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

Multi-omics analysis unveils a four-gene prognostic signature in esophageal squamous carcinoma and the therapeutic potential of PKP1

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies, characterized by high heterogeneity and poor outcomes. Effective classification for patient stratification and identifying reliable markers for prognosis prediction and treatment choice are crucial.

METHODS

Integration of single-cell RNA-sequencing (RNA-seq) and bulk RNA-seq analyses were used to characterize ESCC. Non-negative matrix factorization (NMF) clustering was performed to stratify the ESCC patients into different subtypes and the clinical and pathological features of the ESCC subtypes were compared. Cox regression analysis and LASSO regression analysis were used to select key genes and construct a risk model for ESCC. The associations of the key genes with anti-cancer drug sensitivities in ESCC cell lines were investigated. RT-qRCR experiments, proteomics analysis, and multiplex immunohistochemistry (mIHC) experiments were used to validate the results. Furthermore, one identified gene was selected to investigate its correlation with EGFR expression and the gene effect scores of various potential gene targets across pan-cancer.

RESULTS

The study identified the dysregulated distributions of epithelial cells and fibroblasts as characteristic of ESCC. ESCC patients could be classified into four distinct subtypes with unique cell type features and prognoses. With the gene makers of the cell type features, a four-gene prognostic signature for ESCC was constructed. The CCND1-PKP1-JUP-ANKRD12 model could effectively discriminate the survival status of ESCC patients, independent of various pathological and clinical features. The risk score for the samples was correlated with the expression levels of immunoregulatory genes. The prognostic effects of CCND1, PKP1, and JUP were confirmed at the protein level. The phosphorylation levels of PKP1, JUP, and ANKRD12 were found to be dysregulated in ESCC tumors. Their expression dysregulation and heterogeneity were demonstrated in ESCC cell lines. All four genes were significantly correlated with at least one of the anti-cancer drug sensitivities in ESCC cell lines. PKP1 expression was significantly correlated with EGFR expression and gene effect scores in multiple cancers.

CONCLUSIONS

We conclude that the CCND1-PKP1-JUP-ANKRD12 signature may serve as a novel indicator for ESCC prognosis and diagnosis. PKP1 expression might provide new clues for gene therapy efficacy in multiple cancers.

Association between the expression of specific microRNAs and prostate cancer progression- a systematic review and meta-analysis

Background

This systematic review and metanalysis aimed to summarize the evidence supporting the significance of particular MiRNAs in PrCa progression and to thoroughly examine the body of prior research.

Methods

In accordance with the PRISMA guidelines, this review was conducted using a specifically design data extraction protocol and searching several online databases for relevant articles. The protocol was registered in the PROSPERO database (CRD42023428460).

Results

8 studies were ultimately included in this review. MiRNAs significantly reduced PrCa proliferation, with an odds ratio (OR) of 0.28 (95% CI: 0.21-0.39) and a risk ratio (RR) of 0.51 (95% CI: 0.43-0.61), though moderate heterogeneity was observed (I² = 57%). For two studies investigating MiRNAs as biomarkers for predicting metastasis, the pooled OR was 0.60 (95% CI: 0.47-0.76) and the RR was 0.72 (95% CI: 0.62-0.84), both demonstrating significant predictive value with low heterogeneity (I² = 0%).

Conclusion

The results emphasize the potential of miRNAs as biomarkers for predicting PrCa metastasis and demonstrate that miRNAs have a discernible effect on PrCa proliferation. However, to improve our comprehension of MiRNA's function in this condition, additional studies are required to address the limitations and investigate how MiRNA acts in many areas of PrCa.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42023428460.

Role of non-coding RNA in lineage plasticity of prostate cancer

The treatment of prostate cancer (PCa) has made great progress in recent years, but treatment resistance always develops and can even lead to fatal disease. Exploring the mechanism of drug resistance is of great significance for improving treatment outcomes and developing biomarkers with predictive value. It is increasingly recognized that mechanism of drug resistance in advanced PCa is related to lineage plasticity and tissue differentiation. Specifically, one of the mechanisms by which castration-resistant prostate cancer (CRPC) cells acquire drug resistance and transform into neuroendocrine prostate cancer (NEPC) cells is lineage plasticity. NEPC is a subtype of PCa that is highly aggressive and lethal, with a median survival of only 7 months. With the development of high-throughput RNA sequencing technology, more and more non-coding RNAs have been identified, which play important roles in different diseases through different mechanisms. Several ncRNAs have shown great potential in PCa lineage plasticity and as biomarkers. In the review, the role of ncRNA in PCa lineage plasticity and its use as biomarkers were reviewed.

NMF typing and machine learning algorithm-based exploration of preeclampsia-related mechanisms on ferroptosis signature genes

BACKGROUND

Globally, pre-eclampsia (PE) poses a major threat to the health and survival of pregnant women and fetuses, contributing significantly to morbidity and mortality. Recent studies suggest a pathological link between PE and ferroptosis. We aim to utilize non-negative matrix factorization (NMF) clustering and machine learning algorithms to pinpoint disease-specific genes related to the process of ferroptosis in PE and investigate likely underlying biochemistry mechanisms.

METHODS

The acquisition of four microarray datasets from the Gene Expression Omnibus (GEO) repository, the integration of these datasets, and the elimination of batch effects formed the core procedure. Genes related to ferroptosis in PE (DE-FRG) were identified. NMF clustering was performed on DE-FRG for unsupervised analysis, generating a heatmap for clustering validation via principal component analysis. Immunocyte infiltration differences between different subtypes were compared to elucidate the impact of ferroptosis on immune infiltration in the placental tissue of PE patients. The application of weighted gene co-expression network analysis (WGCNA) revealed important module genes linked to sample subtypes and disease status. The screening of PE feature genes involved employing SVM, RF, GLM, and XGB machine learning algorithms, and their predictive performance was validated using various analyses and an external dataset. The iRegulon tool was utilized to predict upstream transcription factors associated with ferroptosis feature genes, from which differentially expressed transcription factors were screened to construct a "Transcription Factor-FRG-ferroptosis" regulatory network. Finally, in vitro (cultured cells) and in vivo (rat) models were utilized to evaluate the regulatory mechanisms of ferroptosis in normal and PE placental tissues.

RESULTS

Differential analysis of the four merged GEO datasets identified 41 DE-FRGs. NMF clustering based on DE-FRGs revealed two PE subtypes. Immunocyte infiltration analysis indicated significant differences in immune levels between these subtypes. Further WGCNA analysis identified module genes associated with PE and these two subtypes. Subsequently, we developed an integrated machine learning model incorporating five FRGs and validated its predictive efficacy using various analyses and an external validation dataset. Finally, based on the transcription factor ARID3A and ferroptosis feature genes EPHB3 and PAPPA2, we constructed a "Transcription Factor-FRG-ferroptosis" regulatory network, with in vitro and in vivo experiments confirming that ARID3A promotes the progression of PE and ferroptosis by activating the expression of EPHB3 and PAPPA2.

CONCLUSION

This analytical journey illuminated a critical regulatory nexus in PE, underscoring the central influence of ARID3A on PE through ferroptosis-mediated pathways.

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.