POLRMT as a Novel Susceptibility Gene for Cardiotoxicity in Epirubicin Treatment of Breast Cancer Patients

Integrated analysis of lactate-related genes identifies POLRMT as a novel marker promoting the proliferation, migration and energy metabolism of hepatocellular carcinoma via Wnt/β-Catenin signaling

Hepatocellular carcinoma (HCC) is a prevalent and deadly form of cancer globally with typically unfavorable outcomes. Increasing research suggests that lactate serves as an important carbon contributor to cellular metabolism and holds a crucial part in the progression, sustenance, and treatment response of tumors. However, the contribution of lactate-related genes (LRGs) in HCC is still unclear. In this study, we analyzed TCGA datasets and screened 21 differentially expressed LRGs related to long-term survivals in HCC patients. Pan-cancer assays revealed that 21 LRGs expression exhibited a dysregulated level in man types of tumors and associated with clinical prognosis of tumor patients. The analysis of 21 LRGs successfully classified HCC samples into two molecular subtypes, and these two subtypes showed significant differences in clinical information, gene expression, and immune characteristics. Subsequently, based on the aforementioned 21 LRGs, a novel prognostic signature (DTYMK, IRAK1, POLRMT, MPV17, UQCRH, PDSS1, SLC16A3, SPP1 and LDHD) was generated by LASSO-Cox regression analysis. Survival assays demonstrated that the signature performed well in predicting the overall survival of patients with HCC. The results of Gene Set Variation Analysis indicated that the high GSVA scores were associated with poor prognosis. Moreover, we also investigated the correlation between GSVA scores and various signaling pathways in HCC. Among the nine prognostic genes, our attention focused on POLRMT which was highly expressed in HCC specimens based on TCGA datasets and several HCC cell lines. In addition, functional assays indicated that POLRMT distinctly promoted the proliferation, migration and energy metabolism of HCC cells via regulating Wnt/β-Catenin signaling. Overall, through the establishment of a novel prognostic signature, we have provided potential clinical value for assessing the prognosis of HCC patients. Furthermore, our study has identified the high expression of POLRMT in HCC and demonstrated its crucial role in HCC cell proliferation. These findings hold great importance in advancing our understanding of the pathophysiology of HCC, identifying new therapeutic targets, and improving patient survival rates.

Anthracycline Toxicity: Light at the End of the Tunnel?

Anthracycline-induced cardiotoxicity (AIC) is a serious and common side effect of anthracycline therapy. Identification of genes and genetic variants associated with AIC risk has clinical potential as a cardiotoxicity predictive tool and to allow the development of personalized therapies. In this review, we provide an overview of the function of known AIC genes identified by association studies and categorize them based on their mechanistic implication in AIC. We also discuss the importance of functional validation of AIC-associated variants in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) to advance the implementation of genetic predictive biomarkers. Finally, we review how patient-specific hiPSC-CMs can be used to identify novel patient-relevant functional targets and for the discovery of cardioprotectant drugs to prevent AIC. Implementation of functional validation and use of hiPSC-CMs for drug discovery will identify the next generation of highly effective and personalized cardioprotectants and accelerate the inclusion of approved AIC biomarkers into clinical practice.

Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer

BACKGROUND

The largest group of patients with breast cancer are estrogen receptor-positive (ER+) type. The estrogen receptor acts as a transcription factor and triggers cell proliferation and differentiation. Hence, investigating ER-DNA interaction genomic regions can help identify genes directly regulated by ER and understand the mechanism of ER action in cancer progression.

METHODS

In the present study, we employed a workflow to do a meta-analysis of ChIP-seq data of ER+ cell lines stimulated with 10 nM and 100 nM of E2. All publicly available data sets were re-analyzed with the same platform. Then, the known and unknown batch effects were removed. Finally, the meta-analysis was performed to obtain meta-differentially bound sites in estrogen-treated MCF7 cell lines compared to vehicles (as control). Also, the meta-analysis results were compared with the results of T47D cell lines for more precision. Enrichment analyses were also employed to find the functional importance of common meta-differentially bound sites and associated genes among both cell lines.

RESULTS

Remarkably, POU5F1B, ZNF662, ZNF442, KIN, ZNF410, and SGSM2 transcription factors were recognized in the meta-analysis but not in individual studies. Enrichment of the meta-differentially bound sites resulted in the candidacy of pathways not previously reported in breast cancer. PCGF2, HNF1B, and ZBED6 transcription factors were also predicted through the enrichment analysis of associated genes. In addition, comparing the meta-analysis results of both ChIP-seq and RNA-seq data showed that many transcription factors affected by ER were up-regulated.

CONCLUSION

The meta-analysis of ChIP-seq data of estrogen-treated MCF7 cell line leads to the identification of new binding sites of ER that have not been previously reported. Also, enrichment of the meta-differentially bound sites and their associated genes revealed new terms and pathways involved in the development of breast cancer which should be examined in future in vitro and in vivo studies.

New Insights in the Era of Clinical Biomarkers as Potential Predictors of Systemic Therapy-Induced Cardiotoxicity in Women with Breast Cancer: A Systematic Review

Cardiotoxicity induced by breast cancer therapies is a potentially serious complication associated with the use of various breast cancer therapies. Prediction and better management of cardiotoxicity in patients receiving chemotherapy is of critical importance. However, the management of cancer therapy-related cardiac dysfunction (CTRCD) lacks clinical evidence and is based on limited clinical studies.

AIM

To provide an overview of existing and potentially novel biomarkers that possess a promising predictive value for the early and late onset of CTRCD in the clinical setting.

METHODS

A systematic review of published studies searching for promising biomarkers for the prediction of CTRCD in patients with breast cancer was undertaken according to PRISMA guidelines. A search strategy was performed using PubMed, Google Scholar, and Scopus for the period 2013-2023. All subjects were >18 years old, diagnosed with breast cancer, and received breast cancer therapies.

RESULTS

The most promising biomarkers that can be used for the development of an alternative risk cardiac stratification plan for the prediction and/or early detection of CTRCD in patients with breast cancer were identified.

CONCLUSIONS

We highlighted the new insights associated with the use of currently available biomarkers as a standard of care for the management of CTRCD and identified potentially novel clinical biomarkers that could be further investigated as promising predictors of CTRCD.

Magnetic Solid-Phase Microextraction Protocol Based on Didodecyldimethylammonium Bromide-Functionalized Nanoparticles for the Quantification of Epirubicin in Biological Matrices

Due to epirubicin's (EPI) narrow therapeutic index and risk of cardiotoxicity, it is critical to monitor concentrations of this drug when being used to treat cancer patients. In this study, a simple and fast magnetic solid-phase microextraction (MSPME) protocol for the determination of EPI in plasma and urine samples is developed and tested. Experiments were performed using prepared Fe₃O₄-based nanoparticles coated with silica and a double-chain surfactant-namely, didodecyldimethylammonium bromide (DDAB)-as a magnetic sorbent. All the prepared samples were analyzed via liquid chromatography coupled with fluorescence detection (LC-FL). The validation parameters indicated good linearity in the range of 0.001-1 µg/mL with a correlation coefficient > 0.9996 for plasma samples, and in the range of 0.001-10 µg/mL with a correlation coefficient > 0.9997 for urine samples. The limit of detection (LOD) and limit of quantification (LOQ) for both matrices were estimated at 0.0005 µg/mL and 0.001 µg/mL, respectively. The analyte recovery after sample pretreatment was 80 ± 5% for the plasma samples and 90 ± 3% for the urine samples. The developed method's applicability for monitoring EPI concentrations was evaluated by employing it to analyze real plasma and urine samples collected from a pediatric cancer patient. The obtained results confirmed the proposed MSPME-based method's usefulness, and enabled the determination of the EPI concentration-time profile in the studied patient. The miniaturization of the sampling procedure, along with the significant reduction in pre-treatment steps, make the proposed protocol a promising alternative to routine approaches to monitoring EPI levels in clinical laboratories.

Association Studies in Clinical Pharmacogenetics

In recent times, the progress of Clinical Pharmacogenetics has been remarkable [...].

Current Status and Trends of Research on Anthracycline-Induced Cardiotoxicity from 2002 to 2021: A Twenty-Year Bibliometric and Visualization Analysis

Anthracyclines constitute the cornerstone of numerous chemotherapy regimens for various cancers. However, the clinical application of anthracyclines is significantly limited to their dose-dependent cardiotoxicity. A comprehensive understanding of the current status of anthracycline-induced cardiotoxicity is necessary for in-depth research and optimal clinical protocols. Bibliometric analysis is widely applied in depicting development trends and tracking frontiers of a specific field. The present study is aimed at revealing the status and trends of anthracycline-induced cardiotoxicity during the past two decades by employing bibliometric software including R-bibliometric, VOSviewer, and CiteSpace. A total of 3504 publications concerning anthracycline-induced cardiotoxicity from 2002 to 2021 were collected from the Web of Science Core Collection database. Results showed significant growth in annual yields from 90 records in 2002 to 304 papers in 2021. The United States was the most productive country with the strongest collaboration worldwide in the field. Charles University in the Czech Republic was the institution that contributed the most papers, while 7 of the top 10 productive institutions were from the United States. The United States Department of Health and Human Services and the National Institutes of Health are the two agencies that provide financial support for more than 50% of sponsored publications. The research categories of included publications mainly belong to Oncology and Cardiac Cardiovascular Systems. The Journal of Clinical Oncology had a comprehensive impact on this research field with the highest IF value and many publications. Simunek Tomas from Charles University contributed the most publications, while Lipshultz Steven E. from the State University of New York possessed the highest H-index. In addition, the future research frontiers of anthracycline-induced cardiotoxicity might include early detection, pharmacogenomics, molecular mechanism, and cardiooncology. The present bibliometric analysis may provide a valuable reference for researchers and practitioners in future research directions.

LINA: A Linearizing Neural Network Architecture for Accurate First-Order and Second-Order Interpretations

While neural networks can provide high predictive performance, it was a challenge to identify the salient features and important feature interactions used for their predictions. This represented a key hurdle for deploying neural networks in many biomedical applications that require interpretability, including predictive genomics. In this paper, linearizing neural network architecture (LINA) was developed here to provide both the first-order and the second-order interpretations on both the instance-wise and the model-wise levels. LINA combines the representational capacity of a deep inner attention neural network with a linearized intermediate representation for model interpretation. In comparison with DeepLIFT, LIME, Grad*Input and L2X, the first-order interpretation of LINA had better Spearman correlation with the ground-truth importance rankings of features in synthetic datasets. In comparison with NID and GEH, the second-order interpretation results from LINA achieved better precision for identification of the ground-truth feature interactions in synthetic datasets. These algorithms were further benchmarked using predictive genomics as a real-world application. LINA identified larger numbers of important single nucleotide polymorphisms (SNPs) and salient SNP interactions than the other algorithms at given false discovery rates. The results showed accurate and versatile model interpretation using LINA.