Identification of genome-wide copy number variation-driven subtypes for the treatment and prognostic prediction of esophageal carcinoma.

Background: Esophageal carcinoma (ESCA) is a frequently detected gastrointestinal cancer. Copy number variants (CNVs) have a dramatic impact on the screening, diagnosis and prognostic prediction of cancers. However, the mechanism of action of CNVs on ESCA occurrence and progression remains unclear.
Methods: ESCA samples from The Cancer Genome Atlas (TCGA) were typed by consensus clustering using CNV-associated genes. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to section gene modules closely related to the two clusters, and sub-networks were constructed as hub genes. In addition, seven prognosis-correlated genes were further screened and retained by multivariate Cox regression analysis to develop a prognostic assessment model. The ssGSEA algorithm assessed energy metabolism levels in patients from different clusters and risk groups. Finally, quantitative real-time PCR (qRT-PCR) and live-dead cell staining verified the expression of genes associated with CNV risk scores.
Results: ESCA was classified into two subtypes based on CNV values. Compared with cluster 1, cluster 2 had significantly higher level of immune score and tumor-associated immune cell infiltration as well as a noticeably better overall survival. The three modules most associated with the two clusters were identified by WGCNA, and a prognostic model with a strong prediction performance was constructed with their genes. Glycolysis, lactate metabolism, fatty acid synthesis, glutathione, methionine, and tryptophan metabolic pathway enrichment scores were remarkably higher in patients in cluster 1 and the high-risk group than in cluster 2 and the low-risk group. Knockdown PIK3C2A promoted ESCA cells apoptosis and inhibited cell vibiality.
Conclusion: The current research maybe provides new understanding for the pathogenesis of ESCA based on CNV, providing an effective guidance for its clinical diagnosis and prognostic evaluation.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2024 The Authors.)