scholarly journals Fibrinogen as a Prognostic Predictor in Pediatric Patients with Sepsis: A Database Study

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaomeng Tang ◽  
Lujing Shao ◽  
Jiaying Dou ◽  
Yiping Zhou ◽  
Min Chen ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Pediatric Patients ◽  
Cell Injury ◽  
Characteristic Curve ◽  
Vascular Endothelial Cell ◽  
Hospital Death ◽  
Training Set ◽  
Database Study ◽  
Validation Set ◽  
Pediatric Sepsis

Background. Systemic inflammatory response and vascular endothelial cell injury during sepsis lead to coagulopathy. Fibrinogen has been reported as a biomarker of coagulopathy; however, the prognostic value of fibrinogen remains undefined in pediatric patients with sepsis. The aim of this study was to assess fibrinogen level on pediatric intensive care unit (PICU) admission and to elucidate the relationship between fibrinogen levels and in-hospital mortality in children with sepsis. Methods. We conducted a database study. The sepsis database was divided into a training set (between July 2014 and June 2018) and a validation set (from July 2018 to June 2019). The clinical and laboratory parameters on PICU admission and in-hospital mortality in sepsis database were collected and analyzed. Results. A total of 819 pediatric patients were included from database as a training set. The overall hospital mortality was 12.1% (99/819). The fibrinogen levels were significantly lower in nonsurvivors than survivors. Multivariate logistic regression analysis showed significant associations between fibrinogen, lactate level, and hospital mortality (fibrinogen: odds ratio (OR), 0.767 (95% CI: 0.628-0.937), P=0.009; lactate: OR, 1.346 (95% CI: 1.217-1.489), P<0.001, respectively), which was confirmed in a validation set (0.616 [95% CI: 0.457-0.829], P=0.001; 1.397 [95% CI: 1.245-1.569], P<0.001, respectively). The hospital mortality of patients with fibrinogen<1 g/L, 1-2 g/L, 2-3 g/L, or over 3 g/L displayed an obvious difference (62.5% vs. 27.66% vs. 18.1% vs. 4.2%, respectively). Furthermore, the area under the receiver operating characteristic curve (ROC) for fibrinogen in predicting hospital mortality was 0.780 (95% CI: 0.711-0.850) in pediatric patients with sepsis. Conclusions. Fibrinogen is a valuable prognostic biomarker for pediatric sepsis. The level of fibrinogen lower than 2 g/L on PICU admission is closely related to the greater risk of hospital death in pediatric sepsis.

scholarly journals Development of a nomogram to predict in-hospital mortality of sepsis-associated encephalopathy: a retrospective cohort study

2020 ◽  
Author(s):  
Yang Yang ◽  
Shengru Liang ◽  
Jie Geng ◽  
Qiuhe Wang ◽  
Pan Wang ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Medical Information ◽  
Characteristic Curve ◽  
Brier Score ◽  
Hospital Death ◽  
Net Benefit ◽  
Training Set ◽  
Distribution Width ◽  
Death Probability ◽  
Validation Set

Abstract Background Sepsis-associated encephalopathy (SAE) is related to an increased in-hospital mortality in patients with sepsis. We aim to establish a user-friendly nomogram for individual prediction of in-hospital death probability in patients with SAE. MethodsData were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC III) open source clinical database. SAE was defined by a Glasgow Coma Score (GCS) <15 at the presence of sepsis. Prediction model with a nomogram was constructed in the training set by Logistic regression analysis and then internally validated. A decision curve analysis (DCA) was performed to evaluate the net benefit of intervention with the nomogram. Results A total of 669 and 287 patients with SAE were randomly assigned to training set and internal validation set according to an allocation ratio of 7:3, respectively. Parameters eligible for the nomogram included age, Sequential Organ Failure Assessment (SOFA) score, red blood cell distribution width (RDW) and the mean values of heart rate, temperature and respiratory rate at first day of ICU admission. The nomogram exhibited good discrimination with an area under the receiver operating characteristic curve (AUROC) of 0.773 (95%CI: 0.729–0.818) in the training set and 0.741 (95%CI 0.673–0.809) in the validation set, respectively. Calibration of the derived model was also excellent, with Brier score of 0.136 (95%CI: 0.12­–0.153) and 0.168 (95%CI: 0.144–0.192) in both sets. The DCA of the nomogram indicated greater net benefit than SOFA. X-tile analysis showed that the nomogram can clearly stratify patients into three subgroups with different risks of in-hospital death. Conclusions The nomogram presents excellent performance in predicting in-hospital mortality of SAE patients, which can guide the prevention of SAE progression and may be more beneficial once specific treatments towards SAE are developed.

scholarly journals Development of a nomogram to predict in-hospital mortality of sepsis-associated encephalopathy: a retrospective cohort study

2020 ◽  
Author(s):  
Yang Yang ◽  
Shengru Liang ◽  
Jie Geng ◽  
Qiuhe Wang ◽  
Pan Wang ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Medical Information ◽  
Characteristic Curve ◽  
Brier Score ◽  
Hospital Death ◽  
Net Benefit ◽  
Training Set ◽  
Distribution Width ◽  
Death Probability ◽  
Validation Set

Abstract Background: Sepsis-associated encephalopathy (SAE) is related to an increased in-hospital mortality in patients with sepsis. We aim to establish a user-friendly nomogram for individual prediction of in-hospital death probability in patients with SAE.Methods: Data were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC III) open source clinical database. SAE was defined by a Glasgow Coma Score (GCS) <15 at the presence of sepsis. Prediction model with a nomogram was constructed in the training set by Logistic regression analysis and then internally validated. A decision curve analysis (DCA) was performed to evaluate the net benefit of intervention with the nomogram.Results: A total of 669 and 287 patients with SAE were randomly assigned to training set and internal validation set according to an allocation ratio of 7:3, respectively. Parameters eligible for the nomogram included age, Sequential Organ Failure Assessment (SOFA) score, red blood cell distribution width (RDW) and the mean values of heart rate, temperature and respiratory rate at first day of ICU admission. The nomogram exhibited good discrimination with an area under the receiver operating characteristic curve (AUROC) of 0.773 (95%CI: 0.729–0.818) in the training set and 0.741 (95%CI 0.673–0.809) in the validation set, respectively. Calibration of the derived model was also excellent, with Brier score of 0.136 (95%CI: 0.12–0.153) and 0.168 (95%CI: 0.144–0.192) in both sets. The DCA of the nomogram indicated greater net benefit than SOFA. X-tile analysis showed that the nomogram can clearly stratify patients into three subgroups with different risks of hospital death. Conclusions: The nomogram presents excellent performance in predicting in-hospital mortality of SAE patients, which can guide the prevention of SAE progression and may be more beneficial once specific treatments towards SAE are developed.

Discriminating Malignancy in Thyroid Nodules: The Nomogram Versus the Kwak and ACR TI-RADS

2020 ◽  
Vol 163 (6) ◽  
pp. 1156-1165
Author(s):  
Juan Xiao ◽  
Qiang Xiao ◽  
Wei Cong ◽  
Ting Li ◽  
Shouluan Ding ◽  
...  
Keyword(s):  
Thyroid Nodules ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Diagnostic Study ◽  
Diagnostic Efficiency ◽  
Training Set ◽  
Multivariable Logistic Regression Model ◽  
Predictive Values ◽  
Validation Set ◽  
Sensitivity Specificity

Objective To develop an easy-to-use nomogram for discrimination of malignant thyroid nodules and to compare diagnostic efficiency with the Kwak and American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Study Design Retrospective diagnostic study. Setting The Second Hospital of Shandong University. Subjects and Methods From March 2017 to April 2019, 792 patients with 1940 thyroid nodules were included into the training set; from May 2019 to December 2019, 174 patients with 389 nodules were included into the validation set. Multivariable logistic regression model was used to develop a nomogram for discriminating malignant nodules. To compare the diagnostic performance of the nomogram with the Kwak and ACR TI-RADS, the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values were calculated. Results The nomogram consisted of 7 factors: composition, orientation, echogenicity, border, margin, extrathyroidal extension, and calcification. In the training set, for all nodules, the area under the curve (AUC) for the nomogram was 0.844, which was higher than the Kwak TI-RADS (0.826, P = .008) and the ACR TI-RADS (0.810, P < .001). For the 822 nodules >1 cm, the AUC of the nomogram was 0.891, which was higher than the Kwak TI-RADS (0.852, P < .001) and the ACR TI-RADS (0.853, P < .001). In the validation set, the AUC of the nomogram was also higher than the Kwak and ACR TI-RADS ( P < .05), each in the whole series and separately for nodules >1 or ≤1 cm. Conclusions When compared with the Kwak and ACR TI-RADS, the nomogram had a better performance in discriminating malignant thyroid nodules.

scholarly journals Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Mou Li ◽  
Ling Yang ◽  
Yufeng Yue ◽  
Jingxu Xu ◽  
Chencui Huang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Performance ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Set ◽  
Combined Model ◽  
Contrast Enhanced ◽  
Apparent Diffusion ◽  
Validation Set

ObjectiveTo investigate whether a radiomics model can help to improve the performance of PI-RADS v2.1 in prostate cancer (PCa).MethodsThis was a retrospective analysis of 203 patients with pathologically confirmed PCa or non-PCa between March 2015 and December 2016. Patients were divided into a training set (n = 141) and a validation set (n = 62). The radiomics model (Rad-score) was developed based on multi-parametric MRI including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast enhanced (DCE) imaging. The combined model involving Rad-score and PI-RADS was compared with PI-RADS for the diagnosis of PCa by using the receiver operating characteristic curve (ROC) analysis.ResultsA total of 112 (55.2%) patients had PCa, and 91 (44.8%) patients had benign lesions. For PCa versus non-PCa, the Rad-score had a significantly higher area under the ROC curve (AUC) [0.979 (95% CI, 0.940–0.996)] than PI-RADS [0.905 (0.844–0.948), P = 0.002] in the training set. However, the AUC between them was insignificant in the validation set [0.861 (0.749–0.936) vs. 0.845 (0.731–0.924), P = 0.825]. When Rad-score was added to PI-RADS, the performance of the PI-RADS was significantly improved for the PCa diagnosis (AUC = 0.989, P &lt; 0.001 for the training set and AUC = 0.931, P = 0.038 for the validation set).ConclusionsThe radiomics based on multi-parametric MRI can help to improve the diagnostic performance of PI-RADS v2.1 in PCa.

Predictive Value of Nomogram Based on Kyoto Classification of Gastritis to Diagnosis of Gastric Cancer

2021 ◽  
Author(s):  
Jiejun Lin ◽  
Huang Su ◽  
Yaqi Guan ◽  
Qingjie Zhou ◽  
Jie Pan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Multivariate Analysis ◽  
Calibration Curve ◽  
Predictive Value ◽  
Predictive Accuracy ◽  
Bootstrap Method ◽  
Characteristic Curve ◽  
Training Set ◽  
Validation Set

Abstract Background and Aim. It is of importance to predict the risk of gastric cancer (GC) for endoscopists because early detection of GC determines the determines the selection of best treatment strategy and the prognosis of patients. The aim of the study was to evaluate the utility of a predictive nomogram based on Kyoto classification of gastritis for GC. Methods. It was a retrospective study that included 2639 patients who received esophagogastroduodenoscopy and serum pepsinogen (PG) assay from January 2020 to November 2020 at the Endoscopy Center of the Department of Gastroenterology, Wenzhou Central Hospital. Routine biopsy was conducted to determine the benign and malignant lesions pathologically. All cases were randomly divided into the training set (70%) and the validation set (30%) by using bootstrap method. A nomogram was formulated according to multivariate analysis of training set. The predictive accuracy and discriminative ability of the nomogram were assessed by concordance index (C-index), area under the curve (AUC) of receiver operating characteristic curve (ROC) as well as calibration curve and were validated by validation set.Results. Multivariate analysis indicated that age, sex, PG I/II ratio and Kyoto classification scores were independent predictive variables for GC. The C-index of the nomogram of the training set was 0.79 (95% CI: 0.74 to 0.84) and the AUC of ROC is 0.79. The calibration curve of the nomogram demonstrated an optimal agreement between predicted probability and observed probability of the risk of GC. In the validation set, the C-index was 0.86 (95% CI: 0.79 to 0.94) with a calibration curve of better concurrence.Conclusion. The nomogram formulated was proven to be of high predictive value for GC.

scholarly journals Prediction Model of Osteonecrosis of the Femoral Head After Femoral Neck Fracture: Machine Learning–Based Development and Validation Study (Preprint)

2021 ◽  
Author(s):  
Huan Wang ◽  
Wei Wu ◽  
Chunxia Han ◽  
Jiaqi Zheng ◽  
Xinyu Cai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Femoral Head ◽  
Internal Fixation ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Support Vector ◽  
Training Set ◽  
External Data ◽  
Interpretable Model ◽  
Validation Set

BACKGROUND The absolute number of femoral neck fractures (FNFs) is increasing; however, the prediction of traumatic femoral head necrosis remains difficult. Machine learning algorithms have the potential to be superior to traditional prediction methods for the prediction of traumatic femoral head necrosis. OBJECTIVE The aim of this study is to use machine learning to construct a model for the analysis of risk factors and prediction of osteonecrosis of the femoral head (ONFH) in patients with FNF after internal fixation. METHODS We retrospectively collected preoperative, intraoperative, and postoperative clinical data of patients with FNF in 4 hospitals in Shanghai and followed up the patients for more than 2.5 years. A total of 259 patients with 43 variables were included in the study. The data were randomly divided into a training set (181/259, 69.8%) and a validation set (78/259, 30.1%). External data (n=376) were obtained from a retrospective cohort study of patients with FNF in 3 other hospitals. Least absolute shrinkage and selection operator regression and the support vector machine algorithm were used for variable selection. Logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost) were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model, and the external data were used to compare and evaluate the model performance. We compared the accuracy, discrimination, and calibration of the models to identify the best machine learning algorithm for predicting ONFH. Shapley additive explanations and local interpretable model-agnostic explanations were used to determine the interpretability of the black box model. RESULTS A total of 11 variables were selected for the models. The XGBoost model performed best on the validation set and external data. The accuracy, sensitivity, and area under the receiver operating characteristic curve of the model on the validation set were 0.987, 0.929, and 0.992, respectively. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the model on the external data were 0.907, 0.807, 0.935, and 0.933, respectively, and the log-loss was 0.279. The calibration curve demonstrated good agreement between the predicted probability and actual risk. The interpretability of the features and individual predictions were realized using the Shapley additive explanations and local interpretable model-agnostic explanations algorithms. In addition, the XGBoost model was translated into a self-made web-based risk calculator to estimate an individual’s probability of ONFH. CONCLUSIONS Machine learning performs well in predicting ONFH after internal fixation of FNF. The 6-variable XGBoost model predicted the risk of ONFH well and had good generalization ability on the external data, which can be used for the clinical prediction of ONFH after internal fixation of FNF.

scholarly journals Prognostic and Predictive Value of Blood Tumor Mutational Burden in Patients With Lung Cancer Treated With Docetaxel

2020 ◽  
Vol 18 (5) ◽  
pp. 582-589 ◽  
Author(s):  
Wei Nie ◽  
Jie Qian ◽  
Mi-Die Xu ◽  
Kai Gu ◽  
Fang-Fei Qian ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Benefit ◽  
Characteristic Curve ◽  
Next Generation Sequencing Data ◽  
Training Set ◽  
Cutoff Value ◽  
Mutational Burden ◽  
Tumor Mutational Burden ◽  
Validation Set ◽  
Chemotherapy Efficacy

Background: Biomarkers for chemotherapy efficacy in non–small cell lung cancer (NSCLC) are lacking. This retrospective study assesses the association between blood-based tumor mutational burden (bTMB) and clinical benefit of chemotherapy. Methods: Clinical and targeted next-generation sequencing data from the OAK trial (training set; n=318) and POPLAR trial (validation set; n=106) in the docetaxel arm were analyzed. The cutoff value of bTMB for outcome prediction was determined based on a time-dependent receiver operating characteristic curve in the training set, and propensity score matching (PSM) was conducted. The primary outcome was overall survival (OS). Durable clinical benefit (DCB) was defined as OS lasting >12 months. Interaction between treatment and bTMB was assessed in the combined set. Results: A lower bTMB was observed in patients with DCB compared with no durable benefit, and in those with a partial response and stable disease compared with progressive disease. The optimized cutoff value of bTMB for predicting OS was 7 single-nucleotide variants per megabase. In the training set, a low bTMB was significantly associated with longer OS and progression-free survival (PFS). The prognostic value of bTMB was confirmed in the validation set and PSM set. The interaction between bTMB and treatment was significant for PFS (interaction P=.043) in the combined set. Mutations in KEAP1 were associated with high bTMB and a lack of benefit from chemotherapy. Conclusions: Low bTMB is associated with a survival advantage in patients with NSCLC treated with docetaxel, suggesting the prognostic and predictive potential of bTMB for determining chemotherapy efficacy.

scholarly journals Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jessica Pinaire ◽  
Etienne Chabert ◽  
Jérôme Azé ◽  
Sandra Bringay ◽  
Paul Landais
Keyword(s):  
Acute Coronary Syndrome ◽  
Hospital Mortality ◽  
Pattern Mining ◽  
Characteristic Curve ◽  
Model Complexity ◽  
Sequential Patterns ◽  
Hospital Death ◽  
Support Vector ◽  
Machine Model ◽  
Coronary Syndrome

Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research. In sequence prediction modeling, models based on machine learning (ML) techniques have proven their efficiency compared to other models. In addition, reducing model complexity is a challenge. Solutions have been proposed by introducing pattern mining techniques. Based on these results, we developed a new method to extract sets of relevant event sequences for medical events’ prediction, applied to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). From the French Hospital Discharge Database, we mined sequential patterns. They were further integrated into several predictive models using a text string distance to measure the similarity between patients’ patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. We obtained good results in terms of discrimination with the receiver operating characteristic curve scores ranging from 0.71 to 0.99 with a good overall accuracy. We demonstrated the interest of sequential patterns for event prediction. This could be a first step to a decision-support tool for the prevention of in-hospital death by ACS.

scholarly journals Circulating Vitronectin Predicts Liver Injury and Mortality in Children With Sepsis: A Prospective Observational Study

2020 ◽  
Vol 26 ◽  
pp. 107602962093520
Author(s):  
Chunxia Wang ◽  
Yun Cui ◽  
Huijie Miao ◽  
Ting Sun ◽  
Ye Lu ◽  
...  
Keyword(s):  
Liver Injury ◽  
Clinical Utility ◽  
Prospective Observational Study ◽  
Operating Characteristic ◽  
Pediatric Patients ◽  
Characteristic Curve ◽  
Pediatric Intensive Care ◽  
Decision Curve Analysis ◽  
Risk Of Mortality ◽  
Pediatric Sepsis

Vitronectin (VTN) is a key regulator of coagulation, but clinical relevance of serum VTN in pediatric sepsis remains poorly defined. The aim of this study was to access the value of serum VTN level on pediatric intensive care unit (PICU) admission in children with sepsis. Pediatric patients with sepsis were enrolled from January 2018 to December 2018. The serum VTN levels were determined on PICU admission, and the association of serum VTN level with PICU mortality and organ dysfunction was assessed. Serum VTN levels were significantly lower in nonsurvivors compared with survivors, in patients with septic shock compared with patients with sepsis, or in patients with sepsis-associated acute liver injury (ALI) compared with patients without ALI. Serum VTN level was associated with PICU mortality (odds ratio [OR]: 0.958, 95% CI: 0.927-0.996; P = .010) or ALI (OR: 0.956, 95% CI: 0.915-0.999; P = .046), but not shock (OR: 0.996, 95% CI: 0.977-1.016; P =.716). The area under receiver operating characteristic curve for VTN in predicting the occurrence of ALI during PICU stay and PICU mortality were 0.760 (95% CI: 0.627- 0.893) and 0.737 (95% CI: 0.544-0.931), respectively. Moreover, VTN plus pediatric risk of mortality (PRISM) III had a better clinical utility according to decision curve analysis compared with VTN or PRISM III alone. These findings suggest that serum VTN level is associated with sepsis-associated ALI and PICU mortality, and VTN plus PRISM III is a powerful predictor of PICU mortality in pediatric patients with sepsis, which have a better clinical benefit compared with VTN or PRISM III alone.

scholarly journals Identification of a Novel Eight-lncRNA Prognostic Signature for HBV-HCC and Analysis of Their Functions Based on Coexpression and ceRNA Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiaonan Zhao ◽  
Zhenzi Bai ◽  
Chenghua Li ◽  
Chuanlun Sheng ◽  
Hongyan Li
Keyword(s):  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Curve ◽  
Characteristic Curve ◽  
The Cancer Genome Atlas ◽  
Good Prediction ◽  
Prognostic Signature ◽  
Training Set ◽  
Validation Set

Studies have demonstrated the prognosis potential of long noncoding RNAs (lncRNAs) for hepatocellular carcinoma (HCC), but specific lncRNAs for hepatitis B virus- (HBV-) related HCC have rarely been reported. This study was aimed at identifying a lncRNA prognostic signature for HBV-HCC and exploring their underlying functions. The sequencing dataset was collected from The Cancer Genome Atlas database as the training set, while the microarray dataset was obtained from the European Bioinformatics Institute database (E-TABM-36) as the validation set. Univariate and multivariate Cox regression analyses identified that eight lncRNAs (TSPEAR-AS1, LINC00511, LINC01136, MKLN1-AS, LINC00506, KRTAP5-AS1, ZNF252P-AS1, and THUMPD3-AS1) were significantly associated with overall survival (OS). These eight lncRNAs were used to construct a risk score model. The Kaplan-Meier survival curve results showed that this risk score can significantly differentiate the OS between the high-risk group and the low-risk group. Receiver operating characteristic curve analysis demonstrated that this risk score exhibited good prediction effectiveness (area under the curve AUC=0.990 for the training set; AUC=0.903 for the validation set). Furthermore, this lncRNA risk score was identified as an independent prognostic factor in the multivariate analysis after adjusting other clinical characteristics. The crucial coexpression (LINC00511-CABYR, THUMPD3-AS1-TRIP13, LINC01136-SFN, LINC00506-ANLN, and KRTAP5-AS1/TSPEAR-AS1/MKLN1-AS/ZNF252P-AS1-MC1R) or competing endogenous RNA (THUMPD3-AS1-hsa-miR-450a-TRIP13) interaction axes were identified to reveal the possible functions of lncRNAs. These genes were enriched into cell cycle-related biological processes or pathways. In conclusion, our study identified a novel eight-lncRNA prognosis signature for HBV-HCC patients and these lncRNAs may be potential therapeutic targets.

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