Prospective analysis of autophagy in prostate cancer cells based on gene expression databases and investigation of the C-Met regulatory mechanism
10.13431/j.cnki.immunol.j.20250104
- VernacularTitle:基于基因表达数据库的前列腺癌细胞自噬预后分析及C-Met调节机制研究
- Author:
Ru ZHANG
1
;
Yongqiang XIE
;
Qiang ZHAO
;
Keqiang CHAI
;
Yulin LIU
Author Information
1. 甘肃中医药大学第一临床医学院,兰州 730000;甘肃中医药大学第三附属医院皮肤科,甘肃 白银 730900
- Publication Type:Journal Article
- Keywords:
prostate cancer;
gene expression database;
autophagy;
stromal epidermal transition factor;
prognosis
- From:
Immunological Journal
2025;41(10):750-761
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the prognostic value of mitochondrial autophagy-related genes(MRGs)in prostate cancer(PCa),and to reveal their regulatory relationship with interstitial epidermal transforming factor(C-Met)based on the Gene Expression Database(GEO).Methods Single-cell RNA sequencing(scRNA-seq)data of three PCa samples were obtained from the GSE153892 dataset of GEO,and MRGs were collected from the Genecards database and previous literature.The scRNA-seq data were processed and analyzed using the Seurat software package,including quality control,gene expression screening,cell type annotation,differentially expressed genes(DEGs)identification,and intersection analysis with MRGs.The transcriptome data of PCa and control samples were downloaded from the Cancer Genome Atlas Database(TCGA)-PRAD cohort,and differential expression analysis and copy number variation analysis were conducted.The non-negative matrix factorization algorithm is adopted to conduct cluster analysis on PCa samples to identify different PCa subtypes.A prognostic risk model based on intersection genes was constructed,and the predictive ability of the model was analyzed through Kaplan-Meier survival curve analysis and time-dependent receiver operating characteristic(ROC)curve analysis.Conduct independent prognostic analysis,construct a nomogram model based on risk scores and clinical characteristics,and evaluate its ability to predict patient survival rates.The possibility of immune infiltration and tumor immune escape in PCa samples was evaluated by using the single-sample Gene Set Enrichment analysis(ssGSEA)algorithm and the TIDE database.The relationship between intersection genes and C-Met expression was analyzed using Pearson correlation analysis.Results scRNA-seq data analysis identified five cell types including B lymphocytes,epithelial cells,monocytes,natural killer cells and T lymphocytes,and discovered the intersection genes that were highly expressed in different cell types.Through differential expression analysis,genes significantly related to the prognosis of PCa patients were screened out,and a prognostic risk model was constructed.Six genes such as ADH5 and CAT were retained through LASSO analysis.A diagnostic model was constructed and grouped.There was a significant difference in survival time between the two groups in the internal test set(P<0.05).ROC curve evaluation showed that the model had a good predictive ability for 1-,3-,and 5-year survival rates.The external test set verified that there was a statistically significant difference in the expression of intersection genes(P<0.05).Independent prognostic analysis identified T stage and risk score as independent prognostic factors.A nomogram model was constructed.Calibration curve and ROC curve analyses showed that the predictive ability of this model was superior to that of the simple risk model.ssGSEA analysis revealed differences in the abundance of immune cell inflammation and immune function scores between the two groups.Most immune cells,immune function,and risk scores were related to the modeling genes.There were significant differences in TIDE scores and multiple immune checkpoints between the high-risk and low-risk groups(P<0.05).BCAT2,DCXR,OGT and FUS were positively correlated with the expression of C-Met,while ADH5 and CAT were negatively correlated with the expression of C-Met(P<0.05).Conclusion The prognostic risk model based on intersection genes can effectively predict the prognosis of patients with PCa,and the risk score and T stage are independent prognostic factors for PCa.The correlation analysis of intersection genes and C-Met expression provides a new idea for the targeted therapy of PCa.
