| Ovarian cancer is the most lethal gynecological cancer and a common cause of cancer-related deaths in women worldwide.In 2018,the United States is expected to diagnose approximately 22240 new cases of ovarian cancer and 14070 deaths.Most patients with ovarian cancer have been diagnosed with advanced disease(stage III-IV;FIGO).The stage of cancer is closely related to the prognosis of cancer.For advanced ovarian cancer,it is implied that the prognosis is poor.It is crucial to explore the gene expression characteristics associated with ovarian cancer.Despite advances in surgery and chemotherapy,the overall survival rate of patients with ovarian cancer remains unsatisfactory,with a five-year survival rate of only 30%.Therefore,it has been found that the biological mechanisms involved in the progression of ovarian cancer,identifying effective biomarkers of ovarian cancer to assess and predict the clinical outcomes of ovarian cancer patients are also crucial.High-throughput sequencing technology is being used more and more widely in recent years,and it has been used as a very important tool in life sciences,such as early diagnosis of cancer,cancer stage and prognosis prediction.With the development of RNA-Seq sequencing technology and microarray technology,a large amount of gene expression data is generated,which is both an opportunity and a challenge for bioinformatics researchers.Based on gene transcriptome data,computational methods can be used to accurately identify genes associated with cancer stages and assess prognosis.The study in this paper is based on ovarian cancer gene expression data,using bioinformatics analysis methods for the identification and analysis of ovarian cancer markers.The specific work is as follows:(1)Identification and analysis of prognosis-related genes in stage-related differential genes interactive networks of ovarian cancerThe study in this chapter is based on transcriptome data from ovarian cancer provided by TCGA.It is designed to identify,visualize,and combine biological functions with ovarian cancer high-throughput sequencing data to obtain biomarkers related to ovarian cancer.Firstly,the ovarian cancer RNA-Seq gene expression data in the TCGA database was downloaded,and the Edger R analysis package in R software was used to calculate thedifferential expressed genes related to ovarian cancer stage.Subsequently,the two groups of differential expressed genes were screened before the ranking.Twenty core genes and previous differential genes were included in the follow-up study as a candidate gene.Next,the STRING data was used to expand the interaction between genes and genes,and a network of differential gene expression interactions related to the cancer stage was established.Enrichment analysis and gene-protein interaction analysis was used to analyze the biological processes,molecular functions,cellular components and KEGG pathways of differential genes in different stages and constructed six meaningful differential genes interaction network modules.Finally,Kaplan Meier online data(http://kmplot.com/analysis/)was used for the survival analysis of the core differential expressed genes in the six modules(Overall Survival).COL3A1,COL1A1,COL1A2,KRAS,and NRAS were identified as prognostic genes for ovarian cancer and used as markers for judging cancer progression and prognosis in patients with ovarian cancer,this conclusion was further verified in GEO data.It is hoped to provide a basis for subsequent biological experiments,providing meaningful markers for the diagnosis,treatment and prognosis evaluation of ovarian cancer,and guide the precise diagnosis and treatment of ovarian cancer.(2)Neural activities are extremely unfavorable for the prognosis of ovarian cancer patientsIn the third chapter,we identified and screened the differentially expressed genes and genes related to poor prognosis with the progression of ovarian cancer.In order to further identify the gene characteristics significantly related to cancer progression and poor prognosis in the ovarian cancer transcriptome,we conducted the research in the forth chapter.This chapter is based on the RNA-seq gene expression data related to the prognosis of ovarian cancer in the OncoLnc database.We analyzed the main character between the unfavorable and favorable prognostic-related OC mRNAs.Then the DAVID enrichment analysis was conducted in the top 10%,top 250 unfavorable and favorable prognostic mRNAs as well as top 100 unfavorable and favorable differential expressed prognostic OC mRNAs.Unfavorable mRNAs were enriched in many neural activities associated with biological process(BP)and KEGG pathway.Whereas none neural activity enriched BP in favorable mRNAs.Additionally,Axon guidance pathway was significantly enriched in the top 10%,top 250 unfavorable mRNAs,and top 100 unfavorable differential expressed OC mRNAs.NTN1,UNC5 B,EFNB2,EFNA5 were enriched in this pathway.Prognostic genes were explored and the prediction of urinary markers and the immunohistochemical test ofclinical specimens were performed.Moreover,we did the correlation analysis to all the unfavorable neural genes and then constructed PPI network modules.In general,these findings imply that neural activities could promote OC progression and be directly related to poor prognosis,and then the correlation analysis to calculate the closely related genes of the neural genes that promote the progression of ovarian cancer.Three meaningful interaction modules were identified in these closely related genes which are associated with the mitotic process of the cell cycle,collagen catabolism,and chemokine-mediated signaling pathways.Finally,the Bayesian analysis is used to infer the mechanism of interaction between neural genes and their closely related genes.The results of this chapter show that neural activity plays a crucial role in the progression of ovarian cancer and leads to poor prognosis,it also reveals that neurogenesis and axon guidance biological processes may be more important than angiogenesis in the progression of ovarian cancer.Axon guidance KEGG signaling pathway as the most important neural activity signaling pathway provides a particularly complex and multifaceted role for ovarian cancer progression.In addition,NTN1,UNC5 B,EFNB2,EFNA5 may represent novel prognostic markers for ovarian cancer patients and potential therapeutic targets;these neural genes are closely related to genes during the mitotic cell cycle,collagen catabolism,and chemokine-mediated signaling pathways,promoting the development of ovarian cancer.Immune responses regulate neural activity,neural activity such as neurogenesis regulates the function of extracellular matrix and angiogenesis in the tumor microenvironment,and neural activity is significantly involved in the regulation of T cell differentiation.Based on the above analysis results,it provides a reference and basis for the diagnosis and treatment of ovarian cancer and prognosis evaluation from the perspective of bioinformatic gene data analysis. |
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