Construction Of A Prognostic Model Based On Transcriptomic And Proteomic And The Role Of IGF2BP3 In Renal Cell Carcinoma

Backgrounds and purposes: Renal Cell Carcinoma(RCC),also called kidney cancer,is one of the most common malignant tumors in the world.It is estimated to have 73,750 new diagnosed cases in the United States in 2020,accounting for about 4% of the total cancer cases.Radical surgery is the main treatment for early RCC patients.However,due to the lack of specific symptoms,patients usually miss the opportunity for early diagnosis and treatments.About 30% of RCC patients have metastatic at the time of diagnosis.According to the statistics,the 5-year survival rate of RCC patients is 93%,while the 5-year survival rate of metastatic RCC is only 12%.Besides,as RCC is a highly heterogeneous disease,traditional clinicopathplogic characteristics,such as age,TNM stage,are not accurate in predicting the prognosis of patients.Therefore,it is meaningful to explore some reliable biomarkers and potential therapeutic targets for RCC patients,which can help early diagnosis,guide individual treatment,and prolong the survival time of RCC patients.In recent years,with the popularization of proteomics technology and transcriptomics technology,biological data increase quickly.In order to fully tap the potential value of these high-throughput data,bioinformatics technology has developed rapidly.Bioinformatics has been widely used in the screening of differential expressed genes,identification of biomarkers,and establishment of tumor prognostic models in the field of oncology.This study aimed to identify some biomarkers and develop a new prognostic evaluation method through transcriptomic and proteomic methods,which might guide the risk assessments and individualized treatments for RCC patients.This study consisted of three parts.Firstly,we used the quantitative proteomic method to identify the differential expressed proteins(DEPs)and related molecular pathways changes between RCC tumor tissues and normal kidney tissues.Secondly,by combining transcriptomic and proteomic methods,we screened out some core genes which were closely related to the occurrence and prognosis of RCC and established a prognostic model for RCC patients.Thirdly,we identified IGF2BP3 as a key gene of RCC and investigated the function and potential molecular mechanism of this gene in the occurrence of RCC.1 Quantitative proteomics study of renal cell carcinomaMethods: Four pairs of samples of RCC patients were selected from our center in this study.Firstly,protein extraction and trypsin digestion were perfomed.Tandem Mass Tag(TMT)was used to label the peptides,and Liquid Chromatograph Mass Spectrometer/Mass Spectrometer(LC-MS/MS)was used to identify and analyze the petides.Then,the petides were reannotated in Swiss Prot Human database.DEPs were extracted with the criterion of |log2FC|≥0.8 and P<0.05.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analyses were performed to identify the underlying mechanism of DEPs.Results: A total of 300 DEPs were identified,including 165 up-regulated DEPs and 135 down-regulated DEPs.According to the fold changes of these DEPs,we found FABP7,IGF2BP3,NNMT,NEFL and THBS2 were the top five up-regulated proteins,and MUC13,CKMT1 A,UMOD,CALML3 and S100A2 were the top five down-regulated proteins.Then the results of GO enrichment analysis suggested that up-regulated DEPs were related to the biological process of cell adhesion and activation of immune response,and down-regulated DEPs were related to the oxidation-reduction process,small molecule catabolic process and monocarboxylic acid metabolic process.KEGG enrichment analysis suggested that DEPs were involved in multiple metabolic pathways,such as oxidative phosphorylation,amino acid metabolism,and carbon metabolism.The oxidative phosphorylation pathway was the most significant enriched pathway.Conclusion: In this study,through the analysis of the proteomic,we found the cell adhesion and activation of immune response enhanced and the oxidation-reduction process,small molecule metabolism and ion transport weakened during the occurrence of RCC.Besides,dysregulation of metabolic related pathways was an important feature of RCC.2 Construction of a prognostic model in renal cell carcinoma based on transcriptomic and proteomicMethods: The genome sequencing data of RCC were downloaded from the The Cancer Genome Atlas(TCGA)database.Differentially expressed genes were indentified with the criterion of |log2FC|≥0.8 and P<0.05.Genes with significant differences in RNA transcription level and protein translation level were extrated for further analysis.Univariate Cox regression analysis was performed to identify the genes associated with RCC patients’ overall survival(OS).Least absolute shrinkage and selection operator(Lasso)regression and multivariate Cox regression were used to construct the prognostic model in RCC.Receiver operating characteristic(ROC)analysis and Kaplan-Meier survival analysis were operated to evaluate the prediction accuracy and ability of this prognosis model.Multivariate Cox regression analysis was used to assess whether the prognostic signature was an independent prognostic factor for RCC patients.And a nomogram was constructed based on the prognostic model and clinical characteristics.ROC analysis was performed to compare the prognostic value of the nomogram,prognostic model,and clinical characteristics.Finally,the Connectivity Map(CMap)database was performed to predict small molecule drugs for high-risk group patients.Results: In this study,212 genes were found differentially expressed both in the RNA transcription level and protein translation level.91 genes were extrated by univariate Cox regression analysis as they were significantly associated with RCC patients’ OS.Then Lasso regression,multivariate Cox regression were performed and a six-gene prognostic model was constructed,including SH3BGRL3,IGF2BP3,NRBP2,LAD1,HOGA1 and SMIM24.The Kaplan-Meier survival analysis demonstrated that the prognostic model could distinguish RCC patients with different prognosis(P<0.0001).ROC analysis suggested that the prognostic model had a high predictive accuracy for the prognosis of RCC patients and the area under the curve(AUC)of the prognostic model were 0.790 at 1 year,0.739 at 3 years,and 0.749 at 5 years,respectively.Multivariate Cox regression analysis suggested that prognostic model was an independent prognosis factor for the prognosis evaluation of RCC patients.The calibration curve demostrated that the nomogram based on the prognostic model and clinical characteristics had a good prediction ability.The ROC analysis suggested that the nomogram exhibited higher predictive accuracy than other common clinical characteristics.Besides,exisulind was predicted to be a potential small molecule drug for high-risk group patients.Conclusion: The prognostic model and nomogram based on transcriptomic and proteomic had a good ability to predict the prognosis of RCC patients,which could guide the risk assessment and prognosis evaluation of RCC patients.Besides,exisulind might be a potential small molecule drug for high-risk group patients.3 The function and molecular mechanism of IGF2BP3 in renal cell carcinomaMethods: Bioinformatics methods were used to analyze the expression characteristic of IGF2BP3 in RCC and Quantitative Reverse Transcription PCR(q RT-PCR)was performed to validate the IGF2BP3 expression level in RCC tissues and cell lines.CCK-8 assay,cell clone formation assay and flow cytometry assay were used to explore the role of IGF2BP3 in proliferation of RCC cell lines.Three-dimensional cell culture assay was ued to explore the role of IGF2BP3 in vasculogenic mimicry(VM)formation.Western Blot was used to explore the potential mechanism of IGF2BP3 in RCC cell proliferation and VM formation.Results: IGF2BP3 was highly expressed in RCC and significantly related to the RCC patients’ OS and disease-free survival(DFS).CCK-8 assay,cell clone formation assay and three-dimensional cell culture assay demostrated that knowdown of IGF2BP3 could reduce the proliferation activity,clone formation ability and VM formation,while overexpression of IGF2BP3 could enhance the proliferation activity,clone formation ability and VM formation of RCC cell lines.Flow cytometry assay showed knowdown of IGF2BP3 could induce G0/G1 arrest,and overexpression of IGF2BP3 could promote G0/G1 progression.Western Blot assay showed knockdown of IGF2BP3 would induce the downregulation of cell cycle related proteins,including CDK4 and Cyclin D1,and VM related proteins,including MMP9 and Vimentin.Further studies demostrated that IGF2BP3 could participate in the activation of PI3K/AKT signaling pathway,thereby regulate the progression of RCC.Conclusion: IGF2BP3 was highly expressed in RCC and was related to the poor prognosis of RCC patients.IGF2BP3 could affect the proliferation ability and VM formation in RCC cells.Furthermore,IGF2BP3 could participate in the activation of the PI3K/AKT signaling pathway,thereby regulating the progression of RCC.