Analysis of RNA m6A methylation regulators and tumour immune cell infiltration characterization in prostate cancer

Abstract Background Immune infiltration of Prostate cancer (PCa) was highly related to clinical outcomes. However, previous works failed to elucidate the diversity of different immune cell types that make up the function of the immune response system. The aim of the study was to uncover the composition of TIICs in PCa utilizing the CIBERSORT algorithm and further reveal the molecular characteristics of PCa subtypes. Method In the present work, we employed the CIBERSORT method to evaluate the relative proportions of immune cell profiling in PCa and adjacent samples, normal samples. We analyzed the correlation between immune cell infiltration and clinical information. The tumor-infiltrating immune cells of the TCGA PCa cohort were analyzed for the first time. The fractions of 22 immune cell types were imputed to determine the correlation between each immune cell subpopulation and clinical feature. Three types of molecular classification were identified via R-package of “CancerSubtypes”. The functional enrichment was analyzed in each subtype. The submap and TIDE algorithm were used to predict the clinical response to immune checkpoint blockade, and GDSC was employed to screen chemotherapeutic targets for the potential treatment of PCa. Results In current work, we utilized the CIBERSORT algorithm to assess the relative proportions of immune cell profiling in PCa and adjacent samples, normal samples. We investigated the correlation between immune cell infiltration and clinical data. The tumor-infiltrating immune cells in the TCGA PCa cohort were analyzed. The 22 immune cells were also calculated to determine the correlation between each immune cell subpopulation and survival and response to chemotherapy. Three types of molecular classification were identified. Each subtype has specific molecular and clinical characteristics. Meanwhile, Cluster I is defined as advanced PCa, and is more likely to respond to immunotherapy. Conclusions Our results demonstrated that differences in immune response may be important drivers of PCa progression and response to treatment. The deconvolution algorithm of gene expression microarray data by CIBERSOFT provides useful information about the immune cell composition of PCa patients. In addition, we have found a subtype of immunopositive PCa subtype and will help to explore the reasons for the poor effect of PCa on immunotherapy, and it is expected that immunotherapy will be used to guide the individualized management and treatment of PCa patients.

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Abstract 389: The dual mTOR inhibitor, AZD2014, and castration increase intra-tumoral immune cell infiltration and anti-tumour activity in a genetically engineered mouse model of prostate cancer

10.1158/1538-7445.am2016-389 ◽

2016 ◽

Author(s):

Chiranjeevi Sandi ◽

Antonio Ramos-Montoya ◽

Sergio L. Filisbino ◽

Adina Hughes ◽

Suzanne Mosely ◽

...

Keyword(s):

Prostate Cancer ◽

Mouse Model ◽

Mtor Inhibitor ◽

Immune Cell ◽

Genetically Engineered ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Anti Tumour Activity ◽

Genetically Engineered Mouse Model ◽

Tumour Activity

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Characterization of the intra-prostatic immune cell infiltration in androgen-deprived prostate cancer patients

Journal of Immunological Methods ◽

10.1016/j.jim.2009.06.004 ◽

2009 ◽

Vol 348 (1-2) ◽

pp. 9-17 ◽

Cited By ~ 124

Author(s):

Philippe O. Gannon ◽

Alexis O. Poisson ◽

Nathalie Delvoye ◽

Réjean Lapointe ◽

Anne-Marie Mes-Masson ◽

...

Keyword(s):

Prostate Cancer ◽

Cancer Patients ◽

Immune Cell ◽

Cell Infiltration ◽

Immune Cell Infiltration

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lncRNA PCAT14 Is a Diagnostic Marker for Prostate Cancer and Is Associated with Immune Cell Infiltration

Disease Markers ◽

10.1155/2021/9494619 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yunkun Yan ◽

Jianjun Liu ◽

Zhijian Xu ◽

Mushi Ye ◽

Jianchang Li

Keyword(s):

Prostate Cancer ◽

Dendritic Cells ◽

Immune Cells ◽

Noncoding Rna ◽

Immune Cell ◽

Diagnostic Marker ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Primary Therapy ◽

The Relationship

Objective. To investigate the relationship between the long noncoding RNA (lncRNA) Prostate cancer-associated transcription factors 14 (PCAT14) and the clinical characteristics of prostate cancer and immune cell infiltration. Methods. The relationship between PCAT14 expression and the clinicopathological characteristics of prostate cancer was analyzed based on The Cancer Genome Atlas (TCGA) database. Receiver operating characteristic (ROC) curves were used to evaluate the value of PCAT14 as a diagnostic marker for prostate cancer. The relationship between PCAT14 and immune cell infiltration was analyzed to explore the effect of PCAT14 on the immune-related functions of prostate cancer. Results. The ROC curve showed that PCAT14 had a significant diagnostic ability ( area under curve = 0.818 ) for prostate cancer. A reduced expression of PCAT14 in prostate cancer was related to T stage, N stage, primary therapy outcome, residual tumor, Gleason score, and age. The expression of PCAT14 was independently associated with the progression-free interval in prostate cancer patients. The infiltration of immune cells in prostate cancer showed a significant negative correlation between the expression of PCAT14 and plasmacytoid dendritic cells, activated dendritic cells, regulatory T cells, and neutrophils. Conclusions. PCAT14 is highly expressed in prostate cancer and is expected to be a diagnostic marker. PCAT14 might promote the development of prostate cancer through chemokines, antimicrobials, and cytokines that affect the infiltration of immune cells.

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Detailed Molecular and Immune Marker Profiling of Archival Prostate Cancer Samples Reveals an Inverse Association between TMPRSS2:ERG Fusion Status and Immune Cell Infiltration

Journal of Molecular Diagnostics ◽

10.1016/j.jmoldx.2020.02.012 ◽

2020 ◽

Vol 22 (5) ◽

pp. 652-669 ◽

Cited By ~ 1

Author(s):

Srinivasa R. Rao ◽

Nasullah K. Alham ◽

Elysia Upton ◽

Stacey McIntyre ◽

Richard J. Bryant ◽

...

Keyword(s):

Prostate Cancer ◽

Immune Cell ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Inverse Association ◽

Immune Marker

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Molecular mechanisms, immune cell infiltration, and potential drugs for prostate cancer

Cancer Biomarkers ◽

10.3233/cbm-200939 ◽

2021 ◽

pp. 1-10

Author(s):

Yunkun Yan ◽

Xingning Mao ◽

Qingyun Zhang ◽

Yu Ye ◽

Yan Dai ◽

...

Keyword(s):

Prostate Cancer ◽

Molecular Mechanisms ◽

Immune Cell ◽

Kegg Pathway ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Hub Genes ◽

T Cell Infiltration ◽

Rnaseq Data ◽

Hormone Sensitive

BACKGROUND: The molecular mechanisms involved in the prostate cancer and their relationship with immune cell infiltration are not fully understood. The prostate cancer patients undergoing standard androgen deprivation therapy eventually develop castration resistant prostate cancer (CRPC) for which there is no effective treatment currently available, and the hub genes involved in this process remain unclear. OBJECTIVE: To study prostate cancer systematically and comprehensively. METHODS: Differentially expressed genes (DEGs) of prostate cancer were screened in The Cancer Genome Atlas (TCGA) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Connectivity Map (Cmap) software was applied to discover potential treatment drugs. A protein-protein interaction (PPI) analysis was performed to obtained the hub genes, and the relationship between hub genes and immune cell infiltration was investigated. Next, RNAseq data of hormone-sensitive prostate cancer samples and CRPC samples obtained from TCGA database was further analyzed to identify DEGs. Finally, a PPI analysis was performed to obtain the hub genes. RESULTS: A total of 319 DEGs were identified between prostate cancer samples and normal adjacent samples from TCGA database using comparative analysis. The KEGG pathway analysis showed significant correlations with drug metabolism, metabolism of xenobiotics by cytochrome P450, and chemical carcinogenesis. AMACR, FOLH1 and NPY, three hub genes, were found to be upregulated. FOLH1 was positively correlated with CD8+ T cell infiltration. FOLH1, AMACR, and NPY were negatively correlated with CD4+ T cell infiltration. A total of 426 DEGs were identified from RNAseq data of hormone-sensitive prostate cancer samples and CRPC samples using further comparative analysis. KEGG pathway enrichment analysis showed significant correlations with arachidonic acid metabolism, PPAR signaling pathway, AMPK signaling pathway, and metabolic

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

10.21203/rs.3.rs-95052/v1 ◽

2020 ◽

Author(s):

Xiaobo Wu ◽

Qianwen Ge ◽

Chen Yang ◽

Yishuo Wu ◽

Mengbo Hu ◽

...

Keyword(s):

Prostate Cancer ◽

Cancer Progression ◽

Cancer Metastasis ◽

Immune Cell ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Consensus Clustering ◽

Prostate Cancer Progression ◽

Prostate Cancer Metastasis ◽

Prostate Cancers

Abstract BackgroundProstate cancer is one of the most common cancers in men. Usually, most prostate cancers are localized in initial diagnoses and grow slowly. Patients with localized prostate cancers have a nearly 100% 5-year survival rate; however, the 5-year survival rate of metastatic or progressive prostate cancer 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 prostate cancer remain unclear. MethodsHere, we comprehensively examined the patterns of 21 m6A regulators across 494 prostate cancers and systematically correlated m6A regulators with prostate cancer progression and immune cell infiltration. Consensus clustering was utilized for the subtype identification of m6A regulators for prostate cancers. Each subtype signature genes were obtained by the pairwise differentially expressed genes. Featured pathways of m6A subtypes were predicted consequently. The m6A score was constructed to predict the m6A activation. The association of m6A score with patients’ survival, metastasis and immune cell infiltration were also investigated. ResultsWe identified three distinct clusters in prostate cancer 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 prostate cancer. Moreover, we established an m6A score to perceive the m6A regulator activation for prostate cancer. The m6A score is significantly associated with Gleason scores and metastasis in prostate cancer. The predictive capacity of m6A score on prostate cancer metastasis was also validated in another independent cohort. ConclusionOur study revealed the critical role of m6A regulators in prostate cancer progression and m6A score is promising predictive biomarker for prostate cancer metastasis.

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