A novel model associated with tumor microenvironment on predicting prognosis and immunotherapy in triple negative breast cancer.

Triple negative breast cancer (TNBC) is the most aggressive and malignant subtype in breast cancer. Immunotherapy is a currently promising and effective treatment for TNBC, while not all patients are responsive. Therefore, it is necessary to explore novel biomarkers to screen sensitive populations for immunotherapy. All mRNA expression profiles of TNBC from The Cancer Genome Atlas (TCGA) database were clustered into two subgroups by analyzing tumor immune microenvironment (TIME) with single sample gene set enrichment analysis (ssGSEA). A risk score model was constructed based on differently expressed genes (DEGs) identified from two subgroups using Cox and Least Absolute Shrinkage and Selector Operation (LASSO) regression model. And it was validated by Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) analysis in Gene Expression Omnibus (GEO) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Multiplex immunofluorescence (mIF) and Immunohistochemical (IHC) staining were performed on clinical TNBC tissue samples. The relationship between risk score and immune checkpoint blockades (ICB) related signatures was further investigated, as well as the biological processes were performed by gene set enrichment analysis (GSEA). We obtained three DEGs positively related to prognosis and infiltrating immune cells in TNBC. Our risk score model could be an independent prognostic factor and the low risk group exhibited a prolonged overall survival (OS). Patients in low risk group were more likely to present a higher immune infiltration and stronger response to immunotherapy. GSEA revealed the model was associated with immune-related pathways. We constructed and validated a novel model based on three prognostic genes related to TIME in TNBC. The model contributed a robust signature that could predict the prognosis in TNBC, especially for the efficacy of immunotherapy.

Zhang J ，Zhang M ，Tian Q ，Yang J ... -  《-》

A combined hypoxia and immune gene signature for predicting survival and risk stratification in triple-negative breast cancer.

Yang X ，Weng X ，Yang Y ，Zhang M ，Xiu Y ，Peng W ，Liao X ，Xu M ，Sun Y ，Liu X ... -  《Aging-US》

Identification and Validation of a Novel Glycolysis-Related Gene Signature for Predicting the Prognosis and Therapeutic Response in Triple-Negative Breast Cancer.

A high malignancy rate and poor prognosis are common problems with triple-negative breast cancer (TNBC). There is increasing evidence that glycolysis plays vital roles in tumorigenesis, tumor invasion, immune evasion, chemoresistance, and metastasis. However, a comprehensive analysis of the diagnostic and prognostic significance of glycolysis in TNBC is lacking. Transcriptomic and clinical data of TNBC patients were obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases, respectively. Glycolysis-related genes (GRGs) were collected from the Molecular Signatures Database (MSigDB). Differential comparative analysis was performed to obtain the differentially expressed (DE)-GRGs associated with TNBC. Based on the DE-GRGs, a glycolysis-related risk signature was established using Least Absolute Shrinkage and Selector Operation (LASSO) and multivariable Cox regression analyses. The prognostic value, tumor microenvironment, mutation status, and chemotherapy response of different risk groups were analyzed. An independent cohort from the METABRIC database was used for external validation. Furthermore, the expression patterns of five genes derived from the prognostic model were validated by quantitative real-time polymerase chain reaction (RT-qPCR). The glycolysis-related prognostic signature included five genes (IFNG, ACSS2, IRS2, GFUS, and GAL3ST1) and predicted the prognosis of TNBC patients independent of clinical factors (p < 0.05). Patients were divided into high- and low-risk groups based on the median risk score. Compared to low-risk TNBC patients, high-risk patients had significantly decreased overall survival (HR = 2.718, p < 0.001). Receiver operating characteristic and calibration curves demonstrated that the model had high performance in terms of predicting survival and risk stratification. The results remained consistent after external verification. Additionally, the tumor immune microenvironment significantly differed between the risk groups. Low-risk TNBC patients had a better immunotherapy response than high-risk patients. High-risk TNBC patients with a poor prognosis may benefit from targeted therapy. This study developed a novel glycolysis and prognosis-related (GRP) signature based on GRGs to predict the prognosis of TNBC patients, and may aid clinical decision-making for these patients.

Zheng J ，Zhang YF ，Han GH ，Fan MY ，Du MH ，Zhang GC ，Zhang B ，Qiao J ，Zhang SX ，Cao JM ... -  《-》

A novel fatty-acid metabolism-based classification for triple negative breast cancer.

Yang X ，Tang W ，He Y ，An H ，Wang J ... -  《Aging-US》

A novel immune checkpoint-related gene signature for predicting overall survival and immune status in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a highly aggressive subtype and only some of patients could benefit from the immunotherapy. The present study aims to investigate the expression pattern and prognostic value of immune checkpoint genes (ICGs) in TNBC and develop a novel ICGs-signature to predict the prognosis and immune status in TNBC. ICGs expression profiles and clinical characteristics of TNBC samples were obtained from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to construct a multi-gene signature for predicting the prognostic outcome. The risk scores were calculated based on the coefficients of each ICG in LASSO-Cox regression model. The median score was considered as the cut-off value to divide the TNBC patients into a high-risk group and a low-risk group. The Kaplan-Meier survival curves were generated to further explore the association between the risk scores and prognostic outcomes. Finally, single sample gene set enrichment analysis (ssGSEA) was conducted to evaluate the immune status and immunophenoscore (IPS) score was used for the quantitative evaluation of tumor immunogenicity. PDCD1, PDCD1LG2 and KIR3DL2 were included in the ICGs-signature model and the risk scores were calculated for each sample according to the coefficients in LASSO-Cox regression. Patients in high-risk group were associated with unfavorable prognosis. The receiver operating characteristic (ROC) curves showed the area under the curve (AUC) values for predicting 1-, 2- and 3-year overall survival (OS) by ICGs-signature were 0.925,0.822 and 0.835, respectively. The adaptive immunity cells and innate immunity cells were significantly abundant in the low-risk group, and low-risk patients tended to have higher IPS scores of PD-1, CTLA4, PD-L1 and PD-L2. A novel ICGs-signature was developed and validated, which may be not only served as a robust prognostic marker, but also a potential indicator reflecting immunotherapy response.

Liu J ，Ling Y ，Su N ，Li Y ，Tian S ，Hou B ，Luo S ，Zhao L ，Shi M ... -  《-》