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Wang M, Zhang C, Ying Y, Hua M, Meng F, Wang Z, Liu A, Zeng S, Zhang Z, Xu C. PKMYT1 induced by YAP/TEAD1 gives rise to the progression and worse prognosis of bladder cancer. Mol Carcinog 2024; 63:160-172. [PMID: 37787394 DOI: 10.1002/mc.23643] [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: 05/26/2023] [Revised: 09/11/2023] [Accepted: 09/21/2023] [Indexed: 10/04/2023]
Abstract
Protein kinase, membrane-associated tyrosine/threonine 1 (PKMYT1), which is associated with progression of tumor, is upregulated in a variety of cancers. However, its expression and the underlying molecular mechanisms in the context of bladder cancer (BLCA) remain elusive. Here we found that PKMYT1 expression was markedly higher expression in BLCA, which was correlated with poorer prognosis compared with low expression. Knockdown of PKMYT1 significantly inhibited the BLCA cells proliferation in vivo and in vitro, and migration and invasion, reduced G2/M phase in cell cycle and induced apoptosis. Mechanically, YAP and TEAD1 knockdown suppressed PKMYT1 expression in BLCA cells, whereas overexpression of YAP upregulated PKMYT1 expression and YAP prompted PKMYT1 transcriptional expression via TEAD1-mediated direct binding to PKMYT1 promotor. Collectively, these findings suggest that PKMYT1, functioning as a direct gene target regulated by YAP/TEAD1, could serve as a potential indicator of progression and prognosis in BLCA. Further, PKMYT1 could serve as a novel therapeutic target for BLCA.
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Affiliation(s)
- Maoyu Wang
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chen Zhang
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Yidie Ying
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Meimian Hua
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Fang Meng
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Ziwei Wang
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Anwei Liu
- Department of Critical Care Medicine, Hospital of Southern Theatre Command of PLA, Guangzhou, China
| | - Shuxiong Zeng
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Zhensheng Zhang
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chuanliang Xu
- Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
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Three Prognostic Biomarkers Correlate with Immune Checkpoint Blockade Response in Bladder Urothelial Carcinoma. Int J Genomics 2022; 2022:3342666. [PMID: 35664691 PMCID: PMC9162857 DOI: 10.1155/2022/3342666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/09/2022] [Indexed: 11/17/2022] Open
Abstract
Aim We aim to develop a signature that could accurately predict prognosis and evaluate the response to immune checkpoint blockade (ICB) in bladder urothelial carcinoma (BLCA). Methods Based on comprehensive analysis of public database, we identified prognosis-related hub genes and investigated their predictive values for the ICB response in BLCA. Results Among 69 common DEGs, three genes (AURKA, BIRC5, and CKS1B) were associated with poor prognosis, and which were related to histological subtypes, TP53 mutation status, and the C2 (IFN-gamma dominant) subtype. Three genes and their related risk model can effectively predict the response of immunotherapy. Their related drugs were identified through analysis of drug bank database. Conclusions Three genes could predict prognosis and evaluate the response to ICB in BLCA.
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Zhang G, Wang Q, Qi X, Yang H, Su X, Yang M, Jiang C, An Y, Zheng H, Zhang L, Zhu W, Guo J, Guo X. OShnscc: a novel user-friendly online survival analysis tool for head and neck squamous cell carcinoma based on RNA expression profiles and long-term survival information. J Zhejiang Univ Sci B 2022; 23:249-257. [PMID: 35261220 DOI: 10.1631/jzus.b2100512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Head and neck squamous cell carcinoma (HNSCC), as the most common type (>90%) of head and neck cancer, includes various epithelial malignancies that arise in the nasal cavity, oral cavity, pharynx, and larynx. In 2020, approximately 878 000 new cases and 444 000 deaths linked to HNSCC occurred worldwide (Sung et al., 2021). Due to the associated frequent recurrence and metastasis, HNSCC patients have poor prognosis with a five-year survival rate of 40%-50% (Jou and Hess, 2017). Therefore, novel prognostic biomarkers need to be developed to identify high-risk HNSCC patients and improve their disease outcomes.
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Affiliation(s)
- Guosen Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Qiang Wang
- School of Software, Henan University, Kaifeng 475004, China
| | - Xinlei Qi
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Huimin Yang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Xiaodong Su
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Manman Yang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Chao Jiang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Yang An
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Hong Zheng
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Lu Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Wan Zhu
- Department of Anesthesia, Stanford University, Stanford, CA 94305, USA
| | - Jiancheng Guo
- Department of Molecular Pathology, the Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China. .,Precision Medicine Center, Academy of Medical Sciences, Zhengzhou University, Zhengzhou 450052, China. .,Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450052, China.
| | - Xiangqian Guo
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China.
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Yao X, Qi X, Wang Y, Zhang B, He T, Yan T, Zhang L, Wang Y, Zheng H, Zhang G, Guo X. Identification and Validation of an Annexin-Related Prognostic Signature and Therapeutic Targets for Bladder Cancer: Integrative Analysis. BIOLOGY 2022; 11:biology11020259. [PMID: 35205125 PMCID: PMC8869209 DOI: 10.3390/biology11020259] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 02/03/2022] [Accepted: 02/03/2022] [Indexed: 12/12/2022]
Abstract
Abnormal expression and dysfunction of Annexins (ANXA1-11, 13) have been widely found in several types of cancer. However, the expression pattern and prognostic value of Annexins in bladder cancer (BC) are currently still unknown. In this study, survival analysis by our in-house OSblca web server revealed that high ANXA1/2/3/5/6 expression was significantly associated with poor overall survival (OS) in BC patients, while higher ANXA11 was associated with increased OS. Through Oncomine and GEPIA2 database analysis, we found that ANXA2/3/4/13 were up-regulated, whereas ANXA1/5/6 were down-regulated in BC compared with normal bladder tissues. Further LASSO analysis built an Annexin-Related Prognostic Signature (ARPS, including four members ANXA1/5/6/10) in the TCGA BC cohort and validated it in three independent GEO BC cohorts (GSE31684, GSE32548, GSE48075). Multivariate COX analysis demonstrated that ARPS is an independent prognostic signature for BC. Moreover, GSEA results showed that immune-related pathways, such as epithelial-mesenchymal transition and IL6/JAK/STAT3 signaling were enriched in the high ARPS risk groups, while the low ARPS risk group mainly regulated metabolism-related processes, such as adipogenesis and bile acid metabolism. In conclusion, our study comprehensively analyzed the mRNA expression and prognosis of Annexin family members in BC, constructed an Annexin-related prognostic signature using LASSO and COX regression, and validated it in four independent BC cohorts, which might help to improve clinical outcomes of BC patients, offer insights into the underlying molecular mechanisms of BC development and suggest potential therapeutic targets for BC.
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Affiliation(s)
- Xitong Yao
- Cell Signal Transduction Laboratory, Department of Predictive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng 475004, China; (X.Y.); (X.Q.); (Y.W.); (T.H.); (T.Y.); (L.Z.); (Y.W.); (H.Z.)
| | - Xinlei Qi
- Cell Signal Transduction Laboratory, Department of Predictive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng 475004, China; (X.Y.); (X.Q.); (Y.W.); (T.H.); (T.Y.); (L.Z.); (Y.W.); (H.Z.)
| | - Yao Wang
- Cell Signal Transduction Laboratory, Department of Predictive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng 475004, China; (X.Y.); (X.Q.); (Y.W.); (T.H.); (T.Y.); (L.Z.); (Y.W.); (H.Z.)
| | - Baokun Zhang
- Beijing Key Laboratory of New Molecular Diagnosis Technologies for Infectious Diseases, Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China;
| | - Tianshuai He
- Cell Signal Transduction Laboratory, Department of Predictive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng 475004, China; (X.Y.); (X.Q.); (Y.W.); (T.H.); (T.Y.); (L.Z.); (Y.W.); (H.Z.)
| | - Taoning Yan
- Cell Signal Transduction Laboratory, Department of Predictive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng 475004, China; (X.Y.); (X.Q.); (Y.W.); (T.H.); (T.Y.); (L.Z.); (Y.W.); (H.Z.)
| | - Lu Zhang
- Cell Signal Transduction Laboratory, Department of Predictive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng 475004, China; (X.Y.); (X.Q.); (Y.W.); (T.H.); (T.Y.); (L.Z.); (Y.W.); (H.Z.)
| | - Yange Wang
- Cell Signal Transduction Laboratory, Department of Predictive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng 475004, China; (X.Y.); (X.Q.); (Y.W.); (T.H.); (T.Y.); (L.Z.); (Y.W.); (H.Z.)
| | - Hong Zheng
- Cell Signal Transduction Laboratory, Department of Predictive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng 475004, China; (X.Y.); (X.Q.); (Y.W.); (T.H.); (T.Y.); (L.Z.); (Y.W.); (H.Z.)
| | - Guosen Zhang
- Cell Signal Transduction Laboratory, Department of Predictive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng 475004, China; (X.Y.); (X.Q.); (Y.W.); (T.H.); (T.Y.); (L.Z.); (Y.W.); (H.Z.)
- Correspondence: or (G.Z.); (X.G.); Tel.: +86-18237808750 (G.Z.)
| | - Xiangqian Guo
- Cell Signal Transduction Laboratory, Department of Predictive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng 475004, China; (X.Y.); (X.Q.); (Y.W.); (T.H.); (T.Y.); (L.Z.); (Y.W.); (H.Z.)
- Correspondence: or (G.Z.); (X.G.); Tel.: +86-18237808750 (G.Z.)
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OSov: An Interactive Web Server to Evaluate Prognostic Biomarkers for Ovarian Cancer. BIOLOGY 2021; 11:biology11010023. [PMID: 35053021 PMCID: PMC8773055 DOI: 10.3390/biology11010023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/17/2021] [Accepted: 12/21/2021] [Indexed: 11/16/2022]
Abstract
Simple Summary The OSov web server incorporates gene expression profiles with clinical risk factors to estimate the ovarian cancers patients’ survival, and provides a tool for multiple analysis, such as forest-plot, uni/multi-variate survival analysis, Kaplan-Meier plot and nomogram construction. Abstract Ovarian cancer is one of the most aggressive and highly lethal gynecological cancers. The purpose of our study is to build a free prognostic web server to help researchers discover potential prognostic biomarkers by integrating gene expression profiling data and clinical follow-up information of ovarian cancer. We construct a prognostic web server OSov (Online consensus Survival analysis for Ovarian cancer) based on RNA expression profiles. OSov is a user-friendly web server which could present a Kaplan–Meier plot, forest plot, nomogram and survival summary table of queried genes in each individual cohort to evaluate the prognostic potency of each queried gene. To assess the performance of OSov web server, 163 previously published prognostic biomarkers of ovarian cancer were tested and 72% of them had their prognostic values confirmed in OSov. It is a free and valuable prognostic web server to screen and assess survival-associated biomarkers for ovarian cancer.
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Dhall A, Jain S, Sharma N, Naorem LD, Kaur D, Patiyal S, Raghava GPS. In silico tools and databases for designing cancer immunotherapy. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 129:1-50. [PMID: 35305716 DOI: 10.1016/bs.apcsb.2021.11.008] [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: 06/14/2023]
Abstract
Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.
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Affiliation(s)
- Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Shipra Jain
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Neelam Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Leimarembi Devi Naorem
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India.
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Wang Q, An Y, Wang F, Zhang G, Zhang L, Dong H, Xin J, Li Y, Ji S, Guo X. OSchol: an online consensus survival web server for cholangiocarcinoma prognosis analysis. HPB (Oxford) 2021; 23:545-550. [PMID: 32888851 DOI: 10.1016/j.hpb.2020.08.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Revised: 08/12/2020] [Accepted: 08/12/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND As the most common biliary ducts, cholangiocarcinoma (CHOL) is an aggressive malignancy with complex pathological context, high mortality and relapse rate. The current therapy of CHOL is mainly performed with surgery followed by chemoradiotherapy. Due to the high metastasis and relapse rate of CHOL, the prognosis of CHOL is still poor, and the molecular prognostic system is to be constructed. METHODS In this study, we have established an online prognostic analysis web server named OSchol to evaluate the correlation between candidate genes and survival for CHOL. RESULTS The prognostic values of previous published biomarkers in OSchol, including ITIH4, PTEN and DACH1, have been validated by OSchol. In addition, we have identified novel potential prognostic biomarker for CHOL using OSchol, that E2F1 has significant prognostic ability in OSchol (both TCGA and GSE107943 cohorts). CONCLUSION Our study provides a platform for researchers and clinicians to screen, develop and validate their genes of interest to be potential prognostic biomarkers for CHOL and may also help guide the targeted therapies for CHOL.
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Affiliation(s)
- Qiang Wang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
| | - Yang An
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
| | - Fengling Wang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
| | - Guosen Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
| | - Lu Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
| | - Huan Dong
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
| | - Junfang Xin
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
| | - Yongqiang Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
| | - Shaoping Ji
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
| | - Xiangqian Guo
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China.
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Yang J, Lin J, An J, Zhao Y, Jing S, Yu M, Zhu Y, Yao Y. TRIB3 Promotes the Malignant Progression of Bladder Cancer: An Integrated Analysis of Bioinformatics and in vitro Experiments. Front Genet 2021; 12:649208. [PMID: 33841505 PMCID: PMC8033215 DOI: 10.3389/fgene.2021.649208] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 03/01/2021] [Indexed: 11/16/2022] Open
Abstract
Background Bladder cancer is a common malignant tumor characterized by high mortality and high management costs; however, it lacks useful molecular prognostic markers. Tribbles pseudokinase 3 (TRIB3) is a pseudokinase that participates in cell tumor progression and metabolism and whose function in bladder cancer is not precisely known. Main Methods We downloaded transcriptome data and clinical data of bladder cancer from associated databases and extracted the expression matrix of TRIB3 for multiple bioinformatics analysis. RT-PCR detected the expression of TRIB3 in bladder cancer cells. After knockdown of TRIB3 with siRNA, we investigated TRIB3 function using CCK8, Cell Cycle and Transwell assays. Key Findings Kaplan–Meier analysis of TRIB3 in the four cohorts showed that high expression of TRIB3 correlated with poor outcome. Expression of TRIB3 positively correlated with stage and grade and down-regulation of TRIB3 expression significantly inhibited proliferation, migration and cell cycle of bladder cancer cells. Significance TRIB3 is a potential prognostic marker and therapeutic target. It can be used to individualize the treatment of bladder cancer.
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Affiliation(s)
- Jieping Yang
- Department of Urology, The First Hospital of China Medical University, Shenyang, China
| | - Jiaxing Lin
- Department of Urology, The First Hospital of China Medical University, Shenyang, China
| | - Jun An
- Department of Urology, Shenzhen Second People's Hospital, Shenzhen, China
| | - Yongkang Zhao
- Joint Laboratory of Artificial Intelligence and Precision Medicine of China Medical University and Northeastern University, Northeastern University, Shenyang, China
| | - Siyang Jing
- Department of Laboratory Animal Science, China Medical University, Shenyang, China
| | - Meng Yu
- Department of Laboratory Animal Science, China Medical University, Shenyang, China
| | - Yuyan Zhu
- Department of Urology, The First Hospital of China Medical University, Shenyang, China
| | - Yang Yao
- Department of Physiology, Shenyang Medical College, Shenyang, China
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OSmfs: An Online Interactive Tool to Evaluate Prognostic Markers for Myxofibrosarcoma. Genes (Basel) 2020; 11:genes11121523. [PMID: 33352742 PMCID: PMC7766036 DOI: 10.3390/genes11121523] [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: 10/28/2020] [Revised: 12/14/2020] [Accepted: 12/17/2020] [Indexed: 11/17/2022] Open
Abstract
Myxofibrosarcoma is a complex genetic disease with poor prognosis. However, more effective biomarkers that forebode poor prognosis in Myxofibrosarcoma remain to be determined. Herein, utilizing gene expression profiling data and clinical follow-up data of Myxofibrosarcoma cases in three independent cohorts with a total of 128 Myxofibrosarcoma samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we constructed an easy-to-use web tool, named Online consensus Survival analysis for Myxofibrosarcoma (OSmfs) to analyze the prognostic value of certain genes. Through retrieving the database, users generate a Kaplan–Meier plot with log-rank test and hazard ratio (HR) to assess prognostic-related genes or discover novel Myxofibrosarcoma prognostic biomarkers. The effectiveness and availability of OSmfs were validated using genes in ever reports predicting the prognosis of Myxofibrosarcoma patients. Furthermore, utilizing the cox analysis data and transcriptome data establishing OSmfs, seven genes were selected and considered as more potentially prognostic biomarkers through overlapping and ROC analysis. In conclusion, OSmfs is a promising web tool to evaluate the prognostic potency and reliability of genes in Myxofibrosarcoma, which may significantly contribute to the enrichment of novelly potential prognostic biomarkers and therapeutic targets for Myxofibrosarcoma.
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An Y, Wang Q, Sun F, Zhang G, Wang F, Zhang L, Li Y, Ren W, Zhu W, Li Y, Ji S, Guo X. OSucs: An Online Prognostic Biomarker Analysis Tool for Uterine Carcinosarcoma. Genes (Basel) 2020; 11:genes11091040. [PMID: 32899312 PMCID: PMC7563768 DOI: 10.3390/genes11091040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 08/24/2020] [Accepted: 09/02/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Uterine carcinosarcoma (UCS) is a type of rare and aggressive tumor. The standard treatment for UCS involves surgical treatment followed by radiochemotherapy. Clinical outcomes of UCS patients are poor due to high metastasis and relapse rate. Therefore, new targeted therapy strategies for UCS are needed. Because UCS is highly heterogenous, it is critical to identify and develop prognostic biomarkers to distinguish molecular subtypes of UCS for better treatment guidance. METHODS Using gene expression profiles and clinical follow-up data, we developed an online consensus survival analysis tool named OSucs. This web tool allows researchers to conveniently analyze the prognostic abilities of candidate genes in UCS. RESULTS To test the reliability of this server, we analyzed five previously reported prognostic biomarkers, all of which showed significant prognostic impacts. In addition, ETV4 (ETS variant transcription factor 4), ANGPTL4 (Angiopoietin-like protein 4), HIST1H1C (Histone cluster 1 H1 family member c) and CTSV (Cathepsin V) showed prognostic potential in a molecular subtype-specific manner. CONCLUSION We built a platform for researchers to analyze if genes have prognostic potentials in UCS.
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Affiliation(s)
- Yang An
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng 475004, China; (Y.A.); (Q.W.); (F.S.); (G.Z.); (F.W.); (L.Z.); (Y.L.); (W.R.); (Y.L.); (S.J.)
| | - Qiang Wang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng 475004, China; (Y.A.); (Q.W.); (F.S.); (G.Z.); (F.W.); (L.Z.); (Y.L.); (W.R.); (Y.L.); (S.J.)
| | - Fengjie Sun
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng 475004, China; (Y.A.); (Q.W.); (F.S.); (G.Z.); (F.W.); (L.Z.); (Y.L.); (W.R.); (Y.L.); (S.J.)
| | - Guosen Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng 475004, China; (Y.A.); (Q.W.); (F.S.); (G.Z.); (F.W.); (L.Z.); (Y.L.); (W.R.); (Y.L.); (S.J.)
| | - Fengling Wang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng 475004, China; (Y.A.); (Q.W.); (F.S.); (G.Z.); (F.W.); (L.Z.); (Y.L.); (W.R.); (Y.L.); (S.J.)
| | - Lu Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng 475004, China; (Y.A.); (Q.W.); (F.S.); (G.Z.); (F.W.); (L.Z.); (Y.L.); (W.R.); (Y.L.); (S.J.)
| | - Yanan Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng 475004, China; (Y.A.); (Q.W.); (F.S.); (G.Z.); (F.W.); (L.Z.); (Y.L.); (W.R.); (Y.L.); (S.J.)
| | - Weinan Ren
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng 475004, China; (Y.A.); (Q.W.); (F.S.); (G.Z.); (F.W.); (L.Z.); (Y.L.); (W.R.); (Y.L.); (S.J.)
| | - Wan Zhu
- Department of Anesthesia, Stanford University, Stanford, CA 94305, USA;
| | - Yongqiang Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng 475004, China; (Y.A.); (Q.W.); (F.S.); (G.Z.); (F.W.); (L.Z.); (Y.L.); (W.R.); (Y.L.); (S.J.)
| | - Shaoping Ji
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng 475004, China; (Y.A.); (Q.W.); (F.S.); (G.Z.); (F.W.); (L.Z.); (Y.L.); (W.R.); (Y.L.); (S.J.)
| | - Xiangqian Guo
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng 475004, China; (Y.A.); (Q.W.); (F.S.); (G.Z.); (F.W.); (L.Z.); (Y.L.); (W.R.); (Y.L.); (S.J.)
- Correspondence: ; Tel.: +86-0371-22892860
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11
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Lyu L, Xiang W, Zheng F, Huang T, Feng Y, Yuan J, Zhang C. Significant Prognostic Value of the Autophagy-Related Gene P4HB in Bladder Urothelial Carcinoma. Front Oncol 2020; 10:1613. [PMID: 32903592 PMCID: PMC7438560 DOI: 10.3389/fonc.2020.01613] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 07/24/2020] [Indexed: 01/01/2023] Open
Abstract
While hundreds of consistently altered autophagy-related genes (ARGs) have been identified in cancers, their prognostic value in bladder urothelial carcinoma (BUC) remains unclear. In the present study, we collected 232 ARGs from the Human Autophagy Database (HADb), and identified 37 differentially expressed ARGs in BUC based on The Cancer Genome Atlas (TCGA) database. Kaplan-Meier survival analysis based on the Gene Expression Profiling Interactive Analysis (GEPIA) database revealed that among the 37 differentially expressed ARGs, prolyl 4-hydroxylase, beta polypeptide (P4HB), and regulator of G protein signaling 19 (RGS19) were significantly negatively correlated with overall survival (OS) and disease-free survival (DFS). Overexpression of P4HB and RGS19 in BUC was further validated using independent data sets, including those from the Oncomine and Gene Expression Omnibus (GEO) databases. cBioPortal and UALCAN analyses indicated that altered P4HB and RGS19 mRNA expression was significantly associated with mutations and clinical characteristics (nodal metastasis and cancer stage). Moreover, co-expression network analysis and gene set enrichment analysis (GSEA) predicted that the potential functions of P4HB and RGS19 are involved in the endoplasmic reticulum (ER) stress response, cytokine-mediated signaling pathway and inflammatory response. More importantly, multivariate Cox proportional hazards regression analysis demonstrated that P4HB, but not RGS19, is an independent and unfavorable BUC biomarker based on clinical characteristics (age, gender, cancer stage, and pathological TNM stage). Finally, we validated that the mRNA and protein expression levels of P4HB were upregulated in four bladder cancer cell lines (T24, J82, EJ, and SW780) and found that knockdown of P4HB dramatically inhibited the invasion and proliferation of bladder cancer cells. In summary, our study screened ARGs and identified P4HB as a biomarker that can predict the progression and prognosis of BUC and may provide a better understanding of the autophagy regulatory mechanisms involved in BUC.
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Affiliation(s)
- Lei Lyu
- Department of Urology, Wuhan No.1 Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Xiang
- Department of Urology, Wuhan No.1 Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Fuxin Zheng
- Department of Urology, Wuhan No.1 Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Huang
- Department of Urology, Wuhan No.1 Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Feng
- Department of Pathology, Wuhan No.1 Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Jingdong Yuan
- Department of Urology, Wuhan No.1 Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Chuanhua Zhang
- Department of Urology, Wuhan No.1 Hospital, Huazhong University of Science and Technology, Wuhan, China
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12
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Xie L, Cai L, Wang F, Zhang L, Wang Q, Guo X. Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients. Front Bioeng Biotechnol 2020; 8:805. [PMID: 32850704 PMCID: PMC7412969 DOI: 10.3389/fbioe.2020.00805] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Accepted: 06/22/2020] [Indexed: 12/18/2022] Open
Abstract
Gastric cancer (GC) is the second leading cause of cancer mortality and remains the fourth common cancer worldwide. The effective and feasible methods for predicting the possible outcomes for GC patients are still lacking. While genetic profiling might be suitable in some way, the application of gene expression signatures has been show to be a robust tool. Here, by performing a comprehensive search in PubMed, we provided an up-to-date summary of 39 prognostic gene signatures for GC patients, and described the processing procedure of the selection, calculation and construction of gene signature. We also reviewed current web tools including PROGgene and SurvExpress that can be used to analyze the prognostic value of multiple genes for GC. This review will aid in comprehensive understanding of the current prognostic gene signatures to accurately predict the outcome of GC patients, and may guide the future clinical management when the reliability of these signatures is validated in clinics.
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Affiliation(s)
- Longxiang Xie
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Linghao Cai
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Fei Wang
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Lu Zhang
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Qiang Wang
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Xiangqian Guo
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
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13
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Li F, Teng H, Liu M, Liu B, Zhang D, Xu Z, Wang Y, Zhou H. Prognostic Value of Immune-Related Genes in the Tumor Microenvironment of Bladder Cancer. Front Oncol 2020; 10:1302. [PMID: 32850407 PMCID: PMC7399341 DOI: 10.3389/fonc.2020.01302] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 06/23/2020] [Indexed: 12/12/2022] Open
Abstract
The tumor microenvironment (TME) is a complex system that plays an important role in tumor development and progression, but the current knowledge about its effect on bladder cancer (BC) is scarce. In this study, we performed a comprehensive analysis of the relationship between the TME and gene expression profiles to identify prognostic biomarkers for BC. The ESTIMATE algorithm was used to calculate immune and stromal scores of BC patients who were obtained from the Gene Expression Omnibus database. We found that the immune and stromal scores were associated with clinical characteristics and the prognosis of BC patients. Based on these scores, 104 immune-related differentially expressed genes were identified. Further, functional enrichment analysis revealed that these genes were mainly involved in the immune-related biological processes and signaling pathways. Three prognostic genes were then identified and used to establish a risk prediction model using Cox regression analyses. Kaplan–Meier survival analysis showed that the expression levels of COL1A1, COMP, and SERPINE2 significantly correlated with cancer-specific survival and overall survival of BC patients. Additionally, we validated the prognostic values of these genes using two independent cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases. Finally, the relationships between the three prognostic genes and several immune cells were evaluated using Tumor Immune Estimation Resource, indicating that the expression levels of COL1A1, COMP, and SERPINE2 correlated positively with the tumor infiltration levels of CD4+ T cells and macrophages. In conclusion, the current study comprehensively analyzed the TME and presented immune-related prognostic genes for BC, providing new insights into immunotherapeutic strategies for BC patients.
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Affiliation(s)
- Faping Li
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Haolin Teng
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Mingdi Liu
- Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, China
| | - Bin Liu
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Difei Zhang
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Zhixiang Xu
- Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, China
| | - Yishu Wang
- Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, China
| | - Honglan Zhou
- Department of Urology, The First Hospital of Jilin University, Changchun, China
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14
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An Y, Wang Q, Zhang L, Sun F, Zhang G, Dong H, Li Y, Peng Y, Li H, Zhu W, Ji S, Wang Y, Guo X. OSlgg: An Online Prognostic Biomarker Analysis Tool for Low-Grade Glioma. Front Oncol 2020; 10:1097. [PMID: 32775301 PMCID: PMC7381343 DOI: 10.3389/fonc.2020.01097] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 06/02/2020] [Indexed: 12/28/2022] Open
Abstract
Glioma is the most frequent primary brain tumor that causes high mortality and morbidity with poor prognosis. There are four grades of gliomas, I to IV, among which grade II and III are low-grade glioma (LGG). Although less aggressive, LGG almost universally progresses to high-grade glioma and eventual causes death if lacking of intervention. Current LGG treatment mainly depends on surgical resection followed by radiotherapy and chemotherapy, but the survival rates of LGG patients are low. Therefore, it is necessary to use prognostic biomarkers to classify patients into subgroups with different risks and guide clinical managements. Using gene expression profiling and long-term follow-up data, we established an Online consensus Survival analysis tool for LGG named OSlgg. OSlgg is comprised of 720 LGG cases from two independent cohorts. To evaluate the prognostic potency of genes, OSlgg employs the Kaplan-Meier plot with hazard ratio and p value to assess the prognostic significance of genes of interest. The reliability of OSlgg was verified by analyzing 86 previously published prognostic biomarkers of LGG. Using OSlgg, we discovered two novel potential prognostic biomarkers (CD302 and FABP5) of LGG, and patients with the elevated expression of either CD302 or FABP5 present the unfavorable survival outcome. These two genes may be novel risk predictors for LGG patients after further validation. OSlgg is public and free to the users at http://bioinfo.henu.edu.cn/LGG/LGGList.jsp.
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Affiliation(s)
- Yang An
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Qiang Wang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Lu Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Fengjie Sun
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Guosen Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Huan Dong
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Yingkun Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Yanyu Peng
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Haojie Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Wan Zhu
- Department of Anesthesia, Stanford University, Stanford, CA, United States
| | - Shaoping Ji
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Yunlong Wang
- Henan Bioengineering Research Center, Zhengzhou, China
| | - Xiangqian Guo
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
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15
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Nedjadi T, Benabdelkamal H, Albarakati N, Masood A, Al-Sayyad A, Alfadda AA, Alanazi IO, Al-Ammari A, Al-Maghrabi J. Circulating proteomic signature for detection of biomarkers in bladder cancer patients. Sci Rep 2020; 10:10999. [PMID: 32620920 PMCID: PMC7335182 DOI: 10.1038/s41598-020-67929-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 06/15/2020] [Indexed: 12/12/2022] Open
Abstract
The identification of clinically-relevant early diagnostic and prognostic protein biomarkers is essential to maximize therapeutic efficacy and prevent cancer progression. The aim of the current study is to determine whether aberrant plasma protein profile can be applied as a surrogate tool for early diagnosis of bladder carcinoma. Plasma samples from patients with low grade non-muscle invasive bladder cancer and healthy controls were analyzed using combined 2D-DIGE and mass-spectrometry to identify differentially expressed proteins. Validation was performed using western blotting analysis in an independent cohort of cancer patients and controls. Fifteen differentially-expressed proteins were identified of which 12 were significantly up-regulated and three were significantly down-regulated in tumors compared to controls. The Ingenuity Pathways Analysis revealed functional connection between the differentially-expressed proteins and immunological disease, inflammatory disease and cancer mediated through chemokine and cytokine signaling pathway and NF-kB transcription factor. Among the three validated proteins, haptoglobin was able to distinguish between patients with low grade bladder cancer and the controls with high sensitivity and specificity (AUC > 0.87). In conclusion, several biomarker proteins were identified in bladder cancer. Haptoglobin is a potential candidate that merit further investigation to validate its usefulness and functional significance as potential biomarkers for early detection of bladder cancer.
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Affiliation(s)
- Taoufik Nedjadi
- King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, PO Box 9515, Jeddah, 21423, Saudi Arabia.
| | - Hicham Benabdelkamal
- Proteomics Resource Unit, Obesity Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Nada Albarakati
- King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, PO Box 9515, Jeddah, 21423, Saudi Arabia
| | - Afshan Masood
- Proteomics Resource Unit, Obesity Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Ahmed Al-Sayyad
- Department of Urology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Assim A Alfadda
- Proteomics Resource Unit, Obesity Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Ibrahim O Alanazi
- National Center for Biotechnology (NCBT), Life Science and Environment Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
| | - Adel Al-Ammari
- Department of Urology, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia
| | - Jaudah Al-Maghrabi
- Department of Pathology, King Abdulaziz University, Jeddah, Saudi Arabia
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16
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An Y, Wang Q, Zhang G, Sun F, Zhang L, Li H, Li Y, Peng Y, Zhu W, Ji S, Guo X. OSlihc: An Online Prognostic Biomarker Analysis Tool for Hepatocellular Carcinoma. Front Pharmacol 2020; 11:875. [PMID: 32587519 PMCID: PMC7298068 DOI: 10.3389/fphar.2020.00875] [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: 01/12/2020] [Accepted: 05/27/2020] [Indexed: 12/16/2022] Open
Abstract
Liver hepatocellular carcinoma (LIHC) is one of the most common malignant tumors in the world with an increasing number of fatalities. Identification of novel prognosis biomarker for LIHC may improve treatment and therefore patient outcomes. The availability of public gene expression profiling data offers the opportunity to discover prognosis biomarkers for LIHC. We developed an online consensus survival analysis tool named OSlihc using gene expression profiling and long-term follow-up data to identify new prognosis biomarkers. OSlihc consists of 637 cases from four independent cohorts. As a risk assessment tool, OSlihc generates the Kaplan-Meier survival plot with hazard ratio (HR) and p value to evaluate the prognostic value of a gene of interest. To test the reliability of OSlihc, we analyzed 65 previous reported prognostic biomarkers in OSlihc and showed that all of which have significant prognostic values. Furthermore, we identified four novel potential prognostic biomarkers (ATG9A, WIPI1, CXCL1, and CSNK2A2) for LIHC, the elevated expression of which predict the unfavorable survival outcomes. These genes (ATG9A, WIPI1, CXCL1, and CSNK2A2) may be potentially new biomarkers to identify at-risk LIHC patients when further validated. By OSlihc, users can evaluate the prognostic abilities of genes of their interest, which provides a platform for researchers to identify prognostic biomarkers to further develop targeted therapy strategies for LIHC patients. OSlihc is public and free to the users at http://bioinfo.henu.edu.cn/LIHC/LIHCList.jsp.
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Affiliation(s)
- Yang An
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Qiang Wang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Guosen Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Fengjie Sun
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Lu Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Haojie Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Yingkun Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Yanyu Peng
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Wan Zhu
- Department of Anesthesia, Stanford University, Stanford, CA, United States
| | - Shaoping Ji
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Xiangqian Guo
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
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17
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Xiao G, Yang Q, Bao Z, Mao H, Zhang Y, Lin S. Expression of tripartite motif-containing 44 and its prognostic and clinicopathological value in human malignancies:a meta-analysis. BMC Cancer 2020; 20:525. [PMID: 32503466 PMCID: PMC7275359 DOI: 10.1186/s12885-020-07014-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 05/28/2020] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Previous researches have reported that tripartite motif-containing 44 (TRIM44) is related to the prognosis of multiple human tumors. This study was designed to systematically assess the prognostic value of TRIM44 in human malignancies and summarize its possible tumor-related mechanisms. METHODS The available databases were searched for eligible studies that evaluated the clinicopathological and prognostic roles of TRIM44 in patients with malignancies. The hazard ratios (HR) and odds ratios (OR) were combined to assess the predictive role of TRIM44 using Stata/SE 14.1 software. RESULTS A total of 1740 patients from thirteen original studies were finally included in this study. The results of the combined analysis showed that over-expression of TRIM44 protein was significantly correlated with shorter overall survival (OS) (HR = 1.94, 95% CI: 1.60-2.35) and worse disease-free survival (DFS) (HR = 2.13, 95% CI: 1.24-3.65) in cancer patients. Additionally, the combined ORs indicated that elevated expression level of TRIM44 protein was significantly associated with lymph node metastasis (OR = 2.69, 95% CI: 1.71-4.24), distant metastasis (OR = 10.35, 95% CI: 1.01-106.24), poor tumor differentiation (OR = 1.78, 95% CI: 1.03-3.09), increased depth of tumor invasion (OR = 2.72, 95% CI: 1.73-4.30), advanced clinical stage (OR = 2.75, 95% CI: 2.04-3.71), and recurrence (OR = 2.30, 95% CI: 1.34-3.95). Furthermore, analysis results using Gene Expression Profiling Interactive Analysis (GEPIA) showed that the expression level of TRIM44 mRNA was higher in most tumor tissues than in the corresponding normal tissues, and the relationship between TRIM44 mRNA level and prognosis in various malignant tumors also explored in GEPIA and OS analysis webservers. CONCLUSIONS TRIM44 may serve as a valuable prognostic biomarker and a potential therapeutic target for patients with malignancies.
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Affiliation(s)
- Guoliang Xiao
- Department of General Surgery, the First People's Hospital of Neijiang, Neijiang, 641000, Sichuan Province, PR China
| | - Qiuxi Yang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, 570100, Hainan Province, PR China
| | - Ziwei Bao
- Department of medicine, Southwest Medical University, Luzhou, 646000, Sichuan Province, PR China
| | - Haixia Mao
- Department of medicine, Southwest Medical University, Luzhou, 646000, Sichuan Province, PR China
| | - Yi Zhang
- Department of General Surgery, the First People's Hospital of Neijiang, Neijiang, 641000, Sichuan Province, PR China.
| | - Shibu Lin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, 570100, Hainan Province, PR China
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Yan Z, Wang Q, Lu Z, Sun X, Song P, Dang Y, Xie L, Zhang L, Li Y, Zhu W, Xie T, Ma J, Zhang Y, Guo X. OSluca: An Interactive Web Server to Evaluate Prognostic Biomarkers for Lung Cancer. Front Genet 2020; 11:420. [PMID: 32528519 PMCID: PMC7264384 DOI: 10.3389/fgene.2020.00420] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 04/03/2020] [Indexed: 12/24/2022] Open
Abstract
Lung cancer is the principal cause of leading cancer-related incidence and mortality in the world. Various studies have excavated the potential prognostic biomarkers for cancer patients based on gene expression profiles. However, most of these reported biomarkers lack independent validation in multiple cohorts. Herein, we collected 35 datasets with long-term follow-up clinical information from TCGA (2 cohorts), GEO (32 cohorts), and Roepman study (1 cohort), and developed a web server named OSluca (Online consensus Survival for Lung Cancer) to assess the prognostic value of genes in lung cancer. The input of OSluca is an official gene symbol, and the output web page of OSluca displays the survival analysis summary with a forest plot and a survival table from Cox proportional regression in each cohort and combined cohorts. To test the performance of OSluca, 104 previously reported prognostic biomarkers in lung carcinoma were evaluated in OSluca. In conclusion, OSluca is a highly valuable and interactive prognostic web server for lung cancer. It can be accessed at http:// bioinfo.henu.edu.cn/LUCA/LUCAList.jsp.
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Affiliation(s)
- Zhongyi Yan
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Qiang Wang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Zhendong Lu
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Xiaoxiao Sun
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Pengfei Song
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Yifang Dang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Longxiang Xie
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Lu Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Yongqiang Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Wan Zhu
- Department of Anesthesia, Stanford University, Stanford, CA, United States
| | - Tiantian Xie
- Department of Respiratory and Critical Care Medicine, Huaihe Hospital of Henan University, Kaifeng, China
| | - Jing Ma
- Department of Respiratory and Critical Care Medicine, Huaihe Hospital of Henan University, Kaifeng, China
| | - Yijie Zhang
- Department of Respiratory and Critical Care Medicine, Huaihe Hospital of Henan University, Kaifeng, China
| | - Xiangqian Guo
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
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19
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Zhang L, Wang Q, Wang L, Xie L, An Y, Zhang G, Zhu W, Li Y, Liu Z, Zhang X, Tang P, Huo X, Guo X. OSskcm: an online survival analysis webserver for skin cutaneous melanoma based on 1085 transcriptomic profiles. Cancer Cell Int 2020; 20:176. [PMID: 32467670 PMCID: PMC7236197 DOI: 10.1186/s12935-020-01262-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 05/13/2020] [Indexed: 12/11/2022] Open
Abstract
Background Cutaneous melanoma is one of the most aggressive and lethal skin cancers. It is greatly important to identify prognostic biomarkers to guide the clinical management. However, it is technically challenging for untrained researchers to process high dimensional profiling data and identify potential prognostic genes in profiling datasets. Methods In this study, we developed a webserver to analyze the prognostic values of genes in cutaneous melanoma using data from TCGA and GEO databases. The webserver is named Online consensus Survival webserver for Skin Cutaneous Melanoma (OSskcm) which includes 1085 clinical melanoma samples. The OSskcm is hosted in a windows tomcat server. Server-side scripts were developed in Java script. The database system is managed by a SQL Server, which integrates gene expression data and clinical data. The Kaplan–Meier (KM) survival curves, Hazard ratio (HR) and 95% confidence interval (95%CI) were calculated in a univariate Cox regression analysis. Results In OSskcm, by inputting official gene symbol and selecting proper options, users could obtain KM survival plot with log-rank P value and HR on the output web page. In addition, clinical characters including race, stage, gender, age and type of therapy could also be included in the prognosis analysis as confounding factors to constrain the analysis in a subgroup of melanoma patients. Conclusion The OSskcm is highly valuable for biologists and clinicians to perform the assessment and validation of new or interested prognostic biomarkers for melanoma. OSskcm can be accessed online at: http://bioinfo.henu.edu.cn/Melanoma/MelanomaList.jsp.
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Affiliation(s)
- Lu Zhang
- 1Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004 Henan China
| | - Qiang Wang
- 1Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004 Henan China
| | - Lijie Wang
- 1Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004 Henan China
| | - Longxiang Xie
- 1Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004 Henan China
| | - Yang An
- 1Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004 Henan China
| | - Guosen Zhang
- 1Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004 Henan China
| | - Wan Zhu
- 3Department of Anesthesia, Stanford University, Stanford, CA 94305 USA
| | - Yongqiang Li
- 1Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004 Henan China
| | - Zhihui Liu
- 1Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004 Henan China
| | - Xiaochen Zhang
- 1Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004 Henan China
| | - Panpan Tang
- 1Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004 Henan China
| | - Xiaozheng Huo
- 1Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004 Henan China
| | - Xiangqian Guo
- 1Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, 475004 Henan China.,2Henan Provincial Engineering Centre for Tumor Molecular Medicine, Henan University, Kaifeng, 475004 Henan China
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20
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Wang Q, Yan Z, Ge L, Li N, Yang M, Sun X, Xie L, Zhang G, Zhu W, Wang Y, Li Y, Li X, Guo X. OSeac: An Online Survival Analysis Tool for Esophageal Adenocarcinoma. Front Oncol 2020; 10:315. [PMID: 32211334 PMCID: PMC7067743 DOI: 10.3389/fonc.2020.00315] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 02/21/2020] [Indexed: 12/20/2022] Open
Abstract
Esophageal Adenocarcinoma (EAC) is one of the most common gastrointestinal tumors in the world. However, molecular prognostic systems are still lacking for EAC. Hence, we developed an Online consensus Survival analysis web server for Esophageal Adenocarcinoma (OSeac), to centralize published gene expression data and clinical follow up data of EAC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). OSeac includes 198 EAC cases with gene expression profiling and relevant clinical long-term follow-up data, and employs the Kaplan Meier (KM) survival plot with hazard ratio (HR) and log rank test to estimate the prognostic potency of genes of interests for EAC patients. Moreover, we have determined the reliability of OSeac by using previously reported prognostic biomarkers such as DKK3, CTO1, and TXNIP. OSeac is free and publicly accessible at http://bioinfo.henu.edu.cn/EAC/EACList.jsp.
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Affiliation(s)
- Qiang Wang
- Cell Signal Transduction Laboratory, Bioinformatics Department of Predictive Medicine, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Zhongyi Yan
- Cell Signal Transduction Laboratory, Bioinformatics Department of Predictive Medicine, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Linna Ge
- Cell Signal Transduction Laboratory, Bioinformatics Department of Predictive Medicine, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Ning Li
- Cell Signal Transduction Laboratory, Bioinformatics Department of Predictive Medicine, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Mengsi Yang
- Cell Signal Transduction Laboratory, Bioinformatics Department of Predictive Medicine, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Xiaoxiao Sun
- Cell Signal Transduction Laboratory, Bioinformatics Department of Predictive Medicine, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Longxiang Xie
- Cell Signal Transduction Laboratory, Bioinformatics Department of Predictive Medicine, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Guosen Zhang
- Cell Signal Transduction Laboratory, Bioinformatics Department of Predictive Medicine, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Wan Zhu
- Department of Anesthesia, Stanford University, Stanford, CA, United States
| | - Yunlong Wang
- Henan Bioengineering Research Center, Zhengzhou, China
| | - Yongqiang Li
- Cell Signal Transduction Laboratory, Bioinformatics Department of Predictive Medicine, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Xianzhe Li
- Department of Thoracic Surgery, The Affiliated Nanshi Hospital of Henan University, Nanyang, China
| | - Xiangqian Guo
- Cell Signal Transduction Laboratory, Bioinformatics Department of Predictive Medicine, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Institute of Biomedical Informatics, Henan University, Kaifeng, China
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21
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Zhu W, Xie L, Han J, Guo X. The Application of Deep Learning in Cancer Prognosis Prediction. Cancers (Basel) 2020; 12:E603. [PMID: 32150991 PMCID: PMC7139576 DOI: 10.3390/cancers12030603] [Citation(s) in RCA: 120] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 02/28/2020] [Accepted: 03/02/2020] [Indexed: 12/11/2022] Open
Abstract
Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.
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Affiliation(s)
- Wan Zhu
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics center, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China;
- Department of Anesthesia, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA
| | - Longxiang Xie
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics center, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China;
| | - Jianye Han
- Department of Computer Science, University of Illinois, Urbana Champions, IL 61820, USA;
| | - Xiangqian Guo
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics center, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China;
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Lv S, Zhang G, Xie L, Yan Z, Wang Q, Li Y, Zhang L, Han Y, Li H, Du Y, Yang Y, Guo X. High TXLNA Expression Predicts Favourable Outcome for Pancreatic Adenocarcinoma Patients. BIOMED RESEARCH INTERNATIONAL 2020; 2020:2585862. [PMID: 32185195 PMCID: PMC7060861 DOI: 10.1155/2020/2585862] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Accepted: 01/30/2020] [Indexed: 02/06/2023]
Abstract
TXLNA (taxilin alpha), a binding partner of the syntaxin family, was identified as a key factor in the coordination of intracellular vesicle trafficking and highly expressed in various tumor cells. However, the accurate relation between TXLNA and tumorigenesis and progression of pancreatic adenocarcinoma (PAAD) is still unclear. The present study was designed to examine the expression profile of TXLNA and explore its prognostic significance in PAAD patients and the possible molecular regulatory mechanism by analyzing a series of data from databases, including GEPIA, LOGpc, STRING, and GeneMANIA. The results indicate that TXLNA mRNA and protein were remarkably increased in PAAD tissues compared with normal pancreatic tissues. The high TXLNA expression was significantly correlated with superior overall survival (OS), disease-free interval (DFI), disease specific survival (DSS), and progression-free interval (PFI) for PAAD patients. In summary, high TXLNA expression could predict favourable OS, DFI, DSS, and PFI for PAAD patients, and it might be as potential prognostic biomarkers and targets for PAAD.
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Affiliation(s)
- Shuangyu Lv
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Guosen Zhang
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Longxiang Xie
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Zhongyi Yan
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Qiang Wang
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Yongqiang Li
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Lu Zhang
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Yali Han
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Huimin Li
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Yaowu Du
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Yanjie Yang
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Xiangqian Guo
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
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23
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Dong H, Wang Q, Li N, Lv J, Ge L, Yang M, Zhang G, An Y, Wang F, Xie L, Li Y, Zhu W, Zhang H, Zhang M, Guo X. OSgbm: An Online Consensus Survival Analysis Web Server for Glioblastoma. Front Genet 2020; 10:1378. [PMID: 32153627 PMCID: PMC7046682 DOI: 10.3389/fgene.2019.01378] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 12/17/2019] [Indexed: 02/06/2023] Open
Abstract
Glioblastoma (GBM) is the most common malignant tumor of the central nervous system. GBM causes poor clinical outcome and high mortality rate, mainly due to the lack of effective targeted therapy and prognostic biomarkers. Here, we developed a user-friendly Online Survival analysis web server for GlioBlastoMa, abbreviated OSgbm, to assess the prognostic value of candidate genes. Currently, OSgbm contains 684 samples with transcriptome profiles and clinical information from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA). The survival analysis results can be graphically presented by Kaplan-Meier (KM) plot with Hazard ratio (HR) and log-rank p value. As demonstration, the prognostic value of 51 previously reported survival associated biomarkers, such as PROM1 (HR = 2.4120, p = 0.0071) and CXCR4 (HR = 1.5578, p < 0.001), were confirmed in OSgbm. In summary, OSgbm allows users to evaluate and develop prognostic biomarkers of GBM. The web server of OSgbm is available at http://bioinfo.henu.edu.cn/GBM/GBMList.jsp.
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Affiliation(s)
- Huan Dong
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Qiang Wang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Ning Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Jiajia Lv
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Linna Ge
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Mengsi Yang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Guosen Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Yang An
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Fengling Wang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Longxiang Xie
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Yongqiang Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Wan Zhu
- Department of Anesthesia, Stanford University School of Medicine, Stanford, CA, United States
| | - Haiyu Zhang
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | | | - Xiangqian Guo
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
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CD147 Expression Is Associated with Tumor Proliferation in Bladder Cancer via GSDMD. BIOMED RESEARCH INTERNATIONAL 2020; 2020:7638975. [PMID: 32149134 PMCID: PMC7054768 DOI: 10.1155/2020/7638975] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 12/18/2019] [Accepted: 01/22/2020] [Indexed: 12/21/2022]
Abstract
Purpose CD147, also known as BSG, is a type I transmembrane glycoprotein that belonged to immunoglobulin superfamily. Mature CD147 is an N-linked glycosylated protein and exists on the transmembrane and as soluble forms in tumors. However, the function of CD147 in cell proliferation of bladder cancer (BC) remains to be elucidated. Methods The study included 159 patients with BC and 68 healthy controls. The expression of CD147 and gasdermin D (GSDMD) was analyzed by immunohistochemistry (IHC). Western blotting was performed to detect the expression of proteins in BC cells. The relationship between CD147 and GSDMD was analyzed by the IHC score. Results The expression of CD147 was significantly increased in BC when compared to healthy controls, and the level of CD147 was correlated with tumor proliferation characterized by Ki-67, which is a cell proliferation antigen. In addition, CD147 treatment of BC cells increased the expression of GSDMD, leading to increased Ki-67 expression, while CD147 blockade with peptide in BC significantly reduced GSDMD expression, resulting in reduced cell proliferation. Furthermore, overexpression of GSDMD markedly overcame the inhibitory effect of CD147 peptide on tumor proliferation. BC patients with overexpression of CD147 showed correlation with GSDMD and demonstrated significantly poorer prognosis and overall survival rate. Conclusion These findings suggested that high expression of CD147 contributed to tumor proliferation in BC via GSDMD, which might in turn act as an unfavorable prognostic marker.
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Zheng H, Zhang G, Zhang L, Wang Q, Li H, Han Y, Xie L, Yan Z, Li Y, An Y, Dong H, Zhu W, Guo X. Comprehensive Review of Web Servers and Bioinformatics Tools for Cancer Prognosis Analysis. Front Oncol 2020; 10:68. [PMID: 32117725 PMCID: PMC7013087 DOI: 10.3389/fonc.2020.00068] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 01/15/2020] [Indexed: 01/10/2023] Open
Abstract
Prognostic biomarkers are of great significance to predict the outcome of patients with cancer, to guide the clinical treatments, to elucidate tumorigenesis mechanisms, and offer the opportunity of identifying therapeutic targets. To screen and develop prognostic biomarkers, high throughput profiling methods including gene microarray and next-generation sequencing have been widely applied and shown great success. However, due to the lack of independent validation, only very few prognostic biomarkers have been applied for clinical practice. In order to cross-validate the reliability of potential prognostic biomarkers, some groups have collected the omics datasets (i.e., epigenetics/transcriptome/proteome) with relative follow-up data (such as OS/DSS/PFS) of clinical samples from different cohorts, and developed the easy-to-use online bioinformatics tools and web servers to assist the biomarker screening and validation. These tools and web servers provide great convenience for the development of prognostic biomarkers, for the study of molecular mechanisms of tumorigenesis and progression, and even for the discovery of important therapeutic targets. Aim to help researchers to get a quick learning and understand the function of these tools, the current review delves into the introduction of the usage, characteristics and algorithms of tools, and web servers, such as LOGpc, KM plotter, GEPIA, TCPA, OncoLnc, PrognoScan, MethSurv, SurvExpress, UALCAN, etc., and further help researchers to select more suitable tools for their own research. In addition, all the tools introduced in this review can be reached at http://bioinfo.henu.edu.cn/WebServiceList.html.
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Affiliation(s)
- Hong Zheng
- Cell Signal Transduction Laboratory, Bioinformatics Center, School of Basic Medical Sciences, School of Software, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Guosen Zhang
- Cell Signal Transduction Laboratory, Bioinformatics Center, School of Basic Medical Sciences, School of Software, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Lu Zhang
- Cell Signal Transduction Laboratory, Bioinformatics Center, School of Basic Medical Sciences, School of Software, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Qiang Wang
- Cell Signal Transduction Laboratory, Bioinformatics Center, School of Basic Medical Sciences, School of Software, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Huimin Li
- Cell Signal Transduction Laboratory, Bioinformatics Center, School of Basic Medical Sciences, School of Software, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Yali Han
- Cell Signal Transduction Laboratory, Bioinformatics Center, School of Basic Medical Sciences, School of Software, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Longxiang Xie
- Cell Signal Transduction Laboratory, Bioinformatics Center, School of Basic Medical Sciences, School of Software, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Zhongyi Yan
- Cell Signal Transduction Laboratory, Bioinformatics Center, School of Basic Medical Sciences, School of Software, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Yongqiang Li
- Cell Signal Transduction Laboratory, Bioinformatics Center, School of Basic Medical Sciences, School of Software, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Yang An
- Cell Signal Transduction Laboratory, Bioinformatics Center, School of Basic Medical Sciences, School of Software, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Huan Dong
- Cell Signal Transduction Laboratory, Bioinformatics Center, School of Basic Medical Sciences, School of Software, Institute of Biomedical Informatics, Henan University, Kaifeng, China
| | - Wan Zhu
- Department of Anesthesia, Stanford University, Stanford, CA, United States
| | - Xiangqian Guo
- Cell Signal Transduction Laboratory, Bioinformatics Center, School of Basic Medical Sciences, School of Software, Institute of Biomedical Informatics, Henan University, Kaifeng, China
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26
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Dong H, Wang Q, Zhang G, Li N, Yang M, An Y, Xie L, Li H, Zhang L, Zhu W, Zhao S, Zhang H, Guo X. OSdlbcl: An online consensus survival analysis web server based on gene expression profiles of diffuse large B-cell lymphoma. Cancer Med 2020; 9:1790-1797. [PMID: 31918459 PMCID: PMC7050097 DOI: 10.1002/cam4.2829] [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: 08/19/2019] [Revised: 12/11/2019] [Accepted: 12/26/2019] [Indexed: 12/12/2022] Open
Abstract
Diffuse large B‐cell lymphoma (DLBCL) is the most common subtype of non‐Hodgkin lymphoma (NHL) and is a clinical, pathological, and molecular heterogeneous disease with highly variable clinical outcomes. Currently, valid prognostic biomarkers in DLBCL are still lacking. To optimize targeted therapy and improve the prognosis of DLBCL, the performance of proposed biomarkers needs to be evaluated in multiple cohorts, and new biomarkers need to be investigated in large datasets. Here, we developed a consensus Online Survival analysis web server for Diffuse Large B‐Cell Lymphoma, abbreviated OSdlbcl, to assess the prognostic value of individual gene. To build OSdlbcl, we collected 1100 samples with gene expression profiles and clinical follow‐up information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. In addition, DNA mutation data were also collected from the TCGA database. Overall survival (OS), progression‐free survival (PFS), disease‐specific survival (DSS), disease‐free interval (DFI), and progression‐free interval (PFI) are important endpoints to reflect the survival rate in OSdlbcl. Moreover, clinical features were integrated into OSdlbcl to allow data stratifications according to the user's special needs. By inputting an official gene symbol and selecting desired criteria, the survival analysis results can be graphically presented by the Kaplan‐Meier (KM) plot with hazard ratio (HR) and log‐rank p value. As a proof‐of‐concept demonstration, the prognostic value of 23 previously reported survival associated biomarkers, such as transcription factors FOXP1 and BCL2, was evaluated in OSdlbcl and found to be significantly associated with survival as reported (HR = 1.73, P < .01; HR = 1.47, P = .03, respectively). In conclusion, OSdlbcl is a new web server that integrates public gene expression, gene mutation data, and clinical follow‐up information to provide prognosis evaluations for biomarker development for DLBCL. The OSdlbcl web server is available at https://bioinfo.henu.edu.cn/DLBCL/DLBCLList.jsp.
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Affiliation(s)
- Huan Dong
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Qiang Wang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Guosen Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Ning Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Mengsi Yang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Yang An
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Longxiang Xie
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Huimin Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Lu Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Wan Zhu
- Department of Anesthesia, Stanford University School of Medicine, Stanford, CA, USA
| | - Shuchun Zhao
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Haiyu Zhang
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Xiangqian Guo
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
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27
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Xie L, Wang Q, Nan F, Ge L, Dang Y, Sun X, Li N, Dong H, Han Y, Zhang G, Zhu W, Guo X. OSacc: Gene Expression-Based Survival Analysis Web Tool For Adrenocortical Carcinoma. Cancer Manag Res 2019; 11:9145-9152. [PMID: 31749633 PMCID: PMC6817837 DOI: 10.2147/cmar.s215586] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 10/02/2019] [Indexed: 12/30/2022] Open
Abstract
Gene expression profiling data with long-term clinical follow-up information are great resources to screen, develop, evaluate and validate prognostic biomarkers in translational cancer research. However, an easy-to-use interactive online tool is needed to analyze these profiling and clinical data. In the current work, we developed OSacc (Online consensus Survival analysis of ACC), a web tool that provides rapid and user-friendly survival analysis based on seven independent transcriptomic profiles with long-term clinical follow-up information of 259 ACC patients gathered from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. OSacc allows researchers and clinicians to evaluate the prognostic value of genes of interest by Kaplan–Meier (KM) survival plot with hazard ratio (HR) and log-rank test in ACC. OSacc is freely available at http://bioinfo.henu.edu.cn/ACC/ACCList.jsp.
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Affiliation(s)
- Longxiang Xie
- Bioinformatics Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, People's Republic of China
| | - Qiang Wang
- Bioinformatics Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, People's Republic of China
| | - Fangmei Nan
- Bioinformatics Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, People's Republic of China
| | - Linna Ge
- Bioinformatics Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, People's Republic of China
| | - Yifang Dang
- Bioinformatics Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, People's Republic of China
| | - Xiaoxiao Sun
- Bioinformatics Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, People's Republic of China
| | - Ning Li
- Bioinformatics Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, People's Republic of China
| | - Huan Dong
- Bioinformatics Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, People's Republic of China
| | - Yali Han
- Bioinformatics Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, People's Republic of China
| | - Guosen Zhang
- Bioinformatics Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, People's Republic of China
| | - Wan Zhu
- Department of Anesthesia, Stanford University, Stanford, CA, USA
| | - Xiangqian Guo
- Bioinformatics Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, People's Republic of China
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28
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Wang F, Wang Q, Li N, Ge L, Yang M, An Y, Zhang G, Dong H, Ji S, Zhu W, Guo X. OSuvm: An interactive online consensus survival tool for uveal melanoma prognosis analysis. Mol Carcinog 2019; 59:56-61. [PMID: 31646691 DOI: 10.1002/mc.23128] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 09/29/2019] [Accepted: 10/10/2019] [Indexed: 01/30/2023]
Abstract
Uveal melanoma (UM) is a rare, aggressive, but the most frequent primary intraocular malignancy in adults, and up to 50% of patients develop a tendency of liver metastases. Great efforts have been made to develop biomarkers that facilitate diagnosis, prediction of the risk, and response to treatment of UM. However, a biologically informative and highly accurate gold standard system for prognostic evaluation of UM remains to be established. To facilitate assessment of the prognosis of UM patients, we established a user-friendly Online consensus Survival tool for uveal melanoma, named OSuvm, by which users can easily estimate the prognostic values of genes of interest by the Kaplan-Meier survival plot with hazard ratio and log-rank test. OSuvm comprises four independent cohorts including 229 patients with both gene expression profiles and relevant clinical follow-up information, and it has shown great performance in evaluating the prognostic roles of previously reported biomarkers. Using OSuvm enables researchers and clinicians to rapidly and conveniently explore the prognostic value of genes of interest and develop new potential molecular biomarkers for UM. OSuvm can be accessed at http://bioinfo.henu.edu.cn/UVM/UVMList.jsp.
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Affiliation(s)
- Fengling Wang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Qiang Wang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Ning Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Linna Ge
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Mengsi Yang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Yang An
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Guosen Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Huan Dong
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Shaoping Ji
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Wan Zhu
- Department of Anesthesia, School of Medicine, Stanford University, Stanford, California
| | - Xiangqian Guo
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China
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29
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Xie L, Wang Q, Dang Y, Ge L, Sun X, Li N, Han Y, Yan Z, Zhang L, Li Y, Zhang H, Guo X. OSkirc: a web tool for identifying prognostic biomarkers in kidney renal clear cell carcinoma. Future Oncol 2019; 15:3103-3110. [PMID: 31368353 DOI: 10.2217/fon-2019-0296] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Aim: To develop a free and quick analysis online tool that allows users to easily investigate the prognostic potencies of interesting genes in kidney renal clear cell carcinoma (KIRC). Patients & methods: A total of 629 KIRC cases with gene expression profiling data and clinical follow-up information are collected from public Gene Expression Omnibus and The Cancer Genome Atlas databases. Results: One web application called Online consensus Survival analysis for KIRC (OSkirc) that can be used for exploring the prognostic implications of interesting genes in KIRC was constructed. By OSkirc, users could simply input the gene symbol to receive the Kaplan-Meier survival plot with hazard ratio and log-rank p-value. Conclusion: OSkirc is extremely valuable for basic and translational researchers to screen and validate the prognostic potencies of genes for KIRC, publicly accessible at http://bioinfo.henu.edu.cn/KIRC/KIRCList.jsp.
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Affiliation(s)
- Longxiang Xie
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, PR China
| | - Qiang Wang
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, PR China
| | - Yifang Dang
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, PR China
| | - Linna Ge
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, PR China
| | - Xiaoxiao Sun
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, PR China
| | - Ning Li
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, PR China
| | - Yali Han
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, PR China
| | - Zhongyi Yan
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, PR China
| | - Lu Zhang
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, PR China
| | - Yongqiang Li
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, PR China
| | - Haiyu Zhang
- Department of Pathology, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Xiangqian Guo
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475004, PR China
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