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. 2023 Feb 5:2023:6457152.
doi: 10.1155/2023/6457152. eCollection 2023.

Identification of Key MicroRNAs and Genes between Colorectal Adenoma and Colorectal Cancer via Deep Learning on GEO Databases and Bioinformatics

Xin Zhang  1 , Mingxin Jin  1 , Fengjun Liu  1 , Hui Qu  1 , Cheng Chen  1
Affiliations

Identification of Key MicroRNAs and Genes between Colorectal Adenoma and Colorectal Cancer via Deep Learning on GEO Databases and Bioinformatics

Xin Zhang et al. Contrast Media Mol Imaging. .

Retraction in

Abstract

Background: Deep learning techniques are gaining momentum in medical research. Colorectal adenoma (CRA) is a precancerous lesion that may develop into colorectal cancer (CRC) and its etiology and pathogenesis are unclear. This study aims to identify transcriptome differences between CRA and CRC via deep learning on Gene Expression Omnibus (GEO) databases and bioinformatics in the Chinese population.

Methods: In this study, three microarray datasets from the GEO database were used to identify the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) in CRA and CRC. The FunRich software was performed to predict the targeted mRNAs of DEMs. The targeted mRNAs were overlapped with DEGs to determine the key DEGs. Molecular mechanisms of CRA and CRC were evaluated using enrichment analysis. Cytoscape was used to construct protein-protein interaction (PPI) and miRNA-mRNA regulatory networks. We analyzed the expression of key DEMs and DEGs, their prognosis, and correlation with immune infiltration based on the Kaplan-Meier plotter, UALCAN, and TIMER databases.

Results: A total of 38 DEGs are obtained after the intersection, including 11 upregulated genes and 27 downregulated genes. The DEGs were involved in the pathways, including epithelial-to-mesenchymal transition, sphingolipid metabolism, and intrinsic pathway for apoptosis. The expression of has-miR-34c (P = 0.036), hsa-miR-320a (P = 0.045), and has-miR-338 (P = 0.0063) was correlated with the prognosis of CRC patients. The expression levels of BCL2, PPM1L, ARHGAP44, and PRKACB in CRC tissues were significantly lower than normal tissues (P < 0.001), while the expression levels of TPD52L2 and WNK4 in CRC tissues were significantly higher than normal tissues (P < 0.01). These key genes are significantly associated with the immune infiltration of CRC.

Conclusion: This preliminary study will help identify patients with CRA and early CRC and establish prevention and monitoring strategies to reduce the incidence of CRC.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Volcano plot and Venn diagrams of the DEGs and DEMs in several GEO datasets: (a) the DEGs in GSE41657 dataset; (b) the DEGs in GSE71187 dataset; (c) the DEMs in GSE72281 dataset; (d) the upregulated DEGs in three datasets; (e) the downregulated DEGs in three datasets. DEGs: differentially expressed genes; DEMs, differentially expressed microRNAs.
Figure 2
Figure 2
The results of GO and pathway enrichment analysis for DEGs. The top 10 of (a) biological process, (b) cellular component, (c) molecular function, and (d) biological pathways of the DEGs. GO, Gene Ontology.
Figure 3
Figure 3
The PPI and microRNA–mRNA regulatory networks of key DEMs and DEGs: (a) the PPI network of the DEGs; (b) the microRNA–mRNA regulatory network of key DEMs and DEGs. Red indicates upregulated miRNAs and genes, and blue indicates downregulated miRNAs and genes. PPI, protein–protein interaction.
Figure 4
Figure 4
The association between key microRNAs and CRC prognosis. CRC, colorectal cancer.
Figure 5
Figure 5
The expression levels of key genes in COAD samples: (a) BCL2; (b) PPM1L; (c) ARHGAP44; (d) PRKACB; (e) TPD52L2; (f) WNK4.
Figure 6
Figure 6
Correlation between the gene expression of genes and immune cells in CRC: (a) BCL2; (b) PPM1L; (c) ARHGAP44; (d) PRKACB; (e) TPD52L2; (f) WNK4. CRC, colorectal cancer. P < 0.05 was considered statistically significant.
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