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<p>We aimed to identify circRNA as a biomarker in non-small cell lung cancer (NSCLC) and explore the underlying mechanism. circRNA and mRNA data were retrieved from GEO database. A series of bioinformatics analyses including differentially expressed analysis, weighted gene co-expression network analysis (WGCNA), Random Forest, and support vector machine algorithm were applied to identify the key circRNAs in NSCLC. ROC curves were used to evaluate and distinguish the roles of key circRNAs in cancer. The expression levels of circRNAs were validated via qPCR analysis. Finally, a ceRNA network was constructed. Herein, si-hsa_circ_0084443 was transfected into NSCLC cells to investigate its function in NSCLC. Five circRNAs (hsa_circ_0049271, hsa_circ_0029426, hsa_circ_0084443, hsa_circ_0015278, and hsa_circ_0024731) were identified as biomarkers in NSCLC. They exhibited potent diagnostic ability in identifying NSCLC, with AUC &gt; 0.85. qPCR results suggested that hsa_circ_0049271, hsa_circ_0029426, and hsa_circ_0015278 were significantly downregulated and hsa_circ_0084443 and hsa_circ_0024731 were significantly upregulated in tumor tissue compared with the levels in normal tissues (<i>P</i> &lt; 0.05). A ceRNA network was finally constructed. Knockdown of hsa_circ_0084443 inhibited cell growth, migration, invasion, and colony formation, and promoted apoptosis in NSCLC cell line. Five circRNAs were identified as biomarkers and demonstrated abnormal expression in NSCLC. Furthermore, ceRNA network was constructed, which can aid the mechanism exploration in the future.</p>

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Identification of circRNA-Based Biomarkers and ceRNA Mechanism in Non-Small Cell Lung Cancer

  • 期刊论文

  • 发表日期：

    2025年4月18日

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  • Zhengjia Liu,
  • Xiyu Liu,
  • Cong Yin,
  • Zihao Liu,
  • Haixiang Yu

摘要

We aimed to identify circRNA as a biomarker in non-small cell lung cancer (NSCLC) and explore the underlying mechanism. circRNA and mRNA data were retrieved from GEO database. A series of bioinformatics analyses including differentially expressed analysis, weighted gene co-expression network analysis (WGCNA), Random Forest, and support vector machine algorithm were applied to identify the key circRNAs in NSCLC. ROC curves were used to evaluate and distinguish the roles of key circRNAs in cancer. The expression levels of circRNAs were validated via qPCR analysis. Finally, a ceRNA network was constructed. Herein, si-hsa_circ_0084443 was transfected into NSCLC cells to investigate its function in NSCLC. Five circRNAs (hsa_circ_0049271, hsa_circ_0029426, hsa_circ_0084443, hsa_circ_0015278, and hsa_circ_0024731) were identified as biomarkers in NSCLC. They exhibited potent diagnostic ability in identifying NSCLC, with AUC > 0.85. qPCR results suggested that hsa_circ_0049271, hsa_circ_0029426, and hsa_circ_0015278 were significantly downregulated and hsa_circ_0084443 and hsa_circ_0024731 were significantly upregulated in tumor tissue compared with the levels in normal tissues (P < 0.05). A ceRNA network was finally constructed. Knockdown of hsa_circ_0084443 inhibited cell growth, migration, invasion, and colony formation, and promoted apoptosis in NSCLC cell line. Five circRNAs were identified as biomarkers and demonstrated abnormal expression in NSCLC. Furthermore, ceRNA network was constructed, which can aid the mechanism exploration in the future.