| Part Ⅰ Effect of Exosome Biomarkers for Diagnosis and Prognosis of Ovarian Cancer Patients--a Systematic ReviewPurpose:More and more studies show that exosome biomarkers can be used as indicators for the diagnosis and prognosis of ovarian cancer(OC),and we systematically evaluate the value of which in the diagnosis and prognosis of OC.Methods:A search for clinical studies published before December 20,2019 was performed,methods of exosome purification and identification from all studies were extracted.For diagnosis evaluation,the comparison of exosome biomarkers expression between OC patients and healthy women was obtained;for prognosis prediction,the correlation between the expression levels of exosome biomarkers and overall survival(OS)and disease free survival(DFS)of OC was also extracted.Results:A total of 11 studies were included.The exosomes extraction kit method was the most common method for purifying exosomes.Observation of morphology and detection of exosomal marker proteins were the basic methods for identifying exosomes.Serum exosomal miR-200a and miR-145 showed high diagnostic value(AUC of miR-200a was 0.914,sensitivity and specificity of which reached 83.9%and 90.0%,respectively;AUC of miR-145 was 0.91,sensitivity and specificity of which reached 91.7%and 75.0%,respectively).Plasma exosomes containing proteins FGA,GSN,and FGG also had certain diagnostic values(AUC of plasma exosomes containing proteins FGA,GSN,and FGG can reach 84.59%,83.09%,and 74.47%,respectively).The high expression of miR-373,miR-200b,and miR-200c from serum-derived exosomes was closely related to poorer OS in OC patients(HR was 2.1,2.7,and 2.4;95%CI were[1.0-4.3],[1.3-5.7],and[1.2-4.9]);similarly,the high expression of miR-200c in OC patients was also closely related to poorer PFS(HR=2.0;95%CI:[1.1-3.6]).Serum exosome-derived long noncoding RNA aHIF was closely related to poorer OS in patients with OC(HR=3.699;95%CI:[1.825-7.499]).The down-regulation of plasma-derived exosomal protein markers FGG and LBP was closely related to poorer OS in OC patients(HR[95%CI]were 0.79[0.69-0.91],0.81[0.71-0.93],respectively),and was also closely related to poorer PFS in patients with OC(HR[95%CI]were 0.77[0.67-0.89],0.78[0.68-0.89]).Conclusion:Exosome biomarkers can be used for early diagnosis and prognostic evaluation of patient with ovarian cancer.Part Ⅱ Biological Effects of Long non-coding RNA Co-expression Related Genes in Epithelial Ovarian Cancer--Bioinformatics AnalysisObjective:To evaluate the biological effects of genes associated with long non-coding RNA co-expression in epithelial ovarian cancer using bioinformatics methods.Methods:After searching the relevant databases and comprehensively analyzing the published LncRNAs related to the malignant biological behavior of ovarian cancer,we used the Pearson correlation and z-test to test the correlations between the expression level of the target LncRNAs and that of PCGs;the Gene Expression Omnibus(GEO)database was searched to obtain the mRNA expression profile datasets for studying ovarian cancer tissue and normal ovarian tissue.Using the online tool venny to intersect the differentially expressed genes(DEGs)obtained in the PCGs and mRNA expression profile chip datasets associated with the target LncRNAs,that is to obtain both the target LncRNAs expression and different mRNA expression levels of PCGs in ovarian cancer.Results:A total of 9 LncRNAs(LINC01088,SNHG3,SPRY4-IT1,CPS1-IT1,CDKN2B-AS1(Alias:ANRIL),MALAT1,FAM215A,LINC00472,and HOTAIR)were obtained for us to investigate the correlations with protein coding genes(PCGs).Two microarray datasets(GSE14407 and GSE1852)were found to study the mRNA expression profiles of ovarian cancer tissue and normal ovarian tissue.From the datasets of GSE14407 and GSE18520,2328 and 9590 DEGs were identified,respectively.The online tool venny was used to intersect the co-expressed DEGs obtained from GSE18520 and GSE14407 with PCGs co-expressed with LncRNAs to obtain genes(a total of 1421)that were co-expressed with LncRNAs and were differentially expressed mRNAs in ovarian cancer.GO analysis showed that co-expressed differential genes participated in functional enrichment processes such as DNA replication,cell division,cell proliferation,extracellular exosome,and protein binding;KEGG analysis found that 49 of these co-expressed genes participated in the signaling pathway(pathways in cancer).The PPI network from the co-expressed genes of LncRNAs and DEGs in ovarian cancer was composed of 979 nodes and 5060 edges,and then two important modules were screened out(most genes in the module were involved in the process related to malignant biological behavior of ovarian cancer);there were 274 hub genes,and which were interactive between genes.Using OncoLnc to evaluate the correlations between hub genes and the prognosis of OC patients,it was found that high expression levels of CDCA3,BTRC,UBR4,IQGAP1,FBXL3,FGF2,SYT1,TRIM4,REPS1,AGFG1,PCNT,POLK,PTGER3,and QKI were significantly associated with poorer overall survival(OS)in OC patients(p<0.05);low expression levels of EXO1,MCM3,POLR2D,ANAPC11,SPC24,KLHL25,LSM4,PUF60,and EIF3M were significantly associated with poorer OS in OC patients(p<0.05).Conclusions:Coding protein genes that are differentially mRNA-expressed in ovarian cancer and co-expressed with LncRNAs are involved in biological behaviors,such as malignant proliferation,disease progression,and clinical prognosis of ovarian cancer,which can provide us with relevant LncRNA indicators for experimental research.Part Ⅲ Exosomes from Human Fluids Affect Biological Behavior of Ovarian Cancer by Related Genes of Exosomal Contents--Bioinformatics AnalysisObjective:To evaluate the biological role of the target genes from ovarian cancer which were related to exosomal miRNA and LncRNAs.Methods:After searching exosomal miRNAs and LncRNAs related to the malignant biological behavior of ovarian cancer,through the miRWalk database,Pearson correlation coefficient and z-test,the protein encoding genes(PCGs)related to miRNAs and LncRNAs were predicted,respectively.The Gene Expression Omnibus(GEO)database was searched to obtain the mRNA expression profile datasets for studying ovarian cancer tissue and normal ovarian tissue.Using the online tool venny to intersect the differentially expressed genes(DEGs)in ovarian cancer and target genes related to miRNAs or LncRNAs,respectively.To conduct gene ontology(GO)and Kyoto gene and genome encyclopedia(KEGG)pathway enrichment analysis on the selected PCGs by DAVID,to construct the PPI network by STRING database,and to determine the module and hub genes.Subsequently,to perform biological function analysis and pathway enrichment analysis on the important modules,to show the correlations between hub genes,and to perform survival analysis on hub genes.Results:Three exosomal miRNAs(miR-200a,miR-200b,and miR-200c)and four exosomal LncRNAs(LncRNA ESRG,MEG3,MALAT1,and UCA1)were obtained for research on PCGs.Two microarray datasets(GSE14407 and GSE1852)were used to study mRNA expression profiles of ovarian cancer tissue and normal ovarian tissue.From GSE14407 and GSE18520,2328 and 9590 DEGs were identified,respectively.The online tool venny was used to intersect the co-expressed DEGs obtained from GSE18520 and GSE14407 with miRNA-targeted or LncRNA co-expressed PCGs to obtain common PCGs,respectively.1331 and 1113 PCGs were obtained which were not only related with miRNA or co-expressed with LncRNA but also differentially expressed in ovarian cancer,respectively.In the analysis of PCGs which were related to exosomal miRNAs and differentially expressed in ovarian cancer,GO analysis showed that a large number of PCGs were involved in functional enrichment processes such as DNA replication,cell division,protein binding,and microtubule binding.KEGG analysis found that,among these PCGs,28,58 and 33 genes were involved in cell cycle,pathways in cancer,and proteoglycans in cancer,respectively.The PPI network from PCGs of exosomal miRNAs and DEGs in ovarian cancer was composed of 1284 nodes and 9879 edges,and then one important module was screened out(lots of genes that make up this module were involved in the malignant biological behavior of ovarian cancer);there were 256 hub genes,and which were interactive among genes.Using OncoLnc to evaluate the correlations between hub genes and the prognosis of OC patients,it was found that high expression levels of IQGAP1,CDCA3,BTRC,UBR4,FBXL3,FGF2,SYT1,REPS1,PCNT,DOCK4,and QKI were significantly associated with poorer overall survival(OS)in OC patients(p<0.05);low expression levels of MCM3,POLR2D,ANAPC11,SPC24,LSM4,and EXO1 were significantly associated with poorer OS in OC patients(p<0.05).In the analysis of PCGs which were related to exosomal LncRNAs and differentially expressed in ovarian cancer,GO analysis showed that these PCGs participated in functional enrichment processes such as microtubule cytoskeleton,DNA replication,protein binding.KEGG analysis found that,among these PCGs,51 and 30 genes were involved in pathways in cancer and cell cycle,respectively.The PPI network from the co-expressed genes of exosomal LncRNAs and DEGs in ovarian cancer was composed of 1065 nodes and 7634 edges,and then two important modules were screened out(lots of genes that make up these two modules were involved in the malignant biological behavior of ovarian cancer);there were 204 hub genes,and which were interactive among genes.Using OncoLnc to evaluate the correlation between hub genes and the prognosis of OC patients,it was found that high expression levels of IQGAP1,CDCA3,BTRC,UBR4,FBXL3,FGF2,SYT1,TRIM4,REPS1,AGFG1,PCNT,POLK,and DOCK4 were significantly associated with poorer OS in OC patients(p<0.05);low expression levels of MCM3,POLR2D,ANAPC11,SPC24,KLHL25,LSM4,PUF60,EXO1,and EIF3M were significantly associated with poorer OS in OC patients(p<0.05).Conclusion:Exosomes can participate in biological behaviors such as malignant proliferation(cell cycle),disease progression(migration and invasion),and clinical prognosis of ovarian cancer through related protein-encoding genes of its contents,which provides clues for our related experimental research.Part Ⅳ Preparation and Identification of Serum ExosomesObjective:To isolate and purify human serum exosomes,evaluate the quality of the exosomes obtained from multiple perspectives,and lay the foundation for the next stage of experiments.Methods:Human fasting venous blood serum was collected,and serum exosomes were isolated and purified using commercial kits.The morphology of exosomal particles was observed by a transmission electron microscope,and the particle size distribution of the exosomes was analyzed by Nanosight technology.The expression of specific proteins CD63,TSG101,and CD81 of exosomes was analyzed by Western blot.Results:The shape of human serum exosomes observed by transmission electron microscopy was round or oval;the peak diameter of the particles analyzed by Nanosight technology was 127.6 nm,and the particles between 30 and 150 nm accounted for 58.9%;western blot confirmed that the obtained(exosomal)particles could detect the expression of the marker proteins CD63,TSG101,and CD81.Conclusion:Qualified exosomes can be extracted by the commercial kits,and the obtained exosomes can be used for LncRNA research in exosomes.Part Ⅴ Screening of Serum Exosomal LncRNA and Its Prelimanary Diagnostic Value for Epithelial Ovarian CancerObjective:To select LncRANs with significantly different expression in epithelial ovarian cancer by serum exosomes sequencing for QRT-PCR verification of clinical samples,analyze the diagnostic efficacy of single target LncRNA,and construct multi-factor combined diagnostic models by Logistic binary regression model.Methods:1)Four patients with epithelial ovarian cancer and three healthy women of approximately matching age were collected,and fasting vein serum was collected and exosomes were isolated.2)Using next-generation sequencing technology to analyze the differentially expressed LncRNAs in the serum exosomes of epithelial ovarian cancer patients and healthy women,and screened out the significantly differently expressed LncRNAs.3)The screened LncRNA with different expression levels was verified by QRT-PCR in the original clinical samples.4)To select LncRNAs which were confirmed by original serum exosome samples to test the expression levels with QRT-PCR in larger clinical samples.5)To draw the ROC curve of the target LncRNA and evaluate its diagnostic indicators such as sensitivity and specificity.6)To use Logistic binary regression model to construct multi-factor joint diagnostic models.Results:The expression levels of LncRNAs in serum exosomes of patients with epithelial ovarian cancer and healthy women were compared,and 425 LncRNAs with different expression levels(23 up-regulated and 402 down-regulated)were screened out.Six types of LncRNAs with significantly abnormal expression levels(FER1L6-AS2,LINC00470,LINC01811,CXXC4-AS1,LINC02343,LINC02428)were randomly selected for original sample verification,and the results were consistent with the sequencing results.Subsequently,these six LncRNAs(FER1L6-AS2,LINC00470,LINC01811,CXXC4-AS1,LINC02343 and LINC02428)were used for larger samples QRT-PCR verification.The results showed that the AUCs of these six LncRNAs ranged from 0.722-0.805,which had moderate diagnostic efficiency.To use Logistic binary regression model to establish multi-indicator joint diagnostic models and establish different joint factor ROC curves.The results showed that the AUCs of the joint factor prediction model 1(composed of FER1L6-AS2 and LINC01811),the joint factor prediction model 2(composed of CXXC4-AS1,LINC02343 and LINC02428),and the joint factor prediction model 3(composed of FER1L6-AS2,CXXC4-AS1,LINC02343 and LINC02428)were 0.865,0.934,and 0.962,respectively(The diagnostic efficacy of the three combined factor prediction models was higher than that of a single LncRNA,p<0.05).Conclusion:Next-generation sequencing technology is an effective method for screening out the different expression levels of LncRNAs which were from human serum exosomes.The single indicator LncRNA FER1L6-AS2,LINC00470,LINC01811,CXXC4-AS1,LINC02343,and LINC02428 have moderate diagnostic efficacy for epithelial ovarian cancer.The diagnostic efficacy of multiple LncRNA combined prediction models is significantly higher than that of a single LncRNA indicator. |
PDF Full Text Request