Bioinformatics-based Investigation of the Prognostic Value of ESCRT-related Genes in Osteosarcoma Assessment

Binbin MA; Shaoxiong ZHANG; Yongli GAO

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Binbin MA, Shaoxiong ZHANG, Yongli GAO. Bioinformatics-based Investigation of the Prognostic Value of ESCRT-related Genes in Osteosarcoma Assessment[J]. Journal of Kunming Medical University.

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Binbin MA, Shaoxiong ZHANG, Yongli GAO. Bioinformatics-based Investigation of the Prognostic Value of ESCRT-related Genes in Osteosarcoma Assessment[J]. Journal of Kunming Medical University.

Binbin MA, Shaoxiong ZHANG, Yongli GAO. Bioinformatics-based Investigation of the Prognostic Value of ESCRT-related Genes in Osteosarcoma Assessment[J]. Journal of Kunming Medical University.

Citation:

Binbin MA, Shaoxiong ZHANG, Yongli GAO. Bioinformatics-based Investigation of the Prognostic Value of ESCRT-related Genes in Osteosarcoma Assessment[J]. Journal of Kunming Medical University.

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Bioinformatics-based Investigation of the Prognostic Value of ESCRT-related Genes in Osteosarcoma Assessment

1.
Dept. of Emergency Trauma center
2.
Dept. of Orthopedics II
3.
Dept. of Oncology Centre，People’s Hospital of Yuxi，Yuxi Yunnan 653100，China

Received Date: 2024-06-19
Available Online: 2024-11-11

Abstract

Abstract

Objective To evaluate the prognostic value of endosomal sorting complex required for transport （ESCRT）-related genes in osteosarcoma （OS） based on bioinformatics. Methods Preprocessing was performed on 88 OS sequencing samples （with 29 death outcomes） downloaded from the TARGET database and 257 patient clinical information. The Cox proportional hazards model was constructed using the survival package to screen ESCRT genes related to survival. The STRING database was used to construct a protein-protein interaction （PPI） network, and core genes were selected based on PPI. KEGG enrichment analysis was performed on the selected core genes with more than 5 nodes. Lasso regression analysis was applied to identify ESCRT-related genes more closely related to the prognosis of OS patients. Results A total of 1486 ESCRT-related genes were identified, of which 164 were associated with survival. CLTC, MYC, INSR, PTPN1, and TNFRSF1A were identified as core genes related to the prognosis of OS patients. OS patients were randomly divided into a training set （n = 44） and a validation set （n = 44）. In the training set, OS patients in the high-risk group had significantly shorter overall survival than those in the low-risk group （P < 0.05）, and similar results were obtained in the validation set （P < 0.01）. The ROC （receiver operating characteristic） curve showed an AUC of 0.846 in the training set and 0.877 in the validation set. Prognostic survival analysis and differential analysis of core genes revealed no difference in MYC between high- and low-risk groups in the validation set, and no difference in INSR in the training set. In the overall dataset, all prognostic core genes showed significant differences （P < 0.05）. Survival analysis of core genes using the R package Survival showed significant differences in survival rates for four genes （CLTC, INSR, PTPN1, TNFRSF1A） except MYC （P > 0.05）. Univariate independent prognostic analysis identified three genes （TNFRSF1A, PTPN1, MYC） associated with OS survival. Multivariate independent prognostic analysis ultimately identified two key genes （TNFRSF1A, PTPN1） as independent factors influencing OS survival prognosis and closely related to OS patient survival. Conclusion A risk scoring model for OS survival prognosis based on the expression of two key genes, TNFRSF1A and PTPN1, was successfully constructed using bioinformatics, providing more options for clinical treatment and survival prognosis assessment of OS.
- Osteosarcoma,
- ESCRT,
- TNFRSF1A,
- PTPN1,
- Prognosis

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