Glycyl-tRNA Synthetase (GARS) Expression Is Associated with Prostate Cancer Progression and Its Inhibition Decreases Migration, and Invasion In Vitro.
Ealia Khosh KishYaser GamallatMuhammad ChoudhrySunita GhoshSima SeyediTarek A BismarPublished in: International journal of molecular sciences (2023)
Glycyl-tRNA synthetase (GARS) is a potential oncogene associated with poor overall survival in various cancers. However, its role in prostate cancer (PCa) has not been investigated. Protein expression of GARS was investigated in benign, incidental, advanced, and castrate-resistant PCa (CRPC) patient samples. We also investigated the role of GARS in vitro and validated GARS clinical outcomes and its underlying mechanism, utilizing The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA PRAD) database. Our data revealed a significant association between GARS protein expression and Gleason groups. Knockdown of GARS in PC3 cell lines attenuated cell migration and invasion and resulted in early apoptosis signs and cellular arrest in S phase. Bioinformatically, higher GARS expression was observed in TCGA PRAD cohort, and there was significant association with higher Gleason groups, pathological stage, and lymph nodes metastasis. High GARS expression was also significantly correlated with high-risk genomic aberrations such as PTEN , TP53 , FXA1 , IDH1 , SPOP mutations, and ERG , ETV1 , and ETV4 gene fusions. Gene Set Enrichment Analysis (GSEA) of GARS through the TCGA PRAD database provided evidence for upregulation of biological processes such as cellular proliferation. Our findings support the oncogenic role of GARS involved in cellular proliferation and poor clinical outcome and provide further evidence for its use as a potential biomarker in PCa.
Keyphrases
- prostate cancer
- poor prognosis
- radical prostatectomy
- lymph node
- copy number
- single cell
- acute lymphoblastic leukemia
- binding protein
- genome wide
- oxidative stress
- cell proliferation
- emergency department
- atomic force microscopy
- long non coding rna
- dna methylation
- papillary thyroid
- deep learning
- stem cells
- young adults
- locally advanced
- transcription factor
- adverse drug
- bone marrow
- risk assessment
- cell cycle
- electronic health record
- cell therapy
- single molecule
- artificial intelligence
- mesenchymal stem cells