| Chapter Ⅰ Differential Expression and Prognostic Value Analysis of Autophagy-related Genes in Prostate CancerObjective: Prostate cancer(PCa,or prostate adenocarcinoma,PRAD)is the most common malignancy in men worldwide.Studies have shown that autophagy plays an important role in the development and process of PCa.This study aimed to develop an autophagy-related genes(ATGs)signature to predict the prognosis of PCa.Methods: ATGs were extracted from HADb,MSig DB,and Gene Cards databases,and HTSeq-FPKM data and clinical information were downloaded from TCGA database.Differentially expressed autophagy-related genes(DEATGs)were identified by "limma" package.Gene Ontology(GO)annotation and Kyoto Encyclopedia of Genes and Genome(KEGG)analysis were performed to reveal the molecular mechanism of the possible role of DEATGs in PCa.CMAP database was used to mine small-molecule drugs targeting DEATGs.Cox regression analysis was used to create the best prognosis model,then PCa patients were divided into high-risk and low-risk groups according to the median risk score(RS)value,and OS curves and receiver operating characteristic(ROC)curves of patients at 1,3,and 5 years were drawn to assess the prognostic predictive ability of the RS model.Then,univariate Cox regression and multivariate Cox regression analysis were used to evaluate the independent prognostic ability of RS model.Finally,gene set enrichment analysis(GSEA)was used to analyze the different signaling pathways between patients in high and low-risk groups.Results: We identified 117 differently expressed ATGs in PCa.Potential small molecular drugs such as scriptaid,rifabutin,and puromycin which may target DEATGs were identified by CMAP database.19 DEATGs were correlated with OS in PCa patients,among which STK32 A,MYC,and SRPX were prognosis-related key DEATGs.The RS model was developed by using the expression levels and regression coefficients of STK32 A,MYC,and SRPX.OS of patients in high-risk group was significantly shorter than that in low-risk group(P < 0.05),and the area under curve(AUC)of 1 year,3 years,and 5 years was1.000,0.664,and 0.774,respectively.Univariate and multivariate Cox regression analysis showed that the RS model was an independent prognostic factor(HR =1.140,95% CI = 1.051-1.236,P= 0.002).The GSEA results revealed that the highrisk group was mainly enriched in key pathways related to tumorigenesis,including epithelial-mesenchymal transition(EMT),Notch signaling,and xenobiotic metabolism,etc.Conclusions: 117 DEATGs were identified in the PCa cohort,42 were upregulated and 75 were downregulated,of which 19 DEATGs were prognosisrelated ATGs.A novel model to predict the prognosis of PCa patients was developed based on STK32 A,MYC,and SRPX signature,which may be an independent prognostic biomarker and potential therapeutic targets for PCa patients.Chapter Ⅱ Development of a Novel Autophagy-related lncRNAs Signature as a Prognostic Biomarker for Prostate CancerObjective: PCa is one of the leading causes of cancer death in men.To date,there are great challenges to identify novel molecular markers for the prognosis of PCa.Long non-codingRNA(lncRNA)and autophagy are considered to be the key factors in tumorigenesis and malignant progression.However,the expression profile and molecular mechanism of autophagy-related lncRNA(ARlncRNA)in PCa and their clinical relevance of PCa remain unclear.Therefore,it is necessary to identify the signature of ARlncRNAs in PCa and develop a RS model to predict the prognosis of PCa.Methods: ARlncRNAs were identified according to Pearson correlation coefficient |R2| >0.4 and P< 0.001.The prognosis-related ARlncRNAs were identified by Kaplan-meier method and Cox regression analysis.RS model of ARlncRNAs was constructed by univariate and multivariate Cox regression analysis.The predictive value of the model was evaluated by ROC curves and functional enrichment was performed by GSEA.The key ARlncRNA-mRNA coexpression network was constructed using gene co-expression coefficients.Results: A total of 1133 ARlncRNAs were identified in PCa,among which 39 ARlncRNAs were differentially expressed,35 with up-regulated expression and 4 with down-regulated expression.A novel prognostic RS model including AC008969.1,AC079414.3,AC012645.1,AL121820.2,AC022150.2,AC108134.1,SNHG1,SNHG17,and INE1,was constructed.Patients with highrisk scores had worse OS than those with low-risk scores.The AUC values at 1,3,and 5 years were 1.000,0.932,and 0.959,respectively.The results of GSEA suggested that the high-risk group was mainly enriched in pathways associated with tumorigenesis and progression,such as prostate cancer,TGF-β signaling pathway,and Hedgehog signaling pathway.ARlncRNA-mRNA co-expression network suggested that SNHG17 was the most important node in the whole network.Conclusions: we developed a novel ARlncRNAs signature as a prognostic biomarker for PCa.Nine key ARlncRNAs may be autophagy-related therapeutic targets in the clinical practice of PCa.Chapter Ⅲ Expression of Autophagy-related gene HSPB8 and Its Clinical Significance in Prostate CancerObjective: Patients with advanced PCa have a poor prognosis,and there is an urgent need to develop new biomarkers to accurately predict tumor properties and prognosis in individual PCa patients.Heat shock protein beta-8(HSPB8)is a member of the small heat shock protein superfamily.Abnormal HSPB8 expression is closely related to carcinogenesis and progression.However,little is known about the expression,prognosis,and immune infiltration of HSPB8 in PCa.Methods: The expression of HSPB8 and its clinical outcomes in PCa were analyzed by using TCGA,Oncomine,CCLE,CANCERTOOL,PCTA,Driver DBV3,and CANCERTOOL databases.The association between HSPB8 expression and immune infiltration was evaluated by TIMER and TISIDB databases.The co-expression genes and the regulatory factors of HSPB8 were analyzed by Linked Omics,and functional enrichment analysis was performed by Gene Set Enrichment Analysis.Results: We found that HSPB8 expression was down-regulated in multiple PCa cohorts,and was significantly negatively correlated with PCa patients’ TNM grade,Gleason scores,and tumor metastasis.Patients with luminal B subtype had the lowest HSPB8 expression level among the three PAM50 types.Low HSPB8 was associated with poor prognosis and a higher risk of recurrence in PCa patients.Notably,HSPB8 expression was negatively related to the abundance of 24 types of tumor-infiltrating lymphocytes(TILs),such as NK cells,NKT cells,Th2 cells,Th1 cells,Tcm_CD8cells,and Mem_B cells in PCa.Pathway enrichment analysis exhibited that HSPB8 regulated ribosome,spliceosome,base excision repair,proteasome,and focal adhesion.KEGG pathway showed that genes co-expressed with HSPB8 were mainly involved in cell cycle,aminoacyl-tRNA biosynthesis,ribosome biogenesis in eukaryotes,etc.Conclusions: Low expression of HSPB8 in PCa was correlated with unfavorable clinical outcomes and decreased the abundance of immune cells.HSPB8 may be a novel prognostic biomarker and potential therapeutic molecule for PCa.Chapter Ⅳ Biological functions and potential mechanisms of HSPB8 in Prostate CancerObjective: PCa is the most common malignant tumor of the male urinary system.Increasing evidence suggests that HSPB8 was involved in tumorigenesis and development.However,the detailed molecular mechanisms of HSPB8 in PCa have not been fully elucidated.In this study,we aimed to explore the biological functions and underlying mechanism of HSPB8 in PCa.Methods: The expression of HSPB8 in PCa cell lines was detected using CCLE database and RT-qPCR,respectively.Lentivirus(LV)-HSPB8 was used to construct cell lines stably overexpressing HSPB8.which were identified by fluorescence microscopy,RT-qPCR,and western blotting.Cell autophagy was examined by transmission electron microscopy.CCK8 and colony formation assay were performed to detect cell proliferation.Flow cytometry was used to detect cell cycle phase and apoptosis levels.TotalRNA was extracted from cells andRNA sequencing was performed.Raw data were quality controlled by Fast QC.DEGs were identified by edge R.Functional enrichment analysis and proteinprotein interaction(PPI)network analysis were performed using the cluster Profiler package and STRING website,respectively.Hub genes were identified by cytoscape software.Network Analyst was used to analyze transcription factors regulating key DEGs.Validation of DEGs expression levels was performed by RT-qPCR.Results: HSPB8 was lower expressed in PC3 and LNCa P,which were used to construct stable overexpression of HSPB8 by LV-HSPB8.Transmission electron microscopy results showed that overexpression HSPB8 promoted autophagy in PC3 cells.The results of CCK8 assay and colony formation assay showed that overexpression of HSPB8 inhibited cell proliferation and promoted apoptosis.Transcriptome sequencing analysis showed that 3184 DEGs were identified in PC3 cells,of which 1711 were down-regulated and 1473 were upregulated,a total of 1907 DEGs were screened in the LNCa P cell,1063 were low expression and 844 were high expression.DEGs were mainly enriched in phospholipase D signaling pathway,peroxisome,amino-and nucleotide sugar metabolism,and homologous recombination.PPI network analysis showed 103 protein nodes and 244 edges,and 10 hub DEGs were identified,including MYC,BRCA1,RFC5,RFC3,RFC4,RFC2,RAD51,FANCD2,BRCA2,and PALB2.E2F4 was the most critical transcription factor and interacted with 9 hub genes.Conclusions: Overexpression of HSBP8 inhibits PCa cell proliferation and promotes apoptosis.HSPB8 may affect the biological functions of PCa cells through signal pathways of phospholipase D signaling pathway,peroxisome,amino-and nucleotide sugar metabolism,and homologous recombination. |
