Identification of the miRNA-mRNA regulatory network associated with radiosensitivity in esophageal cancer based on integrative analysis of the TCGA and GEO data

Abstract

Background The current study set out to identify the miRNA-mRNA regulatory networks that influence the radiosensitivity in esophageal cancer based on the The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Methods Firstly, esophageal cancer-related miRNA-seq and mRNA-seq data were retrieved from the TCGA database, and the mRNA dataset of esophageal cancer radiotherapy was downloaded from the GEO database to analyze the differential expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) in radiosensitive and radioresistant samples, followed by the construction of the miRNA-mRNA regulatory network and Gene Ontology and KEGG enrichment analysis. Additionally, a prognostic risk model was constructed, and its accuracy was evaluated by means of receiver operating characteristic analysis. Results A total of 125 DEmiRNAs and 42 DEmRNAs were closely related to the radiosensitivity in patients with esophageal cancer. Based on 47 miRNA-mRNA interactions, including 21 miRNAs and 21 mRNAs, the miRNA-mRNA regulatory network was constructed. The prognostic risk model based on 2 miRNAs (miR-132-3p and miR-576-5p) and 4 mRNAs (CAND1, ZDHHC23, AHR, and MTMR4) could accurately predict the prognosis of esophageal cancer patients. Finally, it was verified that miR-132-3p/CAND1/ZDHHC23 and miR-576-5p/AHR could affect the radiosensitivity in esophageal cancer. Conclusion Our study demonstrated that miR-132-3p/CAND1/ZDHHC23 and miR-576-5p/AHR were critical molecular pathways related to the radiosensitivity of esophageal cancer.

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“…In the progression of GC, PPARs play an essential role as metabolic regulators. PPARs have been studied more and more recently, expanding the mechanisms underlying GC development [ 97 ]. Using a visceral-to-subcutaneous fat ratio study, Yu-Ching et al found that patients with high visceral versus subcutaneous adipose tissue ratios expressed fewer PPARs than those with low ratios [ 98 ].…”

Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for muscle-invasive urothelial cancer

“…After downloading, the data were processed using the robust limma package for background correction, log 2 transformation, and quantile normalization and visualized using the boxplot function to assess the uniformity of sample expression abundance value distribution. 33 Similar methods have been applied in previous studies on glioma, 34 esophageal cancer, 35 liver cancer, 36 thyroid cancer, 37 and lung adenocarcinoma. 38 When multiple probes were mapped to a single gene symbol, the probe with the highest expression was annotated as gene expression.…”

Peroxisome proliferator-activated receptors regulate the progression and treatment of gastrointestinal cancers

“…Yue et al analyzed esophageal cancer microarray data to profile microRNA expression and construct networks of differentially expressed genes and microRNAs using a bioinformatics approach. The subsequent gene enrichment analysis identified ADCYAP1R1 , in the PPAR signaling pathway, as potentially involved in the prognosis of esophageal cancer ( Chen et al, 2022a ). A study conducted by Yukio Terashita et al examined the correlation between the mRNA expression of PPARγ and esophageal cancer prognosis in 55 patients with primary ESCCs and found that PPARγ expression decreased significantly compared with the normal esophageal epithelium; thus, they concluded that the mRNA expression level of PPARγ could be regarded as a prognosis marker for esophageal cancer patients ( Terashita et al, 2002 ).…”