The Clinical Prognosis And Its Possible Mechanism Of Long Non-coding RNA In Hepatocellular Carcinoma

Globally,liver cancer is the sixth most commonly diagnosed cancer and ranked fourth in terms of deaths in 2018,with a steadily rising incidence rate.Hepatocellular carcinoma(HCC)is the most frequent type of liver cancer,accounting for 75-80%of all primary liver cancer cases.Despite the major progress in risk factors,early diagnosis,surveillance methods,and treatment techniques,prognose for HCC patients remains poor.It is vital to investigate patients with a high risk of poor outcomes to guide effective clinical management.Thus,considerable effort has been devoted to establishing various staging systems for HCC using clinical information and pathological criteria.Currently,several classification models,including the clinical tumor-node-metastasis(TNM)staging,Barcelona-Clinic Liver Cancer(BCLC),Okuda staging system,Cancer of Liver Italian Program(CLIP),and Chinese University Prognostic Index(CUPI),have been developed and used clinically.Although these staging systems have proven useful,their predictive efficiency remains limited and fails to involve biological characteristics of HCC that might account for clinical heterogeneity when establishing novel predictive modelsLong noncoding RNAs(IncRNAs)are defined as RNA transcripts longer than 200 nucleotides that have no significant protein-coding capacity.lncRNAs exhibit diverse subcellular localization patterns,and it is becoming increasingly clear that the functions of lncRNAs depend on their unique subcellular localization.The potential functions of lncRNAs in regulating the malignancy of tumor cells have been previously examined.To date,many studies have highlighted the indispensable molecular mechanisms and biological characteristics of lncRNA in HCC occurrence and progression.Moreover,an increasing number of aberrantly expressed lncRNAs can also serve as valuable prognostic gene expression signatures for HCC patients.However,only a few studies have attempted to identify lncRNA prognostic signatures for HCC prognosis prediction,resulting in varying efficiencies and unclear mechanisms.Additionally,none of these studies have compared the predictive efficiencies of lncRNA models and other clinical classification systems.Thus,conducting more convincing clinical validation and finding potential mechanisms of lncRNA-based signature in HCC prognosis prediction are necessary.With the development of high-throughput technologies,public databases have enabled researchers to investigate a variety of approaches for identifying potential biomarkers for HCC diagnosis and prognosis prediction.The Cancer Genome Atlas(TCGA)is a project aimed at generating multi-dimensional maps of the key genomic changes in 33 types of cancer to provide great opportunities for analyzing the high throughput data and clinical features using statistical analysis.All of the existing studies that have reported HCC prognostic models based on lncRNAs only used such online databases.However,the transcriptional profiling and RNA sequencing testing methods used in TCGA database are expensive and difficult to perform at hospital.Moreover,the IncRNA signature developed using tumor data from the online database might not be available for the different testing methods and real clinical patients.Therefore,an easier method for the detection of IncRNAs and a new method-based lncRNA signature are urgently needed.In this study,the five-lncRNA formula was shown to have the best prediction efficiency in HBV-HCC patients based on the TCGA database.Based on this result,the expression level of each lncRNA was detected by qRT-PCR in 50 primary HBV-HCC patients who underwent surgical hepatectomy as their primary treatment,and a qRT-PCR data-based lncRNA signature was first established.Our lncRNA model had the highest prediction efficiency compared to other clinical staging systems(TNM,Child-Pugh,Okuda staging system,BCLC,CLIP,and CUP1)in these resected HBV-HCC patients.Finally,we investigated the expression levels and locations of these five lncRNAs in vitro.In summary,we developed a useful innovative five-lncRNA model for survival prediction and individual treatment guidance of resected HBV-HCC patients.High-risk patients should have shorter follow-up times and combined treatments such as TACE,chemotherapy,and targeted agents.Additionally,our study has provided a deeper understanding of the five lncRNAs,which could be considered prognostic biomarkers,as well as clinical treatment guidance and therapeutic targets.Part 1.Constructing a lncRNA model for predicting the survival of patients with hepatocellular carcinoma through TCGA database AimThe aim of the current study was to identify potential prognostic long non-coding RNA(lncRNA)biomarkers for predicting survival in patients with hepatocellular carcinoma(HCC)using The Cancer Genome Atlas(TCGA)dataset.Subsequently,compare and evaluate the predictive power of the lncRNA signature in HCC patients with different risk factors.Methods1.Downloaded the raw RNA-Seq data and the corresponding clinical information of 374 HCC tumor and 50 normal liver tissue specimens from the TCGA database.The differentially expressed lncRNAs were calculated using the Perl and package "edgeR"from R.2.Univariate and multivariate Cox regression analyses were used to assess the prognostic contribution of these lncRNAs and construct the predictive risk score model.3.The cut-off point for risk score was identified with the median to stratify 370 HCC patients with survival data into the high-risk group and low-risk group.K-M analysis and the receiver operating characteristic(ROC)curve analyses was plotted to evaluate the prognostic effacay of the IncRNA model.4.Stratification analysis was performed for different risk factors of HCC.K-M analysis and the receiver operating characteristic(ROC)curve analyses were performed to compare and evaluate the prognostic effacay of the lncRNA model in the different subgroup.Results1.The raw RNA-Seq data of 374 HCC tissues and 50 normal liver tissues were obtained from TCGA database.After differential expression analysis,1029 upregulated and 58 downregulated differentially expressed lncRNAs were screened out by comparing HCC and normal liver tissues(absolute logFC>2.0,adjusted P<0.05)2.LINC01116,LUC AT1,DDX11-AS1,C10orf91,and FIRRE were independent predictors in the multivariate Cox regression analysis(P<0.05).Finally,a lncRNA-based model was developed as follows:risk score=(0.0882×LINC01116)+(0.2504×DDX11AS1)+(0.1001×C10orf91)+(0.0722×LUCAT1)+(0.1138×FIRRE)3.Based on the risk score,370 HCC patients with known survival times were classified as high-risk group and low-risk group according to the cut-off value.The expression pattern of each lncRNA in every HCC patient is shown through heatmap.The K-M curves of the two groups were significantly different(P<0.0001).Furthermore,the areas under the curves(AUCs)of the above model were 0.710 for the 3-year survival times4.We performed stratification analysis for six HCC groups(alcohol consumption,HBV,HCV,NAFLD,no risk history,and other factors)according to the factors provided by TCGA clinical database.This five-lncRNA signature has the highest prediction efficiency in HB V-HCC patients,,the risk score could largely predict the 3-year survival,as the AUC was 0.81 1.ConclusionsBy analyzing the lncRNA expression and clinical information of HCC patients in the TCGA database,a new lncRNA prognostic model was constructed,which can effectively predict the survival of HCC patients.The lncRNA signature has the best prediction performance for HB V-HCC patients.Part 2.LncRNA signature for prognosis prediction in HBV-HCC patients after resectionAimHBV infection accounts for about 80%of all HCC patients in China,and has the best prediction efficiency according to the risk score model in TCGA database.To better guide clinical treatment,we aimed to develop a new 5-lncRNA prognostic score based on qRT-PCR data and compared its predictive efficiency with that of other clinical staging systems,to further assess the robustness and extend the utility of this signature.Methods1.We included 50 tumor tissues from primary HBV-HCC patients and 10 normal liver tissues collected from patients with hepatic trauma.Subsequently,we determined the expression levels of these five lncRNAs in our prospectively collected samples using qRT-PCR.2.To assess the prognostic formula according to the experimental data,K-M and Cox analyses were performed to recalculate the regression coefficients of each lncRNA.Thus,a new risk score model was constructed derived from qRT-PCR results of the lncRNAs to predict the clinical HBV-HCC patients.2.Univariate and multivariate Cox regression analyses were used to assess the prognostic contribution of these lncRNAs and construct the predictive risk score model.3.To testify whether the qRT-PCR data based lncRNA signature is an independent prognostic factor for OS,we performed univariate and multivariate Cox regression analyses with the signature and clinicopathological factors in HBV-HCC patients after surgical resection.4.All independent prognostic factors which based on multivariate Cox regression analysis were used to construct the nomogram(combined prognostic model)for predicting the probability of 1-,2-and 3-year OS of HCC patients.Then,nomogram validation which consist of discrimination and calibration was performed.C-index was calculated to evaluate the discrimination of the nomogram.5.K-M analysis and ROC curve were used to evaluate and compare the predictive power of lncRNA model,TNM,Child-Pugh,Okuda,BCLC,CLIP and CUPI staging system on the prognosis of HBV-HCC patients.Results1.All lncRNAs-LINC01116,LUCAT1,DDX11-AS1,C10orf91 and FIRRE-displayed highly expression patterns in HBV-HCC tumor tissues when comparing with normal samples,which was consistent with the findings in the TCGA cohort.2.The results of univariate and multivariate Cox regression analyses showed that all five lncRNAs were all significantly correlated with HBV-HCC patients' OS However,the HR of C10orf91 was>1 in the univariate analysis but<1 in the multivariate analysis.We thought that the lncRNA C10orf91 may have interactions with other variables.Then we excluded C10orf91 and rebuilt the signature.Finally,we obtained a new four-lncRNA model which was stable and suitable for the data of qRT-PCR method using our clinical samples.The model was described as follows:risk score=0.353×LINC01116+0.037xDDX11-AS1+0.032×LUCAT1+0.136×FIRRE3.HBV-HCC patients were divided into high risk and low risk groups using the median score as the cut-off value.According to K-M analysis,patients in the low risk groups had significantly longer OS(3.67±0.09 years)than patients in the HRG(2.01 ±0.25 years).In addition,the prognostic capacities of individual lncRNAs and the four-lncRNA signature were assessed by the ROC curve analysis.The 3-year AUCs were 0.875(95%CI=0.769-0.981),0.735(95%CI=0.592-0.878),0.770(95%CI=0.612-0.928),0.852(95%CI=0.741-0.964),and 0.809(95%CI=0.674-0.944)for lncRNA signature,DDX11-AS1,LUCAT1,LINC01116,and FIRRE,respectively,indicating that the risk score has the best predictive efficiency.4.In univariate Cox analysis,the prognostic model,total bilirubin,alkaline phosphatase(ALP),and maximum diameter of the tumor were all correlated with the survival of resected HBV-HCC patients.Then,those factors were selected to the multivariate analysis,which showed only our signature(P<0.001),ALP(P=0.011)and maximum diameter of the tumor(P=0.017)to be the independent prognostic factor Thus,our results proved that this lncRNA signature could be used as an independent predictor in clinical postoperative HBV-HCC patients.5.A nomogram was established to predict HCC patients' survival on the basis of the independent factors from the multivariate analysis.The validation of the nomogram was consisted of discrimination and calibration.Calibration curve for the probability of overall survival at 3-year did not indicated optimal agreement between the nomogram'prognostic value and actual observation.However,even this nomogram' C-index was greater than that of other models,the nomogram did not have better prognostic efficiency than the four-lncRNA signature(P=0.175).In addition,the ROC curve analysis indicated that our prognostic model had the best predictive efficiency for survival for the 2-year and 3-year survival times.6.K-M analysis and ROC curve analysis were conducted to assess the prognostic capacity of lncRNA model,TNM,Child-Pugh,Okuda,BCLC,CLIP and CUPI systems.The results showed that the qRT-PCR-based 4-lncRNA formula had the best predictive efficiency for survival(AUC=0.875,95%CI=0.769-0.981)compared to other six clinical staging systems in postoperative HBV-HCC patients.ConclusionsThrough the TCGA database and clinical database,an effective qRT-PCR-based 4-lncRNA model was established.This formula had the best predictive efficiency for survival compared to TNM,Child,Okuda,BCLC,CLIP and CUPI staging systems.And it can be used for postoperative HBV-HCC patietns' survival prediction and treatment guidance.Part 3.The mechanism of LncRNA model predicting the survival of hepatocellular carcinoma patientsAimThe aim of the current study was to performed comprehensive bioinformatics analyses to analysis the deeper potential regulatory mechanisms of these lncRNAs in HCC.Methods1.The qRT-PCR and FISH analyses were used to detect the expression levels and locations of LINC01116,DDX11-AS1,LUCAT1 and FIRRE in LO2 and HCC cell lines(Huh7,HepG2,MHCC97-h,LM3)2.The RNA-Seq data and miRNA-Seq data of 374 HCC samples and 50 normal liver tissue specimens were obtained from the TCGA.The differentially expressed mRNAs and miRNAs were filtered out with |log2FC|?1.0 and adjusted P value<0.05.3.The interactions between the differentially expressed lncRNAs and miRNAs were predicted by the miRcode,miRDB,miRTarBase and TargetScan.Pearson correlation to calculate the relationship between these lncRNAs and the differently expressed mRNAs.Cytoscape software was used to visualize the network.4.Biological functions of the lncRNA target genes were predicted using functional enrichment analysis of Gene Ontology(GO)and The Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways.Results1.All of the HCC cell lines indicated higher expression levels of each lncRNA compared to the normal hepatocyte cell line LO2.LINC01116,DDX11-AS1 and FIRRE are expressed in both nuclear and cytoplasmic compartments in Huh7 cell lines.However,LUCAT1 is mainly expressed in the cytoplasm.2.Using P<0.05 and absolute logFC?1.0 as cutoffs,we found that 4851 mRNAs and 250 miRNAs were differentially expressed between HCC and normal samples according to TCGA database.2.A total of 97 protein-coding genes were selected that correlated with at least two of these four lncRNAs.The visualization of co-expression networks of these lncRNAs and mRNAs was constructed.3.GO terms and KEGG pathways were performed to explore the potential molecular mechanisms of the target genes including in co-expression networks.The KEGG pathways were significantly enriched in "Cell cycle","Progesterone-mediated oocyte maturation","Valine,leucine and isoleucine degradation","Oocyte meiosis","Propanoate metabolism" and "FoxO signaling pathway".ConclusionsThis study has shed new light on the prognostic lncRNAs by identifying their expression patterns and the regulatory mechanism of HCC progression,which could be considered as prognostic biomarkers and therapeutic targets.In addition,it was found that lncRNA DDX11-AS1 is at the center of the regulatory network,suggesting that it plays an important role in the progression of HCC.Part 4.The mechanism of LncRNA DDX11-AS1 in regulating hepatocellular carcinomaAimIn this part of the study,we will further explore the specific mechanism of DDX11-AS1 in regulating the progress of HCC through in vivo and in vitro experiments.Methods1.Divide the HCC patients in the TCGA database into high expression group and low expression group according to the expression level of DDX11-AS1.Gene Set Enrichment Analysis(GSEA)analysis was performed to analysis the enriched pathways in the high expression group.2.Liver tissues of 15 HCC patients and 8 normal control patients were collected.And immunohistochemical staining and FISH were used to detect the expression levels of CD31 and DDX11-AS1.Then analyzed the relationship of DDX11-AS1 and CD31.3.Analyze the correlation between the expression of DDX11-AS1 and the clinicopathological characteristics of HCC patients.4.Small interfering RNA(siRNA)was used to knock down the expression of DDX11-AS1 in HCC lines.The transfection efficiency was observed under a fluorescence microscope,and the knockdown efficiency was detected by qRT-PCR.5.Detect the migration ability of HUVEC in the co-culture system by cell scratch experiment and Transwell experiment.6.Detect the migration ability of HUVEC in the co-culture system with DDX11-AS1 knockdown by cell scratch experiment and Transwell experiment7.qRT-PCR and enzyme linked immunosorbent assay(ELISA)were performed to detect the expression of major angiogenic factors in HCC cells and supernatants of HCC cells.Results1.We found that the DDX11-AS1 high expression group is enriched in pathways related to angiogenesis including the "vascular endothelial growth factor(VEGF)signaling pathway",so we will focus our research on whether it can regulate the angiogenesis in HCC.2.The results showed that the expression of DDX11-AS1 and CD31 in the liver tissue of HCC patients were significantly higher than those of the normal control group.In addition,the expression of DDX1 1-AS1 is positively correlated with the expression of CD31,which reflects the degree of angiogenesis.3.The expression of DDX11-AS1 is closely related to the patient's Alanine aminotransferase(ALT)and alkaline phosphatase(ALP),the number of tumors,and the vascular invasion.4.Two cell lines Huh7 and MHCC97h were selected for the next interference experiment.In addition,siRNA-2 was selected to knock down the expression of lncRNA DDX11-AS1.5.Interfering with lncRNA DDX11-AS 1 can inhibit the migration ability of HUVEC cells in Huh7 and MHCC97h cell co-culture system.6.In addition,interfering with IncRNA DDX11-AS1 can also obviously inhibit the angiogenesis ability of HUVEC cells in the co-culture system of Huh7 and 7MHCC97h cells.7.qRT-PCR showed that after interfering with the expression of lncRNA DDX 11-AS1 in Huh7,the expression of VEGFA,Ang-1 and Ang-2 were significantly reduced.In addition,the expression of VEGFA and Ang-2 in the supernatant of Huh7 decreased significantly.ConclusionsLncRNA DDX11-AS1 affects tumor cells to produce and release vascular regeneration factors such as VEGFA.Thereby indirectly regulating the angiogenesis of HCC and affecting the occurrence and development of HCC.