Construction and validation of a novel prognostic signature for uveal melanoma based on five metabolism-related genes

Han Zhao; Yun Chen; Peijun Shen; Lan Gong; Han Zhao; Yun Chen; Peijun Shen; Lan Gong

doi:10.3934/mbe.2021399

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 6: 8045-8063. doi: 10.3934/mbe.2021399

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Research article Special Issues

Construction and validation of a novel prognostic signature for uveal melanoma based on five metabolism-related genes

1.
Department of Ophthalmology, Eye, Ear, Nose, and Throat Hospital of Fudan University, Shanghai, China
2.
Laboratory of Myopia, NHC Key Laboratory of Myopia (Fudan University), Chinese Academy of Medical Sciences, Shanghai, China
3.
Shanghai Key Laboratory of Visual Impairment and Restoration, Fudan University, Shanghai, China
4.
Department of Stomatology, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China
5.
Department of Gastroenterology, the First Affiliated Hospital of Xinxiang Medical University, Henan, China
^† These authors contributed equally to this work

Received: 03 August 2021 Accepted: 07 September 2021 Published: 15 September 2021

Background
Uveal melanoma (UM) is the most aggressive intraocular tumor worldwide. Accurate prognostic models are urgently needed. The present research aimed to construct and validate a prognostic signature is associated with overall survival (OS) for UM patients based on metabolism-related genes (MRGs).
Methods
MRGs were obtained from molecular signature database (MSigDB). The gene expression profiles and patient clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. In the training datasets, MRGs were analyzed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO) Cox analyses to build a prognostic model. The GSE84976 was treated as the validation cohort. In addition, time-dependent receiver operating characteristic (ROC) and Kaplan-Meier survival curve analyses the reliability of the developed model. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. Nomogram that combined the five-gene signature was used to evaluate the predictive OS value of UM patients.
Results
Five MRGs were identified and used to establish the prognostic model for UM patients. The model was successfully validated using the testing cohort. Moreover, ROC analysis demonstrated a strong predictive ability that our prognostic signature had for UM prognosis. Multivariable Cox regression analysis revealed that the risk model was an independent predictor of prognosis. UM patients with a high-risk score showed a higher level of immune checkpoint molecules.
Conclusion
We established a novel metabolism-related signature that could predict survival and might be therapeutic targets for the treatment of UM patients.
- uveal melanoma,
- metabolism,
- TCGA,
- GEO,
- prognostic model,
- immune cell infiltration
Citation: Han Zhao, Yun Chen, Peijun Shen, Lan Gong. Construction and validation of a novel prognostic signature for uveal melanoma based on five metabolism-related genes[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 8045-8063. doi: 10.3934/mbe.2021399

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Abstract

Background

Uveal melanoma (UM) is the most aggressive intraocular tumor worldwide. Accurate prognostic models are urgently needed. The present research aimed to construct and validate a prognostic signature is associated with overall survival (OS) for UM patients based on metabolism-related genes (MRGs).

Methods

MRGs were obtained from molecular signature database (MSigDB). The gene expression profiles and patient clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. In the training datasets, MRGs were analyzed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO) Cox analyses to build a prognostic model. The GSE84976 was treated as the validation cohort. In addition, time-dependent receiver operating characteristic (ROC) and Kaplan-Meier survival curve analyses the reliability of the developed model. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. Nomogram that combined the five-gene signature was used to evaluate the predictive OS value of UM patients.

Results

Five MRGs were identified and used to establish the prognostic model for UM patients. The model was successfully validated using the testing cohort. Moreover, ROC analysis demonstrated a strong predictive ability that our prognostic signature had for UM prognosis. Multivariable Cox regression analysis revealed that the risk model was an independent predictor of prognosis. UM patients with a high-risk score showed a higher level of immune checkpoint molecules.

Conclusion

We established a novel metabolism-related signature that could predict survival and might be therapeutic targets for the treatment of UM patients.

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