Identification of Prognostic miRNAs Targeting EZH2 in Hepatocellular Carcinoma Using The Cancer Genome Atlas Database

Abstract Background: Enhancer of zeste homolog 2 (EZH2) gene have a prognostic role in hepatocellular carcinoma (HCC). This study aimed to identify the prognostic microRNAs (miRNAs) targeting EZH2 in HCC. Methods and Results: We downloaded the gene and miRNA RNA-seq data from The Cancer Genome Atlas (TCGA) database. Differences in EZH2 expression between tumor and control samples and those between tumors with different clinical variables were analyzed using the Mann-Whitney U test. Association of EZH2 expression with prognosis in HCC patients was detected using Cox regression analysis. We also identified miRNAs targeting EZH2 with negative correlations, compared the miRNA expression profiles between tumor and control tissues, and identified pathways and protein-protein interaction pairs related to EZH2. The miRNA-EZH2-pathway network was constructed accordingly. EZH2 was significantly upregulated in HCC tumors compared with control samples (p<0.0001) and in tumors with advanced T classifications (3/4 vs. 1/2, p=0.0039) and stages (III/IV vs. I/II, p=0.0028). The Cox regression analysis showed that TCGA HCC patients who had high EZH2 expression levels showed a short survival time (HR=1.677, 95% CI 1.316-2.137; p<0.0001). Among miRNAs targeting EZH2, seven miRNAs, including hsa-let-7c-5p, were negatively correlated with EZH2 expression and were significantly downregulated in HCC tumor samples compared with controls (p<0.0001). The miRNA-EZH2-pathway network included seven downregulated miRNAs and four pathways, including hsa00310: Lysine degradation. Hsa-let-7c-5p was associated with prognosis in HCC (HR=0.849 95% CI 0.739-0.975; p=0.021). Conclusions: EZH2-hsa-let-7c-5p has a significant association with HCC prognosis and the mechanism worth investigating.

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Eight-gene prognostic signature associated with hypoxia and ferroptosis for gastric cancer with general applicability

Epigenomics ◽

10.2217/epi-2020-0411 ◽

2021 ◽

Author(s):

Junyu Huo ◽

Liqun Wu ◽

Yunjin Zang

Keyword(s):

Gastric Cancer ◽

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Prognostic Significance ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Cancer Genome Atlas ◽

Genome Atlas

Aims: To investigate the prognostic significance of hypoxia- and ferroptosis-related genes for gastric cancer (GC). Materials & methods: We extracted data on 259 hypoxia- and ferroptosis-related genes from The Cancer Genome Atlas and identified the differentially expressed genes between normal (n = 32) and tumor (n = 375) tissues. A risk score was established by univariate Cox regression analysis and LASSO penalized Cox regression analysis. Results: The risk score contained eight genes showed good performance in predicting overall survival and relapse-free survival in GC patients in both the training cohort (The Cancer Genome Atlas, n = 350) and the testing cohorts (GSE84437, n = 431; GSE62254, n = 300; GSE15459, n = 191; GSE26253, n = 432). Conclusion: The eight-gene signature may help to the improve the prognostic risk classification of GC.

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Systematic analysis and prediction model construction of alternative splicing events in hepatocellular carcinoma: a study on the basis of large-scale spliceseq data from The Cancer Genome Atlas

PeerJ ◽

10.7717/peerj.8245 ◽

2019 ◽

Vol 7 ◽

pp. e8245 ◽

Cited By ~ 3

Author(s):

Lingpeng Yang ◽

Yang He ◽

Zifei Zhang ◽

Wentao Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Prediction Model ◽

Gene Network ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Systematic Analysis ◽

Cox Regression Analysis ◽

Regulation Network ◽

Cancer Genome Atlas ◽

Genome Atlas

Growing evidence showed that alternative splicing (AS) event is significantly related to tumor occurrence and progress. This study was performed to make a systematic analysis of AS events and constructed a robust prediction model of hepatocellular carcinoma (HCC). The clinical information and the genes expression profile data of 335 HCC patients were collected from The Cancer Genome Atlas (TCGA). Information of seven types AS events were collected from the TCGA SpliceSeq database. Overall survival (OS) related AS events and splicing factors (SFs) were identified using univariate Cox regression analysis. The corresponding genes of OS-related AS events were sent for gene network analysis and functional enrichment analysis. Optimal OS-related AS events were selected by LASSO regression to construct prediction model using multivariate Cox regression analysis. Prognostic value of the prediction models were assessed by receiver operating characteristic (ROC) curve and KaplanMeir survival analysis. The relationship between the Percent Spliced In (PSI) value of OS-related AS events and SFs expression were analyzed using Spearman correlation analysis. And the regulation network was generated by Cytoscape. A total of 34,163 AS events were identified, which consist of 3,482 OS-related AS events. UBB, UBE2D3, SF3A1 were the hub genes in the gene network of the top 800 OS-related AS events. The area under the curve (AUC) of the final prediction model based on seven types OS-related AS events was 0.878, 0.843, 0.821 in 1, 3, 5 years, respectively. Upon multivariate analysis, risk score (All) served as the risk factor to independently predict OS for HCC patients. SFs HNRNPH3 and HNRNPL were overexpressed in tumor samples and were signifcantly associated with the OS of HCC patients. The regulation network showed prominent correlation between the expression of SFs and OS-related AS events in HCC patients. The final prediction model performs well in predicting the prognosis of HCC patients. And the findings in this study improve our understanding of the association between AS events and HCC.

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Downregulation of SPINK13 Promotes Metastasis by Regulating uPA in Ovarian Cancer Cells

Cellular Physiology and Biochemistry ◽

10.1159/000487348 ◽

2018 ◽

Vol 45 (3) ◽

pp. 1061-1071 ◽

Cited By ~ 7

Author(s):

Shengyun Cai ◽

Pei Zhang ◽

Suhe Dong ◽

Li Li ◽

Jianming Cai ◽

...

Keyword(s):

Ovarian Cancer ◽

Regression Analysis ◽

Cellular Proliferation ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Cancer Genome Atlas ◽

Genome Atlas

Background/Aims: Ovarian cancer (OC) is the fifth leading cause of cancer-related death in women, and it is difficult to diagnose at an early stage. The purpose of this study was to explore the prognostic biological markers of OC. Methods: Univariate Cox regression analysis was used to identify genes related to OC prognosis from the Cancer Genome Atlas(TCGA) database. Immunohistochemistry was used to analyse the level of SPINK13 in OC and normal tissues. Cell proliferation, apoptosis and invasion were performed using MTT assay, flow cytometric analysis and Transwell assay, respectively. Results: We identified the Kazal-type serine protease inhibitor-13 (SPINK13) gene related to OC prognosis from the Cancer Genome Atlas (TCGA) database by univariate Cox regression analysis. Overexpression of SPINK13 was associated with higher overall survival rate in OC patients. Immunohistochemistry showed that the level of SPINK13 protein was significantly lower in OC tissues than in normal tissues (P < 0.05).In vitro experiments showed that the overexpression of SPINK13 inhibited cellular proliferation and promoted apoptosis. Moreover, SPINK13 inhibited cell migration and epithelial to mesenchymal transition (EMT). SPINK13 was found to inhibit the expression of urokinase-type plasminogen activator (uPA), while recombinant uPA protein could reverse the inhibitory effect of SPINK13 on OC metastasis. Conclusion: These results indicate that SPINK13 functions as a tumour suppressor. The role of SPINK13 in cellular proliferation, apoptosis and migration is uPA dependent, and SPINK13 may be used as a potential biomarker for diagnosis and targeted therapy in OC.

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Identification and validation of an immune-related prognostic signature for hepatocellular carcinoma

10.21203/rs.3.rs-25879/v1 ◽

2020 ◽

Author(s):

Xinxin Xia ◽

Hui Liu ◽

Yuejun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Regression Analyses ◽

Prognostic Signature ◽

Biological Mechanisms ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality. The immune system plays vital roles in HCC initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients. Methods: Gene expression data were retrieved from The Cancer Genome Atlas database. Univariate Cox regression analysis was carried out to identify differentially expressed genes that associated with overall survival. The IRPS was established via Lasso and multivariate Cox regression analysis. Both Cox regression analyses were conducted to determine the independent prognostic factors for HCC. Next, the association between the IRPS and clinical-related factors were evaluated. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset. Gene set enrichment analyses (GSEA) were conducted to understand the biological mechanisms of the IRPS. Results: A total of 62 genes were identified to be candidate immune-related prognostic genes. Transcription factors-immunogenes network was generated to explore the interactions among these candidate genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be an independent risk factor influencing the prognosis of HCC patients. The relationships between the IRPS and infiltration immune cells demonstrated that the IRPS was associated with immune cell infiltration. GSEA identified significantly enriched pathways, which might assist in elucidating the biological mechanisms of the IRPS. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients.Conclusions: The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

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A novel signature based on m6A related genes as the potential prognostic factor in hepatocellular carcinoma.

10.21203/rs.2.21963/v1 ◽

2020 ◽

Author(s):

Zhuomao Mo ◽

Shaoju Luo ◽

Hao Hu ◽

Ling Yu ◽

Zhirui Cao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Roc Curve ◽

Cox Regression ◽

Risk Group ◽

Immune Therapy ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Potential Biomarker ◽

Significant Difference

Abstract Background Many different signatures and models have been established for patients with hepatocellular carcinoma (HCC), but no signature based on m6A related genes was developed. The objective of this research was to establish the signature with m6A related genes in HCC. Methods Data from 377 HCC patients from The Cancer Genome Atlas (TCGA) database was downloaded. The included m6A related genes were selected by Cox regression analysis and the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Furthermore, the nomogram was constructed and evaluated by C-index, calibration plot and ROC curve. Results The signature was established with the four m6A related genes (YTHDF2, YTHDF1, METTL3 and KIAA1429). Under the grouping from signature, patients in high risk group of showed the poor prognosis than those in low risk group. And significant difference was found in two kinds of immune cells (T cell gamma delta and NK cells activated) between two groups. The univariate and multivariate Cox regression analysis indicated that m6A related signature can be the potential independent prognosis factor in HCC. Finally, we developed a clinical risk model predicting the HCC prognosis and successfully verified it in C-index, calibration and ROC curve. Conclusion Our study identified the m6A related signature for predicting prognosis of HCC and provided the potential biomarker between m6A and immune therapy.

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Integrated Analysis of lncRNA-Mediated ceRNA Network Reveals a Prognostic Signature for Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2020.602542 ◽

2020 ◽

Vol 11 ◽

Author(s):

Jian-Rong Sun ◽

Chen-Fan Kong ◽

Kun-Min Xiao ◽

Jia-Lu Yang ◽

Xiang-Ke Qu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Tnm Stage ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Cox Regression Analysis ◽

Cerna Network ◽

Kaplan Meier ◽

Risk Patients

Hepatocellular carcinoma (HCC) is one of the most common types of malignancy and is associated with high mortality. Prior research suggests that long non-coding RNAs (lncRNAs) play a crucial role in the development of HCC. Therefore, it is necessary to identify lncRNA-associated therapeutic biomarkers to improve the accuracy of HCC prognosis. Transcriptomic data of HCC obtained from The Cancer Genome Atlas (TCGA) database were used in the present study. Differentially expressed RNAs (DERNAs), including 74 lncRNAs, 16 miRNAs, and 35 mRNAs, were identified using bioinformatics analysis. The DERNAs were subsequently used to reconstruct a competing endogenous RNA (ceRNA) network. A lncRNA signature was revealed using Cox regression analysis, including LINC00200, MIR137HG, LINC00462, AP002478.1, and HTR2A-AS1. Kaplan-Meier plot demonstrated that the lncRNA signature is highly accurate in discriminating high- and low-risk patients (P < 0.05). The area under curve (AUC) value exceeded 0.7 in both training and validation cohort, suggesting a high prognostic potential of the signature. Furthermore, multivariate Cox regression analysis indicated that both the TNM stage and the lncRNA signature could serve as independent prognostic factors for HCC (P < 0.05). Then, a nomogram comprising the TNM stage and the lncRNA signature was determined to raise the accuracy in predicting the survival of HCC patients. In the present study, we have introduced a ceRNA network that could contribute to provide a new insight into the identification of potential regulation mechanisms for the development of HCC. The five-lncRNA signature could serve as a reliable biosignature for HCC prognosis, while the nomogram possesses strong potential in clinical applications.

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Development of a prognostic signature based on six immune related lncRNAs for hepatocellular carcinoma.

10.21203/rs.3.rs-16348/v1 ◽

2020 ◽

Author(s):

Ze-bing Song ◽

Guo-pei Zhang ◽

shaoqiang li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Cerna Network

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumor in the world which prognosis is poor. Therefore, a precise biomarker is needed to guide treatment and improve prognosis. More and more studies have shown that lncRNAs and immune response are closely related to the prognosis of hepatocellular carcinoma. The aim of this study was to establish a prognostic signature based on immune related lncRNAs for HCC.Methods: Univariate cox regression analysis was performed to identify immune related lncRNAs, which had negative correlation with overall survival (OS) of 370 HCC patients from The Cancer Genome Atlas (TCGA). A prognostic signature based on OS related lncRNAs was identified by using multivariate cox regression analysis. Gene set enrichment analysis (GSEA) and a competing endogenous RNA (ceRNA) network were performed to clarify the potential mechanism of lncRNAs included in prognostic signature. Results: A prognostic signature based on OS related lncRNAs (AC145207.5, AL365203.2, AC009779.2, ZFPM2-AS1, PCAT6, LINC00942) showed moderately in prognosis prediction, and related with pathologic stage (Stage I&II VS Stage III&IV), distant metastasis status (M0 VS M1) and tumor stage (T1-2 VS T3-4). CeRNA network constructed 15 aixs among differentially expressed immune related genes, lncRNAs included in prognostic signature and differentially expressed miRNA. GSEA indicated that these lncRNAs were involved in cancer-related pathways. Conclusion: We constructed a prognostic signature based on immune related lncRNAs which can predict prognosis and guide therapies for HCC.

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Identification and validation of an immune-related prognostic signature for hepatocellular carcinoma

10.21203/rs.3.rs-25879/v2 ◽

2020 ◽

Author(s):

Xinxin Xia ◽

Hui Liu ◽

Yuejun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Regression Analyses ◽

Prognostic Signature ◽

Biological Mechanisms ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality. The immune system plays vital roles in HCC initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients. Methods: Gene expression data were retrieved from The Cancer Genome Atlas database. Univariate Cox regression analysis was carried out to identify differentially expressed genes that associated with overall survival. The IRPS was established via Lasso and multivariate Cox regression analysis. Both Cox regression analyses were conducted to determine the independent prognostic factors for HCC. Next, the association between the IRPS and clinical-related factors were evaluated. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset. Gene set enrichment analyses (GSEA) were conducted to understand the biological mechanisms of the IRPS.Results: A total of 62 genes were identified to be candidate immune-related prognostic genes. Transcription factors-immunogenes network was generated to explore the interactions among these candidate genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be an independent risk factor influencing the prognosis of HCC patients. The relationships between the IRPS and infiltration immune cells demonstrated that the IRPS was associated with immune cell infiltration. GSEA identified significantly enriched pathways, which might assist in elucidating the biological mechanisms of the IRPS. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients. Conclusions: The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

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Interferon-lambda (IFNL) germline variations and their significance for HCC and PDAC progression: an analysis of The Cancer Genome Atlas (TCGA) data

BMC Cancer ◽

10.1186/s12885-020-07589-4 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Henriette Huschka ◽

Sabine Mihm

Keyword(s):

Disease Progression ◽

Cox Regression ◽

Genetic Variations ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Disease Outcome ◽

Ductal Adenocarcinoma ◽

Cox Regression Analysis ◽

Cancer Genome Atlas ◽

Genome Atlas

Abstract Background Hepatocellular carcinoma (HCC) and pancreatic ductal adenocarcinoma (PDAC) are malignancies with a leading lethality. With reference to interferons (IFNs) known to mediate antitumor activities, this study investigated the relationship between germline genetic variations in type III IFN genes and cancer disease progression from The Cancer Genome Atlas (TCGA) data. The genetic variations under study tag a gain-or-loss-of-function dinucleotide polymorphism within the IFNL4 gene, rs368234815 [TT/ΔG]. Methods The entirety of the TCGA sequencing data was used to assess genotypes of 187 patients with HCC and of 162 patients with PDAC matched for ethnicity. Stratified for IFNL genotypes, both cohorts were subjected to time-to-event analyses according to Kaplan-Meier with regard to the length of the specific progression free interval (PFI) and the overall survival (OS) time as two clinical endpoints for disease progression. Results Logrank analysis revealed a significant relationship between IFNL genotypes and disease outcome for PDAC. This relationship was not found for HCC. A multiple Cox regression analysis employing patients’ age, tumor grade and tumor stage as further covariates proved IFNL genotypes to be independent predictors for PDAC disease outcome. Conclusion This repository-based approach unveiled clinical evidence suggestive for an impact of IFNL germline variations for PDAC progression with an IFNL haplotype predisposing for IFNL4 expression being favorable.

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Development and validation of a novel pseudogene pair-based prognostic signature for prediction of overall survival in patients with hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-020-07391-2 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Yajuan Du ◽

Ying Gao

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Model ◽

Cox Regression ◽

Pearson Correlation ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

The Novel ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background There is growing evidence that pseudogenes may serve as prognostic biomarkers in several cancers. The present study was designed to develop and validate an accurate and robust pseudogene pairs-based signature for the prognosis of hepatocellular carcinoma (HCC). Methods RNA-sequencing data from 374 HCC patients with clinical follow-up information were obtained from the Cancer Genome Atlas (TCGA) database and used in this study. Survival-related pseudogene pairs were identified, and a signature model was constructed by Cox regression analysis (univariate and least absolute shrinkage and selection operator). All individuals were classified into high- and low-risk groups based on the optimal cutoff. Subgroups analysis of the novel signature was conducted and validated in an independent cohort. Pearson correlation analyses were carried out between the included pseudogenes and the protein-coding genes based on their expression levels. Enrichment analysis was performed to predict the possible role of the pseudogenes identified in the signature. Results A 19-pseudogene pair signature, which included 21 pseudogenes, was established. Patients in high-risk group demonstrated an increased the risk of adverse prognosis in the TCGA cohort and the external cohort (all P < 0.001). The novel pseudogene signature was independent of other conventional clinical variables used for survival prediction in HCC patients in the two cohorts revealed by the multivariate Cox regression analysis (all P < 0.001). Subgroup analysis further demonstrated the diagnostic value of the signature across different stages, grades, sexes, and age groups. The C-index of the prognostic signature was 0.761, which was not only higher than that of several previous risk models but was also much higher than that of a single age, sex, grade, and stage risk model. Furthermore, functional analysis revealed that the potential biological mechanisms mediated by these pseudogenes are primarily involved in cytokine receptor activity, T cell receptor signaling, chemokine signaling, NF-κB signaling, PD-L1 expression, and the PD-1 checkpoint pathway in cancer. Conclusion The novel proposed and validated pseudogene pair-based signature may serve as a valuable independent prognostic predictor for predicting survival of patients with HCC.

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