The Application of Prognostic Model of Lysosomal Related Genes in Bladder Cancer

Yijun AN; Lidan YU; Meisu ZHAO; Dongmei MA; Chunhua YANG; Yao KONG

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Yijun AN, Lidan YU, Meisu ZHAO, Dongmei MA, Chunhua YANG, Yao KONG. The Application of Prognostic Model of Lysosomal Related Genes in Bladder Cancer[J]. Journal of Kunming Medical University.

Citation:

Yijun AN, Lidan YU, Meisu ZHAO, Dongmei MA, Chunhua YANG, Yao KONG. The Application of Prognostic Model of Lysosomal Related Genes in Bladder Cancer[J]. Journal of Kunming Medical University.

Yijun AN, Lidan YU, Meisu ZHAO, Dongmei MA, Chunhua YANG, Yao KONG. The Application of Prognostic Model of Lysosomal Related Genes in Bladder Cancer[J]. Journal of Kunming Medical University.

Citation:

Yijun AN, Lidan YU, Meisu ZHAO, Dongmei MA, Chunhua YANG, Yao KONG. The Application of Prognostic Model of Lysosomal Related Genes in Bladder Cancer[J]. Journal of Kunming Medical University.

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The Application of Prognostic Model of Lysosomal Related Genes in Bladder Cancer

1.
Dept. of Vascular Oncology
2.
Dept. of Neurology
3.
Dept. of Gynaecology
4.
Dept. of Radiotherapy，The 1st People’s Hospital of Zhaotong，Zhaotong Yunnan 657000，China

Received Date: 2024-01-15
Available Online: 2024-04-29

Abstract

Abstract

Objective To explore the feasibility of applying the prognosis model based on lysosomal related genes（LRGs） in patients with bladder cancer （BC）. Methods Bladder cancer data were downloaded from The Cancer Genome Atlas （TCGA） program and dataset of GSE13507 were downloaded from Gene Expression Omnibus （GEO）. Differentially expressed LRGs related to the survival of BC in the TCGA database were screened by differential analysis and single factor proportional hazards model （COX） regression analysis via R software. LASSO regression was used to construct a prognostic risk model. BC patients were divided into the high and low risk groups according to the median risk score. Survival analysis were used to compare the survival differences between the high-risk and low-risk groups of BC patients, and validate in GEO database. Univariate and multivariate cox regression analysis were used to verify whether the risk scores were an independent risk factor affecting the prognosis of BC patients. The receiver operating characteristic （ROC） curve was used to evaluate the accuracy of prognostic model predictions. GO and KEGG enrichment analysis were used to explore the biological functions and signaling pathways of differentially expressed genes between the high-risk and low-risk groups. Immunoassay was used to explore the differences in immune function between the high-risk and low-risk groups. Results A total of 44 differentially expressed lysosomal related genes were screened, of which 9 genes related to the prognosis were used to construct the prognosis model in this study. Survival analysis showed that the prognosis of the low-risk group was significantly better than that of the high-risk group （P < 0.05）, which was verified in GEO database. The area under the ROC curve （AUC） of the BC prognosis risk scoring model to predict the 1-, 3- and 5-year survival of patients were 0.696, 0.717 and 0.738, respectively. Independent prognostic analysis showed that this prognostic risk model was an independent prognostic factor for BC patients. GO enrichment analysis indicated that the differential genes between the high-risk and low-risk groups were mainly involved in cell structure and function correlation. KEGG enrichment analysis suggested that the differential genes were mainly enriched in PI3K-Akt signaling pathway. Immunological analysis showed that there were significant differences in immune cell infiltration and immune function between the two groups. Conclusion Risk model of lysosomal related genes in BC can accurately and effectively predict the prognosis of BC patients.
- Bladder cancer,
- Lysosomal related genes,
- Prognostic model,
- Bioinformatics

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References(12)

References

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