Analysis And Functional Study Of Immune-Related Prognostic Factors Based On Public Database In Breast Cancer

Breast cancer is a malignant disease with the highest incidence rate and mortality rate in women,and poses a great threat to the health of women worldwide.Traditional breast cancer clinicopathological stage and molecular biological characteristics provide a clinically useful criterion for the prognosis of patients with breast cancer.However,breast cancer is a heterogeneous disease.Those with similar clinical characteristics may have different clinical outcomes.In the era of personalized cancer treatment,these traditional prognostic factors no longer meet the needs of optimal patient management.Therefore,in recent years,mounting research has been devoted to developing and validating molecular biomarkers,which can more accurately assess the prognosis and treatment response,help patients develop personalized treatment protocols,and assist in the development of new therapeutic targets for breast cancer patients.Tumor microenvironment(TME)is a highly heterogeneous network system composed of various cells and cytokines.The heterogeneity of TME leads to different therapeutic responses and clinical outcomes in breast cancer patients,and hinders the realization of precision medicine in breast cancer.As the main cells in TME,tumorinfiltrating immune cells can play a dual role of anti-tumor or pro-tumor.A large number of studies have shown that the immunosuppressive TME has an important impact on the clinical effects of immunotherapy and other cancer therapies.Therefore,further understanding of tumor immunity will help to develop more reliable prognostic and predictive biomarkers for breast cancer,which is essential for assessing prognosis and optimizing treatment decisions.The development of bioinformatics technology has greatly expanded the public data resources,which is of great significance for us to further study the biomarkers of breast cancer.In the first part of the paper,we explored the immune-related prognostic long non-coding RNA(lncRNA)in entire breast cancer patients and successfully constructed the prognostic model.Because triple-negative breast cancer(TNBC)is the most aggressive subtype of breast cancer and lacks specific biological targets,it faces many challenges in clinical treatment.With the advent of cancer immunotherapy,TNBC also show its advantages in immunotherapy.However,there exist many problems in immunotherapy,such as low response rate.How to better predict the prognosis and treatment response of TNBC patients have become an important topic in the current research.Thus,in the second section of this paper,we focused on TNBC subtype and confirmed PPP2R2B as an important immune-related biomarker affecting the prognosis of TNBC patients.In the third section of this paper,we exploited the biological function and molecular mechanism of PPP2R2B in TNBC,and validate some biological functions of PPP2R2B using in vitro experiments.PARTI Screening of immune-related prognostic factors and construction of a novel prognostic model in breast cancerObjective1.Screen immune-related prognosis lncRNA and construct a prognostic model for breast cancer.2.Construct nomogram using lncRNA prognostic model combined with traditional clinicopathological features.3.Explore the potential biological function and molecular mechanism associated with the prognosis model.MethodsFirst,based on breast cancer gene expression profile in TCGA and GEO datasets,immune-related lncRNAs were identified via gene co-expression analysis.Then,the prognostic lncRNAs were screened by Kaplan-Meier method,univariate and lasso Cox regression analysis.Subsequently,the prognostic model was constructed using multivariate Cox regression analysis.After the predicted efficiency of this model was evaluated via external datasets,a nomogram was developed by combining the prognostic model with clinicopathological factors.Finally,we applied integrated bioinformatics analysis to explore potential biological functions and molecular mechanisms in this prognostic model.Results1.We identified immune-related prognosis lncRNAs and constructed a prognostic model of six immune-related lncRNAs,which could distinguish between patients with high and low risk.2.This prognostic model had an excellent performance in external validation datasets,and could function as an independent prognostic factor for breast cancer patients.3.Compared with the current gene detection methods,the prediction efficiency of this lncRNA model is not affected by clinical stage,and it was also valuable to predict the overall survival time of patients with basic-like subtype.4.This nomogram was successfully constructed via combining the lncRNA model with age and AJCC stage,which improved the clinical practicability and prediction accuracy of the lncRNA model.5.The risk value of lncRNA model was significantly negatively correlated with CD8+T cells infiltration,and the signals associated with immune response were evidently inhibited in the high-risk group.6.WGCNA showed that the disorder of endoplasmic reticulum protein processing and antigen processing and presentation pathway may be the key factor affecting the infiltration of CD8+T cells in the high-risk group.Conclusions1.The lncRNA model performed well in predicting the prognosis of breast cancer patients and was more widely available than the current gene detection methods.2.Nomogram provides a useful clinical indicator for prognosis prediction of breast cancer patients.3.The lncRNA model could accurately predict the level of CD8+T cells infiltration,and has potential to become a new therapeutic target for breast cancer patients.PARTⅡ Identification and analysis of immune-related prognostic factors in triple-negative breast cancerObjective1.Integrate and optimize various bioinformatics methods to improve the accuracy of gene screening.2.Screen key immune-related biomarkers that affect the prognosis of TNBC patients.3.Evaluate prognostic value of the key biomarker.MethodsCIBERSORT algorithm combined with WGCNA identified a biological module highly related to CD8+T cell infiltration in TNBC.ssGSEA was used to develop an immune signature based the crucial genes of the highly related module.Lasso Cox regression analysis with 1000 iterations was applied to identify the hub immune-related prognostic genes.External datasets were used to evaluate the effect of the hub gene on immune infiltration.Breast cancer progression cell line model was employed to estimate the role of the candidate gene in the development of breast cancer.Immunohistochemistry was applied to examine the candidate gene expression in TNBC.A variety of survival analysis methods were used to evaluate the prognostic value of the candidate gene.Results1.WGCNA followed by ssGSEA was used to construct an immune signature highly related to CD8+T cell infiltration,which was considered as a core signal of antitumor immunity.2.Lasso analysis showed that PPP2R2B was a key factor affecting the prognosis of TNBC patients.3.Q-PCR assay showed that PPP2R2B expression showed a gradually downregulated tendency from the normal mammary epithelial cells to breast cancer cells.Immunohistochemistry assay showed that the staining intensity of PPP2R2B protein in TNBC tissue was significantly decreased than that in normal breast tissue.4.The external datasets evaluated the relationship between PPP2R2B and immune infiltrating cells.The results showed that PPP2R2B was significantly positively correlated with the infiltration of CD8+T,Thl and M1 cells,while negatively correlated with the infiltration of M2 cells.5.The overall survival of TNBC patients with PPP2R2B overexpression was significantly prolonged,and was not affected by other clinicopathological factors.In addition to TNBC,PPP2R2B also has a significant impact on the prognosis of HER2 patients.Conclusions1.In this study,the integrated and optimized screening method greatly improved the accuracy of gene screening.2.PPP2R2B is the key immune-related factor affecting the prognosis of TNBC patients,and is closely related to the occurrence and development of breast cancer.3.In addition to TNBC,PPP2R2B also has important prognostic value in HER2 patients.PartⅢ Multi-omics analysis and functional study of PPP2R2B in triple-negative breast cancerObjective1.The function and molecular mechanism of PPP2R2B were analyzed by integrating multi-omics data.2.The biological function of PPP2R2B was validated using in vitro experiments.MethodsWe integrated and analyzed breast cancer multi-omics data,including mRNA,copy number variation,somatic mutation,methylation,drug sensitivity data and clinicopathological information,to evaluate the immune function and molecular mechanism of PPP2R2B in TNBC,and finally examined some biological functions of PPP2R2B via in vitro experiments.Results1.GO,GSEA and GSVA analysis showed that PPP2R2B was significantly correlated with immune signals of antigen processing and presentation and T cell response.2.GZMA,PRF1 and IFNG were strongly positively correlated with PPP2R2B expression.Multiple TNBC datasets and TCGA Pancancer dataset proved that the relationship between PPP2R2B and the three genes was highly conservative.3.in vitro experiments showed that PPP2R2B could induce macrophages to polarize into M1 phenotype and promote the migration ability of M1 cells.Meanwhile,PPP2R2B can also inhibit the drug resistance and metastasis of TNBC cells in ways independent of the immune system.4.At the genomic level,the high and low expression groups of PPP2R2B showed obvious differences in copy number variation,somatic mutation and methylation.5.The high expression of PPP2R2B was associated with higher level of lymphocyte infiltration and TCR abundance.6.Patients with high PPP2R2B expression showed higher sensitivity to chemotherapeutic drugs.In addition,patients with high PPP2R2B expression were more likely to obtain the pathological complete response(pCR)after neoadjuvant chemotherapy(NAC).C onclusions1.PPP2R2B is an important immune-related tumor suppressor in TNBC.It plays an important role in anti-tumor immune response,and is closely related to the metastasis and drug resistance of TNBC.2.In TNBC,the decreased expression of PPP2R2B is related to the hypermethylation of specific sites in the promoter region.3.PPP2R2B can be used as a key prognostic and predictive factor of TNBC patients,and has the potential to become a new treatment target.