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Objective <p>The objective of this study is to elucidate the breast cancer (BC)-associated ceRNA regulatory network by examining the expression profiles of lncRNAs, miRNAs, and mRNAs.</p> Methods <p>Using RNA sequencing data from The Cancer Genome Atlas (TCGA), we compared the expression profiles of lncRNAs, miRNAs, and mRNAs between 976 breast cancer (BC) tissues and 104 matched non-cancerous samples. Then, we constructed a BRCA-specific ceRNA network and evaluated the lncRNA-miRNA-mRNA regulatory relationships. The DAVID tool was used to investigate the functional enrichment of the key, significantly dysregulated mRNAs within the ceRNA network. Moreover, we analyzed the associations between the expression levels of key genes in the ceRNA network and the clinicopathological features as well as the overall survival of the BC patients from the TCGA database. Finally, to validate our bioinformatics findings, we utilized datasets from the Gene Expression Omnibus (GEO) database (GSE42568 and GSE65194) and additionally collected 20 tissue samples from BC patients for experimental assessment.</p> Results <p>A total of 231 dysregulated lncRNAs, 50 miRNAs, and 1136 mRNAs were identified in BC samples from the TCGA database. Among these, 52 lncRNAs, 24 miRNAs, and 126 mRNAs were found to potentially interact through ceRNA regulatory mechanisms. Based on these key genes, we analyzed the associations between their expression levels and the clinicopathological characteristics as well as the overall survival of BC patients from TCGA. The expression of 40 lncRNAs and 20 miRNAs was significantly associated with the patients’ clinical features (<i>P</i> &lt; 0.05). Notably, 12 genes (including 4 lncRNAs, 2 miRNAs, and 6 mRNAs) in the ceRNA network were statistically significantly associated with overall survival time (log-rank test, <i>P</i> &lt; 0.05). Comparative analysis of the GSE42568 and GSE65194 datasets and experimental validation by RT-qPCR revealed that the expression levels and changing trends of the key genes in the ceRNA network were highly consistent with the TCGA results, confirming the reliability of our bioinformatics analysis.</p> Conclusion <p>This study identified key BC-related ceRNA network 52 lncRNAs, 24 miRNAs and 126 mRNAs. These key genes are worthy to further explore as potential novel biomarkers for diagnosis, classification, and prognosis of BC.</p>

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Systems biology analysis of long non-coding RNAs and targets to identify key modules and biomarkers in breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Chao Ma,
  • Xueling Zhang,
  • Rui Zhang,
  • Hanteng Yang,
  • Junling Wang,
  • Chengyun Li

摘要

Objective

The objective of this study is to elucidate the breast cancer (BC)-associated ceRNA regulatory network by examining the expression profiles of lncRNAs, miRNAs, and mRNAs.

Methods

Using RNA sequencing data from The Cancer Genome Atlas (TCGA), we compared the expression profiles of lncRNAs, miRNAs, and mRNAs between 976 breast cancer (BC) tissues and 104 matched non-cancerous samples. Then, we constructed a BRCA-specific ceRNA network and evaluated the lncRNA-miRNA-mRNA regulatory relationships. The DAVID tool was used to investigate the functional enrichment of the key, significantly dysregulated mRNAs within the ceRNA network. Moreover, we analyzed the associations between the expression levels of key genes in the ceRNA network and the clinicopathological features as well as the overall survival of the BC patients from the TCGA database. Finally, to validate our bioinformatics findings, we utilized datasets from the Gene Expression Omnibus (GEO) database (GSE42568 and GSE65194) and additionally collected 20 tissue samples from BC patients for experimental assessment.

Results

A total of 231 dysregulated lncRNAs, 50 miRNAs, and 1136 mRNAs were identified in BC samples from the TCGA database. Among these, 52 lncRNAs, 24 miRNAs, and 126 mRNAs were found to potentially interact through ceRNA regulatory mechanisms. Based on these key genes, we analyzed the associations between their expression levels and the clinicopathological characteristics as well as the overall survival of BC patients from TCGA. The expression of 40 lncRNAs and 20 miRNAs was significantly associated with the patients’ clinical features (P < 0.05). Notably, 12 genes (including 4 lncRNAs, 2 miRNAs, and 6 mRNAs) in the ceRNA network were statistically significantly associated with overall survival time (log-rank test, P < 0.05). Comparative analysis of the GSE42568 and GSE65194 datasets and experimental validation by RT-qPCR revealed that the expression levels and changing trends of the key genes in the ceRNA network were highly consistent with the TCGA results, confirming the reliability of our bioinformatics analysis.

Conclusion

This study identified key BC-related ceRNA network 52 lncRNAs, 24 miRNAs and 126 mRNAs. These key genes are worthy to further explore as potential novel biomarkers for diagnosis, classification, and prognosis of BC.