Hui Fei

Comprehensive Analysis of the Control of Cancer Stem Cell Characteristics in Endometrial Cancer by Network Analysis

Background. Cancer stem cells play an important role in endometrial cancer (EC). It is closely related to self-renewal and therapeutic resistance of EC. Methods. In this study, WGCNA (weighted gene coexpression network analysis) was used to analyze the relationship between genes and clinical features. We also performed immune cell infiltration analysis of a key module by using ImmuCellAI (Immune Cell Abundance Identifier). Then, key genes were verified in the GEO database. Finally, causal relationship analysis and protein-protein interaction analysis were performed in DisNor tool and STRING. Result. The mRNA expression-based stemness index (mRNAsi) is significantly lower in normal tissues and is significantly higher in individuals with stage IV or high-grade cancer and those who are obese or postmenopausal. Nineteen key genes (ORC6, C1orf112, RAD54L, SGO2, BUB1, PLK4, KIF18B, BUB1B, TTK, NCAPG, XRCC2, CENPF, KIF15, RACGAP1, ARHGAP11A, TPX2, KIF14, KIF4A, and NCAPH) that were enriched mainly in terms related to the cell cycle and DNA replication were selected by weighted gene coexpression network analysis (WGCNA). Based on the key modules, the numbers of NKT cells, NK cells, and neutrophils in the normal group were significantly higher than those in the cancer group. PLK1, CDK1, and MAD2L1, which were correlated with upstream genes, may be an regulated upstream of key genes. Conclusion. PLK1, CDK1, and MAD2L1 which were strongly correlated with upstream genes may be a regulated upstream of key genes.

A novel lncRNA-mRNA-miRNA signature predicts recurrence and disease-free survival in cervical cancer

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.