Prediction Of Prognosis And Immunotherapy Prognosis Of Breast Cancer By Immune Related Gene Model

Purpose: Breast cancer is a malignant tumor which is the vast majority of female groups.In 2020,the International Cancer Research Institute（IARC）released the cancer incidence data,in which BRCA exceeded lung cancer and became the most popular malignant tumor in the world.The data published by IARC accounted for 11.7% of the new cancers cases.Anti programmed death protein receptor 1/ programmed death protein ligand 1（anti PD1/PDL1）therapy,as a hot immunotherapy target in recent years,has not been widely used in breast cancer for various reasons.In this study,we hope to predict the prognosis of breast cancer patients and the effect of immunotherapy through bioinformatics methods.Method: 1)Download the breast cancer（BRCA）dataset from the TCGA database.2)R（4.1.2）software was used to analyze the difference of gene expression between breast malignant tumor tissue and normall tissue.3)Using R software,the differential genes and immport database immune genes were intersected to obtain the differential immune genes.4)The expression and survival data were combined by R software to obtain immune genes related to prognosis.5)Go and KEGG analysis were used.6)The samples are divided into train group and test group by R software.The model is constructed by using the data of train group,and the effectiveness of the model is verified by using the data of test group.7)Download the NCBI database immune scoring file and score the patients,so as to predict the effect of immunotherapy.The R language version 4.1.2 software is adopted.All data analysis is completed by R language.Results: 1)We identified 4402 differentially expressed genes（DEGs）from the transcriptome RNA sequencing data of breast cancer samples and normal samples.Next,we downloaded the list of immune related genes（Irgs）from the import database,which intersects with DEGs.466 deirgs were finally obtained.2)The biological process（BP）analysis of go showed that these deirgs were mainly involved in cell chemotaxis,response to external stimuli and cytokine mediated signaling pathways.Cell component（CC）analysis showed that these deirgs were mainly enriched in the outer side of plasma membrane,immunoglobulin complex and collagen containing extracellular matrix.The molecular function（MF）analysis of go showed that these deirgs were mainly involved in signal receptor agonist activity,receptor ligand activity and cytokine activity.KEGG analysis showed that the signal pathways of these deirgs,such as cytokine cytokine receptor interaction,neuroactive ligand receptor phase interaction and PI3 K Akt signal pathway,play an important role in tumorigenesis and tumor immunity.3)These deirgs were analyzed with the BRCA clinical data downloaded by TCGA,and 11 deirgs related to prognosis were obtained.We made these genes key genes.These key genes were used to construct the prognosis model.4)Key factors: psme2,rbp1,APOD,NOS1,ighe,igkv2d-40,NRG1,adrb1,Npr3,SDC1.Conclusion: The model we built can effectively divide breast cancer patients into high/low-risk groups.At the same time,therapeutic schedule of antiPD1/PDL1 or CTLA4 can be individualized according to the grouping of patients suffering from malignant breast cancer,improving the therapeutic effect and reducing the burden of patients.