Analyses of DNA Methylation Involved in the Activation of Nuclear Karyopherin Alpha 2 Leading to Identify the Progression and Prognostic Significance Across Human Breast Cancer

Quercetin and taxifolin inhibits TMPRSS2 activity and its interaction with EGFR in paclitaxel-resistant breast cancer cells: An in silico and in vitro study

Transmembrane protease/serine (TMPRSS2), a type II transmembrane serine protease, plays a crucial role in different stages of cancer. Recent studies have reported that the triggering epidermal growth factor receptor (EGFR) activation through protease action promotes metastasis. However, there are no reports on the interaction of TMPRSS2 with EGFR, especially in triple-negative triple negative (TNBC). The current study investigates the unexplored interaction between TMPRSS2 and EGFR, which are key partners mediating metastasis. This interaction is explored for potential targeting using quercetin (QUE) and taxifolin (TAX). TMPRSS2 expression patterns in breast cancer (BC) tissues and subtypes have been predicted, with the prognostic significance assessed using the GENT2.0 database. Validation of TMPRSS2 expression was performed in normal and TNBC tissues, including drug-resistant cell lines, utilizing GEO datasets. TMPRSS2 was further validated as a predictive biomarker for FDA-approved chemotherapeutics through transcriptomic data from BC patients. The study demonstrated the association of TMPRSS2 with EGFR through in silico analysis and validates the findings in TNBC cohorts using the TIMER2.0 web server and the TCGA dataset through C-Bioportal. Molecular docking and molecular dynamic simulation studies identified QUE and TAX as best leads targeting TMPRSS2. They inhibited cell-free TMPRSS2 activity like clinical inhibitor of TMPRSS2, Camostat mesylate. In cell-based assays focused on paclitaxel-resistant TNBC (TNBC/PR), QUE and TAX demonstrated potent inhibitory activity against extracellular and membrane-bound TMPRSS2, with low IC50 values. Furthermore, ELISA and cell-based AlphaLISA assays demonstrated that QUE and TAX inhibit the interaction of TMPRSS2 with EGFR. Additionally, QUE and TAX exhibited significant inhibition of proliferation and cell cycle accompanied by notable alterations in the morphology of TNBC/PR cells. This study provides valuable insights into potential of QUE and TAX targeting TMPRSS2 overexpressing TNBC.

Sex blind: bridging the gap between drug exposure and sex-related gene expression in Danio rerio using next-generation sequencing (NGS) data and a literature review to find the missing links in pharmaceutical and environmental toxicology studies

The sex of both humans and Danio rerio has previously been shown to affect the way individuals respond to drug exposure. Genes which allow identification of sex in juvenile zebrafish show potential to reveal these confounding variables between sex in toxicological and preclinical trials but the link between these is so far missing. These sex-specific, early expressed genes where expression is not altered by drug exposure must be carefully selected for this purpose. We aimed to discover genes which can be used in pharmaceutical trials and environmental toxicology studies to uncover sex-related variations in gene expression with drug application using the model organism Danio rerio. Previously published early sex determining genes from King et al. were evaluated as well as additional genes selected from our zebrafish Next-generation sequencing (NGS) data which are known from previously published works not to be susceptible to changes in expression with drug exposure. NGS revealed a further ten female-specific genes (vtg1, cyp17a1, cyp19a1a, igf3, ftz-f1, gdf9, foxl2a, Nr0b1, ipo4, lhcgr) and five male related candidate genes (FKBP5, apobb1, hbaa1, dmrt1, spata6) which are also expressed in juvenile zebrafish, 28 days post fertilisation (dpf). Following this, a literature review was performed to classify which of these early-expressed sex specific genes are already known to be affected by drug exposure in order to determine candidate genes to be used in pharmaceutical trials or environmental toxicology testing studies. Discovery of these early sex-determining genes in Danio rerio will allow identification of sex-related responses to drug testing to improve sex-specific healthcare and the medical treatment of human patients.

microRNA-17 is a tumor suppressor in oral squamous cell carcinoma and is repressed by LSD1

OBJECTIVE

The effects of epigenetic modifiers have been uncovered on cellular reprogramming and, specifically, on sustaining characteristics of cancer stem cells. We here aim to investigate whether lysine-specific demethylase 1 (LSD1) affects the development of oral squamous cell carcinoma (OSCC) by sustaining the cancer stem cells from OSCC (OSCSCs).

METHODS

RT-qPCR detection was firstly conducted to screen out research gene by determining differential expression of histone demethylases and methylases in identified OSCSCs. Then, microarray analysis was carried out in cells with poor expression of LSD1.

RESULTS

OSCSCs expressed high levels of LSD1, and LSD1 inhibition reduced cell viability, migration, invasion, and sphere formation of OSCSCs. Later mechanistic studies suggested that LSD1 inhibited microRNA (miR)-17 expression through histone demethylation. miR-17 bound to KPNA2, and LSD1 downstream genes were mainly enriched in the PI3K/AKT pathway. Importantly, miR-17 inhibitor reversed the inhibitory effect of si-LSD1 on cell activity, while si-KPNA2 abolished the promotive effect of miR-17 inhibitor on cell activity both in vitro and in vivo.

CONCLUSION

Overall, LSD1 functions as a cancer stem cell supporter in OSCC by catalyzing demethylation of miR-17 and activating the downstream KPNA2/PI3K/AKT pathway, which contributes to understanding of the mechanisms associated with epigenetic regulation in OSCC.

Evaluation of the role of KPNA2 mutations in breast cancer prognosis using bioinformatics datasets

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 the application of a bioinformatics-based approach 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 clinical data 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.

DNA methylation and breast cancer: A way forward (Review)

The current management of breast cancer (BC) lacks specific non‑invasive biomarkers able to provide an early diagnosis of the disease. Epigenetic‑sensitive signatures are influenced by environmental exposures and are mediated by direct molecular mechanisms, mainly guided by DNA methylation, which regulate the interplay between genetic and non‑genetic risk factors during cancerogenesis. The inactivation of tumor suppressor genes due to promoter hypermethylation is an early event in carcinogenesis. Of note, targeted tumor suppressor genes are frequently hypermethylated in patient‑derived BC tissues and peripheral blood biospecimens. In addition, epigenetic alterations in triple‑negative BC, as the most aggressive subtype, have been identified. Thus, detecting both targeted and genome‑wide DNA methylation changes through liquid‑based assays appears to be a useful clinical strategy for early detection, more accurate risk stratification and a personalized prediction of therapeutic response in patients with BC. Of note, the DNA methylation profile may be mapped by isolating the circulating tumor DNA from the plasma as a more accessible biospecimen. Furthermore, the sensitivity to treatment with chemotherapy, hormones and immunotherapy may be altered by gene‑specific DNA methylation, suggesting novel potential drug targets. Recently, the use of epigenetic drugs administered alone and/or with anticancer therapies has led to remarkable results, particularly in patients with BC resistant to anticancer treatment. The aim of the present review was to provide an update on DNA methylation changes that are potentially involved in BC development and their putative clinical utility in the fields of diagnosis, prognosis and therapy.

Oncogenic role of karyopherin α2 (KPNA2) in human tumors: A pan-cancer analysis

BACKGROUND

KPNA2, a nuclear export protein that plays an important role in tumorigenesis, is an emerging hotspot target in oncology. Despite increasing supporting evidence of its importance, no pan-cancer analysis, across multiple databases, is available for in-depth data mining of the gene.

METHODS

Tumor data from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were explored to investigate the potential oncogenic roles of KPNA2. Diverse analytical methods were used to gain a full-scale understanding of KPNA2: gene expression, survival situations, genetic mutations, DNA methylation, sites of protein phosphorylation, immunocyte infiltration, and correlative cellular pathways.

RESULTS

KPNA2 is highly expressed in many cancers, and different correlations exist between KPNA2 expression and prognosis of cancer patients. cBioPortal reported that a nonsense mutation of R285* was considered to be the primary tumorigenic genetic alteration to KPNA2 and was found in cases of LUSC, STAD, and CESC. Enhanced phosphorylation of S62 was found in several cancers and the level of infiltration of cancer-associated fibroblasts was found to be linearly correlated with KPNA2 expression levels in ACC, BRCA, MESO, TGCT, THCA, and THYM. Correlations between KPNA2 DNA methylation and the pathogenesis of various tumors in TCGA were further identified. KEGG and GO enrichment analysis identified cell cycle, microtubule binding, and tubulin binding functions for KPNA2.

CONCLUSION

This is the first pan-cancer analysis focusing on KPNA2. It provides a comprehensive understanding about the role of KPNA2 in tumorigenesis and highlights the potential targeted role of KPNA2 for cancer study.

Increased expression of KPNA2 predicts unfavorable prognosis in ovarian cancer patients, possibly by targeting KIF4A signaling

Background

Karyopherin α-2 (KPNA2) is a member of karyopherin family, which is proved to be responsible for the import or export of cargo proteins. Studies have determined that KPNA2 is associated with the development and prognosis of various cancers, yet the role of KPNA2 in ovarian carcinoma and its potential molecular mechanisms remains unclear.

Materials and methods

The expression and prognosis of KPNA2 in ovarian cancer was investigated using GEPIA and Oncomine analyses. Mutations of KPNA2 in ovarian cancer were analyzed by cBioPortal database. The prognostic value of KPNA2 expression was evaluated by our own ovarian carcinoma samples using RT-qPCR. Subsequently, the cell growth, migration and invasion of ovarian cancer cells were investigated by CCK-8 and transwell assay, respectively. The protein levels of KPNA2 and KIF4A were determined by western blot.

Results

We obtained the following important results. (1) KPNA2 and KIF4A wereoverexpressed in ovairan cancer tissues and cells. (2) Among patients with ovarian cancer, overexpressed KPNA2 was associated with lower survival rate. (3) Mutations (R197* and S140F) in KPNA2 will have some influences on protein structure, and then may cause protein function abnormal. (4) KPNA2 konckdown inhibited proliferation, migration, invasion, as well as the expression of KIF4A.

Conclusion

KPNA2, as a tumorigenic gene in ovarian cancer, accelerated tumor progression by up-regulating KIF4A, suggesting that KPNA2 might be a hopeful indicator of treatment and poor prognosis.

The overexpression of DNA repair genes in invasive ductal and lobular breast carcinomas: Insights on individual variations and precision medicine

In the era of precision medicine, analyzing the transcriptomic profile of patients is essential to tailor the appropriate therapy. In this study, we explored transcriptional differences between two invasive breast cancer subtypes; infiltrating ductal carcinoma (IDC) and lobular carcinoma (LC) using RNA-Seq data deposited in the TCGA-BRCA project. We revealed 3854 differentially expressed genes between normal ductal tissues and IDC. In addition, IDC to LC comparison resulted in 663 differentially expressed genes. We then focused on DNA repair genes because of their known effects on patients' response to therapy and resistance. We here report that 36 DNA repair genes are overexpressed in a significant number of both IDC and LC patients' samples. Despite the upregulation in a significant number of samples, we observed a noticeable variation in the expression levels of the repair genes across patients of the same cancer subtype. The same trend is valid for the expression of miRNAs, where remarkable variations between patients' samples of the same cancer subtype are also observed. These individual variations could lie behind the differential response of patients to treatment. The future of cancer diagnostics and therapy will inevitably depend on high-throughput genomic and transcriptomic data analysis. However, we propose that performing analysis on individual patients rather than a big set of patients' samples will be necessary to ensure that the best treatment is determined, and therapy resistance is reduced.