Establishing a cancer driver gene signature-based risk model for predicting the prognoses of gastric cancer patients

Abstract

Despite the high prevalence of gastric cancer (GC), molecular biomarkers that can reliably detect GC are yet to be discovered. The present study aimed to establish a robust gene signature based on cancer driver genes (CDGs) that can predict GC prognosis. Transcriptional profiles and clinical data from GC patients were analyzed using univariate Cox regression analysis and the least absolute shrinkage and selection (LASSO)-penalized Cox regression analysis to select optimal prognosis-related genes for modeling. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier analyses were done to assess the predictive power of this gene signature. A nomogram model for prediction of survival of GC patients was established using the CDG signature and clinical information, and a seven-CDG signature was identified. Risk scores were calculated using this signature, and patients were subsequently divided into high-and low-risk groups; high-risk patients in the training and validation datasets had poorer prognoses than low-risk patients. Cox regression analysis revealed that the CDG signature is an independent prognostic factor for GC. The signature and other clinical features were used to construct a nomogram for predicting overall GC patient survival. Calibration and decision curve analysis showed that the nomogram accurately predicted survival, highlighting its clinical utility. Thus, we established a novel CDG signature and nomogram for predicting GC prognosis, which may facilitate personalized treatment of GC.

Quantitative Analysis on Molecular Characteristics Evolution of Gastric Cancer Progression and Prognosis

“…Using the expression levels of key genes in the model and the corresponding clinical information (including age, sex, stage) as features, the accuracy of the 3‐gene model was evaluated and compared with the 6‐ and 7‐gene models. [ 49,50 ] Our model had the highest C‐index of the three compared methods in the GSE62254 training cohort ( Table 1 ). In the TCGA‐STAD test cohort, our model achieved a C‐index of 0.68, slightly higher than the 6‐gene model and lower than the 7‐gene model.…”

“…Moreover, the calibration curve and C‐index showed good agreement. The C‐index of the 3‐gene model is higher than that of the 6‐gene [ 49 ] and 7‐gene models [ 50 ] in the training cohort and it also performed well in the test cohort, indicating that the 3‐gene model offers excellent prognostic guidance to GC patients. Since six of 81 EGC‐related DEmRNAs (KRT17, FCGR3A, CTAG1B, CCN6, AGMO, and CBLIF) were not included in the transcriptional expression experiments of GSE62254, they were excluded in the construction of GC prognostic model.…”

Zinc Finger Proteins in the War on Gastric Cancer: Molecular Mechanism and Clinical Potential

“…Some of the ZFPs identified so far may be oncogenes or tumor suppressors in GC progression, and researchers have designed specific ZFPs to regulate the expression of the corresponding target genes in mammals, which have made great progress [206]. In light of the high intertumoral, intratumoral and interpatient heterogeneity of GC, accurate classification and stratification via reference indicators are indispensable for the diagnosis, treatment and prognosis of GC [207][208][209]. With the in-depth development of bioinformatics for discovering more ZFPs and the elucidation of their molecular mechanisms, staging based on various ZFPs can facilitate more optimized early diagnosis and personalized therapeutics for GC.…”

Predicting the prognosis in patients with sepsis by an endoplasmic reticulum stress gene signature

“…Early identification of diagnostic and prognostic biomarkers of sepsis can play a pivotal role in its treatment, and when appropriate treatment is promptly initiated, the mortality rate reduces [ 22 , 23 ]. Recently, high-throughput sequencing and data processing, which can identify biomarkers for prognosis prediction, have gradually become significant tools in biomedical research [ 24 , 25 ]. Accordingly, there is now a significant opportunity to develop biomarkers as predictive indicators of sepsis and to use these biomarkers as the foundation for the development of effective therapies.…”