Targeting E2F Sensitizes Prostate Cancer Cells to Drug-Induced Replication Stress by Promoting Unscheduled CDK1 Activity

Air pollution and prostate cancer: Unraveling the connection through network toxicology and machine learning

BACKGROUND

In recent years, air pollution has been demonstrated to be associated with the occurrence of various diseases. This study aims to explore the potential association between air pollutants and prostate cancer (PCa) and to identify key genes that may play a critical bridging role in this process.

METHODS

This study utilized multiple online databases to obtain relevant target genes associated with air pollutants and PCa. Protein-protein interaction (PPI) analysis and visualization were conducted for the intersecting genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses to explore potential mechanisms. Subsequently, the best predictive model was selected through a combination of 108 machine learning algorithms. A prognostic model was constructed using the Random Survival Forest (RSF) model in conjunction with Lasso regression model, and its performance was validated in four external datasets. Finally, molecular docking analysis was conducted to investigate the interaction between key genes and air pollutants.

RESULTS

Seven common air pollutants (benzene, SO₂, NO, CO, NO₂, toluene, and O₃) were selected for analysis, and 48 intersecting targets related to PCa were identified. GO and KEGG functional enrichment analyses revealed that these targets are primarily involved in regulating biological processes such as apoptosis, carcinogenesis, and cell proliferation. Based on machine learning algorithm selection, the combination of RSF and Lasso regression was identified as the optimal predictive model, which highlighted five key genes associated with air pollutants and PCa. The model exhibited strong predictive performance across all four independent external datasets. Additionally, molecular docking analysis further confirmed the potential interactions between air pollutants and these core targets.

CONCLUSION

The findings suggest that HDAC6, CDK1, DNMT1, NOS3, and DPP4 play crucial roles in the process by which air pollutants influence PCa. The results offer new insights into the molecular mechanisms linking air pollutants and PCa, highlighting the need for greater public awareness of air pollution issues.

The prognostic and neuroendocrine implications of SLC25A29-mediated biomass signature in prostate cancer

Dysregulated solutes are linked to cancer progression, with associated carriers being potential targets for prognosis and treatment. Androgen deprivation therapy (ADT) is essential for prostate cancer (PCa) progression, but secondary resistance often leads to androgen-independent tumor growth, necessitating new prognostic biomarkers. Transcriptome-based datasets identify SLC25A29, an arginine carrier, as upregulated in PCa, correlating with metastatic features and serving as a high-risk prognostic factor, particularly in castration-resistant prostate cancer (CRPC). Molecular simulations indicate that SLC25A29-mediated pathways contribute to mitochondrial metabolism and redox homeostasis, implicating POLD1 regulation and suggesting a link to ferroptosis. Further analysis reveals that SLC25A29 may transactivate POLD1 via E2F1, as shown by RNA-seq profiling of E2F1 knockdown in CRPC-related cells, which demonstrated reduced POLD1 expression. Clinical and cellular studies confirm that SLC25A29, E2F1, and POLD1 levels positively correlate with pathological features, with their downstream effectors serving as prognosis signatures. The SLC25A29/E2F1/POLD1 axis is associated with neuroendocrine PCa (NEPC) development, indicating its role in response to androgen receptor inhibition. Downregulation of E2F1 not only decreases POLD1 levels but also reduces NEPC-related markers. These findings support the SLC25A29/E2F1/POLD1 axis as a prognostic tool for CRPC and NEPC, and targeting E2F1 may offer a therapeutic strategy to disrupt SLC25A29-mediated PCa progression.

A minority of Th1 and Tfh effector cells express survival genes shared by memory cell progeny that require IL-7 or TCR signaling to persist

It is not clear how CD4+ memory T cells are formed from a much larger pool of earlier effector cells. We found that transient systemic bacterial infection rapidly generates several antigen-specific T helper (Th)1 and T follicular helper (Tfh) cell populations with different tissue residence behaviors. Although most cells of all varieties had transcriptomes indicative of cell stress and death at the peak of the response, some had already acquired a memory cell signature characterized by expression of genes involved in cell survival. Each Th1 and Tfh cell type was maintained long term by interleukin (IL)-7, except germinal center Tfh cells, which depended on a T cell antigen receptor (TCR) signal. The results indicate that acute infection induces rapid differentiation of Th1 and Tfh cells, a minority of which quickly adopt the gene expression profile of memory cells and survive by signals from the IL-7 receptor or TCR.

Manzamine A reduces androgen receptor transcription and synthesis by blocking E2F8-DNA interactions and effectively inhibits prostate tumor growth in mice

The androgen receptor (AR) is the main driver in the development of castration-resistant prostate cancer, where the emergence of AR splice variants leads to treatment-resistant disease. Through detailed molecular studies of the marine alkaloid manzamine A (MA), we identified transcription factor E2F8 as a previously unknown regulator of AR transcription that prevents AR synthesis in prostate cancer cells. MA significantly inhibited the growth of various prostate cancer cell lines and was highly effective in inhibiting xenograft tumor growth in mice without any pathophysiological perturbations in major organs. MA suppressed the full-length AR (AR-FL), its spliced variant AR-V7, and the AR-regulated prostate-specific antigen (PSA; also known as KLK3) and human kallikrein 2 (hK2; also known as KLK2) genes. RNA sequencing (RNA-seq) analysis and protein modeling studies revealed E2F8 interactions with DNA as a potential novel target of MA, suppressing AR transcription and its synthesis. This novel mechanism of blocking AR biogenesis via E2F8 may provide an opportunity to control therapy-resistant prostate cancer over the currently used AR antagonists designed to target different parts of the AR gene.

Focused cancer pathway analysis revealed unique therapeutic targets in retinoblastoma

Retinoblastoma (RB) is a pediatric cancer of the eye that occurs in 1/15000 live births worldwide. Albeit RB is initiated by the inactivation of RB1 gene, the disease progression relies largely on transcriptional alterations. Therefore, evaluating gene expression is vital to unveil the therapeutic targets in RB management. In this study, we employed an RT2 Profiler™ PCR array for a focused analysis of 84 cancer-specific genes in RB. An interaction network was built with gene expression data to identify the dysregulated pathways in RB. The key transcript alterations identified in 13 tumors by RT2 Profiler™ PCR array was further validated in 15 tumors by independent RT-qPCR. Out of 84 cancer-specific genes, 68 were dysregulated in RB tumors. Among the 68 genes, 23 were chosen for further analysis based on statistical significance and abundance across multiple tumors. Pathway analysis of altered genes showed the frequent perturbations of cell cycle, angiogenesis and apoptotic pathways in RB. Notably, upregulation of MCM2, MKI67, PGF, WEE1, CDC20 and downregulation of COX5A were found in all the tumors. Western blot confirmed the dysregulation of identified targets at protein levels as well. These alterations were more prominent in invasive RB, correlating with the disease pathogenesis. Our molecular analysis thus identified the potential therapeutic targets for improving retinoblastoma treatment. We also suggest that PCR array can be used as a tool for rapid and cost-effective gene expression analysis.

drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network

Drug development is a lengthy process with a high failure rate. Increasingly, machine learning is utilized to facilitate the drug development processes. These models aim to enhance our understanding of drug characteristics, including their activity in biological contexts. However, a major challenge in drug response (DR) prediction is model interpretability as it aids in the validation of findings. This is important in biomedicine, where models need to be understandable in comparison with established knowledge of drug interactions with proteins. drGAT, a graph deep learning model, leverages a heterogeneous graph composed of relationships between proteins, cell lines, and drugs. drGAT is designed with two objectives: DR prediction as a binary sensitivity prediction and elucidation of drug mechanism from attention coefficients. drGAT has demonstrated superior performance over existing models, achieving 78% accuracy (and precision), and 76% F1 score for 269 DNA-damaging compounds of the NCI60 drug response dataset. To assess the model's interpretability, we conducted a review of drug-gene co-occurrences in Pubmed abstracts in comparison to the top 5 genes with the highest attention coefficients for each drug. We also examined whether known relationships were retained in the model by inspecting the neighborhoods of topoisomerase-related drugs. For example, our model retained TOP1 as a highly weighted predictive feature for irinotecan and topotecan, in addition to other genes that could potentially be regulators of the drugs. Our method can be used to accurately predict sensitivity to drugs and may be useful in the identification of biomarkers relating to the treatment of cancer patients.

The role of E2F2 in cancer progression and its value as a therapeutic target

The E2F family of transcription factors plays a crucial role in the regulation of cell cycle progression and cell proliferation. Accumulative evidence indicates that aberrant expression or activation of E2F2 is a common phenomenon in malignances. E2F2 has emerged as a key player in the development and progression of various types of tumors. A wealth of research has substantiated that E2F2 could contribute to the enhancement of tumor cell proliferation, angiogenesis, and invasiveness. Moreover, E2F2 exerts its influence on a myriad of cellular processes by engaging with a spectrum of auxiliary factors and downstream targets, including apoptosis and DNA repair. The dysregulation of E2F2 in the context of carcinogenesis may be attributable to a multitude of mechanisms, which encompass modifications in upstream regulatory elements or epigenetic alterations. This review explores the function of E2F2 in cancer progression and both established and emerging therapeutic strategies aiming at targeting this oncogenic pathway, while also providing a strong basis for further research on the biological function and clinical applications of E2F2.

Foreword Special Issue Genomic Instability in Tumor Evolution and Therapy Response

From an evolutionary perspective, mutations in the DNA molecule act as a source of genetic variation and thus, are beneficial to the adaptation and survival of the species [...].