Macrophages M1-Related Prognostic Signature in Hepatocellular Carcinoma

A large number of studies have found that macrophages M1 play an important role in the occurrence and development of tumors. The aim of our study is to explore the causes of differential infiltration of macrophages M1 in hepatocellular carcinoma from the perspective of transcriptome and establish a prognostic model of hepatocellular carcinoma. We downloaded gene expression and clinical data from the public database, estimated the content of macrophages M1 in different samples with R software, and found the different genes between high- and low-infiltration groups. Using differentially expressed genes, we constructed a model composed of 7 genes. The risk score of the model has a good ability to predict the prognosis, has a positive correlation with immune checkpoints, and is closely related to other immune cells and immune function. Our model shows good prognostic function and has wide application value.


Introduction
Liver cancer is a kind of malignant tumor disease with high incidence all over the world, which seriously endangers public health. Improving the prognosis of patients with liver cancer and curing liver cancer is one of the goals of researchers. e impact of the tumor immune microenvironment on liver cancer cells has been found to be more and more important. At present, there are a large number of studies on tumor immune microenvironment. Tumor-associated macrophages are a key factor in cancer. Macrophages play an important role in the development of tumors. ey can promote genomic instability, promote the growth of tumor stem cells, promote metastasis, and so on [1]. Rodell et al. found that TLR7/8agonist-loaded nanoparticles enhance cancer immunotherapy by macrophages M1 [2]. Chen et al. found that tumorrecruited M2 macrophages promote gastric and breast cancer metastasis [3]. Choo et al. found that M1 macrophage-derived nanovesicles potentiate the anticancer efficacy of immune checkpoint inhibitors [4]. Rao et al. found that hybrid cellular membrane nanovesicles amplify macrophage immune responses against cancer recurrence and metastasis [5].
At present, a considerable number of studies have found that some genes can affect the prognosis of cancer patients. Conlin et al. found that K-ras, p53, and APC mutations had prognostic significance in colorectal carcinoma [6]. Powell et al. found that p53 is a prognostic significance in breast cancer [7]. Gurung et al. found that AIMP3 predicts survival following radiotherapy in muscle-invasive bladder cancer [8]. In recent years, a large number of models were constructed by multiple genes that can accurately predict the prognosis of patients. Deng et al. found that a five-autophagy-related lncRNA signature was used to be a prognostic model in HCC [9]. Feng et al. found a 7-gene prognostic signature to predict the survival of pancreatic ductal adenocarcinoma [10]. Yin et al. found a novel prognostic six-CpG signature in glioblastomas [11]. e aim of our study is to explore the causes of differential infiltration of macrophages M1 in hepatocellular carcinoma from the perspective of transcriptome. Using differentially expressed genes to construct a reliable prognosis model is expected to improve the prognosis of patients with HCC. In our model, we scored the content of macrophages M1 according to the transcriptome data downloaded from e Cancer Genome Atlas and found the differentially expressed genes between high-and low-infiltration groups. e prognostic model was constructed according to the differential genes and verified on the external database. Our model is also deeply discussed.

Data Download.
We downloaded the expression data of the hepatocellular liver carcinoma project rectified to fragments perkilobase million (FPKM) as the training cohort and clinical data of HCC in e Cancer Genome Atlas (TCGA, https://tcga-data.nci.nih.gov/tcga/). e expression data and clinical data of Liver Cancer-RIKEN, Japan, were downloaded from the International Cancer Genome Consortium (ICGC, https://dcc.icgc.org/). We annotated the data by gene transfer format (GTF) files obtained from Ensembl (http://asia.ensembl.org).

Construction and Validation of the Model.
Screening of DEGs was carried out by "limma" package (https:// bioconductor.org/packages/limma/) in R software (4.0.0). e data were analyzed by Cox hazard analysis and Lasso regression with the "survival" (https://cran.r-project.org/ package�survival), "glmnet" (https://cran.r-project.org/ package�glmnet), and "survminer" (https://cran.r-project. org/package�survminer) package. e "survivalROC" package was used to draw receiver operating characteristic curve, and the "survival" package was used to draw the survival curve. (GSEA). GSEA was utilized in this study to find the differences between different risk groups in the TCGA cohort. An annotated gene set file (c2.cp.kegg.v7.0.symbols.gmt) was selected as the reference. e threshold was confirmed as FDR q-val <0.05.

e Analysis of Immune.
Significant results of immune infiltrate deconvolution were obtained in TCGA patients with HCC by CIBERSORT analysis. e "StromalScore," "ImmuneScore," and "ESTIMATEScore" of each sample in the TCGA cohort are carried out by the "estimate" package. e "GSVA" and "GSEABase" packages were used for ssGSEA analysis for each patient. e correlation analysis of each index was completed by the Spearman test.

Constructing the Prognosis Model in the TCGA Cohort.
After scoring the macrophages M1 of different HCC patients, we ranked the scores from low to high. Analyzing the DEGs between the first quarter of patients (86) and the last quarter of patients (87), 317 DEGs were found in the process. Combined with the clinical prognosis, we screened 55 genes by univariate Cox hazard analysis in the TCGA cohort. We used Lasso regression and multivariate Cox hazard analysis to narrow the number of genes and finally got 7 genes to optimize the model (Figure 1(a)), and the risk score of each sample was calculated (risk score � UAP1L1 * 0.0433 + EPO * 0.0226 + PNMA3 * 0.0307 + NDRG1 * 0.0032 + KCNH2 * 0.0406 + G6PD * 0.0092 + HAVCR1 * 0.0460) and the median of risk score was used to distinguish the high-and low-risk group. In the 0.5, 1, and 3 years, the AUC value under the ROC curve is 0.722, 0.757, and 0.708 (Figure 1(b)). ere were significant differences in prognosis between high-and low-risk groups (Figure 1(c)). e heatmap showed that the expression level of UAP1L1, EPO, PNMA3, NDRG1, KCNH2, G6PD, and HAVCR1 in the high-risk group was higher than that in the low-risk group (Figure 1(d)) and the risk of death in HCC patients increased with the increase in risk score (Figures 1(e) and 1(f )).

Verifying the Prognosis Model.
We validated the model in the GSE14520 cohort. In the 0.5, 1, and 3 years, the AUC values under the ROC curve are 0.706, 0.751, and 0.759 ( Figure 2(a)). e model can significantly distinguish the prognosis of patients in high-and low-risk groups ( Figure 2(b)).

e Risk Score Was an Independent Prognostic Indicator.
We analyzed the relationship between risk score and clinicopathological characteristics (age, gender, histological grade, clinical stage, and TNM). Univariate Cox hazard analysis of clinicopathological features showed that the p value of stage, T, and risk score was less than 0.001 and the hazard ratio was over 1 (Figure 3(a)). Multivariate Cox hazard analysis of clinicopathological features showed that the p value of risk score was less than 0.05 and the hazard ratio was over 1 (Figure 3(b)). e risk score in different ages, genders, grades, stages, and T groups has significant differences (Figures 3(c)-3(f )).
ere are significant differences in the prognosis of different risk score groups in different ages, genders, histological grades, M0, N0, stages, and T ( Figure 3(g)).
3.5. e Risk Score and Immune. We found that the content of macrophages M1 can be well distinguished among different risk score groups.
ere were also significant differences in the content of some immune cells in different risk score groups ( Figure 5(a)).
ere was a 2 Journal of Oncology significant correlation between risk score and macrophages M1 ( Figure 5(b)). e ssGSEA analysis showed that there was no significant difference in the cell content of B cells, CD8 T cells, DCs, mast_cells, neutrophils, pDCs, and T helper cells in the high-and low-risk groups and APC coinhibition, cytolytic activity, inflammation promoting, and type 1 INF reponse (Figures 5(c) and 5(d)). We also found a significant positive correlation between risk score and immune checkpoint (CTLA4 and PDCD1) ( Figure 5(e)).

Discussion
Macrophages M1 in hepatocellular carcinoma have been concerned by a large number of researchers. During the differentiation of monocytes into macrophages, macrophages obtain immunosuppressive function in order to maintain the homeostasis of the immune microenvironment, but the M1 polarization of macrophages has a significant antitumor effect [12]. Macrophages secrete vascular endothelial growth factor, platelet-derived growth factor, and transforming growth factor β which inhibited antitumor immunity and promoted tumor progression [13][14][15]. ese findings also provide a new dimension for the immunosuppressive effect of cancer. Angiogenesis inhibition therapy has also become a promising treatment strategy for HCC [16]. Zhao [19]. Clinical trials that exert influence on macrophages have shown improvement on tumors. e prognostic significance of combining tumor-secreted osteopontin with microenvironment-associated peritumoral macrophages was confirmed in HCC with early stage [20]. e combination of bavituximab with sorafenib could increase the frequency of M1 macrophages in the treatment for advanced HCC patients [21]. Terakawa et al. found that the capability of macrophages to produce TNF-alpha could be useful for prognostis and for monitoring immunocompetence in patients with pancreatic cancer [22]. e immune microenvironment of HCC is quite complex. In particular, the relationship between macrophages and Tregs has been widely concerned. Macrophages aggregate Tregs cells to cancer sites by expressing CCL17, CCL18, and CCL22, thus hindering the activation of cytotoxic T cells [23,24]. Granito et al. [25] found that tumorassociated macrophages (by secreting IL-10) can induce CD4+ CD25+ Foxp3 regulatory T cells, thus indirectly supporting tumor growth and progression. It was found that the IL-10 antibody could partially block the aggregation effect of macrophages on Tregs [26].
e genes in our model play an important role in tumors. Hill et al. identified UAP1L1 is a methylated gene associated with clinical features [27]. Lai et al. found that UAP1L1 is a critical factor for protein O-GlcNAcylation and cell proliferation in human hepatoma cells [28]. Bradbury found that EPO helps children with cancer-related anaemia [29]. Kumar et al. found that EPO receptor contributes to melanoma cell survival [30]. Schüller et al. found that PNMA3 is a novel neuronal protein implicated in paraneoplastic neurological disease [31]. Sevinsky et al. found that NDRG1 regulates neutral lipid metabolism in breast cancer [32]. Villodre et al. found that NDRG1 is an independent prognostic factor in breast cancer [33]. Afrasiabi et al. found that KCNH2 regulated melanoma cell proliferation and migration [34]. Feng et al. found that G6PD regulated paclitaxel resistance in ovarian cancer [35]. Liu et al. found that HAVCR1 might be a novel prognostic factor for gastric cancer [36].
ere is a significant correlation between the risk score in our model and many immune indexes and immune checkpoints, which is a very meaningful discovery. However, our model needs more biological function verification and multicenter patient data to modify our model. It is hoped that our model can provide new ideas for the treatment of hepatocellular carcinoma and improve the prognosis of hepatocellular carcinoma patients.
List of abbreviations: HCC, hepatocellular carcinoma; FPKM, fragments perkilobase million; TCGA, e Cancer       Data Availability e datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.