Construction Of Roadmap Of Non-Coding RNAs Using The Mode Of Big Data As Prior Knowledge And The Molecular Mechanism Of Nucleolar Small RNA12 In Bladder Cancer

The concept of big data as prior knowledge is changing the behavior patterns of various industries.The artificial intelligence medical science supported by clinical big data has shown super high diagnostic efficacy in clinical imaging,clinical pathology and diagnosis of other diseases.It is worth mentioning that bioscientific big data shows unique advantages in the personalized analysis of complex diseases,which helps researchers to fully understand the disease profile,find personalized information,and provide more detailed guidance for basic experimental research and clinical treatment strategies,which further consolidates the inability of the omics technology in the field of precision medicine in recent years.Unfortunately,the big data prior mode is slower in the field of experimental biology.The international large-scale bio-study big data program has accumulated a large amount of data,and the reduction of the cost of omics testing has led to an exponential growth of biological data.The fully use of omics big data can not only save research costs for experimental biology,but also provide researchers with more multi-dimensional and comprehensive information,to shorten the research cycle,and to promote basic experiments to find problems and solve problems more efficiently.Bladder cancer is a type of malignant tumor that poses a serious threat to human health.However,the current research on the molecular mechanism of bladder cancer remains unclear,leading to the suboptimal clinical prevention,monitoring and treatment strategies.The big data of omics makes people realize the importance of non-coding RNA in controlling gene expression,transcription,post-transcriptional modification and signal transduction.A large number of noncoding RNAs have been identified as carcinogenic motivators and tumor suppressors in major tumor types.Unfortunately,there is currently a lack of multilevel personalized high-precision analysis of bladder cancer transcriptomics big data.In this study,we used a model of big data and experiments posterior to study two small molecular events in bladder cancer.In the section of big data prior,we constructed a relatively maturely studied long non-coding RNA(lncRNA)and a relatively immaturely studied nucleolar RNA(snoRNA)expression road map.At the same time,we also constructed a panoramic view of the bladder cancer mRNA and the related transcription factors of the Cajal body small nucleolar RNA 12(SCARNA12),as well as the road map of SCARNA12 related alternative splicing events.In the above process,we provided several computational analysis frameworks.In the experimental posterior part,we verified the computational biology finding and confirmed the biological significance of SCARNA12 using multiple experimental methods at the multi-cell line level and clinical tumor tissue level,including RT-qPCR,CRISPR-Cas9 knockdown assay,high-throughput mRNA-sequencing,in situ hybridization,nude mouse tumorigenicity assay and Chromatin Isolation by RNA Purification(ChIRP)assays.In the first part of the project,we obtained the latest annotated mRNA-sequencing expression data from bladder cancer in the TCGA database including 13,918 lncRNAs.Then we constructed a differential expression profile of lncRNA in bladder cancer,and systematically integrated prognostic information to update and construct a new prognostic prediction model based on lncRNA levels for bladder cancer.This new prognostic model had a significant improvement in predicting patients' survival,tumor size,lymph node infiltration and pathological staging compared with a single index.It is worth mentioning that this predictive model could be an independent factor in the prognosis prediction of bladder cancer.Finally,we partially verified the robustness of the calculated lncRNA at the cell line and clinical tissue sample levels.Through this part of the work,we provided a more comprehensive interpretation of lncRNAs in bladder cancer,offering detailed information for the future lncRNA experimental study in bladder cancer.In the second part of the work,we comprehensively and accurately mapped snoRNA expression profile of bladder cancer including 1,232 snoRNAs,which is the blank area by mining the small RNA-seq level data of bladder cancer tissues.The results showed that there were significant differences in the expression of small molecules in bladder cancer that have not yet received enough attention from researchers between bladder cancer tissues and non-cancerous bladder controls,with the number being as high as 230.Some snoRNAs had favorable value for predicting bladder cancer,suggesting that some snoRNAs may have an important contribution to the biological behavior of bladder cancer.Based on the results of the part of big data prior,we performed RT-qPCR for the verification of the reliability of some snoRNAs at the cellular and tissue levels,which provided a basis for the selection of snoRNA for bladder cancer research.According to the part of big data prior,we selected one SCARNA,SCARNA12,which is related to the previous study of Cajal body in our group,and found that SCARNA12 had marked over-expression in bladder cancer and high predictive ability to distinguish bladder cancer from non-cancerous tissues.We then performed real time RT-qPCR to verify the expression level of SCARNA12 in clinical bladder cancer tissues and multi-cell lines.In situ hybridization was further carried out to determine the distribution characteristics of SCARNA12 at the tissue level.Based on the theory of “the expression-related genes are most likely to be mutually modulated and influenced”,we,for the first time,constructed a functional analysis framework for SCARNA represented by SCARNA12.The gene enrichment analysis algorithm evaluated the main potential mode of SCARNA12 in bladder cancer.In the third part of the thesis,we used the CRISPR-Cas9 gene editing technology to construct SCARNA12 knockdown model with bladder cancer T24 cells for functional exploration.After SCARNA12 knockdown,the growth of bladder cancer cells was significantly inhibited.The cancer cells were arrested in the G0/G1 phase and could not fully enter the S phase of the cell cycle,which led to a slower cell proliferation.We found that SCARNA12 may also suppress the growth of bladder cancer cells by inducing apoptosis.After SCARNA12 knockdown,late apoptosis was significantly increased.In addition,we also found that the invasive ability of T24 cells after knockdown was weakened.The tumor formation of nude mice had a tendency to be shrinking.At present,there is no research program as references on SCARNA in bladder cancer in the world.There is no similar index in bladder cancer to speculate the biological function of SCARNA.We have verified the stability of the big data priori for the first time with the full-scale display of SCARNA12 in bladder cancer in vitro and in vivo.In the fourth part of the work,we tried to interpret the molecular mechanism of SCARNA12 in bladder cancer.For the first time,we used RNA-seq technology to detect the changes in the transcriptome before and after SCARNA12 knockdown at the genome-wide transcription level.SCARNA12 knockdown significantly affected the expression of up to 1155 genes,and the pathways associated with extracellular matrix were the most enriched pathways,consistent with the results of the big data analysis in the second part.This is the first time that high-throughput sequencing data has been used to elucidate the way how SCARNA12 functions at the mRNA level.Subsequently,we successfully used the RNA Purification Chromatin Separation Technology(ChIRP)technology to obtain the SCARNA12 interaction-regulated protein for the first time.It was found that H2 A histone family member Z(H2AFZ)and MYCN proto-oncogene,bHLH transcription factor(MYCN)could bind to the physical space position of SCARNA12.In particular,we first identified that both H2 AFZ and MYCN were likely to be upstream regulators of differential genes after SCARNA12 knockdown with the algorism of the Binding Analysis for Regulation of Transcription(BART).We also for the first time integrated all of the publicly available ChIP-seq big data of this histone-associated gene H2 AFZ and transcription factor MYCN,and found that deltex E3 ubiquitin ligase 3(DTX3)was the potential target gene of H2 AFZ,while junctional adhesion molecule 2(JAM2)and nuclear factor I X(NFIX)were the potential target genes of MYCN.This part of study provides a reference standard for in-depth analysis of transcriptional regulation at the gene level by snoRNA as represented by SCARNA12.In the fifth part of the work,we first demonstrated the overall splicing event spectrum of bladder cancer cells after SCARNA12 knockdown and the splicing event road map of bladder cancer based on RNA-seq data.The alternative splicing event analysis is a more accurate analysis than the mRNA level,with up to 156 significant alternative splicing events occurring after the knocking down of SCARNA12.Interestingly,six new splicing events have never been reported after SCARNA12 knockdown in T24 bladder cancer cells.Combining clinical bladder cancer tissue level data and integrating bladder cancer prognosis information,we found that the occurrence of alternative splicing event in bladder cancer was not an accidental event,as 39,508 alternative splicing events were noted in 9,731 genes.Among all the dysregulated splicing events,the gene filamin A(FLNA)had an alternative splicing event of exon skipping in both tissue and cell level,and this splicing event also had a certain value of prognostic evaluation for bladder cancer.This part of the work provides strong evidence for the development of subsequent SCARNA12 alternative splicing research strategies in bladder cancer.