MicroRNA-regulated transcriptome analysis identifies four major subtypes with prognostic and therapeutic implications in prostate cancer

An 18-gene signature of recurrence-associated endothelial cells predicts tumor progression and castration resistance in prostate cancer

BACKGROUND

The prognostic and therapeutic implications of endothelial cells (ECs) heterogeneity in prostate cancer (PCa) are poorly understood.

METHODS

We investigated associations of EC heterogeneity with PCa recurrence and castration resistance in 8 bulk transcriptomic and 4 single-cell RNA-seq cohorts. A recurrence-associated EC (RAEC) signature was constructed by comparing 11 machine learning algorithms through nested cross-validation. Functional relevances of RAEC-specific genes were also tested.

RESULTS

A subset of ECs was significantly associated with recurrence in primary PCa and named RAECs. RAECs were characteristic of tip and immature cells and were enriched in migration, angiogenesis, and collagen-related pathways. We then developed an 18-gene RAEC signature (RAECsig) representative of RAECs. Higher RAECsig scores independently predicted tumor recurrence and performed better or comparably compared to clinicopathological factors and commercial gene signatures in multiple PCa cohorts. Of the 18 RAECsig genes, FSCN1 was upregulated in ECs from PCa with higher Gleason scores; and the silencing of FSCN1, TMEME255B, or GABRD in ECs either attenuated tube formation or inhibited PCa cell proliferation. Finally, higher RAECsig scores predicted castration resistance in both primary and castration-resistant PCa.

CONCLUSION

This study establishes an endothelial signature that links a subset of ECs to prostate cancer recurrence and castration resistance.

PET imaging of new target CDK19 in prostate cancer

PURPOSE

Prostate-specific membrane antigen (PSMA)-positron emission tomography (PET) is a superior method to predict patients' risk of cancer progression and response to specific therapies. However, its performance is limited for neuroendocrine prostate cancer (NEPC) and PSMA-low prostate cancer cells, resulting in diagnostic blind spots. Hence, identifying novel specific targets is our aim for diagnosing those prostate cancers with low PSMA expression.

METHODS

The Cancer Genome Atlas (TCGA) database and our cohorts from men with biopsy-proven high-risk metastatic prostate cancer were used to identify CDK19 and PSMA expression. PDX lines neP-09 and P-16 primary cells were used for cellular uptake and imaging mass cytometry in vitro. To evaluate in vivo CDK19-specific uptake of gallium(Ga)-68-IRM-015-DOTA, xenograft mice models and blocking assays were used. PET/CT imaging data were obtained to estimate the absorbed dose in organs.

RESULTS

Our study group had reported the overexpression of a novel tissue-specific gene CDK19 in high-risk metastatic prostate cancer and CDK19 expression correlated with metastatic status and tumor staging, independently with PSMA and PSA levels. Following up on this new candidate for use in diagnostics, small molecules targeting CDK19 labeled with Ga-68 (68Ga-IRM-015-DOTA) were used for PET in this study. We found that the 68Ga-IRM-015-DOTA was specificity for prostate cancer cells, but the other cancer cells also took up little 68Ga-IRM-015-DOTA. Importantly, mouse imaging data showed that the NEPC and CRPC xenografts exhibited similar signal strength with 68Ga-IRM-015-DOTA, but 68Ga-PSMA-11 only stained the CRPC xenografts. Furthermore, target specificity was elucidated by a blocking experiment on a CDK19-bearing tumor xenograft. These data concluded that 68Ga-CDK19 PET/CT was an effective technology to detect lesions with or without PSMA in vitro, in vivo, and in the PDX model.

CONCLUSION

Thus, we have generated a novel PET small molecule with predictive value for prostate cancer. The findings indicate that 68Ga-CDK19 may merit further evaluation as a predictive biomarker for PET scans in prospective cohorts and may facilitate the identification of molecular types of prostate cancer independent of PSMA.

Molecular predictors of metastasis in patients with prostate cancer

INTRODUCTION

Prostate cancer is a serious threat to the health of older adults worldwide. The quality of life and survival time of patients sharply decline once metastasis occurs. Thus, early screening for prostate cancer is very advanced in developed countries. The detection methods used include Prostate-specific antigen (PSA) detection and digital rectal examination. However, the lack of universal access to early screening in some developing countries has resulted in an increased number of patients presenting with metastatic prostate cancer. In addition, the treatment methods for metastatic and localized prostate cancer are considerably different. In many patients, early-stage prostate cancer cells often metastasize due to delayed observation, negative PSA results, and delay in treatment time. Therefore, the identification of patients who are prone to metastasis is important for future clinical studies.

AREAS COVERED

this review introduced a large number of predictive molecules related to prostate cancer metastasis. These molecules involve the mutation and regulation of tumor cell genes, changes in the tumor microenvironment, and the liquid biopsy.

EXPERT OPINION

In next decade, PSMA PET/CT and liquid biopsy will be the excellent predicting tools, while ¹⁷⁷ Lu- PSMA-RLT will be showed excellent anti-tumor efficacy in mPCa patients.

Transcriptome-Wide Analysis of microRNA-mRNA Correlations in Tissue Identifies microRNA Targeting Determinants

MicroRNAs are small RNAs that regulate gene expression through complementary base pairing with their target mRNAs. A substantial understanding of microRNA target recognition and repression mechanisms has been reached using diverse empirical and bioinformatic approaches, primarily in vitro biochemical or cell culture perturbation settings. We sought to determine if rules of microRNA target efficacy could be inferred from extensive gene expression data of human tissues. A transcriptome-wide assessment of all the microRNA-mRNA canonical interactions' efficacy was performed using a normalized Spearman correlation (Z-score) between the abundance of the transcripts in the PRAD-TCGA dataset tissues (RNA-seq mRNAs and small RNA-seq for microRNAs, 546 samples). Using the Z-score of correlation as a surrogate marker of microRNA target efficacy, we confirmed hallmarks of microRNAs, such as repression of their targets, the hierarchy of preference for gene regions (3'UTR > CDS > 5'UTR), and seed length (6 mer < 7 mer < 8 mer), as well as the contribution of the 3'-supplementary pairing at nucleotides 13-16 of the microRNA. Interactions mediated by 6 mer + supplementary showed similar inferred repression as 7 mer sites, suggesting that the 6 mer + supplementary sites may be relevant in vivo. However, aggregated 7 mer-A1 seeds appear more repressive than 7 mer-m8 seeds, while similar when pairing possibilities at the 3'-supplementary sites. We then examined the 3'-supplementary pairing using 39 microRNAs with Z-score-inferred repressive 3'-supplementary interactions. The approach was sensitive to the offset of the bridge between seed and 3'-supplementary pairing sites, and the pattern of offset-associated repression found supports previous findings. The 39 microRNAs with effective repressive 3'supplementary sites show low GC content at positions 13-16. Our study suggests that the transcriptome-wide analysis of microRNA-mRNA correlations may uncover hints of microRNA targeting determinants. Finally, we provide a bioinformatic tool to identify microRNA-mRNA candidate interactions based on the sequence complementarity of the seed and 3'-supplementary regions.

A machine learning framework develops a DNA replication stress model for predicting clinical outcomes and therapeutic vulnerability in primary prostate cancer

Recent studies have identified DNA replication stress as an important feature of advanced prostate cancer (PCa). The identification of biomarkers for DNA replication stress could therefore facilitate risk stratification and help inform treatment options for PCa. Here, we designed a robust machine learning-based framework to comprehensively explore the impact of DNA replication stress on prognosis and treatment in 5 PCa bulk transcriptomic cohorts with a total of 905 patients. Bootstrap resampling-based univariate Cox regression and Boruta algorithm were applied to select a subset of DNA replication stress genes that were more clinically relevant. Next, we benchmarked 7 survival-related machine-learning algorithms for PCa recurrence using nested cross-validation. Multi-omic and drug sensitivity data were also utilized to characterize PCa with various DNA replication stress. We found that the hyperparameter-tuned eXtreme Gradient Boosting model outperformed other tuned models and was therefore used to establish a robust replication stress signature (RSS). RSS demonstrated superior performance over most clinical features and other PCa signatures in predicting PCa recurrence across cohorts. Lower RSS was characterized by enriched metabolism pathways, high androgen activity, and a favorable prognosis. In contrast, higher RSS was significantly associated with TP53, RB1, and PTEN deletion, exhibited increased proliferation and DNA replication stress, and was more immune-suppressive with a higher chance of immunotherapy response. In silico screening identified 13 potential targets (e.g. TOP2A, CDK9, and RRM2) from 2249 druggable targets, and 2 therapeutic agents (irinotecan and topotecan) for RSS-high patients. Additionally, RSS-high patients were more responsive to taxane-based chemotherapy and Poly (ADP-ribose) polymerase inhibitors, whereas RSS-low patients were more sensitive to androgen deprivation therapy. In conclusion, a robust machine-learning framework was used to reveal the great potential of RSS for personalized risk stratification and therapeutic implications in PCa.

MDSCs might be "Achilles heel" for eradicating CSCs

During tumor initiation and progression, the complicated role of immune cells in the tumor immune microenvironment remains a concern. Myeloid-derived suppressor cells (MDSCs) are a group of immune cells that originate from the bone marrow and have immunosuppressive potency in various diseases, including cancer. In recent years, the key role of cancer stemness has received increasing attention in cancer development and therapy. Several studies have demonstrated the important regulatory relationship between MDSCs and cancer stem cells (CSCs). However, there is still no clear understanding regarding the complex interacting regulation of tumor malignancy, and current research progress is limited. In this review, we summarize the complicated role of MDSCs in the modulation of cancer stemness, evaluate the mechanism of the relationship between CSCs and MDSCs, and discuss potential strategies for eradicating CSCs with respect to MDSCs.

MicroRNA Dysregulation in Prostate Cancer

Prostate cancer biology is complex, and needs to be deciphered. The latest evidence reveals the significant role of non-coding RNAs, particularly microRNAs (miRNAs), as key regulatory factors in cancer. Therefore, the identification of altered miRNA patterns involved in prostate cancer will allow them to be used for development of novel diagnostic and prognostic biomarkers. Patients and Methods: We performed a miRNAs transcriptomic analysis, using microarray (10 matched pairs tumor tissue versus normal adjacent tissue, selected based on inclusion criteria), followed by overlapping with TCGA data. A total of 292 miRNAs were differentially expressed, with 125 upregulated and 167 downregulated in TCGA patients’ cohort with PRAD (prostate adenocarcinoma), respectively for the microarray experiments; 16 upregulated and 44 downregulated miRNAs were found in our cohort. To confirm our results obtained for tumor tissue, we performed validation with qRT-PCR at the tissue and plasma level of two selected transcripts, and finally, we focused on the identification of altered miRNAs involved in key biological processes. Results: A common signature identified a panel of 12 upregulated and 1 downregulated miRNA, targeting and interconnected in a network with the TP53, AGO2, BIRC5 gene and EGFR as a core element. Among this signature, the overexpressed transcripts (miR-20b-5p, miR-96-5p, miR-183-5p) and the downregulated miR-542-5p were validated by qRT-PCR in an additional patients’ cohort of 34 matched tumor and normal adjacent paired samples. Further, we performed the validation of the expression level for miR-20b-5p, miR-96-5p, miR-183-5p plasma, on the same patients’ cohort versus a healthy control group, confirming the overexpression of these transcripts in the PRAD group, demonstrating the liquid biopsy as a potential investigational tool in prostate cancer. Conclusion: In this pilot study, we provide evidence on miRNA dysregulation and its association with key functional components of the PRAD landscape, where an important role is acted by miR-20b-5p, miR-542-5p, or the oncogenic cluster miR-183-96-182.