Abstract
Hepatocellular carcinoma (HCC) is a highly heterogeneous malignant tumor characterized by a high recurrence rate and poor prognosis. In recent years, the study of miRNAs as potential prognostic markers and therapeutic targets, as well as their regulation of the glucose metabolism pathway in HCC, has attracted widespread attention. This study aims to construct a risk model for predicting the prognosis of HCC by analyzing differentially expressed glycolysis-related miRNAs and further explore their relationship with the immune microenvironment and drug sensitivity. In this study, the original mRNA and miRNA expression data of HCC were downloaded from the TCGA and GEO databases, respectively, with a total of 374 TCGA samples and 97 GSE30297 samples collected. A prognostic risk score model for HCC was constructed using LASSO regression analysis, and survival differences between different risk groups were analyzed using Kaplan-Meier curves. Metascape and GSEA analyses were performed for functional enrichment to explore the potential molecular mechanisms of the model miRNAs. Additionally, the CIBERSORT algorithm was used to analyze the immune microenvironment, and the "pRRophetic" package was employed to predict the sensitivity of HCC patients to commonly used chemotherapy drugs. Real-time quantitative PCR (RT-qPCR) was used to detect the expression levels of these glycolysis-metabolism-related miRNAs with prognostic value in tumor tissues and adjacent normal tissues of HCC patients. Through differential expression analysis, a total of 4,421 differentially expressed mRNAs and 106 differentially expressed miRNAs were screened, and 59 glycolysis-metabolism-related differential miRNAs were identified. Cox univariate regression and LASSO regression analysis were used to select 10 prognosis-related miRNAs, and a risk score model based on these miRNAs was constructed. The validation results of the model in both the training and test sets showed that the overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group (P < 0.05). The nomogram model further validated the independent predictive value of the risk score for the prognosis of HCC patients. Immune microenvironment analysis revealed that the content of immune cells, such as M0 macrophages and regulatory T cells (Tregs), was higher in the high-risk group, while the content of immune cells, such as resting NK cells, was lower. Drug sensitivity analysis showed that the risk score was significantly correlated with the sensitivity to various chemotherapeutic drugs (e.g., Methotrexate, Paclitaxel). The results of RT-qPCR showed that the expression levels of hsa-mir-454 and hsa-mir-149 were up-regulated in HCC, and the expression level of hsa-mir-621 was down-regulated in HCC. However, the expression level of hsa-mir-653 was not significant in HCC, and the difference was not statistically significant. In patients with recurrent and primary HCC, the results showed significant expression differences between hsa-mir-454 and hsa-mir-621. Specifically, hsa-mir-454 was upregulated in recurrent tumor samples, while hsa-mir-621 was downregulated. Notably, hsa-mir-149 and hsa-mir-653 showed no statistically significant differences between the two groups. This study established a reliable prognostic risk scoring model for HCC by screening differentially expressed glycolysis-related miRNAs, which effectively distinguishes between high-risk and low-risk patients and predicts patient survival. Additionally, the model is closely associated with the immune microenvironment and drug sensitivity, offering strong support for personalized treatment and clinical decision-making in HCC.