Exploration Of Hub Genes In Multiple Clinical Prognostic Indicators Of Triple Negative Breast Cancer By Bioinformatics Methods

Objective: In recent years,with the rapid development of precision medicine,the overall level of breast cancer in diagnosis,treatment,prognosis and many other aspects has improved greatly,but the problems related to the prognosis of triple negative breast cancer still plague medical practitioners.Although researchers have done a lot of research on it,the prognosis of triple negative breast cancer has not improved significantly.According to relevant reports from authoritative journals such as the lancet,there were 2.26 million new cases of breast cancer worldwide in 2020.Breast cancer has been identified as the most frequently occurring malignancy in women,among which,triple negative breast cancer(TNBC),which accounts for approximately 16% of all breast cancers,is a subtype of breast cancer that shows negative genetic indicators for estrogen receptor,progesterone receptor and erb B2,and has a poor prognostic effect than other subtypes of breast cancer,with very limited therapeutic options.This study aims at innovative applications of traditional bioinformatics methods to mine biomarkers of triple negative breast cancer among multiple breast cancer prognostic indicators from a multi omics perspective for more precise adjuvant clinical treatment and prognosis assessment.Methods: The genetic data applied in this study were obtained mainly from the geo gene database created and maintained by the National Center for Biotechnology Information(NCBI),and the TCGA Gene Database Co created by the National Cancer Institute(NCI)and the National Human Genome Research Institute(NHGRI).The biomaterials applied in this study were the lesion tissues of 60 triple negative breast cancer patients collected at the First Affiliated Hospital of China Medical University.We performed gene data mining from two datasets(GSE25055,GSE25065)of the geo database and derived hub genes that were significantly associated with triple negative breast cancer prognostic indicators in multiple breast cancer clinical prognostic indicators using relevant bioinformatics techniques such as PPI and WGCNA.We then validated the hub genes using several gene databases and immunohistochemistry experiments including ONCOMINE,TCGA and K-M ploter.Results: A total of 178 sample cases of triple negative breast cancer patients were extracted from the geo dataset.Then,univariate survival analysis was performed on all the genes extracted,resulting in 976 significant genes with p-values less than0.05.Through innovative application of WGCNA,ultimately,we obtained five hub genes that were highly associated with triple negative breast cancer from multiple clinical prognostic indicators.Through ONCOMINE,TCGA and other gene databases and immunohistochemical experiments,the 5 hub genes(NCAPG,CCNB1,ESPL1,TRIP13,NCAPH)showed significant differences in three negative breast cancer.Conclusions: In summary,through innovative application of traditional bioinformatics methods,we mined five hub genes(NCAPG,CCNB1,ESPL1,TRIP13,NCAPH)associated with triple negative breast cancer prognosis,which were verified by multiple databases and biological experiments,showed indeed very significant expression differences in triple negative breast cancer,and they were associated with multiple clinical prognosis Indicators are highly correlated.So,we think that these 5 hub genes are highly likely to be novel targets for triple negative breast cancer.