Abstract
Background and ObjectiveAs a common cancer type in women, cervical cancer remains one of the leading causes of cancer-associated mortalities word wide. Recent evidence has demonstrated the regulatory role of a large number of long non-coding RNAs (lncRNAs) in cervical cancer. Here, we aimed to identify new biomarkers that related with the recurrence through comprehensive bioinformatics analysis.MethodsFirstly, we collected online lncRNA expression data of cervical cancer patients which were divided into training, validation, and test set. Then we developed a nine-lncRNA signature from training set by conducting LASSO Cox regression model along with 10-fold cross validation. The prognostic value of this risk score was validated in all the three sets using Kaplan–Meier analysis, C-index, time-dependent ROC curves and dynamic AUC. Biological function of these lncRNAs in cervical cancer cells were evaluated by performing gene ontology biological process enrichment and Kyoto Encyclopedia of Genes and Genomes signaling pathways analysis.ResultsAccording to the results, a higher predict accuracy was observed in the nine-lncRNA signature than that of FIGO stage in all the three sets. Stratified analysis also demonstrated that the nine-lncRNA signature can predict the recurrence of cervical cancer within FIGO stage. The potential mechanisms underlying the nine-lncRNAs from the signature were also identified according to the gene enrichment analysis.ConclusionIn the present article, we provided a reliable prognostic tool to facilitate the individual management of patients with cervical cancer after treatment.
Highlights
As the second most common cause of female cancer-associated mortalities worldwide, cervical cancer ranks the fourth most frequently diagnosed cancer (Jemal et al, 2011; Torre et al, 2015)
Samples in GEO datasets were firstly quantile normalized and the distributions for the dataset of long non-coding RNAs (lncRNAs) profiles in each patient was shown as box plot using the R software package (Supplementary Figure S1). 300 patients from the GSE44001 cohort were randomly divided into a training cohort (n = 150) and internal validation cohort (n = 150)
We developed a nine-lncRNA signature based on their expression level and coefficients
Summary
As the second most common cause of female cancer-associated mortalities worldwide, cervical cancer ranks the fourth most frequently diagnosed cancer (Jemal et al, 2011; Torre et al, 2015). Previous studies demonstrated that cervical cancer was closely associated with human papillomavirus (HPV) infection (Walboomers et al, 1999; Castellsagué et al, 2006). Identification of the new suitable prognosis biomarkers are critical for cervical cancer. Recent studies have showed that genomic factors could be the indicators for the prognosis of cervical cancer (Mao et al, 2018a). As a common cancer type in women, cervical cancer remains one of the leading causes of cancer-associated mortalities word wide. Recent evidence has demonstrated the regulatory role of a large number of long non-coding RNAs (lncRNAs) in cervical cancer. We aimed to identify new biomarkers that related with the recurrence through comprehensive bioinformatics analysis
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