Developing a 5-gene prognostic signature for cervical cancer by integrating mRNA and copy number variations.
BMC Cancer
; 22(1): 192, 2022 Feb 21.
Article
en En
| MEDLINE
| ID: mdl-35184747
ABSTRACT
BACKGROUND:
Cervical cancer is frequently detected gynecological cancer all over the world. This study was designed to develop a prognostic signature for an effective prediction of cervical cancer prognosis.METHODS:
Differentially expressed genes (DEGs) were identified based on copy number variation (CNV) data and expression profiles from different databases. A prognostic model was constructed and further optimized by stepwise Akaike information criterion (stepAIC). The model was then evaluated in three groups (training group, test group and validation group). Functional analysis and immune analysis were used to assess the difference between high-risk and low-risk groups.RESULTS:
The study developed a 5-gene prognostic model that could accurately classify cervical cancer samples into high-risk and low-risk groups with distinctly different prognosis. Low-risk group exhibited more favorable prognosis and higher immune infiltration than high-risk group. Both univariate and multivariate Cox regression analysis showed that the risk score was an independent risk factor for cervical cancer.CONCLUSIONS:
The 5-gene prognostic signature could serve as a predictor for identifying high-risk cervical cancer patients, and provided potential direction for studying the mechanism or drug targets of cervical cancer. The integrated analysis of CNV and mRNA expanded a new perspective for exploring prognostic signatures in cervical cancer.Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
ARN Mensajero
/
Neoplasias del Cuello Uterino
/
Medición de Riesgo
/
Nomogramas
/
Variaciones en el Número de Copia de ADN
Tipo de estudio:
Etiology_studies
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Prognostic_studies
/
Risk_factors_studies
Límite:
Female
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Humans
Idioma:
En
Revista:
BMC Cancer
Asunto de la revista:
NEOPLASIAS
Año:
2022
Tipo del documento:
Article