Development and validation of a nomogram with an epigenetic signature for predicting survival in patients with lung adenocarcinoma

Wang, Jiao; He, Lei; Tang, Yunliang; Li, Dan; Yang, Yuting; Zeng, Zhenguo

doi:10.18632/aging.104090

Cited by 10 publications

(13 citation statements)

References 22 publications

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“…Nomograms (visualized graphs of a predictive model) are widely applied for prognosis and prediction of various diseases ( 10 , 11 ). To date, no nomogram and corresponding web-based calculator has been developed to predict the prognosis of ARDS patients.…”

Section: Introductionmentioning

confidence: 99%

A Simple-to-Use Web-Based Calculator for Survival Prediction in Acute Respiratory Distress Syndrome

Liu

Huang

2021

Front. Med.

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Background: The aim of this study was to construct and validate a simple-to-use model to predict the survival of patients with acute respiratory distress syndrome.Methods: A total of 197 patients with acute respiratory distress syndrome were selected from the Dryad Digital Repository. All eligible individuals were randomly stratified into the training set (n=133) and the validation set (n=64) as 2: 1 ratio. LASSO regression analysis was used to select the optimal predictors, and receiver operating characteristic and calibration curves were used to evaluate accuracy and discrimination of the model. Clinical usefulness of the model was also assessed using decision curve analysis and Kaplan-Meier analysis.Results: Age, albumin, platelet count, PaO2/FiO2, lactate dehydrogenase, high-resolution computed tomography score, and etiology were identified as independent prognostic factors based on LASSO regression analysis; these factors were integrated for the construction of the nomogram. Results of calibration plots, decision curve analysis, and receiver operating characteristic analysis showed that this model has good predictive ability of patient survival in acute respiratory distress syndrome. Moreover, a significant difference in the 28-day survival was shown between the patients stratified into different risk groups (P < 0.001). For convenient application, we also established a web-based calculator (https://huangl.shinyapps.io/ARDSprognosis/).Conclusions: We satisfactorily constructed a simple-to-use model based on seven relevant factors to predict survival and prognosis of patients with acute respiratory distress syndrome. This model can aid personalized treatment and clinical decision-making.

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Section: Introductionmentioning

confidence: 99%

A Simple-to-Use Web-Based Calculator for Survival Prediction in Acute Respiratory Distress Syndrome

Liu

Huang

2021

Front. Med.

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“…Depending on the training set, Cox proportion hazard regression models were used to screen potential prognostic factors and estimate their weights [ 19 , 20 ]. Univariable Cox regression analysis was performed to explore the potential predictors [ 21 ]. The selected prognostic factors (p value below 0.05 in univariate analysis) were then included in a multivariate Cox regression analysis to obtain an integrated nomogram by a stepwise feature selection algorithm based on the AIC [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

An easy-to-operate web-based calculator for predicting the progression of chronic kidney disease

Wang

Fang

et al. 2021

J Transl Med

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Background This study aimed to establish and validate an easy-to-operate novel scoring system based on simple and readily available clinical indices for predicting the progression of chronic kidney disease (CKD). Methods We retrospectively evaluated 1045 eligible CKD patients from a publicly available database. Factors included in the model were determined by univariate and multiple Cox proportional hazard analyses based on the training set. Results Independent prognostic factors including etiology, hemoglobin level, creatinine level, proteinuria, and urinary protein/creatinine ratio were determined and contained in the model. The model showed good calibration and discrimination. The area under the curve (AUC) values generated to predict 1-, 2-, and 3-year progression-free survival in the training set were 0.947, 0.931, and 0.939, respectively. In the validation set, the model still revealed excellent calibration and discrimination, and the AUC values generated to predict 1-, 2-, and 3-year progression-free survival were 0.948, 0.933, and 0.915, respectively. In addition, decision curve analysis demonstrated that the model was clinically beneficial. Moreover, to visualize the prediction results, we established a web-based calculator (https://ncutool.shinyapps.io/CKDprogression/). Conclusion An easy-to-operate model based on five relevant factors was developed and validated as a conventional tool to assist doctors with clinical decision-making and personalized treatment.

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“…Histologically, lung adenocarcinoma (LUAD) is the most common form of NSCLC, and comprises nearly 40% of all cases ( Hua et al, 2020 ). Therefore, it is necessary to explore the specific molecular pathogenesis of LUAD and develop novel therapeutic targets for its treatment ( Wang J. et al, 2020 ; Tang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A Ubiquitin-Proteasome Gene Signature for Predicting Prognosis in Patients With Lung Adenocarcinoma

Tang

Guo

2022

Front. Genet.

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Background: Dysregulation of the ubiquitin-proteasome system (UPS) can lead to instability in the cell cycle and may act as a crucial factor in both tumorigenesis and tumor progression. However, there is no established prognostic signature based on UPS genes (UPSGs) for lung adenocarcinoma (LUAD) despite their value in other cancers.Methods: We retrospectively evaluated a total of 703 LUAD patients through multivariate Cox and Lasso regression analyses from two datasets, the Cancer Genome Atlas (n = 477) and GSE31210 (n = 226). An independent dataset (GSE50081) containing 128 LUAD samples were used for validation.Results: An eight-UPSG signature, including ARIH2, FBXO9, KRT8, MYLIP, PSMD2, RNF180, TRIM28, and UBE2V2, was established. Kaplan-Meier survival analysis and time-receiver operating characteristic curves for the training and validation datasets revealed that this risk signature presented with good performance in predicting overall and relapsed-free survival. Based on the signature and its associated clinical features, a nomogram and corresponding web-based calculator for predicting survival were established. Calibration plot and decision curve analyses showed that this model was clinically useful for both the training and validation datasets. Finally, a web-based calculator (https://ostool.shinyapps.io/lungcancer) was built to facilitate convenient clinical application of the signature.Conclusion: An UPSG based model was developed and validated in this study, which may be useful as a novel prognostic predictor for LUAD.

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Development and validation of a nomogram with an epigenetic signature for predicting survival in patients with lung adenocarcinoma

Cited by 10 publications

References 22 publications

A Simple-to-Use Web-Based Calculator for Survival Prediction in Acute Respiratory Distress Syndrome

A Simple-to-Use Web-Based Calculator for Survival Prediction in Acute Respiratory Distress Syndrome

An easy-to-operate web-based calculator for predicting the progression of chronic kidney disease

A Ubiquitin-Proteasome Gene Signature for Predicting Prognosis in Patients With Lung Adenocarcinoma

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