Comprehensive Analysis of Autophagy-related Genes in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Li Wang ◽  
Jialin Qu ◽  
Na Zhou ◽  
Man Jiang ◽  
Xiaochun Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Survival Model ◽  
Survival Models ◽  
Clinicopathological Parameters ◽  
Lasso Regression ◽  
Kaplan Meier ◽  
Human Protein Atlas ◽  
Independent Risk Factors

Abstract Background: Hepatocellular carcinoma (HCC) is the common type of cause of cancer-related death among human cancers. There are ample evidences to showing that autophagy-related genes (ARGs) may play a significant role in the biological process of HCC. Methods: In this study, we aim to identify survival model and nomogram that could effectively predict the prognosis of HCC based on ARGs. First, we download the data of HCC patients from TCGA database. Second, we analysis the function of ARGs by utilized GO and the KEGG analysis. Finally, we screen 5 ARGs (SQSTM1, CAPN10, EIF2S1, ATIC, RHEB) for survival model by performed the Cox regression and Lasso regression analysis. We further built and verified a prognostic nomogram base on prognostic ARGs. Moreover, its efficacy was validated by the ICGC database. The expressions level of 5 ARGs was performed using Oncomine database, the Human Protein Atlas and Kaplan-Meier plotter.Result: We found patients the survival of patients in the different groups was significantly different both in the TCGA cohort and ICGC cohort. The survival model showed good performance for predicting the prognosis of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters. Conclusion: our survival models and prognostic ARGs nomogram can be independent risk factors for hepatocellular carcinoma patients.

scholarly journals Identification of an RNA binding protein-related gene signature in hepatocellular carcinoma patients

2020 ◽  
Vol 26 (1) ◽  
Author(s):  
Li Wang ◽  
Na Zhou ◽  
Jialin Qu ◽  
Man Jiang ◽  
Xiaochun Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Characteristic Curve ◽  
Gene Signature ◽  
Clinicopathological Parameters ◽  
Lasso Regression ◽  
Bioinformatics Analyses

Abstract Background Hepatocellular carcinoma (HCC) is a common malignant primary cancer with high mortality. Previous studies have demonstrated that RNA binding proteins (RBPs) are involved in the biological processes of cancers, including hepatocellular cancer. Methods In this study, we aimed to identify the clinical value of RNA-binding proteins for hepatocellular carcinoma. We obtained gene expression and clinical data of hepatocellular carcinoma patients from the TCGA and ICGC databases. The prognostic value of RBP-related genes in patients with hepatocellular carcinoma and their function were studied by comprehensive bioinformatics analyses. The gene signature of SMG5, EZH2, FBLL1, ZNF239, and IGF2BP3 was generated by univariate and multivariate Cox regression and LASSO regression analyses. We built and verified a prognostic nomogram based on RBP-related genes. The gene signature was validated by the ICGC database. The expression of RBP-related genes was validated by the Oncomine database, the Human Protein Atlas and Kaplan–Meier plotter. Result Most RBP-related genes were significantly different in cancer and normal tissues. The survival of patients in the different groups was significantly different. The gene signature showed good performance for predicting the survival of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters. Conclusion Gene signatures based on RNA-binding proteins can be independent risk factors for hepatocellular carcinoma patients.

Identification of Novel Tumor Microenvironment-Related Long Non-coding RNAs to Predict the Prognosis for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Shenglan Huang ◽  
Jian Zhang ◽  
Dan Li ◽  
Xiaolan Lai ◽  
Lingling Zhuang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Clinicopathological Parameters ◽  
Kaplan Meier ◽  
The Impact

Abstract Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Tumor microenvironment (TME) plays a vital role in the tumor progression of HCC. Thus, we aimed to analyze the association of TME with HCC prognosis, and construct an TME-related lncRNAs signature for predicting the prognosis of HCC patients.Methods: We firstly assessed the stromal/immune /Estimate scores within the HCC microenvironment using the ESTIMATE algorithm based on TCGA database, and its associations with survival and clinicopathological parameters were also analyzed. Then, different expression lncRNAs were filtered out according to immune/stromal scores. Cox regression was performed to built an TME-related lncRNAs risk signature. Kaplan–Meier analysis was carried out to explored the prognostic values of the risk signature. Furthermore, we explored the biological functions and immune microenvironment feathers in high- and low risk groups. Lastly, we probed the association of the risk signature with the treatment responses to immune checkpoint inhibitors (ICIs) in HCC by comparing the immunophenoscore (IPS).Results: Stromal/immune /Estimate scores of HCC patients were obtained based on the ESTIMATE algorithm. The Kaplan-Meier curve analysis showed the high stromal/immune/ Estimate scores were significantly associated with better prognosis of the HCC patients. Then, six TME-related lncRNAs were screened for constructing the prognosis model. Kaplan-Meier survival curves suggested that HCC patients in high-risk group had worse prognosis than those with low-risk. ROC curve and Cox regression analyses demonstrated the signature could predict HCC survival exactly and independently. Function enrichment analysis revealed that some tumor- and immune-related pathways associated with HCC tumorigenesis and progression might be activated in high-risk group. We also discovered that some immune cells, which were beneficial to enhance immune responses towards cancer, were remarkably upregulated in low-risk group. Besides, there was closely correlation of immune checkmate inhibitors (ICIs) with the risk signature and the signature can be used to predict treatment response of ICIs.Conclusions: We analyzed the impact of the tumor microenvironment scores on the prognosis of patients with HCC. A novel TME-related prognostic risk signature was established, which may improve prognostic predictive accuracy and guide individualized immunotherapy for HCC patients.

Development and validation of a survival model based on autophagy-associated genes for predicting prognosis of hepatocellular carcinoma

2020 ◽  
Author(s):  
Wanli Yang ◽  
Liaoran Niu ◽  
Xinhui Zhao ◽  
Lili Duan ◽  
Yiding Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Survival Rate ◽  
Prediction Model ◽  
Risk Score ◽  
Cox Regression ◽  
Survival Model ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Prognostic Prediction

Abstract Background: Hepatocellular carcinoma (HCC) is one of the devastating tumors with increasing incidence. Autophagy-associated genes (ARGs) are widely participated in the cellular processes of HCC. This study proposed to identify the novel prognostic gene signature based on ARGs in HCC. Methods: We downloaded the RNA sequencing data and clinical information of HCC and normal tissues from The Cancer Genome Atlas (TCGA) database. The differentially expressed ARGs were screened by the Wilcoxon signed-rank test. Functional enrichment analyses were conducted to explore the biological implications and mechanisms of ARGs in HCC. Cox regression analysis and Lasso regression analysis were performed to screen the ARGs which related to overall survival (OS). The OS-related ARGs were then used to establish a prognostic prediction model. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were both applied to evaluate the accuracy of the model. GSE14520 dataset was downloaded as the testing cohort to validate the prognostic risk model in TCGA. A nomogram based on the clinical features and risk signature was established to predict the 3-year and 5-year survival rate of HCC patients. Results: Totally 27 differentially expressed ARGs were screened in this study. Then, 3 OS-related ARGs (SQSTM1, HSPB8, and BIRC5) were identified via the Cox regression and Lasso regression analyses. Based on these 3 ARGs, a prognostic prediction model was constructed. HCC patients in high-risk group presented poorer prognosis than those with low risk score in TCGA cohort (3-year OS, 53.7% vs 70.2%; 5-year OS, 42.0 % vs 55.2%; P=4.478e-04) and in the testing group (3-year OS, 57.7% vs 73.5%; 5-year OS, 43.2% vs 63.0%; P=1.274e-03). The risk score curve showed a well feasibility in predicting the patients’ survival both in TCGA and GEO cohort with the area under the ROC curve (AUC) of 0.756 and 0.672, respectively. Besides, the calibration curves and C-index indicated that the clinical nomogram performs well to predict the 3-year and 5-year survival rate in HCC patients. Conclusions: The survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients.

scholarly journals Identification of an RNA binding protein-related gene signature in hepatocellular carcinoma patients

2020 ◽  
Author(s):  
Li Wang ◽  
Na Zhou ◽  
Jialin Qu ◽  
Man Jiang ◽  
Xiaochun Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Characteristic Curve ◽  
Gene Signature ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters

Abstract Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related morbidity and mortality among all human cancers. Studies have demonstrated that RNA binding proteins (RBPs) involved in the biological process of cancers including hepatocellular cancer. In this study, we aim to identify clinical value of RNA binding proteins for hepatocellular carcinoma.Methods: We analyses the data of HCC that downloaded from the Cancer Genome Atlas (TCGA) database and determined the differently expressed of RBPs between cancer and normal tissues. We further elucidate the function of RBPs by utilized Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Gene signature of SMG5, EZH2, FBLL1, ZNF239, IGF2BP3 were generated by performed the univariate and multivariate Cox regression and LASSO regression analysis. CIBERSORT analysis was used to evaluation of tumor-infiltrating immune cells in different group. We built and verify a prognosis nomogram base on RBPs-related genes. Gene signature was validated by the International Cancer Genome Consortium (ICGC) database. The expressions of RBPs-related genes were validated by using Oncomine database, and the Human Protein Atlas.Result: Most of RBPs-related genes were significantly different in cancer and normal tissue. The survival of patients in the different group was statistically different. The Gene signature showed good performance for predicting the survival of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters (AUC=0.758). The patients in the high-risk group were more likely to have a higher Macrophages M0. Conclusion: Gene signature constructed by RNA binding proteins can be independent risk factors for hepatocellular carcinoma patients.

scholarly journals Diagnostic and Prognostic Values of MANF Expression in Hepatocellular Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Jingyi He ◽  
Guangbing Li ◽  
Xihan Liu ◽  
Liye Ma ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Value ◽  
Regulatory Networks ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Characteristic Curve ◽  
Prognostic Indicator ◽  
Diagnostic Value ◽  
Expression Level ◽  
Kaplan Meier

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors, and its prognosis is still poor. Mesencephalic astrocyte-derived neurotrophic factor (MANF) plays a key role in endoplasmic reticulum stress. ER stress plays a key role in HCC carcinogenesis. To confirm the clinical and prognostic value of MANF in HCC, we investigated the expression level of MANF in HCC as recorded in databases, and the results were verified by experiment. Survival analysis was probed by the Kaplan–Meier method. Cox regression models were used to ascertain the prognostic value of MANF in HCC tissue microarray. The diagnostic value of MANF in HCC was evaluated by receiver operating characteristic curve analysis. Potential correlation between MANF and selected genes was also analyzed. Results showed that MANF was overexpressed in HCC. Patients with high MANF expression levels had a worse prognosis and higher risk of tumor recurrence. Furthermore, the expression level of MANF had good diagnostic power. Correlation analysis revealed potential regulatory networks of MANF in HCC, laying a foundation for further study of the role of MANF in tumorigenesis. In conclusion, MANF was overexpressed in HCC and related to the occurrence and development of HCC. It is a potential diagnostic and prognostic indicator of HCC.

scholarly journals Expression and prognostic roles of PRDXs gene family in hepatocellular carcinoma

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Mingxing Xu ◽  
Jianliang Xu ◽  
Dun Zhu ◽  
Rishun Su ◽  
Baoding Zhuang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Acid Metabolism ◽  
Family Members ◽  
Interaction Network ◽  
Genetic Alterations ◽  
Database Analysis ◽  
Kaplan Meier ◽  
Canonical Pathways ◽  
Human Protein Atlas ◽  
Kaplan Meier Plotter

Abstract Background As the fourth leading cause of cancer-related death in the world, the therapeutic effect and 5-year overall survival of hepatocellular carcinoma (HCC) are not optimistic. Previous researches indicated that the disorder of PRDXs was related to the occurrence and development of cancers. Methods In this study, PRDXs were found in various tumor cell lines by CCLE database analysis. The analysis results of UALCAN, HCCDB and Human Protein Atlas databases showed the expression of PRDXs mRNA and protein in HCC tissues was dysregulated. Besides, UALCAN was used to assess the correlations between PRDXs mRNA as well as methylation levels and clinical characterization. Results High expression of PRDX1 or low expression of PRDX2/3 suggested poor prognosis for HCC patients which was demonstrated by Kaplan–Meier Plotter. The genetic alterations and biological interaction network of PRDXs in HCC samples were obtained from c-Bioportal. In addition, LinkedOmics was employed to analyze PRDXs related differentially expressed genes, and on this basis, enrichment of KEGG pathway and miRNAs targets of PRDXs were conducted. The results indicated that these genes were involved in several canonical pathways and certain amino acid metabolism, some of which may effect on the progression of HCC. Conclusions In conclusion, the disordered expression of some PRDX family members was associated with the prognosis of HCC patients, suggesting that these PRDX family members may become new molecular targets for the treatment and prognosis prediction of HCC.

scholarly journals NDRG3 overexpression is associated with a poor prognosis in patients with hepatocellular carcinoma

2018 ◽  
Vol 38 (6) ◽  
Author(s):  
Ji-sheng Jing ◽  
Hongbo Li ◽  
Shun-cai Wang ◽  
Jiu-ming Ma ◽  
La-qing Yu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Survival Curve ◽  
Disease Free Survival ◽  
Prognostic Indicator ◽  
Clinicopathological Characteristics ◽  
Pcr Analysis ◽  
Kaplan Meier ◽  
Tumor Tissues ◽  
The Relationship

N-myc downstream-regulated gene 3 (NDRG3), an important member of the NDRG family, is involved in cell proliferation, differentiation, and other biological processes. The present study analyzed NDRG3 expression in hepatocellular carcinoma (HCC) and explored the relationship between expression of NDRG3 in HCC patients and their clinicopathological characteristics. We performed quantitative real-time reverse-transcription polymerase chain reaction (qRT-PCR) analysis and immunohistochemistry (IHC) analyses on HCC tissues to elucidate NDRG3 expression characteristics in HCC patients. Kaplan–Meier survival curve and Cox regression analyses were used to evaluate the prognoses of 102 patients with HCC. The results revealed that compared with non-tumor tissues, HCC tissues showed significantly higher NDRG3 expression. In addition, our analyses showed that NDRG3 expression was statistically associated with tumor size (P=0.048) and pathological grade (P=0.001). Survival analysis and Kaplan–Meier curves revealed that NDRG3 expression is an independent prognostic indicator for disease-free survival (P=0.002) and overall survival (P=0.005) in HCC patients. The data indicate that NDRG3 expression may be considered as a oncogenic biomarker and a novel predictor for HCC prognosis.

scholarly journals Functional lncRNA-miRNA-mRNA Networks in Response to Baicalein Treatment in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Xin Zhao ◽  
Dongyang Tang ◽  
Xiaofei Chen ◽  
Shaoqing Chen ◽  
Cheng Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Treatment Group ◽  
Cancer Progression ◽  
Messenger Rna ◽  
Noncoding Rna ◽  
Mirna Targets ◽  
Kaplan Meier ◽  
Dmso Treatment ◽  
Human Protein Atlas ◽  
Kaplan Meier Plotter

Introduction. Baicalein has been shown to have antitumor activities in several cancer types. However, its acting mechanisms remain to be further investigated. This work is aimed at exploring the functional long noncoding RNA (lncRNA)/microRNA (miRNA)/messenger RNA (mRNA) triplets in response to baicalein in hepatocellular carcinoma (HCC) cell to understand the mechanisms of baicalein in HCC. Methods. Differentially expressed lncRNAs (DELs) and miRNAs (DEMs) in HCC cell treated with baicalein were first screened using GSE95504 and GSE85511, respectively. miRNA targets for DELs were predicted and intersected with DEMs, after which the miRNA expression was validated using ENCORI and its prognostic value was assessed using Kaplan-Meier plotter. Potential miRNA targets were predicted by 3 prediction tools, after which expression level was validated at UALCAN and Human Protein Atlas. Kaplan-Meier plotter was used to evaluate the effects of these genes on overall survival and recurrence-free survival of HCC patients. Enrichment analyses for these genes were performed at DAVID. Results. Here, we identified 14 overlapping DELs and 26 overlapping DEMs in the baicalein treatment group than those in the DMSO treatment group. Subsequently, by analyzing expression and clinical significance of miRNAs, hsa-miR-4443 was found as a highly potential miRNA target. Then, targets of hsa-miR-4443 were predicted and analyzed, and we found AKT1 was the most potential target for hsa-miR-4443. Hence, the lncRNAs-hsa-miR-4443-AKT1 axis that can respond to baicalein was established. Conclusion. Collectively, we elucidated a role of lncRNAs-hsa-miR-4443-AKT1 pathway in response to baicalein treatment in HCC, which could help us understand the roles of baicalein in inhibiting cancer progression and may provide novel insights into the mechanisms behind HCC progression.

scholarly journals Development and Validation an Epigenetic Modification-related Signals for Diagnosis and Prognosis of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Diguang Wen ◽  
Sheng Qiu ◽  
Zuojin Liu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Epigenetic Modification ◽  
Lasso Regression ◽  
Cancer Dataset ◽  
Gene Signatures ◽  
Diagnosis And Prognosis

Abstract Background: Increasing evidence has indicated that abnormal epigenetic modification such as RNAm6a modification, histone modification, DNA methylation modification, RNA binding proteins and transcription factors, is correlated with Hepatocarcinogenesis. However, it is unknown how epigenetic modification associated genes contribute to the occurrence and clinical outcome of hepatocellular carcinoma (HCC). Thus, we constructed epigenetic modification associated model that may enhance the diagnosis and prognosis of HCC.METHODS: In this study, we focused on the clinical values of epigenetic modification associated genes for HCC. Our gene expression data were collected from TCGA and a HCC datasets from GEO dataset in order to ensure the reliability of data. Their function was analyzed by bioinformatics methods. We used lasso regression, SUV, logistic regression and cox regression to construct the diagnosis and prognosis models. We also constructed a nomogram for the practicability of the above-mentioned prognosis model. The above results have been verified in an independent liver cancer dataset from ICGC database. Furthermore, we carried out pan cancer analysis to verify the specificity of the above model.RESULT: A large number of epigenetic modification associated genes were significantly different in HCC and normal liver tissues. The gene signatures showed good performance for predicting the occurrence and survival of HCC patients verified by DCA and ROC curve.CONCLUSION: Gene signatures based on epigenetic modification associated genes can be used to identify the occurrence and prognosis of liver cancer.

scholarly journals Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development (Preprint)

2020 ◽  
Author(s):  
Tao Fan ◽  
Bo Hao ◽  
Shuo Yang ◽  
Bo Shen ◽  
Zhixin Huang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Disease Onset ◽  
Lasso Regression ◽  
Prediction Ability ◽  
Calibration Curves ◽  
Independent Risk Factors ◽  
Critical Patients

BACKGROUND In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. OBJECTIVE The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. METHODS In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. RESULTS A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (<i>P</i>&lt;.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; <i>P</i>&lt;.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; <i>P</i>=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; <i>P</i>=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, <i>P</i>&lt;.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; <i>P</i>&lt;.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; <i>P</i>=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. CONCLUSIONS This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

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