Dataset Information

15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma.

ABSTRACT:

Background

Our study aims to develop a lncRNA-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma (HCC) patients.

Methods

The lncRNA profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a 15-lncRNA-based classifier and compared our classifier to the existing six-lncRNA signature. Besides, a nomogram incorporating the genomic classifier and clinicopathologic factors was also developed. The predictive accuracy and discriminative ability of the genomic-clinicopathologic nomogram were determined by a concordance index (C-index) and calibration curve and were compared with the TNM staging system by the C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate the clinical value of our nomogram.

Results

Fifteen relapse-free survival (RFS-) related lncRNAs were identified, and the classifier, consisting of the identified 15 lncRNAs, could effectively classify patients into the high-risk and low-risk subgroups. The prediction accuracy of the 15-lncRNA-based classifier for predicting 2-year and 5-year RFS was 0.791 and 0.834 in the training set and 0.684 and 0.747 in the validation set, respectively, which was better than the existing six-lncRNA signature. Moreover, the AUC of genomic-clinicopathologic nomogram in predicting RFS were 0.837 in the training set and 0.753 in the validation set, and the C-index of the genomic-clinicopathologic nomogram was 0.78 (0.72-0.83) in the training set and 0.71 (0.65-0.76) in the validation set, which was better than the traditional TNM stage and 15-lncRNA-based classifier. The decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage and 15-lncRNA-based classifier. The results were confirmed externally.

Conclusion

Compared to the TNM stage, the 15-lncRNAs-based classifier-clinicopathologic nomogram is a more effective and valuable tool to identify HCC recurrence and may aid in clinical decision-making.

SUBMITTER: Zhang Q

PROVIDER: S-EPMC7817238 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma.

Zhang Qiong Q Ning Gang G Jiang Hongye H Huang Yanlin Y Piao Jinsong J Chen Zhen Z Tan Xiaojun X Zhang Jiangyu J Liu Genglong G

Disease markers 20201201

<h4>Background</h4>Our study aims to develop a lncRNA-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma (HCC) patients.<h4>Methods</h4>The lncRNA profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis ...[more]

PMID: 33520012

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Project description:Long non-coding RNAs (lncRNAs) which have little or no protein-coding capacity, due to their potential roles in the cancer disease, caught a particular interest. Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the lncRNAs classifier and clinicopathologic factors to help to improve the accuracy of recurrence prediction for head and neck squamous cell carcinoma (HNSCC) patients. The HNSCC lncRNAs profiling data and the corresponding clinicopathologic information were downloaded from TANRIC database and cBioPortal. Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier related to recurrence. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate clinical value of our nomogram. Consequently, fifteen recurrence-free survival (RFS) -related lncRNAs were identified, and the classifier consisting of the established 15 lncRNAs could effectively divide patients into high-risk and low-risk subgroup. The prediction ability of the 15-lncRNAs-based classifier for predicting 3- year and 5-year RFS were 0.833 and 0.771. Independent factors derived from multivariable analysis to predict recurrence were number of positive LNs, margin status, mutation count and lncRNAs classifier, which were all embedded into the nomogram. The calibration curve for the recurrence probability showed that the predictions based on the nomogram were in good coincide with practical observations. The C-index of the nomogram was 0.76 (0.72-0.79), and the area under curve (AUC) of nomogram in predicting RFS was 0.809, which were significantly higher than traditional TNM stage and 15-lncRNAs-based classifier. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage and 15-lncRNAs-based classifier. The results were confirmed externally. In summary, a visually inclusive nomogram for patients with HNSCC, comprising genomic and clinicopathologic variables, generates more accurate prediction of the recurrence probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice.

Project description:MethodsBetween December 2017 and December 2018, 153 HCC patients (134 males and 19 females; mean age, 56.0 ± 10.2 years; range, 28-78 years) treated with radical therapy were enrolled in our retrospective study and were divided into a training cohort (n = 107) and a validation cohort (n = 46). All patients underwent preoperative CTC tests and CEUS examinations before treatment. The ultrasomics signature was extracted and built from CEUS images. Univariate and multivariate logistic regression analyses were used to identify the significant variables related to ER, which were then combined to build a predictive nomogram. The performance of the nomogram was evaluated by its discrimination, calibration and clinical utility. The predictive model was further evaluated in the internal validation cohort.ResultsHBV DNA, serum AFP level, CTC status, tumour size and ultrasomics score were identified as independent predictors associated with ER (all p < 0.05). Multivariable logistic regression analysis showed that the CTC status (OR = 7.02 [95% CI, 2.07 to 28.38], p = 0.003) and ultrasomics score (OR = 148.65 [95% CI, 25.49 to 1741.72], p < 0.001) were independent risk factors for ER. The nomogram based on ultrasomics score, CTC status, serum AFP level and tumour size exhibited C-indexes of 0.933 (95% CI, 0.878 to 0.988) and 0.910 (95% CI, 0.765 to 1.055) in the training and validation cohorts, respectively, fitting well in calibration curves. Decision curve analysis further confirmed the clinical usefulness of the nomogram.ConclusionThe nomogram incorporating CTC, ultrasomics features and independent clinical risk factors achieved satisfactory preoperative prediction of ER in HCC patients after radical treatment.Advances in knowledge1. CTC status and ultrasomics score were identified as independent predictors associated with ER of HCC after radical treatment. 2. The nomogram constructed by ultrasomics score generated by 17 ultrasomics features, combined with CTCs and independent clinical risk factors such as AFP and tumour size. 3. The nomogram exhibited satisfactory discriminative power, and could be clinically useful in the preoperative prediction of ER after radical treatment in HCC patients.

Dataset Information

15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma.

Background

Methods

Results

Conclusion

Publications

15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma.

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