Statistical predictions with glmnet

Engebretsen, Solveig; Bohlin, Jon

doi:10.1186/s13148-019-0730-1

Cited by 649 publications

(470 citation statements)

References 21 publications

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“…To develop the optimal signature for predicting OS prognosis based on IRGs, we performed univariate Cox regression analysis on the obtained DEIRGs, and selected IRGs related to prognosis with a screening criterion of p < 0.05. Next, we used the glmnet ( https://CRAN.R-project.org/package=glmnet ) package 80 to perform a machine learning algorithm-iterative LASSO Cox regression analysis on prognostic-associated IRGs to construct the optimal prognosis signature. LASSO is highly dependent on seeds and requires cross-validation to select samples randomly.…”

Section: Methodsmentioning

confidence: 99%

Development of a novel immune-related genes prognostic signature for osteosarcoma

Deng

Zhang

et al. 2020

Sci Rep

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Immune-related genes (IRGs) are responsible for osteosarcoma (OS) initiation and development. We aimed to develop an optimal IRGs-based signature to assess of OS prognosis. Sample gene expression profiles and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases. IRGs were obtained from the ImmPort database. R software was used to screen differentially expressed IRGs (DEIRGs) and functional correlation analysis. DEIRGs were analyzed by univariate Cox regression and iterative LASSO Cox regression analysis to develop an optimal prognostic signature, and the signature was further verified by independent cohort (GSE39055) and clinical correlation analysis. The analyses yielded 604 DEIRGs and 10 hub IRGs. A prognostic signature consisting of 13 IRGs was constructed, which strikingly correlated with OS overall survival and distant metastasis (p < 0.05, p < 0.01), and clinical subgroup showed that the signature’s prognostic ability was independent of clinicopathological factors. Univariate and multivariate Cox regression analyses also supported its prognostic value. In conclusion, we developed an IRGs signature that is a prognostic indicator in OS patients, and the signature might serve as potential prognostic indicator to identify outcome of OS and facilitate personalized management of the high-risk patients.

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Section: Methodsmentioning

confidence: 99%

Development of a novel immune-related genes prognostic signature for osteosarcoma

Deng

Zhang

et al. 2020

Sci Rep

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“…A Wilcoxon test was performed to contrast the expression of RNA:m 5 C methyltransferases in gliomas stratified by clinicopathological features, while a Chi-square test was used for multivariate groups. Univariate, multivariate, LASSO Cox regression, and Kaplan-Meier analyses were performed using the R packages "glmnet" and "survival" [30,31]. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the prognostic efficacy of the risk score model using the R package "survivalROC".…”

Section: Discussionmentioning

confidence: 99%

Clinicopathological signature and prognostic value of RNA:m5C methyltransferases in gliomas

Wang

Huang

et al. 2020

Preprint

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Background: Glioma is the most common primary intracranial tumor, accounting for the vast majority of intracranial malignant tumors. Aberrant expression of RNA:5-methylcytosine(m5C) methyltransferases has recently been the focus of research relating to the occurrence and progression of tumors. However, the prognostic value of RNA:m5C methyltransferases in glioma remains unclear. This study investigated RNA: m5C methyltransferase expression and defined its clinicopathological signature and prognostic value in gliomas. Methods: We systematically studied the RNA-sequence data of RNA:m5C methyltransferases underlying gliomas in the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets and identified different subtypes using Consensus clustering analysis. Gene Ontology (GO) and Gene Set Enrichment analysis (GSEA) was used to annotate the function of these genes. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm analyses were performed to construct the risk score model. Kaplan-Meier method and Receiver operating characteristic (ROC) curves were used to assess the overall survival of glioma patients. Additionally, Cox proportional regression model analysis was developed to address the connections between the risk scores and clinical factors. Results: Consensus clustering of RNA:m5C methyltransferases identified three clusters of gliomas with different prognostic and clinicopathological features. Meanwhile, Functional annotations demonstrated that RNA:m5C methyltransferases were significantly associated with the malignant progression of gliomas. Thereafter, five RNA:m5C methyltransferase genes were screened to construct a risk score model which can be used to predict not only overall survival but also clinicopathological features in gliomas. ROC curves revealed the significant prognostic ability of this signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for glioma outcome. Conclusion: We demonstrated the role of RNA:m5C methyltransferases in the initiation and progression of glioma. We have expanded on the understanding of the molecular mechanism involved, and provided a unique approach to predictive biomarkers and targeted therapy.

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“…Based on the OS-related IRGs identi ed in the univariate Cox proportional hazard model, we performed the LASSO analysis to avoid over tting [30]. Then, the signi cant genes in the LASSO regression were incorporated into the multivariate Cox analysis, and a prognostic signature was established.…”

Section: Construction and Evaluation Of Irgs Prognostic Signaturementioning

confidence: 99%

Development and validation of an immune gene-set based prognostic signature for soft tissue sarcoma

Shen

Meng

et al. 2020

Preprint

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Background Sarcomas is a group of heterogeneous malignant tumors originated from mesenchymal tissue and different types of sarcomas have disparate outcomes. The present study aims to identify the prognostic value of immune-related genes (IRGs) in sarcoma and establish a prognostic signature based on IRGs. Methods We collected the expression profile and clinical information of 255 soft tissue sarcoma samples from The Cancer Genome Atlas (TCGA) database and 2498 IRGs from the ImmPort database. The LASSO algorithm and Cox regression analysis were used to identify the best candidate genes and construct a signature. The prognostic ability of the signature was evaluated by ROC curves and Kaplan-Meier survival curves and validated in an independent cohort. Besides, a nomogram based on the IRGs and independent prognostic clinical variables was developed. Results A total of 19 IRGs were incorporated into the signature. In the training cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.938, 0.937 and 0.935, respectively. The Kaplan-Meier survival curve indicated that high-risk patients were significantly worse prognosis(P < 0.001). In the validation cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.730, 0.717 and 0.647, respectively. The Kaplan-Meier survival curve also showed significant distinct survival outcome between two risk groups. Furthermore, a nomogram based on the signature and four prognostic variables showed great accuracy in whole sarcoma patients and subgroup analyses. More importantly, the results of the TF regulatory network and immune infiltration analysis revealed the potential molecular mechanism of IRGs. Conclusions In general, we identified and validated an IRG-based signature, which can be used as an independent prognostic signature in evaluating the prognosis of sarcoma patients and provide potential novel immunotherapy targets.

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Statistical predictions with glmnet

Cited by 649 publications

References 21 publications

Development of a novel immune-related genes prognostic signature for osteosarcoma

Development of a novel immune-related genes prognostic signature for osteosarcoma

Clinicopathological signature and prognostic value of RNA:m5C methyltransferases in gliomas

Development and validation of an immune gene-set based prognostic signature for soft tissue sarcoma

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