A 5‐gene prognostic nomogram predicting survival probability of glioblastoma patients

Wang, Lingchen; Yan, Zheng; He, Xiaona; Zhang, Cheng; Yu, Hwa‐Lung; Lu, Quqin

doi:10.1002/brb3.1258

Cited by 8 publications

(9 citation statements)

References 38 publications

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“…However, taking into account of the feasibility of clinical work where the lesser number of the biomarkers in the model, the more advantage it gets in the clinic, the maximum value of k range was set to five [9,16,17]. Then patients from training set were divided into two groups according to the expression of every gene from C sAIC (A, k) screened through BSR: high expression (log 2 TPM higher than the cutpoint, which determined by "survminer" package of R software [18]), and low expression (log 2 TPM lower than the cutpoint).…”

Section: Selection and Verification Of Prognosis-related Genesmentioning

confidence: 99%

“…Nowadays, gene-based prognostic models containing other clinical parameters in predicting OS of cancer patients including ccRCC have been investigated numerously but they have not been widely accepted and exerted on the clinical practice [9][10][11]. Therefore, more novel prognosis-related genes could be uncovered by different bioinformatics analysis process and used to establish a more accurate prognostic models than conventional clinical parameters.…”

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confidence: 99%

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Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

et al. 2020

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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. © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article'

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Section: Selection and Verification Of Prognosis-related Genesmentioning

confidence: 99%

mentioning

confidence: 99%

Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

et al. 2020

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“…26 New genes related to prognosis have been found based on database analysis, and a prognostic prediction model has been constructed. 27,28 With the development of radiology in recent years, research on establishing nomogram models in glioma based on radiomics data has also gradually increased, and this includes the preoperative prediction of tumor grade and the prediction of molecular biomarkers. 29,30 Based on clinical factors and AS events, an epigenetic factor closely related to the genesis, progression, metastasis, and drug resistance of tumors, this study established a nomogram of the prognostic prediction model for LGG patients, demonstrating the value of basic science research and the importance of translational medicine.…”

Section: Discussionmentioning

confidence: 99%

Development of a nomogram for prognostic prediction of lower‐grade glioma based on alternative splicing signatures

Wang

Zhao

et al. 2020

Cancer Medicine

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“…For the selected genes, we used WebGestalt ( http://bioinfo.vanderbilt.edu/webgestalt ) based on Gene Ontology (GO) functions and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to understand the biological significance of the identified genes [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

Wang

Xie

et al. 2021

BMC Cancer

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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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A 5‐gene prognostic nomogram predicting survival probability of glioblastoma patients

Cited by 8 publications

References 38 publications

Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

Development of a nomogram for prognostic prediction of lower‐grade glioma based on alternative splicing signatures

A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

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