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

Lin, Bing-Biao; Lei, Hanqi; Xiong, Haiyun; Fu, Xing; Shi, Fang; Yang, Xiangwei; Yang, Yafei; Liao, Guolong; Feng, Yupeng; Jiang, Donggen; Pang, Jun

doi:10.1016/j.csbj.2021.08.046

Cited by 11 publications

(4 citation statements)

References 53 publications

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“…miRNAs are mentioned in literature as a potential application of biomarkers for numerous diseases, 7 , 9 , 18 including prostate adenocarcinoma (PRAD). 19 Despite this, there is a current lack of validated studies that evaluated the miRNA expression level and direct connection with clinical variables, particularly from the liquid biopsy. 20 , 21 Recent literature data emphasizes the important role of liquid biopsy in patient stratification.…”

Section: Introductionmentioning

confidence: 99%

MicroRNA Dysregulation in Prostate Cancer

Schitcu

Ráduly

Nuțu

et al. 2022

PGPM

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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.

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Section: Introductionmentioning

confidence: 99%

MicroRNA Dysregulation in Prostate Cancer

Schitcu

Ráduly

Nuțu

et al. 2022

PGPM

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“…Given the tissue specificity of microRNA expression, we performed this study within a single tissue type. We chose the PRAD cohort because it is large and extensively studied for microRNAs [ 20 , 21 , 22 , 23 ]; in addition, PRAD is a very heterogeneous cancer, thus biases due to major cancer subtypes are unlikely [ 24 ]. We investigated the expression of the conserved human microRNAs (221 out of the 2606 microRNA entries available in Targetscan 7.2) and all the human protein-coding transcripts annotated in Ensembl (20,440) in the 546 samples of the PRAD-TCGA cohort (20,531 genes and 2588 microRNAs expressed) (see pipeline analysis in Figure 1 ).…”

Section: Resultsmentioning

confidence: 99%

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

Barnech

Fort

Barnech

et al. 2023

ncRNA

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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.

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“…Given the tissue specificity of microRNA expression, we performed this study within a single tissue type. We chose the PRAD cohort because it is large and extensively studied for microRNAs (Lin et al, 2021; Wei et al, 2020; Yang et al, 2019; Ye et al, 2018); in addition, PRAD is a very heterogeneous cancer thus biases result in of cancer subtypes are unlikely (Abeshouse et al, 2015). We investigated the expression of the conserved human microRNAs (221) and all the human protein-coding transcripts annotated in Ensembl (20,440) in the 546 samples of the PRAD-TCGA cohort (20,531) (see pipeline analysis in Figure 1).…”

Section: Resultsmentioning

confidence: 99%

Transcriptome-wide analysis of microRNA-mRNA correlations in unperturbed tissue transcriptomes identifies microRNA targeting determinants

Trinidad

Fort

Trinidad

et al. 2021

Preprint

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MicroRNAs are small RNAs that regulate gene expression through complementary base pairing with their target mRNAs. Given the small size of the pairing region and the large number of mRNAs that each microRNA can control, the identification of biologically relevant targets is difficult. Since current knowledge of target recognition and repression has mainly relied on in vitro studies, we sought to determine if the interrogation of gene expression data of unperturbed tissues could yield new insight into these processes. The transcriptome-wide repression at the microRNA-mRNA canonical interaction sites (seed and 3'-supplementary region, identified by sole base complementarity) was calculated as a normalized Spearman correlation (Z-score) between the abundance of the transcripts in the PRAD-TCGA tissues (RNA-seq and small RNA-seq data of 546 samples). Using the repression values obtained we confirmed established properties or microRNA targeting efficacy, such as the preference for gene regions (3'UTR>CDS>5'UTR), the proportionality between repression and seed length (6mer<7mer<8mer) and the contribution to the repression exerted by the supplementary pairing at 13-16nt of the microRNA. Our results suggest that the 7mer-m8 seed could be more repressive than the 7mer-A1, while they have similar efficacy when they interact using the 3'-supplementary pairing. Strikingly, the 6mer+suppl sites yielded normalized Z-score of repression similar to the sole 7mer-m8 or 7mer-A1 seeds, which raise awareness of its potential biological relevance. We then used the approach to further characterize the 3'-supplementary pairing, using 39 microRNAs that hold repressive 3'-supplementary interactions. The analysis of the bridge between seed and 3'-supplementary pairing site confirmed the optimum +1 offset previously evidenced, but higher offsets appear to hold similar repressive strength. In addition, they show a low GC content at position 13-16, and base preferences that allow the selection of a candidate sequence motif. Overall, our study demonstrates that transcriptome-wide analysis of microRNA-mRNA correlations in large, matched RNA-seq and small-RNA-seq data has the power to uncover hints of microRNA targeting determinants operating in the in vivo unperturbed set. Finally, we made available a bioinformatic tool to analyze microRNA-target mRNA interactions using our approach.

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

Abstract: Graphical abstract

Cited by 11 publications

References 53 publications

MicroRNA Dysregulation in Prostate Cancer

MicroRNA Dysregulation in Prostate Cancer

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

Transcriptome-wide analysis of microRNA-mRNA correlations in unperturbed tissue transcriptomes identifies microRNA targeting determinants

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