Dataset Information

Construction and Validation of a Novel Prognostic Signature for Intestinal Type of Gastric Cancer.

ABSTRACT:

Background

Intestinal type of gastric cancer (IGC) is the largest subtype of gastric cancer (GC) by Lauren classification. The purpose of this present study was to construct a prognostic signature for IGC patients, based on the high-grade dysplasia (HGD) and IGC tissues, to improve and enhance the prognostic accuracy.

Methods

The microarray datasets and associated clinical characteristics of HGD and IGC were obtained from the Gene Expression Omnibus (GEO) database. Based on the differential expression analysis between HGD and IGC, the prognostic-related differential expression genes (DEGs) were identified in a training set by univariate COX regression analysis. The least absolute shrinkage and selection operator (LASSO) regression was used to construct an optimal prognostic signature. The enrichment analysis was performed by using Gene Set Enrichment Analysis (GSEA). The performance of the nomogram was assessed by the calibration curve and concordance index (C-index). The results were validated by using a testing set.

Results

We identified 35 prognostic-related DGEs in the training set. The nine-gene signature was established by LASSO analysis. The nine-gene signature was an independent risk factor in both the training and testing sets. The areas under the curve (AUC) values of receiver operating characteristic (ROC) analysis were 0.733 and 0.700 for the training and testing sets, respectively. In GSEA analysis, the gene expression in high-risk group was enriched in hedgehog signaling, epithelial mesenchymal transition, and angiogenesis. The nomogram for IGC showed good performance with C-index of 0.81 (95% CI: 0.76-0.86) and 0.70 (95% CI: 0.63-0.77) in the training and testing sets, respectively.

Conclusion

We identified and verified a nine-gene signature for the prognostic prediction of IGC patients, which might identify subgroups of IGC patients and select more suitable therapeutic options.

SUBMITTER: Zhang F

PROVIDER: S-EPMC8376432 | biostudies-literature |

REPOSITORIES: biostudies-literature

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