Fifteen-MiRNA-Based Signature Is a Reliable Prognosis-Predicting Tool for Prostate Cancer Patients

Bian, Zichen; Huang, Xinbo; Chen, Yiding; Meng, Jialin; Feng, Xingliang; Zhang, Meng; Zhang, Li; Zhou, Jun; Liang, Chaozhao

doi:10.7150/ijms.49412

Cited by 5 publications

(5 citation statements)

References 61 publications

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“…A few attempts have been made to predict metastatic PCa from non-metastatic primary PCa but there is no consensus regarding a reliable biomarker yet [ 44 , 45 , 46 , 47 , 48 ]. In addition, other studies fail to demonstrate the relationship of the expression of the m 6 A regulator with PCa metastasis, though they can indicate patients with PCa outcomes [ 49 , 50 ].…”

Section: Discussionmentioning

confidence: 99%

m6A Methylation Regulators Are Predictive Biomarkers for Tumour Metastasis in Prostate Cancer

Liang

Zhang

et al. 2022

Cancers

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Prostate cancer (PCa) is one of the most common cancers in men. Usually, most PCas at initial diagnosis are localized and hormone-dependent, and grow slowly. Patients with localized PCas have a nearly 100% 5-year survival rate; however, the 5-year survival rate of metastatic or progressive PCa is still dismal. N6-methyladenosine (m6A) is the most common post-transcriptional mRNA modification and is dynamically regulated by m6A regulators. A few studies have shown that the abnormal expression of m6A regulators is significantly associated with cancer progression and immune cell infiltration, but the roles of these regulators in PCa remain unclear. Here, we examined the expression profiles and methylation levels of 21 m6A regulators across the Cancer Genome Atlas (TCGA), 495 PCas by consensus clustering, and correlated the expression of m6A regulators with PCa progression and immune cell infiltration. Consensus clustering was applied for subtyping Pca samples into clusters based on the expression profiles of m6A regulators. Each subtype’s signature genes were obtained by a pairwise differential expression analysis. Featured pathways of m6A subtypes were predicted by Gene Ontology. The m6A score was developed to predict m6A activation. The association of the m6A score with patients’ survival, metastasis and immune cell infiltration was also investigated. We identified three distinct clusters in PCa based on the expression profiles of 21 m6A regulators by consensus clustering. The differential expression and pathway analyses on the three clusters uncovered the m6A regulators involved in metabolic processes and immune responses in PCa. Moreover, we developed an m6A score to evaluate the m6A regulator activation for PCa. The m6A score is significantly associated with Gleason scores and metastasis in PCa. The predictive capacity of the m6A score on PCa metastasis was also validated in another independent cohort with an area under the curve of 89.5%. Hence, our study revealed the critical role of m6A regulators in PCa progression and the m6A score is a promising predictive biomarker for PCa metastasis.

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Section: Discussionmentioning

confidence: 99%

m6A Methylation Regulators Are Predictive Biomarkers for Tumour Metastasis in Prostate Cancer

Liang

Zhang

et al. 2022

Cancers

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“…For miRNAs, we evaluated and constructed 15 miRNA-based prognostic signatures in the TCGA-PRAD cohort and validated the findings in the MSKCC cohort, with preferable results 38 . Several genes and miRNAs identified in the abovementioned studies were also used in the current multiomics study.…”

Section: Discussionmentioning

confidence: 99%

Integrated Multi-Omics Data Reveals the Molecular Subtypes of Prostate Cancer

Meng

Jin

et al. 2021

Preprint

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Prostate cancer (PCa), the second most common male malignancy, is the fifth leading cause of cancer-related death and places notable burdens on medical resources. Most of the previous subtypes only focused on one or fewer types of data or ignored the genomic heterogeneity among PCa patients with diverse genetic backgrounds. Therefore, it is essential to precisely identify the specific molecular features and judge potential clinical outcomes based on multi-omics data. In the current study, we first identified the PCa multi-omics classification (PMOC) system based on the multi-omics, including mRNA, miRNA, lncRNA, DNA methylation, and gene mutation, using a total of ten state-of-the-art clustering algorithms. The PMOC1 subtype, also called the inflammatory subtype, contains the highest expression levels of immune checkpoint proteins, moderate activated immune-associated pathways. The PMOC2 tumor-activated subtype demonstrated the worst prognosis, which might be impacted by the activated cell cycle and DNA repair pathways, and also characterized by the most genetic alterations of mutant TP53, mutant APC and copy number alteration of 8q24.21 region. The PMOC3 subtype is likely to be a balance subtype, with the activated oncogenic signaling pathways, including hypoxia, angiogenesis, epithelial mesenchymal transition, and PI3K/AKT pathways. As well as with the activated proinflammatory pathways, including IL6/JAK/STAT3, IL2/STAT5, Notch and TNF-α signaling. Additionally, PMOC3 subtype also linked with the activation of the androgen response and the high response rate of ARSI treatment. Taken together, we defined the PMOC system for PCa patients via multi-omics data and consensus results of ten algorithms, this multi-omics consensus PCa molecular classification can further assist in the precise clinical treatment and development of targeted therapy.

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“…Combinations of multiple miRNAs can be used to predict the prognosis of PCa patients. Bian et al obtained the combination of 15 miRNAs by TCGA databases, which was adopted to predict the relapse-free survival (RFS) rate of PCa patients [ 142 ]. Similar to diagnosis, urinary miRNAs served as the prognostic markers for PCa.…”

Section: Mirnas and Cancer Diagnosis And Prognosismentioning

confidence: 99%

Overview of MicroRNAs as Diagnostic and Prognostic Biomarkers for High-Incidence Cancers in 2021

Sun

Zhao

Wang

et al. 2022

IJMS

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MicroRNAs (miRNAs) are small non-coding RNAs (ncRNAs) about 22 nucleotides in size, which play an important role in gene regulation and are involved in almost all major cellular physiological processes. In recent years, the abnormal expression of miRNAs has been shown to be associated with human diseases including cancer. In the past ten years, the link between miRNAs and various cancers has been extensively studied, and the abnormal expression of miRNAs has been reported in various malignant tumors, such as lung cancer, gastric cancer, colorectal cancer, liver cancer, breast cancer, and prostate cancer. Due to the high malignancy grade of these cancers, it is more necessary to develop the related diagnostic and prognostic methods. According to the study of miRNAs, many potential cancer biomarkers have been proposed for the diagnosis and prognosis of diseases, especially cancer, thus providing a new theoretical basis and perspective for cancer screening. The use of miRNAs as biomarkers for diagnosis or prognosis of cancer has the advantages of being less invasive to patients, with better accuracy and lower price. In view of the important clinical significance of miRNAs in human cancer research, this article reviewed the research status of miRNAs in the above-mentioned cancers in 2021, especially in terms of diagnosis and prognosis, and provided some new perspectives and theoretical basis for the diagnosis and treatment of cancers.

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Fifteen-MiRNA-Based Signature Is a Reliable Prognosis-Predicting Tool for Prostate Cancer Patients

Cited by 5 publications

References 61 publications

m6A Methylation Regulators Are Predictive Biomarkers for Tumour Metastasis in Prostate Cancer

m6A Methylation Regulators Are Predictive Biomarkers for Tumour Metastasis in Prostate Cancer

Integrated Multi-Omics Data Reveals the Molecular Subtypes of Prostate Cancer

Overview of MicroRNAs as Diagnostic and Prognostic Biomarkers for High-Incidence Cancers in 2021

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