High expression of centromere protein A and its molecular mechanism and clinical significance in prostate cancer: A study based on data mining and immunohistochemistry.
Fang-Cheng JiangGao-Qiang ZhaiJia-Lin LiuRui-Gong WangYuan-Ping YangHarivignesh MurugesanXiao-Xiang YuXiu-Fang DuJuan HeZhen-Bo FengShang Ling PanGang ChenSheng-Hua LiZhi-Guang HuangPublished in: IET systems biology (2023)
The progression of prostate cancer (PCa) leads to poor prognosis. However, the molecular mechanism of PCa is still not completely clear. This study aimed to elucidate the important role of centromere protein A (CENPA) in PCa. Large numbers of bulk RNA sequencing (RNA-seq) data and in-house immunohistochemistry data were used in analysing the expression level of CENPA in PCa and metastatic PCa (MPCa). Single-cell RNA-seq data was used to explore the expression status of CENPA in different prostate subpopulations. Enrichment analysis was employed to detect the function of CENPA in PCa. Clinicopathological parameters analysis was utilised in analysing the clinical value of CENPA. The results showed that CENPA was upregulated in PCa (standardised mean difference [SMD] = 0.83, p = 0.001) and MPCa (SMD = 0.61, p = 0.029). CENPA was overexpressed in prostate cancer stem cells (CSCs) with androgen receptor (AR) negative compared to epithelial cells with AR positive. CENPA may influence the development of PCa through affecting cell cycle. Patients with nodal metastasis had higher expression level of CENPA. And patients with high CENPA expression had poor disease-free survival. Taken together, Overexpression of CENPA may influence the development of PCa by regulating cell cycle and promoting metastasis.
Keyphrases
- poor prognosis
- rna seq
- single cell
- prostate cancer
- cell cycle
- long non coding rna
- cell proliferation
- binding protein
- electronic health record
- radical prostatectomy
- free survival
- cancer stem cells
- high throughput
- big data
- small cell lung cancer
- squamous cell carcinoma
- small molecule
- radiation therapy
- machine learning
- lymph node
- data analysis