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

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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.

Project description:Oral squamous cell carcinoma (OSCC) is a life?threatening disease with a poor prognosis. Although previous studies have reported that the methylation of certain genes is associated with the pathogenesis of OSCC, the methylation of genes that have relevance to OSCC progression is not clearly documented. The present study aimed to gain insights into the mechanisms underlying DNA methylation regulation associated with OSCC progression and to identify potential prognostic markers for OSCC treatment. DNA methylation dataset GSE41114 and gene expression dataset GSE74530 were downloaded from the Gene Expression Omnibus database. The global methylation status of OSCC tumor samples and normal control samples was determined, and differentially methylated genes (DMGs) in OSCC samples compared with control samples were identified. The mRNA expression data were then integrated to identify differentially expressed genes (DEGs) in OSCC samples compared with control samples. Overlapping genes between DEGs and DMGs were identified, and functional enrichment analysis was performed. In addition, survival analysis of the overlapping genes was performed to screen genes with prognostic significance in OSCC. A total of 40,115 differential methylation CpG sites spanning 3,360 DMGs were identified; CpG sites in the promoter, gene body and intergenic regions were generally highly hypermethylated or hypomethylated. Additionally, 508 DEGs in OSCC samples were identified, including 332 upregulated and 176 downregulated genes. A total of 82 overlapping genes between DEGs and DMGs were found, which were mainly involved in protein metabolism, regulation of the metabolic process and the immune system. Additionally, differential methylation or expression of several genes, including fibroblast activation protein ? (FAP), interferon ? inducible protein 27 (IFI27), laminin subunit ?2 (LAMC2), matrix metallopeptidase 1 (MMP1), serine peptidase inhibitor Kazal?type 5 (SPINK5) and zinc finger protein 662 (ZNF662), was significantly associated with the survival of OSCC patients, and their differential expression in OSCC patients was further confirmed by reverse transcription?quantitative polymerase chain reaction in OSCC and normal oral cell lines. Overall, FAP, IFI27, LAMC2, MMP1, SPINK5 and ZNF662 genes caused by epigenetic changes via DNA methylation may be associated with the development and progression of OSCC, and should be valuable OSCC therapeutic biomarkers.

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.

Similar Datasets

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