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. 2022 Dec 1;15(1):249.
doi: 10.1186/s12920-022-01392-9.

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

Hongmin Chen #  1 , Xiaoxiao Shi #  2 , Li Ren  1 , Hongyu Zhuo  1 , Li Zeng  1 , Qing Qin  1 , Yuming Wan  1 , Wangmu Sangdan  3 , Lin Zhou  4   5
Affiliations

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

Hongmin Chen et al. BMC Med Genomics. .

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.

Keywords: AHR; CAND1; Esophageal cancer; Radiosensitivity; ZDHHC23; miR-132-3p; miR-576-5p; miRNA-mRNA regulatory network.

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Conflict of interest statement

The authors have no competing interests to declare.

Figures

Fig. 1
Fig. 1
Flow chart of the analysis in the current study. The miRNA-seq and mRNA-seq data of esophageal cancer were obtained from the TCGA database, and the expression differences of miRNAs and mRNAs in radiosensitive samples and radioresistant samples were analyzed, and the radiotherapy-related mRNA dataset of esophageal cancer was obtained from the GEO database. Then, DEmRNAs before and after radiotherapy were obtained. After that, the miRNA-mRNA regulatory networks were constructed through the online databases, and GO and KEGG enrichment analyses were utilized to predict the biological functions involved in mRNAs. A prognostic risk model was constructed using univariate Cox regression analysis, LASSO regression analysis and multivariate Cox regression analysis. Finally, a ROC curve analysis was performed to evaluate the predictive accuracy of candidate factors
Fig. 2
Fig. 2
Identification of the radiosensitivity-related DEmiRNAs and DEmRNAs in patients with esophageal cancer. A Volcano map of DEmiRNAs between radiosensitive and radioresistant groups in TCGA, Red dots indicate upregulated miRNAs, green dots indicate downregulated miRNAs, and black dots indicate miRNAs without differential expression. B Volcano map of DEmRNAs between radiosensitive and radioresistant groups in TCGA, Red dots indicate upregulated mRNAs, green dots indicate downregulated mRNAs, and black dots indicate mRNAs without differential expression. C Volcano map of DEmRNAs from four patients with esophageal cancer before and after radiotherapy in GSE137867 dataset, Red dots indicate upregulated mRNAs, green dots indicate downregulated mRNAs, and black dots indicate mRNAs without differential expression. D Venn diagram of intersection of downregulated DEmRNAs between TCGA and GSE137867 dataset. E Venn diagram of intersection of upregulated DEmRNAs between TCGA and GSE137867 dataset
Fig. 3
Fig. 3
Construction of the miRNA-mRNA regulatory network affecting the radiosensitivity in esophageal cancer using Cytoscape software. The triangles refer to miRNAs, and the circles refer to mRNA. Red indicates upregulated genes in radiosensitive group, and green indicates downregulated genes in radiosensitive group
Fig. 4
Fig. 4
DEmRNAs in the miRNA-mRNA regulatory network involved in the radiosensitivity in esophageal cancer based on GO and KEGG enrichment analysis. A GO functional enrichment analysis of DEmRNAs. B KEGG enrichment analysis of DEmRNAs
Fig. 5
Fig. 5
Evaluation of efficacy of the radiosensitivity-related miRNAs in the prognosis of patients with esophageal cancer. A Univariate Cox regression analysis for the prognosis-related miRNAs in esophageal cancer patients. The left part indicates the miRNA name, and the middle part indicates the p value. The Hazard ratio represents the risk rate. Risk rate greater than 1 represents a high risk of this gene, and risk rate less than 1 represents a low risk. The right part indicates the risk rate distribution. B LASSO coefficient distribution of 2 miRNAs in esophageal cancer. C Selection of the optimal parameters (lambda) in the LASSO analysis of esophageal cancer. D Multivariate Cox regression analysis for the prognosis-related miRNAs in esophageal cancer patients. E A Kaplan-Meier survival curve analysis of patients with high- and low-risk. F ROC analysis of the prognostic risk model. The lower right corner indicates the AUC value
Fig. 6
Fig. 6
Evaluation of efficacy of the radiosensitivity-related mRNAs in the prognosis of patients with esophageal cancer. A Univariate Cox regression analysis for the prognosis-related mRNAs in esophageal cancer patients. The left part indicates the miRNA name, and the middle part indicates the p value. The Hazard ratio represents the risk rate. Risk rate greater than 1 represents a high risk of this gene, and risk rate less than 1 represents a low risk. The right part indicates the risk rate distribution. B LASSO coefficient distribution of 4 mRNAs s in esophageal cancer. C Selection of the optimal parameters (lambda) in the LASSO analysis of esophageal cancer. D Multivariate Cox regression analysis for the prognosis-related mRNAs in esophageal cancer patients. E A Kaplan-Meier survival curve analysis of patients with high- and low-risk. F ROC analysis of the prognostic risk model. The lower right corner indicates the AUC value
Fig. 7
Fig. 7
Screening and efficacy evaluation of the miRNA-mRNA regulatory network related to the radiosensitivity in esophageal cancer. A Distribution of DEmiRNAs between the radiosensitive and radioresistant groups; B Distribution of DEmRNAs between the radiosensitive and radioresistant groups. C ROC curve of 2 key miRNAs. D ROC curve of 3 key mRNAs. *p < 0.05; **p < 0.01. RR: radioresistant; RS: radiosensitive
Fig. 8
Fig. 8
Molecular mechanism of the miRNA-mRNA regulatory network related to the radiosensitivity in esophageal cancer
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