Abstract
Cervical cancer (CC) patients have a poor prognosis due to the high recurrence rate. However, there are still no effective molecular signatures to predict the recurrence and survival rates for CC patients. Here, we aimed to identify a novel signature based on three types of RNAs [messenger RNA (mRNAs), microRNA (miRNAs), and long non-coding RNAs (lncRNAs)]. A total of 763 differentially expressed mRNAs (DEMs), 46 lncRNAs (DELs), and 22 miRNAs (DEMis) were identified between recurrent and non-recurrent CC patients using the datasets collected from the Gene Expression Omnibus (GSE44001; training) and The Cancer Genome Atlas (RNA- and miRNA-sequencing; testing) databases. A competing endogenous RNA network was constructed based on 23 DELs, 15 DEMis, and 426 DEMs, in which 15 DELs, 13 DEMis, and 390 DEMs were significantly associated with disease-free survival (DFS). A prognostic signature, containing two DELs (CD27-AS1, LINC00683), three DEMis (hsa-miR-146b, hsa-miR-1238, hsa-miR-4648), and seven DEMs (ARMC7, ATRX, FBLN5, GHR, MYLIP, OXCT1, RAB39A), was developed after LASSO analysis. The built risk score could effectively separate the recurrence rate and DFS of patients in the high- and low-risk groups. The accuracy of this risk score model for DFS prediction was better than that of the FIGO (International Federation of Gynecology and Obstetrics) staging (the area under receiver operating characteristic curve: training, 0.954 vs 0.501; testing, 0.882 vs 0.656; and C-index: training, 0.855 vs 0.539; testing, 0.711 vs 0.508). In conclusion, the high predictive accuracy of our signature for DFS indicated its potential clinical application value for CC patients.
Highlights
Cervical cancer (CC) is one of the most common gynecological cancers worldwide
Accumulating evidence has demonstrated that the advanced International Federation of Gynecology and Obstetrics (FIGO) stage correlates with a high risk of recurrence and shorter length of 5-year survival [3]
DEMis were predicted by the DIANA-LncBase database, while 1,038 paired interactions between 15 DEMis and 426 differentially expressed messenger RNAs (mRNAs) (DEMs) were predicted by the starBase database [such as hsa-miR-1275-ANKRD6, hsamiR-595/4648-GHR, hsa-miR1238-FBLN5, hsa-miR-146b-ACAP2 (ArfGAP with coiled-coil, ankyrin repeat and PH domains 2), hasmiR-128-1-ATRX (ATRX chromatin remodeler), hsa-miR29b-1-MYLIP, hsa-miR-604-ARMC7/OXCT1 (3-oxoacid CoA-transferase 1), and hsa-miR1304-RAB39A (RAB39A, member RAS oncogene family)]
Summary
Cervical cancer (CC) is one of the most common gynecological cancers worldwide. According to statistics using the Global Cancer Observatory database, there were approximately 570,000 cases of CC in 2018 [1]. Great advances have been made in the therapeutic options (such as surgery, radiotherapy, and chemotherapy), a considerable proportion of patients can develop relapse or metastasis, which may be the possible reason associated with a high mortality-to-incidence ratio in CC (about 30– 50%) [1,2]. Accumulating evidence has demonstrated that the advanced International Federation of Gynecology and Obstetrics (FIGO) stage correlates with a high risk of recurrence and shorter length of 5-year survival [3]. The FIGO staging system has been well recognized as a prognostic biomarker for CC in clinically. Some studies indicate that the prognostic effectiveness of the FIGO staging system should be improved because survival differences could be observed in patients within the same stage [4]. Some molecular biomarkers were proven to have better predictive abilities than the FIGO staging system for survival in CC patients. Zhao et al [5] identified a prognostic signature that consisted of five protein-encoding
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