Multiomics Analysis Identifies Prognostic Signatures for Sepsis-Associated Hepatocellular Carcinoma in Emergency Medicine.

Objectives: Sepsis, caused by the body's response to infection, poses a life-threatening condition and represents a significant global health challenge. Characterized by dysregulated immune response to infection, sepsis may lead to organ dysfunction and failure, ultimately resulting in high mortality rates. The liver plays a crucial role in sepsis, yet the role of differentially expressed genes in septic patients remains unclear in hepatocellular carcinoma (HCC). In this study, we aim to investigate the significance of differentially expressed genes related to sepsis in the occurrence and prognosis of tumors in HCC.

Methods: We conducted analyses by obtaining gene transcriptome data and clinical data of HCC cases from The Cancer Genome Atlas (TCGA). Furthermore, we obtained transcriptomic sequencing results of septic patients from the Gene Expression Omnibus (GEO) database, identified intersecting differentially expressed genes between the two, and performed survival analysis on the samples using LASSO and Cox regression analysis. Combining analyses of tumor mutation burden (TMB) and immune function, we further elucidated the mechanisms of sepsis-related genes in the prognosis and treatment of HCC.

Results: We established a prognostic model consisting of four sepsis-related genes: KRT20, PAEP, CCR3, and ANLN. Both the training and validation sets showed excellent outcomes in the prognosis of tumor patients, with significantly longer survival times observed in the low-risk group based on this model compared to the high-risk group. Furthermore, analyses, such as differential analysis of tumor mutation burden, immune function analysis, GO/KEGG pathway enrichment analysis, and drug sensitivity analysis, also demonstrated the potential mechanisms of action of sepsis-related genes.

Conclusions: Models constructed based on sepsis-related genes have shown excellent predictive ability in prognosis and differential analysis of drug sensitivity among tumor patients. These predictive models can enhance patient prognosis and inform the creation of early treatment protocols for sepsis, consequently aiding in the prevention of sepsis-induced HCC development through the modulation of the overall immune status. This may play a crucial role in patient management and immunotherapy, providing valuable reference for subsequent research.