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. 2022 Sep 12:2022:6787791.
doi: 10.1155/2022/6787791. eCollection 2022.

Quantitative Expression of SFN, lncRNA CCDC18-AS1, and lncRNA LINC01343 in Human Breast Cancer as the Regulator Biomarkers in a Novel ceRNA Network: Based on Bioinformatics and Experimental Analyses

Mehrnoush Rishehri  1 , Tahereh Etemadi  1 , Leila Pisheh  1   2 , Ghazaleh Koufigar  1 , Mansoureh Azadeh  1   3
Affiliations

Quantitative Expression of SFN, lncRNA CCDC18-AS1, and lncRNA LINC01343 in Human Breast Cancer as the Regulator Biomarkers in a Novel ceRNA Network: Based on Bioinformatics and Experimental Analyses

Mehrnoush Rishehri et al. Genet Res (Camb). .

Abstract

Breast cancer (BC) is one of the leading cancers in the world, which has become an increasing serious problem. In this context, reports demonstrate that some long noncoding RNAs (lncRNAs) play significant regulatory roles in breast tumorigenesis and BC progression via various pathways and act as endogenous RNAs. Finding their dysregulation in cancer and evaluating their interaction with other molecules, such as short noncoding RNAs "microRNA (miRNAs)" as well as various genes, are the most important parts in cancer diagnostics. In this study, after performing GSEA and microarray analysis on the GSE71053 dataset, a new ceRNA network of CCDC18-AS1, LINC01343, hsa-miR4462, and SFN in BC was detected by bioinformatics analysis. Therefore, the expression of SFN, CCDC18-AS1, and LINC01343 was quantitatively measured in 24 BC and normal paired tissues using qRT-PCR. CCDC18-AS1, LINC01343, and SFN were expressed higher in BC than in the control (normal paired) tissues based on qRT-PCR data. Furthermore, a significant positive correlation was observed between CCDC18-AS1 and LINC01343 expression in the samples investigated in this study. The investigation of clinicopathological parameters showed that SFN was highly expressed in tumor size of <5 cm and in nonmenopausal ages, while CCDC18-AS1 and LINC01343 indicated a high expression in stages II-III and III of BC, respectively. The overall survival analysis displayed high and low survival in patients with high expression of SFN and CCDC18-AS1, respectively. The ROC curve analysis disclosed that SFN, CCDC18-AS1, and LINC01343 might be suggested as potential biological markers in BC patients. The high expression of CCDC18-AS1, LINC01343, and SFN in BC samples suggests their potential role in BC tumorigenesis and could be considered hallmarks for the diagnosis and prognosis of BC, although this will require further clinical investigations.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Figures display heatmaps and enrichment plots from GSEA. (a) Heatmap shows the name of groups in a row and the name of genes in a column. The red color in the heatmap and enrichment plot indicates a strong association between gene expression levels and the phenotype of BC. In contrast, the blue color shows a negative correlation between the level of gene expression and the phenotype of the normal breast. (b) The results of enrichment by GSEA show the major signaling pathways linked to the selected gene.
Figure 2
Figure 2
Analysis of DEGs in dataset GSE71053 using R software (a, b, c). (a) The heatmap figure represents the correlation between the two groups normal and BC in 54675 DEGs; (b) principal component analysis (PCA) between healthy and BC tissues based on the GSE71053 dataset. (c) Volcano plot of DEGs in the GSE71053 dataset. SFN is showed by a black dot in the graph. X-axis represents log2 fold change (FC) and Y-axis represents −log10 (adjusted p value). Red dots depict the low expression of genes (n = 8711), and green dots indicate the high expression of genes (n = 8712) in the GSE71053 database.
Figure 3
Figure 3
The novel ceRNA interaction (lncRNAs-miRNA-mRNA) associated with tumor networks of BC shows that hsa-miR-4462 and LINC01343 targeted SFN. miRNA and LINC01343 both have interaction with CCDC18-AS1.
Figure 4
Figure 4
The expression level of SFN, CCDC18-AS1, and LINC01343 in tumor tissues (n = 24) compared to the normal tissues (n = 24) by qRT-PCR. Figures demonstrated the high expression of SFN, CCDC18-AS1, and LINC01343 in tumor samples compared to the normal samples (● outlying data of control groups, ■ outlying data of tumor groups, ∗∗∗p value <0.001,∗∗∗∗p value <0.0001).
Figure 5
Figure 5
The ROC curve of sensitivity versus specificity of SFN, LINC01343, and CCDC18-AS1 in BC based on their expression resulting from qRT-PCR was constructed by GraphPad. In these plots, an excellent model with AUC near 1 has a good measure of separability.
Figure 6
Figure 6
According to the spearman correlation analysis, there was a significant positive correlation between the expression level of CCDC18-AS1 and LINC01343 in BC samples.
Figure 7
Figure 7
Clinicopathological analysis of BC tissues. QRT-PCR data of SFN, LINC01343, and CCDC18-AS1 were analyzed according to the clinicopathological parameters (stage, tumor size, and menopause ages) by KruskalWallis test.
Figure 8
Figure 8
The overall survival (OS) analyses based on the cancer type in BC using GEPIA2 data. (a) SFN, (b) CCDC18-AS1.
Figure 9
Figure 9
Possible mechanisms of SFN role in tumorigenesis. Although SFN dependent on P53 induces the cell cycle arrest, it can prevent apoptosis by sequestrating Bad and Bax, which symbolizes an excellent capability of SFN as an oncogene [65]. In addition, it may have roles in proliferation, growth, motility, differentiation, and metabolism through PI3K/Akt pathway [61].
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