Identification of a 3‑mRNA signature as a novel potential prognostic biomarker in patients with ovarian serous cystadenocarcinoma in G2 and G3

Abstract

The use of mRNAs as biomarkers serves to diagnose, treat, as well as aid the prognosis of cancer. The present study involved an analysis of mRNAs in the cell cycle at the G2 and G3 tumor grades for the prognosis of ovarian serous cystadenocarcinoma (OSC) using 364 clinical samples (G2:G3=42:322). Statistics aided the identification of NPFFR2, XPNPEP2 and CELA3B; the 3-mRNA model that allows for classification of patients into high- and low-risk groups using a median value of 0.9580745. The rates of survival varied (P=0.00149) and the independent detection of stratification of the risk of this disease was validated with success using the 3-mRNA signature, which was demonstrated to be more successful than the weight model. This approach was revealed to provide the prognosis of grade G2 and G3 in patients with OSC compared with factors used traditionally. Compared with traditional factors, this 3-mRNA model was demonstrated to be the only and independent prognostic factor for patients with G2 and G3 stage OSC. A literature survey was also performed in the present study in order to assess the role of the 3 genes and indirectly prove their effectiveness. The establishment of this new genetic model will enhance prospective prognosis and treatment for patients with OSC.

Comprehensive pathway-related genes signature for prognosis and recurrence of ovarian cancer

“…A number of studies have shown that combined biomarkers could improve prognostic accuracy compared to single biomarkers ( Barata & Rini, 2017 ). Thus, extensive studies have attempted to establish molecular signatures based on gene expression to predict survival of patients, including mRNA- ( Zhou et al, 2019 ), microRNA- ( Li et al, 2019 ), and long non-coding RNA-based signatures ( Xu et al, 2019 ). However, only a small number of prognostic signatures have been developed, and none have been directly applied in clinical practice.…”

Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients

“…To exclude the effect of heterogeneity, only signatures that were developed based on the TCGA database were included. The studies on markers for predicting specific types of prognosis for patients with OV were excluded from our comprehensive evaluation 32–34 . Finally, 22 OS‐related prognostic signatures were included for comparison with our gene signature.…”

“…The studies on markers for predicting specific types of prognosis for patients with OV were excluded from our comprehensive evaluation. 32 , 33 , 34 Finally, 22 OS‐related prognostic signatures were included for comparison with our gene signature. The results demonstrated that our signature yielded remarkably good performance in predicting OS in patients with OV (Table 4 ).…”

Construction and validation of a novel ten miRNA-pair based signature for the prognosis of clear cell renal cell carcinoma

“…The aberrant expression of some miRNAs between ccRCC and the normal samples has been universally measured, presenting its value as marker in diagnosis, predicting prognosis, or potential therapeutic targets [21] , [22] , [23] . Nowadays, more and more studies applying gene and miRNA pairs risk signatures are used to predict the prognosis of different kinds of tumors [ [24] , [25] , [26] , [27] ]. However, due to the differences in testing or quantitative methods (miRNA-seq, microarray or qRT-PCR) and histological variations in study cohort, the effectiveness of miRNAs serving as prognosis biomarkers is controversial in clinical practice.…”