Mutation status coupled with RNA-sequencing data can efficiently identify important non-significantly mutated genes serving as diagnostic biomarkers of endometrial cancer

Autor: Zhichao Liu, Qifan Kuang, Yuan Liu, Keqin Liu, Menglong Li, Li He, Zhining Wen, Junmei Xu
Rok vydání: 2018
Předmět:
0301 basic medicine
Oncology
medicine.medical_specialty
Population
Biology
lcsh:Computer applications to medicine. Medical informatics
medicine.disease_cause
Biochemistry
03 medical and health sciences
0302 clinical medicine
Germline mutation
Endometrial cancer
Structural Biology
Internal medicine
medicine
Clinical endpoint
Biomarkers
Tumor
Humans
education
lcsh:QH301-705.5
Molecular Biology
Gene
Neoplasm Staging
education.field_of_study
Mutation
Models
Genetic
Sequence Analysis
RNA
Applied Mathematics
Research
Somatic mutation
Cancer
RNA sequencing
Middle Aged
medicine.disease
Computer Science Applications
Endometrial Neoplasms
030104 developmental biology
lcsh:Biology (General)
030220 oncology & carcinogenesis
Clinical phenotype characteristics
Differentially expressed genes
lcsh:R858-859.7
Female
DNA microarray
Genes
Neoplasm
Zdroj: BMC Bioinformatics
BMC Bioinformatics, Vol 18, Iss S14, Pp 39-49 (2017)
ISSN: 1471-2105
Popis: Background Endometrial cancers (ECs) are one of the most common types of malignant tumor in females. Substantial efforts had been made to identify significantly mutated genes (SMGs) in ECs and use them as biomarkers for the classification of histological subtypes and the prediction of clinical outcomes. However, the impact of non-significantly mutated genes (non-SMGs), which may also play important roles in the prognosis of EC patients, has not been extensively studied. Therefore, it is essential for the discovery of biomarkers in ECs to further investigate the non-SMGs that were highly associated with clinical outcomes. Results For the 9681 non-SMGs reported by the mutation annotation pipeline, there were 1053, 1273 and 395 non-SMGs differentially expressed between the patient groups divided by the clinical endpoints of histological grade, histological type as well as the International Federation of Gynecology and Obstetrics (FIGO) stage of ECs, respectively. In the gene set enrichment analysis, the cancer-related pathways, namely neuroactive ligand-receptor interaction signaling pathway, cAMP signaling pathway and calcium signaling pathway, were significantly enriched with the differentially expressed non-SMGs for all the three endpoints. We further identified 23, 19 and 24 non-SMGs, which were highly associated with histological grade, histological type and FIGO stage, respectively, from the differentially expressed non-SMGs by using the variable combination population analysis (VCPA) approach and found that 69.6% (16/23), 78.9% (15/19) and 66.7% (16/24) of the identified non-SMGs had been previously reported to be correlated with cancers. In addition, the averaged areas under the receiver operating characteristic curve (AUCs) achieved by the predictive models with identified non-SMGs as predictors in predicting histological type, histological grade, and FIGO stage were 0.993, 0.961 and 0.832, respectively, which were superior to those achieved by the models with SMGs as features (averaged AUCs = 0.928, 0.864 and 0.535, resp.). Conclusions Besides the SMGs, the non-SMGs reported in the mutation annotation analysis may also involve the crucial genes that were highly associated with clinical outcomes. Combining the mutation status with the gene expression profiles can efficiently identify the cancer-related non-SMGs as predictors for cancer prognostic prediction and provide more supplemental candidates for the discovery of biomarkers. Electronic supplementary material The online version of this article (10.1186/s12859-017-1891-6) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE
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