lncRNA Expression-Based Risk Scoring System Can Predict Survival of Tumor-Positive Patients with Hepatocellular Carcinoma

Background. Hepatocellular carcinoma (HCC) remains an important cause of cancer death. The molecular mechanism of hepatocarcinogenesis and prognostic factors of HCC have not been completely uncovered. Methods. In this study, we screened out differentially expressed lncRNAs (DE lncRNAs), miRNAs (DE miRNAs), and mRNAs (DE mRNAs) by comparing the gene expression of HCC and normal tissue in The Cancer Genome Atlas (TCGA) database. DE mRNAs were used to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the miRNA and lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a competitive endogenous RNA (ceRNA) network by weighted gene coexpression network analysis (WGCNA). Moreover, univariable Cox regression and Kaplan-Meier curve analyses of DE lncRNAs and DE mRNAs were conducted. Finally, the lasso-penalized Cox regression analysis and nomogram model were used to establish a new risk scoring system and predict the prognosis of patients with liver cancer. The expression of survival-related DE lncRNAs was verified by qRT-PCR. Results. A total of 1896 DEmRNAs, 330 DElncRNAs, and 76 DEmiRNAs were identified in HCC and normal tissue samples. Then, the turquoise miRNA and turquoise lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a ceRNA network by WGCNA. In this ceRNA network, there were 566 lncRNA-miRNA-mRNA regulatory pairs, including 30 upregulated lncRNAs, 16 downregulated miRNAs, and 75 upregulated mRNAs. Moreover, we screened out 19 lncRNAs and 14 hub mRNAs related to prognosis from this ceRNA network by univariable Cox regression and Kaplan-Meier curve analyses. Finally, a new risk scoring system was established by selecting the optimal risk lncRNAs from the 19 prognosis-related lncRNAs through lasso-penalized Cox regression analysis. In addition, we established a nomogram model consisting of independent prognostic factors to predict the survival rate of HCC patients. Finally, the correlation between the risk score and immune cell infiltration and gene set enrichment analysis were determined. Conclusions. In conclusion, the results may provide potential biomarkers or therapeutic targets for HCC and the establishment of the new risk scoring system and nomogram model provides the new perspective for predicting the prognosis of HCC.

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Development of a Genetic and Clinical Data-Based (GC) Risk Score for Predicting Survival of Hepatocellular Carcinoma Patients After Tumor Resection

Cellular Physiology and Biochemistry ◽

10.1159/000491779 ◽

2018 ◽

Vol 48 (2) ◽

pp. 491-502 ◽

Cited By ~ 3

Author(s):

Shengsen Chen ◽

Chao Wang ◽

An Cui ◽

Kangkang Yu ◽

Chong Huang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Cancer ◽

Clinical Data ◽

Scoring System ◽

Cancer Tissue ◽

Predictive Tool ◽

Internal Validation ◽

Scoring Model ◽

Risk Scoring ◽

Risk Scoring System

Background/Aims: Carnitine palmitoyltransferase 1A (CPT1A) is a rate-limiting enzyme in the transport of long-chain fatty acids for β-oxidation. Increasing evidence has indicated that CPT1A plays an important role in carcinogenesis. However, the expression and prognostic value of CPT1A in hepatocellular carcinoma (HCC) have not been extensively studied. Methods: Here, we collected 66 post-operative liver cancer tissue samples. Gene profile expression was tested by RT-PCR. Receiver operating characteristic (ROC) analysis was performed and multivariate analysis with Cox’s Proportional Hazard Model was used for confirming the selected markers’ predictive efficiency for HCC patients’ survival. A simple risk scoring system was created based on Cox’s regression modeling and bootstrap internal validation. Results: Cox multivariate regression analysis demonstrated that CPT1A, tumor size, intrahepatic metastasis, TNM stage and histological grade were independent risk factors for the prognosis of HCC patients after surgery. Our genetic and clinical data-based (GC) risk scoring system revealed that HCC patients whose total score≥3 are more likely to relapse and die than patients whose total score < 3. Finally, the good discriminatory power of our risk scoring model was validated by bootstrap internal validation. Conclusions: The genetic and clinical data-based risk scoring model can be a promising predictive tool for liver cancer patients’ prognosis after operation.

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Development of risk scoring system for stratifying population for hepatocellular carcinoma screening

Hepatology ◽

10.1002/hep.27610 ◽

2015 ◽

Vol 61 (6) ◽

pp. 1934-1944 ◽

Cited By ~ 26

Author(s):

Yi-Chun Hung ◽

Chih-Lin Lin ◽

Chun-Jen Liu ◽

Hung Hung ◽

Shi-Ming Lin ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Scoring System ◽

Risk Scoring ◽

Hepatocellular Carcinoma Screening ◽

Risk Scoring System

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Screening prognosis-related lncRNAs based on WGCNA to establish a new risk score for predicting prognosis in patients with hepatocellular carcinoma

10.21203/rs.2.23241/v1 ◽

2020 ◽

Author(s):

Xueliang Zhou ◽

Mengmeng Dou ◽

Zaoqu Liu ◽

Dechao Jiao ◽

Zhaonan Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Scoring System ◽

Normal Tissue ◽

Cox Regression ◽

Cox Regression Analysis ◽

Cerna Network ◽

Kaplan Meier ◽

Risk Scoring ◽

Risk Scoring System

Abstract Background: Hepatocellular carcinoma (HCC) remains an important cause of cancer death. The molecular mechanism of hepatocarcinogenesis and prognostic factors of HCC have not been completely uncovered. Methods: In this study, we screened out differentially expressed lncRNAs (DE lncRNAs), miRNAs (DE miRNAs), and mRNAs (DE mRNAs) by comparing the gene expression of HCC and normal tissue in the The Cancer Genome Atlas (TCGA) database. DE mRNAs were used to perform gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the miRNA and lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a competitive endogenous RNA (ceRNA) network by weighted gene coexpression network analysis (WGCNA). Moreover, univariable Cox regression and Kaplan-Meier curve analyses of DE lncRNAs and DE mRNAs were conducted. Finally, the lasso‐penalized Cox regression analysis and nomogram model were used to establish new risk scoring system and predict the prognosis of patients with liver cancer. Results: A total of 1896 DEmRNAs, 330 DElncRNAs, and 76 DEmiRNAs were identified in HCC and normal tissue samples. Then, the turquoise miRNA and turquoise lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a ceRNA network by WGCNA. In this ceRNA network, there were 566 lncRNA-miRNA-mRNA regulatory pairs, including 30 upregulated lncRNAs, 16 downregulated miRNAs and 75 upregulated mRNAs. Moreover, we screened out 19 lncRNAs and 14 hub mRNAs related to prognosis from this ceRNA network by univariable Cox regression and Kaplan-Meier curve analyses. Finally, a new risk scoring system was established by selecting the optimal risk lncRNAs from the 19 prognosis-related lncRNAs through lasso‐penalized Cox regression analysis. In addition, we established a nomogram model consisting of independent prognostic factors to predict the survival rate of HCC patients. Conclusions: In conclusion, the results may provide potential biomarkers or therapeutic targets for HCC, and the establishment of new risk scoring system and nomogram model provide the new perspective for predicting the prognosis of HCC.

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