| | - Jianyao Gao
- Department of Radiation Oncology, the First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiatong Li
- Department of Orthopedics, The First Affiliated Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
| | - Lingming Kong
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, 110004, China
| | - Ling Qin
- Department of Physiology, School of Life Science, China Medical University, No. 77 Puhe Road, Shenyang North New AreaLiaoning Province, Shenyang, 110122, People's Republic of China.
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15
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Niu J, Yan T, Guo W, Wang W, Zhao Z, Ren T, Huang Y, Zhang H, Yu Y, Liang X. Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy. Front Oncol 2020; 10:1628. [PMID: 32974202 PMCID: PMC7471873 DOI: 10.3389/fonc.2020.01628] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 07/27/2020] [Indexed: 02/06/2023] Open
Abstract
Osteosarcoma is one of the most aggressive malignant bone tumors worldwide. Although great advancements have been made in its treatment owing to the advent of neoadjuvant chemotherapy, the problem of lung metastasis is a major obstacle in the improvement of survival outcomes. Thus, the aim of the present study is to screen novel and key biomarkers, which may act as potential prognostic markers and therapeutic targets in osteosarcoma. We utilized the robust rank aggregation (RRA) method to integrate three osteosarcoma microarray datasets downloaded from the Gene Expression Omnibus (GEO) database, and we identified the robust differentially expressed genes (DEGs) between primary and metastatic osteosarcoma tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functions of robust DEGs. The results of enrichment analysis showed that the robust DEGs were closely associated with osteosarcoma development and progression. Immune cell infiltration analysis was also conducted by CIBERSORT algorithm, and we found that macrophages are the most principal infiltrating immune cells in osteosarcoma, especially macrophages M0 and M2. Then, the protein–protein interaction network and key modules were constructed by Cytoscape, and 10 hub genes were selected by plugin cytoHubba from the whole network. The survival analysis of hub genes was also carried out based on the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. The integrated bioinformatics analysis was utilized to provide new insight into osteosarcoma development and metastasis and identified EGR1, CXCL10, MYC, and CXCR4 as potential biomarkers for prognosis of osteosarcoma.
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Affiliation(s)
- Jianfang Niu
- Musculoskeletal Tumor Center, Peking University People's Hospital, Peking University, Beijing, China.,Beijing Key Laboratory of Musculoskeletal Tumor, Peking University People's Hospital, Beijing, China
| | - Taiqiang Yan
- Musculoskeletal Tumor Center, Peking University People's Hospital, Peking University, Beijing, China.,Beijing Key Laboratory of Musculoskeletal Tumor, Peking University People's Hospital, Beijing, China
| | - Wei Guo
- Musculoskeletal Tumor Center, Peking University People's Hospital, Peking University, Beijing, China.,Beijing Key Laboratory of Musculoskeletal Tumor, Peking University People's Hospital, Beijing, China
| | - Wei Wang
- Musculoskeletal Tumor Center, Peking University People's Hospital, Peking University, Beijing, China.,Beijing Key Laboratory of Musculoskeletal Tumor, Peking University People's Hospital, Beijing, China
| | - Zhiqing Zhao
- Musculoskeletal Tumor Center, Peking University People's Hospital, Peking University, Beijing, China.,Beijing Key Laboratory of Musculoskeletal Tumor, Peking University People's Hospital, Beijing, China
| | - Tingting Ren
- Musculoskeletal Tumor Center, Peking University People's Hospital, Peking University, Beijing, China.,Beijing Key Laboratory of Musculoskeletal Tumor, Peking University People's Hospital, Beijing, China
| | - Yi Huang
- Musculoskeletal Tumor Center, Peking University People's Hospital, Peking University, Beijing, China.,Beijing Key Laboratory of Musculoskeletal Tumor, Peking University People's Hospital, Beijing, China
| | - Hongliang Zhang
- Musculoskeletal Tumor Center, Peking University People's Hospital, Peking University, Beijing, China.,Beijing Key Laboratory of Musculoskeletal Tumor, Peking University People's Hospital, Beijing, China
| | - Yiyang Yu
- Musculoskeletal Tumor Center, Peking University People's Hospital, Peking University, Beijing, China.,Beijing Key Laboratory of Musculoskeletal Tumor, Peking University People's Hospital, Beijing, China
| | - Xin Liang
- Musculoskeletal Tumor Center, Peking University People's Hospital, Peking University, Beijing, China.,Beijing Key Laboratory of Musculoskeletal Tumor, Peking University People's Hospital, Beijing, China
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16
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Li R, Shui L, Jia J, Wu C. Construction and Validation of Novel Diagnostic and Prognostic DNA Methylation Signatures for Hepatocellular Carcinoma. Front Genet 2020; 11:906. [PMID: 32922438 PMCID: PMC7456968 DOI: 10.3389/fgene.2020.00906] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Accepted: 07/22/2020] [Indexed: 12/20/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the most prevalent life-threatening human cancers and the leading cause of cancer-related mortality, with increased global incidence within the last decade. Identification of effective diagnostic and prognostic biomarkers would enable reliable risk stratification and efficient screening of high-risk patients, thereby facilitating clinical decision-making. Herein, we performed a comprehensive, robust DNA methylation analysis based on genome-wide DNA methylation profiling. We constructed a diagnostic signature with five DNA methylation markers, which precisely distinguished HCC patients from normal controls. Cox regression and LASSO analysis were applied to construct a prognostic signature with four DNA methylation markers. A one-to-one correlation analysis was carried out between genes of the whole genome and our prognostic signature. Exploration of the biological function and the role of the underlying significantly correlated genes was conducted. A mixed dataset of 463 HCC patients and 253 normal controls, derived from six independent datasets, was used to valid the diagnostic signature. Results showed a specificity of 96.84% and sensitivity of 96.77%. Class scores for the diagnostic signature were significantly different between normal controls, individuals with liver diseases, and HCC patients. The present signature has the potential to serve as a biomarker to monitor health in normal controls. Additionally, HCC patients were successfully separated into low-risk and high-risk groups by the prognostic signature, with a better prognosis for patients in the low-risk group. Kaplan-Meier and ROC analysis confirmed that the prognostic signature performed well. We found eight of the top ten genes to positively correlate with risk scores of the prognostic signature, and to be involved in cell cycle regulation. This eight-gene panel also served as a prognostic signature. The robust evidence presented in this study therefore demonstrates the effectiveness of the prognostic signature. In summary, we constructed diagnostic and prognostic signatures, which have potential for use in diagnosis, surveillance, and prognostic prediction for HCC patients. Eight genes that were significantly and positively correlated with the prognostic signature were strongly associated with cell cycle processes. Therefore, the prognostic signature can be used as a guide by which to measure responsiveness to cell-cycle-targeting agents.
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Affiliation(s)
- Ran Li
- Life Sciences Institute, Zhejiang University, Hangzhou, China
| | - Liyan Shui
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Junling Jia
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Innovation Center for Precision Medicine, Zhongtong-Lanbo Diagnostic Ltd, Beijing, China
| | - Chao Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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17
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Cui H, Xu L, Li Z, Hou KZ, Che XF, Liu BF, Liu YP, Qu XJ. Integrated bioinformatics analysis for the identification of potential key genes affecting the pathogenesis of clear cell renal cell carcinoma. Oncol Lett 2020; 20:1573-1584. [PMID: 32724399 PMCID: PMC7377202 DOI: 10.3892/ol.2020.11703] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 04/15/2020] [Indexed: 12/17/2022] Open
Abstract
Clear cell renal cell carcinoma (CCRCC) is a typical type of RCC with the worst prognosis among the common epithelial neoplasms of the kidney. However, its molecular pathogenesis remains unknown. Therefore, the aim of the present study was to screen for effective and potential pathogenic biomarkers of CCRCC. The gene expression profile of the GSE16441, GSE36895, GSE40435, GSE46699, GSE66270 and GSE71963 datasets were downloaded from the Gene Expression Omnibus database. First, the limma package in R language was used to identify differentially expressed genes (DEGs) in each dataset. The robust and strong DEGs were explored using the robust rank aggregation method. A total of 980 markedly robust DEGs were identified (429 upregulated and 551 downregulated). According to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, these DEGs exhibited an obvious enrichment in various cancer-related biological pathways and functions. The Search Tool for the Retrieval of Interacting Genes/Proteins database was used for the construction of a protein-protein interaction (PPI) network, the Cytoscape MCODE plug-in for module analysis and the cytoHubba plug-in to identify hub genes from the aforementioned DEGs. A total of four key modules were identified in the PPI network. A total of six hub genes, including C-X-C motif chemokine ligand 12, bradykinin receptor B2, adenylate cyclase 7, calcium sensing receptor (CASR), kininogen 1 and lysophosphatidic acid receptor 5, were identified. The DEG results of the hub genes were verified using The Cancer Genome Atlas database, and CASR was found to be significantly associated with the prognosis of patients with CCRCC. In conclusion, the present study provided new insight and potential biomarkers for the diagnosis and prognosis of CCRCC.
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Affiliation(s)
- Hao Cui
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China
| | - Lei Xu
- Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China
| | - Zhi Li
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China
| | - Ke-Zuo Hou
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China
| | - Xiao-Fang Che
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China
| | - Bo-Fang Liu
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China
| | - Yun-Peng Liu
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China
| | - Xiu-Juan Qu
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China
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18
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Ma X, Mo M, Tan HJJ, Tan C, Zeng X, Zhang G, Huang D, Liang J, Liu S, Qiu X. LINC02499, a novel liver-specific long non-coding RNA with potential diagnostic and prognostic value, inhibits hepatocellular carcinoma cell proliferation, migration, and invasion. Hepatol Res 2020; 50:726-740. [PMID: 32039538 DOI: 10.1111/hepr.13491] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 01/07/2020] [Accepted: 01/29/2020] [Indexed: 12/14/2022]
Abstract
AIM Liver-specific non-coding RNAs have been reported to play crucial roles in hepatocellular carcinoma (HCC). We investigated the possible biological performance of a novel liver-specific long non-coding RNA, LINC02499, in HCC. METHODS The association between LINC02499 expression and HCC was evaluated based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and then confirmed in a HCC cohort by quantitative real-time polymerase chain reaction. The effects of LINC02499 on HCC cells were verified by gain- and loss-of-function assays. Pathway enrichment analyses were used to explore the potential mechanism of LINC02499 in HCC. RESULTS LINC02499 expression was remarkably decreased in HCC tissues compared to adjacent non-tumor tissues based on TCGA (P < 0.001) and GEO databases (P < 0.001) and our HCC cohort (P < 0.001). Decreased LINC02499 was also significantly associated with poorer overall survival in both the TCGA database (P = 0.009) and our HCC cohort (P = 0.002). Furthermore, the receiver operating characteristic analysis indicated that LINC02499 showed a good performance in HCC diagnosis (area under the curve = 0.879, P < 0.001), and both sensitivity and specificity were 83.8%. In addition, up- and downregulated LINC02499 significantly impacted proliferation, migration, and invasion abilities of HCC cells in vitro. Pathway enrichment analyses revealed that the potential target genes of LINC02499 were involved in "Complement and coagulation cascades" and "Butanoate metabolism" pathways. CONCLUSION LINC02499 could be a potential novel diagnostic and prognostic biomarker for HCC patients, and it could exert a tumor suppressor role in the progression of HCC.
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Affiliation(s)
- Xiaoyun Ma
- Department of Epidemiology, School of Public Health, Guangxi Medical University, Nanning, China
| | - Meile Mo
- Department of Epidemiology, School of Public Health, Guangxi Medical University, Nanning, China
| | | | - Chao Tan
- Department of Epidemiology, School of Public Health, Guangxi Medical University, Nanning, China
| | - Xiaoyun Zeng
- Department of Epidemiology, School of Public Health, Guangxi Medical University, Nanning, China
| | - Guoqiang Zhang
- Hospital-acquired Infection Control Department, Affiliated Cancer Hospital of Guangxi Medical University, Nanning, China
| | - Dongping Huang
- Department of Sanitary Chemistry, School of Public Health, Guangxi Medical University, Nanning, China
| | - Jun Liang
- Department of Epidemiology, School of Public Health, Guangxi Medical University, Nanning, China
| | - Shun Liu
- Department of Epidemiology, School of Public Health, Guangxi Medical University, Nanning, China
| | - Xiaoqiang Qiu
- Department of Epidemiology, School of Public Health, Guangxi Medical University, Nanning, China
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Liu Y, Chen TY, Yang ZY, Fang W, Wu Q, Zhang C. Identification of hub genes in papillary thyroid carcinoma: robust rank aggregation and weighted gene co-expression network analysis. J Transl Med 2020; 18:170. [PMID: 32299435 PMCID: PMC7161219 DOI: 10.1186/s12967-020-02327-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 04/02/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Papillary thyroid carcinoma (PTC), which is the most common endocrine malignancy, has been steadily increasing worldwide in incidence over the years, while mechanisms underlying the pathogenesis and diagnostic for PTC are incomplete. The purpose of this study is to identify potential biomarkers for diagnosis of PTC, and provide new insights into pathogenesis of PTC. METHODS Based on weighted gene co-expression network analysis, Robust Rank Aggregation, functional annotation, GSEA and DNA methylation, were employed for investigating potential biomarkers for diagnosis of PTC. RESULTS Black and turquoise modules were identified in the gene co-expression network constructed by 1807 DEGs that from 6 eligible gene expression profiles of Gene Expression Omnibus database based on Robust Rank Aggregation and weighted gene co-expression network analysis. Hub genes were significantly down-regulated and the expression levels of the hub genes were different in different stages in hub gene verification. ROC curves indicated all hub genes had good diagnostic value for PTC (except for ABCA6 AUC = 89.5%, the 15 genes with AUC > 90%). Methylation analysis showed that hub gene verification ABCA6, ACACB, RMDN1 and TFPI were identified as differentially methylated genes, and the decreased expression level of these genes may relate to abnormal DNA methylation. Moreover, the expression levels of 8 top hub genes were correlated with tumor purity and tumor-infiltrating immune cells. These findings, including functional annotations and GSEA provide new insights into pathogenesis of PTC. CONCLUSIONS The hub genes and methylation of hub genes may as potential biomarkers provide new insights for diagnosis of PTC, and all these findings may be the direction to study the mechanisms underlying of PTC in the future.
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Affiliation(s)
- Yang Liu
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, No. 32, South Renmin Road, Shiyan, 442000, China
| | - Ting-Yu Chen
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, No. 32, South Renmin Road, Shiyan, 442000, China
| | - Zhi-Yan Yang
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, No. 32, South Renmin Road, Shiyan, 442000, China
| | - Wei Fang
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, No. 32, South Renmin Road, Shiyan, 442000, China
| | - Qian Wu
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, Shaanxi, People's Republic of China.
| | - Chao Zhang
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, No. 32, South Renmin Road, Shiyan, 442000, China.
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20
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Identification of hub genes in hepatocellular carcinoma using integrated bioinformatic analysis. Aging (Albany NY) 2020; 12:5439-5468. [PMID: 32213663 PMCID: PMC7138582 DOI: 10.18632/aging.102969] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 02/19/2020] [Indexed: 12/24/2022]
Abstract
The molecular mechanisms underlying hepatocellular carcinoma (HCC) progression remain largely undefined. Here, we identified 176 commonly upregulated genes in HCC tissues based on three Gene Expression Omnibus datasets and The Cancer Genome Atlas (TCGA) cohort. We integrated survival and methylation analyses to further obtain 12 upregulated genes for validation. These genes were overexpressed in HCC tissues at the transcription and protein levels, and increased mRNA levels were related to higher tumor grades and cancer stages. The expression of all markers was negatively associated with overall and disease-free survival in HCC patients. Most of these hub genes can promote HCC proliferation and/or metastasis. These 12 hub genes were also overexpressed and had strong prognostic value in many other cancer types. Methylation and gene copy number analyses indicated that the upregulation of these hub genes was probably due to hypomethylation or increased gene copy numbers. Further, the methylation levels of three genes, KPNA2, MCM3, and LRRC1, were associated with HCC clinical features. Moreover, the levels of most hub genes were related to immune cell infiltration in HCC microenvironments. Finally, we identified three upregulated genes (KPNA2, TARBP1, and RNASEH2A) that could comprehensively and accurately provide diagnostic and prognostic value for HCC patients.
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21
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Wu Y, Zhang S, Yan J. IRF1 association with tumor immune microenvironment and use as a diagnostic biomarker for colorectal cancer recurrence. Oncol Lett 2020; 19:1759-1770. [PMID: 32194669 PMCID: PMC7039159 DOI: 10.3892/ol.2020.11289] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 11/27/2019] [Indexed: 12/13/2022] Open
Abstract
Colorectal cancer (CRC) is considered to be one of the most lethal cancer types globally, and its recurrence is a major treatment challenge. Identifying the factors involved when determining the risk of CRC recurrence is required to improve personalized therapy for patients with CRC. Based on the GSE39582 dataset, the present study demonstrated that a higher ratio of M1 macrophages and activated memory CD4+ T cells indicated a better recurrence-free survival (RFS) time for CRC, using CIBERSORT and Pearson's correlation analysis. Through weighted correlation network analysis (WGCNA), an immune-associated module was identified that was significantly positively correlated with the ratio of M1 macrophages and activated memory CD4+ T cells. In this module, using WGCNA and a protein-protein interaction network, interferon regulatory factor 1 (IRF1), chemokine ligand 5, ubiquitin/ISG15-conjugating enzyme E2 L6, guanylate binding protein 1 and interleukin 2 receptor subunit beta were identified as hub genes. Among these genes, univariate Cox and multivariate Cox analysis revealed that IRF1 may be a potential diagnostic biomarker for RFS in patients with CRC. This was further validated using The Cancer Genome Atlas data. Gene set enrichment analysis demonstrated that IRF1 influenced the genes and pathways that are associated with immune cell recruitment and activation. Additionally, the DNA methylation of cg27587780 and cg15375424 CpG sites in the IRF1 gene region was indicated to be negatively correlated with IRF1 mRNA expression and positively correlated with the recurrence of CRC. Collectively, the results of the present study demonstrated that IRF1 may be a potential diagnostic biomarker for RFS in patients with CRC.
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Affiliation(s)
- Yanfang Wu
- Department of Gastroenterology, The Fourth People's Hospital of Shaanxi, Xi'an, Shanxi 710032, P.R. China
| | - Shuju Zhang
- Hunan Children's Research Institute, Hunan Children's Hospital, University of South China, Changsha, Hunan 410007, P.R. China
| | - Jun Yan
- Center of Hepatobiliary Pancreatic Disease, Beijing Tsinghua Changgung Hospital, Beijing 102218, P.R. China
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22
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Deng X, Bi Q, Chen S, Chen X, Li S, Zhong Z, Guo W, Li X, Deng Y, Yang Y. Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma. Front Mol Biosci 2020; 7:611626. [PMID: 33505990 PMCID: PMC7831610 DOI: 10.3389/fmolb.2020.611626] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 12/02/2020] [Indexed: 12/21/2022] Open
Abstract
Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.
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Affiliation(s)
- Xiaoyu Deng
- Institute of Materia Medica, College of Pharmacy, Army Medical University (Third Military Medical University), Chongqing, China
| | - Qinghua Bi
- Institute of Materia Medica, College of Pharmacy, Army Medical University (Third Military Medical University), Chongqing, China
- *Correspondence: Yao Yang
| | - Shihan Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, China
| | - Xianhua Chen
- Diagosis and Treatment Center for Servicemen, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, China
| | - Shuhui Li
- Department of Clinical Biochemistry, Faculty of Pharmacy and Laboratory Medicine, Army Medical University (Third Military Medical University), Chongqing, China
| | - Zhaoyang Zhong
- Cancer Center, Daping Hospital and Research Institute of Surgery, Army Medical University (Third Military Medical University), Chongqing, China
| | - Wei Guo
- Department of Pharmacy, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Xiaohui Li
- Institute of Materia Medica, College of Pharmacy, Army Medical University (Third Military Medical University), Chongqing, China
- Youcai Deng
| | - Youcai Deng
- Institute of Materia Medica, College of Pharmacy, Army Medical University (Third Military Medical University), Chongqing, China
- Xiaohui Li
| | - Yao Yang
- Institute of Materia Medica, College of Pharmacy, Army Medical University (Third Military Medical University), Chongqing, China
- Qinghua Bi
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23
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Kong J, Wang T, Shen S, Zhang Z, Yang X, Wang W. A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma. PeerJ 2019; 7:e7942. [PMID: 31687273 PMCID: PMC6825747 DOI: 10.7717/peerj.7942] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 09/23/2019] [Indexed: 02/05/2023] Open
Abstract
Liver resection surgery is the most commonly used treatment strategy for patients diagnosed with hepatocellular carcinoma (HCC). However, there is still a chance for recurrence in these patients despite the survival benefits of this procedure. This study aimed to explore recurrence-related genes (RRGs) and establish a genomic-clinical nomogram for predicting postoperative recurrence in HCC patients. A total of 123 differently expressed genes and three RRGs (PZP, SPP2, and PRC1) were identified from online databases via Cox regression and LASSO logistic regression analyses and a gene-based risk model containing RRGs was then established. The Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves showed that the model performed well. Finally, a genomic-clinical nomogram incorporating the gene-based risk model, AJCC staging system, and Eastern Cooperative Oncology Group performance status was constructed to predict the 1-, 2-, and 3-year recurrence-free survival rates (RFS) for HCC patients. The C-index, ROC analysis, and decision curve analysis were good indicators of the nomogram’s performance. In conclusion, we identified three reliable RRGs associated with the recurrence of cancer and constructed a nomogram that performed well in predicting RFS for HCC patients. These findings could enrich our understanding of the mechanisms for HCC recurrence, help surgeons predict patients’ prognosis, and promote HCC treatment.
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Affiliation(s)
- Junjie Kong
- Department of Liver Surgery & Liver Transplantation Center, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Tao Wang
- Department of Liver Surgery & Liver Transplantation Center, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Shu Shen
- Department of Liver Surgery & Liver Transplantation Center, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Zifei Zhang
- Department of Liver Surgery & Liver Transplantation Center, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Xianwei Yang
- Department of Liver Surgery & Liver Transplantation Center, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Wentao Wang
- Department of Liver Surgery & Liver Transplantation Center, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
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Feng J, Hu J, Xia Y. Identification of RAD54 homolog B as a promising therapeutic target for breast cancer. Oncol Lett 2019; 18:5350-5362. [PMID: 31612045 PMCID: PMC6781656 DOI: 10.3892/ol.2019.10854] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 07/26/2019] [Indexed: 12/24/2022] Open
Abstract
Breast cancer is a recognized threat to the health of women globally. Due to the lack of the knowledge about the molecular pathogenesis of breast cancer, therapeutic strategies remain inadequate, especially for aggressive breast cancer. In the present study, sequential bioinformatics analysis was performed using data from the GSE20711 dataset, and the results demonstrated that three genes may impact the survival of patients with breast cancer. One of these genes, RAD54 homolog B (RAD54B), may be a potential prognostic factor for breast cancer. A signature was established that could evaluate the overall survival for patients with breast cancer based on the risk score calculated from RAD54B expression and the Tumor-Node-Metastasis (TNM) stage [risk score=expRAD54B × 0.236 + TNM stage (I/II=0 or III/IV=1) ×1.025]. In addition, based on the GSE85871 dataset and inhibitory assay, the study identified a natural compound, Japonicone A, which may reduce the proliferation of breast cancer cells by inhibiting the expression of RAD54B. Overall, the present study identified a novel candidate gene and a candidate compound as promising therapeutic targets for the treatment of breast cancer.
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Affiliation(s)
- Jing Feng
- Institute of Chemical Component Analysis of Traditional Chinese Medicine, Chongqing Medical and Pharmaceutical College, Chongqing 401331, P.R. China
- Engineering Research Center of Pharmaceutical Sciences, Chongqing 401331, P.R. China
| | - Juanjuan Hu
- Institute of Chemical Component Analysis of Traditional Chinese Medicine, Chongqing Medical and Pharmaceutical College, Chongqing 401331, P.R. China
- Engineering Research Center of Pharmaceutical Sciences, Chongqing 401331, P.R. China
| | - Ying Xia
- Institute of Chemical Component Analysis of Traditional Chinese Medicine, Chongqing Medical and Pharmaceutical College, Chongqing 401331, P.R. China
- Engineering Research Center of Pharmaceutical Sciences, Chongqing 401331, P.R. China
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25
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Mo X, Liu W, Yang Y, Imani S, Lu S, Dan G, Nie X, Yan J, Zhan R, Li X, Deng Y, Chen B, Cai Y. NCF2, MYO1F, S1PR4, and FCN1 as potential noninvasive diagnostic biomarkers in patients with obstructive coronary artery: A weighted gene co-expression network analysis. J Cell Biochem 2019; 120:18219-18235. [PMID: 31245869 PMCID: PMC6771964 DOI: 10.1002/jcb.29128] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 05/05/2019] [Accepted: 05/07/2019] [Indexed: 12/11/2022]
Abstract
This study aims to explore the predictive noninvasive biomarker for obstructive coronary artery disease (CAD). By using the data set GSE90074, weighted gene co-expression network analysis (WGCNA), and protein-protein interactive network, construction of differentially expressed genes in peripheral blood mononuclear cells was conducted to identify the most significant gene clusters associated with obstructive CAD. Univariate and multivariate stepwise logistic regression analyses and receiver operating characteristic analysis were used to predicate the diagnostic accuracy of biomarker candidates in the detection of obstructive CAD. Furthermore, functional prediction of candidate gene biomarkers was further confirmed in ST-segment elevation myocardial infarction (STEMI) patients or stable CAD patients by using the datasets of GSE62646 and GSE59867. We found that the blue module discriminated by WGCNA contained 13 hub-genes that could be independent risk factors for obstructive CAD (P < .05). Among these 13 hub-genes, a four-gene signature including neutrophil cytosol factor 2 (NCF2, P = .025), myosin-If (MYO1F, P = .001), sphingosine-1-phosphate receptor 4 (S1PR4, P = .015), and ficolin-1 (FCN1, P = .012) alone or combined with two risk factors (male sex and hyperlipidemia) may represent potential diagnostic biomarkers in obstructive CAD. Furthermore, the messenger RNA levels of NCF2, MYO1F, S1PR4, and FCN1 were higher in STEMI patients than that in stable CAD patients, although S1PR4 showed no statistical difference (P > .05). This four-gene signature could also act as a prognostic biomarker to discriminate STEMI patients from stable CAD patients. These findings suggest a four-gene signature (NCF2, MYO1F, S1PR4, and FCN1) alone or combined with two risk factors (male sex and hyperlipidemia) as a promising prognostic biomarker in the diagnosis of STEMI. Well-designed cohort studies should be implemented to warrant the diagnostic value of these genes in clinical purpose.
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Affiliation(s)
- Xian‐Gang Mo
- Department of GeriatricsThe Affiliated Hospital of Guizhou Medical UniversityGuiyangGuizhouChina
| | - Wei Liu
- Health Physical Examination CenterThe Affiliated Hospital of Qingdao UniversityQingdaoShandongChina
| | - Yao Yang
- Institute of Materia Medica, College of PharmacyArmy Medical University (Third Military Medical University)ChongqingChina
| | - Saber Imani
- Department of OncologyThe Affiliated Hospital of Southwest Medical UniversityLuzhouSichuanChina
| | - Shan Lu
- Institute of Materia Medica, College of PharmacyArmy Medical University (Third Military Medical University)ChongqingChina
| | - Guorong Dan
- Institute of Materia Medica, College of PharmacyArmy Medical University (Third Military Medical University)ChongqingChina
| | - Xuqiang Nie
- Institute of Materia Medica, College of PharmacyArmy Medical University (Third Military Medical University)ChongqingChina
| | - Jun Yan
- Center of Hepatobiliary Pancreatic DiseaseBeijing Tsinghua Changgung HospitalBeijingChina
| | - Rixing Zhan
- Institute of Burn Research, Southwest HospitalArmy Medical University (Third Military Medical University)ChongqingChina
| | - Xiaohui Li
- Institute of Materia Medica, College of PharmacyArmy Medical University (Third Military Medical University)ChongqingChina
| | - Youcai Deng
- Institute of Materia Medica, College of PharmacyArmy Medical University (Third Military Medical University)ChongqingChina
| | - Bingbo Chen
- Laboratory Animal CenterArmy Medical University (Third Military Medical University)ChongqingChina
| | - Yue Cai
- Department of Cardiology, Xijing HospitalFourth Military Medical UniversityXi'anShaanxiChina
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26
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Li G, Xu W, Zhang L, Liu T, Jin G, Song J, Wu J, Wang Y, Chen W, Zhang C, Chen X, Ding Z, Zhu P, Zhang B. Development and validation of a CIMP-associated prognostic model for hepatocellular carcinoma. EBioMedicine 2019; 47:128-141. [PMID: 31492561 PMCID: PMC6796541 DOI: 10.1016/j.ebiom.2019.08.064] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 08/26/2019] [Accepted: 08/27/2019] [Indexed: 12/23/2022] Open
Abstract
Background CpG island methylator phenotype (CIMP), a common biological phenomenon characterized by a subset of concurrently methylated genes, can have an influence on the progression of multiple cancers. However, the potential mechanism of CIMP in hepatocarcinogenesis and its clinical relevance remains only partially understood. Methods: We used a methylation array from the cancer genome atlas (TCGA) to stratify HCC patients into different CIMP subtypes, and evaluated their correlation with clinical characteristics. In addition, mutation, CNV, and transcriptome profiles were also utilized to evaluate the distinctive genomic patterns correlated with CIMP. Finally, a CIMP-associated prognostic model (CPM) was trained and validated using four independent datasets. Findings A subgroup of patients was identified as having CIMP-H, which was associated with worse OS and DFS. Gene enrichment analysis indicated that the terms “liver cancer with EPCAM up”, “tumor invasiveness up”, “methyltransferase complex”, and “translational initiation” were enriched in CIMP-H subgroup. Notably, somatic mutation analysis indicated that CIMP-H patients presented with a higher mutation burden of BRD4, DDIAS and NOX1. Moreover, four CPM associated genes could significantly categorize patients into low- and high-risk groups in the training dataset and another 3 independent validation datasets. Finally, a nomogram incorporating a classifier based on four mRNAs, pathological M stage and CIMP status was established, which showed a favorable discriminating ability and might contribute to clinical decision-making for HCC. Interpretation Our work highlights the potential clinical application value of CPM in predicting the overall survival of HCC patients and the mechanisms underlying the role of CIMP in hepatocarcinogenesis. Fund This work was supported by the State Key Project on Infectious Diseases of China (2018ZX10723204-003), the National Nature Science Foundation of China (Nos. 81874065, 81500565, 81874149, 81572427, and 81401997), the Hepato-Biliary-Pancreatic Malignant Tumor Investigation Fund of Chen Xiao-ping Foundation for the Development of Science and Technology of Hubei Province (CXPJJH11800001-2018356).
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Affiliation(s)
- Ganxun Li
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weiqi Xu
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lu Zhang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tongtong Liu
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guannan Jin
- Institute of Nephrology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jia Song
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jingjing Wu
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuwei Wang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weixun Chen
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chuanhan Zhang
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoping Chen
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zeyang Ding
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Peng Zhu
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Bixiang Zhang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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27
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Song ZY, Chao F, Zhuo Z, Ma Z, Li W, Chen G. Identification of hub genes in prostate cancer using robust rank aggregation and weighted gene co-expression network analysis. Aging (Albany NY) 2019; 11:4736-4756. [PMID: 31306099 PMCID: PMC6660050 DOI: 10.18632/aging.102087] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 07/04/2019] [Indexed: 12/20/2022]
Abstract
The pathogenic mechanisms of prostate cancer (PCa) remain to be defined. In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate 10 eligible PCa microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between tumor samples and normal, matched specimens. To explore potential associations between gene sets and PCa clinical features and to identify hub genes, we utilized WGCNA to construct gene co-expression networks incorporating the DEGs screened with the use of RRA. From the key module, we selected LMNB1, TK1, ZWINT, and RACGAP1 for validation. We found that these genes were up-regulated in PCa samples, and higher expression levels were associated with higher Gleason scores and tumor grades. Moreover, ROC and K-M plots indicated these genes had good diagnostic and prognostic value for PCa. On the other hand, methylation analyses suggested that the abnormal up-regulation of these four genes likely resulted from hypomethylation, while GSEA and GSVA for single hub gene revealed they all had a close association with proliferation of PCa cells. These findings provide new insight into PCa pathogenesis, and identify LMNB1, TK1, RACGAP1 and ZWINT as candidate biomarkers for diagnosis and prognosis of PCa.
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Affiliation(s)
- Zhen-yu Song
- Department of Urology, Jinshan Hospital of Fudan University, Shanghai, China
| | - Fan Chao
- Department of Urology, Jinshan Hospital of Fudan University, Shanghai, China
| | - Zhiyuan Zhuo
- Department of Urology, Jinshan Hospital of Fudan University, Shanghai, China
| | - Zhe Ma
- Department of Urology, Jinshan Hospital of Fudan University, Shanghai, China
| | - Wenzhi Li
- Department of Urology, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Gang Chen
- Department of Urology, Jinshan Hospital of Fudan University, Shanghai, China
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Qiao GJ, Chen L, Wu JC, Li ZR. Identification of an eight-gene signature for survival prediction for patients with hepatocellular carcinoma based on integrated bioinformatics analysis. PeerJ 2019; 7:e6548. [PMID: 30918751 PMCID: PMC6431139 DOI: 10.7717/peerj.6548] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 01/25/2019] [Indexed: 11/25/2022] Open
Abstract
Background Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related death worldwide. Despite recent advances in imaging techniques and therapeutic intervention for HCC, the low overall 5-year survival rate of HCC patients remains unsatisfactory. This study aims to find a gene signature to predict clinical outcomes in HCC. Methods Bioinformatics analysis including Cox’s regression analysis, Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analysis and the random survival forest algorithm were performed to mine the expression profiles of 553 hepatocellular carcinoma (HCC) patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public database. Results We selected a signature comprising eight protein-coding genes (DCAF13, FAM163A, GPR18, LRP10, PVRIG, S100A9, SGCB, and TNNI3K) in the training dataset (AUC = 0.77 at five years, n = 332). The signature stratified patients into high- and low-risk groups with significantly different survival in the training dataset (median 2.20 vs. 8.93 years, log-rank test P < 0.001) and in the test dataset (median 2.68 vs. 4.24 years, log-rank test P = 0.004, n = 221, GSE14520). Further multivariate Cox regression analysis showed that the signature was an independent prognostic factor for patients with HCC. Compared with TNM stage and another reported three-gene model, the signature displayed improved survival prediction power in entire dataset (AUC signature = 0.66 vs. AUC TNM = 0.64 vs. AUC gene model = 0.60, n = 553). Stratification analysis shows that it can be used as an auxiliary marker for many traditional staging models. Conclusions We constructed an eight-gene signature that can be a novel prognostic marker to predict the survival of HCC patients.
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Affiliation(s)
- Guo-Jie Qiao
- Institute of Tropical Agriculture and Forestry, Hainan University, Hainkou, China.,Department of Hepatobiliary Surgery, Hainan Provincial People's Hospital, Hainan Medical College, Hainkou, China
| | - Liang Chen
- Department of Hepatobiliary Surgery, Hainan Provincial People's Hospital, Hainan Medical College, Hainkou, China
| | - Jin-Cai Wu
- Department of Hepatobiliary Surgery, Hainan Provincial People's Hospital, Hainan Medical College, Hainkou, China
| | - Zhou-Ri Li
- Institute of Tropical Agriculture and Forestry, Hainan University, Hainkou, China.,Department of Hepatobiliary Surgery, Hainan Provincial People's Hospital, Hainan Medical College, Hainkou, China
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29
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Long J, Wang A, Bai Y, Lin J, Yang X, Wang D, Yang X, Jiang Y, Zhao H. Development and validation of a TP53-associated immune prognostic model for hepatocellular carcinoma. EBioMedicine 2019; 42:363-374. [PMID: 30885723 PMCID: PMC6491941 DOI: 10.1016/j.ebiom.2019.03.022] [Citation(s) in RCA: 215] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 03/07/2019] [Accepted: 03/08/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND TP53 mutation is the most common mutation in hepatocellular carcinoma (HCC), and it affects the progression and prognosis of HCC. We investigated how TP53 mutation regulates the HCC immunophenotype and thus affects the prognosis of HCC. METHODS We investigated TP53 mutation status and RNA expression in different populations and platforms and developed an immune prognostic model (IPM) based on immune-related genes that were differentially expressed between TP53WT and TP53MUT HCC samples. Then, the influence of the IPM on the immune microenvironment in HCC was comprehensively analysed. FINDINGS TP53 mutation resulted in the downregulation of the immune response in HCC. Thirty-seven of the 312 immune response-related genes were differentially expressed based on TP53 mutation status. An IPM was established and validated based on 865 patients with HCC to differentiate patients with a low or high risk of poor survival. A nomogram was also established for clinical application. Functional enrichment analysis showed that the humoral immune response and immune system diseases pathway represented the major function and pathway, respectively, related to the IPM genes. Moreover, we found that the patients in the high-risk group had higher fractions of T cells follicular helper, T cells regulatory (Tregs) and macrophages M0 and presented higher expression of CTLA-4, PD-1 and TIM-3 than the low-risk group. INTERPRETATION TP53 mutation is strongly related to the immune microenvironment in HCC. Our IPM, which is sensitive to TP53 mutation status, may have important implications for identifying subgroups of HCC patients with low or high risk of unfavourable survival. FUND: This work was supported by the International Science and Technology Cooperation Projects (2016YFE0107100), the Capital Special Research Project for Health Development (2014-2-4012), the Beijing Natural Science Foundation (L172055 and 7192158), the National Ten Thousand Talent Program, the Fundamental Research Funds for the Central Universities (3332018032), and the CAMS Innovation Fund for Medical Science (CIFMS) (2017-I2M-4-003 and 2018-I2M-3-001).
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Affiliation(s)
- Junyu Long
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Anqiang Wang
- Department of Gastrointestinal Surgery, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital & Institute, China
| | - Yi Bai
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jianzhen Lin
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xu Yang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Dongxu Wang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xiaobo Yang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | | | - Haitao Zhao
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
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30
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Zhang X, Kang C, Li N, Liu X, Zhang J, Gao F, Dai L. Identification of special key genes for alcohol-related hepatocellular carcinoma through bioinformatic analysis. PeerJ 2019; 7:e6375. [PMID: 30755830 PMCID: PMC6368834 DOI: 10.7717/peerj.6375] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 12/31/2018] [Indexed: 02/06/2023] Open
Abstract
Background Alcohol-related hepatocellular carcinoma (HCC) was reported to be diagnosed at a later stage, but the mechanism was unknown. This study aimed to identify special key genes (SKGs) during alcohol-related HCC development and progression. Methods The mRNA data of 369 HCC patients and the clinical information were downloaded from the Cancer Genome Atlas project (TCGA). The 310 patients with certain HCC-related risk factors were included for analysis and divided into seven groups according to the risk factors. Survival analyses were applied for the HCC patients of different groups. The patients with hepatitis B virus or hepatitis C virus infection only were combined into the HCC-V group for further analysis. The differentially expressed genes (DEGs) between the HCCs with alcohol consumption only (HCC-A) and HCC-V tumors were identified through limma package in R with cutoff criteria│log2 fold change (logFC)|>1.0 and p < 0.05. The DEGs between eight alcohol-related HCCs and their paired normal livers of GSE59259 from the Gene Expression Omnibus (GEO) were identified through GEO2R (a built-in tool in GEO database) with cutoff criteria |logFC|> 2.0 and adj.p < 0.05. The intersection of the two sets of DEGs was considered SKGs which were then investigated for their specificity through comparisons between HCC-A and other four HCC groups. The SKGs were analyzed for their correlations with HCC-A stage and grade and their prognostic power for HCC-A patients. The expressional differences of the SKGs in the HCCs in whole were also investigated through Gene Expression Profiling Interactive Analysis (GEPIA). The SKGs in HCC were validated through Oncomine database analysis. Results Pathological stage is an independent prognostic factor for HCC patients. HCC-A patients were diagnosed later than HCC patients with other risk factors. Ten SKGs were identified and nine of them were confirmed for their differences in paired samples of HCC-A patients. Three (SLC22A10, CD5L, and UROC1) and four (SLC22A10, UROC1, CSAG3, and CSMD1) confirmed genes were correlated with HCC-A stage and grade, respectively. SPP2 had a lower trend in HCC-A tumors and was negatively correlated with HCC-A stage and grade. The SKGs each was differentially expressed between HCC-A and at least one of other HCC groups. CD5L was identified to be favorable prognostic factor for overall survival while CSMD1 unfavorable prognostic factor for disease-free survival for HCC-A patients and HCC patients in whole. Through Oncomine database, the dysregulations of the SKGs in HCC and their clinical significance were confirmed. Conclusion The poor prognosis of HCC-A patients might be due to their later diagnosis. The SKGs, especially the four stage-correlated genes (CD5L, SLC22A10, UROC1, and SPP2) might play important roles in HCC development, especially alcohol-related HCC development and progression. CD5L might be useful for overall survival and CSMD1 for disease-free survival predication in HCC, especially alcohol-related HCC.
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Affiliation(s)
| | | | | | - Xiaoli Liu
- Henan Province People's Hospital, Zhengzhou, China
| | | | | | - Liping Dai
- Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
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Yue C, Ren Y, Ge H, Liang C, Xu Y, Li G, Wu J. Comprehensive analysis of potential prognostic genes for the construction of a competing endogenous RNA regulatory network in hepatocellular carcinoma. Onco Targets Ther 2019; 12:561-576. [PMID: 30679912 PMCID: PMC6338110 DOI: 10.2147/ott.s188913] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is an extremely common malignant tumor with worldwide prevalence. The aim of this study was to identify potential prognostic genes and construct a competing endogenous RNA (ceRNA) regulatory network to explore the mechanisms underlying the development of HCC. METHODS Integrated analysis was used to identify potential prognostic genes in HCC with R software based on the GSE14520, GSE17548, GSE19665, GSE29721, GSE60502, and the Cancer Genome Atlas databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway-enrichment analyses were performed to explore the molecular mechanisms of potential prognostic genes. Differentially expressed miRNAs (DEMs) and lncRNAs (DELs) were screened based on the Cancer Genome Atlas database. An lncRNA-miRNA-mRNA ceRNA regulatory network was constructed based on information about interactions derived from the miRcode, TargetScan, miRTarBase, and miRDB databases. RESULTS A total of 152 potential prognostic genes were screened that were differentially expressed in HCC tissue and significantly associated with overall survival of HCC patients. There were 13 key potential prognostic genes in the ceRNA regulatory network: eleven upregulated genes (CCNB1, CEP55, CHEK1, EZH2, KPNA2, LRRC1, PBK, RRM2, SLC7A11, SUCO, and ZWINT) and two downregulated genes (ACSL1 and CDC37L1) whose expression might be regulated by eight DEMs and 61 DELs. Kaplan-Meier curve analysis showed that nine DELs (AL163952.1, AL359878.1, AP002478.1, C2orf48, C10orf91, CLLU1, CLRN1-AS1, ERVMER61-1, and WARS2-IT1) in the ceRNA regulatory network were significantly associated with HCC-patient prognoses. CONCLUSION This study identified potential prognostic genes and constructed an lncRNA- miRNA-mRNA ceRNA regulatory network of HCC, which not only has important clinical significance for early diagnoses but also provides effective targets for HCC treatments and could provide new insights for HCC-interventional strategies.
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Affiliation(s)
- Chaosen Yue
- Department of General Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China, ;
| | - Yaoyao Ren
- Department of Anesthesiology, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Hua Ge
- Department of General Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China, ;
| | - Chaojie Liang
- Department of General Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China, ;
| | - Yingchen Xu
- Department of General Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China, ;
| | - Guangming Li
- Department of General Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China, ;
| | - Jixiang Wu
- Department of General Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China, ;
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