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Analysis of CASP12 diagnostic and prognostic values in cervical cancer based on TCGA database.

Bioscience reports | 2019

The present study aims to find a differential protein-coding gene caspase 12 (CASP12) in cervical cancer (CC) based on the (TCGA) database and verify its clinical diagnostic and prognostic values. The transcriptome and clinicopathological data of CC were downloaded from the TCGA database and through screening, we found that PDE2A and CASP12 were independent prognostic factors for CC patients. According to the median expression, the patients were divided into groups with high and low CASP12 and PDE2A expression. There was no difference in survival between PDE2A high and low expression groups (P=0.099), whereas there was a significant difference between CASP12 high and low expression groups (P=0.033). The serum from 68 CC patients (experimental group) and 50 healthy people (control group) was collected to detect the relative expression of CASP12 using qRT-PCR and plotted the ROC curve. The relative expression of CASP12 in the experimental group was significantly lower than in the control group (P<0.05). The area under the curve (AUC) of CASP12 was 0.865. There were statistically significant differences between CASP12 groups with high and low expression in terms of differentiation, lymph node metastasis, tumor size, FIGO staging, and clinical outcomes (P<0.05), but not in terms of age, HPV types and pathological types (P>0.05). The 3-year survival in the CASP12 low expression group was significantly worse than in the CASP12 high expression group (P=0.028). In conclusion, the expression level of CASP12 can be used as a diagnostic and prognostic biomarker for patients with CC.

Pubmed ID: 31804677 RIS Download

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Thermo Fisher Scientific (tool)

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Commercial vendor and service provider of laboratory reagents and antibodies. Supplier of scientific instrumentation, reagents and consumables, and software services.

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edgeR (tool)

RRID:SCR_012802

Bioconductor software package for Empirical analysis of Digital Gene Expression data in R. Used for differential expression analysis of RNA-seq and digital gene expression data with biological replication.

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