Advances in biomarkers for esophageal cancer

Biological and clinical relevance of metastasis-associated long noncoding RNAs in esophageal squamous cell carcinoma: A systematic review

Esophageal squamous cell carcinoma (ESCC) is a foremost cancer-related death worldwide owing to rapid metastasis and poor prognosis. Metastasis, as the most important reason for death, is biologically a multifaceted process involving a range of cell signaling pathways. Long noncoding RNAs (lncRNAs), as transcriptional regulators, can regulate numerous genomic processes and cellular processes such as cell proliferation, migration, and invasion. LncRNAs have also been shown to involve in/regulate the cancer metastasis-related signaling pathways. Hence, they have increasingly been brought to international attention in molecular oncology research. A number of researchers have attempted to reveal the biological and clinical relevance of lncRNAs in ESCC tumourigenesis and metastasis. The aberrant expression of these molecules in ESCC has regularly been reported to involve in various cellular processes and clinical features, including diagnosis, prognosis, and therapeutic responses. Here, we especially consider the pathways in which lncRNAs act as metastasis-mediated effectors, mainly by interacting with epithelial-mesenchymal transition-associated factors. We review the biological roles of lncRNAs through involving in ESCC metastasis as well as the clinical significance of the metastasis-related lncRNAs in cancer diagnosis and prognosis.

Robust analysis of novel mRNA-lncRNA cross talk based on ceRNA hypothesis uncovers carcinogenic mechanism and promotes diagnostic accuracy in esophageal cancer

Background

ceRNAs have emerged as pivotal players in the regulation of gene expression and play a crucial role in the physiology and development of various cancers. Nevertheless, the function and underlying mechanisms of ceRNAs in esophageal cancer (EC) are still largely unknown.

Methods

In this study, profiles of DEmRNAs, DElncRNAs, and DEmiRNAs between normal and EC tumor tissue samples were obtained from the Cancer Genome Atlas database using the DESeq package in R by setting the adjusted P<0.05 and |log₂(fold change)|>2 as the cutoff. The ceRNA network (ceRNet) was initially constructed to reveal the interaction of these ceRNAs during carcinogenesis based on the bioinformatics of miRcode, miRDB, miRTarBase, and TargetScan. Then, independent microarray data of GSE6188, GSE89102, and GSE92396 and correlation analysis were used to validate molecular biomarkers in the initial ceRNet. Finally, a least absolute shrinkage and selection operator logistic regression model was built using an oncogenic ceRNet to diagnose EC more accurately.

Results

We successfully constructed an oncogenic ceRNet of EC, crosstalk of hsa-miR372-centered CADM2-ADAMTS9-AS2 and hsa-miR145-centered SERPINE1-PVT1. In addition, the risk-score model -0.0053*log₂(CADM2)+0.0168*log₂(SERPINE1)-0.0073*log₂(ADAMTS9-AS2)+0.0905*log₂(PVT1)+0.0047*log₂(hsa-miR372)-0.0193*log₂(hsa-miR145), (log₂[gene count]) could improve diagnosis of EC with an AUC of 0.988.

Conclusion

We identified two novel pairs of ceRNAs in EC and its role of diagnosis. The pairs of hsa-miR372-centered CADM2-ADAMTS9-AS2 and hsa-miR145-centered SERPINE1-PVT1 were likely potential carcinogenic mechanisms of EC, and their joint detection could improve diagnostic accuracy.