Abstract
BackgroundWith characteristic self-renewal and multipotent differentiation, cancer stem cells (CSCs) have a crucial influence on the metastasis, relapse and drug resistance of gastric cancer (GC). However, the genes that participates in the stemness of GC stem cells have not been identified.MethodsThe mRNA expression-based stemness index (mRNAsi) was analyzed with differential expressions in GC. The weighted gene co-expression network analysis (WGCNA) was utilized to build a co-expression network targeting differentially expressed genes (DEG) and discover mRNAsi-related modules and genes. We assessed the association between the key genes at both the transcription and protein level. Gene Expression Omnibus (GEO) database was used to validate the expression levels of the key genes. The risk model was established according to the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Furthermore, we determined the prognostic value of the model by employing Kaplan-Meier (KM) plus multivariate Cox analysis.ResultsGC tissues exhibited a substantially higher mRNAsi relative to the healthy non-tumor tissues. Based on WGCNA, 17 key genes (ARHGAP11A, BUB1, BUB1B, C1orf112, CENPF, KIF14, KIF15, KIF18B, KIF4A, NCAPH, PLK4, RACGAP1, RAD54L, SGO2, TPX2, TTK, and XRCC2) were identified. These key genes were clearly overexpressed in GC and validated in the GEO database. The protein-protein interaction (PPI) network as assessed by STRING indicated that the key genes were tightly connected. After LASSO analysis, a nine-gene risk model (BUB1B, NCAPH, KIF15, RAD54L, KIF18B, KIF4A, TTK, SGO2, C1orf112) was constructed. The overall survival in the high-risk group was relatively poor. The area under curve (AUC) of risk score was higher compared to that of clinicopathological characteristics. According to the multivariate Cox analysis, the nine-gene risk model was a predictor of disease outcomes in GC patients (HR, 7.606; 95% CI, 3.037–19.051; P < 0.001). We constructed a prognostic nomogram with well−fitted calibration curve based on risk score and clinical data.ConclusionThe 17 mRNAsi-related key genes identified in this study could be potential treatment targets in GC treatment, considering that they can inhibit the stemness properties. The nine-gene risk model can be employed to predict the disease outcomes of the patients.
Highlights
Gastric cancer (GC) is a leading cause of morbidity and death globally
Based on the TCGA database and applying bioinformatic method, we identified key genes correlated with GC stemness by merging mRNA expression-based stemness index (mRNAsi) with Weighted gene co-expression network analysis (WGCNA)
Our focus was on the key genes related to gastric cancer stem cells (GCSCs) using WGCNA based on an mRNAsi index, as calculated by Tathiane et al via the one-class logistic regression machine learning algorithm (OCLR) algorithm
Summary
Gastric cancer (GC) is a leading cause of morbidity and death globally. According to the GLOBOCAN 2018 estimation, the disease is ranked fifth in terms of incidence and third in mortality, with regards to the total cancer cases worldwide. Cancer stem cells (CSCs) have been implicated in poor treatment outcomes. CSCs, a subpopulation of tumors, take the main responsibility for the maintenance and spreading of tumor. Given that these cells have a high capacity to proliferate and self-renew, they generate many differentiated cells and normally are the main constituents of tumor population (Reya et al, 2001). Accumulating evidence suggests that gastric cancer stem cells (GCSCs) may play a crucial part in tumor recurrence, metastasis and therapeutic resistance (Xu et al, 2013; Stojnev et al, 2014). With characteristic self-renewal and multipotent differentiation, cancer stem cells (CSCs) have a crucial influence on the metastasis, relapse and drug resistance of gastric cancer (GC). The genes that participates in the stemness of GC stem cells have not been identified
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
