scholarly journals DNA Methylation Modification Regulator-Mediated Molecular Clusters and Tumor Metabolic Characterization in Prostate Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Yanlong Zhang ◽  
Xuezhi Liang ◽  
Liyun Zhang ◽  
Dongwen Wang
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
Drug Sensitivity ◽  
Meta Analysis ◽  
Principal Component ◽  
Tumor Metabolism ◽  
Mrna Levels ◽  
Close Link ◽  
Metabolic Characteristics ◽  
Number Variation

Background. An increasing number of studies have indicated a close link between DNA methylation and tumor metabolism. However, the overall influence of DNA methylation on tumor metabolic characteristics in prostate cancer (PCa) remains unclear. Methods. We first explored the subtypes of DNA methylation modification regulators and tumor metabolic features of 1,205 PCa samples using clustering analysis and gene set variation analysis based on the mRNA levels of DNA methylation modification regulators. A DNA methylation-related score (DMS) was calculated using principal component analysis and the DNA methylation modification-related gene signatures to quantify DNA methylation characteristics. We then performed a meta-analysis to identify the hazard ratio of DMS in the six cohorts. In addition, a nomogram was drawn using univariate and multivariate Cox analyses based on the DMS and clinical variables. Finally, a drug sensitivity analysis of the DMS was performed based on the genomics of drug sensitivity in cancer datasets. Results. Three PCa clusters showing different DNA methylation modification patterns and tumor metabolic features were identified. A DMS system was established to quantify the characteristics of DNA methylation modification. PCa samples showed a differential metabolic landscape between the high and low DMS groups. The prognostic value of the DMS and nomogram was independently validated in multiple cohorts. A high DMS was associated with increases in the tumor mutation burden, copy number variation, and microsatellite instability; high tumor heterogeneity; and poor prognosis. Finally, DMS was closely related to different types of antitumor treatment. Conclusion. Improving the understanding of tumor metabolism by characterizing DNA methylation modification patterns and using the DMS may help clinicians predict prognosis and aid in more personalized antitumor therapy strategies for PCa.

scholarly journals Metabolic characterization and metabolism-score of tumor to predict the prognosis in prostate cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yanlong Zhang ◽  
Xuezhi Liang ◽  
Liyun Zhang ◽  
Dongwen Wang
Keyword(s):  
Prostate Cancer ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Tumor Metabolism ◽  
Activity Level ◽  
Gene Set Enrichment Analysis ◽  
Dna Mutation ◽  
Metabolic Characteristics

AbstractTumor metabolism patterns have been reported to be associated with the prognosis of many cancers. However, the metabolic mechanisms underlying prostate cancer (PCa) remain unknown. This study aimed to explore the metabolic characteristics of PCa. First, we downloaded mRNA expression data and clinical information of PCa samples from multiple databases and quantified the metabolic pathway activity level using single-sample gene set enrichment analysis (ssGSEA). Through unsupervised clustering and principal component analyses, we explored metabolic characteristics and constructed a metabolic score for PCa. Then, we independently validated the prognostic value of our metabolic score and the nomogram based on the metabolic score in multiple databases. Next, we found the metabolic score to be closely related to the tumor microenvironment and DNA mutation using multi-omics data and ssGSEA. Finally, we found different features of drug sensitivity in PCa patients in the high/low metabolic score groups. In total, 1232 samples were analyzed in the present study. Overall, an improved understanding of tumor metabolism through the characterization of metabolic clusters and metabolic score may help clinicians predict prognosis and aid the development of more personalized anti-tumor therapeutic strategies for PCa.

scholarly journals Prediction of Prognosis in Patients with Prostate Cancer by DNA Methylation Classification

2020 ◽  
Author(s):  
Lei Chen ◽  
Deshen Pan ◽  
Minglei Sha ◽  
Deng Li ◽  
Chaoliang Xu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
High Risk ◽  
Copy Number Variation ◽  
Copy Number ◽  
Gene Copy Number ◽  
Tumor Burden ◽  
Gene Copy ◽  
Gene Copy Number Variation ◽  
Number Variation

Abstract Background: Prostate cancer is the second most frequently diagnosed cancer and the fifth leading cause of cancer-related death. It is estimated that the incidence of prostate cancer is on the rise worldwide. Epigenetic changes in tumors play an important role in the occurrence and development of prostate cancer. DNA methylation is one of the mechanisms of tumor epigenetic regulation and may be a new biomarker that has great potential in early tumor screening, treatment guidance and prognosis prediction. The purpose of this study was to explore a classification method from the perspective of DNA methylation.Methods: The least absolute shrinkage and selection operator (LASSO) method was used to analyze DNA methylation and RNA-seq data from the Cancer Genome Atlas (TCGA). The methylation sites with small differences were eliminated, and the 21 methylation sites with the most significant differences were retained for analysis. Using their corresponding gene expression levels, a recurrence prediction model for prostate cancer patients was constructed to distinguish high-risk, medium-risk, and low-risk cases. Immune cell abundance analysis, gene enrichment analysis, Tumor burden mutation analysis and gene copy number variation analysis were then used to analyze the differences among these three subtypes and their underlying mechanisms. Results: We observed the difference in disease-free survival (DFS) of the three methylated subtypes in the test set, which was verified in the validation set. We found three subtypes have different proportions of immune cells, especially in memory B cells, M2 macrophages, Treg cells. GSVA and GSEA analysis revealed that the relevant metastasis gene sets of prostate cancer were enriched in high-risk cases. In addition, the mutation frequencies of TP53, TTN and KMT2D were the highest, and gradually increased in the three genotypes according to Tumor burden mutation (TMB) analysis. Gene copy number variation (CNV) showed that AR, LAPTM4B, and MTDH were significantly amplified, while ATP1B2 and FAM92B were significantly deleted. Finally, univariate and multivariate analysis showed that there were statistical differences among the three methylation subtypes, which can be used as an index to predict prostate cancer recurrence.Conclusions: Our study suggests that classification based on DNA methylation is an independent factor for predicting tumor recurrence in patients with prostate cancer.

MiR-493 Induces Cytotoxic Autophagy in Prostate Cancer Cells through Regulation on PHLPP2

2020 ◽  
Vol 21 (14) ◽  
pp. 1451-1456 ◽  
Author(s):  
Jun Deng ◽  
Ming Ma ◽  
Wei Jiang ◽  
Liangliang Zheng ◽  
Suping Cui
Keyword(s):  
Prostate Cancer ◽  
Protein Expression ◽  
Cell Function ◽  
Mrna Levels ◽  
Prostate Cancer Cells ◽  
Potent Inducer ◽  
Qrt Pcr ◽  
Autophagy Gene ◽  
The Relationship ◽  
Cancer Inhibition

Background: MiR-493 promotes the proliferation of prostate cancer (PC) cells by targeting PHLPP2. We aimed to explore the relationship between miR-493 and autophagy in PC. Methods: qRT-PCR and western blotting were used to determine the mRNA levels and protein expression of miR-493, PHLPP2, autophagy gene BECN1 and ATG7 in PC cells. The autophagy gene expression was determined after PC cells transfected with miR-493 precursor or PHLPP2 precursor. Corresponding changes of autophagy phenotype and PC cell function were also studied. Results: The mRNA levels and protein expression of miR-493, PHLPP2, BECN1 and ATG7 in PC cells were significantly decreased in PC cells. Overexpression of miR-493 or PHLPP2 markedly upregulated the expression levels of BECN1 and ATG7 in PC cells. Overexpression of miR-493 and PHLPP2 markedly promoted autophagy, and inhibited the invasion and cloning formation of PC cells. Conclusion: MiR-493 is a potent inducer of cytotoxic autophagy that leads to prostate cancer inhibition by regulating on PHLPP2.

A Systematic Review and Meta-Analysis about the Effect of Bisphosphonates on the Risk of Skeletal-Related Event in Men with Prostate Cancer

2020 ◽  
Vol 20 (13) ◽  
pp. 1604-1612
Author(s):  
Congcong Wu ◽  
Hua Jiang ◽  
Jianghua Chen
Keyword(s):  
Prostate Cancer ◽  
Fixed Effects ◽  
Data Extraction ◽  
Controlled Trial ◽  
Meta Analysis ◽  
Control Group ◽  
Fixed Effects Model ◽  
Related Event ◽  
Mineral Density ◽  
Skeletal Related Event

Background: Although the adjuvant therapy of bisphosphonates in prostate cancer is effective in improving bone mineral density, it is still uncertain whether bisphosphonates could decrease the risk of Skeletal- Related Event (SRE) in patients with prostate cancer. We reviewed and analyzed the effect of different types of bisphosphonates on the risk of SRE, defined as pathological fracture, spinal cord compression, radiation therapy to the bone, surgery to bone, hypercalcemia, bone pain, or death as a result of prostate cancer. Methods: A systemic literature search was conducted on PubMed and related bibliographies. The emphasis during data extraction was laid on the Hazard Ratio (HR) and the corresponding 95% Confidence Interval (CI) from every eligible Randomized Controlled Trial (RCT). HR was pooled with the fixed effects model, and preplanned subgroup analyses were performed. Results: 5 RCTs (n = 4651) were included and analyzed finally after screening 51 articles. The meta-analysis of all participants showed no significant decrease in the risk of SRE when adding bisphosphonates to control group (HR = 0.968, 95% CI = 0.874 - 1.072, p = 0.536) with low heterogeneity (I2 = 0.0% (d.f. = 4) p = 0.679). There was no significant improvement on SRE neither in the subgroups with Metastases (M1) or Castration-Sensitive Prostate Cancer (CSPC) (respectively HR = 0.968, 95% CI = 0.874 - 1.072, p = 0.536, I2 = 0.0% (d.f. = 4) p = 0.679; HR = 0.954, 95% CI = 0.837 - 1.088, p = 0.484, I2 = 0.0% (d.f. = 3) p = 0.534). Conclusion: Our study demonstrated that bisphosphonates could not statistically significantly reduce the risk of SRE in patients with prostate cancer, neither in the subgroups with M1 or CSPC.

scholarly journals Transcriptional landscape of PTEN loss in primary prostate cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Eddie Luidy Imada ◽  
Diego Fernando Sanchez ◽  
Wikum Dinalankara ◽  
Thiago Vidotto ◽  
Ericka M. Ebot ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Meta Analysis ◽  
Pten Loss ◽  
Primary Prostate Cancer ◽  
Expression Atlas ◽  
Transcriptomic Signature ◽  
Transcriptional Changes ◽  
New Biomarkers ◽  
Adaptive Immune Systems ◽  
Transcriptional Landscape

Abstract Background PTEN is the most frequently lost tumor suppressor in primary prostate cancer (PCa) and its loss is associated with aggressive disease. However, the transcriptional changes associated with PTEN loss in PCa have not been described in detail. In this study, we highlight the transcriptional changes associated with PTEN loss in PCa. Methods Using a meta-analysis approach, we leveraged two large PCa cohorts with experimentally validated PTEN and ERG status by Immunohistochemistry (IHC), to derive a transcriptomic signature of PTEN loss, while also accounting for potential confounders due to ERG rearrangements. This signature was expanded to lncRNAs using the TCGA quantifications from the FC-R2 expression atlas. Results The signatures indicate a strong activation of both innate and adaptive immune systems upon PTEN loss, as well as an expected activation of cell-cycle genes. Moreover, we made use of our recently developed FC-R2 expression atlas to expand this signature to include many non-coding RNAs recently annotated by the FANTOM consortium. Highlighting potential novel lncRNAs associated with PTEN loss and PCa progression. Conclusion We created a PCa specific signature of the transcriptional landscape of PTEN loss that comprises both the coding and an extensive non-coding counterpart, highlighting potential new players in PCa progression. We also show that contrary to what is observed in other cancers, PTEN loss in PCa leads to increased activation of the immune system. These findings can help the development of new biomarkers and help guide therapy choices.

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