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Alternations of gene expression in PI3K and AR pathways and DNA methylation features contribute to metastasis of prostate cancer

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Abstract

Objective

The molecular heterogeneity of prostate cancer (PCa) gives rise to distinct tumor subclasses based on epigenetic modification and gene expression signatures. Identification of clinically actionable molecular subtypes of PCa is key to improving patient outcome, and the balance between specific pathways may influence PCa outcome. It is also urgent to identify progression-related markers through cytosine-guanine (CpG) methylation in predicting metastasis for patients with PCa.

Methods

We performed bioinformatics analysis of transcriptomic, and clinical data in an integrated cohort of 551 prostate samples. The datasets included retrospective The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. Two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to select significant CpGs.

Results

We found that PCa progression is more likely to occur after the third year through conditional survival (CS) analysis, and prostate-specific antigen (PSA) was a better predictor of Progression-free survival (PFS) than Gleason score (GS). Our study first demonstrated that PCa tumors have distinct expression profiles based on the expression of genes involved in androgen receptor (AR) and PI3K-AKT, which influence disease outcome. Our results also indicated that there are multiple phenotypes relevant to the AR-PI3K axis in PCa, where tumors with mixed phenotype may be more aggressive or have worse outcome than quiescent phenotype. In terms of epigenetics, we obtained CpG sites and their corresponding genes which have a good predictive value of PFS. However, various evidences showed that the predictive value of CpGs corresponding genes was much lower than GpG sites in Overall survival (OS) and PFS.

Conclusions

PCa classification specific to AR and PI3K pathways provides novel biological insight into previously established PCa subtypes and may help develop personalized therapies. Our results support the potential clinical utility of DNA methylation signatures to distinguish tumor metastasis and to predict prognosis and outcomes.

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Availability of data and materials

All data obtained and/or analyzed in this study were available from the corresponding authors in a reasonable request.

Abbreviations

PCa:

Prostate cancer

TCGA:

The Cancer Genome Atlas

GEO:

Gene Expression Omnibus

CpG:

Cytosine-guanine

CS:

Conditional survival

PSA:

Prostate-specific antigen

GS:

Gleason score

OS:

Overall survival

PFS:

Progression-free survival

AR:

Androgen receptor

CRPC:

Castration-resistant prostate cancer

DEGs:

Differentially expressed genes

GO:

Gene ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

BP:

Biological process

CC:

Cellular component

MF:

Molecular function

GSCA:

Gene Set Cancer Analysis

GDSC:

Genomics of Drug Sensitivity in Cancer

LASSO:

Least Absolute Shrinkage and Selector Operation

SVM-RFE:

Support Vector Machine-Recursive Feature Elimination

EMT:

Epithelial-mesenchymal transition

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Acknowlegements

Not applicable.

Funding

This work was supported by the Natural Science Foundation of China (No. 81972373) and Scientific Research Foundation for Advanced Talents, Xiang’an Hospital of Xiamen University (NO. PM201809170001).

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Authors and Affiliations

  1. Department of Urology, School of Medicine, Xiang’an Hospital of Xiamen University, Xiamen University, Xiamen, 361000, China

    Yue Zhao, Huimin Sun & Chen Shao

  2. School of Life Science and Technology, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150001, China

    Xin Hu, Haoran Yu & Xin Liu

Authors
  1. Yue Zhao
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  2. Xin Hu
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  3. Haoran Yu
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  4. Xin Liu
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  6. Chen Shao
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Contributions

Conception and design: YZ, CS; provision of study materials: XH, HRY, XL, and HMS; collection and assembly of data: YZ, XH, HRY; data analysis and interpretation: YZ, XH, XL; manuscript writing: YZ, CS. The authors read and approved the final manuscript.

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Correspondence to Chen Shao.

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Zhao, Y., Hu, X., Yu, H. et al. Alternations of gene expression in PI3K and AR pathways and DNA methylation features contribute to metastasis of prostate cancer. Cell. Mol. Life Sci. 79, 436 (2022). https://doi.org/10.1007/s00018-022-04456-2

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