Dataset Information

Construction and validation of a novel aging-related gene signature and prognostic nomogram for predicting the overall survival in ovarian cancer.

ABSTRACT:

Background

Ovarian cancer (OC) is the most lethal gynecological malignancy. The objective of this study was to establish and validate an individual aging-related gene signature and a clinical nomogram that can powerfully predict independently the overall survival rate of patients with ovarian cancer.

Methods

Data on transcriptomic profile and relevant clinical information were retrieved from The Cancer Genome Atlas (TCGA) database as a training group, and the same data from three public Gene Expression Omnibus (GEO) databases as validation groups. Univariate Cox regression analysis, lasso regression analysis, and multiple multivariate Cox analysis were analyzed sequentially to select the genes to be included in the aging-associated signature. A risk scoring model was established and verified, the predictive value of the model was evaluated, and a clinical nomogram was established.

Results

We found eight genes that were most relevant to prognosis and constructed an eight-mRNA signature. Based on the model, each OC patient's risk score was able to be calculated and patients were split into groups of low and high risks with a distinct outcome. Survival analysis confirmed that the outcome of patients in the high-risk group was dramatically shorter than that of those in the low-risk group, and the eight-mRNA signature can be considered as a powerful and independent predictor that could predict the outcome of OC patient. Additionally, the risk score and age can be used to construct a clinical nomogram as a simpler tool for predicting prognosis. We also explored the association between the risk score and immunity and drug sensitivity.

Conclusion

This study suggested that the aging-related gene signature could be used as an intervention point and latent prognostic predictor in OC, which may provide new perceptions for postoperative treatment strategies.

SUBMITTER: Liu L

PROVIDER: S-EPMC8683552 | biostudies-literature |

REPOSITORIES: biostudies-literature

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