A nomogram for predicting the risk of postoperative recurrence of hepatitis B virus-related hepatocellular carcinoma in patients with high preoperative serum glutamyl transpeptidase

Zhang, Jia-Si; Wang, Zhiheng; Guo, Xinggang; Ji, Zhang; Ni, Jingwei

doi:10.21037/jgo-21-450

Cited by 6 publications

(5 citation statements)

References 51 publications

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“…However, the HCC cure rate remains unsatisfactory, which is due in part to a lack of biomarkers for treatment and prognosis. Traditionally used biomarkers, such as alpha-fetoprotein (AFP), can guide the diagnosis and monitoring of recurrence to some extent, but do not provide guidance for treatment (37). In addition, compared with a single clinical biomarker, combining multiple biomarkers into a single model can improve the prediction accuracy and help to develop an accurate personalized treatment plan (38).…”

Section: Discussionmentioning

confidence: 99%

The development and validation of a novel senescence-related long-chain non-coding RNA (lncRNA) signature that predicts prognosis and the tumor microenvironment of patients with hepatocellular carcinoma

Huang¹,

Ma²,

Zhou³

et al. 2022

Ann Transl Med

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Background: The epigenetic regulators of cellular senescence, especially long non-coding RNAs (lncRNAs), remain unclear. The expression levels of lncRNA were previously known to be prognostic indicators for tumors. We hypothesized that lncRNAs regulating cellular senescence could also predict prognosis in patients with hepatocellular carcinoma (HCC) and developed a novel lncRNA predictive signature.Methods: Using RNA sequencing data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database, a co-expression network of senescence-related messenger RNAs (mRNAs) and lncRNAs was constructed. Using univariate Cox regression analysis and a stepwise multiple Cox regression analysis, we constructed a prognostic HCC senescence-related lncRNA signature (HCCSenLncSig).Kaplan-Meier analysis was used to compare the overall survival (OS) of high-and low-risk groups stratified by the HCCSenLncSig. Furthermore, the HCCSenLncSig risk score and other clinical characteristics were included to develop an HCC prognostic nomogram. The accuracy of the model was evaluated by the time dependent receiver operating characteristic (ROC) and calibration curves, respectively.

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Section: Discussionmentioning

confidence: 99%

The development and validation of a novel senescence-related long-chain non-coding RNA (lncRNA) signature that predicts prognosis and the tumor microenvironment of patients with hepatocellular carcinoma

Huang¹,

Ma²,

Zhou³

et al. 2022

Ann Transl Med

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show abstract

“…However, the prognosis of HCC patients remains poor, in large part owing to a lack of therapeutic and prognostic biomarkers. Markers currently considered in clinical practice, such as alpha fetoprotein (AFP), can be used as diagnostic markers or for monitoring recurrence, but they do not provide treatment or prognostic data[ 25 ]. The combination of several biomarkers into a single model improves both therapeutic and prognostic prediction accuracy compared with a single biomarker[ 26 ].…”

Section: Discussionmentioning

confidence: 99%

Cuproptosis-related long non-coding RNAs model that effectively predicts prognosis in hepatocellular carcinoma

Huang¹,

Ma²,

Ma³

et al. 2022

World J Gastrointest Oncol

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BACKGROUND Cuproptosis has recently been considered a novel form of programmed cell death. To date, long-chain non-coding RNAs (lncRNAs) crucial to the regulation of this process remain unelucidated. AIM To identify lncRNAs linked to cuproptosis in order to estimate patients' prognoses for hepatocellular carcinoma (HCC). METHODS Using RNA sequence data from The Cancer Genome Atlas Live Hepatocellular Carcinoma (TCGA-LIHC), a co-expression network of cuproptosis-related genes and lncRNAs was constructed. For HCC prognosis, we developed a cuproptosis-related lncRNA signature (CupRLSig) using univariate Cox, lasso, and multivariate Cox regression analyses. Kaplan-Meier analysis was used to compare overall survival among high- and low-risk groups stratified by median CupRLSig risk score. Furthermore, comparisons of functional annotation, immune infiltration, somatic mutation, tumor mutation burden (TMB), and pharmacologic options were made between high- and low-risk groups. RESULTS Three hundred and forty-three patients with complete follow-up data were recruited in the analysis. Pearson correlation analysis identified 157 cuproptosis-related lncRNAs related to 14 cuproptosis genes. Next, we divided the TCGA-LIHC sample into a training set and a validation set. In univariate Cox regression analysis, 27 LncRNAs with prognostic value were identified in the training set. After lasso regression, the multivariate Cox regression model determined the identified risk equation as follows: Risk score = (0.2659 × PICSAR expression) + (0.4374 × FOXD2-AS1 expression) + (-0.3467 × AP001065.1 expression). The CupRLSig high-risk group was associated with poor overall survival (hazard ratio = 1.162, 95%CI = 1.063-1.270; P < 0.001) after the patients were divided into two groups depending upon their median risk score. Model accuracy was further supported by receiver operating characteristic and principal component analysis as well as the validation set. The area under the curve of 0.741 was found to be a better predictor of HCC prognosis as compared to other clinicopathological variables. Mutation analysis revealed that high-risk combinations with high TMB carried worse prognoses (median survival of 30 mo vs 102 mo of low-risk combinations with low TMB group). The low-risk group had more activated natural killer cells (NK cells, P = 0.032 by Wilcoxon rank sum test) and fewer regulatory T cells (Tregs, P = 0.021) infiltration than the high-risk group. This finding could explain why the low-risk group has a better prognosis. Interestingly, when checkpoint gene expression (CD276, CTLA-4, and PDCD-1) and tumor immune dysfunction and rejection (TIDE) scores are considered, high-risk patients may respond better to immunotherapy. Finally, most drugs commonly used in preclinical and clinical systemic therapy for HCC, such as 5-fluoro...

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“…However, HCC prognosis remains poor, in large part due to a lack of therapeutic and prognostic biomarkers. Markers currently considered in clinical practice, such as AFP, can be used as diagnostic markers or for monitoring recurrence, but they do not provide treatment or prognostic data (23). The combination of several biomarkers into a single model improves both therapeutic and prognostic prediction accuracy as compared to a single biomarker (24).…”

Section: Discussionmentioning

confidence: 99%

A novel cuproptosis-related long non-coding RNAs model that effectively predicts prognosis in hepatocellular carcinoma

Huang

et al. 2022

Preprint

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Background: Cuproptosis has recently been considered a novel form of programmed cell death. To date, factors crucial to the regulation of this process remain unelucidated. Here, we aimed to identify long-chain non-coding RNAs (lncRNAs) associated with cuproptosis in order to predict the prognosis of patients with hepatocellular carcinoma (HCC). Methods: Using RNA sequence data from The Cancer Genome Atlas Live Hepatocellular Carcinoma (TCGA-LIHC), a co-expression network of cuproptosis-related mRNAs and lncRNAs was constructed. For HCC prognosis, we developed a cuproptosis-related lncRNA signature (CupRLSig) using univariate Cox, lasso, and multivariate Cox regression analyses. Kaplan-Meier analysis was used to compare overall survival among high- and low-risk groups stratified by median CupRLSig score. Furthermore, comparisons of functional annotation, immune infiltration, somatic mutation, TMB (tumor mutation burden), and pharmacologic options were made between high- and low-risk groups. Results: Our prognostic risk model was constructed using the cuproptosis-related PICSAR, FOXD2-AS1, and AP001065.1 lncRNAs. The CupRLSig high-risk group was associated with poor overall survival (hazard ratio = 1.162, 95% CI = 1.063-1.270; p < 0.001). Model accuracy was further supported by receiver operating characteristic and principal component analysis as well as internal validation cohorts. A prognostic nomogram developed considering CupRLSig data and a number of clinical characteristics were found to exhibit adequate performance in survival risk stratification. Mutation analysis revealed that high-risk combinations with high TMB carried worse prognoses. Finally, differences in immune checkpoint expression and responses to chemotherapy as well as in targeted therapy among CupRLSig stratified high- and low-risk groups were explored. Conclusions: The lncRNA signature constructed in this study is valuable in prognostic estimation in the setting of HCC.

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A nomogram for predicting the risk of postoperative recurrence of hepatitis B virus-related hepatocellular carcinoma in patients with high preoperative serum glutamyl transpeptidase

Cited by 6 publications

References 51 publications

The development and validation of a novel senescence-related long-chain non-coding RNA (lncRNA) signature that predicts prognosis and the tumor microenvironment of patients with hepatocellular carcinoma

The development and validation of a novel senescence-related long-chain non-coding RNA (lncRNA) signature that predicts prognosis and the tumor microenvironment of patients with hepatocellular carcinoma

Cuproptosis-related long non-coding RNAs model that effectively predicts prognosis in hepatocellular carcinoma

A novel cuproptosis-related long non-coding RNAs model that effectively predicts prognosis in hepatocellular carcinoma

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