Prognostic Prediction Using a Stemness Index-Related Signature in a Cohort of Gastric Cancer

Abstract

Background 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). However, the genes that participates in the stemness of GC stem cells have not been identified. Methods The 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. Results GC 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. Conclusion The 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.

Transcription Elongation Factor A-like 7 is a Stemness-Related Prognostic Factor in Gastric Cancer

“…Cox model 2 showed better accuracy and discrimination than Cox model 1 and Cox model 3 in both two GC cohorts. Moreover, the accuracy and discrimination of this one gene-based model are comparable with a nine-mRNAsi-relatedgene risk model of a previous study [11]. These results veri ed the prognostic value of TCEAL7, and the TCEAL7 expression based model could be expected to serve as an e cient tool to predict the prognosis of GC patients for clinicians.…”

“…Since the pan-cancer cohorts study of Malta et al provided a new way for describing stemness features of cancer, many studies have focuesed on stemness features-associated key genes and possible signal pathways in cancers based on the OCLR-based stemness index [7][8][9][10][11]. These studies veri ed that tumor samples had a higher stemness index when compared with normal samples in BLCA, BRCA, LUAD, LGG, and STAD.…”

“…By applying WGCNA, we identi ed more than one gene module related to mRNAsi. Different from the study of Chen et al who selected the highest positive correlation module with mRNAsi [11], the blue module which had the highest correlation value (r = -0.79, P = 6E − 79 ) with mRNAsi was selected and 55 out of 504 hub genes in this module were further analyzed according to the threshold limits. The KEGG signaling pathway annotation analysis and GO enrichment analysis showed that these gene were more associated with some organismal system-speci c functions such as secretion of some digestive juices, which preliminarily explained the negative correlation between the key genes in blue modul and stemness features.…”

“…Many scholars have identi ed stemness featuresassociated genes and possible signal pathways in bladder cancer (BLCA), breast cancer (BRCA), and lung adenocarcinoma (LUAD) based on the OCLR-based stemness index [7][8][9]. Preceding studies also suggested the importance of mRNAsi-related genes in risk strati cation and survival prediction in lowergrade glioma (LGG) and gastric cancer [10,11]. However, the predictive power of the nine-mRNAsi-relatedgene risk model in the study of gastric cancer was not very satisfactory compared to that of LGG, and this gene model also lacked external validation.…”

A signature of 24 aging‑related gene pairs predict overall survival in gastric cancer

“…The rapid development of high-throughput sequencing technologies and bioinformatics tools has allowed us to study cancer in greater depth. For GC, many previous studies have established gene signature to predict the prognosis of patients [ 17 – 19 ]. However, for various reasons (technical limitations and diversity of data types) these gene signature have not been widely used in daily clinical work.…”