Malignant Evaluation and Clinical Prognostic Values of M6A RNA Methylation Regulators in Prostate Cancer

Zhang, Qijie; Luan, Jiaochen; Song, Lebin; Wei, Xiao‐Hong; Xia, Jun; Song, Ninghong

doi:10.7150/jca.55140

Cited by 8 publications

(9 citation statements)

References 54 publications

(55 reference statements)

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“…Some researchers analyzed the important m6A RNA regulators, which act as prognosis factors in prostate cancer, and identified RBMX, NXF1, YTHDF1, HNRNPA2B1, and TRMT112 as critical genes that have great effects on the prognosis of PCa patients (Xu et al, 2020). Many studies have also explored expression patterns and clinical prognostic values of m6A regulators in PCa, which plays a great function in the treatment of PCa (Ou- Yang et al, 2020;Zhang et al, 2021). Nevertheless, in prostate cancer with different Gleason scores, there was still heterogeneity of PCa.…”

Section: Discussionmentioning

confidence: 99%

RNA m6A Methylation Regulators Multi-Omics Analysis in Prostate Cancer

Wang

2021

Front. Genet.

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RNA N6-methyladenosine (m6A) methylation is known to be the most popular RNA modification in animals. Many research reports have elaborated on the effects of m6A regulators in medical practice, such as diagnosis, prognosis, and treatment. M6A modification has evident impacts on many aspects of RNA metabolism, just like RNA splicing, processing, translation, and stability. M6A also has a magnificent role in numerous types of cancers. We analyzed the prostate cancer datasets, from The Cancer Genome Atlas (TCGA) database, for every recognized m6A regulator in their gene expression, DNA methylation status and copy number variations (CNVs). We also systematically analyzed the relationship between different m6A regulators and the prognosis of prostate cancer. The results illustrated considerable differences in the expression of various m6A regulators between the prostate and normal cancer samples. At the same time, there were evident differences in the expression of various m6A regulators in prostate cancers with different Gleason scores. Subsequently, we determined CBLL1, FTO, YTHDC1, HNRNPA2B1 as crucial m6A regulators of prostate cancer. Premised on the expression of CBLL1, we also identified potential therapeutic agents for prostate cancer, and knockdown of HNRNPA2B1 prominently inhibited prostate cells migration and invasion in vitro experiment.

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Section: Discussionmentioning

confidence: 99%

RNA m6A Methylation Regulators Multi-Omics Analysis in Prostate Cancer

Wang

2021

Front. Genet.

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“…5 C). Researchers have studied prognosis models of PCa with modification conditions such as ferroptosis, m6A, and immune score, such as seven-gene signature discovered by Liu et al [ 25 ], eleven-gene signature discovered by Zhang et al [ 26 ], and seven-gene signature discovered by Lv et al [ 27 ]. Through comparison, we found that the accuracy of our risk signature was superior to other prognostic models according to AUC values of the ROC curve (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, Risk score focuses directly on PCa cell death patterns compared to other models. Researchers have studied prognosis models of PCa with modification conditions such as ferroptosis [ 25 ], m6A [ 26 ], and immune score [ 27 ]. The higher AUC values in our model can be found by plotting the ROC curve, which means that the accuracy of our risk signature is better than other prognostic models.…”

Section: Discussionmentioning

confidence: 99%

A novel defined risk signature based on pyroptosis-related genes can predict the prognosis of prostate cancer

Cao

Tong

et al. 2022

BMC Med Genomics

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Background Pyroptosis can not only inhibit the occurrence and development of tumors but also develop a microenvironment conducive to cancer growth. However, pyroptosis research in prostate cancer (PCa) has rarely been reported. Methods The expression profile and corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients were divided into different clusters using consensus clustering analysis, and differential genes were obtained. We developed and validated a prognostic biomarker for biochemical recurrence (BCR) of PCa using univariate Cox analysis, Lasso-Cox analysis, Kaplan–Meier (K–M) survival analysis, and time-dependent receiver operating characteristics (ROC) curves. Results The expression levels of most pyroptosis-related genes (PRGs) are different not only between normal and tumor tissues but also between different clusters. Cluster 2 patients have a better prognosis than cluster 1 patients, and there are significant differences in immune cell content and biological pathway between them. Based on the classification of different clusters, we constructed an eight genes signature that can independently predict the progression-free survival (PFS) rate of a patient, and this signature was validated using a GEO data set (GSE70769). Finally, we established a nomogram model with good accuracy. Conclusions In this study, PRGs were used as the starting point and based on the expression profile and clinical data, a prognostic signature with a high predictive value for biochemical recurrence (BCR) following radical prostatectomy (RP) was finally constructed, and the relationship between pyroptosis, immune microenvironment, and PCa was explored, providing important clues for future research on pyroptosis and immunity.

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“…The landscape of m6A variation in PCa was recently reported [ 26 , 36 – 40 ], although no scholars have analyzed sufficient m6A regulators and verified them using clinical PCa tissues. To overcome these shortcomings, we explored all 25 widely-acknowledged m6A regulators and assessed relevant associations of various clinicopathological characteristics, such as TN, GS, pT, TP53 mutation, and survival analysis of RFS using TCGA-PRAD data.…”

Section: Discussionmentioning

confidence: 99%

Construction of a risk prediction model using m6A RNA methylation regulators in prostate cancer: comprehensive bioinformatic analysis and histological validation

2022

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Background Epigenetic reprogramming reportedly has a crucial role in prostate cancer (PCa) progression. RNA modification is a hot topic in epigenetics, and N6-methyladenosine (m6A) accounts for approximately 60% of RNA chemical modifications. The aim of this study was to evaluate the m6A modification patterns in PCa patients and construct a risk prediction model using m6A RNA regulators. Materials and methods Analyses were based on the levels of 25 m6A regulators in The Cancer Genome Atlas (TCGA). Differentially expressed gene (DEG) and survival analyses were performed according to TCGA-PRAD clinicopathologic and follow-up information. To detect the influences of m6A regulators and their DEGs, consensus clustering analysis was performed, and tumor mutational burden (TMB) estimation and tumor microenvironment (TME) cell infiltration were assessed. mRNA levels of representative genes were verified using clinical PCa data. Results Diverse expression patterns of m6A regulators between tumor and normal (TN) tissues were detected regarding Gleason score (GS), pathological T stage (pT), TP53 mutation, and survival comparisons, with HNRNPA2B1 and IGFBP3 being intersecting genes. HNRNPA2B1 was upregulated in advanced stages (GS > 7, pT3, HR > 1, and TP53 mutation), as verified using clinical PCa tissue. Three distinct m6A modification patterns were identified through consensus clustering analysis, but no significant difference was found among these groups in recurrence-free survival (RFS) analysis. Six DEGs of m6A clusters (m6Aclusters) were screened through univariate Cox regression analysis. MMAB and PAIAP2 were intersecting genes for the five clinical factors. MMAB, which was upregulated in PCa compared with TN, was verified using clinical PCa samples. Three distinct subgroups were established according to the 6 DEGs. Cluster A involved the most advanced stages and had the poorest RFS. The m6A score (m6Ascore) was calculated based on the 6 genes, and the low m6Ascore group showed poor RFS with a negative association with infiltration for 16 of 23 immune-related cells. Conclusion We screened DEGs of m6Aclusters and identified 6 genes (BAIAP2, TEX264, MMAB, JAGN1, TIMM8AP1, and IMP3), with which we constructed a highly predictive model with prognostic value by dividing TCGA-PRAD into three distinct subgroups and performing m6Ascore analysis. This study helps to elucidate the integral effects of m6A modification patterns on PCa progression.

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Malignant Evaluation and Clinical Prognostic Values of M6A RNA Methylation Regulators in Prostate Cancer

Cited by 8 publications

References 54 publications

RNA m6A Methylation Regulators Multi-Omics Analysis in Prostate Cancer

RNA m6A Methylation Regulators Multi-Omics Analysis in Prostate Cancer

A novel defined risk signature based on pyroptosis-related genes can predict the prognosis of prostate cancer

Construction of a risk prediction model using m6A RNA methylation regulators in prostate cancer: comprehensive bioinformatic analysis and histological validation

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