Model for predicting immunotherapy based on M2 macrophage infiltration in TNBC.

Introduction: Compared to other types of breast cancer, triple-negative breast cancer (TNBC) does not effectively respond to hormone therapy and HER2 targeted therapy, showing a poor prognosis. There are currently a limited number of immunotherapeutic drugs available for TNBC, a field that requires additional development. Methods: Co-expressing genes with M2 macrophages were analyzed based on the infiltration of M2 macrophages in TNBC and the sequencing data in The Cancer Genome Atlas (TCGA) database. Consequently, the influence of these genes on the prognoses of TNBC patients was analyzed. GO analysis and KEGG analysis were performed for exploring potential signal pathways. Lasso regression analysis was conducted for model construction. The TNBC patients were scored by the model, and patients were divided into high- and low-risk groups. Subsequently, the accuracy of model was further verified using GEO database and patients information from the Cancer Center of Sun Yat-sen University. On this basis, we analyzed the accuracy of prognosis prediction, correlation with immune checkpoint, and immunotherapy drug sensitivity in different groups. Results: Our findings revealed that OLFML2B, MS4A7, SPARC, POSTN, THY1, and CD300C genes significantly influenced the prognosis of TNBC. Moreover, MS4A7, SPARC, and CD300C were finally determined for model construction, and the model showed good accuracy in prognosis prediction. And 50 immunotherapy drugs with therapeutic significance in different groups were screened, which were assessed possible immunotherapeutics that have potential application and demonstrated the high precision of our prognostic model for predictive analysis. Conclusion: MS4A7, SPARC, and CD300C, the three main genes used in our prognostic model, offer good precision and clinical application potential. Fifty immune medications were assessed for their ability to predict immunotherapy drugs, providing a novel approach to immunotherapy for TNBC patients and a more reliable foundation for applying drugs in subsequent treatments.

Prognostic value of comprehensive typing based on m6A and gene cluster in TNBC.

BACKGROUND: Triple-negative breast cancer (TNBC) is resistant to targeted therapy with HER2 monoclonal antibodies and endocrine therapy, because it lacks the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). TNBC is a subtype of breast cancer with the worst prognosis and the highest mortality rate compared to other subtypes. N6-methyladenosine (m6A) modification is significant in cancer and metastasis, because it can alter gene expression and function at numerous levels, such as RNA splicing, stability, translocation, and translation. There are limited investigations into the connection between TNBC and m6A. MATERIALS AND METHODS: Breast cancer-related data were retrieved from the Cancer Genome Atlas (TCGA) database, and 116 triple-negative breast cancer cases were identified from the data. The GSE31519 data set, which included 68 cases of TNBC, was obtained from the Gene Expression Omnibus (GEO) database. Survival analysis was used to determine the prognosis of distinct m6A types based on their m6A group, gene group, and m6A score. To investigate the potential mechanism, GO and KEGG analyses were performed on the differentially expressed genes. RESULTS: The expression of m6A-related genes and their impact on prognosis in TNBC patients were studied. According to the findings, m6A was crucial in determining the prognosis of TNBC patients, and the major m6A-linked genes in this process were YTHDF2, RBM15B, IGFBP3, and WTAP. YTHDF2, RBM15B and IGFBP3 are associated with poor prognosis, while WTAP is associated with good prognosis. By cluster analysis, the gene cluster and the m6A cluster were beneficial in predicting the prognosis of TNBC patients. The m6A score based on m6A and gene clusters was more effective in predicting the prognosis of TNBC patients. Furthermore, the tumor microenvironment may play an important role in the process of m6A, influencing TNBC prognosis. CONCLUSIONS: N6-adenylic acid methylation (m6A) was important in altering the prognosis of TNBC patients, and the key m6A-associated genes in this process were YTHDF2, RBM15B, IGFBP3, and WTAP. Furthermore, the comprehensive typing based on m6A and gene clusters was useful in predicting TNBC patients' prognosis, showing potential as valuable evaluating tools for TNBC.

Peripheral blood lymphocytes subtypes as new predictors for neoadjuvant therapy efficacy in breast cancer.

BACKGROUND: Host immunity plays an important role in tumor development and treatment. Tumor-infiltrating lymphocytes (TILs) have been proven to predict the efficacy of neoadjuvant therapy (NAT) in breast cancer (BC) patients, but their application is limited due to various reasons. This study aims to explore the relationship between peripheral blood lymphocytes (PBLs) subsets distribution and the efficacy of NAT. METHODS: Between December 2017 and March 2021, a total of 116 BC patients appropriate for NAT in Sun Yat-Sen University cancer center were enrolled, pre-NAC baseline blood samples were taken for further flow cytometry analysis to quantitatively evaluate the PBLs subsets distribution, and corresponding clinical information including pathological complete response (pCR) rate of NAT response were recorded. RESULTS: Baseline CD3+ T cells(OR 1.11, 1.03-1.21, p = 0.011), CD8+ T cells (OR 1.09, 1.02-1.18, p = 0.015), and NK cells (OR 0.91, 0.83-0.98, p = 0.028) in PBLs subgroup distribution were independent predictors of pCR in BC patients receiving NAT, in which CD8+ T cells had the highest predictive ability (AUC = 0.76). Compared with some previous prediction indicators, its prediction ability has been improved to some extent. CONCLUSION: Peripheral baseline CD3+ T cells, CD8+ T cells, and NK cells were independent predictors of pCR in BC patients receiving NAT, in which CD8+ T cells had the highest predictive ability. Therefore, it can provide newly non-invasive, relatively accurate and easily accessible predictors for corresponding patients, and help clinicians better understand tumor immunity.

Identification of Immune-Related Risk Characteristics and Prognostic Value of Immunophenotyping in TNBC.

Background: Triple-negative breast cancer (TNBC) is not sensitive to targeted therapy with HER-2 monoclonal antibody and endocrine therapy due to lack of ER, PR, and HER-2 receptors. TNBC is a breast cancer subtype with the worst prognosis and the highest mortality rate compared with other subtypes. Materials and Methods: Breast cancer-related data were retrieved from The Cancer Genome Atlas (TCGA) database, and 116 cases of triple-negative breast cancer were identified from the data. GSE31519 dataset was retrieved from Gene Expression Omnibus (GEO) database, comprising a total of 68 cases with TNBC. Survival analysis was performed based on immune score, infiltration score and mutation score to explore differences in prognosis of different immune types. Analysis of differentially expressed genes was conducted and GSEA analysis based on these genes was conducted to explore the potential mechanism. Results: The findings showed that comprehensive immune typing is highly effective and accurate in assessing prognosis of TNBC patients. Analysis showed that MMP9, CXCL9, CXCL10, CXCL11 and CD7 are key genes that may affect immune typing of TNBC patients and play an important role in prediction of prognosis in TNBC patients. Conclusion: The current study presents an evaluation system based on immunophenotyping, which provides a more accurate prognostic evaluation tool for TNBC patients. Differentially expressed genes can be targeted to improve treatment of TNBC.

Identification of Key Genes Affecting the Tumor Microenvironment and Prognosis of Triple-Negative Breast Cancer.

Although the tumor microenvironment (TME) plays an important role in the development of many cancers, its roles in breast cancer, especially triple-negative breast cancer (TNBC), are not well studied. This study aimed to identify genes related to the TME and prognosis of TNBC. Firstly, we identified differentially expressed genes (DEG) in the TME of TNBC, using Expression data (ESTIMATE) datasets obtained from the Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues. Next, survival analysis was performed to analyze the relationship between TME and prognosis of TNBC, as well as determine DEGs. Genes showing significant differences were scored as alternative genes. A protein-protein interaction (PPI) network was constructed and functional enrichment analysis conducted using the DEG. Proteins with a degree greater than 5 and 10 in the PPI network correspond with hub genes and key genes, respectively. Finally, CCR2 and CCR5 were identified as key genes in TME and prognosis of TNBC. Finally, these results were verified using Gene Expression Omnibus (GEO) datasets and immunohistochemistry of TNBC patients. In conclusion, CCR2 and CCR5 are key genes in the TME and prognosis of TNBC with the potential of prognostic biomarkers in TNBC.

A Prognostic Model for Breast Cancer Based on Cancer Incidence-Related DNA Methylation Pattern.

Breast cancer (BC) is the most diagnosed cancer and the leading cause of cancer-related deaths in women. The purpose of this study was to develop a prognostic model based on BC-related DNA methylation pattern. A total of 361 BC incidence-related probes (BCIPs) were differentially methylated in blood samples from women at high risk of BC and BC tissues. Twenty-nine of the 361 BCIPs that significantly correlated with BC outcomes were selected to establish the BCIP score. BCIP scores based on BC-related DNA methylation pattern were developed to evaluate the mortality risk of BC. The correlation between overall survival and BCIP scores was assessed using Kaplan-Meier, univariate, and multivariate analyses. In BC, the BCIP score was significantly correlated with malignant BC characteristics and poor outcomes. Furthermore, we assessed the BCIP score-related gene expression profile and observed that genes with expressions associated with the BCIP score were involved in the process of cancer immunity according to GO and KEGG analyses. Using the ESTIMATE and CIBERSORT algorithms, we discovered that BCIP scores were negatively correlated with both T cell infiltration and immune checkpoint inhibitor response markers in BC tissues. Finally, a nomogram comprising the BCIP score and BC prognostic factors was used to establish a prognostic model for patients with BC, while C-index and calibration curves were used to evaluate the effectiveness of the nomogram. A nomogram comprising the BCIP score, tumor size, lymph node status, and molecular subtype was developed to quantify the survival probability of patients with BC. Collectively, our study developed the BCIP score, which correlated with poor outcomes in BC, to portray the variation in DNA methylation pattern related to BC incidence.

Comprehensive analysis of the NME gene family functions in breast cancer.

BACKGROUND: An increasing amount of research over recent years on the anti-metastasis function of the non-metastatic (NME) gene family has been challenged, with some studies identifying its involvement in the promotion of oncogenesis. Therefore, the specific functions of the NME gene family require redefining through a comprehensive analysis of tumor heterogeneity and survival benefit. However, the functions of NME genes have not been comprehensively investigated in breast cancer (BC). METHODS: In this study, ONCOMINE, GEPIA, Kaplan-Meier plotter, cBioPortal, String, and metascape databases were utilized for comparison of the mRNA expression, patient survival and network analysis of NME-associated signaling pathways in BC patients. RESULTS: The mRNA expression of NME1 and NME2 was significantly increased in BC. Additionally, high NME 1 and NME2 levels were related to poor overall survival (OS), while the upregulated expression of NME3, NME5, and NME7 indicated prolonged survival. Moreover, increased mRNA level, amplification, or deep deletions in the NME gene family were identified in approximately 41% (450/1098) of all included BC specimens. NME1 and NME2 genes displayed the highest correlation with genetic correlations of the human NME genes in BC. The following pathways were regulated by NME gene upregulation: R-HAS-380270: Recruitment of mitotic centrosome and complexes; GO:0006228: UTP biosynthetic process; R-HAS-380259: Loss of NlP from mitotic centrosomes; hsa03410: Base excision repair; and CORUM:3714: Pericenrin-GCP complex, which was significantly modulated by changes influencing the NME genes. CONCLUSIONS: Collectively, our findings revealed that the elevated expression of NME1 and NME2 could act as a biomarker and predictive tool for BC patients with poor prognosis. Furthermore, our findings indicated that NME3, NME5, and NME7 might play the roles of tumor suppressor genes, which require validation through further experiments.