Identification and validation of cigarette smoking-related genes in predicting prostate cancer development through bioinformatic analysis and experiments

The morbidity and mortality rates of prostate cancer (PCa) are high among elderly men worldwide. Several factors, such as heredity, obesity, and environment are associated with the occurrence of PCa. Cigarette smoking, which is also an important factor in the development of PCa, can lead to genetic alterations and consequently promote PCa development. However, the smoking-induced genetic alterations in PCa are unclear. This study aimed to identify the potential smoking-related genes associated with PCa development. The smoking-related differentially expressed genes (DEGs) were identified using the Gene Expression Omnibus (GEO) which included lots of PCa datasets. DEGs were subjected to protein-protein interaction (PPI) network analysis to identify the hub genes. The pathways in which these hub genes were enriched were identified. The Cancer Genome Atlas (TCGA) dataset was used to examine the expression of smoking-related genes in PCa samples and estimate their value in predicting tumor progression and prognosis. In total, 110 smoking-related DEGs were got from GSE68135 dataset which included microarray data of PCa patients with smoking or not and 14 smoking-related key genes associated with PCa were identified from PPI network. The expression of the following seven key genes was altered in TCGA PCa patients: EWSR1, SRSF6, COL6A3, FBLN1, DCN, CYP2J2, and PLA2G2A. EWSR1, SRSF6, FBLN1, and CYP2J2 also influenced PCa progression. Additionally, EWSR1 influenced disease-free survival. In the logistic regression model, CYP2J2, which exhibited the highest risk scores, was identified as the risk gene for PCa. We also found one of the smoking-related genes: EWSR1 was truly upregulated in clinical PCa patients and influenced PCa cells invasion and proliferation. This study identified the function of smoking-related genes involved in the progression of PCa.

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Publication Details

Journal
Discover Oncology
Published
2024-12-03
DOI
https://doi.org/10.1007/s12672-024-01645-2
Citations
3
Primary Topic
Prostate Cancer Treatment and Research
Type
article
Field-Weighted Citation Impact
1.26

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article

Identification and validation of cigarette smoking-related genes in predicting prostate cancer development through bioinformatic analysis and experiments

Xinan Wang, Dujian Li, Duocheng Qian, Tengfei Lv et al.
3 citations
Discover Oncology
Prostate Cancer Treatment and Research
1.26
article

Identification and validation of cigarette smoking-related genes in predicting prostate cancer development through bioinformatic analysis and experiments

Xinan Wang, Dujian Li, Duocheng Qian, Tengfei Lv, Xi Chen
article en
3 citations

Abstract

The morbidity and mortality rates of prostate cancer (PCa) are high among elderly men worldwide. Several factors, such as heredity, obesity, and environment are associated with the occurrence of PCa. Cigarette smoking, which is also an important factor in the development of PCa, can lead to genetic alterations and consequently promote PCa development. However, the smoking-induced genetic alterations in PCa are unclear. This study aimed to identify the potential smoking-related genes associated with PCa development. The smoking-related differentially expressed genes (DEGs) were identified using the Gene Expression Omnibus (GEO) which included lots of PCa datasets. DEGs were subjected to protein-protein interaction (PPI) network analysis to identify the hub genes. The pathways in which these hub genes were enriched were identified. The Cancer Genome Atlas (TCGA) dataset was used to examine the expression of smoking-related genes in PCa samples and estimate their value in predicting tumor progression and prognosis. In total, 110 smoking-related DEGs were got from GSE68135 dataset which included microarray data of PCa patients with smoking or not and 14 smoking-related key genes associated with PCa were identified from PPI network. The expression of the following seven key genes was altered in TCGA PCa patients: EWSR1, SRSF6, COL6A3, FBLN1, DCN, CYP2J2, and PLA2G2A. EWSR1, SRSF6, FBLN1, and CYP2J2 also influenced PCa progression. Additionally, EWSR1 influenced disease-free survival. In the logistic regression model, CYP2J2, which exhibited the highest risk scores, was identified as the risk gene for PCa. We also found one of the smoking-related genes: EWSR1 was truly upregulated in clinical PCa patients and influenced PCa cells invasion and proliferation. This study identified the function of smoking-related genes involved in the progression of PCa.

Discover OncologyVol. 15(1)
Tongji University (CN), Jiaxing University (CN), Tongji Hospital (CN)
National Natural Science Foundation of China
Good health and well-being
Openalex Percentile: Top 18%
Prostate Cancer Treatment and Research
1.26
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