ABSTRACT
Breast cancer remains the most common malignant cancer in women, with a staggering incidence of two million cases annually worldwide; therefore, it is crucial to explore novel biomarkers to assess the diagnosis and prognosis of breast cancer patients. NIMA-related kinase (NEK) protein kinase contains 11 family members named NEK1-NEK11, which were discovered from Aspergillus Nidulans; however, the role of NEK family genes for tumor development remains unclear and requires additional study. In the present study, we investigate the prognosis relationships of NEK family genes for breast cancer development, as well as the gene expression signature via the bioinformatics approach. The results of several integrative analyses revealed that most of the NEK family genes are overexpressed in breast cancer. Among these family genes, NEK2/6/8 overexpression had poor prognostic significance in distant metastasis-free survival (DMFS) in breast cancer patients. Meanwhile, NEK2/6 had the highest level of DNA methylation, and the functional enrichment analysis from MetaCore and Gene Set Enrichment Analysis (GSEA) suggested that NEK2 was associated with the cell cycle, G2M checkpoint, DNA repair, E2F, MYC, MTORC1, and interferon-related signaling. Moreover, Tumor Immune Estimation Resource (TIMER) results showed that the transcriptional levels of NEK2 were positively correlated with immune infiltration of B cells and CD4+ T Cell. Collectively, the current study indicated that NEK family genes, especially NEK2 which is involved in immune infiltration, and may serve as prognosis biomarkers for breast cancer progression.
ABSTRACT
According to statistics 2020, female breast cancer (BRCA) became the most commonly diagnosed malignancy worldwide. Prognosis of BRCA patients is still poor, especially in population with advanced or metastatic. Particular functions of each members of the solute carrier 35A (SLC35A) gene family in human BRCA are still unknown regardless of awareness that they play critical roles in tumorigenesis and progression. Using integrated bioinformatics analyses to identify therapeutic targets for specific cancers based on transcriptomics, proteomics, and high-throughput sequencing, we obtained new information and a better understanding of potential underlying molecular mechanisms. Leveraging BRCA dataset that belongs to The Cancer Genome Atlas (TCGA), which were employed to clarify SLC35A gene expression levels. Then we used a bioinformatics approach to investigate biological processes connected to SLC35A family genes in BRCA development. Beside that, the Kaplan-Meier estimator was leveraged to explore predictive values of SLC35A family genes in BCRA patients. Among individuals of this family gene, expression levels of SLC35A2 were substantially related to poor prognostic values, result from a hazard ratio of 1.3 (with 95 percent confidence interval (95% CI: 1.18-1.44), the p for trend (ptrend) is 3.1 × 10-7). Furthermore, a functional enrichment analysis showed that SLC35A2 was correlated with hypoxia-inducible factor 1A (HIF1A), heat shock protein (HSP), E2 transcription factor (E2F), DNA damage, and cell cycle-related signaling. Infiltration levels observed in specific types of immune cell, especially the cluster of differentiation found on macrophages and neutrophils, were positively linked with SLC35A2 expression in multiple BRCA subclasses (luminal A, luminal B, basal, and human epidermal growth factor receptor 2). Collectively, SLC35A2 expression was associated with a lower recurrence-free survival rate, suggesting that it could be used as a biomarker in treating BRCA.
ABSTRACT
The complexity of breast cancer includes many interacting biological processes, and proteasome alpha (PSMA) subunits are reported to be involved in many cancerous diseases, although the transcriptomic expression of this gene family in breast cancer still needs to be more thoroughly investigated. Consequently, we used a holistic bioinformatics approach to study the PSMA genes involved in breast cancer by integrating several well-established high-throughput databases and tools, such as cBioPortal, Oncomine, and the Kaplan-Meier plotter. Additionally, correlations of breast cancer patient survival and PSMA messenger RNA expressions were also studied. The results demonstrated that breast cancer tissues had higher expression levels of PSMA genes compared to normal breast tissues. Furthermore, PSMA2, PSMA3, PSMA4, PSMA6, and PSMA7 showed high expression levels, which were correlated with poor survival of breast cancer patients. In contrast, PSMA5 and PSMA8 had high expression levels, which were associated with good prognoses. We also found that PSMA family genes were positively correlated with the cell cycle, ubiquinone metabolism, oxidative stress, and immune response signaling, including antigen presentation by major histocompatibility class, interferon-gamma, and the cluster of differentiation signaling. Collectively, these findings suggest that PSMA genes have the potential to serve as novel biomarkers and therapeutic targets for breast cancer. Nevertheless, the bioinformatic results from the present study would be strengthened with experimental validation in the future by prospective studies on the underlying biological mechanisms of PSMA genes and breast cancer.