The Role of High-Risk Human Papillomavirus-Related Long Non-Coding RNAs in the Prognosis of Cervical Squamous Cell Carcinoma.
Clicks: 367
ID: 94117
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.
Reader Engagement
Steady Performance
69.6
/100
367 views
245 readers
Trending
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #1 of 3 articles by views in dna and cell biology
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate 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
Abstract
Cervical cancer (CC) is a malignant tumor that could seriously endanger women's life and health, of which cervical squamous cell carcinoma (CESC) accounts for more than 80%. High-risk human papillomavirus (HR-HPV) infection is the primary cause of CC. The 5-year survival rate is low due to poor prognosis. We need to explore the pathogenesis of CC and seek effective biomarkers to improve prognosis. The purpose of this research is to construct an HR-HPV-related long non-coding RNA (lncRNA) signature for predicting the survival and finding the biomarkers related to CC prognosis. First, we downloaded the CESC data from The Cancer Genome Atlas (TCGA) database to find HR-HPV-related lncRNAs in CC. Then, the differentially expressed lncRNAs were analyzed by univariate and multivariate Cox regression. Six lncRNAs were found to be associated with the prognosis and can be used as independent prognostic factors. Next, based on these prognostic genes, we established a risk score model, which showed that patients with higher score had poorer prognosis and higher mortality. Moreover, the Kaplan-Meier curve of the model indicated that the model was statistically significant ( < 0.05). The survival-receiver operating characteristic curve showed that the model could also predict the survival of CC patients (the area under the curve, AUC = 0.65). More importantly, nomogram was drawn with clinical features and risk score, which verified the above conclusion, and its calibration curve and c-index index fully demonstrated that the prediction model could predict the progress of CC. We also validated the risk score model in head and neck cancer, and the results indicated that the model had obvious prognostic ability. Finally, we analyzed the correlation between clinical features and survival, and found that neoplasm cancer ( < 0.000) and risk score ( < 0.000) were independent prognostic factors for CC. In conclusion, the study established HR-HPV-related lncRNA signature, which provided a reliable prognostic tool, and was of great significance for finding the biomarkers related to HR-HPV infection in CC.
| Reference Key |
cheng2020thedna
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Cheng, Yanping;Yang, Sheng;Shen, Yang;Ding, Bo;Wu, Wenjuan;Zhang, Yanqiu;Liang, Geyu; |
| Journal | dna and cell biology |
| Year | 2020 |
| DOI |
10.1089/dna.2019.5167
|
| URL | |
| Keywords |
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.