Clinical Prediction Model Of Triple Negative Breast Cancer Based On NCAPG Gene

OBJECTIVE: To find the potential target gene NCAPG of triple-negative breast cancer by using bioinformatics methods,and to perform the public database verification of the target genes,predict their possible functions,and finally combine their biological functions and clinical information.A Nomogram model was established to assess the risk of 1-year recurrence after triple-negative breast cancer surgery.METHODS: We used the R script to download 843 samples of breast cancer patients with complete RNA_SEQ and clinical data from the TCGA database,and then normalized the transcriptome raw data of 843 patients.The format was changed from the original COUNT to TPM.Subsequently,the patients were divided into Lumina A,Lumina B,Her2 and TNBC according to the molecular biological characteristics,and the gene with the greatest difference in TPM data expression of RNA_SEQ(the first 15% of the maximum variance)was extracted for WGCNA.Excavate the core modules of potential biological targets.Then we performed a protein interaction network analysis on all the genes contained in the core modules mined by WGCNA.Using the DEGREE algorithm to find the multiple core genes that occur in the driver module,that is,we are looking for a new BIOMARK.Then look at the new BIOMARK in cancer and The expression in the paracancerous tissues confirmed the stable expression of the genes.We searched again and looked for BIOMARK that has not been studied for follow-up studies.Finally,we performed GO and KEGG enrichment analysis on all genes in the core module to predict the biological functions that the genes we care about may play.Then,in order to realize the transformation of basic research into clinical application,we combined this gene with clinical information of patients to construct a Nomogram model to evaluate the risk of recurrence after triple-year surgery for triple-negative breast cancer.Finally,we evaluate the stability of the model through the C-INDEX and ROC curves.RESULTS: Two core modules were screened using WGCNA(BROWN and GREY60).The two modules contained a total of 30 module genes.After PPI(protein interaction network),seven core genes were identified.They were KIF2 C.PLK1,KIF20 A,NCAPG,CENPA,EXO1,BUB1.We performed a differential analysis of these seven genes in cancer and normal tissues.The results showed that all genes were highly expressed in cancer tissues,and there were significant statistical differences between the groups.We searched for the NCAPG gene as a follow-up study.In order to clarify the potential biological functions of NCAPG,we performed GO and KEGG enrichment analysis on 30 genes of the original core module.The results showed that NCAPG was enriched in the function of lectin complex,which means that NCAPG may be The function of cell migration and adhesion is played in the cells of triple negative breast cancer tumors.To this end,we performed a Logrank test to assess the recurrence-free survival(RFS)of patients with high and low expression.The results showed that compared with patients in the low-expression group,the tumor recurrence time was significantly shortened and the tumor recurrence rate was significantly increased.P = 0.001).The NCAPG gene was used as a clinical variable and clinical patient characteristic variables(age,histological type)to construct a Nomogram model to evaluate the risk of 1-year recurrence after triple-negative breast cancer surgery.The stability of this model was evaluated after internal verification.The corrected C-INDEX was 0.807,and the area evaluation result under the ROC curve indicated that AUC=0.666.Conclusion: The scale-free network analysis of WGCNA suggests that NCAPG can be used as a potential biological target for triple-negative breast cancer.The 1-year recurrence risk Nomogram model for triple-negative breast cancer based on this potential target gene can provide a reference for clinicians in assessing postoperative recurrence of triple-negative breast cancer.