Dataset Information

Identification of significant genes signatures and prognostic biomarkers in cervical squamous carcinoma via bioinformatic data.

ABSTRACT:

Background

Cervical squamous cancer (CESC) is an intractable gynecological malignancy because of its high mortality rate and difficulty in early diagnosis. Several biomarkers have been found to predict the prognose of CESC using bioinformatics methods, but they still lack clinical effectiveness. Most of the existing bioinformatic studies only focus on the changes of oncogenes but neglect the differences on the protein level and molecular biology validation are rarely conducted.

Methods

Gene set data from the NCBI-GEO database were used in this study to compare the differences of gene and protein levels between normal and cancer tissues through significant pathway selection and core gene signature analysis to screen potential clinical biomarkers of CESC. Subsequently, the molecular and protein levels of clinical samples were verified by quantitative transcription PCR, western blot and immunohistochemistry.

Results

Three differentially expressed genes (RFC4, MCM2, TOP2A) were found to have a significant survival (P < 0.05) and highly expressed in CESC tissues. Molecular biological verification using quantitative reverse transcribed PCR, western blotting and immunohistochemistry assays exhibited significant differences in the expression of RFC4 between CESC and para-cancerous tissues (P < 0.05).

Conclusion

This study identified three potential biomarkers (RFC4, MCM2, TOP2A) of CESC which may be useful to clarify the underlying mechanisms of CESC and predict the prognosis of CESC patients.

SUBMITTER: He Y

PROVIDER: S-EPMC7718800 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of significant genes signatures and prognostic biomarkers in cervical squamous carcinoma via bioinformatic data.

He Yunan Y Hu Shunjie S Zhong Jiaojiao J Cheng Anran A Shan Nianchun N

PeerJ 20201202

<h4>Background</h4>Cervical squamous cancer (CESC) is an intractable gynecological malignancy because of its high mortality rate and difficulty in early diagnosis. Several biomarkers have been found to predict the prognose of CESC using bioinformatics methods, but they still lack clinical effectiveness. Most of the existing bioinformatic studies only focus on the changes of oncogenes but neglect the differences on the protein level and molecular biology validation are rarely conducted.<h4>Method ...[more]

PMID: 33344075

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Project description:BACKGROUND:Cervical cancer (CC) represents the fourth most frequently diagnosed malignancy affecting women all over the world. However, effective prognostic biomarkers are still limited for accurately identifying high-risk patients. Here, we provided a combination machine learning algorithm-based signature to predict the prognosis of cervical squamous cell carcinoma (CSCC). METHODS AND MATERIALS:After utilizing RNA sequencing (RNA-seq) data from 36 formalin-fixed and paraffin-embedded (FFPE) samples, the most significant modules were highlighted by the weighted gene co-expression network analysis (WGCNA). A candidate genes-based prognostic classifier was constructed by the least absolute shrinkage and selection operator (LASSO) and then validated in an independent validation set. Finally, based on the multivariate analysis, a nomogram including the FIGO stage, therapy outcome, and risk score level was built to predict progression-free survival (PFS) probability. RESULTS:A mRNA-based signature was developed to classify patients into high- and low-risk groups with significantly different PFS and overall survival (OS) rate (training set: p?<?0.001 for PFS, p?=?0.016 for OS; validation set: p?=?0.002 for PFS, p?=?0.028 for OS). The prognostic classifier was an independent and powerful prognostic biomarker for PFS in both cohorts (training set: hazard ratio [HR]?=?0.13, 95% CI 0.05-0.33, p?<?0.001; validation set: HR?=?0.02, 95% CI 0.01-0.04, p?<?0.001). A nomogram that integrated the independent prognostic factors was constructed for clinical application. The calibration curve showed that the nomogram was able to predict 1-, 3-, and 5-year PFS accurately, and it performed well in the external validation cohorts (concordance index: 0.828 and 0.864, respectively). CONCLUSION:The mRNA-based biomarker is a powerful and independent prognostic factor. Furthermore, the nomogram comprising our prognostic classifier is a promising predictor in identifying the progression risk of CSCC patients.

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Dataset Information

Identification of significant genes signatures and prognostic biomarkers in cervical squamous carcinoma via bioinformatic data.

Background

Methods

Results

Conclusion

Publications

Identification of significant genes signatures and prognostic biomarkers in cervical squamous carcinoma via bioinformatic data.

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