Identification and External Validation of a Prognostic Signature Based on Myeloid-Derived Suppressor Cell-Related lncRNAs for Hepatocellular Carcinoma

Background Myeloid-derived suppressor cells (MDSCs) exhibit notable immunosuppressive and pro-tumorigenic properties. However, the relationship between long noncoding RNAs (lncRNAs) and hepatocellular carcinoma (HCC) remains insufficiently explored. Our research established a forecast system relying on lncRNAs linked to MDSCs for evaluating HCC patient survival. Methods Data from HCC patients were collected from The Cancer Genome Atlas (TCGA) database, and MDSC-associated lncRNAs were identified to establish a prognostic risk model. A nomogram integrating risk markers and clinical factors was developed and validated for real-world applicability. To explore the model’s mechanistic basis and clinical significance, enrichment analysis, tumor mutation burden (TMB) analysis, tumor microenvironment (TME) evaluation, immunotherapy response prediction, and drug sensitivity assessment were performed. RT-qPCR was utilized to confirm lncRNA expression. Results We identified a prognostic signature consisting with 7 MDSCs-related lncRNAs. Kaplan-Meier (K-M) survival plots revealed a worse prognosis for the high-risk cohort. The nomogram, integrating the risk signature, provided greater accuracy than approaches excluding the risk evaluation. Enrichment analysis exposed that metabolic and immune-associated pathways were predominant within low-risk cohort, while cell proliferation and gene expression regulation dominant across the high-risk population. Meanwhile, an increased TMB and a degraded TME appeared in the high-risk population. The drug responsiveness evaluation revealed that sorafenib, axitinib, and others exhibited enhanced effectiveness among the low-risk population. High-risk individuals displayed enhanced reactions to medications like volitinib, savolitinib, and others. Conclusions The prognostic model constructed based on the seven MDSCs-associated lncRNAs showed good application value in assessing prognosis and guiding clinical therapy.