Prediction Of Prognosis And Identification Of Mechanism Under Sorafenib Resistance In Patients With Hepatocellular Carcinoma Based On Cancer Stemness

Background and Purpose:Hepatocellular carcinoma(HCC)is one of the most common malignancies worldwide and is the second leading cause of cancer-related deaths in China.There is no specific clinical manifestation of HCC development,and most patients already have severe liver dysfunction at the time of the first diagnosis,and their survival and response to treatment cannot be accurately predicted based on their morphological features alone.HCC is highly heterogeneous,which is related to its complex tumor microenvironment.The tumor microenvironment can cause phenotypic changes in cancer cells,with some cells dedifferentiating and regaining their differentiation potential.The dedifferentiation ability of tumor cells is one of the molecular bases of heterogeneity and drug resistance of tumor tissue.The prognosis of cancer patients and the effect of drug therapy are closely related to the degree of differentiation of tumor cells.Sorafenib resistance in HCC patients is one of the reasons for poor therapeutic outcomes.Currently,there is still a lack of biomarkers that accurately predict the prognosis of HCC patients and the efficacy of sorafenib treatment.We used high-throughput sequencing data from public databases to assess the role of cancer stemness in predicting the prognosis of HCC patients and identifying mechanisms of sorafenib resistance.Experimental Design:The datasets of HCC,including mRNA expression,somatic mutations,and clinical information were collected from three databases.The mRNA expression-based stemness=ndex(mRNA expression based-index,mRNAsi),which can represent degrees of dedifferentiation of HCC samples,was calculated to predict prognosis and drug response to sorafenib therapy.Survival analysis of five cohorts revealed that the cutoff value of mRNAsi could predict the prognosis of HCC patients.Next,an unsupervised cluster analysis was conducted to distinguish mRNAsibased subgroups for the sorafenib cohort using ConsensusClusterPlus.We performed gene/geneset function enrichment analysis to identify the resistant characteristics in response to sorafenib therapy.The key sorafenib resistance-related pathways and the regulation of key gene expression were dissected by CBNplot.Principal component analysis(PCA)was performed to examine whether four genes’ expressions can differentiate responses to sorafenib.The PPARscore,which can predict response to sorafenib,further was trained and validated through the results of PCA in sorafenib cohorts.We also combined with other omics data to discuss and verify the regulations of key genes.Conclusions:Our study demonstrates that mRNAsi is a promising biomarker to predict both the prognosis and the response to sorafenib therapy in HCC.We reveal fatty acid metabolic-related PPAR signaling pathway is a potential sorafenib resistance pathway that doesn’t been reported before.By analyzing the core regulatory genes of the PPAR pathway,we provided four candidate target genes,RXRB,NR1H3,CYP8B1,and SCD,for understanding the possible mechanisms of sorafenib-resistance.And the signature based on four genes can distinguish responses to sorafenib.The mechanistic hypothesis has been proposed that four genes in the PPAR signaling pathway cascade to result in sorafenib resistance based on our result.