Multi-omics identification of an antibody dependent cellular phagocytosis related signature for hepatocellular carcinoma.

Background: The reduced phagocytic activity of macrophages in the tumor microenvironment (TME) and the presence of anti-phagocytic factors in cancer cells hinder the effectiveness of immune checkpoint inhibitors (ICIs) in clinical settings. Consequently, our objective was to create a risk assessment model using antibody-dependent cellular phagocytosis (ADCP) regulators to serve as prognostic indicators and immunotherapy markers for individuals with hepatocellular carcinoma (HCC). Methods: This research incorporated The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) group, the GSE14520 group, the International Cancer Genome Consortium (ICGC) group, and the single-cell dataset GSE166635. Machine learning was used to discover ADCP genes closely linked to prognosis, helping to determine the molecular subtypes of ADCP in HCC samples. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression algorithms were used to develop the ADCP-related signature (ADCPRS). This signature was evaluated using both training and external validation datasets. Furthermore, a nomogram that integrates ADCPRS was created to function as a quantitative instrument for predicting outcomes in clinical settings. A comprehensive multi-omics investigation was performed, integrating genomic, single-cell transcriptomic, and bulk transcriptomic analyses, with the aim of gaining a more profound understanding of the prognostic signature. We assessed how different risk categories respond to immunotherapy and identified drugs tailored to target these specific groups for customized treatment. Results: We identified two ADCP subgroups by analyzing 91 genes linked to HCC prognosis. The two ADCP subgroups showed distinct outcomes, pathway activation, immune checkpoint gene expression levels, and immune cell infiltration within the TME. We developed a unified ADCPRS that excels in forecasting clinicopathological characteristics, patient outcomes, and TME stromal activity in HCC. The nomogram incorporating ADCPRS offered a numerical tool for use in medical practice. Notably, the high-risk group presented inflamed TME with an improved response to ICI. In addition, the high-ADCPRS group presented a high sensitivity to the derivative compounds provided by Cancer Therapeutics Response Portal (CTRP) and Profiling Relative Inhibition Simultaneously in Mixtures (PRISM). Conclusions: Our research developed a signature linked to ADCP, which shows promise as a key tool for predicting outcomes, implementing prevention, and enabling personalized treatment in HCC.