Bioinformatics Analysis And Potential Compounds Identification Based On Hepatocellular Carcinoma Transcriptomic Stemness

Background and objective:Stemness refers to the potential of normal stem cells to self-renew and differentiate.Cancer stemness has been reported to drive tumor progression and induce resistance in tumor therapy.In recent years,one-class logistic regression(OCLR)machine learning algorithm has been used to calculate transcriptomic stemness for a variety of tumors.These studies have found that transcriptomic stemness is a risk factor for disease progression and poor prognosis of different tumors.Based on the multiple HCC cohorts and OCLR algorithm,the aim of this study was to explore the clinical and biological implications of hepatocellular carcinoma(HCC)transcriptomic stemness and subsequently identify potential compounds that targeting HCC transcriptomic stemness.Methods and results:1.Clinical implications of HCC transcriptomic stemnessIn this study,1059 patients from five HCC cohorts were collected to calculate the RNA-based stemness index(mRNAsi)by using OCLR algorithm.Analysis based on the The Cancer Genome Atlas-Liver Hepatocellular Carcinoma(TCGA-LIHC)cohort found that mRNAsi was associated with pathological grade,TNM stage and poor prognosis.Multivariate COX regression analysis indicated that mRNAsi might be an independent risk factor for HCC overall survival(OS).Therefore,establishing a prognostic model based on mRNAsi may help to distinguish OS of HCC patients.To this end,the TCGA-LIHC cohort was used as the training dataset to establish HCC stemness risk model(HSRM).Based on the TCGA-LIHC cohort,we first identified 626 mRNAsi-related genes by spearman correlation analysis(|R|>0.5).Subsequently,HSRM is established based on these genes by using LASSO(Least Absolute Shrinkage and Selection Operator)algorithm and multivariate COX regression analysis.Finally,the predictive efficacy of HSRM for HCC OS was verified by four other independent HCC cohorts that were mentioned above.In addition,we also found that HSRM might be associated with HCC tumor growth rate and treatment response of transarterial chemoembolization in two cohorts,GSE54236 and GSE104580.2.Biological implications of HCC transcriptomic stemness Based on the 626 mRNAsi-related genes,we classified 1059 patients from five HCC cohorts into two sternness subtypes by consensus clustering,in which subtype I had significantly higher mRNAsi than subtype II.Single sample gene set enrichment analysis(ssGSEA)found that biological process such as cell cycle and DNA replication were enriched in stemness subtype I tumor samples,while a series of liver-associated metabolisms were inhibited in these tumor samples.Besides,somatic mutation analysis found that mutations in tumor suppressor genes TP53 and RB1 were more frequent in stemness subtype I,which were associated with higher mRNAsi.Moreover,tumor microenvironment analysis based on CIBERSORT algorithm and ssGSEA suggested that the correlation between HCC stemness subtypes and anti-tumor immunity was relatively weak.3.Identification of potential compounds that targeting HCC transcriptomic stemness Based on the Connectivity Map(CMap)database,we identified 81 potential compounds that targeting HCC transcriptomic stemness,which also suggested that topoisomerases,cyclin-dependent kinases and histone deacetylases are potential therapeutic targets to inhibit HCC transcriptomic stemness.According to the prediction of CMap analysis and the results of literature search,aminopurvalanol-a and NCH-51 were selected from these potential compounds to verify by in vitro experiments.CCK-8 assay,colony formation assay and EdU assay showed that aminopurvalanol-a and NCH-51 could significantly inhibit the proliferation of liver cancer cell lines(Hep3B and Huh7).The oncosphere formation assay showed that these two compounds could also inhibit the tumor stemness of Hep3B and Huh7 cells.We then performed RNA sequencing of aminopurvalanol-a-treated HCC cells,which demonstrated that aminopurvalanol-a treatment could significantly inhibit its transcriptomic stemness.Antibody array assays demonstrated that aminopurvalanol-a might suppress HCC via several molecules,such as CCND1(pThr286)and RB(pSer608).Conclusion:Transcriptomic stemness is associated with HCC disease progression and may be an independent risk factor of HCC poor prognosis.In this study,a prognostic model based on transcriptomic stemness was able to significantly discriminate OS in several independent HCC cohorts.Two HCC stemness subtypes have distinct biological functional patterns and somatic mutation profiles.This study also identified a series of potential compounds targeting the HCC transcriptomic stemness.The therapeutic effects of two potential compounds,aminopurvalanol-a and NCH-51,on liver cancer cells have been verified in vitro,which might provide more HCC treatment options in the future.