Bioinformatics Analysis Identifies PLA2G7 as a Key Antigen-Presenting Prognostic Related Gene Promoting Hepatocellular Carcinoma through the STAT1/PD-L1 Axis

Background : Antigen presentation may be an important factor contributing to immune evasion in cancer. This study investigated antigen-presenting prognostic related genes (APPGs) and their potential mechanisms in hepatocellular carcinoma (HCC). Methods : We constructed a score built upon the core APPGs (APP.Score) through nonnegative matrix factorization (NMF) clustering, weighted gene co-expression network analysis (WGCNA), random forest (RF), and least absolute shrinkage and selection operator (LASSO) methods. We also compared the clinical and molecular characteristics of different APP.Score. Furthermore, in vitro experiments were conducted to validate the expression of core APPGs and investigate the effects of phospholipase A2, group 7 (PLA2G7) knockdown on HCC cell development and programmed death-ligand 1 (PD-L1) expression. Results : APP.Score was positively correlated with immune cell infiltration and levels of immune checkpoint inhibitor-related genes, and negatively correlated with overall survival (OS). The area under the curve values were 0.734, 0.747, and 0.679 for survival periods of 1, 2, and 3 years, respectively, indicating that APP.Score could be an independent prognostic factor for patients with HCC. OS of the high expression group of these genes, including PLA2G7 , musculin, heat shock protein family A, secreted phosphoprotein 1, and neutrophil cytosolic factor 2 ( NCF2 ) was lower than that of their low expression group. Moreover, the upregulation of key components of APPGs, except NCF2 , was observed in HCC. The inhibition of PLA2G7 suppressed HCC progression and reduced PD-L1 and phosphorylated signal transducer and activator of transcription 1 (p-STAT1)/STAT1 levels in HepG2 and Huh-7 cells. Remarkably, the decrease in PD-L1 expression caused by PLA2G7 silencing was reversed upon treatment with a STAT1 activator. Conclusion : The results of this study show that APP.Score could be an independent prognostic factor for patients with HCC, and that PLA2G7 silencing inhibits cancer cell development and PD-L1 expression. We provide a new perspective and potential target for immune research on antigen presentation in HCC.


Introduction
Hepatocellular carcinoma (HCC) is a lethal malignancy globally with a low survival rate [1,2].It has been estimated that the incidence of HCC has tripled in the last 30 years [3], and the incidence and mortality rates continue to increase [4].Chemotherapy and immunotherapy are currently the best treatment options for HCC patients [1].Although preclinical and clinical studies have shown that immune checkpoint inhibitor (ICI) therapy provides survival benefits to a larger population of HCC patients, including those with cholangiocarcinoma [5], most cancer patients still exhibit resistance to ICI blockade [6].The molecular mechanisms underlying the immune response and evasion in HCC are not fully understood and thus need to be further researched.
There are many predictive biomarkers of immunotherapy in HCC patients, such as tumor mutation burden, the tumor microenvironment (TME), and T cell inflammation.However, these markers have limitations that hinder their clinical application [7][8][9].Antigen presentation is essential for triggering the T cell immune response, serving as a link between nonspecific and specific immunity [10].Antigenpresenting cells (APCs), such as dendritic cells (DCs) and macrophages (MACs), present peptides on major histocompatibility complex class I or II (MHC-I or MHC-II) to naive T cells (CD8 + or CD4 + ) to activate the immune response [11].The human MHC is usually referred to as human leukocyte antigen (HLA) [12].Tumor antigens allow tumor cells to be recognized and killed by CD8 + T cells through the antigen-presenting mechanism [13].Tumors use various escape mechanisms in AP to evade immune recognition, ultimately leading to tumor immune escape [14].However, we did not find any relevant studies on how antigen-presenting prognostic (APP) related genes (APPGs) of HCC affect the therapeutic immune response and whether they can predict the prognosis.
Currently, bioinformatics analyses such as nonnegative matrix factorization (NMF) clustering, weighted gene co-expression network analysis (WGCNA), random forest (RF) models, and least absolute shrinkage and selection operator (LASSO) have been widely applied to data min-ing, genomics, proteomics, and other research fields related to disease [15][16][17][18].NMF clustering, a matrix decomposition method, has been widely used in prognosisrelated gene studies of various cancers [15,19].For example, Gao et al. [16] utilized NMF clustering to analyze m6A RNA methylation regulators, revealing that the methylation-mediated TME regulates intercellular communication in tumor growth and antitumor immune regulation processes.WGCNA can describe the interaction between genes by constructing a gene co-expression network.For instance, Tian et al. [17] identified core modules and central genes associated with breast cancer using WGCNA.RF, an ensemble learning algorithm, can be used to screen prognostic markers for various cancers such as prostate, breast, and lung cancers [20][21][22].LASSO is a linear regression model and bioinformatics algorithm commonly used for data analyses in cancer.For example, Kang et al. [23] used a LASSO model combined with other indicators to predict lymph node metastasis in patients with T1 rectal cancer.
Here, we screened the APPGs of HCC using public datasets and bioinformatics analysis to predict the prognosis of patients with HCC.Furthermore, we conducted preliminary in vitro experiments to explore the impact of key APPGs on HCC cells.Our study lays the foundation for HCC diagnosis and immunotherapy.

Construction of a Prognostic Model for Scores Built upon the Core APPGs
Based on the TCGA-LIHC dataset, prognosis-related APPGs were obtained by univariate Cox analysis (p < 0.01).Based on these genes, APP-related genotyping (APP.cluster 1 and APP.cluster 2) was obtained by performing NMF clustering and subjected to survival analysis.The expression of all APPGs was analyzed.For the TCGA-LIHC dataset, WGCNA was performed according to AP-related genotyping.The module that was most relevant to APP-related genotyping was found by screening.Genes in the modules were subjected to univariate Cox analysis (p < 0.01).Then nine genes were obtained by performing RF analysis (relative importance >0.3).The LASSO model was constructed to obtain the core APPGs, including KLRB1, neutrophil cytosolic factor 2 (NCF2), PLA2G7, msculin (MSC), HSPA6, secreted phosphoprotein 1 (SPP1), FABP5P7, in LIHC.A score built upon the core APPGs (APP.Score) was obtained by calculating the for-mula (APP.Score = -2.2047× KLRB1 + 0.0197 × NCF2 + 1.2247 × PLA2G7 + 0.7626 × MSC + 0.78 × HSPA6 + 0.7743 × SPP1 + 0.8317 × FABP5P7).GSE14520 was used to externally test the prognostic predictive power of the model.The calculated APP.Score was subjected to survival and receiver operator characteristic curve (ROC) analyses.The seven genes, including KLRB1, NCF2, PLA2G7, MSC, HSPA6, SPP1, FABP5P7, in LIHC obtained from the final screening were subjected to survival and expression analysis.The LIHC patients were categorized into two groups (high APP.Score and low APP.Score).Supplementary Fig. 1 illustrates the study design.

Immune Cells Infiltration and GSE Analysis
The R package limma (version 3.6.1)was utilized to process the processing of gene expression matrix data.With the addition of APP.Score, immune scores and immune cell infiltration were assessed by the ESTIMATE algorithm and TIMER database, respectively [24,25].In addition, we analyzed the expression of immune cell marker genes with different APP.Scores of LIHC samples using the TIMER database.The Gene Set Variation Analysis (GSVA) package was used for analyzing the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.genome.jp/kegg/) pathway via GSVA [26].Additionally, correlation analyses were conducted among prognostic scoring, gene scoring, and functional enrichment pathways.Subsequently, the ClusterProfiler R package was utilized for performing GSE analysis (GSEA) based on the APP.Score.

Mutation and Copy Number Variation Analysis
The mutated genes in the high and low APP.Score groups were analyzed using the "maftools" R package [27].The copy number variation (CNV) landscape and the assessment of copy number gains or losses at amplified or deleted peaks, was evaluated using GISTIC 2.0 analysis (https://gatk.broadinstitute.org)[28].

Prediction of Drug Sensitivity
The somatic mutations and CNV profiles were collected from the TCGA-LIHC dataset.The drug sensitivity of the high APP.Score and low APP.Score groups was analyzed using the oncoPredict package (version 0.2) [29].

Quantitative Real-time Polymerase chain reaction
The six selected genes were validated by quantitative real-time PCR (qRT-PCR) using a fluorescence qRT-PCR instrument (PIKOREAL96, Thermo, Waltham, MA, USA).Primer sequences were designed utilizing primer 5 software, with β-actin serving as an internal mRNA reference and listed in Supplementary Table 1.Relative gene expression was analyzed employing the 2 −∆∆Ct method.

Western Blotting
The cells were lysed, centrifuged, and concentrated.Then the proteins were separated by electrophoresis and electrotransferred to a nitrocellulose membrane.The membrane was incubated overnight at 4 ℃ with the following primary antibodies: killer cell lectin like receptor B1 (KLRB1), PLA2G7, musculin (MSC), heat shock protein family A (HSPA6), secreted phosphoprotein 1 (SPP1), neutrophil cytosolic factor 2 (NCF2), programmed deathligand 1 (PD-L1), phosphoinositide 3-kinase (PI3K), AKT, phosphorylated signal transducer and activator of transcription 1 (p-STAT1), STAT1, phosphorylated nuclear factor kappa B (p-NF-κB), and NF-κB with β-actin as the internal reference.The membrane was incubated with horseradish peroxidase-conjugated goat anti-mouse/rabbit immunoglobulin G (IgG) secondary antibody at 37 ℃ with shaking for 90 min, followed by the development and visualization of the proteins.After washing with phosphate buffer, the membrane was immersed in ECL reaction solution and incubated at room temperature for 1 minute.The protein was exposed and imaged using a chemiluminescence imaging system (ChemiScope 6100, CLINX, Shanghai, China).Detailed antibody information was provided in Supplementary Table 2.

Cell Counting Kit-8 Assay
The Cell Counting Kit-8 (CCK-8) assay was used to measure cell proliferation.Cells were separately cultured for different time points (12, 24, 48 h) and then added to medium containing 10% CCK-8 (NU679, DOJINDO Laboratories, Kumamoto, Japan).The cells were incubated at 37 ℃ for an additional 4 h, and their optical density values were analyzed at 450 nm.

Colony Formation Assay
The cells were digested into single cells, and 200 cells/per well were seeded and maintained for 10 days.After the cells were fixed in 4% paraformaldehyde and stained with crystalline violet, photographs were taken for observation.

Wound Healing Assay
As previously described [30], a monolayer of cells was scratched.The cells were rinsed three times with phosphate-buffered saline to remove the scratched cells, and serum-free medium was added.Cell healing was observed under an optical microscope (DSZ2000X, Beijing Zhongyantaihe Medical Instrument Co., Ltd., Beijing, China) at 0 and 48 h.

Transwell Assay
Matrigel (354262, Becton Dickinson and Co., Franklin, NJ, USA) that had been prediluted was added to the upper chamber, followed by the addition of a cell suspension of 2 × 10 4 to the same chamber.The lower chamber was filled with medium containing 20% fetal bovine serum.After incubating and wiping down the upper chamber, the cells were fixed, stained, and observed under an optical microscope.The results were analyzed using ImageJ software (version 1.49, National Institutes of Health, Bethesda, MD, USA).

Statistical Evaluation
We conducted our statistical analysis and data visualization by utilizing R version 3.6.1 along with the gg-plot2 package.Additionally, for variables that were not normally distributed, we employed both Wilcoxon and Kruskal-Wallis tests as part of our analytical approach.To calculate correlation coefficients, we used Pearson correlation.To generate and display survival curves for our subgroups, we used the Kaplan-Meier method.All of our tests were two-sided.To determine statistical significance, we used GraphPad Prism software package version 8.0 to perform analysis of variance (ANOVA) or unpaired Student's t tests.Statistical significance was determined by a p-value less than 0.05.

Recognition of Antigen-presenting Prognostic (APP)-Related Genotyping
Based on the 23 APP genes, we obtained two genotypes through cluster analysis: APP cluster 1 and APP cluster 2 (Fig. 1A).The principal component analysis plot showed that APP cluster 1 and APP cluster 2 had good dispersion (Fig. 1B).We conducted survival analysis on the two clusters.When the survival rate was 0.5, the survival time of cluster 2 was longer than that of cluster 1 (Fig. 1C).Subsequently, the differential expression of the 23 APP genes in cluster 1 and cluster 2 was analyzed (Fig. 1D,E).The results showed that compared with cluster 1, 17 genes including adaptor-related protein complex 3 subunit beta 1, Fc Fragment of IgE Receptor Ig (FCER1G), HSP90AA1, HSP90AB1, HSPA4, HSPA6, HSPA8, nuclear transcription factor Y subunit gamma, proteasome 26S subunit ATPase 4, proteasome 26S subunit, non-ATPase 11 (PSMD11), PSMD14, PSMD2, PSMD7, proteasome activator complex subunit 3, retinoic acid early transcript 1G (RAET1G), RAET1L, and transient receptor potential cation channel, subfamily C, member 4 associated protein were downregulated in cluster 2. The above results reveal that the APP-related genotyping analysis is reasonable.

Acquisition of Green Gene Modules Using WGCNA
The minimum soft threshold value for building a scale-free network was 5 (scale-free fit index = 0.9).Therefore, 5 was chosen as the optimal soft threshold value for subsequent analyses.Another Figure showed the network connectivity under soft thresholding power (Fig. 2A).Subsequently, a gene clustering tree was constructed (Fig. 2B).
The correlation and significance of different gene modules with cluster 1 and cluster 2 are shown in Fig. 2C.The green gene module exhibited the highest correlation with both cluster 1 and cluster 2. Further analyses revealed a positive correlation between module membership and gene significance (Fig. 2D), suggesting that genes highly related to cluster 1 and cluster 2 were also crucial in the green gene module.

Mutation and CNV Analyses
We further analyzed the correlation of mutated genes in the high and low APP.Score groups.In the high APP.Score group, tumor protein p53 (TP53) and retinoblastoma 1 (RB1) might have co-occurring mutations; β-catenin (CTNNB1) might have co-occurring mutations with WD repeat domain 87 (WDR87), obscurin (OBSCN), and mucin 16 (MUC16); TTN might have co-occurring mutations with low-density lipoprotein receptor-related protein 1B (LRP1B) and dedicator of cytokinesis protein 2 (DOCK2); MUC16 might have co-occurring mutations with ATPbinding cassette sub-family A member 13 (ABCA13); albumin (ALB) might have co-occurring mutations with Piccolo presynaptic cytomatrix protein (PCLO); ryanodine receptor 2 (RYR2) might have co-occurring mutations with WDR87, adhesion G protein-coupled receptor V1 (AD-GRV1), and DOCK2; OBSCN might have co-occurring mutations with filaggrin (FLG); PCLO might have cooccurring mutations with WDR87 and ABCA13; ABCA13 might have co-occurring mutations with CUB and Sushi domain-containing protein 3; DOCK2 might have cooccurring mutations with FLG; and ADGRV1 might have co-occurring mutations with WDR87.In the low APP.Score group, TP53 might have co-occurring mutations with KMT2D; MUC16 might have co-occurring mutations with RYR2; ALB might have co-occurring mutations with XIRP2; HMCN1 might have co-occurring mutations with RYR2; LRP1B might have co-occurring mutations with FBN2; and CTNNB1 might not have co-occurring mutations with AXIN1 and TP53.These results indicate that the number of gene pairs with simultaneous mutations would be greater in the high APP.Score group than in the low APP.Score group (Fig. 4A).In addition, there were differences in CNV frequency between the high and low APP.Score groups (Fig. 4B).These results indicate the significant involvement of APP.Score in both mutations and CNVs.

Prediction of Drug Sensitivity
We further analyzed the difference in drug sensitivity between the high and low APP.Score groups.In all of the drug sensitivity analyses, including temozolomide 1375, otx015 1626, leflunomide 1578, i-bet-762 1624, gdc0810 1925, dasatinib 1079, bpd-00008900 1998, bdp-00009066 1866, and azd5153 1706, the drug sensitivity of the high APP.Score group was higher than that of the low APP.Score group (Fig. 5).

In Vitro Validation of Gene Expression of APP.Score
We further analyzed the expression differences in KLRB1, PLA2G7, MSC, HSPA6, SPP1, and NCF2 between the tumor and normal groups using the TCGA-LIHC dataset.Compared with the LX-2 group, the expression of KLRB1, PLA2G7, MSC, HSPA6, and SPP1 but not NCF2 was significantly upregulated in the HepG2 and Huh-7 groups.The difference in KLRB1 and PLA2G7 expres-  sion at the gene and protein levels was the most significant (Fig. 6A,B).The OS rate was 0.5, and the survival time of patients with high expression of PLA2G7, MSC, HSPA6, and SPP1 was shorter, whereas that with high KLRB1 expression was longer (Fig. 6C).Therefore, PLA2G7 was selected for further study.

Inhibiting PLA2G7 Affects the Functions of HCC Cells HepG2 and Huh-7 Cells
Firstly, PLA2G7 was silenced in HepG2 and Huh-7 HCC cell lines (Fig. 7A,B).Compared to the si-NC group, the cell proliferation, migration, and invasion abilities of the si-PLA2G7 group were suppressed, while the level of cell apoptosis was significantly increased (Fig. 7C-G).

Discussion
Dysfunctional antigen presentation has been identified as a key factor that causes tumor progression and immune therapy resistance [31].Antigen presentation gene disruption is one of the reasons for the loss of antigen presentation and a common event leading to immune escape in cancer [13,32].This study focused on APPGs, and screened for core APPGs that might affect HCC.Based on this, APP.Score was calculated.APP.Score was positively associated with immune cell infiltration, expression of ICIrelated genes, immune and inflammatory pathways, antigen mutations occurring simultaneously, and drug sensitivity, and was negatively correlated with OS.The AUC values were 0.734, 0.747, and 0.679 for survival periods of 1, 2, and 3 years, respectively, indicating that APP.Score may serve as an independent prognostic factor for patients with HCC.SPP1 was obviously overexpressed in HCC in the TCGA-LIHC dataset.With the exception of KLRB1, the high expression group of the five core APPGs (PLA2G7, MSC, HSPA6, SPP1, and NCF2) had lower survival rates than the low expression group.Furthermore, in vitro experiments showed that the significant expression of the five core APPGs (KLRB1, PLA2G7, MSC, HSPA6, and SPP1) was increased in HCC cell lines, and PLA2G7 silencing inhibited the development of cancer cells.
In recent years, several studies have used methods such as WGCNA, RF, and LASSO to reveal the related risk factors and potential mechanisms of immune escape in different tumors [33,34].For example, Zhong et al. [33] mainly used WGCNA to reveal that syndecan-1 may be related to immune infiltration and regulate AP.Chen et al. [34] mainly used WGCNA and LASSO methods to find that the high-risk group (based on the hypoxia-related gene risk model) displayed a unique immune-suppressive microenvironment, lower levels of AP, and higher levels of suppressive cytokines.However, the screening of core APPGs and its potential immune function prediction in HCC have been poorly reported.The current study found that seven core APPGs (KLRB1, PLA2G7, MSC, HSPA6, SPP1, NCF2, and FABP5P7) and APP.Score were obtained through NMF clustering, WGCNA, RF, and LASSO methods in HCC.KLRB1, encoded by the killer cell lectin-like receptor B1 gene, is a newly reported candidate inhibitor of tumorinfiltrating T cells [35].KLRB1 is a potential a potential new immune checkpoint, and may synergize with other ICIs to regulate the immune microenvironment, thus it can be used to develop new immunotherapeutically targeted drugs [36].CD8 + T cells overexpressing KLRB1 are in a state of low innate cytotoxicity in recurrent HCC [37].MSC (also known as activated B-cell factor 1, ABF-1) inhibits plasma cell differentiation but promotes memory B-cell formation [38].HSPA6 is an antigen processing and presentation gene.When HSPA6 is released into the extracellular space, it serves as a source of antigen as it enables peptide conjugation and induces cross-presentation of antigen from DCs to T cells [39].In cervical cancer, the expression level of HSPA6 is negatively correlated with survival [40].High levels of HSPA6 may be associated with the early recurrence of HCC [41].Similarly, our study found that the expression of HSPA6 (one of the core APPGs) was significantly elevated and negatively correlated with survival.Liu et al. [42] found that SPP1 is an immunerelated predictive factor for low survival rates in HCC patients.SPP1 upregulates PD-L1-mediated macrophage polarization and promotes immune escape from lung adenocarcinoma [43].NCF2 is a subunit of a multiprotein complex known as nicotinamide adenine dinucleotide phos-phate (NADPH) oxidase, which has been shown to regulate antigen processing and MHC-I cross-presentation in dendritic cells.Thus NCF2 has been shown to be an APPGs [44].PLA2G7 is a protein produced by macrophages.Reduction of PLA2G7 may mediate the immunometabolic effects of caloric restriction and be used to reduce inflammation and extend healthy lifespan [45,46].Bioinformatics analysis and experiments confirmed that PLA2G7 can serve as a potential immune-related biomarker and contribute to chronic obstructive pulmonary disease progression by promoting the expansion and inhibitory function of myeloidderived suppressor cells [47].PLA2G7 has also been found to be strongly associated with TME composition, so that patients with diffuse large B-cell lymphoma expressing higher levels of this gene exhibit higher levels of localized monocytes and gamma delta T cells [48].PLA2G7 is highly expressed in subgroups of metastatic and invasive breast cancer and metastatic samples from various origin tissues, promoting the development of cultured breast cancer cells [49].However, the occurrence of core APPGs, such as KLRB1, PLA2G7, MSC, HSPA6, and SPP1, in HCC is rarely documented.In short, the current research showed the significant upregulation of five core APPGs (KLRB1, PLA2G7, MSC, HSPA6, and SPP1) in HCC cell lines, and PLA2G7 silencing inhibited cancer cell growth and promoted cells apoptosis.In addition, our study identified PLA2G7, a novel regulator of PD-L1, as a potential target for cancer therapy.PD-L1 may increase the inflammatory response in the TME and promote neoantigen presentation, as well as anti-tumor immune escape [50].Therefore, we hypothesize that PLA2G7 may regulate antigen presentation through PD-L1.However, this needs to be further investigated in the future.
It is well known that common APCs include DCs, MACs, and B cells [51].Dysregulation of APCs is an important cause of tumor immune escape [14].In cancer, APCs present peptides on their HLA to naïve T cells (CD8 + or CD4 + ) to activate an immune response [11,12].APCs are associated with many genes related to immune function, such as CD80, CD274 (encoded PD-L1), and CD276 [52,53].This study found that APP.Score was positively correlated with immune cell infiltration (e.g., CD8 + T cells, DCs, and MACs), expression of ICI-related genes (e.g., APCs, receptor, co-inhibitor, and cell adhesion molecules), and immune and inflammatory-related pathways (e.g., PD-L1 expression and PD-1 checkpoint, antigen processing and presentation, and p53 pathways).It is known that the successful completion of antigen-presenting machinery (APM), i.e., tumor antigens are correctly recognized and processed, and then presented to immune effector cells such as CD8 + cytotoxic T-cells, which is a key prerequisite for the efficacy of immunotherapy [54].However, the mechanism between APPGs and immune evasion in HCC is still not fully investigated, which is a limitation of our study.
Mutations or heterozygous loss of MHC-I can also cause damage to the antigen presentation system [55].Cai et al. [56] found that the most common mutated genes predicting MHC-II neoantigens in lung adenocarcinoma patients included TTN, RYR2, MUC16, and TP53.In breast cancer, TP53 mutation is linked to both a poor prognosis and immune cell infiltration [57].Long et al. [58] found that TP53 mutation is commonly associated with HCC and negatively impacts its progression and outcome.It also impairs the immune response of patients with HCC.Wang et al. [59] reported that these are the most frequently mutated genes in Chinese patients with HCC include TP53, RB1, CCND1, and AT-rich interactive domain-containing protein 1A.Among these mutated genes, TTN mutation is usually associated with poor immune infiltration and a worse HCC prognosis [60].Our study found that a high APP.Score might be associated with more gene pairs with a concurrent mutation in tumor antigens (e.g., TP53 and RB1), suggesting that APP.Score might be related to poor prognosis.Zhou et al. [61] reported that MHC-I antigenpresenting enhanced cancer immunotherapy.Enhanced tumor antigen-presenting contributes to synergistic anti-PD-1 therapy for metastatic breast cancer [62].Our study discovered a positive correlation between APP.Score and expression of ICI-related genes, a positive correlation between APP.Score and drug sensitivity, and a negative correlation between the APP.Score and survival.ROC curve analysis showed the AUC values were 0.734, 0.747, and 0.679 for survival periods of 1, 2, and 3 years, respectively, indicating that the APP.Score may serve as an independent prognostic factor for patients with HCC.In conclusion, APP.Score may help to facilitate the clinical management of HCC.
Activation of STAT1 promotes PD-L1 expression, enhancing the effect of PD-L1 blockade in a mouse model of lung cancer [63].STAT1 can bind to the PD-L1 promoter and thus transcriptionally regulate PD-L1 expression [64].In HCC, activation of STAT1 promotes PD-L1 expression [65].Our results suggest that si-PLA2G7 may inhibit PD-L1 expression by repressing the STAT1 pathway.Our study identified PLA2G7, a novel regulator of PD-L1, as a potential target for cancer therapy.PLA2G7 may be a novel target for ICI therapy.
Due to the complexity and difficulty of collecting clinical specimens, the expression of the core APPGs in HCC tissue has not been further validated.We did not explore the effects of PLA2G7 on immune cells for the study, which is a limitation of our study.In addition, considering that FABP5P7 is a mostly non-coding pseudo genes [66], we have not yet studied its expression and functional effects in HCC.We plan to study this further in the future.

Conclusions
In this study, a risk scoring model, APP.Score, was successfully constructed based on seven core APPGs in LIHC, namely, KLRB1, PLA2G7, MSC, HSPA6, SPP1, NCF2, and FABP5P7.In vitro experiments showed the significant upregulation of five core APPGs (KLRB1, PLA2G7, MSC, HSPA6, and SPP1) in HCC cell lines, and inhibition of PLA2G7 significantly suppressed cancer cell development and PD-L1 expression.The study provides a new perspective and potential target for immune research on antigen presentation in HCC.