Screening of significant biomarkers related with prognosis of cervical cancer and functional study based on lncRNA-associated ceRNA regulatory network.

Clicks: 288
ID: 158050
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #15 of 36 articles by views in combinatorial chemistry & high throughput screening

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Cervical cancer (CESC), which threatens the health of women, has a very high recurrence rate.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.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. Fianlly, a competing endogenous RNAs (ceRNA) regulatory network was proposed to study the functions of these genes.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-AS1and LINC00323). Subsequently, a nomogram recurrence rate model, proposed with Pathologic N and RS model status, was found to have good prediction ability for CESC. In ceRNA regulatory network,LINC00323 and DLEU1were 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, PRKAA1and HMGCS1 may influence the prognosis of CESC.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.
Reference Key
ding2020screeningcombinatorial Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ding, Haiyan;Zhang, Li;Zhang, Chunmiao;Song, Jie;Jiang, Ying;
Journal combinatorial chemistry & high throughput screening
Year 2020
DOI
10.2174/1386207323999200729113028
URL
Keywords
cervical cancer long non-coding rnas survival probability cerna regulatory network independent clinical factors. nomogramrecurrence rate model

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.