Title:Screening of Significant Biomarkers Related to Prognosis of Cervical Cancer and Functional Study Based on lncRNA-associated ceRNA Regulatory Network
Volume: 24
Issue: 3
Author(s): Haiyan Ding, Li Zhang, Chunmiao Zhang, Jie Song and Ying Jiang*
Affiliation:
- Department of Obstetrics and Gynecology, Second Hospital of Jilin University, Changchun, Jilin Province 130041,China
Keywords:
Cervical cancer, long non-coding RNAs, survival probability, nomogram recurrence rate model, ceRNA regulatory
network, independent clinical factors.
Abstract:
Background: Cervical cancer (CESC), which threatens the health of women, has a very
high recurrence rate.
Purposes: This study aimed to identify the signature long non-coding RNAs (lncRNAs) associated
with the prognosis of CESC and predict the prognostic survival rate with the clinical risk factors.
Methods: The CESC gene expression profiling data were downloaded from TCGA database and
NCBI Gene Expression Omnibus. Afterwards, the differentially expressed RNAs (DERs) were
screened using limma package of R software. R package “survival” was then used to screen the
signature lncRNAs associated with independently recurrence prognosis, and a nomogram
recurrence rate model based on these signature lncRNAs was constructed to predict the 3-year and
5-year survival probability of CESC. Finally, a competing endogenous RNAs (ceRNA) regulatory
network was proposed to study the functions of these genes.
Results: We obtained 305 DERs significantly associated with prognosis. Afterwards, a risk score
(RS) prediction model was established using the screened 5 signature lncRNAs associated with
independently recurrence prognosis (DLEU1, LINC01119, RBPMS-AS1, RAD21-AS1 and
LINC00323). Subsequently, a nomogram recurrence rate model, proposed with Pathologic N and
RS model status, was found to have a good prediction ability for CESC. In ceRNA regulatory
network, LINC00323 and DLEU1 were hub nodes which targeted more miRNAs and mRNAs.
After that, 15 GO terms and 3 KEGG pathways were associated with recurrence prognosis and
showed that the targeted genes PTK2, NRP1, PRKAA1 and HMGCS1 might influence the
prognosis of CESC.
Conclusion: The signature lncRNAs can help improve our understanding of the development and
recurrence of CESC and the nomogram recurrence rate model can be applied to predict the survival
rate of CESC patients in clinical practice.