MAGEA3 serves as an independent indicator for predicting the prognosis of ESCC

Development of a prognostic nomogram based on an eight-gene signature for esophageal squamous cell carcinoma by weighted gene co-expression network analysis (WGCNA)

Background

Esophageal squamous cell carcinoma (ESCC) is a highly aggressive malignant tumor. This study aims to develop a robust prognostic model for ESCC.

Methods

Expression profiles of ESCC were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Co-expressed modules were constructed by weighted gene co-expression network analysis (WGCNA). Differentially expressed genes (DEGs) between ESCC and normal samples were identified with the screening criteria of adjusted P value <0.05 and log |fold change (FC)| >1. After univariate and multivariate Cox regression analysis, an 8-gene module was constructed. A receiver operating characteristic (ROC) curve for overall survival (OS) was used to assess the prediction efficacy of the risk score. A nomogram was developed based on the risk score, age, gender, and stage for 1-, 2- and 3-year survival. The potential biological functions and pathways of the 8 genes were predicted using the Metascape database.

Results

The 2 ESCC-related co-expression modules were built via WGCNA. Among all DEGs, 55 survival-related genes were identified for ESCC. Based on these genes, an 8-gene module was constructed, composed of CFAP53, FCGR2A, FCGR3A, GNGT1, IGF2, LINC01524, MAGEA3, and MAGEA6. The area under the curve (AUC) was 0.961, suggesting that the risk score could effectively predict the OS of patients with ESCC. Furthermore, the nomogram exhibited high accuracy in predicting the survival rate of ESCC patients at 1, 2, and 3 years. These genes were mainly involved in ESCC-related pathways such as extracellular matrix organization, collagen formation, and blood vessel development.

Conclusions

Our nomogram based on the 8-gene risk score could be a reliable prognostic tool for ESCC.

Prognostic Implications of Novel Gene Signatures in Gastric Cancer Microenvironment

Background

Increasing studies have shown the important clinical role of immune and stromal cells in gastric cancer microenvironment. Based on information of immune and stromal cells in The Cancer Genome Atlas, this study aimed to construct a prognostic risk assessment model for gastric cancer.

Material/Methods

Based on the immune/structural scores, differentially expressed genes (DEGs) were filtered and analyzed. Afterwards, DEGs associated with prognosis were screened and the risk assessment model was constructed in the training set. Moreover, the validity of the model was verified both in the testing set and the overall sample.

Results

In this study, patients were divided into high-score and low-score groups based on immune/stromal score, and 919 DEGs were identified. By applying least absolute shrinkage and selection operator (LASSO) and Cox analysis, 10 mRNAs were selected to form a prognostic risk assessment model, risk score=(0.294*SLC17A9) + (−0.477*FERMT3) + (0.866*NRP1) + (0.350*MMRN1) + (0.381*RNASE1) + (0.189*TRIB3) + (0.230*PGAP3) + (0.087*MAGEA3) + (0.182*TACR2) + (0.368*CYP51A1). In the training set, the low-risk group divided by the model was found to have better overall survival, and the prediction efficiency of the model was demonstrated to be good. Multivariate Cox analysis indicated that the model could work as a prognostic factor independently. Similar results were shown in the testing group and overall patients cohort group. Finally, the risk assessment model and other clinical variables were integrated to construct a nomogram.

Conclusions

In general, this study constructs a prognostic risk assessment model for gastric cancer, which could improve the prognosis stratification of patients combined with other clinical indicators.