Comprehensive Analysis Of Prognostic Markers Of Hepatocellular Carcinoma Based On Bioinformatics

[Background]Long noncoding RNAs(lncRNAs)can act as microRNA(miRNA)sponges to regulate protein-coding gene expression;therefore,IncRNAs are considered a major part of the competitive endogenous RNA(ceRNA)network and have attracted growing attention.The present study explored the regulatory mechanisms and functional roles of IncRNAs as ceRNAs in hepatocellular carcinoma(HCC)and their potential impact on HCC patient prognosis.[Method]In this study,we systematically studied the expression profiles and prognostic value of lncRNA,miRNA,and mRNA from a total of 838 HCC patients from five HCC cohorts(TCGA,GSE54236,GSE76427,GSE64041 and GSE14520).The TCGA,GSE54236 and GSE76427 HCC cohorts were utilized to establish a prognosis-related network of dysregulated ceRNAs by bioinformatics methods.The GSE64041 and GSE14520 HCC cohorts were utilized to verify the expression of candidate genes.[Result]In total,721 lncRNAs,73 miRNAs,and 1,563 mRNAs were aberrantly expressed in HCC samples.A ceRNA network including 26 lncRNAs,4 miRNAs,and 6 mRNAs specific to HCC was established.The survival analysis showed that 4 lncRNAs(MYCNOS,DLX6-AS1,LINC00221,and CRNDE)and 2 mRNAs(CCNB1 and SHCBP1)were prognostic biomarkers for patients ’with HCC in both the TCGA and GEO databases.[Conclusion]The proposed ceRNA network may help elucidate the regulatory mechanism by which IncRNAs function as ceRNAs and contribute to the pathogenesis of HCC.Importantly,the candidate lncRNAs,miRNAs,and mRNAs involved in the ceRNA network can be further evaluated as potential therapeutic targets and prognostic biomarkers for HCC.[Background]HCC is one of the deadliest malignant tumors worldwide,and its incidence increases every year.With the development of HCC management and imaging technology,high-risk groups(such as people with hepatitis virus)are closely monitored,and some HCC can be diagnosed early.Nevertheless,most patients with HCC have a high recurrence rate,and the prognosis is still unsatisfactory.The accurate prognosis prediction of HCC is helpful for early detection of patients with poor prognosis and active and effective treatment,while avoiding over-treatment of patients with good prognosis,and achieving the goal of optimizing the use of valuable medical resources,which has important science and society significance.Currently,traditional methods for predicting prognosis with a single biomarker or clinical data lack systematic assessment,resulting in a lack of high sensitivity and specificity.Because HCC involves complex molecular regulation mechanisms,the current clinical pathologic stage cannot fully reflect tumor heterogeneity and predict poor prognosis.Therefore,the prediction model based on gene sequencing data and clinical data has become an urgent need to improve clinical efficacy,which has great potential for clinical transformation.This study is the first prediction model combined gene sequencing data with well-reviewed clinical data of large HCC samples.Using artificial intelligence simulation machine learning method to construct a stable,high-efficiency,reproducible,economical individualized prognosis model for HCC patients in China,to achieve accurate prediction and accurate treatment of HCC patients.[Method]In this study,mRNA-seq data(level 3)and corresponding clinical information of 371 HCC patients were downloaded from the TCGA public database and used as a training dataset.Microarray data and corresponding clinical information of 78 HCC patients were downloaded from the GEO data as a validation set.The gene differentially expressed more than 16-fold between the cancerous tissue and the adjacent noncancerous tissues,and the corrected P value of less than 0.001 is considered to be a differentially expressed gene.Univariate,Lasso and multivariate Cox regression analyses were employed to investigate the correlation between patient overall survival(OS)and the expression level of each gene.The gene was considered significant when the P<0.001 in the univariate Cox regression analysis.Next,we applied a Lasso-penalized Cox regression to further reduce genes for patients with HCC.For the Lasso-penalized Cox regression selection operator,we subsampled the dataset with replacement 1000 times and selected the markers with repeat occurrence frequencies of more than 900.Finally,a multivariate Cox regression analysis was conducted to assess the contribution of a gene as an independent prognostic factor for patient survival.A prognosis risk score was established based on a linear combination of the regression coefficient derived from the multivariate Cox regression model multiplied with its expression level.Univariate and multivariate Cox regression analyses were used to evaluate the independent predictive value of the four-gene prognostic model in HCC patients with complete clinical information from the TCGA HCC cohort.Then,the prognostic model was validated in the GEO dataset.Then we use the combined risk model and the clinicopathologic factors to construct a combined model(shown in the form of a nomogram)to see if it can improve the prognostic value.[Result]A total of 339 differentially expressed genes(DEGs)were obtained between the HCC and normal tissues.Four genes(CENPA,SPP1,MAGEB6 and HOXD9)were screened by univariate,Lasso and multivariate Cox regression analyses to develop the prognostic model.High-risk group was significantly lower than that in the low-risk group(median overall survival(OS),3.42 years vs 6.15 years,P<0.0001).Further analysis revealed the independent prognostic capacity of the prognostic model in relation to other clinical characteristics.Then,the prognostic model was validated using the Gene Expression Omnibus(GEO)dataset.Consistent with the result in the TCGA,the OS of the HCC patients in the GSE54236 data in the high-risk group was significantly lower than that in the low-risk group(median survival:0.99 years vs 2.26 years;P<0.0001).A nomogram comprising the prognostic model to predict the overall survival was established,and internal validation in the TCGA cohort was performed.Compared with the traditional pathologic stage,the predictive power of the nomogram is increased by 12%(C-index:0.57 vs 0.69;P<0.01)[Conclusion]The prognostic model and nomogram constructed based on machine learning method can accurately predict the 3-year and 5-year survival rates of HCC patients,and can be used as a basis for the treatment and follow-up of patients with HCC.[Background]TP53 mutation is the most common mutation in hepatocellular carcinoma(HCC),and it affects the progression and prognosis of HCC.We investigated how TP53 mutation regulates the HCC immunophenotype and thus affects the prognosis of HCC.[Method]We investigated TP5 3 mutation status and RNA expression in different populations and platforms and developed an immune prognostic model based on immune-related genes that were differentially expressed between TP53WT and TP53MUT HCC samples.Then,the influence of the immune prognostic model on the immune microenvironment in HCC was comprehensively analysed.[Result]TP53 mutation resulted in the downregulation of the immune response in HCC.Thirty-seven of the 312 immune response-related genes were differentially expressed based on TP53 mutation status.An immune prognostic model was established and validated based on 865 patients with HCC to differentiate patients with a low or high risk of poor survival.A nomogram was also established for clinical application.Functional enrichment analysis showed that the humoral immune response and immune system diseases pathway represented the major function and pathway,respectively,related to the immune prognostic model genes.Moreover,we found that the patients in the high-risk group had higher fractions of T cells follicular helper,T cells regulatory(Tregs)and macrophages MO and presented higher expression of cytotoxic T-lymphocyte antigen-4(CTLA-4),programmed death-1(PD-1)and T cell immunoglobulin and mucin-domain containing-3(TIM-3)than the low-risk group.[Conclusion]TP53 mutation is strongly related to the immune microenvironment in HCC.Our immune prognostic model,which is sensitive to TP53 mutation status,may have important implications for identifying subgroups of HCC patients with low or high risk of unfavorable survival.