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The POR rs10954732 polymorphism decreases susceptibility to hepatocellular carcinoma and hepsin as a prognostic biomarker correlated with immune infiltration based on proteomics


The effect of the cytochrome P450 oxidoreductase (POR) rs10954732 (G > A) polymorphism on hepatocellular carcinoma (HCC) susceptibility is unknown. Here we found that A allele carriers showed a 69% decrease in susceptibility to HCC with overall survival (OS) prolonged to 199%, accompanied by lower activity for cytochrome P450 2E1. A total of 222 differentially expressed proteins were mainly enriched in neutrophil and T cell activation and involved in the immune and inflammatory responses, constituting the altered immune tumor microenvironment related with A allele by proteomics analysis. Hepsin (HPN) showed significant down-regulation in HCC and up-regulation in A allele carriers. A lower HPN level was associated with increased susceptibility to HCC and a worse prognosis. Moreover, HPN is a potential independent prognostic biomarker for HCC and is strongly associated with clinicopathological features, tumor-infiltrating status of immune cells both in our discovery cohort and database surveys. Our findings provide a new potential mechanism by which HPN may play an important role in the susceptibility of rs10954732 A allele carriers to HCC and their prognosis through tumor immune infiltration, thus offering potential insights for future studies on tumor immunotherapy.


Hepatocellular carcinoma (HCC) accounts for approximately 70%-90% of primary liver cancer and is the seventh most common cancer and the third leading cause of cancer-related deaths worldwide [1]. Due to its characteristics of poor clinical prognosis, high mortality rates, and frequent recurrence, identifying early risk factors or potential biomarkers that identify high-risk individuals for early detection, diagnosis, and treatment to improve HCC outcomes is of paramount importance. Although much effort has been directed toward identifying multifactorial risk factors for HCC, the predictive and/or prognostic value of currently identified risk factors for HCC is limited, and a more reliable biological risk marker has yet to be identified [2].

It is now well established that a complex combination of genetic and environmental factors contributes to HCC hepatocarcinogenesis, including polymorphisms of the IL-23R gene3, ErbB4 [4], p535, TNF-α [6] and XPC codon [7], hepatitis virus infection, carcinogenic exposure, and so on. During the past several years, increasing evidence indicated that genes that encode metabolic enzymes can serve as tumor susceptibility genes and/or prognostic signatures, in part through their roles in activation of carcinogens; these genes include cytochrome P450 2E1 (CYP2E1) [8, 9], CYP2D6 [10], GSTP1 [11], NAT2 and GSTM1 [12]. Our previous systematic studies on 10 major CYP enzymes responsible for hepatic drug metabolism showed significant changes in CYP polymorphism incidence [10] and enzyme activity [13,14,15] in HCC patients, some of which were confirmed as related to increased susceptibility to hepatofibrosis and HCC [16, 17], especially for increased activity of CYP2D6 and CYP2E1, decreased activity of CYP2C8, and decreased frequency of the CYP2D6*2 allele.

Cytochrome P450 oxidoreductase (POR), is the unique electron donor for all microsomal CYP enzymes. This enzyme directly participates in the metabolism of exogenous chemicals and drugs [18, 19]. POR gene knock-out or liver-specific deletion has demonstrated to be lethal in the embryonic stage and causes a severe disruption of hepatic drug metabolism. In addition to the widespread expression of POR in multiple normal and tumor tissues [20, 21], the POR gene is highly polymorphic, making it possible that POR variations contribute to cancer risk, either by changes in the metabolic activation of environmental carcinogens or by affecting the electron transfer process and metabolic activity of CYP enzymes [22]. Therefore, it is reasonable to study in detail POR polymorphisms and HCC susceptibility.

A growing number of studies have identified POR polymorphisms as risk factors associated with prognosis in multiple cancers, including breast cancer [23] and bladder cancer [21]. However, only isolated reports have reported that one POR variant, A503V, is associated with HCC risk and prognosis, and the detailed mechanism by which POR polymorphisms confer HCC susceptibility and progression has not been addressed. The reason is partly because phenotypic changes associated with diseases, especially tumors, resulting from genotypic changes are a complex process involving many proteins, making it difficult to study potential mechanisms in detail.

Thus, this study aimed to characterize the association between POR polymorphisms and HCC susceptibility and prognosis in 85 normal subjects and 100 HCC patients. Mass spectrometry (MS)-based proteomics emerged as the preferred strategy and provides a sensitive and accurate method for large-scale proteomic analyses [24]. We used this approach with 34 normal livers and peritumors of 60 HCC samples to elucidate changes in the tumor microenvironment (TME) related to the POR rs10459732 (G > A) polymorphism and to identify mechanisms for HCC susceptibility and prognosis associated with this polymorphism. Among the altered TME, hepsin (HPN), which has been associated with the growth and progression of of various cancers [25,26,27,28], showed the most significant alteration in our discovery cohort. In contrast to the significant down-regulation in the HCC group, expression of HPN was firstly discovered with higher expression in HCC bearing POR rs10459732 A allele mutation compared with wild-type, which may partly account for decreased HCC susceptibility underlying the rs10954732 A allele. Furthermore, apart from evaluation in in our discovery cohort, expression of HPN, its correlation with prognosis in HCC, and the status of different tumor-infiltrating immune cells based on expression of specific markers were also comprehensively validated by experimental research and database surveys from ONCOMINE, Kaplan–Meier plotter, Gene Expression Profiling Interactive Analysis (GEPIA), and the Tumor Immune Estimation Resource (TIMER) databases.


Study design

The study consisted of a discovery cohort and some validation cohorts. A schematic diagram of our workflow is presented in Fig. 1. The discovery cohort is composed of liver samples from 85 healthy subjects and 100 HCC patients undergoing hepatic surgery (Additional file 1: Table S1). The validation cohorts consisted of experimental research studies and database surveys. Experimental research was conducted with human and mouse liver tissues and paraffin specimens. Database surveys were performed from GTEx, TCGA, TIMER, GEPIA, Kaplan–Meier plotter and TISIDB databases.

Fig. 1
figure 1

An overview of the experimental design workflow. Illustration of the potential mechanism underlying the role of POR rs10954732 (G > A) polymorphisms in decreased HCC susceptibility and increased OS of HCC using label-free quantitative proteomic analysis. POR cytochrome P450 oxidoreductase, HCC hepatocellular carcinoma, OS overall survival time. A carriers refers to rs10954732 GA+AA genotypes; GG refers to wild type. DEPs differentially expressed proteins, HPN hepsin, AUC area under the ROC curves. Go enrichment for BP, assigning Gene Ontology (GO) terms to proteins, specifying involvement in biological processes (BP). Nor means normal group based on human liver samples. Con means control group based on HCC mouse model. RNA-seq RNA sequencing, HCC hepatocellular carcinoma, ELISA enzyme-linked immunosorbent assay, WB western blot, IHC immunohistochemistry

Human liver samples

Normal liver samples were obtained from patients undergoing hepatic surgery and usually diagnosed with hepatic haemangioma, and only tissue specimens with normal liver function and histological appearance were collected. Peritumor liver samples were obtained from HCC patients without receiving tumor radiotherapy, chemotherapy or targeted drug therapy before surgery; tissue specimens were taken 2 cm away from the tumor tissues. Liver specimens were stored in liquid nitrogen within 30 min after resection. Well-documented demographic information including gender, age and laboratory tests were obtained from an inpatient case record [13]. All liver samples were collected from the First Affiliated Hospital of Zhengzhou University, the Affiliated People's Hospital of Zhengzhou University and the Affiliated Cancer Hospital of Zhengzhou University. This study was carried out in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of Zhengzhou University. Written informed consent was provided by all donors.

Genotypes of POR and Activity of CYP2E1

The genomic DNA purification kit (Beijing ComWin Biotech Co., Ltd., China) was used to isolate genomic DNA from liver tissues. Four polymorphisms for POR with frequencies more than 1% in the Chinese population based on consideration of linkage disequilibrium (LD) analysis were genotyped by polymerase chain reaction (PCR) sequencing according to our previous study [29]. Human liver microsomes (HLMs) were prepared by differential centrifugation and enzyme activity of CYP2E1 in HLMs was determined by measuring the rate of chlorzoxazone 6-hydroxylation by high-performance liquid chromatography with eight chlorzoxazone concentrations (7.8–1000 µM) as reported previously [30]. Chlorzoxazone and 6-OH-chlorzoxazone were purchased from the National Institute for the Food and Drug Control (China) and Toronto Research Chemicals, Inc. (Canada), respectively. The incubation system contained HLMs (0.3 mg/mL protein), 100 mM phosphate buffer (pH 7.4) and serial concentrations of substrate. The reaction was initiated with 1 mM NADPH with an optimal incubation time of 30 min. One mL of ethyl acetate was added to terminate the reactions and the metabolite of 6-OH-chlorzoxazone was determined by HPLC–UV. Examination on linear range, precision, relative recovery, and stability were all determined. The Michaelis–Menten affinity constant (Km) and maximum reaction velocity (Vmax) values was determined according to nonlinear regression analysis by GraphPad Prism 8.03 software. The intrinsic clearance CLint was estimated by the Michaelis–Menten parameter estimates (CLint = Vmax / Km).

Proteomics and data analysis

The proteomic analysis based on normal livers and peritumor tissue from HCC patients was conducted using liquid chromatography-mass spectrometry-tandem mass spectrometry (Q-Exactive HF LC–MS/MS) by the State Key Laboratory of Proteomics, Beijing Proteome Research Center, (Beijing, China). Raw data from mass spectrometry were processed against the human UniProt protein sequence database (version 20,140,922, 20,193 sequences). Protein expression is shown as intensity-based absolute protein quantification (iBAQ) based on peak intensity. Proteomic data was normalized by log2 transformation. Proteins that were undetectable in more than 50% of samples were excluded, and missing values for each protein were replaced with half of the minimum. Further detailed methods on proteomic processing and analysis are according to a previous report [31].

Functional enrichment analysis

The R/Bioconductor package limma v.3.24.15 34 [32] was used to identify differentially expressed proteins (DEPs) between 34 normal livers and peritumor tissues of 60 HCC patients as HCC related proteins, or between GG genotype and GA + AA genotypes in peritumor tissues of 60 HCC patients as A allele-related proteins. Proteins with both differences greater than 1.2 and P < 0.05 were considered significant. Potential biological functions of DEPs were identified by Gene ontology (GO) analysis for Enrich GO function in the R Package “clusterProfiler”. GO analysis was according to the threshold of P < 0.05 and q < 0.05.

Allograft transplantation model

Male BALB/c mice (18–22 g) were purchased from Beijing Vital River Laboratory Animal Technology Co. Ltd. Mice were first anesthetized with 300 mg/kg chloral hydrate by intraperitoneal injection. Each mouse was implanted in the left liver lobe with 10 μL of an H22 cell suspension (1.5 × 106 cells/mL;10 μL for each mouse) or 10 μL of serum-free RPMI-1640 medium (sham operation group) by a subcapsular intrahepatic injection. The mice were kept in a Specific Pathogen Free (SPF) warm incubator until they had recovered from the anesthesia and then returned to the animal room.

Validation of HPN expression in HCC

HPN expression in the livers of 6 normal subjects and 6 peritumor tissues of HCC patients was determined by western blotting (WB) with primary anti-HPN antibody (ab189246, optimal dilutions at 1:1500). Expression of CD68, CD163 and IL-10 in liver samples for the HCC mouse model was determined by immunohistochemistry (IHC). Validation of HPN expression in various cancers including HCC was obtained by exploring the GTEx, TCGA and TIMER databases.

Validation of prognostic value of HPN in HCC

The correlation of HPN mRNA level with clinical prognosis was evaluated by analyzing the TCGA database, the Kaplan–Meier plotter database and the GEPIA database. Subsequently, the correlation of HPN with the tumor immune cell infiltration level was assessed by exploring the TIMER platform and TISIDB website. The prognostic value of HPN expression in predicting prognosis based on immune cells was determined by analyzing the Kaplan–Meier plotter database.

Statistical analysis

Statistical analyses were carried out using SPSS version 24.0 software (SPSS Inc. Chicago, IL, USA). Data are expressed as mean ± SE. The Wilcoxon Mann–Whitney test was used for pairwise comparison between two independent groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the models' predictive values in terms of sensitivity, specificity, and respective areas under the curves (AUCs). P values are 2-sided and a P value of less than 0.05 is defined as statistically significant.


rs10954732 (G > A) polymorphisms and HCC susceptibility

A Hardy–Weinberg equilibrium test indicated that the genotype distribution of the four SNPS (rs10954732, rs2286822, rs1135612 and rs1057868) for POR included in the study were in Hardy–Weinberg equilibrium (Additional file 1: Table S2). The rs10954732 (G > A) polymorphism was associated with significantly lowered susceptibility to HCC, demonstrated by a 68.8% decreased risk of HCC in individuals with GA + AA genotype (A allele carriers) ((Odds Ratio (OR) = 0.312, 95% confidence interval (CI), 0.116–0.838, P = 0.021)) in comparison with GG wild-type (Fig. 2a), suggesting that the rs10954732 A allele carrier is a protective factor for HCC. For the remaining polymorphisms (rs2286822, rs1135612 and rs1057868) no significant relationship was detected between these three polymorphisms and HCC risks (Additional file 1: Table S2). The protective factor of the rs10954732 A allele for HCC was further substantiated by examining the prognosis of HCC patients. As depicted in the Kaplan–Meier survival curve, patients with the A allele were more likely to have longer overall survival (OS) compared with the GG wild-type (median OS, 437 vs 220 days, P = 0.0150, Fig. 2b). Taken together, these results suggest an important role for the rs10954732 GA + AA genotype in decreased HCC susceptibility and longer OS.

Fig. 2
figure 2

POR rs10954732 polymorphisms related to decreased HCC susceptibility and increased OS of HCC patients as well as enzyme activity for CYP2E1 in healthy subjects and HCC patients. a Univariate logistic regression analysis of the association between rs10954732 polymorphisms and the risk of HCC. b Kaplan–Meier curve for overall survival (OS) of HCC patients between the GG genotype and GA + AA genotypes in HCC patients. Biochemical data (c) and CYP2E1 activity (d) in subjects between GG genotype and A allele carriers in healthy subjects and HCC patients. A allele carriers, GA + AA genotypes. Normal, healthy subjects. HCC, hepatocellular carcinoma. AST transaminase, ALT alanine aminotransferase, Vmax maximum reaction velocity, CLint intrinsic clearance. Data are expressed as mean ± SEM, P value was calculated by Mann–Whitney U test

Moreover, serum biochemical measurements revealed higher levels of aspartate transaminase (AST) and alanine aminotransferase (ALT) in patients than in the normal group, while lower levels in patients with the A allele when compared with individuals carrying the GG genotype (Fig. 2c), results which further corroborated the protective role of the A allele. To explore the underlying mechanism, the effect of the G > A mutation on activity of cytochrome P450 2E1 (CYP2E1), an enzyme involved in hepatocarcinogenesis, was evaluated. In line with AST and ALT, significantly increased CYP2E1 activity was found in the HCC group (Fig. 2d), whereas decreased CYP2E1 activity was found in A allele carriers (Fig. 2e). These results suggest that the decreased HCC susceptibility associated with the rs10954732 A allele might be related to the lowered metabolic activity of CYP2E1.

Proteomic mechanisms underlying A allele-related susceptibility to HCC

To explore the mechanism underlying the reduced HCC susceptibility associated with the rs10954732 polymorphism, proteomic analysis of liver tissues from 16 GG genotypes and 44 GA + AA genotypes derived from the 60 HCC patients was performed to identify rs10954732-related DEPs. There were 222 DEPs identified with the heat-map-based screening criteria of |log2-fold change (FC)|≥ 0.20 and P value < 0.05 (Fig. 3a). Subsequently, 117 statistically significant DEPs including 86 up-regulated and 31 down-regulated proteins in the GA + AA genotypes were selected after adjusting for P < 0.05, as shown in the volcano plot (Fig. 3b).

Fig. 3
figure 3

Proteomic mechanisms for POR rs10954732 polymorphisms to confer decreased susceptibility to HCC. a A total of 222 DEPs related to rs10954732 A allele compared with the GG genotype in HCC patients as shown in a heat map. GO enrichment analysis (b) and Volcano plot (c) for 222 DEPs with dysregulation related with A allele carriers. d Wayne figure shows 46 DEPs intersections between 222 A allele-related DEPs and 1342 HCC-related DEPs. e Heat map of 34 DEPs with protein changes showing the opposite direction in the HCC group and A allele carriers. f Expression profiles of the top eight significantly up-regulated and one down-regulated DEPs in peritumoral tissues of HCC. Data are shown as mean ± SEM, **P < 0.01, ***P < 0.001 vs Normal group by Mann–Whitney U test. DEPs differentially expressed proteins, HCC hepatocellular carcinoma

Further functional analysis of the DEPs by gene ontology (GO) enrichment analysis shows that dysregulated proteins were significantly enriched in immune, inflammation and metabolism, as demonstrated by neutrophil and T cell activation, immune and inflammatory response, energy derivation, ATP, oxidoreduction coenzyme and glycogen metabolic processes, regulation of endocytosis, and electron transport chain (Fig. 3c). These results suggest an altered tumor-extrinsic microenvironment in GA + AA genotypes by regulation of the immune response, inflammatory response and altered metabolic reprogramming, which in part provides an underlying mechanism by which the A allele confers a lower risk of HCC.

In addition to A allele-related proteins, a further proteomic analysis with 60 HCC patients and 34 normal subjects identified 1342 DEPs, which are considered as HCC-related proteins. The intersection between the 222 A allele-related DEPs and the 1342 HCC-related DEPs identified 46 proteins (Fig. 3d). Changing trends of expression for these 46 A allele-related DEPs in HCC compared with normal group and in the rs10954732 GA + AA genotype compared with GG genotype was next investigated. Surprisingly, an opposite trend was observed in HCC patients when grouped by rs10954732 mutation. Specifically, we found that 27/36 (75.0%) proteins up-regulated in the A allele carriers showed significant down-regulation, and 7/10 (70.0%) proteins down-regulated in individuals carrying A allele were up-regulated in HCC patients (Fig. 3e). The opposite direction for protein changes in the HCC group and A allele carriers once again confirmed the protective role of the A allele mutation.

Hepsin may play an underlying protective role with the A allele in HCC

We speculate that the above 34 proteins with opposite expression trends in HCC patients when grouped by the rs10954732 mutation might be involved in lowered susceptibility to HCC. Therefore, we focused the 34 A allele-related DEPs with regard to already-known cancer-related proteins based on a thorough literature review, and receiver operating characteristic (ROC) curves was constructed. Nine out of 34 proteins showed the most notable changes in expression in HCC patients with a high AUC (> 0.70) (Fig. 3f), indicating high power to distinguish HCC patients from healthy subjects. The significant top DEPs included 8 down-regulated proteins (HPN, CLEC4G, ABCB10, CTSZ, PACSIN3, ENPP1, C14orf1 and DHRS3) and one up-regulated protein (MYLK). Among them, hepsin (HPN) showed the largest AUC at 0.802.

Hepsin was the only DEP with an AUC > 0.8 (Fig. 4a). In contrast to the significant down-regulation in the HCC group, expression of HPN was higher in the A allele carriers compared with wild-type (P = 0.014). Moreover, there was no significant difference in HPN expression when grouped by G > A mutation in normal subjects (Fig. 4b). That’s to say, lowered HPN expression was detected in HCC patients, while A allele carriers were more likely to have higher expression of HPN, findings further validating that the decreased HCC susceptibility underlying the rs10954732 A allele might be related to increased HPN expression.

Fig. 4
figure 4

Significantly decreased HPN expression in HCC and associated with worse prognosis. a Decreased protein abundance of HPN in HCC patients and an ROC curve of HPN for diagnosis of HCC in the discovery cohort. b Increased protein abundance of HPN with rs10954732 GA + AA genotypes in HCC patients. c Low expression of HPN conferring a poor prognosis in overall HCC patients in the discovery cohort. d Significantly altered OS between HPN high-expressing and low-expressing groups only in A allele carriers. e Patients with higher HPN levels were more likely to have lower AST, ALT, GGT and GLO. f Differential expression and ROC curve of HPN in adjacent tissues in HCC patients in TCGA database by comparison with normal subjects in the GTEx database. g Levels of HPN expression in tumor tissues of different tumor types from the TCGA database in TIMER. Note: *P < 0.05, **P < 0.01, ***P < 0.001. HCC hepatocellular carcinoma, AST transaminase, ALT alanine aminotransferase, GGT γ-glutamyl transpeptadase, GLO globulin

To further elucidate the prognostic value of HPN, a Kaplan–Meier survival analysis was performed with our discovery cohort. As shown in Fig. 4c, a lower level of HPN was associated with worse prognosis in HCC patients, evidenced by a prominently decreased (61.6%) OS in the HPN low-expressing group (290 vs 756 days, P = 0.0057). However, this association was only in patients genotyped with the A allele, and no difference in OS was found between HPN high- and low-expressing patients in the GG wild-type group (Fig. 4d). Similarly, patients with higher HPN expression were more likely to have lower levels of AST, ALT,γ-glutamyl transpeptidase (GGT) and globulin (GLO) (Fig. 4e), indicating that lower HPN expression is an indicator for poorer prognosis of HCC.

Validation of lowered expression and the prognostic significance of HPN

Altogether, results from the discovery cohort strongly indicate that lower expression of HPN in A allele carriers is a risk factor for HCC occurrence and is correlated with poorer prognosis.

Lowered expression of HPN in HCC peritumoral tissues as compared with normal tissues was first confirmed by the validation set obtained from the GEx and TCGA databases (Fig. 4f), with a AUC at 0.736. Next, the analysis of RNA-seq data from the TIMER database also showed deregulated expression in tumor tissues from various cancers, including significantly lower expression in HCC (Fig. 4g). The lower expression of HPN in peritumor tissues and tumor tissues of HCC patients indicate a protective role for HPN in HCC occurrence.

The prognostic value of lower HPN expression was substantiated by a remarkably shortened OS of HCC patients (by 34.5%) from the TCGA database (1397 vs 2131 days, P = 0.0056) (Fig. 5a) and poor prognosis from the GEPIA database (HR = 0.68, P = 0.03) (Fig. 5b). Furthermore, this finding was again corroborated by analysis of the Kaplan–Meier plotter database. Low HPN expression was associated with worse prognosis in HCC (OS: HR = 0.54, 95% CI = 0.38 to 0.76, P = 0.00044; PFS: HR = 0.68, 95% CI = 0.51 to 0.91, P = 0.0094; Fig. 5c, d).

Fig. 5
figure 5

Prognostic value of highly-expressed and lowly-expressed HPN in HCC patients. a, b Low HPN expression was correlated with worse OS in the TCGA database (a) and the GEPIA database (b). Survival curves of OS (c) and PFS (d) in HCC using the Kaplan–Meier plot database (n = 364, n = 370). Correlation of HPN mRNA expression and OS (e) and PFS (f) in HCC with different clinicopathological factors by Kaplan–Meier plot (n = 364, n = 370). OS overall survival, PFS progression-free survival

Subsequently, association between HPN expression and prognosis of HCC patients with different clinical characteristics was investigated. Notably, the expression of HPN was not always associated with prognosis of HCC. Specifically, a low HPN level correlated well with both worse OS (Fig. 5e) and PFS (Fig. 5f) in males (OS: HR = 0.47, P = 0.001; PFS: HR = 0.66, P = 0.022), Asians (OS: HR = 0.33, P < 0.001; PFS: HR = 0.51, P = 0.002) and patients without alcohol use (OS: HR = 0.53, P = 0.007; PFS: HR = 0.621, P = 0.021), without hepatitis viral infection (OS: HR = 0.59, P = 0.021; PFS: HR = 0.54, P = 0.005). In other cases, low expression of HPN was weakly or uncorrelated with poor prognosis, suggesting that the prognostic value of HPN expression was influenced by the clinical characteristics of HCC patients.

Expression and prognostic value of HPN based on immune infiltration

Since the important role of HPN in the immune system and the quantity and activity status of tumor-infiltrating lymphocytes can influence the prognosis of HCC patients, the correlation of HPN expression with markers of different subsets of immune cells in our data set was next analyzed. Our results revealed that HPN abundance correlated positively with immune cell gene marker levels, such as CD14, CD36, CD40, CD46, CD276 and VSIG4 and correlated negatively with the markers CD59, HLA-DRA, HLA-DAP1, NRP1 and STAT1 (Fig. 6a). These significant correlations between HPN expression and marker genes from immune cells like tumor-infiltrating monocytes, M2 macrophages, dendritic cells and T-helper cells suggests that HPN in A allele-related HCC might be involved in the regulation of tumor immune infiltration in HCC.

Fig. 6
figure 6

Correlation analysis of HPN expression and infiltration levels of immune cells in HCC tissues. a Correlation analysis of HPN abundance and expression of markers of immune cells based on proteomic analysis in the discovery cohort (n = 60). b Correlation between HPN expression and infiltration levels of immune cells in HCC by the TIMER database (n = 371). c Correlation between HPN level and twenty-eight tumor immune infiltrating cell subtypes among human heterogeneous cancers. d Top four immune cell subtypes with the greatest Spearman’s correlation value for HPN expression in HCC. HCC hepatocellular carcinoma

Consequently, this assumption was further tested by investigating the correlation between HPN expression and tumor-infiltrating immune cell subtypes in HCC patients in the TIMER database. We determined that the HPN level in tumor tissues was negatively associated with the infiltration levels of B cells (r = − 0.222, P = 3.27e−05), CD8 + T cells (r = − 0.26, P = 1.05e-06), macrophages (r = − 0.257, P = 1.53e−06), neutrophils (r = − 0.221, P = 3.39e-05) and dendritic cells (r = − 0.29, P = 5.01e-08) (Fig. 6b). No significant association was observed with CD4 + T cells. These results strongly suggest that HPN can modulate the infiltration of immune cells into tumor tissues in HCC.

To further support this inference, correlation between HPN expression in HCC and the levels of immune marker gene expression, which can represent the status of tumor-infiltrating immune cells, was assessed by analysis of the TIMER and GEPIA databases. The results indicate that HPN expression was in strongly negative correlation with 35 marker genes of infiltrating immune cells regardless of whether the correlation analysis was adjusted for purity or not (Additional file 1: Table S3), such as tumor-associated macrophages (TAM), M2 macrophages, monocytes, and neutrophils. Moreover, to further explore the regulation of immune molecules by HPN, correlation between HPN expression and 28 tumor-infiltrating lymphocytes (TILs) was analyzed across human heterogeneous cancers in the TISIDB database. The results show that HPN is associated with 13 TILs in HCC (Fig. 6c). Especially, monocytes (r = 0.486, P < 2.2e−16) and CD56dim natural killer cells (CD56dim) (r = 0.296, P = 6.42e−09) displayed the greatest positive correlations, while activated CD4 T cells (Act-CD4) (r = − 0.42, P < 2.2e−16) and Type 2T helper cells (Th2) (r = − 0.38, P = 3.54e−14) displayed the greatest negative correlations. These findings provide a potential mechanism underlying the oncogenic mechanisms of HPN, which might be by regulating the tumor microenvironment through tumor immunity and recruiting and regulating immune cells in HCC.

Kaplan–Meier plot analyses of HPN expression in HCC in B cells, CD4 + memory T cells, CD8 + T cells, macrophages, NK T cells, Treg T cells, Th1 cells, Th2 cells was next performed to assess the predictive potential of HPN in HCC based on immune cells. We found that HCC patients with lower HPN levels in enriched B cells (P = 2.1e−006), CD4 + memory T cells (P = 8.8e−03), CD8 + T cells (P = 8e−03), natural killer T cells (p = 5.8e−03), Treg T cells (p = 3.5e−03), type 1 T helper cells (P = 4.7e−03), type 2 T-helper cells (P = 3.5e−03) cohort were more likely to have a worse prognosis (Fig. 7a–h). A similar trend was found in macrophages, though it was not statistically significant. The above analysis suggests that low expression of HPN affects the prognosis of HCC patients through immune infiltration.

Fig. 7
figure 7

Kaplan–Meier survival curves between HPN high- and low-expressing groups in HCC based on immune cell subgroups. Low HPN level in enriched B cells (a), CD4 + memory T cells (b), CD8 + T cells (c), NK T cells (e), Treg cells (f), Th1 cells (g) and Th2 cells (h) is correlated with worse OS in HCC. For the enriched macrophages, low HPN level showed a decreasing trend in OS, though it did not reach statistical significance. HCC hepatocellular carcinoma

Finally, to validate the proteomic results and database surveys, expression of HPN was measured by a WB method in another set of human liver samples from HCC patients and in a mouse HCC model. In agreement with proteomic findings, HPN levels were dramatically lower in peritumoral specimens of HCC patients than in normal livers (P < 0.001, Fig. 8a). Consistently, significantly lower HPN expression also was detected in a mouse HCC model (P < 0.01, Fig. 8b–d). The results further verified that lower HPN expression conferred increased HCC occurrence. Furthermore, expression of marker of tumor-infiltrating immune cells like CD68, CD163 and IL-10 significantly increased in a mouse HCC model group by paraffin specimen analysis (P < 0.001, Fig. 8e), which further demonstrates HPN-related tumorigenesis and progression related to tumor immune cell infiltration in HCC.

Fig. 8
figure 8

Verification of decreased HPN expression in HCC and correlation with expression of marker genes for immune cells. a Western blot analysis of HPN in another cohort of human samples. General picture (b), representative images of H&E staining (c) and western blot analysis of HPN (d) in a BALB/c mouse model. e Representative images of immunohistochemical staining for CD68, CD163 and IL-10 in a BALB/c mouse model (Scale bar, 100 μm). Data are expressed as mean ± SEM. Statistical analysis was performed according to a Mann–Whitney test. Normal means healthy subjects based on human liver samples. Control means control group based on the HCC mouse model. H&E, haematoxylin and eosin. **P < 0.01, ***P < 0.001 vs Normal or Control. HCC hepatocellular carcinoma. (n = 6 for each group)


This study reports the association of the POR rs10459732 (G > A) polymorphism with decreased susceptibility to HCC and better prognosis of HCC patients, which might be related to decreased CYP2E1 activity, and further with altered TME, mainly involved in immune responses. We further demonstrate that significantly down-regulated HPN expression is related to HCC risk and is a potential independent prognostic biomarker for HCC, which is strongly associated with clinicopathological features, poor prognosis, and infiltration status of immune cells both in our discovery cohort and in database surveys. Our study provides a new potential mechanism by which HPN may play an important role in A allele carriers for HCC susceptibility and prognosis through tumor immune infiltration.

Although there have been extensive studies on various genetic variations associated with either susceptibility or progression of HCC [33], few studies have been performed regarding POR polymorphisms, and with limited polymorphic sites. To date, only POR A503V has been confirmed as an HCC susceptibility polymorphism [20]. Here we report the novel finding that the POR rs10459732 (G > A) polymorphism is correlated with decreased susceptibility to HCC and prognosis. Decreased CYP2E1 activity associated with POR rs10459732 A carriers can at least in part explain the decreased HCC susceptibility, which might be related to the critical role POR plays as an obligate electron donor for CYP enzymes [34]. This affects the activities of CYPs including CYP2E1, and the important role of CYP2E1 in the activation of procarcinogens and protoxins. This speculation is supported by previous reports from other groups [9, 35,36,37,38,39] as well as ours [16, 17, 40] that increased CYP2E1 activity is closely associated with oxidative stress, inflammation, and inflammation-related diseases like nonalcoholic steatohepatitis (NASH), hepatofibrosis, HCC, ovarian cancer, and so on.

High-resolution mass spectrometry (MS) was utilized here for proteome analysis of complex protein samples to uncover the potential mechanism underlying the rs10459732 (G > A) polymorphism related to HCC susceptibility. We identified a significantly altered TME, mainly in immune responses, followed by inflammatory responses, which may be related to reduced HCC risk and better prognosis in the A allele carriers. Among the altered TME, HPN, which has been associated with the growth and progression of various cancers [25,26,27,28], might partly account for the A allele carriers association with HCC susceptibility and prognosis. Moreover, in line with our assumptions, further genotyping in validated cohorts in six HCC patients showed that four samples beared the A gene had higher expression trend of HPN compared with the two wild type GG.

Further database surveys and validated cohorts in HCC patients and an HCC mouse model (Fig. 8a, b) corroborated the down-regulation of HPN in HCC. It seems paradoxical that HPN plays a role in HCC based on its down-regulation in our and other reports [41, 42], as its level was up-regulated in other cancers such as ovarian carcinoma, renal cell carcinoma, and prostate cancer as reported in previous studies [43,44,45]. Thus, the effect of HPN on carcinogenesis might be tissue- and cell type-specific, which may explain the conflicting literature on the influence of HPN in different cancer cells.

TME, consisting of tumor cells, stromal cells, extracellular matrix and infiltrating immune cells, is known to be highly immunosuppressive for HCC [46]. Tumor-infiltrating lymphocytes play vital roles in tumor development. HPN, a protein involved in immune reactions and metabolism, was previously reported to regulate the TME [26, 28, 47,48,49,50]. Here, we report that HPN expression is associated with several immune infiltrating cells and many immune cell makers, including T helper cells (Th1, Th2 and Tfh), in HCC through correlation analysis. These connections may indicate underlying mechanisms for HPN regulation of T cell function in HCC. Therefore, it may be related to the poor prognosis of HCC by recruiting and regulating immune cells, which was validated by lower expression of HPN in a variety of immune cells in a cohort of HCC showing a worse prognosis from the Kaplan Meier-Plot database analysis (Fig. 7). We speculate that lowered HPN expression, along with tumor associated macrophages (TAMs), regulatory T cells (Tregs), and myeloid-derived suppressor cells (MDSCs) as well as tumor-associated dendritic cells (tDCs) together suppress anti-tumor immune responses, and lead to tumor-mediated immune escape, constituting the immunosuppressive TME [51].

In summary, our study suggests that POR rs10954732 (G > A) is significantly associated with decreased HCC risk and prolonged OS in HCC patients, with the underlying mechanism being down-regulated HPN expression. The lower HPN level is a potential independent prognostic biomarker for HCC, and is strongly associated with clinicopathological features, poor prognosis, and immune cell infiltration. Furthermore, our study offers insights for further studies on tumor immunotherapy based on the potential that HPN may affect the prognosis of HCC through tumor immune infiltration. Relatively low levels of HPN in HCC may indicate greater risk of tumor relapse after treatment and close medical supervision will be necessary for such patients. The current study is a preliminary part of a larger study, validation of the POR polymorphism-related HCC risk and prognosis in additional larger HCC cohorts, and the exact detailed mechanism by which HPN regulates tumor infiltration of immune cells in HCC are needed to verify our results.

Availability of data and materials

The datasets generated during and/or analysed during the current study are not publicly available due (REASON WHY DATA ARE NOT PUBLIC) but are available from the corresponding author on reasonable request.


  1. Ioannou GN. HCC surveillance after SVR in patients with F3/F4 fibrosis. J Hepatol. 2021;74:458–65.

    Article  PubMed  Google Scholar 

  2. Wang W, Wei C. Advances in the early diagnosis of hepatocellular carcinoma. Genes Dis. 2020;7:308–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Labib HA, Ahmed HS, Shalaby SM, Wahab EA, Hamed EF. Genetic polymorphism of IL-23R influences susceptibility to HCV-related hepatocellular carcinoma. Cell Immunol. 2015;294:21–4.

    Article  CAS  PubMed  Google Scholar 

  4. Yu Q, Zhou CX, Chen NS, Zheng SD, Shen LM, Zhang JK. A polymorphism within ErbB4 is associated with risk for hepatocellular carcinoma in Chinese population. World J Gastroenterol. 2012;18:383–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zhu ZZ, Cong WM, Liu SF, Xian ZH, Wu WQ, Wu MC, et al. A p53 polymorphism modifies the risk of hepatocellular carcinoma among non-carriers but not carriers of chronic hepatitis B virus infection. Cancer Lett. 2005;229:77–83.

    Article  CAS  PubMed  Google Scholar 

  6. Wungu CD, Ariyanto FC, Prabowo GI, Handajani R. Association between five types of Tumor Necrosis Factor-alpha gene polymorphism and hepatocellular carcinoma risk: a meta-analysis. BMC Cancer. 2020;20:1134.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Long XD, Ma Y, Zhou YF, Ma AM, Fu GH. Polymorphism in xeroderma pigmentosum complementation group C codon 939 and aflatoxin B1-related hepatocellular carcinoma in the Guangxi population. Hepatology. 2010;52:1301–9.

    Article  CAS  PubMed  Google Scholar 

  8. Wang Y, Yang H, Li L, Wang H, Zhang C, Yin G, et al. Association between CYP2E1 genetic polymorphisms and lung cancer risk: a meta-analysis. Eur J Cancer. 2010;46:758–64.

    Article  CAS  PubMed  Google Scholar 

  9. Guengerich FP. Cytochrome P450 2E1 and its roles in disease. Chem Biol Interact. 2020;1322:09056.

    Article  CAS  Google Scholar 

  10. Hu G, Gao F, Wang G, Fang Y, Guo Y, Zhou J, et al. Use of proteomics to identify mechanisms of hepatocellular carcinoma with the CYP2D6*10 polymorphism and identification of ANGPTL6 as a new diagnostic and prognostic biomarker. J Transl Med. 2021;19:359.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wu K, Wang X, Xie Z, Liu Z, Lu Y. Glutathione S-transferase P1 gene polymorphism and bladder cancer susceptibility: an updated analysis. Mol Biol Rep. 2013;40:687–95.

    Article  CAS  PubMed  Google Scholar 

  12. Garcia-Closas M, Malats N, Silverman D, Dosemeci M, Kogevinas M, Hein DW, et al. NAT2 slow acetylation, GSTM1 null genotype, and risk of bladder cancer: results from the Spanish Bladder Cancer Study and meta-analyses. Lancet. 2005;366:649–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhou J, Wen Q, Li SF, Zhang YF, Gao N, Tian X, et al. Significant change of cytochrome P450s activities in patients with hepatocellular carcinoma. Oncotarget. 2016;7:50612–23.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Gao J, Tian X, Zhou J, Cui MZ, Zhang HF, Gao N, et al. From genotype to phenotype: cytochrome P450 2D6-mediated drug clearance in humans. Mol Pharm. 2017;14:649–57.

    Article  CAS  PubMed  Google Scholar 

  15. Fang Y, Wang T, Guo YY, Zhang HF, Wen Q, Xing YR, et al. From genotype to phenotype: content and activities of cytochromes P450 2A6 in human liver in vitro and predicted in vivo. J Pharmacol Exp Ther. 2020;372:320–30.

    Article  CAS  PubMed  Google Scholar 

  16. Gao J, Wang Z, Wang GJ, Zhang HX, Gao N, Wang J, et al. Higher CYP2E1 activity correlates with hepatocarcinogenesis induced by diethylnitrosamine. J Pharmacol Exp Ther. 2018;365:398–407.

    Article  CAS  PubMed  Google Scholar 

  17. Gao J, Wang Z, Wang GJ, Gao N, Li J, Zhang YF, Zhou J, et al. From hepatofibrosis to hepatocarcinogenesis: Higher cytochrome P450 2E1 activity is a potential risk factor. Mol Carcinog. 2018;57:1371–82.

    Article  CAS  PubMed  Google Scholar 

  18. Han JF, Wang SL, He XY, Liu CY, Hong JY. Effect of genetic variation on human cytochrome p450 reductase-mediated paraquat cytotoxicity. Toxicol Sci. 2006;91:42–8.

    Article  CAS  PubMed  Google Scholar 

  19. Wang SL, Han JF, He XY, Wang XR, Hong JY. Genetic variation of human cytochrome p450 reductase as a potential biomarker for mitomycin C-induced cytotoxicity. Drug Metab Dispos. 2007;35:176–9.

    Article  CAS  PubMed  Google Scholar 

  20. Huang K, Liao X, Han C, Wang X, Yu T, Yang C, et al. Genetic variants and EXPRESSION OF CYTOchrome p450 oxidoreductase predict postoperative survival in patients with hepatitis B virus-related hepatocellular carcinoma. J Cancer. 2019;10:1453–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Xiao X, Ma G, Li S, Wang M, Liu N, Ma L, et al. Functional POR A503V is associated with the risk of bladder cancer in a Chinese population. Sci Rep. 2015;5:11751.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Miller WL, Agrawal V, Sandee D, Tee MK, Huang N, Choi JH, et al. Consequences of POR mutations and polymorphisms. Mol Cell Endocrinol. 2011;336:174–9.

    Article  CAS  PubMed  Google Scholar 

  23. Haiman CA, Setiawan VW, Xia LY, Le Marchand L, Ingles SA, Ursin G, et al. A variant in the cytochrome p450 oxidoreductase gene is associated with breast cancer risk in African Americans. Cancer Res. 2007;67:3565–8.

    Article  CAS  PubMed  Google Scholar 

  24. Karczewski KJ, Snyder MP. Integrative omics for health and disease. Nat Rev Genet. 2018;19:299–310.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Willbold R, Wirth K, Martini T, Sultmann H, Bolenz C, Wittig R. Excess hepsin proteolytic activity limits oncogenic signaling and induces ER stress and autophagy in prostate cancer cells. Cell Death Dis. 2019;10:601.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhang C, Zhang M, Song S. Cathepsin D enhances breast cancer invasion and metastasis through promoting hepsin ubiquitin-proteasome degradation. Cancer Lett. 2018;438:105–15.

    Article  CAS  PubMed  Google Scholar 

  27. Miao J, Mu D, Ergel B, Singavarapu R, Duan Z, Powers S, et al. Hepsin colocalizes with desmosomes and induces progression of ovarian cancer in a mouse model. Int J Cancer. 2008;123:2041–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Li S, Wang L, Sun S, Wu Q. Hepsin: a multifunctional transmembrane serine protease in pathobiology. FEBS J. 2021;288:5252–64.

    Article  CAS  PubMed  Google Scholar 

  29. Fang Y, Gao N, Tian X, Zhou J, Zhang HF, Gao J, et al. Effect of P450 oxidoreductase polymorphisms on the metabolic activities of ten cytochrome P450s varied by polymorphic CYP genotypes in human liver microsomes. Cell Physiol Biochem. 2018;47:1604–16.

    Article  CAS  PubMed  Google Scholar 

  30. Gao N, Tian X, Fang Y, Zhou J, Zhang H, Wen Q, et al. Gene polymorphisms and contents of cytochrome P450s have only limited effects on metabolic activities in human liver microsomes. Eur J Pharm Sci. 2016;92:86–97.

    Article  CAS  PubMed  Google Scholar 

  31. Jiang Y, Sun A, Zhao Y, Ying W, Sun H, Yang X, et al. Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma. Nature. 2019;567:257–61.

    Article  CAS  PubMed  Google Scholar 

  32. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43: e47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Rebouissou S, Nault JC. Advances in molecular classification and precision oncology in hepatocellular carcinoma. J Hepatol. 2020;72:215–29.

    Article  CAS  PubMed  Google Scholar 

  34. Pandey AV, Fluck CE. NADPH P450 oxidoreductase: structure, function, and pathology of diseases. Pharmacol Ther. 2013;138:229–54.

    Article  CAS  PubMed  Google Scholar 

  35. Chen J, Jiang S, Wang J, Renukuntla J, Sirimulla S, Chen J. A comprehensive review of cytochrome P450 2E1 for xenobiotic metabolism. Drug Metab Rev. 2019;51:178–95.

    Article  CAS  PubMed  Google Scholar 

  36. Wang G, Wakamiya M, Wang J, Ansari GAS, Khan MF. Cytochrome P450 2E1-deficient MRL+/+ mice are less susceptible to trichloroethene-mediated autoimmunity: involvement of oxidative stress-responsive signaling pathways. Free Radic Biol Med. 2019;143:324–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Sutti S, Rigamonti C, Vidali M, Albano E. CYP2E1 autoantibodies in liver diseases. Redox Biol. 2014;3:72–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Trousil S, Lee P, Edwards RJ, Maslen L, Lozan-Kuehne JP, Ramaswami R, et al. Altered cytochrome 2E1 and 3A P450-dependent drug metabolism in advanced ovarian cancer correlates to tumour-associated inflammation. Br J Pharmacol. 2019;176:3712–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Liu Y, Xu W, Zhai T, You J, Chen Y. Silibinin ameliorates hepatic lipid accumulation and oxidative stress in mice with non-alcoholic steatohepatitis by regulating CFLAR-JNK pathway. Acta Pharm Sin B. 2019;9:745–57.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wang G, Xiao K, Gao J, Jiang S, Wang S, Weng S, et al. Inhibitory effect of chlormethiazole on the toxicokinetics of diethylnitrosamine in normal and hepatofibrotic rats. Drug Chem Toxicol. 2019;42:600–7.

    Article  CAS  PubMed  Google Scholar 

  41. Chen CH, Su KY, Tao MH, Lin SW, Su YH, Tsai YC, et al. Decreased expressions of hepsin in human hepatocellular carcinomas. Liver Int. 2006;26:774–80.

    Article  CAS  PubMed  Google Scholar 

  42. Hsu YC, Huang HP, Yu IS, Su KY, Lin SR, Lin WC, et al. Serine protease hepsin regulates hepatocyte size and hemodynamic retention of tumor cells by hepatocyte growth factor signaling in mice. Hepatology. 2012;56:1913–23.

    Article  CAS  PubMed  Google Scholar 

  43. Xuan JA, Schneider D, Toy P, Lin R, Newton A, Zhu Y, Finster S, Vogel D, Mintzer B, Dinter H, Light D, Parry R, Polokoff M, Whitlow M, Wu Q, Parry G. Antibodies neutralizing hepsin protease activity do not impact cell growth but inhibit invasion of prostate and ovarian tumor cells in culture. Cancer Res. 2006;66:3611–9.

    Article  CAS  PubMed  Google Scholar 

  44. Betsunoh H, Mukai S, Akiyama Y, Fukushima T, Minamiguchi N, Hasui Y, et al. Clinical relevance of hepsin and hepatocyte growth factor activator inhibitor type 2 expression in renal cell carcinoma. Cancer Sci. 2007;98:491–8.

    Article  CAS  PubMed  Google Scholar 

  45. Ma X, Guo J, Liu K, Chen L, Liu D, Dong S, et al. Identification of a distinct luminal subgroup diagnosing and stratifying early stage prostate cancer by tissue-based single-cell RNA sequencing. Mol Cancer. 2020;19:147.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Song M, Yeku OO, Rafiq S, Purdon T, Dong X, Zhu L, et al. Tumor derived UBR5 promotes ovarian cancer growth and metastasis through inducing immunosuppressive macrophages. Nat Commun. 2020;11:6298.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ganesan R, Kolumam GA, Lin SJ, Xie MH, Santell L, Wu TD, et al. Proteolytic activation of pro-macrophage-stimulating protein by hepsin. Mol Cancer Res. 2011;9:1175–86.

    Article  CAS  PubMed  Google Scholar 

  48. Hsin F, Hsu YC, Tsai YF, Lin SW, Liu HM. The transmembrane serine protease hepsin suppresses type I interferon induction by cleaving STING. Sci Signal. 2021.

    Article  PubMed  Google Scholar 

  49. Belitskin D, Pant SM, Munne P, Suleymanova I, Belitskina K, Hongisto HA, et al. Hepsin regulates TGFbeta signaling via fibronectin proteolysis. EMBO Rep. 2021.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Morrissey C, True LD, Roudier MP, Coleman IM, Hawley S, Nelson PS, et al. Differential expression of angiogenesis associated genes in prostate cancer bone, liver and lymph node metastases. Clin Exp Metastasis. 2008;25:377–88.

    Article  CAS  PubMed  Google Scholar 

  51. Cai DL, Jin LP. Immune cell population in ovarian tumor microenvironment. J Cancer. 2017;8:2915–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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This work was supported by National Natural Science Foundation of China (NSFC) (No.81473279, No.81673507, No.81872931 and No.82073930), Zhengzhou Major Scientific and Technological Innovation Projects (No.2020CXZX0076), China Postdoctoral Science Foundation (No. 2020M672300), Program for Innovation Research Team (in Science and Technology) in University of Henan Province (No. 20IRTSTHN026).


This work was supported by National Natural Science Foundation of China (NSFC) (No.81473279, No.81673507, No.81872931 and No.82073930), Zhengzhou Major Scientific and Technological Innovation Projects (No.2020CXZX0076), China Postdoctoral Science Foundation (No. 2020M672300), Program for Innovation Research Team (in Science and Technology) in University of Henan Province (No. 20IRTSTHN026).

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HQ: Conceptualization, Supervision, Project administration, Writing—review & editing. YF, Data curation; Methodology, Writing—original draft, Writing—review & editing. HY, Formal analysis, Resources, Validation. GH, Funding acquisition, Project administration, Validation. JL, Methodology, Resources; Software. JZ, Methodology, Formal analysis, Resources. NG, Data curation, Funding acquisition, Visualization. YG, Software, Validation; Visualization. CZ, Methodology, Validation, Visualization. JQ, Formal analysis, Visualization. YG, Data curation; Formal analysis, Resources. YZ, Formal analysis, Resources, Visualization. QW, Funding acquisition, Investigation, Supervision. All authors read and approved the final manuscript.

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Correspondence to Hailing Qiao.

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Additional file 1

: Table S1. Basic demographic and clinical parameters characteristics of human liver tissue samples. Table S2. Hardy-Weinberg test for SNP loci of POR. Table S3. Correlation analysis between HPN and relate genes and markers of immune cells in TIMER

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Fang, Y., Yang, H., Hu, G. et al. The POR rs10954732 polymorphism decreases susceptibility to hepatocellular carcinoma and hepsin as a prognostic biomarker correlated with immune infiltration based on proteomics. J Transl Med 20, 88 (2022).

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