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. 2025 Sep 29;16:1766. doi: 10.1007/s12672-025-03573-1

Pan-cancer analysis reveals AOC3 as a potential therapeutic biomarker for colorectal cancer

Guanlong Wang 1,2, Lidan Zhu 2, Ling Lu 2, Guangyang Wu 2, Bangjie Wang 2, Changjun Yu 1,✉
PMCID: PMC12480328  PMID: 41021158

Abstract

Amine oxidase copper containing 3 (AOC3) had been reported to play an important regulatory role in the biological functional pathways, immune microenvironment and cellular function. However, the potential function of AOC3 in cancer had not been fully studied. We conducted a comprehensive analysis of the potential function of AOC3 in pan-cancer using multiple online databases and analytical methods, including mutations and differential expression analysis, pathway analysis and immune analysis. Then, the function of the AOC3 was assessed in colorectal cancer (CRC) cells. Pan-cancer analysis results show that the highest mutation frequency of ACO3 in embryonic tumor patients, in most cancers, the expression of AOC3 is significantly downregulated and the differential expression of AOC3 was mainly concentrated in CRC and female tumors. Enrichment analysis showed that AOC3 was involved in PPAR signaling pathway. In addition, bioinformatic investigation revealed a link between the AOC3 with immune cell infiltration in the tumor microenvironment. Cell experiments have confirmed that AOC3 can significantly regulate the apoptosis and cycle progression of CRC cells. In summary, our study not only comprehensively analyzed the potential mechanisms of AOC3 in pan-cancer, but also validated the potential regulatory role of AOC3 through CRC cell experiments.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03573-1.

Keywords: AOC3, Immune microenvironment, Cellular function, Pan-cancer

Introduction

The latest statistics show that approximately 20 million people worldwide are diagnosed with cancer every year, and the number of cancer cases is increasing every year [1]. Cancer is a major disease that threatens health. Although technological advancements have improved our diagnostic and treatment methods, there are still a large number of cancer patients who do not receive effective treatment and lose their lives [2]. In recent years, with the continuous improvement of cancer treatment methods, more and more anti-tumor drugs have been approved for market and put into clinical use. In addition, some treatment methods in clinical trials, such as gene therapy, are constantly making new breakthroughs. But for advanced cancer patients whose tumors have already metastasized to other organs, existing treatment methods are difficult to sustain their lives [3].

And currently, there is still little understanding of the immune escape of cancer cells and how to control the mechanism of cancer cell proliferation [4]. Cancer cells originate from normal tissue cells, which may undergo mutations due to physical or chemical stimuli that alter normal physiological functions or may be the result of genetic factors leading to certain gene changes [5]. In addition, as our research on cancer mechanisms deepens, gene changes have become increasingly important in the pathological and immune mechanisms of cancer [6]. Therefore, exploring cancer treatment at genetic level is particularly important.

Amine oxidase copper containing 3 (AOC3), also known as plasma amine oxidase, semicarbazide-sensitive amine oxidase, or vascular adhesion protein-1 (VAP-1), catalyzes the oxidative deamination of primary amines to aldehydes using copper and a quinone as cofactors [7]. AOC3 had been reported as a copper metabolism related protein. Copper participates in various metabolic pathways within cells, including lipid metabolism [8]. AOC3 had been reported to play an important role in lipid metabolism. AOC3 was expressed in adipose tissue and its activity was closely related to the metabolism and function of adipocytes. AOC3 affects the occurrence and development of obesity by regulating adipocyte differentiation, metabolism, and other pathways [9–10]. AOC3 had an impact on the activity and function of immune cells, which can activate immune cells and further affect immune responses [11]. AOC3 can act as a regulatory or synergistic factor of inflammatory factors during the inflammatory process, jointly participating in the inflammatory response [12]. Meanwhile, AOC3 had also been reported to be associated with programmed cell death in cancer [13]. Previous studies had demonstrated a potential biological association between AOC3 and cancer [14]. Therefore, AOC3 may play a crucial role in lipid metabolism related pathways, immunity and cellular function in cancer. However, the potential biological mechanisms of AOC3 in tumors were currently unclear. Therefore, our study not only comprehensively analyzed the potential pathways and immune mechanisms of AOC3 in pan-cancer, but also validated the potential regulatory role of AOC3 in cancer cells through cell experiments. This result provides a theoretical basis for the development of new therapeutic targets in clinical practice.

Materials and methods

Data collection and preprocessing

RNA-sequencing expression profiles and corresponding clinical information for pan-cancer were downloaded from the The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov), Genotype Tissue Expression (GTEx) database(https://gtexportal.org/home/), as follows: TCGA database: 33 types of cancer, GTEx database has catalogued gene expression in > 9,000 samples across 53 tissues from 544 healthy individuals. The data from TCGA database and GTEx database were merged and subjected to batch processing effect checks, the ComBat () function in the SVA package of R software was used to remove batch processing effects. As in the case of the same dataset and platform but in different batches, we used the removeBatchEffect function of the limma package in the R software to remove batch effects. After there was no batch effect in the data, we conducted subsequent differential analysis [15].

Mutations and differential expression of AOC3 in pan-cancer

Information on AOC3 genetic alteration features of all pan-cancer samples was queried by logging into the cBioPortal database (http://www.cbioportal.org/), such as mutation frequencies, alteration types, copy number alteration (CNA) [16]. The TCGA database was used to analyze the pan-cancer expression levels of AOC3. Gene Expression Profiling Interaction Analysis (GEPIA, http://gepia.cancer-pku.cn/index/html) can be used to evaluate the RNA that expression data of 9736 tumor samples and 8587 normal samples. When obtaining AOC3’s expression profile, LIMMA method used for comparison between cancer group and control group, | log2FoldChange (FC) | cutof = 1, LogScale = log2 (TPM + 1) and q-value cutof = 0.01 were considered to have significant differences [17]. Red represents significant upregulation, green represents significant downregulation.

AOC3 co-expressed gene/protein interaction network and enrichment analysis

Analyze the top 100 AOC3 co-expressed genes in pan-cancer on GEPIA2.0 online website (http://gepia2.cancer-pku.cn/#index) (‘Similar Genes Detection’ module) and select the top 6 genes for correlation analysis. The correlation results are presented using a heatmap, red represents positive correlation, blue represents negative correlation, P < 0.05 represents significant correlation. P < 0.05 identified as significant. Then further investigate the potential impact of AOC3 on protein interaction networks. A Protein-Protein Interaction Networks network that was centered on AOC3 was constructed by GeneMANIA (https://genemania.org/).Metascape (https://metascape.org/gp/index.html#/main/step1) was a website for analyzing protein lists or gene, which was used to analyze the functional clustering of gene sets [18].

AOC3 and immune infiltration, immune checkpoint and immune factors

The correlation between immune cell infiltration levels and AOC3 expression was first studied, and the XCELL method (XCELL package in R software) was used to analyze AOC3 landscapes associated with various immune cell infiltrations. Subsequently, Spearman correlation analysis was conducted between AOC3 expression levels and immune infiltration scores in various types of tissues, and the results were presented using heatmaps. Blue represents positive correlation, red represents negative correlation. *p < 0.05, **p < 0.01, ***p < 0.001. The Spearman correlation analysis method was used to detect the expression levels of AOC3 and immune checkpoint related genes in pan-cancer tissues. Blue represents positive correlation, red represents negative correlation. *p < 0.05, **p < 0.01, ***p < 0.001 [19].

AOC3 and immune factors

The Tumor Immune System Interaction Database (TISIB)(http://cis.hku.hk/TISIDB/) was used to analyze the correlation between chemokines and AOC3 and immunostimulants. Red represents positive correlation, blue represents negative correlation [19].

AOC3 is a potential biomarker in colorectal cancer

We analyzed the gene expression level of AOC3 in colorectal cancer using the online database GEPIA. In order to evaluate the prognostic role of AOC3 in colorectal cancer, based on GSE24551 dataset, the prognosis of colorectal cancer patients was evaluated by analyzing the high and low expression of AOC3 with Kaplan-Meier curve, the surve_cutpoint function in R package “surviminer” was used to calculate the optional cutoff value of AOC3, and the samples were classified into high- and low-expression groups accordingly [20].

Construction of AOC3 overexpression CRC cell model

HTC116 cells are sourced from the cell bank of The First Affiliated Hospital of Anhui Medical University in Anhui Province. HTC116 cells are cell lines of CRC cancer. HTC116 cells were inoculated in DMEM medium containing 10% fetal bovine serum (containing 100 U/mL penicillin and 100 mg/mL streptomycin) and cultured at 37 ℃ in 5% CO2 incubator. When the adherent parietal cell grows into a compact monolayer, it is subcultured. Partial stably growing colon cancer cells were randomly divided into two groups: the empty control group (HCT116 + NC) and the AOC3 overexpression group (HTC116 + AOC3-OE). The AOC3 overexpression plasmid vector and the nonsense sequence AOC3 plasmid vector were transfected into colon cancer cells, respectively. Quantitative Real-time PCR (qPCR) was used to verify AOC3 ‘s overexpression effect in HTC116 cells (GAPDH as an internal reference). The 2-ΔΔCt method was used to determine the relative AOC3 expression. The primer sequences utilized were as follows: Forward: GCTGGAAAGGATTTGGTGG, Reverse: AAAGAAGTTATAGGGTCGGAGG. Student’s t-test (unpaired) was used to check the statistical significance while comparing the means of two groups (HCT116 + NC and HTC116 + AOC3-OE), each group has three replicates. P < 0.05 was considered significant [21]. *p < 0.05, **p < 0.01, ***p < 0.001.

Cell cycle experiments of CRC cells

Transfer HTC116 cells (HCT116 + NC group cells and HTC116 + AOC3-OE group cells) and culture medium into centrifuge tubes. After centrifugation at 4 °C for 5 min (1000 rpm), remove the supernatant from the centrifuge tube. Then, add pre-cooled 75% alcohol to the centrifuge tube to fix and resuspend the cells, and place the centrifuge tube in a refrigerator at 4 °C overnight to secure it. After centrifugation at 4 °C for 5 min (1000 rpm), remove the supernatant from the centrifuge tube. Finally, add propidine iodide staining solution to the centrifuge tube, stain in the dark at 37 °C for 30 min, and then perform flow cytometry detection. We analyzed three independent repeated data and plotted them, Student’s t-test (unpaired) was used to check the statistical significance while comparing the means of two groups. P < 0.05 was considered significant [22]. *p < 0.05, **p < 0.01, ***p < 0.001.

Apoptosis experiments of CRC cells

Pre-cooled PBS was used to wash the collected HTC116 cells (HCT116 + NC group cells and HTC116 + AOC3-OE group cells) (1 × 106 cells/time). Then, cells were resuspended by 1 ml of 1X binding buffer, and after reaching a density of 1 × 106 cells/ml in the test tube, in the dark and at room temperature conditions, 5 µL Annexin V-FITC was added to the test tube and gently mixed for 10 min. Finally, 5 µL propidine iodide was added to the test tube and incubated in the dark for 5 min before being detected within 1 h by flow cytometry. After counting total cells and apoptotic cells, we analyzed three independent repeated data and plotted them. Student’s t-test (unpaired) was used to check the statistical significance while comparing the means of two groups. P < 0.05 was considered significant [23]. *p < 0.05, **p < 0.01, ***p < 0.001.

Statistical analysis

Differential gene expression’s most statistical analyses were performed using on-line databases and R 4.2.1. Student t-test and Welch’s T-test were used for comparison between the two groups. The Spearman analysis method is used for correlation analysis. For the analysis of gene expression differences between different groups, | log2FoldChange (FC) | cutof = 1 and P < 0.01 were considered significant. Except for the analysis of gene expression differences between different groups. All analyses with P < 0.05 were considered significant. For cell experiments, Student’s t-test (unpaired) was used to check the statistical significance while comparing the means of two groups, each group has three replicates. P < 0.05 was considered significant [24].

Results

Mutations and differential expression of AOC3 in pan-cancer

The overall workflow of this study is shown in Fig. 1. We used the cBioPortal database to study the mutation of ACO3 in pan-cancer. The results show that the highest mutation frequency of ACO3 in Embryonal Tumor patients is about 14% (Figure S1). The GEPIA2.0 results showed that AOC3 mRNA in pan-cancer was highly expressed in DLBC, PAAD and significantly downregulated in ACC, BLCA, BRCA, CESC, COAD, ESCA, KICH, KIRP, LUAD, LUSC, OV, PRAD, SKCM, READ, TGCT, THCA, UCEC, UCS (Fig. 2). Red represents significant upregulation, green represents significant downregulation. Therefore, ACO3 may play an important role in cancer. The top five in terms of fold change were colorectal cancer (COAD and READ), female tumors (UCEC and UCS), LUSC.

Fig. 1.

Fig. 1

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The workflow of this study

Fig. 2.

Fig. 2

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Low expression of AOC3 in multiple cancers. Comparison of AOC3 expression between tumor samples and normal samples. (| log2FoldChange (FC) | cutof = 1 and P < 0.01 were considered significant). Red represents significant upregulation, green represents significant downregulation

AOC3 co-expressed gene/protein interaction network and enrichment analysis

GEPIA2.0 was used to analyze the top 100 co-expressed genes of AOC3 in pan-cancer, the top 6 genes (LMOD1, KCNMB1, MYL9, TAGLN, CNN1 and CSRP1) were highly correlated with AOC3 in most cancer types (Fig. 3A). Enrichment analysis showed that AOC3 was involved in some pathways, including actin cytoskeleton, cytoskeleton in muscle cells (Fig. 3B). Then, further research was conducted on the potential impact of AOC3 on protein interaction networks, and 20 genes related to AOC3 were extracted from the GeneMANIA database for enrichment analysis (Fig. 3C). Enrichment analysis showed that AOC3 was involved in some pathways, including actin cytoskeleton, primary methylamine oxidase activity, PPAR signaling pathway (Fig. 3D).

Fig. 3.

Fig. 3

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AOC3 co-expressed gene network and enrichment analysis in pan-cancer. GEPIA2.0 was used to analyze the top 100 co-expressed genes of AOC3 in pan-cancer, (A). Functional enrichment analysis of AOC3 in pan-cancer. (B). 20 genes related to AOC3 were extracted from the GeneMANIA database (C). Functional enrichment analysis of AOC3 (D). P < 0.05 was considered significant

AOC3 and immune infiltration, immune checkpoint and immune factors

As shown in Fig. 4, in many tumors, AOC3 is positively and significantly correlated with immune score and microenvironment score, especially colorectal cancer (COAD and READ). In most tumors, AOC3 expression also shows a significant positive correlation with immune cell infiltration. In solid tumors (COAD and READ), AOC3 showed a significant positive correlation with Monocyte, Myeloid dendritic cell, Endothelial cell, Macrophage. In SKCM, PRAD, KIRP, COAD and READ, AOC3 was significantly positively correlated with 9 immune checkpoint genes (Figure S2). We analyzed the association between AOC3 and chemokines and immunostimulants. As shown in the heatmap, AOC3 was positively correlated with chemokines, many receptors and immune stimulatory factors in pan-cancer (Figure S3).

Fig. 4.

Fig. 4

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Relationship between the expression of AOC3 and tumor immune infiltration. Correlation analyses of the AOC3 expression with tumor immune infiltration in pan-cancers. Blue represents positive correlation, red represents negative correlation. *p < 0.05, **p < 0.01, ***p < 0.001

AOC3 is a potential biomarker in colorectal cancer

We analyzed the gene expression level of AOC3 in colorectal cancer using the online database GEPIA. According to this result, the expression level of AOC3 in colorectal cancer was significantly downregulated compared to normal samples (P < 0.01) (Figure S4A-B). Kaplan-Meier curve showed that there was a significant difference between the survival curves of patients with high and low expression of AOC3 (P < 0.05) (Figure S4C), this difference has been validated in MSS type patients (Figure S4D-E). The red line represents the high expression group, and the blue line represents the low expression group.

Construction of AOC3 overexpression CRC cell model and cell cycle experiments

The qPCR results (three replicates per group) showed significant overexpression of AOC3 in the HTC116 + AOC3-OE group compared to the control group (HCT116 + NC) (Figure S5). Cell cycle’s results showed that compared with the control group (HCT116 + NC) cells, the HTC116 + AOC3-OE group showed significant inhibition of CRC cells in the G1 phase (Fig. 5A-B).

Fig. 5.

Fig. 5

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Comparison of cell cycle progression between two groups (HCT116 + NC group and HTC116 + AOC3-OE group). Flow cytometry cell cycle diagrams of HCT116 + NC group and HTC116 + AOC3-OE group (three replicates per group) (A); Cell cycle bar charts of HCT116 + NC group and HTC116 + AOC3-OE group (B). *p < 0.05, **p < 0.01, ***p < 0.001

Apoptosis experiments of CRC cells

The apoptosis experiment of HTC116 cells showed that compared with the control group (HCT116 + NC) cells, the apoptosis rate of HTC116 + AOC3-OE group cells was significantly increased (Fig. 6A-B).

Fig. 6.

Fig. 6

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Comparison of apoptosis rate between two groups (HCT116 + NC group and HTC116 + AOC3-OE group). Flow cytometry apoptosis rate of HCT116 + NC group and HTC116 + AOC3-OE group (three replicates per group) (A); Apoptosis rate bar charts of HCT116 + NC group and HTC116 + AOC3-OE group (B). *p < 0.05, **p < 0.01, ***p < 0.001

Discussion

Global cancer burden data for 2022. Data shows that there are 19.96 million new cases of cancer and 9.74 million cancer deaths worldwide. About one-fifth of men or women will develop cancer in their lifetime, while approximately one ninth of men and one twelfth of women will die from cancer. Cancer is the leading cause of death and disease burden in humans [25]. The occurrence of cancer is related to genes. Because cancer is a genetic disorder at the cellular level, it is a genetic disease. When normal human cells turn into cancer cells, it means that the growth of the cells is out of control, and the growth of the cells is controlled by genes [26–27]. In recent years, it has been confirmed that AOC3 can participate in tumor pathological and immune mechanisms [14]. However, the reporting of AOC3 is limited to a few solid tumors, and the potential functional mechanisms of AOC3 in pan-cancer have not been reported yet. This study is the first to conduct a pan-cancer analysis of the functional role of AOC3 in various types of tumors, which will provide new directions and ideas for further research on the functional role of AOC3 in tumors in the future.

Enrichment analysis showed that AOC3 was involved in PPAR signaling pathway. Research reports that the main target genes of the PPAR signaling pathway were related to fatty acid oxidation and metabolism. The PPAR signaling pathway regulates lipid metabolism related genes, promotes fatty acid oxidation, provides energy for cancer cells, and helps them adapt to unfavorable microenvironments [28]. A study found that the PPAR signaling pathway was commonly abnormally activated in colorectal cancer. Blocking the PPAR pathway inhibits the growth of colorectal cancer organoids in vitro and promotes their apoptosis, indicating that abnormal activation of the PPAR signaling pathway plays a key role in the development of colorectal cancer [29]. AOC3 had been reported as a gene related to fatty acid metabolism [30]. Therefore, the PPAR signaling pathway may affect the occurrence and development of colorectal cancer by regulating AOC3, and its specific mechanism needs further research.In many cancers, AOC3 is positively correlated with immune score, matrix score and microenvironment score. In most cancers, the expression of AOC3 is also significantly positively correlated with immune cell infiltration. Immune cell infiltration plays an important role in the tumor microenvironment, the immune process is usually regulated by certain cytokines [31]. The differential expression of AOC3 may also regulate the function and stability of certain cytokines, thereby modulating the immune process of cancer cells, which will be the biological mechanism for our next research. One of the fundamental goals of various tumor immunotherapies is to induce apoptosis of tumor cells [32]. Immune cells can deliver granzyme through death ligands or trigger tumor cell apoptosis [33–34]. Our immune analysis results show that there was a significant correlation between tumor microenvironment and immune cells and AOC3 in cancer, especially in colorectal cancer patients. The expression of AOC3 had been confirmed to significantly promote apoptosis in colorectal cancer cells. Therefore, AOC3 may be a potential therapeutic target for colorectal cancer.

This study has several limitations. The data from public databases were used for this study, therefore, there are inevitably potential confounding factors that require prospective study cohorts to validate. Cell experiments have confirmed that AOC3 can significantly regulate the function of CRC cells. However, the impact of AOC3 expression (transcriptional level) on cellular function may be regulated by its downstream and upstream mechanisms. Single cell lines have limitations and should be included in multiple cell lines for functional testing. In addition, in vitro cell experiments do not involve the regulation of the neuroendocrine system or interactions between cells in vivo. Therefore, further animal experiments are the next research direction.

Conclusions

In summary, multiple bioinformatics methods had been used to comprehensively investigate the mutations and differential expression analysis, pathway analysis and immune analysis of AOC3 in various tumors and validated the cellular function of AOC3 in colorectal cancer. All these findings will provide new ideas and support for the regulatory role of AOC3 in tumor treatment, and its clinical significance was crucial.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (1.8MB, docx)

Acknowledgements

Not applicable.

Author contributions

Guanlong Wang, Lidan Zhu: Conceived and designed the experiments; Guanlong Wang, Lidan Zhu, Ling Lu: Performed the experiments; Guanlong Wang, Guangyang Wu: Analyzed and interpreted the data; Changjun Yu: Contributed reagents, materials, analysis tools or data; Guanlong Wang, Bangjie Wang: Wrote the paper.

Funding

Not applicable.

Data availability

The datasets supporting this study are publicly available in the following repositories: The Cancer Genome Atlas (TCGA) database ( [https://portal.gdc.cancer.gov](https:/portal.gdc.cancer.gov) ) and Genotype Tissue Expression (GTEx) database(https://gtexportal.org/home/).

Declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent to publish

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Supplementary Materials

Supplementary Material 1 (1.8MB, docx)

Data Availability Statement

The datasets supporting this study are publicly available in the following repositories: The Cancer Genome Atlas (TCGA) database ( [https://portal.gdc.cancer.gov](https:/portal.gdc.cancer.gov) ) and Genotype Tissue Expression (GTEx) database(https://gtexportal.org/home/).

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