Dataset Information

ATP2C2 Has Potential to Define Tumor Microenvironment in Breast Cancer.

ABSTRACT: Tumor microenvironment (TME) is vital for the occurrence and development of breast cancer (BRCA). However, it remains challenging to understand the dynamic modulation of the stromal and immune components comprehensively in TME. Herein, we used ESTIMATE and CIBERSORT algorithm to estimate the number of stromal and immune components and the abundance of tumor-infiltrating immune cells (TICs) in 582 BRCA cases from gene expression omnibus (GEO) database. We employed three regression models including univariable Cox proportion, LASSO regression model and multivariate Cox regression, and identified 7 immune-specific genes related to BRCA survival. Of 7 genes, ATPase Secretory Pathway Ca²⁺ Transporting 2 (ATP2C2) attracts our attention for significantly predicting prognosis of BRCA patients. Further analysis indicated that ATP2C2 expression was closely related to the clinicopathological features (age, T- and N-staging) and negatively correlated with patients' survival in BRCA. Gene Set Enrichment Analysis (GSEA) was performed to reveal pathway enrichment between ATP2C2^high and ATP2C2^low groups. The low ATP2C2 expression groups' genes were mainly enriched for immune-related activities, while those in the ATP2C2 high-expression group were largely enriched in metabolic-related pathways. Notably, Pearson's correlation analysis identified that ATP2C2 expression was positively correlated with T follicular helper (Tfh) cells, and negatively correlated with gamma delta (γδ) T cell, suggesting that ATP2C2 might be accountable for the maintenance of immune-dominant status for TME. To sum up, this study comprehensively analyzed the TME and shed light on prognostic immune-related biomarkers for BRCA. In particular, ATP2C2 might be helpful for predicting the prognosis of BRCA patients, which provided an extra insight for BRCA treatment.

SUBMITTER: Liu J

PROVIDER: S-EPMC8079766 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundTriple-negative breast cancer (TNBC) is an aggressive cancer that affects about 13/100,000 women yearly. Patients with TNBC are often resistant to endocrine and molecular targeted therapy, making clinical treatment challenging. Researches indicate that tumor microenvironment (TME) is related to prognosis in many cancers. Therefore, we aim to identify TME immune-related biomarkers to enhance the prognosis and immunotherapy efficacy in patients with TNBC.MethodsThe bulk mRNA transcriptome data and clinical information of the (GSE58812) and (GSE25055) datasets were downloaded from the Gene Expression Omnibus (GEO) database, and the ESTIMATE algorithm was used to calculate the ImmuneScore, StromalScore, and ESTIMATEScore. Patients were divided into low and high groups according to the quartiles of ImmuneScore, StromalScore, and the median of ESTIMATEScore to filter differential expression genes (DEGs), respectively. The DEGs were then evaluated using univariate and multivariate Cox regression to identify TME-related genes and its association with survival rate for the construction of a TMErisk model with three biomarkers. Then Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) data were used to compare the gene expression in cancer and normal tissues. xCell analysis calculated the proportion of tumor-infiltrating immune cells in low and high expression of ATPase Secretory Pathway Ca2+ Transporting 2 (ATP2C2). In addition, samples from 20 TNBC patients admitted to our institution were used for immunohistochemical (IHC) examination.ResultsThree immune-related DEGs were identified, including prolyl 3-hydroxylase 2 (P3H2), sodium voltage-gated channel beta subunit 3 (SCN3B), and ATP2C2 and a TMErisk model was constructed and validated. However, only ATP2C2 was selected for further analysis. ATP2C2 mRNA level of TNBC patients was higher than that of normal breast tissue. Survival analysis showed that patients with high expression of ATP2C2 had a bad prognosis. xCell analysis demonstrated that the expression of ATP2C2 was associated with 16 kinds of tumor-infiltrating immune cells. Protein expression of ATP2C2 in TNBC tissues was higher compared to paired normal tissues in IHC.ConclusionsThis study constructed and validated a TMErisk model that can effectively predict 3- and 5-year survival rate for TNBC patients. TNBC patients with lower expression of ATP2C2 had a good prognosis.

Project description:Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In our study, we first estimated the tumor microenvironment (TME) infiltration using the xCell algorithm. Based on TME infiltration, the three main TME clusters were identified using consensus clustering. Our results showed that the three main TME clusters cause significant differences in survival rates and TME infiltration patterns (log-rank test, p = 0.006). Then, multiple machine learning algorithms were used to develop a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer (BRCA) patients (the immune-related pathway-based risk score, defined as IPRS). Based on the IPRS, BRCA patients were divided into two subgroups, and patients in the IPRS-low group presented significantly better overall survival (OS) rates than the IPRS-high group (log-rank test, p < 0.0001). Correlation analysis revealed that the IPRS-low group was characterized by increases in immune-related scores (cytolytic activity (CYT), major histocompatibility complex (MHC), T cell-inflamed immune gene expression profile (GEP), ESTIMATE, immune, and stromal scores) while exhibiting decreases in tumor purity, suggesting IPRS-low patients may have a strong immune response. Additionally, the gene-set enrichment analysis (GSEA) result confirmed that the IPRS-low patients were significantly enriched in several immune-associated signaling pathways. Furthermore, multivariate Cox analysis revealed that the IPRS was an independent prognostic biomarker after adjustment by clinicopathologic characteristics. The prognostic value of the IPRS model was further validated in three external validation cohorts. Altogether, our findings demonstrated that the IPRS was a powerful predictor to screen out certain populations with better prognosis in breast cancer and may serve as a potential biomarker guiding clinical treatment decisions.

Dataset Information

ATP2C2 Has Potential to Define Tumor Microenvironment in Breast Cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets