==== Front J Cell Mol Med J Cell Mol Med 10.1111/(ISSN)1582-4934 JCMM Journal of Cellular and Molecular Medicine 1582-1838 1582-4934 John Wiley and Sons Inc. Hoboken 37248957 10.1111/jcmm.17780 JCMM17780 JCMM-01-2023-135.R1 Original Article Original Articles An inflammation‐associated ferroptosis signature optimizes the diagnosis, prognosis evaluation and immunotherapy options in hepatocellular carcinoma Ruan et al. Ruan Wan‐Yuan 1 2 Zhang Lu 2 Lei Shan 3 Zeng Zhi‐Rui https://orcid.org/0000-0001-9547-9074 3 Yang Yu‐Shi 4 Cao Wen‐Peng 3 Hao Qiu‐Yao 2 Lu Min 5 lumin2009amy@163.com Tian Xiao‐Bin 2 txb1216@126.com Peng Pai‐Lan 1 2 pengpailan@163.com 1 Department of Gastroenterology The Affiliated Hospital of Guizhou Medical University Guiyang China 2 School of Clinical Medicine Guizhou Medical University Guiyang China 3 School of Basic Medicine Guizhou Medical University Guiyang China 4 Department of Pathology The Affiliated Hospital of Guizhou Medical University Guiyang China 5 Department of Gastroenterology, Zhujiang Hospital Southern Medical University Guangzhou China * Correspondence Pai‐Lan Peng, Department of Gastroenterology, The Affiliated Hospital of Guizhou Medical University, Guiyang 550009, China. Email: pengpailan@163.com Xiao‐Bin Tian and Min Lu, School of Clinical Medicine, Guizhou Medical University, Guiyang 550025, China. Email: txb1216@126.com and lumin2009amy@163.com 30 5 2023 7 2023 27 13 10.1111/jcmm.v27.13 18201835 17 4 2023 31 1 2023 08 5 2023 © 2023 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Abstract Inflammation and ferroptosis crosstalk complexly with immune microenvironment of hepatocellular carcinoma (HCC), thus affecting the efficacy of immunotherapy. Herein, our aim was to identify the inflammation‐associated ferroptosis (IAF) biomarkers for contributing HCC. A total of 224 intersecting DEGs identified from different inflammation‐ and ferroptosis‐subtypes were set as IAF genes. Seven of them including ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 were used for construction of a risk model which classified HCC patients into two groups (high and low risk). HCC patients in the high‐risk group exhibited shorter survival rate and higher immune score, and were predicted to have higher respond rate in immune checkpoint inhibition (ICI) therapy. Levels of the seven genes were significantly changed in HCC tissues in comparison to adjacent tissues. After inserting the gene expression into the risk model, we found that the risk model exhibited the higher diagnostic value for distinguish HCC tissues compared each single gene. Furthermore, HCC tissues from our research group with high‐risk score exhibited more cases of microsatellite instability (MSI), heavier tumour mutational burden (TMB), higher expression level of PDL1 and cells with CD8. Knockdown of APOA5 reduced HCC cell proliferation combining with elevating inflammation and ferroptosis levels. In conclusion, we considered APOA5 maybe a novel target for suppressing HCC via simultaneously elevating inflammation and ferroptosis levels, and signature constructed by seven IAF genes including ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 can act as a biomarker for optimising the diagnosis, prognosis evaluation and immunotherapy options in HCC patients. diagnosis hepatocellular carcinoma immunotherapy optimisation inflammation‐associated ferroptosis gene prognosis evaluation Doctoral research start‐up project of Affiliated Hospital of Guizhou Medical Universitygyfybsky‐2021‐25 National Natural Science Foundation of China 10.13039/501100001809 82060541 National nature Cultivation Project of Affiliated Hospital of Guizhou Medical Universitygyfynsfc(2020)‐4 Project of Guizhou Science and Technology DepartmentQiankehe support (2021) general 089 Project of Health Commission of Guizhou Provincegzwkj2023‐170 source-schema-version-number2.0 cover-dateJuly 2023 details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.3.0 mode:remove_FC converted:03.07.2023 Ruan W‐Y , Zhang L , Lei S , et al. An inflammation‐associated ferroptosis signature optimizes the diagnosis, prognosis evaluation and immunotherapy options in hepatocellular carcinoma. J Cell Mol Med. 2023;27 :1820‐1835. doi:10.1111/jcmm.17780 Wan‐Yuan Ruan, Lu Zhang, Shan Lei and Zhi‐Rui Zeng equal contribution to this paper. ==== Body pmc1 INTRODUCTION Hepatocellular carcinoma (HCC) ranks sixth in morbidity and third in mortality of tumour‐related deaths worldwide. 1 Surgical resection is the main therapeutic strategy for early‐stage HCC. However, most of the individuals are diagnosed at an advanced stage, thus losing the opportunity for surgical treatment. 2 Moreover, HCC cells have a high resistance to conventional cytotoxic chemotherapeutic agents, limiting the benefit of chemotherapy in patients with HCC. 3 Since 2008, several tyrosine kinase inhibitors, including sorafenib and regorafenib, have been approved for first‐ and second‐line treatments of HCC, whereas the overall survival rate still has no improvement. 4 In recent years, approval of immunotherapy, especially immune checkpoint inhibitor (ICI) therapy, has greatly relieved this embarrassing treatment status, and significantly improves the outcome of part of individuals. Unfortunately, majority of HCC individuals are defeated to respond to ICI. 5 In patients who fail respond to ICI, immune‐related adverse reactions including dermatotoxicity, endocrine system toxicity, pneumonia and gastrointestinal toxicity turn into greater challenge during therapy. 6 Therefore, exploration of novel biomarkers for immunotherapy optimisation in HCC individuals was urgent. Inflammation is a hallmark of cancer, and its link with the progression of cancer is well established. In fact, inflammation plays a double‐edged role in tumours. 7 Studies have shown that transient inflammation exhibit anti‐tumour effects, while chronic persistent inflammation can affect the plasticity of tumour cells by regulating cell differentiation, immune cell polarisation in microenvironment and other processes, hence facilitating tumour proliferation, metastasis and drug resistance. 8 Similarly, the role of inflammation in immune regulation is complex. Immunosuppressive inflammatory microenvironment is commonly formed in tumour tissues; therefore, tumour cells often escape from the killing of immune cells including T cells. 9 However, during immune checkpoint inhibition (ICI) therapy, the tumour microenvironment is remodelled, and tumour‐promoting inflammation is transformed into tumour‐suppressive inflammation. 10 Ferroptosis is a specific cell death model that depends on lipid peroxidation process, and it was different from procedural death in morphology and molecular mechanism. 11 Clinical researchers found that ferroptosis commonly exist in cancer cells while patients obtain radiotherapy, chemotherapy and tumour immunotherapy, indicating that ferroptosis activation is the strategy to inhibit cancer. 12 Ferroptosis has crosstalk with inflammation and immunotherapy. Previous studies indicated that lipid peroxides produced during ferroptosis are identifying signals to accelerate recognition, phagocytosis and processing of tumour antigens by dendritic cells, thus intentinonally activating cytotoxic T lymphocytes to improve the efficacy of tumour immunotherapy. 13 Inflammation is an important factor involve in the activation of ferroptosis, and this phenomenon can be observed in the tissues after ICI therapy. 14 Therefore, inflammation induced the activation of ferroptosis during ICI therapy is generally beneficial to anti‐tumour, and can act as biomarker to reflect the response of ICI. Herein, our aim was to identify inflammation‐associated ferroptosis (IAF) biomarkers for diagnosis and therapy of HCC. We demonstrated that the signature constructed by seven IAF genes including ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 can act as a biomarker for optimising the diagnosis, prognosis evaluation and immunotherapy options in patients with HCC. Among the IAF genes, APOA5 was a novel target for suppressing HCC via simultaneously elevating inflammation and ferroptosis levels. 2 MATERIALS AND METHODS 2.1 Data acquisition and pretreatment Train cohort containing the RNA‐sequencing profile of 369 patients with HCC and their clinical characters information was downloaded from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/). Test cohort containing the RNA‐sequencing information and clinical characters of 237 HCC patients was gained in International Cancer Genome Consortium (ICGC; https://dcc.icgc.org/). Prior to analysis, genes with missing value >50% were removed, and then gene expression profile was performed to normalize. 2.2 Subtypes cluster Inflammation‐related genes were referred to the research of Danaher et al., 15 while ferroptosis‐related genes were referred to the terms of ‘WP_FERROPTOSIS’ in Gene Set Enrichment Analysis (GSEA, http://www.gsea‐msigdb.org/gsea/index.jsp). Then, the inflammation‐ and ferroptosis‐associated genes were used to conduct cluster analysis by the R package ‘ConsensusClusterPlus’, respectively. The cluster number was set as 3. Then, the difference of the overall survival (OS) between the subtypes was determined by Kaplan–Meier method. p < 0.05 was considered different. 2.3 Identification of differentially expressed genes (DEGs) The DEGs between the different subtypes of HCC were analysed by the limma package (Version: 3.1.6). Genes with the |Log2 foldchange (LogFC)| ≥ 1 and adjust p value <0.05 were DEGs. Intersecting DEGs between inflammation‐ and ferroptosis‐subtypes were set as IAF genes, and used for further analysis. 2.4 Enrichment analysis of IAF genes KEGG and GO analysis of IAF genes were analysed in the DAVID Bioinformatics Resources (https://david.ncifcrf.gov/; version: v2022q4). Enrichment GO terms contained biological process (BP) and molecular function (MF) terms. Enrichment terms with p < 0.05 were visualized. 2.5 Risk model establish based on significant IAF genes In order to construct a risk model by IAF genes, we first performed LASSO analysis to remove similarity factors and reduce the scope by adding penalty parameter using the R package ‘glmnet’ (Version: 4.1‐6). Then, the effects on the OS of HCC patients of residual IAF genes was detected by performing univariate and multivariate regression analysis. By multiplying levels of genes and their corresponding regression coefficients deduced from multivariate Cox regression analysis, risk model was constructed. The HCC tissues were then divided into high‐ and low‐risk groups followed by calculating the risk score of each tissue. The OS difference between the two groups was analysed by Kaplan–Meier method, while the effects of risk score on OS of HCC patients were analysed by univariate and multivariate regression analysis. p < 0.05 was cut‐off. 2.6 Immune‐related analysis ESTIMATE (https://bioinformatics.mdanderson.org/estimate/; version: 1.0.11) was utilized to measure the immune signatures of HCC tissues, including immune, stromal and ESTIMATE score. For immune cell infiltration analysis, built‐in data LM22 containing gene expression characteristics of 22 immune cells was set as reference. Cibersort package (version: 1.04) was conducted to measure the levels of 22 immune cells in HCC tissues via matching the reference data. The t‐test was performed to analysed the differences via the cut‐off as p < 0.05. 2.7 Tumour immune dysfunction and exclusion (TIDE) analysis For prediction of the respond rate of HCC tissues in the two groups (high and low risk) after ICI treatment, TIDE database (http://tide.dfci.harvard.edu/) was conducted. The t‐test was performed to analysed the differences of dysregulation, exclusion and TIDE score between the two groups, while difference of respond ratio was determined via chi‐squared test. p < 0.05 was cut‐off. 2.8 Tissues collection Total 40 pair HCC tissues and corresponding adjacent tissues were collected from Affiliated Hospital of Guizhou Medical University with the approve of the Human Ethics Committee of Guizhou Medical University (Approval number: 2022‐282). All individuals had obtaining written informed consent. All of them had not receive chemotherapy, target therapy and immunotherapy prior to tissues collection. Similarly, all patients had complete medical records including the information of microsatellite instability (MSI) and tumour mutational burden (TMB). MSI was determined by detecting the mutation of NR‐21, BAT‐26, NR‐24, BAT‐25 and MONO‐27 site. While HCC patients with mutation site = 0, =1 or ≥2, was set as microsatellite stability (MSS), MSI‐low (MSI‐L) and MSI‐high (MSI‐H), respectively. All tissue specimen was collected in −80°C prior to perform experiments. 2.9 RT‐qPCR experiments Total RNA in HCC tissues and corresponding adjacent tissues were obtained by TRIZOL reagent (Solarbio, Beijing, China). Then, total 800 ng mRNA was used to synthesize first‐strand cDNA utilising RT Master Mix for qPCR II (gDNA digester plus; MCE). Fluorescence amplification quantification was performed using SYBR green reagent (MCE). Relative expression of target genes was referred to glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH). Primers used in fluorescence amplification quantification was shown as follows: alcohol dehydrogenase 4 (ADH4) forward primer 5′‐ AGTTCGCATTCAGATCATTGCT ‐3′, ADH4 reverse primer 5′‐ CTGGCCCAATACTTTCCACAA ‐3′; apolipoprotein A5 (APOA5) forward primer 5′‐ GCTGGTGGGCTGGAATTTG ‐3′, APOA5 reverse primer 5′‐ CTCGGCGTATGGGTGGAAG‐3′; complement factor H‐related 3 (CFHR3) forward primer 5′‐ TGCTAATGGACAAGTGAAACCTT ‐3′, CFHR3 reverse primer 5′‐ GGCAACTTCTGTAGAGTTACCC‐3′; C‐X‐C motif chemokine ligand 8 (CXCL8) forward primer 5′‐ TTTTGCCAAGGAGTGCTAAAGA ‐3′, CXCL8 reverse primer 5′‐ AACCCTCTGCACCCAGTTTTC ‐3′; formimidoyltransferase cyclodeaminase (FTCD) forward primer 5′‐ GGAATGCGTCCCCAACTTTTC ‐3′, FTCD reverse primer 5′‐ TGTCGATAAGTCGGGAAGCTAC ‐3′; glucose‐6‐phosphate dehydrogenase (G6PD) forward primer 5′‐ CGAGGCCGTCACCAAGAAC ‐3′, G6PD reverse primer 5′‐ GTAGTGGTCGATGCGGTAGA −3′; paraoxonase 1 (PON1) forward primer 5′‐ CTGATTGCGCTCACCCTCTT ‐3′, PON1 reverse primer 5′‐ CGGAGAGCATTAAGTCGTGTTTG ‐3′; GAPDH forward primer 5′‐ GGAGCGAGATCCCTCCAAAAT ‐3′, GAPDH reverse primer 5′‐ GGCTGTTGTCATACTTCTCATGG −3′. 2.10 Immumohistochemical staining Total 40 HCC tissues were cut into 2 μm‐thick paraffin specimens. The specimens were conducted antigen‐repair in citrate (Beyotime) followed by dewaxing. The tissues were then infiltrated in 3% H2O2 for 10 min and 8% bovine serum albumin for 15 min for prevention of non‐specific binding. Diluted anti‐PDL1 antibody (1:50,00; Cat No. 66248‐1‐Ig; Proteintech) and anti‐CD8 antibody (1:10,000; Cat No. 66868‐1‐Ig; Proteintech) were incubated with the sections overnight. Sections were washed by PBS for twice, and second antibodies were incubated with sections for 2 h. DAB solution (Solarbio) and haematoxylin (Solarbio) were used to stain the sections for 30 and 10 s, respectively. Finally, the HCC sections after treatment were dehydrated, transparent and sealed. 2.11 Cell culture and transfection of small interfering RNAs (siRNAs) HCC cell HepG2 and Huh7 were obtained from ATCC database, and cultured in DMEM medium (Hyclone) with 10% FBS (Hyclone) at 37°C environment. Targeting APOA5 siRNAs and scramble siRNA were constructed by iGeneBio. The sequences of siRNAs for targeting APOA5 was 5′‐ GAGCAAGACCUCAACAAUAUG −3′ and 5′‐ CGAUGGAUCUGAUGGAGCAGG −3′. The transfection of siRNAs were conducted by lipo2000 (Invitrogen) according to process provided by manufacturer. 2.12 Western blotting Total protein in HCC cells were extracted by RIPA buffer (Boster) containing with 1% PMSF (Boster). Following by detecting concentration by BCA method, proteins were separated by 12.5% SDS‐PAGE, and transfected onto PVDF membranes (Invitrogen). After blocking with TBST containing 5% milk powder, anti‐APOA5 (1:500; cat no. A1424; Abconal) and anti‐β‐actin (1:10,000; cat no. AC026; Abconal) were added overnight at 4°C. After washing by secondary antibodies and visualising by ECL reagent (Invitrogen), relative expression of APOA5 was calculated via normalising to β‐actin. 2.13 Enzyme‐linked Immunosorbent assay (ELISA) experiment Total 1 × 105 negative control (NC) HepG2 and Huh7 cells and cells with APOA5 knockdown were set into per well for 24 h and then lysed. The TNF‐α and IFN‐γ in cell lysate of per sample was determined with corresponding ELISA kit (Beijing Tiantan Biological Products co., LTD). The relative levels of TNF‐α and IFN‐γ were normalized to protein concentration. 2.14 Relative experiments for detecting ferroptosis Based on manufacturer's instructions, Glutathione Assay Kits (Invitrogen) were utilized to analyse the levels of glutathione (GSH) in HepG2 and Huh7 cells; Lipid Peroxidation (MDA) Assay Kit (Invitrogen) was used to detect the levels of MDA in HepG2 and Huh7 cells; while the iron concentrations were analysed using an Iron Assay Kit (Invitrogen). 2.15 Reactive oxygen (ROS) detection The cells were seeded in 6‐well plates in triplicate and cultured for the appropriate period of time until 70% confluence was reached. To measure ROS levels in whole cells, the cells were cultured for 24 h in fresh DMEM and stained with 10 μM 2′,7′‐dichlorodihydrofluorescein diacetate (DCFH‐DA; Invitrogen, USA). The ROS level was assessed using flow cytometry and analysed using FlowJo (version: 7.4.1). 2.16 Cell prolfieration and colony formation detection For CCK‐8 assays, HepG2 and Huh7 cells were seeded in 96‐well plates in triplicate with the density of 3 × 103 per well. After culturing for 48 h, 10 μL CCK‐8 was added in per well for 2 h. Finally, the absorbancy of each well was detected in 450 nm in microplate reader. For colony formation assays, HepG2 and Huh7 cells were seeded in 6‐well plates in triplicate with the density of 2 × 103 per well. After 14 days, culture medium was removed, while cell colony was fixed with paraformaldehyde and fixed with crystal violet. The condition of cell colony in per well was photographed and calculated. 2.17 Data statistics The data results were analysed in SPSS 19.0. Differences between the two groups were determined by t‐test, while one‐way analysis of variance was used to measure the differences between multigroups. p < 0.05 was significant. 2.18 Inflammation‐ and ferroptosis‐ molecular subtypes was associated with HCC patient prognosis In order to obtain different subtype HCC tissues with inflammation signature, characteristic inflammation‐related genes were conducted for consensus clustering analysis (Figure 1A,B). Total three subtypes (C1, C2 and C3 inflammation cluster) of HCC with different inflammation signature were obtained (Figure 1C). Kaplan–Meier method suggested that, compared with the OS of HCC patients in C1 and C2 inflammation cluster, HCC patients in C3 inflammation cluster had the shortest OS time (Figure 1D; HR = 1.55, 95% CI = 1.17–2.05). Similarly, characteristic ferroptosis‐related genes enrolled in consensus clustering analysis (Figure 2A,B), and three subtypes (C1, C2 and C3 ferroptosis cluster) of HCC with different ferroptosis signature were gained (Figure 2C). Compared with the OS of HCC patients in C2 and C3 ferroptosis cluster, HCC patients in C1 ferroptosis cluster had the shortest OS time (Figure 2D; HR = 1.75, 95% CI = 1.30–2.33). These evidences indicated that inflammation‐ and ferroptosis‐ molecular subtypes was associated with HCC patient prognosis. FIGURE 1 Cluster of HCC samples based on inflammation‐related genes. (A) Consensus clustering CDF with k valued 2–6. (B) Relative change in area under CDF curve for k = 3. (C) Consensus clustering matrix when k = 3. (D) The overall survival rate of C1, C2, C3 inflammation‐related cluster. FIGURE 2 Cluster of HCC samples based on ferroptosis‐related genes. (A) Consensus clustering CDF with k valued 2–6. (B) Relative change in area under CDF curve for k = 3. (C) Consensus clustering matrix when k = 3. (D) The overall survival rate of C1, C2, C3 ferroptosis‐related cluster. 2.19 Identification of IAF genes in HCC We performed DEGs analysis in inflammation clusters (C3 vs. others clusters), total 106 upregulated genes and 146 downregulated genes were found (Figure 3A,B; Supplemental Table S1). Similarly, through performing DEGs analysis in ferroptosis clusters (C1 vs. others clusters), total 289 upregulated genes and 234 downregulated genes were found (Figure 3C,D; Table S2). A total of 224 intersecting DEGs were found (Figure 4A) and set as IAF genes in HCC. KEGG analysis indicated that enrich terms of IAF genes were ‘metabolic pathways’, ‘retinol metabolism’, ‘xenobiotics metabolism’, ‘PPAR pathway’ and ‘glycolysis’ (Figure 4B). GO analysis exhibited that BP terms of IAF genes were enriched in ‘lipid metabolic process’, ‘monocarboxylic acid metabolic process’, ‘fatty acid metabolic process’, ‘alcohol metabolic process’ and ‘steroid metabolic process’ (Figure 4C), while MF terms of IAF genes were enriched in ‘oxidoreductase activity’, ‘vitamin binding’, ‘arachidonic acid monooxygenase activity’, ‘arachidonic acid epoxygenase activity’ and ‘retinol dehydrogenase activity’ (Figure 4D). FIGURE 3 Identification of differentially expressed genes (DEGs) between inflammation‐ and ferroptosis‐related clusters. (A, B) DEGs between C3 inflammation cluster and other inflammation clusters. (C, D) DEGs between C1 ferroptosis cluster and other ferroptosis clusters. FIGURE 4 Analysis of intersecting DEGs identified from different inflammation‐ and ferroptosis‐subtypes. (A) Intersecting DEGs identified from different inflammation‐ and ferroptosis‐subtypes, and set as inflammation‐associated ferroptosis (IAF) genes. (B) KEGG terms of IAF genes enriched in. (C) Biological process (BP) terms of IAF genes enriched in. (D) Molecular function (MF) terms of IAF genes enriched in. 2.20 Construction of risk model based on significant IAF genes Total 224 IAF genes were enrolled in LASSO Cox analysis, and 201 genes with high similarity were removed (Figure 5A,B). Then, residual 23 genes were conducted for univariate Cox regression analysis. ADH4 (HR = 0.690), APOA5 (HR = 0.765), CDC20 (HR = 2.268), CFHR3 (HR = 0.670), CXCL8 (HR = 1.454), CYP2C9 (HR = 0.678), FTCD (HR = 0.614), G6PD (HR = 2.737), MARCKSL1 (HR = 3.552), PLPP2 (HR = 1.274), PON1 (HR = 0.671), RAP1GAP (HR = 1.797), SPP1 (HR = 1.601), TPX2 (HR = 2.606) and TRNP1 (HR = 1.701) was found to correlate with the OS of HCC patients (Figure 5C). These genes were used for multivariate Cox regression analysis, while ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 were conducted for construction of a risk model (Figure 5D). The risk model was risk score = −0.209 × ADH4 + 0.941 × APOA5 – 0.213 × CFHR3 + 0.316 × CXCL8–0.551 × FTCD + 1.008 × G6PD – 0.289 × PON1. FIGURE 5 Construction of risk model based on inflammation‐associated ferroptosis (IAF) genes. (A, B) Lasso Cox analysis for IAF genes, total 23 IAF genes were retained. (C) Univariate Cox regression analysis for the 23 IAF genes. (D) Multivariate Cox regression analysis for the 23 IAF genes. 2.21 Risk model served as prognostic biomarker in HCC individuals We checked the effects of risk model on prognosis evaluation in HCC tissues from the train cohort TCGA first. The HCC tissues in TCGA was divided into high‐ and low‐risk groups based on the median of risk score (Figure 6A). A higher proportion of deaths was found in the HCC individuals with high risk (Figure 6B). Compared the OS time in the low‐risk group, the OS time in HCC individuals with high risk were shorter (Figure 6C; HR = 2.27, 95% CI = 1.61–3.03). The prognostic values of risk model for evaluating 1‐year, 3‐year and 5‐year survival were 0.82, 0.74 and 0.72 (Figure 6D). Moreover, higher level of APOA5, CXCL8 and G6PD was observed in high‐risk HCC tissues, while level of ADH4, CFHR3, FTCD and PON1 was reduced (Figure 6D). Furthermore, we enrolled the information of age, sex, BMI, T stage, N stage, M stage, tumour stage, tumour grade and risk score for univariate and multivariate Cox regression analysis in HCC individuals in TCGA. It was demonstrated that the risk score was an independent biomarker to evaluate OS in HCC patients in TCGA (Figure 6F,G). We then analysed the prognostic value in HCC tissues from test cohort ICGC. Following classifying HCC patients of ICGC into the two groups (Figure 7A), higher proportion of death cases (Figure 7B) and lower OS time (Figure 7C; HR = 2.86, CI = 1.52–5.26) were observed in the high‐risk group. The prognostic value of risk model in HCC tissues in ICGC for evaluating 1‐year and 3‐year survival were 0.81 and 0.70 (Figure 7D). Expression of APOA5, CXCL8 and G6PD was also elevated in HCC tissues from ICGC, whereas expression of ADH4, CFHR3, FTCD and PON1 was decreased (Figure 7E). Similarly, enrolling the information of sex, age, tumour stage and risk score into univariate and multivariate Cox regression analysis, risk score both had significance (Figure 7F,G). All these evidences exhibited that the risk model established by the significant IAF genes can act as prognostic biomarkers for HCC tissues. FIGURE 6 Risk model can act as prognostic biomarker for HCC tissues in TCGA. (A) HCC patients in TCGA were divided into high‐ and low‐risk group according to medium of risk score. (B) Higher death rate of HCC patients in the high‐risk group in TCGA. (C) Shorter overall survival rate in HCC patients in the high‐risk group in TCGA. (D) Prediction rate of risk model on the 1‐, 3‐ and 5‐year survival of HCC patients in TCGA. (E) Expression of ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 in HCC tissues between the high‐ and low‐risk groups in TCGA. (F, G) Univariate Cox regression analysis and multivariate Cox regression analysis for detecting the prognostic value of risk model in HCC patient in TCGA. FIGURE 7 Risk model can act as prognostic biomarker for HCC tissues in ICGC. (A) HCC patients in ICGC were divided into high‐ and low‐risk groups according to medium of risk score. (B) Higher death rate of HCC patients in the high‐risk group in ICGC. (C) Shorter overall survival rate in HCC patients in the high‐risk group in ICGC. (D) Prediction rate of risk model on the 1‐year and 3‐year survival of HCC patients in ICGC. (E) Expression of ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 in HCC tissues between the high‐ and low‐risk groups in ICGC. (F, G) Univariate Cox regression analysis and multivariate Cox regression analysis for detecting the prognostic value of risk model in HCC patient in ICGC. 2.22 The risk model reflected the immune characteristic and helped to predict the respond of ICI in HCC As inflammation and ferroptosis crosstalk with immune environment, we then analysed whether the risk model can reflect the immune characteristic in HCC. HCC tissues from train cohort (TCGA) and test cohort (ICGC) was merged, and ESTIMATE algorithm was performed to measure immune parameters in HCC tissues in the high‐ and low‐risk groups. Elevated immune score and ESTIMATE score were observed in the HCC tissues in the high‐risk group (Figure 8A). Moreover, expression levels of 22 immune cells were calculated by Cibersort (Figure 8B). Higher levels of CD8 T cell, naïve CD4 T cell, activated memory T cell, M0 macrophages and resting dendritic cell were observed in HCC tissues in the high‐risk group, while the level of follicular helper T cell, resting NK cell, monocytes, M2 macrophages and resting mast cells were reduced (Figure 8C). These evidences indicated that HCC tissues in the high‐risk group tend to be ‘hot tumours’, and risk model may have the function of identifying ‘hot tumours’. Therefore, we then analysed whether risk model can help to predict the respond of ICI treatment in HCC. TIDE analysis was performed, risk score was positively related to IFNG score (R = 0.32, p < 0.01; Figure 8D) and Merck18 (R = 0.32, p < 0.01; Figure 8E). Moreover, through TIDE analysis, MSI score was found higher in HCC tissues in the high‐risk group compared with those in the low‐risk group (Figure 8F). Elevated dysregulation score combined with reduced exclusion and TIDE score were discovered in HCC tissues with high risk (Figure 8G). Furthermore, HCC tissues in the high‐risk group was predicted to had higher respond rate in ICI treatment (Figure 8H). These evidences indicated that the risk model may had function to predict ‘hot tumour’ and the respond of ICI treatment. FIGURE 8 The risk model can reflect the immune characteristic and help to predict the respond of ICI in HCC. (A) Stomal score, immune score and ESTIMATE score between the high‐ and low‐risk groups. (B) Cibersort was used to translate the gene expression matrix of HCC tissues into expression level of 22 immune cells. (C) Differential levels of 22 immune cells between the high‐ and low‐risk groups. (D) The co‐relationship between risk score and IFNG score. (E) The co‐relationship between risk score and Merck18 score. (F) MSI score between the high‐ and low‐risk groups. (G) Exclusion score, dysregulation score and TIDE score between HCC tissues in the high‐ and low‐risk score. (H) The responder rate was predicted in HCC tissues between the high‐ and low‐risk groups. *, p < 0.05; **, p < 0.01. 2.23 The risk model served as diagnostic biomarker for distinguish adjacent tissues and HCC tissues We verified the practicability of risk model in 40 pair HCC tissues and adjacent tissues from our research group. Levels of the seven IAF genes including ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 in each HCC tissue and adjacent tissue were checked by qRT‐PCR (Figure 9A). Levels of ADH4, CFHR3, FTCD and PON1 were decreased in the HCC tissues compared with adjacent tissues, and APOA5, CXCL8 and G6PD expression was elevated (Figure 9B). We substituted the expression of these genes into the formula (risk model). Interestingly, the risk score was also highly expressed in HCC tissues (Figure 9C). Followed by performing ROC analysis, we exhibited that the diagnostic value of risk score (AUC = 0.91) was higher than each single gene including ADH4 (AUC = 0.70), APOA5 (AUC = 0.78), CFHR3 (AUC = 0.72), CXCL8 (AUC = 0.65), FTCD (AUC = 0.69), G6PD (AUC = 0.73) and PON1 (AUC = 0.70) to distinguish HCC tissues and adjacent tissues. These evidences exhibited that the risk model may be a remarkable tool for HCC diagnosis. FIGURE 9 The risk model can act as diagnostic biomarker for distinguish adjacent tissues and HCC tissues. (A) qRT‐PCR was used to detect the expression of ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 in HCC tissues (n = 40) and adjacent tissues (n = 40) from our research group. (B) The expression differences of ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 between HCC tissues (n = 40) and adjacent tissues (n = 40) from our research group. (C) The expression of ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 in per tissues were substituted into the formula (risk model), and higher risk score was found in HCC tissues compared with adjacent tissues. (D) The diagnostic value of ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD, PON1 and risk score for distinguishing HCC tissues and adjacent tissues. **, p < 0.01. 2.24 The risk model helped for optimising the choice of immunotherapy We then reviewed the clinical characters of patients with HCC tissues (n = 40) from our research group, and tried to find the correlationship between the risk model calculating by substituting the expression of genes and patient clinical characters. Among the patients, total 2, 4, 2, 3 and 4 patients had NR‐21, BAT‐26, NR‐24, BAT‐25 and MONO‐27 site mutation (Figure 10A). We divided our HCC patients into high‐ and low‐risk groups based on medium of risk score, and we found that the patients with MSI‐H and MSI‐L was significantly more in the high‐risk group in comparison with the low‐risk group (Figure 10B). Similarly, we found that patients from our research group in the high‐risk group had higher TMB in comparison to the low‐risk group. Furthermore, through performing IHC, higher PDL1 expression and more immune cells with CD8 expression were found in the HCC tissues having high risk (Figure 10C,D). As higher MSI, TMB, PDL1 and more CD8 cells were biomarkers for respond of ICI, 16 we considered that risk model can help us to distinguish patients may respond for ICI, thus optimising the choice of immunotherapy. FIGURE 10 The risk model can help to optimize the choice of immunotherapy. (A) NR‐21, BAT‐26, NR‐24, BAT‐25 and MONO‐27 site mutation condition in HCC tissues from our research group. (B) Different distribution of MSS, MSI‐L and MSI‐H in the high‐and low‐risk group. (C) The difference of TMB between HCC tissues in the high‐ and low‐risk groups. (D) The expression of PDL1 in HCC tissues in the high‐ and low‐risk groups. (E) The positive rate of CD8 in immune cells in HCC tissues in the high‐ and low‐risk groups. *, p < 0.05; **, p < 0.01. 2.25 APOA5 was a novel target for inhibiting HCC via upregulating inflammation and ferroptosis Moreover, after reviewing previous studies, we found that, as a gene in lipid regulation, 17 the effects of APOA5 on HCC progression, inflammation and ferroptosis has not been reported in previous studies, thus catching our attention. Utilising two siRNAs, APOA5 knockdown cells were constructed (Figure 11A,B). Through performing ELISA, we found that HepG2 and Huh7 with APOA5 knockdown had elevating TNF‐α (Figure 11C) and IFN‐γ levels (Figure 11D). Then, we detected the levels of MDA, a product of lipid peroxidation in HCC cells, and found that suppression of APOA5 in HCC cells increased the MDA levels in HepG2 and Huh7 cells (Figure 11E). Moreover, we found that ROS levels (Figure 11F) were significantly increased in HepG2 and Huh7 cells with APOA5 knockdown, while the GSH (Figure 11G) in cells were reduced. Moreover, we found that the iron levels in HCC cells were increased (Figure 11H). These results indicated that knockdown of APOA5 may increase inflammation and ferroptosis. Furthermore, we found that knockdown of APOA5 reduced cell proliferation (Figure 1I) and colony formation (Figure 1J). Therefore, we considered that APOA5 was a novel target for inhibiting HCC via upregulating inflammation and ferroptosis. FIGURE 11 Knockdown of APOA5 can significant reduce the proliferation and colony formation combining with elevating inflammation and ferroptosis levels. (A) RT‐qPCR was used to detect the mRNA level of APOA5 in HepG2 and Huh7 cells after APOA5 knockdown. (B) Western blotting was used to detect the protein level of APOA5 in HepG2 and Huh7 cells after APOA5 knockdown. (C, D) Elisa was used to detect the levels of TNF‐α and IFN‐γ in HepG2 and Huh7 cells after APOA5 knockdown. Levels of MDA (E), ROS (F), GSH (G) and iron (H) were measured in HepG2 and Huh7 cells after APOA5 knockdown. (I) CCK‐8 was used to detect the proliferation rate of HepG2 and Huh7 cells after APOA5 suppression. (J) Colony formation assay was used to detect the colony formation ability of HepG2 and Huh7 cells after APOA5 suppression. **, p < 0.01. 3 DISCUSSION Recently, immunotherapy, radio‐chemotherapy and targeting therapy were used jointly to suppress HCC progression and elevate the prognosis. 18 HCC was highly heterogeneous, and the degree of inflammation and immune cell infiltration in its microenvironment affect the efficacy of treatment of HCC, especially immunotherapy. However, due to low MSI and immune suppressive microenvironment, patients with HCC commonly had low respond rate of immunotherapy including ICI. 19 More and more recognized that inflammation plays as ‘double‐edged sword’ in HCC progression. On the one hand, inflammation in tumour tissue can induce inflammation‐related death of tumour cells, such as ferroptosis, thus suppressing the progression of HCC. Similarly, previous studies indicated that, during the radio‐therapy, chemotherapy and immunotherapy, immune cells including CD8 T cells and NK cells are activated, and secrete a series of inflammatory factors including TNF‐α and INF‐γ to induce inflammation‐related death of tumour cells. 20 However, chronic inflammation accelerates the progression of tumour via activating a series of inflammation relative pathways, including NF‐kappaB and JAKs‐STATs. 21 Moreover, tumour cells would change the inflammatory microenvironment to form properties of immunosuppression. Inflammation‐induced ferroptosis is favourable for cancer therapy, while the products of cells undergoing ferroptosis can act as immunogen to induce immune cell infiltration. 22 Previous studies indicated that ICI based on the data obtaining from high‐throughput sequencing is helpful for the precise treatment of HCC. 23 , 24 Herein, therefore, we first aimed to explore novel biomarker associated with inflammation and ferroptosis in HCC tissues, and its effect on guiding immunotherapy via analysing high‐throughput sequencing data. In the current study, analysis of the gene expression profile of HCC tissues in TCGA, total 224 IAF genes were identified. Then, through selection of lasso Cox, univariate Cox regression analysis and multivariate Cox regression analysis, seven IAF genes including ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 were used for construction of a risk model. Following checking in train corhort (TCGA) and test cohort (ICGC), risk model was found to have potential to play as an independent biomarker for prognostic evaluation in HCC. Previous studies indicated that ‘hot tumour’ with more immune cell infiltration, especially T cell infiltration, had higher responder rate for ICI treatment. 25 Interesting, the high‐risk group classifying by the risk model was found to had more immune score and higher levels of CD8 T cell, naïve CD4 T cell, activated memory T cell, which were predicted to have higher respond rate to ICI. These results indicated that the risk model may help to identify ‘hot tumour’, and had potential to guide the ICI choice. ADH4‐encoded protein belong to the family of alcohol dehydrogenase. In a physiological state, it oxidizes long chain omega‐hydroxy fatty acids into 20‐oxoarachidonate, and induces the reduction of benzoquinones. 26 APOA5‐encoded protein is an apolipoprotein, involved in regulating the plasma triglyceride levels. 27 APOA5‐encoded protein is a component of high‐density lipoprotein. 28 CFHR3‐encoded protein is a secreted protein, which belongs to the complement factor H‐related protein family. CFHR3‐encoded protein is a key factor for complement‐regulated inflammatory responses. 29 CXCL8‐encoded protein is also named as interleukin‐8 (IL‐8), and is a major mediator of the inflammatory response. 30 FTCD‐encoded protein exhibits both transferase and deaminase activity, induces channels 1‐carbon units from formiminoglutamate to the folate pool. 31 G6PD‐encoded protein is the rate‐limiting step of the oxidative pentose‐phosphate pathway, via providing reducing power (NADPH) and pentose phosphates for fatty acid and nucleic acid synthesis. 32 PON1‐encoded protein belongs to the paraoxonase family, possessing lactonase and ester hydrolase activity. 33 The protein is secreted by the kidney and liver, and has potential to bind with high‐density lipoprotein, regulating the process of lipid synthesis. 34 Dysregulation of these genes had been observed in atherosis, autoimmune disease and tumours, including HCC. For example, ADH4 level was reduced in HCC, and low level of ADH4 was related to poor prognosis. 35 Elevated G6PD was observed in HCC tissues, while it promotes the progression of HCC via suppressing ferroptosis. 36 Similarly, our previous study indicated that CFHR3 was respond to hypoxia, and reduced in HCC with hypoxic microenvironment. 37 Consistent with previous studies, through qRT‐PCR, ADH4, CFHR3, FTCD and PON1 levels were found to reduce in the HCC tissues compared with adjacent tissues, and APOA5, CXCL8 and G6PD expression was elevated. It is worth noting that the risk model constructed by them exhibited higher diagnostic value than each single genes. This may be one of shining points of the point. Moreover, we found that APOA5 was a novel target in HCC. Knockdown of APOA5 can significant reduce the proliferation and colony formation combining with elevating inflammation and ferroptosis levels. Previous studies that patients with higher MSI and TMB commonly respond for ICI, and they were set as biomarkers to decide whether to proceed ICI. 38 Therefore, we plugged in the expression of each gene into the formula (risk model), and classifying the patients from our research group into the high‐ and low‐risk groups. Through reviewing clinical characteristic, it was indicated that HCC individuals in the high‐risk group had higher MSI and TMB. Finally, we suggested that tissues in the high‐risk group had higher PDL1 expression and immune cells with CD8 using IHC. These results were consistent with above‐mentioned bioinformatics analysis, verifying that the risk model can distinguish ‘hot tumour’. Therefore, we consider the risk model had potential to optimize the choice of ICI in patients with HCC by reflecting the immune characteristics in HCC tissues. 4 CONCLUSIONS In conclusion, we considered APOA5 was a novel target for suppressing HCC via simultaneously elevating inflammation and ferroptosis levels, and the signature constructed by seven IAF genes including ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 can act as a biomarker for optimising the diagnosis, prognosis evaluation and immunotherapy options in patients with HCC. AUTHOR CONTRIBUTIONS Wan‐Yuan Ruan: Data curation (lead); methodology (lead). Lu Zhang: Data curation (equal); methodology (lead). Shan Lei: Formal analysis (equal); investigation (supporting); methodology (equal). Zhi‐Rui Zeng: Data curation (supporting); formal analysis (supporting); methodology (equal); writing – original draft (lead); writing – review and editing (lead). Yu‐Shi Yang: Data curation (supporting); resources (supporting); validation (supporting); visualization (equal). Wen‐Peng Cao: Data curation (equal); resources (equal); software (equal). Qiu‐Yao Hao: Investigation (supporting); resources (supporting); software (supporting). Min Lu: Conceptualization (lead). Xiao‐Bin Tian: Conceptualization (equal); funding acquisition (lead); project administration (lead); supervision (equal). Pai‐Lan Peng: Conceptualization (equal); funding acquisition (lead); project administration (lead); supervision (lead). FUNDING INFORMATION The study was supported by National Natural Science Foundation of China (No. 82060541), Project of Guizhou Science and Technology Department [No. Qiankehe support (2021) general 089], National nature Cultivation Project of Affiliated Hospital of Guizhou Medical University [No. gyfynsfc(2020)‐4], Doctoral research start‐up project of Affiliated Hospital of Guizhou Medical University (No. gyfybsky‐2021‐25) and Project of Health Commission of Guizhou Province (No. gzwkj2023‐170). CONFLICT OF INTEREST STATEMENT The authors declare no conflicts of interest. Supporting information Table S1. Click here for additional data file. Table S2. Click here for additional data file. DATA AVAILABILITY STATEMENT The data used to support the findings of this study are available from the corresponding author upon reasonable request. ==== Refs REFERENCES 1 Chakraborty E , Sarkar D . Emerging therapies for hepatocellular carcinoma (HCC). 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