scholarly journals Prediction of Poor Prognosis of HCC by Early Warning Model for Co-Expression of miRNA and mRNA Based on Bioinformatics Analysis

2020 ◽  
Vol 19 ◽  
pp. 153303382095935
Author(s):  
Zi-jian Su ◽  
Chun-cheng Lin ◽  
Jian-hui Pan ◽  
Jian-hua Zhang ◽  
Tao Han ◽  
...  
Keyword(s):  
High Risk ◽  
Poor Prognosis ◽  
Target Genes ◽  
Risk Model ◽  
Prognostic Significance ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Venn Diagram ◽  
Kaplan Meier ◽  
Cox Analysis

Objective: Hepatocellular Carcinoma (HCC) has the highest mortality rate worldwide with the intractability of its extremely complicated pathogenesis and unclear mechanism. The limited survival highlights the need for the further detection of prognosis for HCC. MicroRNAs (miRNAs) and messenger RNAs (mRNAs) have been identified as regulatory factors and target genes in human cancers, while some studies also found post-transcriptional modification plays a crucial role in the occurrence and development of HCC. The present study aimed to elucidate the prognostic significance of miRNA and mRNA models in HCC. Methods: Data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. The miRNA and mRNA expressions were tested by the Wilcoxon and used funrich software to predict mRNA that might be related to miRNA. Then we determined the intersection with overlapped mRNA and miRNA Venn diagram, and screened out hub gene by using Degree algorithm in Cytoscape software. The COX models, with TCGA data as the training set and ICGC data as the test set, were constructed. All patients were divided into high-risk and low-risk groups. Data on overall survival of different groups were collected and analyzed by Kaplan-Meier method, and independent risk factors affecting prognosis were assessed by Cox analysis. Results: The miRNA and mRNA polygenic risk model showed a good true positive rate. Kaplan-Meier curve and Cox analysis suggested that the high-risk group was associated with poor prognosis, and the risk score could be used as an independent risk factor for HCC. Conclusion: Tumor risk models constructed in this study could effectively predict the prognosis of patients, which is expected to provide a reference for the prognostic stratification and treatment strategy development of HCC.

scholarly journals Comprehensive Analysis of the Immune Infiltrates of Pyroptosis in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhuolun Sun ◽  
Changying Jing ◽  
Xudong Guo ◽  
Mingxiao Zhang ◽  
Feng Kong ◽  
...  
Keyword(s):  
High Risk ◽  
Cell Carcinoma ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Risk Model ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Kidney renal clear cell carcinoma (KIRC) has long been identified as a highly immune-infiltrated tumor. However, the underlying role of pyroptosis in the tumor microenvironment (TME) of KIRC remains poorly described. Herein, we systematically analyzed the prognostic value, role in the TME, response to ICIs, and drug sensitivity of pyroptosis-related genes (PRGs) in KIRC patients based on The Cancer Genome Atlas (TCGA) database. Cluster 2, by consensus clustering for 24 PRGs, presented a poor prognosis, likely because malignancy-related hallmarks were remarkably enriched. Additionally, we constructed a prognostic prediction model that discriminated well between high- and low-risk patients and was further confirmed in external E-MTAB-1980 cohort and HSP cohort. By further analyzing the TME based on the risk model, higher immune cell infiltration and lower tumor purity were found in the high-risk group, which presented a poor prognosis. Patients with high risk scores also exhibited higher ICI expression, indicating that these patients may be more prone to profit from ICIs. The sensitivity to anticancer drugs that correlated with model-related genes was also identified. Collectively, the pyroptosis-related prognosis risk model may improve prognostic information and provide directions for current research investigations on immunotherapeutic strategies for KIRC patients.

scholarly journals OSmfs: An Online Interactive Tool to Evaluate Prognostic Markers for Myxofibrosarcoma

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1523
Author(s):  
Huimin Li ◽  
Longxiang Xie ◽  
Qiang Wang ◽  
Yifang Dang ◽  
Xiaoxiao Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Poor Prognosis ◽  
Analysis Data ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Prognostic Biomarkers ◽  
Rank Test ◽  
Web Tool ◽  
Kaplan Meier ◽  
Cox Analysis

Myxofibrosarcoma is a complex genetic disease with poor prognosis. However, more effective biomarkers that forebode poor prognosis in Myxofibrosarcoma remain to be determined. Herein, utilizing gene expression profiling data and clinical follow-up data of Myxofibrosarcoma cases in three independent cohorts with a total of 128 Myxofibrosarcoma samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we constructed an easy-to-use web tool, named Online consensus Survival analysis for Myxofibrosarcoma (OSmfs) to analyze the prognostic value of certain genes. Through retrieving the database, users generate a Kaplan–Meier plot with log-rank test and hazard ratio (HR) to assess prognostic-related genes or discover novel Myxofibrosarcoma prognostic biomarkers. The effectiveness and availability of OSmfs were validated using genes in ever reports predicting the prognosis of Myxofibrosarcoma patients. Furthermore, utilizing the cox analysis data and transcriptome data establishing OSmfs, seven genes were selected and considered as more potentially prognostic biomarkers through overlapping and ROC analysis. In conclusion, OSmfs is a promising web tool to evaluate the prognostic potency and reliability of genes in Myxofibrosarcoma, which may significantly contribute to the enrichment of novelly potential prognostic biomarkers and therapeutic targets for Myxofibrosarcoma.

scholarly journals Clinical characteristics and prognostic value of MEX3A mRNA in liver cancer

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8252 ◽  
Author(s):  
Dingquan Yang ◽  
Yan Jiao ◽  
Yanqing Li ◽  
Xuedong Fang
Keyword(s):  
Liver Cancer ◽  
Clinical Data ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Prognostic Significance ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Diagnostic Ability ◽  
Kaplan Meier ◽  
Cox Analysis

Background MEX3A is an RNA-binding proteins (RBPs) that promotes the proliferation, invasion, migration and viability of cancer cells. The aim of this study was to explore the clinicopathological characteristics and prognostic significance of MEX3A mRNA expression in liver cancer. Methods RNA-Seq and clinical data were collected from The Cancer Genome Atlas (TCGA). Boxplots were used to represent discrete variables of MEX3A. Chi-square tests were used to analyze the correlation between clinical features and MEX3A expression. Receiver operating characteristic (ROC) curves were used to confirm diagnostic ability. Independent prognostic ability and values were assessed using Kaplan–Meier curves and Cox analysis. Results We acquired MEX3A RNA-Seq from 50 normal liver tissues and 373 liver cancer patients along with clinical data. We found that MEX3A was up-regulated in liver cancer which increased according to histological grade (p < 0.001). MEX3A showed moderate diagnostic ability for liver cancer (AUC = 0.837). Kaplan–Meier curves and Cox analysis revealed that the high expression of MEX3A was significantly associated with poor survival (OS and RFS) (p < 0.001). Moreover, MEX3A was identified as an independent prognostic factor of liver cancer (p < 0.001). Conclusions MEX3A expression shows promise as an independent predictor of liver cancer prognosis.

scholarly journals Identification and Validation of Ubiquitin-Specific Proteases as a Novel Prognostic Signature for Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Wenkai Ni ◽  
Saiyan Bian ◽  
Mengqi Zhu ◽  
Qianqian Song ◽  
Jianping Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Cancer Genome ◽  
Advanced Tumor Stage ◽  
Genomic Alterations ◽  
Advanced Tumor

PurposeUbiquitin-specific proteases (USPs), as a sub-family of deubiquitinating enzymes (DUBs), are responsible for the elimination of ubiquitin-triggered modification. USPs are recently correlated with various malignancies. However, the expression features and clinical significance of USPs have not been systematically investigated in hepatocellular carcinoma (HCC).MethodsGenomic alterations and expression profiles of USPs were investigated in CbioPortal and The Cancer Genome Atlas (TCGA) Liver hepatocellular carcinoma (LIHC) dataset. Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were conducted to establish a risk signature for HCC prognosis in TCGA LIHC cohort. Subsequently, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves and univariate/multivariate analyses were performed to evaluate the prognostic significance of the risk signature in TCGA LIHC and international cancer genome consortium (ICGC) cohorts. Furthermore, we explored the alterations of the signature genes during hepatocarcinogenesis and HCC progression in GSE89377. In addition, the expression feature of USP39 was further explored in HCC tissues by performing western blotting and immunohistochemistry.ResultsGenomic alterations and overexpression of USPs were observed in HCC tissues. The consensus analysis indicated that the USPs-overexpressed sub-Cluster was correlated with aggressive characteristics and poor prognosis. Cox regression with LASSO algorithm identified a risk signature formed by eight USPs for HCC prognosis. High-risk group stratified by the signature score was correlated with advanced tumor stage and poor survival HCC patients in TCGA LIHC cohort. In addition, the 8-USPs based signature could also robustly predict overall survival of HCC patients in ICGC(LIRI-JP) cohort. Furthermore, gene sets enrichment analysis (GSEA) showed that the high-risk score was associated with tumor-related pathways. According to the observation in GSE89377, USP39 expression was dynamically increased with hepatocarcinogenesis and HCC progression. The overexpression of USP39 was further determined in a local HCC cohort and correlated with poor prognosis. The co-concurrence analysis suggested that USP39 might promote HCC by regulating cell-cycle- and proliferation- related genes.ConclusionThe current study provided a USPs-based signature, highlighting its robust prognostic significance and targeted value for HCC treatment.

scholarly journals Identification of Metabolism-Associated Prostate Cancer Subtypes and Construction of a Prognostic Risk Model

2020 ◽  
Vol 10 ◽  
Author(s):  
Yanlong Zhang ◽  
Ruiqiao Zhang ◽  
Fangzhi Liang ◽  
Liyun Zhang ◽  
Xuezhi Liang
Keyword(s):  
Prostate Cancer ◽  
Drug Sensitivity ◽  
Risk Model ◽  
External Validation ◽  
Disease Free Survival ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Predictive Values ◽  
Cox Analysis

BackgroundDespite being the second most common tumor in men worldwide, the tumor metabolism-associated mechanisms of prostate cancer (PCa) remain unclear. Herein, this study aimed to investigate the metabolism-associated characteristics of PCa and to develop a metabolism-associated prognostic risk model for patients with PCa.MethodsThe activity levels of PCa metabolic pathways were determined using mRNA expression profiling of The Cancer Genome Atlas Prostate Adenocarcinoma cohort via single-sample gene set enrichment analysis (ssGSEA). The analyzed samples were divided into three subtypes based on the partitioning around medication algorithm. Tumor characteristics of the subsets were then investigated using t-distributed stochastic neighbor embedding (t-SNE) analysis, differential analysis, Kaplan–Meier survival analysis, and GSEA. Finally, we developed and validated a metabolism-associated prognostic risk model using weighted gene co-expression network analysis, univariate Cox analysis, least absolute shrinkage and selection operator, and multivariate Cox analysis. Other cohorts (GSE54460, GSE70768, genotype-tissue expression, and International Cancer Genome Consortium) were utilized for external validation. Drug sensibility analysis was performed on Genomics of Drug Sensitivity in Cancer and GSE78220 datasets. In total, 1,039 samples and six cell lines were concluded in our work.ResultsThree metabolism-associated clusters with significantly different characteristics in disease-free survival (DFS), clinical stage, stemness index, tumor microenvironment including stromal and immune cells, DNA mutation (TP53 and SPOP), copy number variation, and microsatellite instability were identified in PCa. Eighty-four of the metabolism-associated module genes were narrowed to a six-gene signature associated with DFS, CACNG4, SLC2A4, EPHX2, CA14, NUDT7, and ADH5 (p &lt;0.05). A risk model was developed, and external validation revealed the strong robustness our risk model possessed in diagnosis and prognosis as well as the association with the cancer feature of drug sensitivity.ConclusionsThe identified metabolism-associated subtypes reflected the pathogenesis, essential features, and heterogeneity of PCa tumors. Our metabolism-associated risk model may provide clinicians with predictive values for diagnosis, prognosis, and treatment guidance in patients with PCa.

scholarly journals A prognostic risk model constructed by proteins in predicting prognosis for ovarian cancer

2021 ◽  
Author(s):  
yan rong ◽  
Liangchen Niu ◽  
Li Li
Keyword(s):  
Ovarian Cancer ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cox Analysis ◽  
Survival Risk ◽  
Related Proteins

Abstract BackgroundsOvarian cancer is the most lethal malignant tumor in gynecological cancers worldwide. Approximately 70% of patients have a poor prognosis, who experienced progression or recurrence within 5 years. The aim of this study attempts is to screen out the potential prognosis-related proteins and establish a prognostic risk model for predicting the prognostic risk for patients with ovarian cancer.MethodData were obtained from the Cancer Proteome Atlas (TCPA) and the Cancer Genome Atlas (TCGA). The proteins significantly related to survival risk in ovarian cancer patients were screened out by Kaplan-Meier test and COX regression analysis. A prognostic risk model was constructed based on the optimal proteins selected by multivariate Cox analysis. The prognostic risk model was validated in different clinical characteristics. The sankyl diagram was used to visualize the relationship between the prognosis-related proteins and their co-expression proteins.ResultsA prognostic risk model consisting of seven proteins that significantly related to prognosis was established. Patients with high risk score were associated with poor survival and relative protein expression. In the multivariate cox regress analysis, only age and the risk score were the independence prognosis factors. The AUC for the risk score was 0.721 in ROC curve for patients under 70 years old. Pearson’s correlation analysis showed that 25 co-expression proteins correlated with the prognosis-related proteins.ConclusionOur study demonstrated that a novel prognostic risk model constructed by proteins could predict prognosis for patients with ovarian cancer.

scholarly journals Identification of novel autophagy-related lncRNAs associated with a poor prognosis of colon adenocarcinoma through bioinformatics analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dejun Wu ◽  
Zhenhua Yin ◽  
Yisheng Ji ◽  
Lin Li ◽  
Yunxin Li ◽  
...  
Keyword(s):  
High Risk ◽  
Poor Prognosis ◽  
Notch Pathway ◽  
Colon Adenocarcinoma ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Future Research ◽  
Risk Patients ◽  
Cancer Genome Atlas ◽  
Immune Score

AbstractLncRNAs play a pivotal role in tumorigenesis and development. However, the potential involvement of lncRNAs in colon adenocarcinoma (COAD) needs to be further explored. All the data used in this study were obtained from The Cancer Genome Atlas database, and all analyses were conducted using R software. Basing on the seven prognosis-related lncRNAs finally selected, we developed a prognosis-predicting model with powerful effectiveness (training cohort, 1 year: AUC = 0.70, 95% Cl = 0.57–0.78; 3 years: AUC = 0.71, 95% Cl = 0.6–0.8; 5 years: AUC = 0.76, 95% Cl = 0.66–0.87; validation cohort, 1 year: AUC = 0.70, 95% Cl = 0.58–0.8; 3 years: AUC = 0.73, 95% Cl = 0.63–0.82; 5 years: AUC = 0.68, 95% Cl = 0.5–0.85). The VEGF and Notch pathway were analyzed through GSEA analysis, and low immune and stromal scores were found in high-risk patients (immune score, cor =  − 0.15, P < 0.001; stromal score, cor =  − 0.18, P < 0.001) , which may partially explain the poor prognosis of patients in the high-risk group. We screened lncRNAs that are significantly associated with the survival of patients with COAD and possibly participate in autophagy regulation. This study may provide direction for future research.

scholarly journals Evaluation of the prognostic value of CBXs in gastric cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mengya He ◽  
Limin Yue ◽  
Haiyan Wang ◽  
Feiyan Yu ◽  
Mingyang Yu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Target Genes ◽  
Prognostic Significance ◽  
Disease Free Survival ◽  
Genetic Alterations ◽  
Genetic Mutation ◽  
The Cancer Genome Atlas ◽  
Normal Tissues ◽  
Interactive Analysis ◽  
Gastric Cancer Patients

AbstractChromobox (CBX) proteins were suggested to exert epigenetic regulatory and transcriptionally repressing effects on target genes and might play key roles in the carcinogenesis of a variety of carcinomas. Nevertheless, the functions and prognostic significance of CBXs in gastric cancer (GC) remain unclear. The current study investigated the roles of CBXs in the prognosis of GC using the Oncomine, The Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, The Cancer Genome Atlas (TCGA), and cBioPortal databases. CBX1/2/3/4/5 were significantly upregulated in GC tissues compared with normal tissues, and CBX7 was downregulated. Multivariate analysis showed that high mRNA expression levels of CBX3/8 were independent prognostic factors for prolonged OS in GC patients. In addition, the genetic mutation rate of CBXs was 37% in GC patients, and genetic alterations in CBXs showed no association with OS or disease-free survival (DFS) in GC patients. These results indicated that CBX3/8 can be prognostic biomarkers for the survival of GC patients.

scholarly journals CYSTM1: A Novel Biomarker for Hepatocellular Carcinoma Prognosis

2021 ◽  
Author(s):  
Jun Du ◽  
Jinguo Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Early Stage ◽  
Human Tumor ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Kaplan Meier ◽  
Cancer Genome Atlas ◽  
Cox Proportional Hazard Regression ◽  
The Relationship

Abstract Background: The expression and molecular mechanism of cysteine rich transmembrane module containing 1 (CYSTM1) in human tumor cells remains unclear. The aim of this study was to determine whether CYSTM1 could be used as a potential prognostic biomarker for hepatocellular carcinoma (HCC).Methods: We first demonstrated the relationship between CYSTM1 expression and HCC in various public databases. Secondly, Kaplan–Meier analysis and Cox proportional hazard regression model were performed to evaluate the relationship between the expression of CYSTM1 and the survival of HCC patients which data was downloaded in the cancer genome atlas (TCGA) database. Finally, we used the expression data of CYSTM1 in TCGA database to predict CYSTM1-related signaling pathways through bioinformatics analysis.Results: The expression level of CYSTM1 in HCC tissues was significantly correlated with T stage (p = 0.039). In addition, Kaplan–Meier analysis showed that the expression of CYSTM1 was significantly associated with poor prognosis in patients with early-stage HCC (p = 0.003). Multivariate analysis indicated that CYSTM1 is a potential predictor of poor prognosis in HCC patients (p = 0.036). The results of biosynthesis analysis demonstrated that the data set of CYSTM1 high expression was mainly enriched in neurodegeneration and oxidative phosphorylation pathways.Conclusion: CYSTM1 is an effective biomarker for the prognosis of patients with early-stage HCC and may play a key role in the occurrence and progression of HCC.

scholarly journals Prognostic Significance of NBEAL2 in Liver hepatocellular carcinoma

2021 ◽  
Author(s):  
Yanghui Wen ◽  
Hui Su ◽  
Wuke Wang ◽  
Feng Ren ◽  
Haitao Jiang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters ◽  
Chi Square ◽  
Kaplan Meier ◽  
Chi Square Test ◽  
Liver Hepatocellular Carcinoma ◽  
The Relationship

Abstract Background: NBEAL2 is a member of the BEACH domain–containing protein (BDCP) family and little is known about the relationship between NBEAL2 and malignancy.Methods: We downloaded the Gene expression profiles and clinical data of Liver hepatocellular carcinoma(LIHC) form the Cancer Genome Atlas (TCGA) dataset. The expression difference of NBEAL2 in LIHC tissues and adjacent nontumor tissues was analyzed by R software. The relationship between NBEAL2 expression and clinicopathological parameters was evaluate by Chi-square test. The effect of NBEAL2 expression on survival were assessed by Kaplan–Meier survival analysis and Cox proportional hazards regression model. GSEA was used to explore the potential molecular mechanism of NBEAL2 in LIHC.Results: Up-regulation of NBEAL2 expression was detected in the LIHC tissue compared with adjacent nontumor tissues(P < 0.001). The chi-square test showed that no significant correlation between the expression level of NBEAL2 and various clinicopathological parameters (including T, N and M classifications) were detected. The Kaplan–Meier curves suggested that lower NBEAL2 expression was related with poor prognosis. The results of Multivariate analysis revealed that a lower expression of NBEAL2 in LIHC was an independent risk of poor overall survival (HR, 8.873; 95% CI, 1.159-67.936; P = 0.035). GSEA suggested that multiple tumor-related metabolic pathways were evidently enriched in samples with the low-NBEAL2 expression phenotype. Conlusion: NBEAL2 might act as an tumor suppressor gene in the progression of LIHC but the precise role of NBELA2 in LIHC needs further vertification.

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