Establishment Of Nomogram For Prediction Of Hepatocellular Carcinoma Prognosis And Mechanism Study Of OIT3 Facilitating Hepatocellular Carcinoma Progression

BackgroundHepatocellular carcinoma(HCC),the prevalent form of primary liver cancer with high incidence and cancer-related mortality,threatens the human health seriously.Although the immunotherapies for cancer have been significantly developed in the past few decades,the overall objective response rate(ORR)in HCC is about 20%.The tumor staging system(TNM staging)and serum alpha-fetoprotein(AFP)are commonly used in predicting HCC prognosis.Due to the heterogeneity of HCC,it is hard to predict the prognosis accurately,especially to predict the ORR of immunotherapy.Currently,transcriptomic sequencing and micro-array platform have been widely used in tumor study.Bioinformatics analysis have shown the utility of high-throughput screening of different variables and constructing more accurate and efficient prognostic models.Several studies have been published,while just two of them were externally validated(one based on microarray dataset,and the other one only focused on HCV-related HCC).None of the prognostic models integrative analyzed on multi-platform datasets,clinical characteristics of HCC,and external validation at the same time.Thus,it is urgent to establish a reliable prognostic model based on multi-platform datasets with external valid cohorts,integrating the transcriptional expression level of prognostic genes and pivotal clinical characteristics.Further,analyzing the characteristics of tumor microenvironment(TME)and exploring the potential therapeutic targets of HCC according to the prognostic model.According to the analysis results of the micro-array data of Nomogram model,we found and validated that infiltrated immune cells converged in paracancer tissues.Therefore,we aim to reveal new markers of immune cells by using the weighted correlation network analysis(WGCNA)and the CIBERSORT algorithm in the data of paracancer tissues.Moreover,exploring the signatures of central immune cells(such as macrophages)in paracancer tissues of HCC highlights the important role of paracancer tissues in the tumor research.The immune microenvironment of HCC is mainly composed of immunosuppressive cells with massive tumor-associated macrophages(TAMs)infiltered.Most of TAMs,interchangeable with M2-macrophages,inhibit the immune response in tumor microenvironment by secreting IL10 and other anti-inflammatory cytokines and chemokines.Macrophages also play a central role in the induction,maintenance and restriction of tumor related inflammation in liver cancer.Therefore,the research of anti-tumor mechanisms targeting at macrophages is also one of the hot spots in the current immunological research of HCC.Methods and Results1.Establishing a reliable prognosis model for HCC1.1 Identification and evaluation of prognosis hub genesThe 11 hub genes were identified by overlapping the three different expression genes(DEGs)sets and two prognosis gene sets.CCNB1 and ANXA10 were validated to be the crucial genes for HCC prognostic by comparing the results of elastic net penalty(ENP)analysis and best subsets regression(BSR)analysis.1.2 Development and constitution of the HCC prognostic nomogramBMI＿cut(body mass index with cutoff value of 23.4)and TNM stages were identified as the HCC prognosis related clinical characteristics by using the univariate Cox analysis and Kaplan–Meier curves for overall survival(OS)time.The nomogram prognostic model was constructed on the results of multivariate Cox regression analysis with the above four covariates.1.3 Residuals methods to validate the Cox model assumptionsSchoenfeld residuals(p > 0.05),Magnitudes results and Martingale residuals validated that the Cox regression model was adequately fitted to describe these data.1.4 Satisfactory performance of the prognostic modelWe used the Kaplan–Meier curves and receiver operating characteristic(ROC)curve plots to check the performance of the Nomogram model,ENP model and BSR model.Nomogram model outperformed in the accuracy prediction than the other two prognostic models with area under curve(AUC)of 0.71.Furthermore,the Nomogram model was the satisfactory prognostic model in the validation datasets.The accuracy of the nomogram model was determined by Cstatistic discriminatory index and visualized in the bootstrapped calibration plots.2.Analysis of TME in HCC based on the prognostic model2.1 Infiltrated immune cells associated with HCC prognosisBy using survival analysis,inter-group compared analysis and univariate cox regression,we found the percentages of common lymphocyte progenitor(CLP)and T helper 2 cell(Th2)were hazard factors for HCC prognosis,while endothelial cell(EC)and hematopoietic stem cell(HSC)were benefit factors for HCC patients.2.2 Hypoxia microenvironment indicates poor prognosis of HCCThe expression levels of the genes in the PID＿HIF1A＿PATHWAY was extracted from the dataset.Then,we analyzed and visualized the gene interactions by using Cytoscape.Thus,hypoxia signaling pathways were found to be closely associated with high-risk scores in HCC subgroups.2.3 Angiogenesis in TME associated with HCC progressThe transcriptome expression level of angiogenesis-related genes in VEGF signaling pathway were extracted and calculated the intergroup differences.Most of them were upregulated in the Risk High group of HCC when compared with Risk Low group(p < 0.05),so were the expression levels of the gene signatures in EC.3.Immune cells in paracancer tissues pave a novel way to investigate the TME in HCC3.1 Infiltrated immune cells converge in paracancer tissuesAfter analyzing GEO microarray datasets from the Nomogram model,the up-regulated genes in paracancer tissues were found to be associated with the inflammatory immune response process based on the Gene Ontology(GO)annotation terms.The immune and stromal scores were higher in the paracancer tissues than in cancer tissues according to the ESTIMATE algorithm.The classic immune cell markers were significantly upregulated in paracancer tissues than in cancer tissues(p < 0.01).3.2 Immunocytochemistry to identify the infiltrated immune cells of paracancer tissuesFour HCC samples with well-demarcated boundary between cancer and paracancer tissue were infiltered in this study.After stained with anti-CD3 and anti-CD45 antibodies respectively,the positively stained regions were frequently located in the paracancer tissues by using Immunohistochemistry.3.3 Construction of gene networks and identification of modules in paracancer tissuesHierarchical clustering of paracancer samples was performed to exclude two outliers.The adequate soft-threshold power was 4 according to the tradeoff between scale-free topology and mean connectivity.The red and black modules were identified as the independent modules using average linkage hierarchical clustering and the dynamic tree cut.3.4 Association of specific module with composition of infiltrated immune cellsBy calculating the correlation between the modules of WGCNA and immune cell fractions estimated by using CIBERSORT,we found the red module was more correlated to the M2macrophages(r = 0.62,p < 0.001)than other modules.Performing correlation analysis of gene significance(GS)for M2 macrophage versus module membership(MM)in the red module(cor= 0.79,p = 3e-21)illustrated the above results integrally.3.5 Identification of the novel markers of M2 macrophagesThe expression levels of the genes in red module were more significant correlated with the abundance of M2 macrophages than the other immune cells(p = 0).We imported WGCNA edge and node files into Cytoscape to target the meaningful hub genes.Based on the correlation analysis,the expression of OIT3 was found to be significantly correlated with the abundance of M2 macrophages,with maximum correlation coefficient among the candidate genes.we speculated that OIT3 might be a novel marker of M2 macrophages.4.Role of OIT3 on the polarization of macrophages4.1 Validation of OIT3 to be a marker of M2 macrophage in the micro-array dataset of bone marrow-derived macrophage(BMDM).4.2 Validation of OIT3 to be a marker of M2 macrophage in the RNA-Seq dataset of BMDM.4.3 OIT3 intermediates macrophage polarizing to M2 type in biological experimentsThe maturated BMDMs were polarized to M1 or M2 type macrophage,respectively.The expression of OIT3 was found to be highly expressed in M2 macrophage than in M0 and M1 macrophage in transcriptome expression and protein level.Similar results were found in the cell lines of i BMDM.4.4 Study the function of OIT3 in M2 macrophageThe overexpression lentiviral vector of OIT3(LV-Oit3)was transfected into i BMDMs.Similar to OIT3,m TOR and AMPKa were highly expressed in the LV-Oit3-M2 group than in the other groups.The transcriptome expression of anti-inflammatory cytokines or chemokines,key enzymes in fatty acid metabolism were also enhanced in LV-Oit3-M2 macrophages.5.The preliminary mechanism of macrophage OIT3 facilitating hepatocellular carcinoma progression5.1 Expression of OIT3 in macrophage associated with the invasion and metastasis of cancer cellsThe expression of Mmp2,Mmp9,Vegf and Pdgf in LV-OIT3-M2 macrophages after cocultured with H22 cells were significantly up-regulated than control groups by q PCR analysis.The protein levels of Vimentin and Snail expressed highly in H22 cells co-cultured with LVOit3-i BMDM than in H22 alone.5.2 Expression of OIT3 in macrophage facilitates the invasion and migration of hepatoma carcinoma cellsTranswell co-cultured systems were performed in the invasion and migration assays.In the migration assays,the maximum count of Hepa1-6 through the filter was found in the LVOit3-i BMDM–HCC cell coculture groups,when compared with negative control and mock groups.In the invasion assays,the maximum count of Hepa1-6 through the filter and Matrixlgel was found in the LV-Oit3-i BMDM–HCC cell coculture groups,when compared with negative control and mock groups.5.3 Expression of OIT3 in macrophage promotes tumor progressionIn the nude mouse tumorigenicity assays,the tumor volumes increased significantly in the H22 group co-injected with LV-Oit3-i BMDM than in mock group and control group(p < 0.05).Similar results were observed in tumor weights or ratios of tumor weight versus the body weight.Conclusions and Implications1.We developed a Nomogram prognostic model for HCC based on the expression of CCNB1 and ANXA10,BMI＿cut and TNM stage.It is suitable for analyzing microarray or RNASeq dataset with different pathogenic factors.2.HIF1 A related oxygen homeostasis,VEGF angiogenesis process and the infiltration of Th2 cells was associated with poor prognosis of HCC,while EC might play a bidirectional role of destruction and proliferation in the HCC progression.3.OIT3 in the red module might be a novel biomarker of M2 macrophages according to the analyzing of infiltered immune cells in paracancer tissues.These results highlight the indispensable role of paracancer tissue in the research of TME to reveal the underlying molecular mechanism.4.OIT3 was identified to be a novel biomarker of M2 macrophage,and the key role of OIT3 in macrophages was explored in the reprogrammed polarization and metabolism.5.OIT3 was determined to mediate macrophage to M2 type polarization,which facilitates the invasion and migration of hepatoma carcinoma cells and hepatocellular carcinoma progression.It may be a promising target for macrophage-targeted immunotherapy for HCC,which pave a novel way for prolong survival time of HCC patients.