Background: Studies which focused on the character of miR-144-3p in hepatocellular carcinoma (HCC) are limited. This study aimed to explore the expression, clinical significance and the potential targets of miR-144-3p in HCC.
Methods: The Cancer Genome Atlas (TCGA) and a cohort of 95 cases of HCC were applied to investigate aberrant miR-144-3p expression in HCC. A meta-analysis was performed to accumulate data on miR-144-3p expression in HCC based on TCGA, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Gene Expression Omnibus (GEO). Additionally, the potential regulatory mechanisms of miR-144-3p in HCC were explored by bioinformatics.
Results: MiR-144-3p expression was downregulated distinctly in HCC compared to para-HCC tissue both in TCGA data (8.9139±1.5986 vs 10.7721±0.9156, P <0.001) and in our qRT-PCR validation (1.3208±0.7594 vs 2.6200±0.9263, P <0.001). The meta-analysis based on TCGA, qRT-PCR and GEO data confirmed a consistent result (standard mean difference =-0.854, 95% CI: -1.224 to -0.484, P <0.001). The receiver operating characteristic curve of miR-144-3p gained a significant diagnostic value both in TCGA data (area under the curve [AUC] =0.852, 95% CI: 0.810 to 0.894, P <0.001) and in qRT-PCR validation (AUC =0.867, 95% CI: 0.817 to 0.916, P <0.001), especially in alpha-fetoprotein–negative HCC patients (AUC =0.900, 95% CI: 0.839 to 0.960, P <0.001). Furthermore, we identified 119 potential targets of miR-144-3p in HCC by bioinformatics. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that several significant biologic functions and pathways correlated with the pathogenesis of HCC, including the p53 signaling pathway.
Conclusion: MiR-144-3p may function as a cancer suppressor microRNA, which is essential for HCC progression through the regulation of various signaling pathways. Thus, interactions with miR-144-3p may provide a novel treatment strategy for HCC in the future.
Keywords: miR-144-3p, hepatocellular carcinoma, TCGA, qRT-PCR, GEO, gene functional enrichment analysis