A long non-coding RNA-based signature predicts early recurrence in hepatocellular carcinoma

2021 ◽  
pp. 1-10
Author(s):  
Shuai He ◽  
Jin-Feng Li ◽  
Hao Tian ◽  
Ye Sang ◽  
Xiao-Jing Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Curative Resection ◽  
Risk Model ◽  
Cox Regression ◽  
Early Recurrence ◽  
Support Vector ◽  
Term Survival ◽  
Cox Regression Analysis ◽  
Non Coding Rna ◽  
Long Non Coding Rna

BACKGROUND: Early recurrence is the main obstacle for long-term survival of hepatocellular carcinoma (HCC) patients after curative resection. OBJECTIVE: We aimed to develop a long non-coding RNA (lncRNA) based signature to predict early recurrence. METHODS: Using bioinformatics analysis and quantitative reverse transcription PCR (RT-qPCR), we screened for lncRNA candidates that were abnormally expressed in HCC. The expression levels of candidate lncRNAs were analyzed in HCC tissues from 160 patients who underwent curative resection, and a risk model for the prediction of recurrence within 1 year (early recurrence) of HCCs was constructed with linear support vector machine (SVM). RESULTS: A lncRNA-based classifier (Clnc), which contained nine differentially expressed lncRNAs including AF339810, AK026286, BC020899, HEIH, HULC, MALAT1, PVT1, uc003fpg, and ZFAS1 was constructed. In the test set, this classifier reliably predicted early recurrence (AUC, 0.675; sensitivity, 72.0%; specificity, 63.1%) with an odds ratio of 4.390 (95% CI, 2.120–9.090). Clnc showed higher accuracy than traditional clinical features, including tumor size, portal vein tumor thrombus (PVTT) in predicting early recurrence (AUC, 0.675 vs 0.523 vs 0.541), and had much higher sensitivity than Barcelona Clinical Liver Cancer (BCLC; 72.0% vs 50.0%), albeit their AUCs were comparable (0.675 vs 0.678). Moreover, combining Clnc with BCLC significantly increased the AUC, compared with Clnc or BCLC alone in predicting early recurrence (all P< 0.05). Finally, logistic and Cox regression analysis suggested that Clnc was an independent prognostic factor and associated with the early recurrence and recurrence-free survival of HCC patients after resection, respectively (all P= 0.001). CONCLUSIONS: Our lncRNA-based classifier Clnc can predict early recurrence of patients undergoing surgical resection of HCC.

scholarly journals Factors influencing early recurrence of hepatocellular carcinoma after curative resection

2020 ◽  
Vol 48 (8) ◽  
pp. 030006052094555
Author(s):  
Yu Zhu ◽  
Lingling Gu ◽  
Ting Chen ◽  
Guoqun Zheng ◽  
Chao Ye ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Curative Resection ◽  
Cox Regression ◽  
Postoperative Recurrence ◽  
Early Recurrence ◽  
Tumor Diameter ◽  
Cox Regression Analysis ◽  
Factors Influencing ◽  
Linear Manner ◽  
Hcc Recurrence

Objective To identify the factors influencing early recurrence in patients with hepatocellular carcinoma (HCC) after curative resection. Methods Clinical data for 99 patients with HCC undergoing curative resection were analyzed. The clinicopathological factors influencing early recurrence were analyzed by Cox regression. Results Twenty-five of 99 patients (25.3%) suffered from early recurrence. There were significant differences between patients with and without recurrence in terms of tumor diameter, tumor capsular integrity, and preoperative alpha fetoprotein level. Cox regression analysis revealed that a tumor diameter >2.6 cm and preoperatively increased total bilirubin (TBL) level were risk factors for postoperative recurrence, while tumor capsular integrity had a protective effect on postoperative recurrence. After adjusting for preoperative TBL level and tumor capsular integrity, the risk of HCC recurrence was markedly increased in line with increasing tumor diameter in a non-linear manner. Conclusion Tumor diameter >2.6 cm and preoperatively increased TBL level are associated with a higher risk of early recurrence after curative resection in patients with HCC, while tumor capsular integrity is associated with a lower risk of early recurrence.

Long non-coding RNA expression profile can predict early recurrence in hepatocellular carcinoma after curative resection

2018 ◽  
Vol 48 (13) ◽  
pp. 1140-1148 ◽  
Author(s):  
Yufeng Lv ◽  
Wenhao Wei ◽  
Zhong Huang ◽  
Zhichao Chen ◽  
Yuan Fang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Profile ◽  
Curative Resection ◽  
Early Recurrence ◽  
Rna Expression ◽  
Non Coding Rna ◽  
Long Non Coding Rna

scholarly journals Spleen Radiomics Signature: A Potential Biomarker for Prediction of Early and Late Recurrences of Hepatocellular Carcinoma After Resection

2021 ◽  
Vol 11 ◽  
Author(s):  
Pinxiong Li ◽  
Lei Wu ◽  
Zhenhui Li ◽  
Jiao Li ◽  
Weitao Ye ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Curative Resection ◽  
Mixed Model ◽  
Cox Regression ◽  
Predictive Performance ◽  
Late Recurrence ◽  
Early Recurrence ◽  
Cox Regression Analysis ◽  
Potential Biomarker ◽  
Radiomics Signature

ObjectivesTo explore the usefulness of spleen radiomics features based on contrast-enhanced computed tomography (CECT) in predicting early and late recurrences of hepatocellular carcinoma (HCC) patients after curative resection.MethodsThis retrospective study included 237 HCC patients who underwent CECT and curative resection between January 2006 to January 2016. Radiomic features were extracted from CECT images, and then the spleen radiomics signatures and the tumor radiomics signatures were built. Cox regression analysis was performed to identify the independent risk factors of early and late recurrences. Then, multiple models were built to predict the recurrence-free survival of HCC after resection, and the incremental value of the radiomics signature to the clinicopathologic model was assessed and validated. Kaplan–Meier survival analysis was used to assess the association of the models with RFS.ResultsThe spleen radiomics signature was independent risk factor of early recurrence of HCC. The mixed model that integrated microvascular invasion, tumor radiomics signature and spleen radiomics signature for the prediction of early recurrence achieved the highest C-index of 0.780 (95% CI: 0.728,0.831) in the primary cohort and 0.776 (95% CI: 0.716,0.836) in the validation cohort, and presented better predictive performance than clinicopathological model and combined model. In the analysis of late recurrence, the spleen radiomics signature was the only prognostic factor associated with late recurrence of HCC.ConclusionsThe identified spleen radiomics signatures are prognostic factors of both early and late recurrences of HCC patients after surgery and improve the predictive performance of model for early recurrence.

scholarly journals Construction and Validation of a Prognostic Risk Model Based on Autophagy-Related Genes in Hepatocellular Carcinoma Cells

2021 ◽  
Author(s):  
Rui Feng ◽  
Jian Li ◽  
Weiling Xuan ◽  
Hanbo Liu ◽  
Dexin Cheng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Differential Expression ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Differential Expression Analysis ◽  
Cox Regression Analysis ◽  
Dismal Prognosis

Abstract Background Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer and the main cause of cancer mortality. Its high complexity and dismal prognosis bring dramatic difficulty to treatment. Due to the disclosed dual functions of autophagy in cancer development, understanding autophagy-related genes devotes into seeking novel biomarkers for HCC. Methods Differential expression of genes in normal and tumor groups was analyzed to acquire autophagy-related genes in HCC. GO and KEGG pathway analyses were conducted on these genes. Genes were then screened by univariate regression analysis. The screened genes were subjected to multivariate Cox regression analysis to build a prognostic model. The model was validated by ICGC validation set. Results Altogether, 42 autophagy-related differential genes were screened by differential expression analysis. Enrichment analysis showed that they were mainly enriched in pathways including regulation of autophagy and cell apoptosis. Genes were screened by univariate analysis and multivariate Cox regression analysis to build a prognostic model. The model was constituted by 6 feature genes: EIF2S1, BIRC5, SQSTM1, ATG7, HDAC1, FKBP1A. Validation confirmed the accuracy and independence of this model in predicting HCC patient’s prognosis. Conclusion A total of 6 feature genes were identified to build a prognostic risk model. This model is conducive to investigating interplay between autophagy-related genes and HCC prognosis.

scholarly journals Prognostic Value and Regulatory Network of Immune-Related Genes in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Xiang Zhou ◽  
Keying Zhang ◽  
Fa Yang ◽  
Chao Xu ◽  
Jianhua Jiao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Risk Model ◽  
Cox Regression ◽  
Wilcoxon Test ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background: Hepatocellular carcinoma (HCC) is a disease with higher morbidity, mortality, and poor prognosis in the whole world. Understanding the crosslink between HCC and the immune system is essential for people to uncover a few potential and valuable therapeutic strategies. This study aimed to reveal the correlation between HCC and immune-related genes and establish a clinical evaluation model. Methods: We had analyzed the clinical information consisted of 373 HCC and 49 normal samples from the cancer genome atlas (TCGA). The differentially expressed genes (DEGs) were selected by the Wilcoxon test and the immune-related differentially expressed genes (IRDEGs) in DEGs were identified by matching DEGs with immune-related genes downloaded from the ImmPort database. Furthermore, the univariate Cox regression analysis and multivariate Cox regression analysis were performed to construct a prognostic risk model. Then, twenty-two types of tumor immune-infiltrating cells (TIICs) were downloaded from Tumor Immune Estimation Resource (TIMER) and were used to construct the correlational graphs between the TIICs and risk score by the CIBERSORT. Subsequently, the transcription factors (TFs) were gained in the Cistrome website and the differentially expressed TFs (DETFs) were achieved. Finally, the KEGG pathway analysis and GO analysis were performed to further understand the molecular mechanisms between DETFs and PDIRGs.Results: In our study, 5839 DEGs, 326 IRDEGs, and 31 prognosis-related IRDEGs (PIRDEGs) were identified. And 8 optimal PIRDEGs were employed to construct a prognostic risk model by multivariate Cox regression analysis. The correlation between risk genes and clinical characterizations and TIICs has verified that the prognostic model was effective in predicting the prognosis of HCC patients. Finally, several important immune-related pathways and molecular functions of the eight PIRDEGs were significantly enriched and there was a distinct association between the risk IRDEGs and TFs. Conclusion: The prognostic risk model showed a more valuable predicting role for HCC patients, and produced many novel therapeutic targets and strategies for HCC.

scholarly journals rs3200401 MALAT1 GENE POLYMORPHISM IS NOT RELATED TO AGE OF KIDNEY CANCER ONSET

2020 ◽  
Vol 20 (2) ◽  
pp. 119-123
Author(s):  
A.D. Volkohon ◽  
V. Yu. Harbuzova ◽  
O.V. Ataman
Keyword(s):  
Renal Cell Carcinoma ◽  
Gene Polymorphism ◽  
Cell Carcinoma ◽  
Kidney Cancer ◽  
Renal Cell ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Today, the long non-coding RNA MALAT1 is considered to be one of the major RNAs involved in the emergence and metastasizing of various malignant tumours. Recent experiments have shown that MALAT1 plays an important role in the onset and progression of kidney cancer as well. It was found that cancer patient survival depends on the level of MALAT1 gene expression. The aim of the study was to investigate the possible association between rs3200401 MALAT1 gene polymorphism and age of kidney cancer onset among Ukrainian patients. Materials and methods. The venous blood of 101 patients with clear cell renal cell carcinoma (42 women and 59 men) was used for study. Determination of MALAT1 gene rs320040 polymorphism was performed by the Real-Time polymerase chain reaction method using TaqManSNP Assay C_3246069_10 components. Statistical analysis of the data obtained was performed using SPSS (version 17.0). To test the possible association between rs3200401 genotypes and the age of kidney cancer onset Kaplan-Meier and Cox regression techniques were used. P value < 0.05 was considered as statistically significant. Results. The obtained results of MALAT1 gene rs3200401 polymorphic site genotyping revealed that 71 (70.3%) patients with renal cell carcinoma had CC genotype, 29 (28.7%) – CT, 1 (1%) – TT genotype. Survival analysis by Kaplan-Meier method showed that life expectancy until the tumour occurrence was not related to rs3200401 locus (log rank P = 0.449 – for codominant model; log rank P = 0.847 – for dominant model). The results of Cox regression analysis also showed no link between MALAT gene rs3200401-site and risk of renal cell carcinoma development (P > 0.05). No statistically significant results were found after adjustment for sex, body mass index, metastasis, smoking and drinking habits (P > 0.05). Conclusions. The rs3200401 gene polymorphism of long non-coding RNA MALAT1 is not associated with the age of kidney cancer onset in Ukrainian population.

scholarly journals Immunoautophagy-Related Long Noncoding RNA (IAR-lncRNA) Signature Predicts Survival in Hepatocellular Carcinoma

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1301
Author(s):  
Yulu Wang ◽  
Fangfang Ge ◽  
Amit Sharma ◽  
Oliver Rudan ◽  
Maria F. Setiawan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Immune Cells ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Clinical Samples ◽  
Special Role ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Background: The dysregulation of autophagy and immunological processes has been linked to various pathophysiological conditions, including cancer. Most notably, their particular involvement in hepatocellular carcinoma (HCC) is becoming increasingly evident. This has led to the possibility of developing a prognostic signature based on immuno-autophagy-related (IAR) genes. Given that long non-coding RNAs (lncRNAs) also play a special role in HCC, a combined signature utilizing IAR genes and HCC-associated long noncoding RNAs (as IARlncRNA) may potentially help in the clinical scenario. Method: We used Pearson correlation analysis, Kaplan–Meier survival curves, univariate and multivariate Cox regression, and ROC curves to generate and validate a prognostic immuno-autophagy-related long non-coding RNA (IARlncRNA) signature. The Chi-squared test was utilized to investigate the correlation between the obtained signature and the clinical characteristics. CIBERSORT algorithms and the Wilcoxon rank sum test were applied to investigate the correlation between signature and infiltrating immune cells. GO and KEGG analyses were performed to derived signature-dependent pathways. Results: Herein, we build an IAR-lncRNA signature (as first in the literature) and demonstrate its prognostic ability in hepatocellular carcinoma. Primarily, we identified three IARlncRNAs (MIR210HG, AC099850.3 and CYTOR) as unfavorable prognostic determinants. The obtained signature predicted the high-risk HCC group with shorter overall survival, and was further associated with clinical characteristics such as tumor grade (t = 10.918, p = 0.001). Additionally, several infiltrating immune cells showed varied fractions between the low-risk group and the high-risk HCC groups in association with the obtained signature. In addition, pathways analysis described by the signature clearly distinguishes both risk groups in HCC. Conclusions: The immuno-autophagy-related long non-coding RNA (IARlncRNA) signature we established exhibits a prognostic ability in hepatocellular carcinoma. To our knowledge, this is the first attempt in the literature to combine three determinants (immune, autophagy and LnRNAs), thus requiring molecular validation of this obtained signature in clinical samples.

scholarly journals Human Nuclear Receptors (NRs) Genes have Prognostic Significance in Hepatocellular Carcinoma Patients

2020 ◽  
Author(s):  
Guangtao Sun ◽  
Kejian Sun ◽  
Chao Shen
Keyword(s):  
Hepatocellular Carcinoma ◽  
Nuclear Receptors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Prognostic Signature ◽  
Term Survival ◽  
Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality in the world. Human nuclear receptors (NRs) have been identified to closely related to various cancer. However, the prognostic significance of NRs on HCC patients has not been studied in detail.Method: We downloaded the mRNA profiles and clinical information of 371 HCC patients from TCGA database and analyzed the expression of 48 NRs. The consensus clustering analysis with the mRNA levels of 48 NRs was performed by the "ConsensusClusterPlus". The Univariate cox regression analysis was performed to predict the prognostic significance of NRs on HCC. The risk score was calculated by the prognostic model constructed based on eight optimal NRs which were selected. Then Multivariate Cox regression analysis was performed to determine whether the risk score is an independent prognostic signature. Finally, the nomogram based on multiple independent prognostic factors including risk score and TNM Stage was used to predict the long-term survival of HCC patients.Results: NRs could effectively separate HCC samples with different prognosis. The prognostic model constructed based on the eight optimal NRs (NR1H3, ESR1, NR1I2, NR2C1, NR6A1, PPARD, PPARG and VDR) could effectively predict the prognosis of HCC patients as an independent prognostic signature. Moreover, the nomogram was constructed based on multiple independent prognostic factors including risk score and TNM Stage and could better predict the long-term survival for 3- and 5-year of HCC patients.Conclusion: Our results provided novel evidences that NRs could act as the potential prognostic signatures for HCC patients.

scholarly journals AC099850.3 Promotes Cell Proliferation and is One of Five Costimulatory Molecule-Related LncRNAs That Predict Overall Survival of Liver Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Meimei Liu ◽  
Qiong Fang ◽  
Yanping Huang ◽  
Jin Zhou ◽  
Qi Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Proliferation ◽  
Risk Model ◽  
Cox Regression ◽  
Costimulatory Molecule ◽  
Costimulatory Molecules ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Liver Hepatocellular Carcinoma

Abstract Background: Extensive research has revealed that costimulatory molecules play central roles in mounting anti-tumor immune responses and long non‐coding RNA (lncRNA) is an important regulatory factor in the development of various cancers. However, their roles in liver hepatocellular carcinoma (HCC) remain unexplored. In this study, we aimed to explore costimulatory molecule-related lncRNAs in HCC and construct a prognostic signature to predict prognosis and improve clinical outcomes with HCC patients.Methods: The data we used for bioinformatics analysis were downloaded from The Cancer Genome Atlas database. Costimulatory molecules were obtained from the known literature. The R software, SPSS and GraphPad Prism were used for mapping and statistical analysis.Results: A five costimulatory molecule-related lncRNAs based risk model was initially constructed through lasso and Cox regression analysis. Moreover, multivariate regression suggested that the risk score was a significant prognostic risk factor in HCC. Samples in high- and low-risk groups exhibited significantly different in gene set enrichment analysis and immune infiltration analysis. Importantly, we found that the AC099850.3 were significantly related to cell proliferation in HCC according to the colony formation and CCK8 assays.Conclusion: In summary, we first identified and validated a novel costimulatory molecule-related survival model and we found that AC099850.3 is closely associated with clinical stage and could remarkably facilitate cell proliferation in HCC, making it potential to be a novel therapeutic target.

scholarly journals A Novel Pyroptosis-related Prognostic Model for Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Qianqian Wu ◽  
Sutian Jiang ◽  
Tong Cheng ◽  
Manyu Xu ◽  
Bing Lu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Prognostic Biomarkers ◽  
Cox Regression Analysis ◽  
Public Data

Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor because of its significant heterogeneity and complicated molecular pathogenesis. Novel prognostic biomarkers are urgently needed because no effective and reliable prognostic biomarkers currently exist for HCC patients. Increasing evidence has revealed that pyroptosis plays a role in the occurrence and progression of malignant tumors. However, the relationship between pyroptosis-related genes (PRGs) and HCC patient prognosis remains unclear. In this study, 57 PRGs were obtained from previous studies and GeneCards. The gene expression profiles and clinical data of HCC patients were acquired from public data portals. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a risk model using TCGA data. Additionally, the risk model was further validated in an independent ICGC dataset. Our results showed that 39 PRGs were significantly differentially expressed between tumor and normal liver tissues in the TCGA cohort. Functional analysis confirmed that these PRGs were enriched in pyroptosis-related pathways. According to univariate Cox regression analysis, 14 differentially expressed PRGs were correlated with the prognosis of HCC patients in the TCGA cohort. A risk model integrating two PRGs was constructed to classify the patients into different risk groups. Poor overall survival was observed in the high-risk group of both TCGA (p &lt; 0.001) and ICGC (p &lt; 0.001) patients. Receiver operating characteristic curves demonstrated the accuracy of the model. Furthermore, the risk score was confirmed as an independent prognostic indicator via multivariate Cox regression analysis (TCGA cohort: HR = 3.346, p &lt; 0.001; ICGC cohort: HR = 3.699, p &lt; 0.001). Moreover, the single-sample gene set enrichment analysis revealed different immune statuses between high- and low-risk groups. In conclusion, our new pyroptosis-related risk model has potential application in predicting the prognosis of HCC patients.

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