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Lai W, Li D, Kuang J, Deng L, Lu Q. Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma. Brain Behav 2022; 12:e2575. [PMID: 35429411 PMCID: PMC9120724 DOI: 10.1002/brb3.2575] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/20/2022] [Accepted: 03/20/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5-year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA-seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice. METHODS We studied single-cell RNA-seq dataset and The Cancer Genome Atlas-glioblastoma multiforme (TCGA-GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model. RESULTS We identified 2128 DEGs from the single-cell RNA-seq dataset and 6461 DEGs from the bulk RNA-seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low-risk group showed better OS than those in the high-risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients. CONCLUSION We constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA-seq dataset and a bulk RNA-seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients.
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Affiliation(s)
- Wenwen Lai
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, China
| | - Defu Li
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, China
| | - Jie Kuang
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Libin Deng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, China
| | - Quqin Lu
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, China
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Qu S, Qiu O, Hu Z. The prognostic factors and nomogram for patients with high-grade gliomas. FUNDAMENTAL RESEARCH 2021. [DOI: 10.1016/j.fmre.2021.07.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
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Wang W, Wang L, Xie X, Yan Y, Li Y, Lu Q. A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma. BMC Cancer 2021; 21:6. [PMID: 33402113 PMCID: PMC7786458 DOI: 10.1186/s12885-020-07692-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 11/25/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice. METHODS Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model's effectiveness. RESULTS We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases. CONCLUSION Our study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.
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Affiliation(s)
- Wenhua Wang
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, 330006, Jiangxi, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Lingchen Wang
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, 330006, Jiangxi, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Xinsheng Xie
- Center for Experimental Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Yehong Yan
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Yue Li
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, 330006, Jiangxi, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Quqin Lu
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, 330006, Jiangxi, China. .,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, 330006, Jiangxi, China.
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Wang Y, Wang Z, Zhao B, Chen W, Wang Y, Ma W. Development of a nomogram for prognostic prediction of lower-grade glioma based on alternative splicing signatures. Cancer Med 2020; 9:9266-9281. [PMID: 33047900 PMCID: PMC7774734 DOI: 10.1002/cam4.3530] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 09/17/2020] [Accepted: 09/24/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The prognosis of lower-grade glioma (LGG) differs from that of other grades gliomas. Although lots of studies on the prognostic biomarkers of LGG have been reported, few have significant clinical impact. Alternative splicing (AS) events can affect cell function by splicing precursor mRNA. Therefore, a prognostic model for LGG based on AS events are important to establish. METHODS RNA sequencing, clinical, and AS event data of 510 LGG patients from the TCGA database were downloaded. Univariate Cox regression analysis was used to screen out prognostic-related AS events and LASSO regression and multivariate Cox regression were used to establish prognostic risk scores for patients in the training set (n = 340). After validation, a nomogram model was established based on the AS signature and clinical information, which was able to predict 1-, 3-, and 5-year survival rates. Finally, considering the regulatory effect of splicing factors (SFs) on AS events, an AS-SF regulatory network was analyzed. RESULTS The most common AS event was exon skipping and the least was mutually exclusive exons. All the seven AS events were related to the prognosis of LGG patients, regardless of whether they were separated or considered as a whole event (integrated AS event), and the integrated AS event had the most significant correlation. After further inclusion of clinical indicators, eight factors were screened out: age, new event, KPS, WHO grade, treatment, integrated AS signature, IDH1 and TP53 mutation status, and a nomogram model was established. The study also constructed an AS-SF regulatory network. CONCLUSION The AS events and clinical factors that can predict the prognosis of LGG patients were screened, and a prognostic prediction model was established. The results of this study can play an important role in clinical work to better evaluate the prognosis of patients and impact treatment options.
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Affiliation(s)
- Yaning Wang
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zihao Wang
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Binghao Zhao
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenlin Chen
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Wang
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenbin Ma
- Departments of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Zhang Z, Lin E, Zhuang H, Xie L, Feng X, Liu J, Yu Y. Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma. Cancer Cell Int 2020; 20:27. [PMID: 32002016 PMCID: PMC6986036 DOI: 10.1186/s12935-020-1113-6] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 01/17/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial. METHODS Raw count of RNA-sequencing data and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA). Tumor samples were divided into two sets. Differentially expressed genes (DEGs) were screened in the whole set and prognosis-related genes were identified from the training set. Their common genes were used in LASSO and best subset regression which were performed to identify the best prognostic 5 genes. The gene-based risk score was developed based on the Cox coefficient of the individual gene. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess its prognostic power. GSE29609 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to screen independent prognostic parameters to construct a nomogram. The predictive power of the nomogram was revealed by time-dependent ROC curves and the calibration plot and verified in the validation set. Finally, Functional enrichment analysis of DEGs and 5 novel genes were performed to suggest the potential biological pathways. RESULTS PADI1, ATP6V0D2, DPP6, C9orf135 and PLG were screened to be significantly related to the prognosis of ccRCC patients. The risk score effectively stratified the patients into high-risk group with poor overall survival (OS) based on survival analysis. AJCC-stage, age, recurrence and risk score were regarded as independent prognostic parameters by Cox regression analysis and were used to construct a nomogram. Time-dependent ROC curves showed the nomogram performed best in 1-, 3- and 5-year survival predictions compared with AJCC-stage and risk score in validation sets. The calibration plot showed good agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis suggested several enriched biological pathways related to cancer. CONCLUSIONS In our study, we constructed a gene-based model integrating clinical prognostic parameters to predict prognosis of ccRCC well, which might provide a reliable prognosis assessment tool for clinician and aid treatment decision-making in the clinic.
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Affiliation(s)
- Zedan Zhang
- Department of Urology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Enyu Lin
- Department of Urology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Hongkai Zhuang
- Department of Urology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou, China
| | - Lu Xie
- Department of Urology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xiaoqiang Feng
- Department of Immunology, School of Basic Medical Science, Southern Medical University, Guangzhou, China
| | - Jiumin Liu
- Department of Urology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yuming Yu
- Department of Urology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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Wang L, Yan Z, He X, Zhang C, Yu H, Lu Q. A 5-gene prognostic nomogram predicting survival probability of glioblastoma patients. Brain Behav 2019; 9:e01258. [PMID: 30859746 PMCID: PMC6456771 DOI: 10.1002/brb3.1258] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 12/07/2018] [Accepted: 02/13/2019] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Glioblastoma (GBM) remains the most biologically aggressive subtype of gliomas with an average survival of 10 to 12 months. Considering that the overall survival (OS) of each GBM patient is a key factor in the treatment of individuals, it is meaningful to predict the survival probability for GBM patients newly diagnosed in clinical practice. MATERIAL AND METHODS Using the TCGA dataset and two independent GEO datasets, we identified genes that are associated with the OS and differentially expressed between GBM tissues and the adjacent normal tissues. A robust likelihood-based survival modeling approach was applied to select the best genes for modeling. After the prognostic nomogram was generated, an independent dataset on different platform was used to evaluate its effectiveness. RESULTS We identified 168 differentially expressed genes associated with the OS. Five of these genes were selected to generate a gene prognostic nomogram. The external validation demonstrated that 5-gene prognostic nomogram has the capability of predicting the OS of GBM patients. CONCLUSION We developed a novel and convenient prognostic tool based on five genes that exhibited clinical value in predicting the survival probability for newly diagnosed GBM patients, and all of these five genes could represent potential target genes for the treatment of GBM. The development of this model will provide a good reference for cancer researchers.
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Affiliation(s)
- Lingchen Wang
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, China.,Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, P.R. China
| | - Zhengwei Yan
- Center for Experimental Medicine, The First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Xiaona He
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, China.,Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, P.R. China
| | - Cheng Zhang
- Center for Experimental Medicine, The First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Huiqiang Yu
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, China.,Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, P.R. China
| | - Quqin Lu
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, China.,Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, P.R. China
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