| Background and Objective:Malignant pleural mesothelioma,an aggressive tumor on the pleura,has a very poor prognosis after diagnosis and a median survival period of less than one year.The characteristics used to predict the survival of patients with tumor can be divided into clinical and molecular characteristics.Clinical features include clinical staging,pathological type,initial diagnosis age,etc.Molecular features include gene mutation and expression.Studies found that clinical features have certain limitations in predicting the survival of patients with tumor,so it is important to screen the prognostic molecular features associated with malignant pleural mesothelioma and study its function.Therefore,this study decided to use molecular characteristics as a breakthrough in the prediction of the survival of patients with malignant pleural mesothelioma,and hoped to find a molecular mechanism that could predict the survival of patients with malignant pleural mesothelioma.This study used a series of bioinformatics analysis methods to screen and mine genes and micro RNAs associated with the prognosis of malignant pleural mesothelioma.In addition,prognostic models based on miRNAs and genes were constructed as biological markers of MPM prognosis.At the same time,the correlation between the corresponding target genes of miRNAs and the prognostic model genes was further predicted,and then exploring possible biological mechanisms of these genes in the development of malignant pleural mesothelioma,the results will provide new ideas for the basic study of malignant pleural mesothelioma.Methods:Malignant pleural mesothelioma gene expression profiles and clinical data,as well as malignant pleural mesothelioma miRNA expression profiles and corresponding clinical data were downloaded from the TCGA database,the expression data were preprocessed and matched with clinical information respectively,and randomly divided into training set and testing set,through Cox regression analysis in training set data,prognostic correlation factors were filtered and prognostic predictive models were built,and then prognostic models were validated in testing set and complete set.For the miRNAs of prognostic model,the expression level of which in mesothelioma cells and normal cells was detected by q RT-PCR,the biological functions of prognostic model miRNAs were studied by target gene prediction,target gene network construction,target gene function enrichment analysis,key gene recognition of target gene and exploring the relationship between target gene and immune cell infiltration.Then we used the bioinformatics database to verify the differential expression of prognostic model genes,analyzed their expression patterns in malignant pleural mesothelioma and performed GSEA analysis,finally,correlation analysis was used to further explore the potential link between prognostic model genes and key genes of target genes.Results:1.A miRNA prognostic model consisting of hsa-miR-181a-2-3p,hsa-miR-491-5p,hsa-miR-503-5p and hsa-miR-3934-5p and a gene prognostic model composed of UHRF1,KIF4 A and NEK2 were developed.Validation results of the two models showed that compared to low-risk groups,the overall survival of patients in the highrisk group was worse(P<0.05).2.Levels of miRNAs expression in mesothelioma cells and normal cells were detected by q RT-PCR.The expression of hsa-miR-181a-2-3p,hsa-miR-503-5p and hsamiR-3934-5p in MPM tumor cells was higher than that of normal cell strains.The expression of hsa-miR-491-5p in MPM tumor cells was lower than that of normal cell strains.The differences are statistical(P<0.05),indicating that the prognosis model has certain predictive value.3.Target genes prediction of four miRNAs in prognostic model was performed through Targetscan,miRDB and miRTar Base databases.A total of 172 downstream target genes were predicted,and GO and KEGG enrichment analysis showed that these target genes were mainly involved in cell cycle transition,cell-cell junction,cadherin binding,and multiple tumor-related signaling pathways and functions.Multiple interaction relationships between miRNAs and target genes were then discovered by constructing miRNA-target gene networks.Two core target genes,KIF23 and VPS37 B,were obtained after the MPM differential genes screened from the GEO database were intersected with the target genes and removed the target genes with the same expression trend as the miRNAs,both of which were significantly correlated with the prognosis of patients with malignant pleural mesothelioma(P<0.05),and higher expression indicated worse OS.4.Immune infiltration analysis found that KIF23 and VPS37 B were significantly correlated with multiple immune cell infiltrations(P<0.05),and the infiltration level of multiple immune cells had a significant effect on the survival of patients with malignant pleural mesothelioma(P<0.05).In addition,the activated NK cells in the high expression group of VPS37 B were significantly downregulated(P<0.05).5.The differential analysis of UHRF1,KIF4 A and NEK2 in the gene prognostic model in the TCGA and GTEx databases showed that all three genes were significantly high in MPM tumor tissues compared to normal lung tissues(P<0.0001).6.Expression pattern analysis of genes in prognostic model in MPM patients showed that UHRF1 was significantly different in different stages,tumor subtypes and lymph node metastasis of MPM(P<0.05),KIF4 A was significantly different in different ages and tumor subtypes in patients with MPM(P<0.05),NEK2 was significantly different in different ages of patients with MPM(P<0.05).7.GSEA enrichment analysis found that prognostic model genes UHRF1,KIF4 A and NEK2 high expression group were significantly enriched in DNA replication,m RNA surveillance pathway,nucleocytoplasmic transport,ribosome biogenesis in eukaryotes and spliceosome.8.The correlation analysis of expression between KIF23 and VPS37 B and UHRF1,KIF4 A and NEK2 in the prognostic model showed that there was a significant positive correlation between KIF23 and VPS37 B and the expression of genes in the prognostic model,respectively(Rho>0.3,P<0.001).Conclusions:The gene and miRNA prognostic risk models of patients with MPM based on the TCGA database can effectively distinguish the high-risk and low-risk groups of MPM patients and have good sensitivity and specificity to assess the prognosis of patients with MPM and can be used as biomarkers to predict prognosis of MPM patients.Target genes of miRNA in prognostic model and prognostic model genes UHRF1,KIF4 A and NEK2 are involved in multiple tumor-associated signaling pathways and functions in MPM.The prognostic model genes UHRF1,KIF4 A and NEK2 are significantly different in MPM staging,tumor subtype,age and metastasis and are significantly related to key target genes expression.These genes may affect the prognosis of patients with MPM by influencing multiple immune cell infiltrations and participating in the regulation of the tumor immune microenvironment.In addition,VPS37 B may affect the activation of NK cells. |
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