BackgroundM2-like tumor-associated macrophages (M2-like TAMs) have important roles in the progression and therapeutics of cancers. We aimed to detect novel M2-like TAM-related biomarkers in hepatocellular carcinoma (HCC) via integrative analysis of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data to construct a novel prognostic signature, reveal the “immune landscape”, and screen drugs in HCC.MethodsM2-like TAM-related genes were obtained by overlapping the marker genes of TAM identified from scRNA-seq data and M2 macrophage modular genes identified by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were carried out to screen prognostic genes from M2-like TAM-related genes, followed by a construction of a prognostic signature, delineation of risk groups, and external validation of the prognostic signature. Analyses of immune cells, immune function, immune evasion scores, and immune-checkpoint genes between high- and low-risk groups were done to further reveal the immune landscape of HCC patients. To screen potential HCC therapeutic agents, analyses of gene–drug correlation and sensitivity to anti-cancer drugs were conducted.ResultsA total of 127 M2-like TAM-related genes were identified by integrative analysis of scRNA-seq and bulk-seq data. PDLIM3, PAM, PDLIM7, FSCN1, DPYSL2, ARID5B, LGALS3, and KLF2 were screened as prognostic genes in HCC by univariate Cox regression and LASSO regression analyses. Then, a prognostic signature was constructed and validated based on those genes for predicting the survival of HCC patients. In terms of drug screening, expression of PAM and LGALS3 was correlated positively with sensitivity to simvastatin and ARRY-162, respectively. Based on risk grouping, we predicted 10 anticancer drugs with high sensitivity in the high-risk group, with epothilone B having the lowest half-maximal inhibitory concentration among all drugs tested.ConclusionsOur findings enhance understanding of the M2-like TAM-related molecular mechanisms involved in HCC, reveal the immune landscape of HCC, and provide potential targets for HCC treatment.

Background. m6A modification plays a key role in the development of hepatocellular carcinoma (HCC). Angiogenesis-related genes (ARGs) are increasingly being used to define signatures predicting patient prognosis. The correlations between m6A-related ARGs (mARGs), clinical outcomes, and the immune and oxidative stress landscape are unclear. Methods. Univariate Cox regression analysis of 24 mARGs yielded 13 prognostic genes, which were then analyzed for their enriched functions and pathways. After LASSO regression analysis, a prognostic signature was constructed and its reliability validated. Patients were grouped by risk using the signature score, and then the clinical prognosis, the immune landscape, and the oxidative stress landscape between the two groups were analyzed. Drug sensitivity analysis was performed to identify potentially efficient therapeutic agents. Results. Thirteen prognosis-related mARGs consistently clustered patients with HCC into four groups with significantly different prognosis. Four mARGs (EGF, ITGA5, ITGAV, and PLG) were used to construct a prognostic signature and define risk groups. Among them, EGF, ITGA5, and ITGAV, were defined as prognostic risk factors, while PLG was defined as a prognostic protective factor. Compared to low-risk patients, HCC patients in the high-risk group had a poorer prognosis and showed significant differences in clinical characteristics, enriched pathways, tumor stemness, and tumor microenvironment. The drug sensitivity of oxaliplatin and LDK-378 negatively correlated with ITGAV expression. Ten drugs had lower IC50s in the high-risk group, indicating better antitumor efficacy than in the low-risk group, with epothilone B having the lowest IC50 value. Conclusions. A prognostic model consisting of mARGs can be used to predict the prognosis of HCC patients. The risk grouping of our model can be used to reveal differences in the tumor immune microenvironment of patients with HCC. Further in-depth study may provide new targets for future treatment.