Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis

Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death among women worldwide. Therefore, the need for effective breast cancer treatment is urgent. Transcription factors (TFs) directly participate in gene transcription, and their dysregulation plays a key role in breast cancer. Our study identified 459 differentially expressed TFs between tumor and normal samples from The Cancer Genome Atlas database. Based on gene expression analysis and weighted gene co-expression network analysis, the co-expression yellow module was found to be integral for breast cancer progression. A total of 121 genes in the yellow module were used for function enrichment. To further confirm prognosis-related TFs, COX regression and LASSO analyses were performed; consequently, a prognostic risk model was constructed, and its validity was verified. Ten prognosis-related TFs were identified according to their expression profile, survival probability, and target genes. COPS5, HDAC2, and NONO were recognized as hub TFs in breast cancer. These TFs were highly expressed in human breast cancer cell lines and clinical breast cancer samples; this result was consistent with the information from multiple databases. Immune infiltration analysis revealed that the proportions of resting dendritic and mast cells were greater in the low-risk group than those in the high-risk group. Thus, in this study, we identified three hub biomarkers related to breast cancer prognosis. The results provide a framework for the co-expression of TF modules and immune infiltration in breast cancer.

The role of KPNA2 mutations in breast cancer prognosis: A survey of publicly available databases

Breast cancer, comprising of several sub-phenotypes, is a leading cause of female cancer-related mortality in the UK and accounts for 15% of all cancer cases. Chemoresistant sub phenotypes of breast cancer remain a particular challenge. However, the rapidly-growing availability of clinical datasets, presents the scope to underpin a data driven precision medicine-based approach exploring new targets for diagnostic and therapeutic interventions. We report a survey of several publicly available databases probing the expression and prognostic role of Karyopherin-2 alpha (KPNA2) in breast cancer prognosis. Aberrant KPNA2 overexpression is directly correlated with aggressive tumour phenotypes and poor patient survival outcomes. We examined the existing information available on a range of commonly occurring mutations of KPNA2 and their correlation with patient survival. Our analysis of clinical gene expression datasets show that KPNA2 is frequently amplified in breast cancer, with differences in expression levels observed as a function of patient age and clinicopathologic parameters. We also found that aberrant KPNA2 overexpression is directly correlated with poor patient prognosis, warranting further investigation of KPNA2 as an actionable target for patient stratification or the design of novel chemotherapy agents. In the era of big data, the wealth of datasets available in the public domain can be used to underpin proof of concept studies evaluating the biomolecular pathways implicated in chemotherapy resistance in breast cancer.

Systematic Analysis Uncovers Associations of PGK1 with Prognosis and Immunological Characteristics in Breast Cancer

Objective. Phosphoglycerate kinase 1 (PGK1) is an essential enzyme in the process of glycolysis and mitochondrial metabolism. Herein, we conducted a systematic analysis to uncover the clinical implication of PGK1 deregulation in breast cancer. Methods. Expression pattern and prognostic significance of PGK1 were comprehensively assessed across pan-cancer based on RNA-seq profiles from the TCGA project. Associations of PGK1 with immunological features in the tumor microenvironment (immune checkpoints, immune response predictors (tumor mutation burden (TMB) and microsatellite instability (MSI)), and tumor-infiltrating immune cells) were systematically analyzed. The role of PGK1 in the prediction of breast cancer prognosis was also evaluated. GSEA was presented for investigating biological pathways involved in PGK1. Results. PGK1 was specifically overexpressed in most of cancer types, including breast cancer. High PGK1 expression was indicative of undesirable overall survival, progression-free interval, disease-specific survival, and disease-free interval for various cancers. Furthermore, high PGK1 levels exhibited prominent correlations to immune checkpoints and high response to immunotherapy across pan-cancer. Notably, ROC curves confirmed that PGK1 can robustly predict breast cancer prognosis. Furthermore, PGK1 might shape an inflamed tumor microenvironment following the evidence that PGK1 was positively correlated to the abundance levels of tumor-infiltrating immune cells such as CD8+ T cell and NK cell in breast cancer. GSEA results revealed that PGK1 participated in metabolism and carcinogenic pathways. Conclusion. Collectively, PGK1 was capable of robustly predicting the prognosis and response to cancer immunotherapy in breast cancer.

Stable breast cancer prognosis

Predicting breast cancer prognosis helps improve the treatment and management of the disease. In the last decades, many prediction models have been developed for breast cancer prognosis based on transcriptomic data. A common assumption made by these models is that the test and training data follow the same distribution. However, in practice, due to the heterogeneity of breast cancer and the different environments (e.g. hospitals) where data are collected, the distribution of the test data may shift from that of the training data. For example, new patients likely have different breast cancer stage distribution from those in the training dataset. Thus these existing methods may not provide stable prediction performance for breast cancer prognosis in situations with the shift of data distribution. In this paper, we present a novel stable prediction method for reliable breast cancer prognosis under data distribution shift. Our model, known as Deep Global Balancing Cox regression (DGBCox), is based on the causal inference theory. In DGBCox, firstly high-dimensional gene expression data is transferred to latent network-based representations by a deep auto-encoder neural network. Then after balancing the latent representations using a proposed causality-based approach, causal latent features are selected for breast cancer prognosis. Causal features have persistent relationships with survival outcomes even under distribution shift across different environments according to the causal inference theory. Therefore, the proposed DGBCox method is robust and stable for breast cancer prognosis. We apply DGBCox to 12 test datasets from different breast cancer studies. The results show that DGBCox outperforms benchmark methods in terms of both prediction accuracy and stability. We also propose a permutation importance algorithm to rank the genes in the DGBCox model. The top 50 ranked genes suggest that the cell cycle and the organelle organisation could be the most relevant biological processes for stable breast cancer prognosis.

DEAD-box helicase 27 enhances stem cell-like properties with poor prognosis in breast cancer

Background Although the rapid development of diagnosis and treatment has improved prognosis in early breast cancer, challenges from different therapeutic response remain due to breast cancer heterogeneity. DEAD-box helicase 27 (DDX27) had been proved to influence ribosome biogenesis and identified as a promoter in gastric and colorectal cancer associated with stem cell-like properties, while the impact of DDX27 on breast cancer prognosis and biological functions is unclear. We aimed to explore the influence of DDX27 on stem cell-like properties and prognosis in breast cancer. Methods The expression of DDX27 was evaluated in 24 pairs of fresh breast cancer and normal tissue by western blot. We conducted Immunohistochemical (IHC) staining in paraffin sections of 165 breast cancer patients to analyze the expression of DDX27 and its correlation to stemness biomarker. The Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) database and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database were used to analyze the expression of DDX27 in breast cancer. Kaplan–Meier survival analysis were used to investigate the implication of DDX27 on breast cancer prognosis. Western blot, CCK-8 assay, Transwell assay and wound-healing assay were carried out to clarify the regulation of DDX27 on stem cell-like properties in breast cancer cells. Gene Set Enrichment Analysis (GSEA) was performed to analyze the potential molecular mechanisms of DDX27 in breast cancer. Results DDX27 was significantly high expressed in breast cancer compared with normal tissue. High expression of DDX27 was related to larger tumor size (p = 0.0005), positive lymph nodes (p = 0.0008), higher histological grade (p = 0.0040), higher ki-67 (p = 0.0063) and later TNM stage (p < 0.0001). Patients with high DDX27 expression turned out a worse prognosis on overall survival (OS, p = 0.0087) and disease-free survival (DFS, p = 0.0235). Overexpression of DDX27 could enhance the expression of biomarkers related to stemness and promote stem cell-like activities such as proliferation and migration in breast cancer cells. Conclusion DDX27 can enhance stem cell-like properties and cause poor prognosis in breast cancer, also may be expected to become a potential biomarker for breast cancer therapy.

Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis

Background Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice. Results In this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation. Conclusions The experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.