Introduction
Globally, liver cancer has an increasing incidence and mortality, which poses a severe threat to human health and the economy [
1,
2]. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, accounting for approximately 80% of all cases [
3]. According to statistics, there are more than 900,000 new diagnoses and more than 800,000 deaths each year [
4]. In the past decade, even though great progress has been made in the treatment and diagnosis of hepatocellular carcinoma, the overall survival rate of patients is still low [
5]. Since the majority of patients with HCC were diagnosed in the advanced stages, or with invasion and metastasis within and outside the liver, the optimal time for surgical treatment was lost [
6,
7]. The oncogenesis of HCC is considered to be a complex multifactorial process, and the biological and clinical diversity of HCC presents a great challenge for individualized clinical treatment. Therefore, exploring biomarkers of HCC is crucial to improve early diagnosis and finding therapeutic targets.
AP4M1 is a component of the adaptor protein complex 4 and is involved in the coding of the adaptor protein complex 4, also known as SPG50. AP-4 compounds have been involved in trafficking of transmembrane proteins from the trans-Golgi network to early and late endosome [
8,
9]. The
AP4M1 gene is highly expressed in the brain, especially during fetal development [
10]. Interruptions in
AP4M1’s ability to affect its function can impair normal brain development and may impair the excitability of neurons. Studies have shown that
AP4M1 is involved in the pathological process of congenital anthropogenic paralysis, suggesting that it may be damaged by analogous glucose-mediated proteins through the early neural axis and sequential white loss [
11].
At present, there is no relevant studies report on
AP4M1 in cancers. It has been reported that the autophagy protein ATG9A is a product of AP-4, and that deletion of AP-4 leads to mislocalization of
ATG9A, which may affect the transport and function of
ATG9A in axons [
12‐
14]. Given the close relationship between autophagy regulation and tumorigenesis, the evidence for the role and clinical significance of
AP4M1 in the diagnosis, disease progression, and prognosis of HCC is insufficient. Therefore, this study proposed to investigate the expression of
AP4M1 in HCC and its role in HCC development and prognosis.
In this study, we conducted a comprehensive analysis using clinical characteristics and survival data of HCC in a public database to assess the significance of AP4M1expression in HCC. We found that the high expression of AP4M1 was related to the inferior prognosis and cancer-immune regulation in HCC. The upregulated AP4M1 also accelerated the proliferation and invasion ability of HCC. Thus, our research identified the potential role of AP4M1 in the onset and development of HCC, and could be a novel diagnostic and prognostic biomarker.
Materials & methods
Data Collection
The expression data of AP4M1 in pan-cancer were obtained from the Cancer Genome Atlas (TCGA) database, and the RNAseq data of patients with hepatocellular carcinoma in the TCGA-LIHC dataset were extracted. The formatted RNAseq data were converted to TPM format, and the clinical data of 424 patients with hepatocellular carcinoma were obtained for subsequent analysis after removing the patients without clinical information.
Comparison of the expression differences of AP4M1 in HCC and normal tissues
Firstly, the expression of
AP4M1 in the different types of cancer tissue including HCC tissues was analyzed through the Xiantao tools. Then, the Biomarker Exploration of Solid Tumors (BEST,
https://rookieutopia.com/app_direct/BEST/) network tool was used to compare the expression of
AP4M1 in hepatocellular carcinoma and normal tissues in GSE144269, GSE14520, GSE54236 and TCGA-LIHC datasets. The gene expression levels were transformed into a Z score. Also, AP4M1 protein expression in HCC and normal tissues were obtained from the CPTAC database, a proteomic database that includes a variety of cancers and enables users to obtain proteomic and genomic information on a large scale [
15,
16].
Analysis of the association of AP4M1 and HCC clinicopathological parameters
After analyzing the protein and mRNA expression levels of
AP4M1 in HCC, the TCGA-LIHC dataset was utilized to assess the clinical relevance of pathological parameters for hepatocellular carcinoma. In this study, we carried out the normality test for the numerical type of variables and used the expression median to categorize the groups. When the data meet the normal distribution, we will calculate the mean ± standard deviation (SD) with the Z-score transform of the corresponding variables; If it does not conform to the normal distribution, the median of the related variables (upper quartile and lower quartile) will be calculated [
17,
18].
Survival analysis
To investigate the prognostic impact of
AP4M1 mRNA on HCC samples. We first analyzed the effects of
AP4M1 on overall survival (OS), recurrence-free survival (RFS), progression-free survival (PFS) and disease-specific survival (DSS) in hepatocellular carcinoma patients in the Kaplan-Meier (KM) plotter website (
http://www.kmplot.com/analysis/) [
19]. In addition, hazard ratios (HRs) and log-rank p-values with 95% confidence intervals (CI) were determined [
20]. Subsequently, HCC patients were divided into high and low groups according to
AP4M1 expression levels, and proportional risk hypothesis tests were performed using “survival” packages and the prognostic value of
AP4M1 on overall survival in HCC was assessed by univariate and multifactorial Cox regression analysis.
Analysis of AP4M1 gene alternations in HCC
The cBioPortal database (version 3.7.1,
http://cbioportal.org) was primarily used to investigate multivariate cancer genomics datasets containing resources from 20 cancer studies and more than 5,000 tumor samples [
21,
22]. A TCGA-LIHC (Firehose Legacy) dataset containing 379 samples was selected, and normalized RNA Seq V2 RSEM data was used for mutation analysis.
Correlations between AP4M1 and the immune environment
The relationship between
AP4M1 expression and immune cell infiltration was analyzed by the ssGSEA method using Xiantao Tools and presented with a lollipop plot. ssGSEA calculated the number of immune cells in tumor specimens based on gene expression data and the R package (gsva.20), and then used Spearman rank correlation analysis to determine the relevance of
AP4M1 and 24 immune cell infiltration level and used the ggplot2 package for visualization. The Tumor Immune Evaluation Resource (TIMER,
https://cistrome.shinyapps.io/TIMER/) is used to analyze immune infiltration in different types of cancer [
23]. We explored the relationship between the altered somatic copy number of the
AP4M1 gene and infiltrating immune cells in HCC by using the SCNA module. A cut-off value of P < 0.05 was used. The TISIDB database was used to further analyze the expression of
AP4M1 in immune subtypes of liver cancer [
24].
AP4M1 co-expression gene analysis and gene enrichment analysis
The LinkedOmics database (
http://www.linkedomics.org/) is a combined multi-omics dataset from the CPTAC and TCGA databases, including clinical data with 32 cancer types and mass spectrometry-based proteomics data [
25]. We used the LinkedOmics database for
AP4M1 co-expression gene analysis. The top 50 genes positively and negatively associated with
AP4M1 in LIHC were obtained through the LinkFinder module. The
AP4M1 gene set was enriched for analysis in the LinkInterpreter module and 500 simulations were performed.
Cell culture, antibodies, siRNA and plasmids
The human liver cancer and normal liver cell lines were acquired from American Type Culture Collection (ATCC). All cells were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM, Life Technologies) with 10% fetal bovine serum (FBS). Cells were cultured in a humidified incubator at 37 °C and in an atmosphere of 5% CO2. All the cell lines tested negative for mycoplasma contamination. Additionally, prior to their use, all cell lines underwent authentication through short tandem repeat profiling. Furthermore, these cell lines were passaged fewer than ten times after being initially revived from frozen stocks. The primary antibodies were Beta Actin Monoclonal antibody (Cat# 60318-1-lg; Proteintech, Wuhan, China; 1:10000), and AP4M1 Polyclonal antibody (Cat# 11653-1-AP; Proteintech, Wuhan, China; 1:500). The siRNA of AP4M1 were purchased from RibBio (RibBio, Guangzhou, China). HanBio designed and established the AP4M1 overexpression plasmid (HanBio, Shanghai, China). Plasmid and siRNA transfection was performed with Lipofectamine® 3000 following the manufacturer’s instructions.
Western blot analysis
The Western blot analysis was performed as we previously described [
26]. The difference is that quantity of 20 µg of total protein was used for western blot analysis. Primary antibodies against
AP4M1 and Beta Actin were purchased from Proteintech (Proteintech, Wuhan, China).
Cell proliferation assay
Cells were seeded at a density of 1 × 103 cells/well in DMEM medium (100 µl) into 96 well plates. Each group had five replicate wells after 24, 48, 72 and 96 h, cell viability was determined by the Cell Counting Kit-8 (CCK8) method. Adding 10 µl CCK8 solution to each well (be careful not to create bubbles) and put the culture plate in the incubator and incubating for 2 h. The absorbance at 450 nm was measured with an enzyme label and a cell proliferation curve was plotted.
Cells (500/well) were seeded into 6-well plates and cultured in 3 ml DMEM supplemented with 10% FBS for about 2 weeks, changing the culture medium every five days during the period. After the colony grew, it was fixed with methanol and stained with 0.1% crystal violet for 30 min, then scored using a microscope and Image J software.
Cell migration and invasion assays
Cell migration assays : Add 200 µl of DMEM without FBS into the transwells and incubate for 30 min. We then add 4 × 104/well cells and 200 µl DMEM without FBS in the upper layer and 800 µl DMEM with 10% FBS in the lower layer of the transwell. Put the culture plates in the incubator and incubate for 24 h. Then wipe the cells in the upper transwell, and fix them with 10% methanol for 10 min and stain them with 0.1% crystal violet for 30 min.
Cell invasion assays: Add 70 µl of 10% matrigel matrix onto the upper chamber and place it in the cell incubator for solidification. Then add 200 µl of DMEM without FBS to the transwells and incubate for 30 min. Next, add 1 × 105 cells in 200 µl of DMEM without FBS to the upper layer, and 800 µl of DMEM with 10% FBS to the lower layer of the transwell. Place the culture plates in the incubator and incubate for 48 h. Afterward, remove the cells in the upper transwell, fix them with methanol for 10 min, and stain with 0.1% crystal violet for 30 min.
Discussion
In most cases of HCC, patients are diagnosed at an advanced stage and do not have the opportunity to undergo surgical resection. Therefore, reliable biomarkers could help diagnose HCC earlier and accurately predict survival prognosis. In this study, we identified the diagnostic and prognostic value of AP4M1 in HCC, and the biological function thataffect the development of HCC. In addition, AP4M1 can be used for the prediction of immune cell infiltration and immune phenotype in hepatocellular carcinoma and positively correlates with various immune checkpoint-related genes, which laid a foundation for future new immunotherapies for HCC.
The expression of
AP4M1 and its potential effect on prognosis in HCC patients have not yet been evaluated. In the present study, we measured that the mRNA level of
AP4M1 was higher in HCC tissues compared to normal tissues in both GEO and TCGA databases. We also found that AP4M1 protein expression was upregulated in HCC tissues compared with normal tissues from the CPTAC database. Furthermore, we discovered that
AP4M1 has a relatively higher ROC score with an AUC of 0.963 in HCC. In addition, we compared
AP4M1 with three other biomarkers. In Suda et al.’s study, Dickkopf-1 (
DKK-1), a secreted glycoprotein, was reported as a promising biomarker for diagnosing HCC [
28]. In Sun Y et al.’s study, Annexin A2 (
AXAN2), a phospholipid-binding protein, was reported to be involved in the growth and metastasis of HCC, and was also a potential biomarker for HCC [
29]. In addition, several studies have revealed Glypican-3 (
GPC3) as a promising diagnostic biomarker in HCC [
30]. Therefore, we analyzed its predictive ability in the TCGA HCC cohort, and showed the ROC curve of AUC values were 0.749, 0.895 and 0.919 respectively. Taken together,
AP4M1 presented a better performance in the diagnosis of HCC patients, and may be applied to further large-scale study in the future. By exploring the correlation between
AP4M1 gene and clinical features, it was found that the overexpression of
AP4M1 was significantly correlated with various clinical features, and a trend toward increased
AP4M1 expression with advanced cancer stages (T3 and T4) and grades (G3 and G4). However, there was no significant difference in
AP4M1 expression between lymph node metastasis and distant metastasis, which may be due to insufficient sample size and the need to increase the number of cases to facilitate future analytical studies. AFP is one of the most widely used biomarker for liver cancer [
31]. We also explored differences in
AP4M1 expression among different AFP expression, suggesting that
AP4M1 is able to identify changes in AFP levels and may be used as a candidate biomarker for early diagnosis of HCC. Therefore, we found that
AP4M1 contributes significantly to HCC progression, which aroused our interest to investigate its biological role.
We further analyzed the prognostic impact of AP4M1 on patients with hepatocellular carcinoma. KM survival curve showed that high AP4M1 expression was may associated with inferior prognosis in HCC, and patients with high AP4M1 expression had lower OS and DFS. Univariate and multifactorial COX regression analyses demonstrated that AP4M1 was an independent risk factor affecting the prognosis of HCC. Thus, our results revealed that AP4M1 had a predictable effect on clinical features and could serve as a potential prognostic biomarker in HCC.
Specific genetic alterations may promote the tumorigenesis. To investigate whether AP4M1 mutation played a crucial role in hepatocarcinogenesis, we investigated specific genetic alterations in HCC. The percentage of AP4M1 genetic alterations in HCC was 11%, and the these genetic alterations presented a significant association with unfavorable OS and DFS. Additionally, our results showed that patients with AP4M1 high expression levels also displayed higher TP53 mutation in HCC.
The heterogeneity of the tumor immune microenvironment is an important factor in promoting tumor progression, recurrence and drug resistance. Immune infiltrating immune cells (TIICs) could modulate the process of development as well as the progression of tumors [
32]. Studies have shown that a high infiltration of cytotoxic T lymphocytes usually suggested a favorable prognosis for patients, but cytotoxic T lymphocyte deactivation and depletion in hepatocellular carcinoma may cause dysregulation of the tumor microenvironment [
33]. In the present study, we observed a significant negative correlation between
AP4M1 and the degree of infiltration of multiple antitumor immune response cells, including CD8
+ T cells, Th17 cells, DC cells, and pDC cells in HCC by ssGSEA analysis. In recent years, the recommendation of immunotherapeutic strategies including immune checkpoint inhibitors, either as a single agent or in combination with approved local and systemic therapies, has significantly altered the treatment outcome of HCC. Thus, we further analyzed the relationship between
AP4M1 and immune checkpoint-related genes. Our results displayed a significant positive correlation between
AP4M1 and the levels of T-cell failure markers such as PD-1 and CTLA4 in HCC. These markers are key suppressive immune checkpoint proteins that naturally inhibit T-cell activity and allow tumor cells to escape immune surveillance, and playing an important role in maintaining self-tolerance. Meanwhile, the upregulation of these markers enhances the suppressive effect of anti-tumor immunity. Although our observation was preliminary and no study reported the exact effect of
AP4M1 in immune-related processes, we revealed a possible role of
AP4M1 in tumor immune microenvironment, which was proposed to be an in-depth exploration for future investigation.
Furthermore, we validated the impact of AP4M1 on the ability of proliferation, invasion, and migration of HCCin vitro. We found that the malignant phenotype of HCC cells was suppressed when AP4M1 knocked down, indicating an oncogenic role of AP4M1 in HCC. Our study provides a new idea for the molecular function of AP4M1 and can be further investigated.
Few studies have reported the role of
AP4M1 in tumors. To explore the biological functions of
AP4M1 in HCC, we analyzed the co-expression genes in HCC and performed functional enrichment analysis. Our results revealed that
AP4M1 were associated with Spliceosome, Proteasome, Cell cycle, cell cycle G2/M phase transition, etc. Based on the results of the GSEA-Hallmark signaling pathway enrichment analysis, we found that
AP4M1-related genes were enriched in the cell proliferation pathway (G2M checkpoint, E2F targets, Myc targets V1). E2F is located downstream of the cell cycle signaling pathway and can regulate the expression of target genes related to the cell cycle process, controlling important processes such as cell proliferation and differentiation [
34]. It has been reported that E2F genes have important roles in the mid-cell regulation of a variety of tumors [
35,
36]. These results suggest that
AP4M1 may be involved in regulating the malignant proliferation and progression of HCC, which also provides new insights into exploring the mechanism of
AP4M1 in HCC.
Although our study presents an integrative analysis of the prognostic and biological functional values of AP4M1 in HCC, there are still some limitations. First, some vital clinical information, such as therapeutic modalities, tumor site and other factors, were not available for analysis in the majority of datasets, which need further prospective studies in the future. Second, AP4M1-related signaling pathways and downstream regulatory molecules need to be further explored, and more in vivo and in vitro experiments are required to further validate our observations, which will be the direction of our future study. Third, all of the samples used in our study were collected retrospectively, and analyses were conducted using data from public databases. Therefore, a more convincing prospective study is required to confirm our findings and can be our future research direction.
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