1
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Hu J, Wang S, Li X. A comprehensive review of m 6A research in cervical cancer. Epigenomics 2024; 16:753-773. [PMID: 38639713 PMCID: PMC11318741 DOI: 10.2217/epi-2024-0002] [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: 01/03/2024] [Accepted: 03/27/2024] [Indexed: 04/20/2024] Open
Abstract
Cervical cancer (CC) remains one of the most common malignancies among women worldwide, posing a serious threat to women's health. N6-methyladenosine (m6A) modification, as the most abundant type of RNA methylation modification, and has been found to play a crucial role in various cancers. Current research suggests a close association between RNA m6A modification and the occurrence and progression of CC, encompassing disruptions in m6A levels and its regulatory machinery. This review summarizes the current status of m6A modification research in CC, explores the mechanisms underlying m6A levels and regulators (methyltransferases, demethylases, reader proteins) in CC and examines the application of small-molecule inhibitors of m6A regulators in disease treatment. The findings provide new insights into the future treatment of CC.
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Affiliation(s)
- Jing Hu
- Key Laboratory of Environmental Medicine & Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China
| | - Shizhi Wang
- Key Laboratory of Environmental Medicine & Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China
| | - Xiuting Li
- Department of Public Health, Jiangsu Health Vocational College, Nanjing, 210000, China
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2
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Rodrigues MMDS, Júnior AMP, Fukutani ER, Bergamaschi KB, Araújo-Pereira M, Salgado VR, de Queiroz ATL. The impact of ZIKV infection on gene expression in neural cells over time. PLoS One 2024; 19:e0290209. [PMID: 38512822 PMCID: PMC10956780 DOI: 10.1371/journal.pone.0290209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 11/22/2023] [Indexed: 03/23/2024] Open
Abstract
Zika virus (ZIKV) outbreak caused one of the most significant medical emergencies in the Americas due to associated microcephaly in newborns. To evaluate the impact of ZIKV infection on neuronal cells over time, we retrieved gene expression data from several ZIKV-infected samples obtained at different time point post-infection (pi). Differential gene expression analysis was applied at each time point, with more differentially expressed genes (DEG) identified at 72h pi. There were 5 DEGs (PLA2G2F, TMEM71, PKD1L2, UBD, and TNFAIP3 genes) across all timepoints, which clearly distinguished between infected and healthy samples. The highest expression levels of all five genes were identified at 72h pi. Taken together, our results indicate that ZIKV infection greatly impacts human neural cells at early times of infection, with peak perturbation observed at 72h pi. Our analysis revealed that all five DEGs, in samples of ZIKV-infected human neural stem cells, remained highly upregulated across the timepoints evaluated. Moreover, despite the pronounced inflammatory host response observed throughout infection, the impact of ZIKV is variable over time. Finally, the five DEGs identified herein play prominent roles in infection, and could serve to guide future investigations into virus-host interaction, as well as constitute targets for therapeutic drug development.
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Affiliation(s)
| | | | - Eduardo Rocha Fukutani
- Laboratório de Pesquisa Clínica e Translacional (LPCT), Instituto Gonçalo Moniz, Salvador, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil
| | | | - Mariana Araújo-Pereira
- Laboratório de Pesquisa Clínica e Translacional (LPCT), Instituto Gonçalo Moniz, Salvador, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil
| | | | - Artur Trancoso Lopo de Queiroz
- Laboratório de Pesquisa Clínica e Translacional (LPCT), Instituto Gonçalo Moniz, Salvador, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil
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3
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Zhao S, Francois A, Kidane D. Inhibition of DHODH Enhances Replication-Associated Genomic Instability and Promotes Sensitivity in Endometrial Cancer. Cancers (Basel) 2023; 15:5727. [PMID: 38136273 PMCID: PMC10741824 DOI: 10.3390/cancers15245727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/28/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023] Open
Abstract
Endometrial carcinoma (EC) is the most common gynecological malignancy in the United States. De novo pyrimidine synthesis pathways generate nucleotides that are required for DNA synthesis. Approximately 38% of human endometrial tumors present with an overexpression of human dihydroorotate dehydrogenase (DHODH). However, the role of DHODH in cancer cell DNA replication and its impact on modulating a treatment response is currently unknown. Here, we report that endometrial tumors with overexpression of DHODH are associated with a high mutation count and chromosomal instability. Furthermore, tumors with an overexpression of DHODH show significant co-occurrence with mutations in DNA replication polymerases, which result in a histologically high-grade endometrial tumor. An in vitro experiment demonstrated that the inhibition of DHODH in endometrial cancer cell lines significantly induced replication-associated DNA damage and hindered replication fork progression. Furthermore, endometrial cancer cells were sensitive to the DHODH inhibitor either alone or in combination with the Poly (ADP-ribose) polymerase 1 inhibitor. Our findings may have important clinical implications for utilizing DHODH as a potential target to enhance cytotoxicity in high-grade endometrial tumors.
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Affiliation(s)
- Shengyuan Zhao
- Division of Pharmacology and Toxicology, Dell Pediatric Research Institute, College of Pharmacy, The University of Texas at Austin, 1400 Barbara Jordan Blvd. R1800, Austin, TX 78723, USA
| | - Aaliyah Francois
- Division of Pharmacology and Toxicology, Dell Pediatric Research Institute, College of Pharmacy, The University of Texas at Austin, 1400 Barbara Jordan Blvd. R1800, Austin, TX 78723, USA
| | - Dawit Kidane
- Division of Pharmacology and Toxicology, Dell Pediatric Research Institute, College of Pharmacy, The University of Texas at Austin, 1400 Barbara Jordan Blvd. R1800, Austin, TX 78723, USA
- Department of Physiology and Biophysics, College of Medicine, Howard University, 520 W Street N.W., Washington, DC 20059, USA
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4
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Hao Q, Wu H, Liu E, Wang L. BUB1, BUB1B, CCNA2, and CDCA8, along with miR-524-5p, as clinically relevant biomarkers for the diagnosis and treatment of endometrial carcinoma. BMC Cancer 2023; 23:995. [PMID: 37853361 PMCID: PMC10585751 DOI: 10.1186/s12885-023-11515-9] [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: 03/10/2023] [Accepted: 10/11/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Endometrial carcinoma (EC) is a malignant tumor of the female reproductive tract that has been associated with increased morbidity and mortality. This study aimed to identify biomarkers and potential therapeutic targets for EC. METHODS A publicly available transcriptome data set comprising 587 EC cases was subjected to a comprehensive bioinformatics analysis to identify candidate genes responsible for EC occurrence and development. Next, we used clinical samples and cell experiments for validation. RESULTS A total of 1,617 differentially expressed genes (DEGs) were identified. Analysis of patient survival outcomes revealed that BUB1, BUB1B, CCNA2, and CDCA8 were correlated with prognosis in patients with EC. Moreover, assessment of clinical samples confirmed that BUB1, BUB1B, CCNA2 and CDCA8 were strongly expressed in EC tissues. Additionally, bioinformatics and luciferase reporter assays confirmed that miR-524-5p can target and regulate these four genes. Overexpression of miR-524-5p significantly inhibited EC Ishikawa cells viability, migration and invasion. Inhibition of miR-524-5p showed the opposite results. CONCLUSIONS Expression of miR-524-5p reduced the migration and invasion of Ishikawa EC cells, and decreased BUB1, BUB1B, CCNA2, and CDCA8 expression. miR-524-5p, as well as BUB1, BUB1B, CCNA2, and CDCA8, may be clinically relevant biomarkers for the diagnosis and treatment of EC.
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Affiliation(s)
- Qirong Hao
- Department of Obstetrics and Gynecology, the Second Hospital of Shanxi Medical University, Taiyuan, 030001, China.
| | - Hongqin Wu
- Department of Obstetrics and Gynecology, the Second Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Erniao Liu
- Department of Obstetrics and Gynecology, the Second Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Lina Wang
- Department of Obstetrics and Gynecology, the Second Hospital of Shanxi Medical University, Taiyuan, 030001, China
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5
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Yuan Y, Chen Z, Cai X, He S, Li D, Zhao W. Identification of Hub Genes Correlated With Poor Prognosis for Patients With Uterine Corpus Endometrial Carcinoma by Integrated Bioinformatics Analysis and Experimental Validation. Front Oncol 2021; 11:766947. [PMID: 34868993 PMCID: PMC8639584 DOI: 10.3389/fonc.2021.766947] [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: 08/30/2021] [Accepted: 10/29/2021] [Indexed: 12/13/2022] Open
Abstract
Uterine Corpus Endometrial Carcinoma (UCEC) is one of the most common malignancies of the female genital tract and there remains a major public health problem. Although significant progress has been made in explaining the progression of UCEC, it is still warranted that molecular mechanisms underlying the tumorigenesis of UCEC are to be elucidated. The aim of the current study was to investigate key modules and hub genes related to UCEC pathogenesis, and to explore potential biomarkers and therapeutic targets for UCEC. The RNA-seq dataset and corresponding clinical information for UCEC patients were obtained from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened between 23 paired UCEC tissues and adjacent non-cancerous tissues. Subsequently, the co-expression network of DEGs was determined via weighted gene co-expression network analysis (WGCNA). The Blue and Brown modules were identified to be significantly positively associated with neoplasm histologic grade. The highly connected genes of the two modules were then investigated as potential key factors related to tumor differentiation. Additionally, a protein-protein interaction (PPI) network for all genes in the two modules was constructed to obtain key modules and nodes. 10 genes were identified by both WGCNA and PPI analyses, and it was shown by Kaplan-Meier curve analysis that 6 out of the 10 genes were significantly negatively related to the 5-year overall survival (OS) in patients (AURKA, BUB1, CDCA8, DLGAP5, KIF2C, TPX2). Besides, according to the DEGs from the two modules, lncRNA-miRNA-mRNA and lncRNA-TF-mRNA networks were constructed to explore the molecular mechanism of UCEC-related lncRNAs. 3 lncRNAs were identified as being significantly negatively related to the 5-year OS (AC015849.16, DUXAP8 and DGCR5), with higher expression in UCEC tissues compared to non-tumor tissues. Finally, quantitative Real-time PCR was applied to validate the expression patterns of hub genes. Cell proliferation and colony formation assays, as well as cell cycle distribution and apoptosis analysis, were performed to test the effects of representative hub genes. Altogether, this study not only promotes our understanding of the molecular mechanisms for the pathogenesis of UCEC but also identifies several promising biomarkers in UCEC development, providing potential therapeutic targets for UCEC.
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Affiliation(s)
- Yi Yuan
- Department of Laboratory Medicine, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Zhengzheng Chen
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Xushan Cai
- Department of Clinical Laboratory, Maternal and Child Health Hospital of Jiading District, Shanghai, China.,School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Shengxiang He
- School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Dong Li
- Department of Laboratory Medicine, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Weidong Zhao
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
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6
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Zheng M, Hu Y, Gou R, Li S, Nie X, Li X, Lin B. Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer. MOLECULAR THERAPY-ONCOLYTICS 2021; 22:294-306. [PMID: 34553020 PMCID: PMC8426172 DOI: 10.1016/j.omto.2021.07.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 07/02/2021] [Indexed: 02/06/2023]
Abstract
Uterine corpus endometrial carcinoma locally infiltrates numerous immune cells and other tumor immune microenvironment components. These cells are involved in malignant tumor growth and proliferation and the process of resistance toward immunotherapies. Here, we aimed to develop a tumor immune microenvironment-related prognostic signature for high-risk grade III endometrial carcinoma based on The Cancer Genome Atlas. The signature was systematically correlated with immune infiltration characteristics of the tumor microenvironment. The seven-gene Riskscore signature was robust and performed well in training, testing, and Gene Expression Omnibus-independent cohorts. A nomogram comprising the gene signature accurately predicted patient prognosis, with our model performing better than other endometrial cancer-related signatures. Analysis of the IMvigor210 immunotherapy cohort revealed that subgroups with a low Riskscore had a better prognosis than subgroups with a high Riskscore. Subgroups with a low Riskscore exhibited immune cell infiltration and inflammatory profiles, whereas subgroups with a high Riskscore experienced progressive disease. The receiver operating characteristic curve indicated that risk score, neoantigen, and tumor mutation burden models together accurately predicted treatment response. Taken together, we developed a tumor microenvironment-based seven-gene prognostic stratification system to predict the prognosis of patients with high-risk endometrial cancer and guide more effective immunotherapy strategies.
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Affiliation(s)
- Mingjun Zheng
- Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, Liaoning 110004, China.,Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, China.,Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany
| | - Yuexin Hu
- Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, Liaoning 110004, China.,Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, China
| | - Rui Gou
- Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, Liaoning 110004, China.,Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, China
| | - Siting Li
- Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, Liaoning 110004, China.,Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, China
| | - Xin Nie
- Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, Liaoning 110004, China.,Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, China
| | - Xiao Li
- Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, Liaoning 110004, China.,Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, China
| | - Bei Lin
- Department of Gynecology and Obstetrics, Shengjing Hospital of China Medical University, Liaoning 110004, China.,Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, China
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7
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Lottini T, Iorio J, Lastraioli E, Carraresi L, Duranti C, Sala C, Armenio M, Noci I, Pillozzi S, Arcangeli A. Transgenic mice overexpressing the LH receptor in the female reproductive system spontaneously develop endometrial tumour masses. Sci Rep 2021; 11:8847. [PMID: 33893331 PMCID: PMC8065064 DOI: 10.1038/s41598-021-87492-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 03/18/2021] [Indexed: 11/26/2022] Open
Abstract
The receptor for the luteinizing hormone (LH-R) is aberrantly over expressed in cancers of the reproductive system. To uncover whether LH-R over expression has a causative role in cancer, we generated a transgenic (TG) mouse which overexpresses the human LH-R (hLH-R) in the female reproductive tract, under the control of the oviduct-specific glycoprotein (OGP) mouse promoter (mogp-1). The transgene was highly expressed in the uterus, ovary and liver, but only in the uterus morphological and molecular alterations (increased proliferation and trans-differentiation in the endometrial layer) were detected. A transcriptomic analysis on the uteri of young TG mice showed an up regulation of genes involved in cell cycle control and a down regulation of genes related to the immune system and the metabolism of xenobiotics. Aged TG females developed tumor masses in the uteri, which resembled an Endometrial Cancer (EC). Microarray and immunohistochemistry data indicated the deregulation of signaling pathways which are known to be altered in human ECs. The analysis of a cohort of 126 human ECs showed that LH-R overexpression is associated with early-stage tumors. Overall, our data led support to conclude that LH-R overexpression may directly contribute to trigger the neoplastic transformation of the endometrium.
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Affiliation(s)
- Tiziano Lottini
- Department of Experimental and Clinical Medicine, Section of Internal Medicine, University of Florence, Viale G.B. Morgagni, 50, 50134, Florence, Italy
| | - Jessica Iorio
- Department of Experimental and Clinical Medicine, Section of Internal Medicine, University of Florence, Viale G.B. Morgagni, 50, 50134, Florence, Italy
| | - Elena Lastraioli
- Department of Experimental and Clinical Medicine, Section of Internal Medicine, University of Florence, Viale G.B. Morgagni, 50, 50134, Florence, Italy
| | | | - Claudia Duranti
- Department of Experimental and Clinical Medicine, Section of Internal Medicine, University of Florence, Viale G.B. Morgagni, 50, 50134, Florence, Italy
| | - Cesare Sala
- Department of Experimental and Clinical Medicine, Section of Internal Medicine, University of Florence, Viale G.B. Morgagni, 50, 50134, Florence, Italy
| | - Miriam Armenio
- Department of Experimental and Clinical Medicine, Section of Internal Medicine, University of Florence, Viale G.B. Morgagni, 50, 50134, Florence, Italy
| | - Ivo Noci
- Department of Biochemical, Experimental and Clinical Science, University of Florence, Florence, Italy
| | - Serena Pillozzi
- Department of Experimental and Clinical Medicine, Section of Internal Medicine, University of Florence, Viale G.B. Morgagni, 50, 50134, Florence, Italy
| | - Annarosa Arcangeli
- Department of Experimental and Clinical Medicine, Section of Internal Medicine, University of Florence, Viale G.B. Morgagni, 50, 50134, Florence, Italy.
- CSDC-Center for the Study of Complex Dynamics, 50019, Sesto Fiorentino, Florence, Italy.
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Fu X, Cheng S, Wang W, Shi O, Gao F, Li Y, Wang Q. TCGA dataset screening for genes implicated in endometrial cancer using RNA-seq profiling. Cancer Genet 2021; 254-255:40-47. [PMID: 33588182 DOI: 10.1016/j.cancergen.2021.01.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 01/12/2021] [Accepted: 01/28/2021] [Indexed: 01/20/2023]
Abstract
The molecular basis of the mechanism and the potential biomarkers of endometrial cancer (EC) remain to be studied. In the present study, we hypothesized that the comprehensive characterization of transcriptional changes in EC could help achieve this aim. By taking advantage of RNA-seq data from The Cancer Genome Atlas, we determined the profile of differently expressed genes (DEGs) between EC tumor tissues and normal samples. On this basis, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways enrichment analyses. The interacting partners for each of the DEGs were explored and a protein-protein interaction network was constructed. Consequently, 10 hub genes were identified and their association with mortality in EC patients was investigated. The genes, AURKA, CENPA, and KIF2C, were found to be potential biomarkers for EC with a significant prognostic effect. Our work provided a basis for EC studies in both biological and clinical settings.
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Affiliation(s)
- Xiaoli Fu
- College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | - Shuai Cheng
- College of Public Health, Zhengzhou University, Zhengzhou 450001, China; The Key Laboratory of Nanomedicine and Health Inspection of Zhengzhou, Zhengzhou 450001, China
| | - Wei Wang
- College of Public Health, Zhengzhou University, Zhengzhou 450001, China; The Key Laboratory of Nanomedicine and Health Inspection of Zhengzhou, Zhengzhou 450001, China
| | - Oumin Shi
- Health Science Center, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518020, China
| | - Fuxiao Gao
- China Canada Medical and Health Science Association, Toronto L3R 1A3, Canada
| | - Yong Li
- Department of Oncology, Guizhou Provincial People's Hospital, Guiyang, Guizhou 550002, China
| | - Qi Wang
- College of Public Health, Zhengzhou University, Zhengzhou 450001, China; China Canada Medical and Health Science Association, Toronto L3R 1A3, Canada.
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9
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Fang Z, Xu S, Xie Y, Yan W. Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis. World J Surg Oncol 2021; 19:13. [PMID: 33441161 PMCID: PMC7807455 DOI: 10.1186/s12957-020-02116-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 12/25/2020] [Indexed: 12/17/2022] Open
Abstract
Background Colon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer. Methods The gene expression profile data of colon cancer were obtained from the TCGA, GSE44861, and GSE44076 datasets. The WGCNA module genes and common differentially expressed genes (DEGs) were used to screen out the prognosis-associated DEGs, which were used to construct a prognostic model. The performance of the prognostic model was assessed and validated in the TCGA training and microarray validation sets (GSE38832 and GSE17538). At last, the model and prognosis-associated clinical factors were used for the construction of the nomogram. Results Five colon cancer-related WGCNA modules (including 1160 genes) and 1153 DEGs between tumor and normal tissues were identified, inclusive of 556 overlapping DEGs. Stepwise Cox regression analyses identified there were 14 prognosis-associated DEGs, of which 12 DEGs were included in the optimized prognostic gene signature. This prognostic model presented a high forecast ability for the prognosis of colon cancer both in the TCGA training dataset and the validation datasets (GSE38832 and GSE17538; AUC > 0.8). In addition, patients’ age, T classification, recurrence status, and prognostic risk score were associated with the prognosis of TCGA patients with colon cancer. The nomogram was constructed using the above factors, and the predictive 3- and 5-year survival probabilities had high compliance with the actual survival proportions. Conclusions The 12-gene signature prognostic model had a high predictive ability for the prognosis of colon cancer. Supplementary Information The online version contains supplementary material available at 10.1186/s12957-020-02116-y.
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Affiliation(s)
- Zhengyu Fang
- Department of Anorectal Surgery, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310006, Zhejiang Province, China
| | - Sumei Xu
- Department of General Practice, The First Affiliated Hospital of Zhejiang Chinese Medical University, #54 Youdian Road, Shangcheng District, Hangzhou, 310006, Zhejiang Province, China.
| | - Yiwen Xie
- Department of General Practice, The First Affiliated Hospital of Zhejiang Chinese Medical University, #54 Youdian Road, Shangcheng District, Hangzhou, 310006, Zhejiang Province, China
| | - Wenxi Yan
- Department of Clinical Laboratory, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310006, Zhejiang Province, China
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10
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Wang Y, Liu Y, Guan Y, Li H, Liu Y, Zhang M, Cui P, Kong D, Chen X, Yin H. Integrated analysis of immune-related genes in endometrial carcinoma. Cancer Cell Int 2020; 20:477. [PMID: 33024415 PMCID: PMC7531161 DOI: 10.1186/s12935-020-01572-6] [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: 07/29/2020] [Accepted: 09/23/2020] [Indexed: 12/16/2022] Open
Abstract
Background Exploring novel and sensitive targets is urgent due to the high morbidity of endometrial cancer (EC). The purpose of our study was to explore the transcription factors and immune-related genes in EC and further identify immune-based lncRNA signature as biomarker for predicting survival prognosis. Methods Transcription factors, aberrantly expressed immune-related genes and immune-related lncRNAs were explored through bioinformatics analysis. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were conducted to identify the immune and overall survival (OS) related lncRNAs. The accuracy of model was evaluated by Kaplan-Meier method and receiver operating characteristic (ROC) analysis, and the independent prognostic indicator was identified with Cox analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) were conducted to detect the accuracy of our results. Results A network of 29 transcription factors and 17 immune-related genes was constructed. Furthermore, four immune-prognosis-related lncRNAs were screened out. Kaplan-Meier survival analysis and time-dependent ROC analysis revealed a satisfactory predictive potential of the 4-lncRNA model. Consistency was achieved among the results from the training set, testing set and entire cohort. The distributed patterns between the high- and low-risk groups could be distinguished in principal component analysis. Comparisons of the risk score and clinical factors confirmed the four-lncRNA-based signature as an independent prognostic indicator. Last, the reliability of the results was verified by qRT-PCR in 29 cases of endometrial carcinoma and in cells. Conclusions Overall, our study constructed a network of transcription factors and immune-related genes and explored a four immune-related lncRNA signature that could serve as a novel potential biomarker of EC.
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Affiliation(s)
- Yiru Wang
- The Department of Gynecologic Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040 Heilongjiang China
| | - Yunduo Liu
- The Department of Gynecologic Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040 Heilongjiang China
| | - Yue Guan
- The Department of Gynecologic Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040 Heilongjiang China
| | - Hao Li
- The Department of Gynecologic Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040 Heilongjiang China
| | - Yuan Liu
- The Department of Radiotherapy Oncology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang China
| | - Mengjun Zhang
- The Department of Radiotherapy Oncology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang China
| | - Ping Cui
- The Department of Gynecologic Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040 Heilongjiang China
| | - Dan Kong
- The Department of Gynecologic Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040 Heilongjiang China
| | - Xiuwei Chen
- The Department of Gynecologic Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040 Heilongjiang China
| | - Hang Yin
- The Department of Radiotherapy Oncology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang China
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11
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Whole transcriptome signature for prognostic prediction (WTSPP): application of whole transcriptome signature for prognostic prediction in cancer. J Transl Med 2020; 100:1356-1366. [PMID: 32144347 PMCID: PMC7483260 DOI: 10.1038/s41374-020-0413-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 02/20/2020] [Accepted: 02/21/2020] [Indexed: 11/08/2022] Open
Abstract
Developing prognostic biomarkers for specific cancer types that accurately predict patient survival is increasingly important in clinical research and practice. Despite the enormous potential of prognostic signatures, proposed models have found limited implementations in routine clinical practice. Herein, we propose a generic, RNA sequencing platform independent, statistical framework named whole transcriptome signature for prognostic prediction to generate prognostic gene signatures. Using ovarian cancer and lung adenocarcinoma as examples, we provide evidence that our prognostic signatures overperform previous reported signatures, capture prognostic features not explained by clinical variables, and expose biologically relevant prognostic pathways, including those involved in the immune system and cell cycle. Our approach demonstrates a robust method for developing prognostic gene expression signatures. In conclusion, our statistical framework can be generally applied to all cancer types for prognostic prediction and might be extended to other human diseases. The proposed method is implemented as an R package (PanCancerSig) and is freely available on GitHub ( https://github.com/Cheng-Lab-GitHub/PanCancer_Signature ).
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Li N, Yu K, Lin Z, Zeng D. Development and Validation of a Five-immune Gene Pair Signature in Endometrial Carcinoma. Comb Chem High Throughput Screen 2020; 24:233-245. [PMID: 32729416 DOI: 10.2174/1386207323999200729113641] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 06/09/2020] [Accepted: 06/20/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Endometrial cancer (EC) is a common gynecological malignancy worldwide. Immunity is closely related to the occurrence and prognosis of EC. At the same time, immune-related genes have great potential as prognostic markers in many types of cancer. OBJECTIVE Therefore, we attempt to develop immune-related gene markers to enhance prognosis prediction of EC. METHODS 542 samples of EC gene expression data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA). The samples were randomly divided into two groups, one group as a training set (N=271), and one set as a validation set. (N=271). In the training set, the gene pairs were established based on the relative expression levels of 271 immune genes, and the prognosis-related gene pairs were screened. The lasso was used to select the features, and finally, the robust biomarkers were screened. Finally, the prognostic model of the immune gene pair was established and verified by the validation data set. RESULTS 10030 immune gene pair (IRGPs) were obtained, and univariate survival analysis was used to identify 1809 prognostic-related IRGPs (p<0.05). 5-IRGPs were obtained by lasso regression feature selection, and multivariate regression was used to establish 5-IRGPs signature, 5-IRGPs signature is an independent prognostic factor for EC patients, and could be risk stratified in patients with TCGA datasets, age, ethnicity, stage, and histological classification (p<0.05). The mean AUC of survival in both the training set and the validation set was greater than 0.7, indicating that 5-IRGPs signature has superior classification performance in patients with EC. In addition, 5-IRGPs have the highest average C index (0.795) compared to the prognostic characteristics of the three endometrial cancers reported in the past and Stage and Age. CONCLUSION This study constructed a 5-IRGPs signature as a novel prognostic marker for predicting survival in patients with EC.
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Affiliation(s)
- Nan Li
- Reproductive Medicine Center, Liuzhou Maternity and Child Health Care Hospital, Liuzhou, Guangxi Autonomous Region, 545001, China
| | - Kai Yu
- College of Life Science and Technology, Guangxi University, Nanning, 530004, China
| | - Zhong Lin
- Reproductive Medicine Center, Liuzhou Maternity and Child Health Care Hospital, Liuzhou, Guangxi Autonomous Region, 545001, China
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Besso MJ, Montivero L, Lacunza E, Argibay MC, Abba M, Furlong LI, Colas E, Gil-Moreno A, Reventos J, Bello R, Vazquez-Levin MH. Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools. Oncol Rep 2020; 44:873-886. [PMID: 32705231 PMCID: PMC7388212 DOI: 10.3892/or.2020.7648] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 04/22/2020] [Indexed: 01/08/2023] Open
Abstract
Endometrial cancer (EC) is the sixth most common cancer in women worldwide. Early diagnosis is critical in recurrent EC management. The present study aimed to identify biomarkers of EC early recurrence using a workflow that combined text and data mining databases (DisGeNET, Gene Expression Omnibus), a prioritization algorithm to select a set of putative candidates (ToppGene), protein-protein interaction network analyses (Search Tool for the Retrieval of Interacting Genes, cytoHubba), association analysis of selected genes with clinicopathological parameters, and survival analysis (Kaplan-Meier and Cox proportional hazard ratio analyses) using a The Cancer Genome Atlas cohort. A total of 10 genes were identified, among which the targeting protein for Xklp2 (TPX2) was the most promising independent prognostic biomarker in stage I EC. TPX2 expression (mRNA and protein) was higher (P<0.0001 and P<0.001, respectively) in ETS variant transcription factor 5-overexpressing Hec1a and Ishikawa cells, a previously reported cell model of aggressive stage I EC. In EC biopsies, TPX2 mRNA expression levels were higher (P<0.05) in high grade tumors (grade 3) compared with grade 1–2 tumors (P<0.05), in tumors with deep myometrial invasion (>50% compared with <50%; P<0.01), and in intermediate-high recurrence risk tumors compared with low-risk tumors (P<0.05). Further validation studies in larger and independent EC cohorts will contribute to confirm the prognostic value of TPX2.
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Affiliation(s)
- María José Besso
- Laboratorio de Estudios de Interacción Celular en Reproducción y Cáncer, Instituto de Biología y Medicina Experimental (IBYME), Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina (CONICET)‑Fundación IBYME (FIBYME), Ciudad Autónoma de Buenos Aires 1428ADN, Argentina
| | - Luciana Montivero
- Laboratorio de Estudios de Interacción Celular en Reproducción y Cáncer, Instituto de Biología y Medicina Experimental (IBYME), Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina (CONICET)‑Fundación IBYME (FIBYME), Ciudad Autónoma de Buenos Aires 1428ADN, Argentina
| | - Ezequiel Lacunza
- Centro de Investigaciones Inmunológicas, Básicas y Aplicadas, Facultad de Ciencias Médicas, Universidad Nacional de La Plata, La Plata, Buenos Aires 1900, Argentina
| | - María Cecilia Argibay
- Laboratorio de Estudios de Interacción Celular en Reproducción y Cáncer, Instituto de Biología y Medicina Experimental (IBYME), Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina (CONICET)‑Fundación IBYME (FIBYME), Ciudad Autónoma de Buenos Aires 1428ADN, Argentina
| | - Martín Abba
- Centro de Investigaciones Inmunológicas, Básicas y Aplicadas, Facultad de Ciencias Médicas, Universidad Nacional de La Plata, La Plata, Buenos Aires 1900, Argentina
| | - Laura Inés Furlong
- Integrative Biomedical Informatics Group, Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, 08002 Barcelona, Spain
| | - Eva Colas
- Biomedical Research Group in Gynecology, Vall d´Hebron Research Institute (VHIR), Universitat Autónoma de Barcelona, CIBERONC, 08035 Barcelona, Spain
| | - Antonio Gil-Moreno
- Biomedical Research Group in Gynecology, Vall d´Hebron Research Institute (VHIR), Universitat Autónoma de Barcelona, CIBERONC, 08035 Barcelona, Spain
| | - Jaume Reventos
- Biomedical Research Group in Gynecology, Vall d´Hebron Research Institute (VHIR), Universitat Autónoma de Barcelona, CIBERONC, 08035 Barcelona, Spain
| | - Ricardo Bello
- Departamento de Metodología, Estadística y Matemática, Universidad de Tres de Febrero, Sáenz Peña, Buenos Aires B1674AHF, Argentina
| | - Mónica Hebe Vazquez-Levin
- Laboratorio de Estudios de Interacción Celular en Reproducción y Cáncer, Instituto de Biología y Medicina Experimental (IBYME), Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina (CONICET)‑Fundación IBYME (FIBYME), Ciudad Autónoma de Buenos Aires 1428ADN, Argentina
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Li X, Jiao M, Hu J, Qi M, Zhang J, Zhao M, Liu H, Xiong X, Dong X, Han B. miR-30a inhibits androgen-independent growth of prostate cancer via targeting MYBL2, FOXD1, and SOX4. Prostate 2020; 80:674-686. [PMID: 32294305 DOI: 10.1002/pros.23979] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 03/22/2020] [Accepted: 03/26/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND Castrate-resistant prostate cancer (CRPC) is an aggressive and lethal disease. The pathogenesis of CRPC is not fully understood and novel therapeutic targets need to be identified to improve the patients' prognosis. MicroRNA-30a (miR-30a) has been demonstrated to be a tumor suppressor in many types of solid malignancies. However, its role in androgen-independent (AI) growth of prostate cancer (PCa) received limited attention as yet. METHODS The clinical association of miR-30a and its potential targets with AI growth was characterized by bioinformatics analyses. Regulation of cell proliferation and colony formation rates by miR-30a were tested using PCa cell models. Xenograft models were used to measure the regulation of prostate tumor growth by miR-30a. The real-time quantitative polymerase chain reaction was used to validate whether miR-30a and its targets regulate cell cycle control genes and androgen receptor (AR)-dependent transcription. Bioinformatics tools, Western blot, and luciferase reporter assays were utilized to identify miR-30a targets. RESULTS Bioinformatic analysis showed that low expression of miR-30a is associated with castration resistance of PCa patients and poor outcomes. Transfection of miR-30a mimics inhibited the AI growth of PCa cells in vitro and in vivo. Upregulation of miR-30a in 22RV1 cells altered the expression of cell cycle control genes and AR-mediated transcription, while downregulation of miR-30a in LNCaP cells had the opposite effects to AR-mediated transcription. MYBL2, FOXD1, and SOX4 were identified as miR-30a targets. Downregulation of MYBL2, FOXD1, and SOX4 affected the expression of cell cycle control genes and AR-mediated transcription and suppressed the AI growth of 22RV1 cells. CONCLUSIONS Our results suggest that miR-30a inhibits AI growth of PCa by targeting MYBL2, FOXD1, and SOX4. They provide novel insights into developing new treatment strategies for CRPC.
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Affiliation(s)
- Xinjun Li
- The Key Laboratory of Experimental Teratology, Ministry of Education and Department of Pathology, School of Basic Medical Sciences, Shandong University, Jinan, China
- Department of Pathology, Binzhou People's Hospital, Binzhou, China
- School of Medicine, Shandong University, Jinan, China
| | - Meng Jiao
- Department of Pathology, The Second Hospital of Shandong University, Jinan, China
| | - Jing Hu
- Department of Pathology, Shandong University QiLu Hospital, Jinan, China
| | - Mei Qi
- Department of Pathology, Shandong University QiLu Hospital, Jinan, China
| | - Jing Zhang
- Department of Pharmacy, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Mingfeng Zhao
- Department of Pathology, Binzhou Medical University, Binzhou, China
| | - Hui Liu
- The Key Laboratory of Experimental Teratology, Ministry of Education and Department of Pathology, School of Basic Medical Sciences, Shandong University, Jinan, China
| | - Xueting Xiong
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Xuesen Dong
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Bo Han
- The Key Laboratory of Experimental Teratology, Ministry of Education and Department of Pathology, School of Basic Medical Sciences, Shandong University, Jinan, China
- Department of Pathology, Shandong University QiLu Hospital, Jinan, China
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Akter S, Xu D, Nagel SC, Bromfield JJ, Pelch KE, Wilshire GB, Joshi T. GenomeForest: An Ensemble Machine Learning Classifier for Endometriosis. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2020; 2020:33-42. [PMID: 32477621 PMCID: PMC7233069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Endometriosis is a complex and high impact disease affecting 176 million women worldwide with diagnostic latency between 4 to 11 years due to lack of a definitive clinical symptom or a minimally invasive diagnostic method. In this study, we developed a new ensemble machine learning classifier based on chromosomal partitioning, named GenomeForest and applied it in classifying the endometriosis vs. the control patients using 38 RNA-seq and 80 enrichment-based DNA-methylation (MBD-seq) datasets, and computed performance assessment with six different experiments. The ensemble machine learning models provided an avenue for identifying several candidate biomarker genes with a very high F1 score; a near perfect F1 score (0.968) for the transcriptomics dataset and a very high F1 score (0.918) for the methylomics dataset. We hope in the future a less invasive biopsy can be used to diagnose endometriosis using the findings from such ensemble machine learning classifiers, as demonstrated in this study.
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Affiliation(s)
| | - Dong Xu
- Informatics Institute
- Electrical Engineering and Computer Science
- Christopher S. Bond Life Sciences Center
| | - Susan C Nagel
- OB/GYN and Women's Health , University of Missouri, Columbia, MO
| | - John J Bromfield
- OB/GYN and Women's Health , University of Missouri, Columbia, MO
| | | | | | - Trupti Joshi
- Informatics Institute
- Christopher S. Bond Life Sciences Center
- Health Management and Informatics, University of Missouri, Columbia, MO
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16
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Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma. BIOMED RESEARCH INTERNATIONAL 2020; 2020:3968279. [PMID: 32420338 PMCID: PMC7199595 DOI: 10.1155/2020/3968279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 12/17/2019] [Indexed: 12/19/2022]
Abstract
Uterine corpus endometrial carcinoma (UCEC) is the second most common type of gynecological tumor. Several research studies have recently shown the potential of different ncRNAs as biomarkers for prognostics and diagnosis in different types of cancers, including UCEC. Thus, we hypothesized that long noncoding RNAs (lncRNAs) could serve as efficient factors to discriminate solid primary (TP) and normal adjacent (NT) tissues in UCEC with high accuracy. We performed an in silico differential expression analysis comparing TP and NT from a set of samples downloaded from the Cancer Genome Atlas (TCGA) database, targeting highly differentially expressed lncRNAs that could potentially serve as gene expression markers. All analyses were performed in R software. The receiver operator characteristics (ROC) analyses and both supervised and unsupervised machine learning indicated a set of 14 lncRNAs that may serve as biomarkers for UCEC. Functions and putative pathways were assessed through a coexpression network and target enrichment analysis.
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17
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Wang P, Zeng Z, Shen X, Tian X, Ye Q. Identification of a Multi-RNA-Type-Based Signature for Recurrence-Free Survival Prediction in Patients with Uterine Corpus Endometrial Carcinoma. DNA Cell Biol 2020; 39:615-630. [PMID: 32105510 DOI: 10.1089/dna.2019.5148] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Uterine corpus endometrial carcinoma (UCEC) is one of the leading causes of death from gynecological cancer due to the high recurrence rate. A recent study indicated that molecular biomarkers can enhance the recurrence prediction power if they were integrated with clinical information. In this study, we attempted to identify a new multi-RNA-type-based molecular biomarker for predicting the recurrence risk and recurrence-free survival (RFS). Matched mRNA (including lncRNA) and miRNA RNA-sequencing data from 463 UCEC patients (n = 75, recurrent; n = 388, non-recurrent) were downloaded from The Cancer Genome Atlas database. LASSO (least absolute shrinkage and selection operator) analysis was used to screen the optimal combination of prognostic RNAs and then the risk score model was constructed. Moreover, the molecular mechanisms of prognostic RNAs were explored by establishing various interaction networks based on corresponding predictive databases. A multi-RNA-type-based signature (including three miRNAs: hsa-miR-6511b, hsa-miR-184, hsa-miR-4461; three lncRNAs: ENO1-IT1, MCCC1-AS1, AATBC; and 7 mRNAs: EPPK1, ASB9, BDNF, CYP11A1, ECEL1, EN2, F13A1) was developed for the prediction of RFS. The risk scoring system established by these signature genes was effective for the discrimination of the 5-year RFS in the high-risk from low-risk patients in the training [an area under the receiver operating characteristic curve (AUC) = 0.960], validation (AUC = 0.863), and entire datasets (AUC = 0.873). This risk score model was also proved to be a more excellent, independent prognostic discriminator than the single-RNA-type (overall AUC: 0.947 vs. 0.677, lncRNAs; 0.709, miRNAs; 0.899, mRNAs) and clinical staging (overall AUC: 0.947 vs. 0.517). Furthermore, the downstream mechanisms for some prognostic miRNAs or lncRNAs (HAND2-AS1-hsa-miR-6511b-APC2, PAX8-AS1-hsa-miR-4461-TNIK and MCCC1-AS1/ENO1-IT1-TNIK) were newly predicted based on the coexpression or competitive endogenous RNA theories. In conclusion, our findings may provide novel biomarkers for recurrence prediction and targets for treatment of UCEC.
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Affiliation(s)
- Peizhi Wang
- Department of Obstetrics and Gynecology, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Zhi Zeng
- Center of Reproductive Medicine, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiaoting Shen
- Reproductive Medicine Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiaohui Tian
- Department of Obstetrics and Gynecology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Qingjian Ye
- Department of Gynecology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
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Toro-Domínguez D, Villatoro-García JA, Martorell-Marugán J, Román-Montoya Y, Alarcón-Riquelme ME, Carmona-Sáez P. A survey of gene expression meta-analysis: methods and applications. Brief Bioinform 2020; 22:1694-1705. [PMID: 32095826 DOI: 10.1093/bib/bbaa019] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 01/09/2020] [Accepted: 02/04/2020] [Indexed: 02/07/2023] Open
Abstract
The increasing use of high-throughput gene expression quantification technologies over the last two decades and the fact that most of the published studies are stored in public databases has triggered an explosion of studies available through public repositories. All this information offers an invaluable resource for reuse to generate new knowledge and scientific findings. In this context, great interest has been focused on meta-analysis methods to integrate and jointly analyze different gene expression datasets. In this work, we describe the main steps in the gene expression meta-analysis, from data preparation to the state-of-the art statistical methods. We also analyze the main types of applications and problems that can be approached in gene expression meta-analysis studies and provide a comparative overview of the available software and bioinformatics tools. Moreover, a practical guide for choosing the most appropriate method in each case is also provided.
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Affiliation(s)
- Daniel Toro-Domínguez
- GENYO (Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 114, 18016 Granada, Spain
| | - Juan Antonio Villatoro-García
- GENYO (Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 114, 18016 Granada, Spain
| | - Jordi Martorell-Marugán
- GENYO (Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 114, 18016 Granada, Spain
| | - Yolanda Román-Montoya
- Department of Statistics and Operations Research, University of Granada, Granada, Spain
| | - Marta E Alarcón-Riquelme
- GENYO (Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 114, 18016 Granada, Spain.,Unit of Inflammatory Diseases, Department of Environmental Medicine, Karolinska Institute, 171 67, Solna, Sweden
| | - Pedro Carmona-Sáez
- GENYO (Centre for Genomics and Oncological Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Avenida de la Ilustración, 114, 18016 Granada, Spain
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Fukutani ER, Ramos PIP, Kasprzykowski JI, Azevedo LG, Rodrigues MMDS, Lima JVDOP, de Araújo Junior HFS, Fukutani KF, de Queiroz ATL. Meta-Analysis of HTLV-1-Infected Patients Identifies CD40LG and GBP2 as Markers of ATLL and HAM/TSP Clinical Status: Two Genes Beat as One. Front Genet 2019; 10:1056. [PMID: 31781157 PMCID: PMC6857459 DOI: 10.3389/fgene.2019.01056] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 10/02/2019] [Indexed: 01/18/2023] Open
Abstract
Human T-lymphotropic virus 1 (HTLV-1) was the first recognized human retrovirus. Infection can lead to two main symptomatologies: adult T-cell lymphoma/leukemia (ATLL) and HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP). Each manifestation is associated with distinct characteristics, as ATLL presents as a leukemia-like disease, while HAM/TSP presents as severe inflammation in the central nervous system, leading to paraparesis. Previous studies have identified molecules associated with disease development, e.g., the downregulation of Foxp3 in Treg cells was associated with increased risk of HAM/TSP. In addition, elevated levels of CXCL10, CXCL9, and Neopterin in cerebrospinal fluid also present increased risk. However, these molecules were only associated with specific patient groups or viral strains. Furthermore, the majority of studies did not jointly compare all clinical manifestations, and robust analysis entails the inclusion of both ATLL and HAM/TSP. The low numbers of samples also pose difficulties in conducting gene expression analysis to identify specific molecular relationships. To address these limitations and increase the power of manifestation-specific gene associations, meta-analysis was performed using publicly available gene expression data. The application of supervised learning techniques identified alterations in two genes observed to act in tandem as potential biomarkers: GBP2 was associated with HAM/TSP, and CD40LG with ATLL. Together, both molecules demonstrated high sample-classification accuracy (AUC values: 0.88 and 1.0, respectively). Next, other genes with expression correlated to these genes were identified, and we attempted to relate the enriched pathways identified with the characteristic of each clinical manifestation. The present findings contribute to knowledge surrounding viral progression and suggest a potentially powerful new tool for the molecular classification of HTLV-associated diseases.
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Affiliation(s)
- Eduardo Rocha Fukutani
- Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, FIOCRUZ, Salvador, Brazil
| | - Pablo Ivan Pereira Ramos
- Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, FIOCRUZ, Salvador, Brazil
| | - José Irahe Kasprzykowski
- Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, FIOCRUZ, Salvador, Brazil
| | - Lucas Gentil Azevedo
- Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, FIOCRUZ, Salvador, Brazil
| | | | | | | | - Kiyoshi Ferreira Fukutani
- Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, FIOCRUZ, Salvador, Brazil.,Fundação José Silveira, Multinational Organization Network Sponsoring Translational and Epidemiological Research, FJS, Salvador, Brazil.,Faculdade de Medicina, Faculdade de Tecnologia e Ciências, Salvador, Brazil
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20
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Liu L, Lin J, He H. Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer. Front Genet 2019; 10:373. [PMID: 31105744 PMCID: PMC6499025 DOI: 10.3389/fgene.2019.00373] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Accepted: 04/09/2019] [Indexed: 12/13/2022] Open
Abstract
Background and Objective Endometrial cancer (EC) is a common gynecological malignancy worldwide. Despite advances in the development of strategies for treating EC, prognosis of the disease remains unsatisfactory, especially for advanced EC. The aim of this study was to identify novel genes that can be used as potential biomarkers for identifying the prognosis of EC and to construct a novel risk stratification using these genes. Methods and Results An mRNA sequencing dataset, corresponding survival data and expression profiling of an array of EC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Common differentially expressed genes (DEGs) were identified based on sequencing and expression as given in the profiling dataset. Pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction network was established using the string online database in order to identify hub genes. Univariate and multivariable Cox regression analyses were used to screen prognostic DEGs and to construct a prognostic signature. Survival analysis based on the prognostic signature was performed on TCGA EC dataset. A total of 255 common DEGs were found and 11 hub genes (TOP2A, CDK1, CCNB1, CCNB2, AURKA, PCNA, CCNA2, BIRC5, NDC80, CDC20, and BUB1BA) that may be closely related to the pathogenesis of EC were identified. A panel of 7 DEG signatures consisting of PHLDA2, GGH, ESPL1, FAM184A, KIAA1644, ESPL1, and TRPM4 were constructed. The signature performed well for prognosis prediction (p < 0.001) and time-dependent receiver-operating characteristic (ROC) analysis displayed an area under the curve (AUC) of 0.797, 0.734, 0.729, and 0.647 for 1, 3, 5, and 10-year overall survival (OS) prediction, respectively. Conclusion This study identified potential genes that may be involved in the pathophysiology of EC and constructed a novel gene expression signature for EC risk stratification and prognosis prediction.
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Affiliation(s)
- Li Liu
- Department of Obstetrics and Gynecology, Liuzhou Worker's Hospital, Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China
| | - Jiajing Lin
- Department of Obstetrics and Gynecology, Liuzhou Worker's Hospital, Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China
| | - Hongying He
- Department of Obstetrics and Gynecology, Liuzhou Worker's Hospital, Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China
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Wu W, Sui J, Liu T, Yang S, Xu S, Zhang M, Huang S, Yin L, Pu Y, Liang G. Integrated analysis of two-lncRNA signature as a potential prognostic biomarker in cervical cancer: a study based on public database. PeerJ 2019; 7:e6761. [PMID: 31065456 PMCID: PMC6482937 DOI: 10.7717/peerj.6761] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 03/08/2019] [Indexed: 01/14/2023] Open
Abstract
Background Cervical cancer (CC) is a common gynecological malignancy in women worldwide. Evidence suggests that long non-coding RNAs (lncRNAs) can be used as biomarkers in patients with CC. However, prognostic biomarkers for CC are still lacking. The aim of our study was to find lncRNA biomarkers which are able to predict prognosis in CC based on the data from The Cancer Genome Atlas (TCGA). Methods The patients were divided into three groups according to FIGO stage. Differentially expressed lncRNAs were identified in CC tissue compared to adjacent normal tissues based on a fold change >2 and <0.5 at P < 0.05 for up- and downregulated lncRNA, respectively. The relationship between survival outcome and lncRNA expression was assessed with univariate and multivariate Cox proportional hazards regression analysis. We constructed a risk score as a method to evaluate prognosis. We used receiver operating characteristic (ROC) curve and the area under curve (AUC) analyses to assess the diagnostic value of a two-lncRNA signature. We detected the expression levels of the two lncRNAs in 31 pairs of newly diagnosed CC specimens and paired adjacent non-cancerous tissue specimens, and also in CC cell lines. Finally, the results were statistically compared using t-tests. Results In total, 289 RNA sequencing profiles and accompanying clinical data were obtained. We identified 49 differentially expressed lncRNAs, of which two related to overall survival (OS) in CC patients. These two lncRNAs (ILF3-AS1 and RASA4CP) were found together as a single prognostic signature. Meanwhile, the prognosis of patients with low-risk CC was better and positively correlated with OS (P < 0.001). Further analysis showed that the combined two-lncRNA expression signature could be used as an independent biomarker to evaluate the prognosis in CC. qRT-PCR results were consistent with TCGA, confirming downregulated expression of both lncRNAs. Furthermore, upon ROC curve analysis, the AUC of the combined lncRNAs was greater than that of the single lncRNAs alone (0.723 vs 0.704 and 0.685), respectively; P < 0.05. Conclusions Our study showed that the two-lncRNA signature of ILF3-AS1 and RASA4CP can be used as an independent biomarker for the prognosis of CC, based on bioinformatic analysis.
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Affiliation(s)
- Wenjuan Wu
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Jing Sui
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Tong Liu
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Sheng Yang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Siyi Xu
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Man Zhang
- Department of Medical Insurance, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Shaoping Huang
- Department of Histology and Embryology, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Lihong Yin
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Yuepu Pu
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Geyu Liang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, China
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22
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Liu Y. Immune response characterization of endometrial cancer. Oncotarget 2019; 10:982-992. [PMID: 30847026 PMCID: PMC6398181 DOI: 10.18632/oncotarget.26630] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 01/16/2019] [Indexed: 01/02/2023] Open
Abstract
Background The comprehensive characterization and prognostic relevance of immune activation in endometrial cancer remain largely unknown. Results We systematically reported a subset of endometrioid-type endometrial cancer characterized by multifaceted immune features such as low tumor purity, high leukocyte percentage, and striking CD8 lymphocytic infiltration with anti-tumor efficacy along with marked upregulation of immunosuppressive gene markers. We also showed that genes whose expression was significantly correlated with better survival were significantly enriched in the immune-related signaling pathways, suggesting that tumor-infiltrating lymphocytes give rise to a favorable prognosis in endometrial cancer. Furthermore, we showed that immune cell recruitment in this subset of tumors is likely due to the transcriptional activation of the STAT1 signaling network. Methods We obtained the multi-dimensional genomic data from publicly available databases and correlated them with the four gene expression-based subtypes we recently identified in endometrial cancer. Upstream regulator analysis was used to identify the most significantly enriched transcription regulators and Ingenuity pathway analysis was applied to determine enrichment of signaling pathways in survival-associated genes. Gene set enrichment analysis was performed on the 200-gene T-cell tumor infiltration gene signature comparing Cluster IV with the other three clusters combined. All statistical tests were two-sided, and a P value of less than 0.05 is considered significant across all analyses performed. Conclusion This study helps to identify patients with immune activation who are likely to benefit from emerging immune checkpoint inhibitors.
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Affiliation(s)
- Yuexin Liu
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Wang Y, Ren F, Chen P, Liu S, Song Z, Ma X. Identification of a six-gene signature with prognostic value for patients with endometrial carcinoma. Cancer Med 2018; 7:5632-5642. [PMID: 30306731 PMCID: PMC6247034 DOI: 10.1002/cam4.1806] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 09/09/2018] [Accepted: 09/10/2018] [Indexed: 12/13/2022] Open
Abstract
Uterine corpus endometrial carcinoma (UCEC) is frequently diagnosed among women worldwide. However, there are different prognostic outcomes because of heterogeneity. Thus, the aim of the current study was to identify a gene signature that can predict the prognosis of patients with UCEC. UCEC gene expression profiles were first downloaded from the The Cancer Genome Atlas (TCGA) database. After data processing and forward screening, 11 390 key genes were selected. The UCEC samples were randomly divided into training and testing sets. In total, 996 genes with prognostic value were then examined by univariate Cox survival analysis with a P-value <0.01 in the training set. Next, using robust likelihood-based survival modeling, we developed a six-gene signature (CTSW, PCSK4, LRRC8D, TNFRSF18, IHH, and CDKN2A) with a prognostic function in UCEC. A prognostic risk score system was developed by multivariate Cox proportional hazard regression based on this six-gene signature. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer overall survival (OS) outcomes than those in the low-risk group (log-rank test P-value <0.0001). This signature was further validated in the testing dataset and the entire TCGA dataset. In conclusion, we conducted an integrated study to develop a six-gene signature for the prognostic prediction of patients with UCEC. Our findings may provide novel biomarkers for prognosis and have significant implications in the understanding of therapeutic targets for UCEC.
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Affiliation(s)
- Yizi Wang
- Department of Obstetrics and GynecologyShengjing Hospital of China Medical UniversityShenyangChina
| | - Fang Ren
- Department of Obstetrics and GynecologyShengjing Hospital of China Medical UniversityShenyangChina
| | - Peng Chen
- Department of Obstetrics and GynecologyShengjing Hospital of China Medical UniversityShenyangChina
| | - Shuang Liu
- Department of Obstetrics and GynecologyShengjing Hospital of China Medical UniversityShenyangChina
| | - Zixuan Song
- Department of Obstetrics and GynecologyShengjing Hospital of China Medical UniversityShenyangChina
| | - Xiaoxin Ma
- Department of Obstetrics and GynecologyShengjing Hospital of China Medical UniversityShenyangChina
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Md Fuzi AA, Omar SZ, Mohamed Z, Mat Adenan NA, Mokhtar NM. High throughput silencing identifies novel genes in endometrioid endometrial cancer. Taiwan J Obstet Gynecol 2018; 57:217-226. [PMID: 29673664 DOI: 10.1016/j.tjog.2018.02.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/17/2017] [Indexed: 01/18/2023] Open
Abstract
OBJECTIVE To validate the gene expression profile obtained from the previous microarray analysis and to further study the biological functions of these genes in endometrial cancer. From our previous study, we identified 621 differentially expressed genes in laser-captured microdissected endometrioid endometrial cancer as compared to normal endometrial cells. Among these genes, 146 were significantly up-regulated in endometrial cancer. MATERIALS AND METHODS A total of 20 genes were selected from the list of up-regulated genes for the validation assay. The qPCR confirmed that 19 out of the 20 genes were up-regulated in endometrial cancer compared with normal endometrium. RNA interference (RNAi) was used to knockdown the expression of the upregulated genes in ECC-1 and HEC-1A endometrial cancer cell lines and its effect on proliferation, migration and invasion were examined. RESULTS Knockdown of MIF, SOD2, HIF1A and SLC7A5 by RNAi significantly decreased the proliferation of ECC-1 cells (p < 0.05). Our results also showed that the knockdown of MIF, SOD2 and SLC7A5 by RNAi significantly decreased the proliferation and migration abilities of HEC-1A cells (p < 0.05). Moreover, the knockdown of SLC38A1 and HIF1A by RNAi resulted in a significant decrease in the proliferation of HEC1A cells (p < 0.05). CONCLUSION We have identified the biological roles of SLC38A1, MIF, SOD2, HIF1A and SLC7A5 in endometrial cancer, which opens up the possibility of using the RNAi silencing approach to design therapeutic strategies for treatment of endometrial cancer.
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Affiliation(s)
- Afiqah Alyaa Md Fuzi
- Department of Pharmacology, Faculty of Medicine, University Malaya, 50603 Kuala Lumpur, Malaysia
| | - Siti Zawiah Omar
- Department of Obstetrics and Gynecology, Faculty of Medicine, University Malaya, 50603 Kuala Lumpur, Malaysia
| | - Zahurin Mohamed
- Department of Pharmacology, Faculty of Medicine, University Malaya, 50603 Kuala Lumpur, Malaysia
| | - Noor Azmi Mat Adenan
- Department of Obstetrics and Gynecology, Faculty of Medicine, University Malaya, 50603 Kuala Lumpur, Malaysia
| | - Norfilza Mohd Mokhtar
- Department of Physiology, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, 56000 Cheras, Kuala Lumpur, Malaysia.
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25
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Ying J, Xu T, Wang Q, Ye J, Lyu J. Exploration of DNA methylation markers for diagnosis and prognosis of patients with endometrial cancer. Epigenetics 2018; 13:490-504. [PMID: 29912667 DOI: 10.1080/15592294.2018.1474071] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The accurate diagnosis of endometrial cancer (EC) holds great promise for improving its treatment choice and prognosis prediction. This work aimed to identify diagnostic biomarkers for differentiating EC tumors from tumors in other tissues, as well as prognostic signatures for predicting survival in EC patients. We identified 48 tissue-specific markers using a cohort of genome-wide methylation data from three common gynecological tumors and their corresponding normal tissues. A diagnostic classifier was constructed based on these 48 CpG markers that could predict cancerous versus normal tissue with an overall correct rate of 98.3% in the entire repository. Fifteen CpG markers associated with the overall survival (OS) and development of EC were also identified based on the methylation patterns of the EC samples. A prognostic model that aggregated these prognostic CpG markers was established and shown to have a higher discriminative ability to distinguish EC patients with an elevated risk of mortality than the FIGO staging system and several other clinical prognostic variables. This study presents the utility of DNA methylation in identifying biomarkers for the diagnosis and prognosis of EC and will help improve our understanding of the underlying mechanisms involved in the development of EC.
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Affiliation(s)
- Jianchao Ying
- a Key Laboratory of Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science , Wenzhou Medical University , Wenzhou , China
| | - Teng Xu
- a Key Laboratory of Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science , Wenzhou Medical University , Wenzhou , China
| | - Qian Wang
- b Department of Clinical Laboratory, Wenzhou People's Hospital , The Third Clinical Institute Affiliated to Wenzhou Medical University , Wenzhou , China
| | - Jun Ye
- a Key Laboratory of Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science , Wenzhou Medical University , Wenzhou , China.,c Department of Clinical Laboratory , The Second Affiliated Hospital of Guizhou Medical University , Kaili , China
| | - Jianxin Lyu
- a Key Laboratory of Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science , Wenzhou Medical University , Wenzhou , China
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26
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Ying J, Wang Q, Xu T, Lyu J. Establishment of a nine-gene prognostic model for predicting overall survival of patients with endometrial carcinoma. Cancer Med 2018; 7:2601-2611. [PMID: 29665298 PMCID: PMC6010780 DOI: 10.1002/cam4.1498] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 03/04/2018] [Accepted: 03/21/2018] [Indexed: 12/11/2022] Open
Abstract
Endometrial carcinoma (EC) is the most common malignant tumor of the female genital tract in developed countries. The prognosis of early stage EC is favorable, but a subset faces high risk of cancer progression or recurrence. EC has a poor prognosis upon progression to advanced or metastatic stages. Therefore, our goal is to build a robust prognostic model for predicting overall survival (OS) in EC patients. In this study, 1571 genes were identified as being associated with OS based on genomewide expression profiles using a training dataset. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that these genes were involved in various cancer-related signaling pathways. Nine signature genes were further selected using stepwise selection, and their potential role in the development of EC was demonstrated by performing differential expression analysis between EC and normal uterine tissues. A prognostic model that aggregated these nine signature genes was ultimately established and effectively divided EC patients into two risk groups. OS for patients in the high-risk group was significantly poorer compared with that of the low-risk group. This nine-gene model was subsequently validated and evaluated using the TCGA dataset and shown to have a high discriminating power to distinguish EC patients with an elevated risk of mortality based on the FIGO staging system and other prognostic factors. This study provides a novel prognostic model for the identification of EC patients with elevated risk of mortality and will help to improve our understanding of the underlying mechanisms involved in prognostic EC factors.
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Affiliation(s)
- Jianchao Ying
- Key Laboratory of Laboratory MedicineMinistry of EducationZhejiang Provincial Key Laboratory of Medical GeneticsSchool of Laboratory Medicine and Life ScienceWenzhou Medical UniversityWenzhouChina
| | - Qian Wang
- Department of Clinical LaboratoryWenzhou People's HospitalThe Third Clinical Institute Affiliated to Wenzhou Medical UniversityWenzhouChina
| | - Teng Xu
- Key Laboratory of Laboratory MedicineMinistry of EducationZhejiang Provincial Key Laboratory of Medical GeneticsSchool of Laboratory Medicine and Life ScienceWenzhou Medical UniversityWenzhouChina
| | - Jianxin Lyu
- Key Laboratory of Laboratory MedicineMinistry of EducationZhejiang Provincial Key Laboratory of Medical GeneticsSchool of Laboratory Medicine and Life ScienceWenzhou Medical UniversityWenzhouChina
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27
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Ashraf N, Basu S, Narula K, Ghosh S, Tayal R, Gangisetty N, Biswas S, Aggarwal PR, Chakraborty N, Chakraborty S. Integrative network analyses of wilt transcriptome in chickpea reveal genotype dependent regulatory hubs in immunity and susceptibility. Sci Rep 2018; 8:6528. [PMID: 29695764 PMCID: PMC5916944 DOI: 10.1038/s41598-018-19919-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 01/05/2018] [Indexed: 12/12/2022] Open
Abstract
Host specific resistance and non-host resistance are two plant immune responses to counter pathogen invasion. Gene network organizing principles leading to quantitative differences in resistant and susceptible host during host specific resistance are poorly understood. Vascular wilt caused by root pathogen Fusarium species is complex and governed by host specific resistance in crop plants, including chickpea. Here, we temporally profiled two contrasting chickpea genotypes in disease and immune state to better understand gene expression switches in host specific resistance. Integrative gene-regulatory network elucidated tangible insight into interaction coordinators leading to pathway determination governing distinct (disease or immune) phenotypes. Global network analysis identified five major hubs with 389 co-regulated genes. Functional enrichment revealed immunome containing three subnetworks involving CTI, PTI and ETI and wilt diseasome encompassing four subnetworks highlighting pathogen perception, penetration, colonization and disease establishment. These subnetworks likely represent key components that coordinate various biological processes favouring defence or disease. Furthermore, we identified core 76 disease/immunity related genes through subcellular analysis. Our regularized network with robust statistical assessment captured known and unexpected gene interaction, candidate novel regulators as future biomarkers and first time showed system-wide quantitative architecture corresponding to genotypic characteristics in wilt landscape.
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Affiliation(s)
- Nasheeman Ashraf
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India
| | - Swaraj Basu
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India
| | - Kanika Narula
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India
| | - Sudip Ghosh
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India
| | - Rajul Tayal
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India
| | - Nagaraju Gangisetty
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India
| | - Sushmita Biswas
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India
| | - Pooja R Aggarwal
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India
| | - Niranjan Chakraborty
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India
| | - Subhra Chakraborty
- National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India.
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28
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Wu WJ, Shen Y, Sui J, Li CY, Yang S, Xu SY, Zhang M, Yin LH, Pu YP, Liang GY. Integrated analysis of long non‑coding RNA competing interactions revealed potential biomarkers in cervical cancer: Based on a public database. Mol Med Rep 2018; 17:7845-7858. [PMID: 29620291 DOI: 10.3892/mmr.2018.8846] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Accepted: 03/21/2018] [Indexed: 11/05/2022] Open
Abstract
Cervical cancer (CC) is a common gynecological malignancy in women worldwide. Using an RNA sequencing profile from The Cancer Genome Atlas (TCGA) and the CC patient information, the aim of the present study was to identify potential long non‑coding RNA (lncRNA) biomarkers of CC using bioinformatics analysis and building a competing endogenous RNA (ceRNA) co‑expression network. Results indicated several CC‑specific lncRNAs, which were associated with CC clinical information and selected some of them for validation and evaluated their diagnostic values. Bioinformatics analysis identified 51 CC‑specific lncRNAs (fold‑change >2 and P<0.05), and 42 of these were included in ceRNA network consisting of lncRNA‑miRNA‑mRNA interactions. Further analyses revealed that differential expression levels of 19 lncRNAs were significantly associated with different clinical features (P<0.05). A total of 11 key lncRNAs in the ceRNA network for reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) analysis to detect their expression levels in 31 pairs of CC clinical samples. The results indicated that 7 lncRNAs were upregulated and 4 lncRNAs were downregulated in CC patients. The fold‑changes between the RT‑qPCR experiments and the TCGA bioinformatics analyses were the same. Furthermore, the area under the receiver operating characteristic (ROC) curve of four lncRNAs (EMX20S, MEG3, SYS1‑DBNDD2 and MIR9‑3HG) indicated that their combined use may have a significant diagnostic value in CC (P<0.05). To the best of our knowledge, the present study is the first to have identified CC‑specific lncRNAs to construct a ceRNA network and has also provided new insights for further investigation of a lncRNA‑associated ceRNA network in CC. In additon, the verification results suggested that the method of bioinformatics analysis and screening of lncRNAs was accurate and reliable. To conclude, the use of multiple lncRNAs may thus improve diagnostic efficacy in CC. In addition, these specific lncRNAs may serve as new candidate biomarkers for clinical diagnosis, classification and prognosis of CC.
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Affiliation(s)
- Wen-Juan Wu
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, P.R. China
| | - Yang Shen
- Department of Obstetrics and Gynecology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, P.R. China
| | - Jing Sui
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, P.R. China
| | - Cheng-Yun Li
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, P.R. China
| | - Sheng Yang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, P.R. China
| | - Si-Yi Xu
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, P.R. China
| | - Man Zhang
- Department of Medical Insurance, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, P.R. China
| | - Li-Hong Yin
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, P.R. China
| | - Yue-Pu Pu
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, P.R. China
| | - Ge-Yu Liang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, P.R. China
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Liu Y, Li Z, Lu J, Zhao M, Qu H. CMGene: A literature-based database and knowledge resource for cancer metastasis genes. J Genet Genomics 2017; 44:277-279. [PMID: 28527662 DOI: 10.1016/j.jgg.2017.04.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 04/27/2017] [Accepted: 04/30/2017] [Indexed: 12/26/2022]
Affiliation(s)
- Yining Liu
- The School of Public Health, Institute for Chemical Carcinogenesis, Guangzhou Medical University, Guangzhou 510182, China
| | - Zhe Li
- Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing 100871, China
| | - Jiachun Lu
- The School of Public Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou 510120, China
| | - Min Zhao
- School of Engineering, Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Maroochydore DC, Queensland 4558, Australia.
| | - Hong Qu
- Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing 100871, China.
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