A Visual Standardized (ViSta) map integrating imaging (MRI), biopsy findings and Specimen Prostate Cancer location
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YAP/TEAD4/SP1-induced VISTA expression as a tumor cell-intrinsic mechanism of immunosuppression in colorectal cancer
Hyperactivation of the YAP/TEAD transcriptional complex in cancers facilitates the development of an immunosuppressive tumor microenvironment. Herein, we observed that the transcription factor SP1 physically interacts with and stabilizes the YAP/TEAD complex at regulatory genomic loci in colorectal cancer (CRC). In response to serum stimulation, PKCζ (protein kinase C ζ) was found to phosphorylate SP1 and enhance its interaction with TEAD4. As a result, SP1 enhanced the transcriptional activity of YAP/TEAD and coregulated the expression of a group of YAP/TEAD target genes. The immune checkpoint V-domain Ig suppressor of T-cell activation (VISTA) was identified as a direct target of the SP1-YAP/TEAD4 complex and found to be widely expressed in CRC cells. Importantly, YAP-induced VISTA upregulation in human CRC cells was found to strongly suppress the antitumor function of CD8+ T cells. Consistently, elevated VISTA expression was found to be correlated with hyperactivation of the SP1-YAP/TEAD axis and associated with poor prognosis of CRC patients. In addition, we found by serendipity that enzymatic deglycosylation significantly improved the anti-VISTA antibody signal intensity, resulting in more accurate detection of VISTA in clinical tumor samples. Overall, our study identified SP1 as a positive modulator of YAP/TEAD for the transcriptional regulation of VISTA and developed a protein deglycosylation strategy to better detect VISTA expression in clinical samples. These findings revealed a new tumor cell-intrinsic mechanism of YAP/TAZ-mediated cancer immune evasion.
Enhancer transcription profiling reveals an enhancer RNA-driven ferroptosis and new therapeutic opportunities in prostate cancer
Enhancer RNAs (eRNAs), a subclass of non-coding RNAs transcribed from enhancer regions, have emerged as critical regulators of gene expression; however, their functional roles in prostate cancer remain largely unexplored. In this study, we performed integrated chromatin accessibility and transcriptomic analyses using ATAC-seq and RNA-seq on twenty pairs of prostate cancer and matched benign tissues. By incorporating chromatin immunoprecipitation sequencing data, we identified a subset of differentially expressed eRNAs significantly associated with genes involved in prostate development and oncogenic signaling pathways. Among these, lactotransferrin-eRNA (LTFe) was markedly downregulated in prostate cancer tissues, with functional analyses revealing its tumor-suppressive role. Mechanistically, LTFe promotes the transcription of its target gene, lactotransferrin (LTF), by interacting with heterogeneous nuclear ribonucleoprotein F (HNRNPF) and facilitating enhancer-promoter chromatin interactions. Furthermore, we demonstrate that the LTFe-LTF axis facilitates ferroptosis by modulating iron transport. Notably, androgen receptor (AR) signaling disrupts LTFe-associated chromatin looping, leading to ferroptosis resistance. Therapeutically, co- administration of the AR inhibitor enzalutamide and the ferroptosis inducer RSL3 significantly suppressed tumor growth, offering a promising strategy for castration-resistant prostate cancer. Collectively, this study provides novel insights into the mechanistic role of eRNAs in prostate cancer, highlighting the LTFe-LTF axis as a critical epigenetic regulator and potential therapeutic target for improved treatment outcomes.
LKB1 inactivation promotes epigenetic remodeling-induced lineage plasticity and antiandrogen resistance in prostate cancer
Epigenetic regulation profoundly influences the fate of cancer cells and their capacity to switch between lineages by modulating essential gene expression, thereby shaping tumor heterogeneity and therapy response. In castration-resistant prostate cancer (CRPC), the intricacies behind androgen receptor (AR)-independent lineage plasticity remain unclear, leading to a scarcity of effective clinical treatments. Utilizing single-cell RNA sequencing on both human and mouse prostate cancer samples, combined with whole-genome bisulfite sequencing and multiple genetically engineered mouse models, we investigated the molecular mechanism of AR-independent lineage plasticity and uncovered a potential therapeutic strategy. Single-cell transcriptomic profiling of human prostate cancers, both pre- and post-androgen deprivation therapy, revealed an association between liver kinase B1 (LKB1) pathway inactivation and AR independence. LKB1 inactivation led to AR-independent lineage plasticity and global DNA hypomethylation during prostate cancer progression. Importantly, the pharmacological inhibition of TET enzymes and supplementation with S-adenosyl methionine were found to effectively suppress AR-independent prostate cancer growth. These insights shed light on the mechanism driving AR-independent lineage plasticity and propose a potential therapeutic strategy by targeting DNA hypomethylation in AR-independent CRPC.
SREBF1-based metabolic reprogramming in prostate cancer promotes tumor ferroptosis resistance
Metabolic reprogramming in prostate cancer has been widely recognized as a promoter of tumor progression and treatment resistance. This study investigated its association with ferroptosis resistance in prostate cancer and explored its therapeutic potential. In this study, we identified differences in the epithelial characteristics between normal prostate tissue and tissues of various types of prostate cancer using single-cell sequencing. Through transcription factor regulatory network analysis, we focused on the candidate transcription factor, SREBF1. We identified the differences in SREBF1 transcriptional activity and its association with ferroptosis, and further verified this association using hdWGCNA. We constructed a risk score based on SREBF1 target genes associated with the biochemical recurrence of prostate cancer by combining bulk RNA analysis. Finally, we verified the effects of the SREBPs inhibitor Betulin on the treatment of prostate cancer and its chemosensitization effect. We observed characteristic differences in fatty acid and cholesterol metabolism between normal prostate tissue and prostate cancer tissue, identifying high transcriptional activity of SREBF1 in prostate cancer tissue. This indicates that SREBF1 is crucial for the metabolic reprogramming of prostate cancer, and that its mediated metabolic changes promoted ferroptosis resistance in prostate cancer in multiple ways. SREBF1 target genes are associated with biochemical recurrence of prostate cancer. Finally, our experiments verified that SREBF1 inhibitors can significantly promote an increase in ROS, the decrease in GSH, and the decrease in mitochondrial membrane potential in prostate cancer cells and confirmed their chemosensitization effect in vivo. Our findings highlighted a close association between SREBF1 and ferroptosis resistance in prostate cancer. SREBF1 significantly influences metabolic reprogramming in prostate cancer cells, leading to ferroptosis resistance. Importantly, our results demonstrated that SREBF1 inhibitors can significantly enhance the therapeutic effect and chemosensitization of prostate cancer, suggesting a promising therapeutic potential for the treatment of prostate cancer.
Focal cortical dysplasia (type II) detection with multi-modal MRI and a deep-learning framework
Focal cortical dysplasia type II (FCD-II) is a prominent cortical development malformation associated with drug-resistant epileptic seizures that leads to lifelong cognitive impairment. Efficient MRI, followed by its analysis (e.g., cortical abnormality distinction, precise localization assistance, etc.) plays a crucial role in the diagnosis and supervision (e.g., presurgery planning and postoperative care) of FCD-II. Involving machine learning techniques particularly, deep-learning (DL) approaches, could enable more effective analysis techniques. We performed a comprehensive study by choosing six different well-known DL models, three image planes (axial, coronal, and sagittal) of two MRI modalities (T1w and FLAIR), demographic characteristics (age and sex) and clinical characteristics (brain hemisphere and lobes) to identify a suitable DL model for analysing FCD-II. The outcomes show that the DenseNet201 model is more suitable because of its superior classification accuracy, high-precision, F1-score, and large area under the receiver operating characteristic (ROC) curve and precision–recall (PR) curve.
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