Angiotensin‑converting enzyme 2 as an immune and prognostic biomarker in colorectal cancer

Guoqian Liu; Xiaoqian Yu; Wenying Jiang; Xiaoyun He; Hui Nie; Chunlin Ou

doi:10.1038/s41598-026-43588-4

. 2026 Mar 9;16:12650. doi: 10.1038/s41598-026-43588-4

Angiotensin‑converting enzyme 2 as an immune and prognostic biomarker in colorectal cancer

Guoqian Liu ^1,^#, Xiaoqian Yu ^1,^#, Wenying Jiang ¹, Xiaoyun He ², Hui Nie ^1,^✉, Chunlin Ou ^1,^3,^✉

PMCID: PMC13086949 PMID: 41796239

Abstract

Angiotensin-converting enzyme 2 (ACE2) has been implicated as an oncogene in certain cancer types; however, there is a lack of analysis on the role of ACE2 in the predictive value for prognosis and immunotherapy response in various tumor types. This study used data from the Cancer Genome Atlas (TCGA), Tumor Immune Estimation Resource (TIMER 2.0), cBioPortal, and ROC Plotter databases to analyze the expression, prognosis, and immune cell infiltration of ACE2 in various tumor types. Furthermore, we analyzed the correlation between the expression of ACE2 and clinicopathological characteristics in 119 pairs of colorectal cancer (CRC) tissues using immunohistochemistry analysis, and then conducted the in vitro experiments to verify the role of ACE2 in the migration and proliferation of CRC cells. We found that ACE2 was highly expressed in CRC tissues compared with adjacent normal tissues, and that CRC patients with high ACE2 expression levels showed poor survival. Additionally, combined bioinformatics and qRT-PCR analysis identified a strong negative correlation between ACE2 expression and natural killer cell infiltration in CRC. Meanwhile, ACE2 expression was significantly elevated in patients resistant to anti-CTLA-4 and anti-PD-L1 therapy and was linked to poor prognosis. In vitro experiments showed that silencing ACE2 inhibits the proliferation and invasion of CRC cells. These results highlight ACE2’s involvement in CRC pathogenesis and cancer-immune interactions, positioning it as a promising prognostic and therapeutic biomarker in CRC.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-43588-4.

Keywords: ACE2, Colorectal cancer, Immune response, Biomarker, Therapy

Subject terms: Biomarkers, Cancer, Computational biology and bioinformatics, Oncology

Introduction

Angiotensin-converting enzyme (ACE), a metalloproteinase consisting of 805 amino acids, is a type I transmembrane glycoprotein characterized by a single extracellular catalytic domain¹. It facilitates the conversion of inactive angiotensin (Ang) I to Ang II, a key regulator of vasoconstriction. Ang II serves as the primary effector molecule of the renin-angiotensin system (RAS), exerting diverse biological functions through angiotensin receptors². ACE2, a homologue of ACE, cleaves Ang II to produce Ang (1–7), indirectly contributing to various physiological processes³. Additionally, ACE2 modulates innate immunity, influences gut microbiota composition, and is directly involved in the initiation of inflammatory responses⁴. It also plays a crucial role in regulating neutral amino acid transporters on epithelial cell surfaces and influences insulin secretion and islet cell growth⁵. Recent studies have demonstrated ACE2’s involvement in the pathogenesis and progression of multiple cancers, including breast, lung, skin, stomach, thyroid, oral, and colorectal cancers^6–13. However, its specific roles and mechanisms in colorectal cancer (CRC) remain poorly understood.

CRC, a malignancy arising in the colorectal region, ranks as the third most common cancer globally^14,15. It is categorised into several pathological types, including adenocarcinoma, mucinous adenocarcinoma, and undifferentiated carcinoma¹⁶. The incidence of CRC varies by geographical region and is influenced by factors such as sex, age, and lifestyle¹⁷. Despite significant advancements in CRC treatment, many patients still face poor outcomes, largely due to late-stage diagnosis and high recurrence rates following surgery^18–20. These challenges underscore the critical need for early diagnosis and improved therapeutic strategies²¹. In this study, we aimed to investigate the role of ACE2 in CRC and elucidate its potential mechanisms of action, providing a novel perspective for future CRC diagnosis and treatment.

Materials and methods

Expression analysis of ACE2 in pan-cancer

GEPIA is an online platform that enables researchers to analyze human cancer gene expression and interactions. It offers customisable tools for differential expression analysis, patient survival analysis, and identification of similar genes²². Xiantao Academic provides statistical analysis of ACE2 expression across various cancers using bioinformatics techniques, along with visualisation services for the resulting data²³. The UALCAN platform serves as a resource for validating target genes and identifying potential biomarkers specific to tumour subgroups²⁴. Furthermore, the Cancer RNA-Seq Nexus (CRN) database directly provides information on gene expression²⁵.

Prognostic analysis of ACE2 in pan-cancer

The BEST (Biomarker Exploration for Solid Tumors) tool, covering 27 different types of solid tumours and including clinical data from nearly 50,000 samples, facilitated the evaluation of ACE2’s prognostic significance across various cancer types, particularly focusing on its role in immunotherapy for colon cancer²⁶.

Enrichment analysis

The cBioPortal database integrates data from major sources such as CCLE, TCGA, and other large-scale cancer research projects, allowing for the exploration, visualization, and analysis of multidimensional cancer genomics data²⁷. This platform helped simplify the identification of ACE2 co-expressed genes in colorectal cancer (CRC) tissues. Subsequently, a protein-protein interaction network was constructed using these co-expressed genes via the STRING database²⁸. Detailed visual analysis was performed using Cytoscape (version 3.7.2) and the KOBAS 2.0 platform²⁹. Xiantao Academic was also employed for performing Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis.

Functional analysis of ACE2 at the single-cell level

The CancerSEA (Cancer Single-Cell Expression Atlas), a specialised tool, was used for studying cancer cell functionality at the single-cell level. It encompasses data from 900 samples across 25 cancer types, providing insights into various tumour-related functions³⁰.

Methylation analysis

MethSurv, a comprehensive platform, was used for assessing the methylation status of cancer biomarkers and rapidly evaluating their potential for disease prognosis. This resource contains methylation data from 25 types of human cancers, encompassing 7,358 methylation sites³¹.

Immune infiltration analysis

Sangerbox integrates data from GEO, TCGA, and ICGC databases, positioning itself as a comprehensive bioinformatics repository. This dataset was used to investigate the relationship between ACE2 and immune cell infiltration in various tumours³². TIMER (Tumour Immune Estimation Resource), another tool based on the TCGA database, was employed to examine the correlations between ACE2 expression and markers of tumour-associated macrophages, monocytes, M1 macrophages, and M2 macrophages. Log₂ RSEM was used to compute gene expression levels, and purity-corrected partial Spearman correlation was applied to assess these associations³³. The BEST tool was used to further analyze immune cell infiltration in the tumour microenvironment, providing precise data for targeted therapeutic strategies and drug response predictions. The American Joint Committee on Cancer (AJCC) first introduced the concept of residual tumor grading (R grading) in 1978. The radicality of resection of malignant tumors, namely residual tumor grading, is an indicator for evaluating the residual tumor status after resection of malignant tumors, expressed as complete tumor resection (R0), microscopic residual (R1), and macroscopic residual tumor (R2)³⁴. The TISIDB platform was used to explore the correlations between ACE2 and immune regulatory factors, chemokines, and other relevant components across different cancer types.

Tissue samples

Exactly 119 pairs of matched adjacent normal tissue samples and paraffin-embedded archival CRC specimens were obtained from Xiangya Hospital (Changsha, P. R. China). None of the patients had received any form of therapy, such as chemotherapy, radiotherapy, or immunotherapy, before the resection. Written informed consent was obtained from all participants, adhering to the Helsinki Declaration principles. The Ethics Committee of Xiangya Hospital of Central South University approved the usage of these samples (Approval No. 202308166).

Immunohistochemistry (IHC)

Immunohistochemical staining was performed on the tissue samples. The slides were first deparaffinised using xylene, followed by treatment with graded alcohol (80%, 95%, and 100%). Antigen retrieval was conducted by boiling the slides in a pressure cooker for 2 min and 30 s in an antigen-reactive solution. To block endogenous peroxidase activity, the slides were immersed in 3% hydrogen peroxide for 15 min, rinsed, and incubated with goat serum for 30 min. Afterwards, a rabbit polyclonal ACE2 antibody (19245-1-AP, Proteintech, USA) at a dilution of 1:100 was applied to each slide and incubated overnight at 4 °C. The next day, slides were incubated at room temperature with goat anti-rabbit IgG polymer (PV-6000, Beijing Jinqiao Biotechnology, China) for 1 min and 30 s, followed by DAB application for the same duration. Haematoxylin counterstaining, dehydration, and neutral bone sealing were performed to complete the IHC process. The expression of ACE2 protein in both CRC and normal tissues was assessed using this method.

ACE2 staining was primarily observed in the cytoplasm. A semi-quantitative scoring system was employed, where the total score equalled the product of the staining intensity and staining area³⁵. Five random fields were examined under a Nikon microscope (200x magnification, Tokyo, Japan). The staining intensity was scored from 0 to 3, with 0 indicating negative staining, and 1, 2, and 3 corresponding to weak (light yellow), moderate (light brown), and strong (dark brown) staining, respectively. The area score ranged 0–4, representing < 5%, 6–25%, 26–50%, 51–75%, and > 75% of the stained area, respectively. A total score ≥ 2 was classified as positive staining, while a score < 2 was considered negative staining.

Cell culture and transfection

CRC cell lines (SW480, RKO, and CaCO-2) and human normal colon mucosal cell line (NCM460) were obtained from the American Type Culture Collection (ATCC, Manassas, USA). All colon cancer cells were cultivated in RPMI-1640 medium with 10% FBS. RPMI-1640 medium contained Penicillin-Streptomycin purchased from Gibco (15140-122), 100x diluted to 1x when using. In addition, the cultivation environment was sterile incubator at a temperature of 37 ℃ containing 5% CO₂.

In the experiment, siRNA was used to knockdown the ACE2 gene. The siRNA was synthesized by Company GenePharma (Shanghai), and transfection was carried out using Lipofectamine 3000 (Invitrogen, USA) according to the instruction manual. Cells were collected 24 h after transfection for subsequent experiments.

Isolation of RNA from formalin-fixed and paraffin-embedded samples

Following formalin fixation and paraffin embedding (FFPE) of the CRC tissues or deparaffinisation of normal tissues using xylene, total RNA was extracted using the AmoyDx FFPE RNA Extraction Kit (Catalogue No. #8.02.0019; AmoyDx, Xiamen, China)³⁶.

Quantitative real-time polymerase chain reaction (qRT-PCR)

qRT-PCR was performed on the extracted RNA. The thermal cycling conditions were as follows: initial denaturation at 95 °C for 30 s, followed by 40 cycles at 60 °C for 30 s and 72 °C for 30s. The primer sequences for the qRT-PCR are provided in Table 1.

Table 1.

Primer sequence for qRT-PCR.

Gene	Primer (Forward)	Primer (Reverse)
ACE2	CAAGAGCAAACGGTTGAACAC	CCAGAGCCTCTCATTGTAGTCT
KLRD1(CD94)	CAGGACCCAACATAGAACTCCA	GGAAATGAAGTAACAGTTGCACC
CCNB1	AATAAGGCGAAGATCAACATGGC	TTTGTTACCAATGTCCCCAAGAG
CCND1	GCTGCGAAGTGGAAACCATC	CCTCCTTCTGCACACATTTGAA
CCNE1	GGCCAAAATCGACAGGACGG	ATCCGAGGCTTGCACGTTGAGT
Snail	TCGGAAGCCTAACTACAGCGA	AGATGAGCATTGGCAGCGAG
β-catenin	CATCTACACAGTTTGATGCTGCT	GCAGTTTTGTCAGTTCAGGGA
MMP9	TGTACCGCTATGGTTACACTCG	GGCAGGGACAGTTGCTTCT
GAPDH	AACGGATTTGGTCGTATTGG	TTGATTTTGGAGGGATCTCG
U6	CTCGCTTCGGCAGCACA	AACGCTTCACGAATTTGCGT

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Transwell invasion assay

Cell invasion ability was assessed using Matrigel coated Transwell chambers (8 μm pore size, Corning, USA). Twenty-four hours after transfection, the cells were harvested and the cell density was adjusted to 5 × 10⁴ cells/mL. The cells were suspended in serum-free medium and added to the upper chamber of the Transwell. RPMI-1640 medium with 10% FBS was added to the lower chamber. After 24 h of incubation, non-invaded cells were removed. After washing with PBS, the cells were fixed and stained with crystal violet. Three random fields of view were selected under a microscope to count the number of invaded cells.

5-Azacitidine treatment

5-Azacitidine (MCE, China) was used to inhibit DNA methylation of RKO and SW480 cells. Cells were treated with 5nM and 10nM Azacitidine for 6 days. After 6 day treatment, cells were harvested and measured mRNA expression of ACE2.

CCK-8 cell proliferation assay

After transfection, cells were seeded in 96-well plates at a density of 1 × 10³ cells per well, with 6 replicates in each group. At 24, 48 and 72 h, respectively, 10 µL CCK-8 reagent (Dojindo, Japan) was added. After incubation at 37 °C for 2 h, the absorbance (OD value) was measured at 450 nm.

Western blot

After 48 h transfection, cells were collected and lysised. Then electrophoresis, membrane transfer, and blocking steps, all primary antibodies (CST, USA) were diluted at 1:1000 and incubated overnight, followed by incubation with the secondary antibody and color development.

Statistical analysis

The difference in ACE2 expression between cancerous and adjacent normal tissues was assessed using Student’s t-test, with data analyzed using the SPSS statistical software (SPSS 23.0, IBM Analytics). The chi-square test was employed to evaluate the association between ACE2 expression and clinicopathological characteristics of patients with CRC. Multivariate analysis was conducted using multiple linear regression, and Pearson’s correlation coefficient was used to determine gene correlations. A p-value of ≤ 0.05 was considered statistically significant.

Results

Differential expression of ACE2 in pan‑cancer analysis

Using the GEPIA database, we systematically analyzed the ACE2 expression across various cancer types. As illustrated in Fig. 1A, ACE2 was highly expressed in multiple types of tumors: colon adenocarcinoma (COAD), kidney renal papillary cell carcinoma (KIRP), pancreatic adenocarcinoma (PAAD), rectum adenocarcinoma (READ), and stomach adenocarcinoma (STAD). These results were corroborated by further analysis using the Xiantao Academic plat form the TCGA dataset (Fig. 1B).

In order to gain further insight into the role of ACE2 in the progression of these cancers, we conducted an evaluation of its protein expression at different tumour stages. In COAD, there was an increase in ACE2 protein expression in all stages in comparison to normal tissues, and stages 2 and 3 were found to be statistically significant (Fig. 2A). In pancreatic cancer, there is a downregulation of ACE2 expression in comparison to normal tissues at all stages (Fig. 2B). A similar trend was observed in renal cell carcinoma, where each stage of the disease was significantly downregulated in comparison to normal tissue. The downregulation of stages 1, 3, and 4 were found to be statistically significant in comparison to normal tissue (Fig. 2C). In comparison to normal tissues, the expression of ACE2 protein was observed to be moderately elevated in subtypes S2, S3, S4, S6, S7, S8, and S10 of gastric cancer, yet no statistically significant difference was identified (Fig. 2D).

Fig. 2 — Correlation between the expression of the ACE2 protein and the staging of tumours. **(A)** Correlation between the expression of the ACE2 protein and tumour staging in patients with colon adenocarcinoma (COAD). **(B)** Correlation between the expression of the ACE2 protein and tumour staging in patients with pancreatic cancer (PAAD). **(C)** Correlation between the expression of the ACE2 protein and tumour staging in patients with renal cell carcinoma (RCC). **(D)** Correlation between the expression of the ACE2 protein and tumour staging in patients with Gastric cancer (GC). *p < 0.05; ** p < 0.01; *** p < 0.001.

Diagnostic and prognostic value of ACE2 in pan-cancers

Receiver operating characteristic (ROC) curves were employed to evaluate the diagnostic potential of ACE2 across the multiple types of tumors. The areas under the curve (AUCs) demonstrated that ACE2 could serve as a promising diagnostic biomarker: COAD (AUC = 0.772), KIRP (AUC = 0.734), PAAD (AUC = 0.758), READ (AUC = 0.858), and STAD (AUC = 0.741) (Fig. 3A). Furthermore, survival analysis using the log-rank test (Fig. 3B) revealed that elevated ACE2 expression was significantly associated with poorer overall survival (OS) in patients with CRC (p = 0.029). However, in patients with KIRP, higher ACE2 levels were correlated with favourable OS (p = 0.14). In PAAD, lower ACE2 expression was linked to a better prognosis (p = 0.017). In STAD, reduced ACE2 expression was associated with an improved OS, though not statistically significant (p = 0.096). Collectively, these findings indicate that the ACE2 expression may have distinct prognostic implications across different cancers, suggesting variable biological roles.

Fig. 3 — Diagnostic and prognostic value of ACE2 in multiple types of tumors. **(A)** Diagnostic performance of ACE2 in COAD, READ, PAAD, KIRP, and STAD cancers. **(B)** Survival curves showing the prognostic relevance of the ACE2 expression in CRC, KIRP, PAAD, and STAD.

Association between ACE2 expression and clinicopathological features of CRC

Based on the aforementioned research findings, CRC was selected as the focus of our study. Our preliminary analysis of ACE2 expression in CRC, using TCGA data, demonstrated a significant overexpression of ACE2 in CRC samples compared to normal intestinal epithelium (Fig. 4A). Additionally, using data from the GEO dataset (GSE1737), we analyzed 40 paired CRC and adjacent normal tissues, revealing significantly higher ACE2 mRNA expression in tumour tissues compared to adjacent normal tissues (p < 0.05, Fig. 4B). Furthermore, the relative mRNA expression levels of ACE2 in CRC cell lines (SW480, RKO, and CaCO-2) were upregulated than in normal colon cells (NCM460) (all p < 0.05, Fig. 4C).

Fig. 4 — ACE2 expression in CRC. **(A)** ACE2 expression in patients with CRC based on TCGA data. **(B)** ACE2 mRNA expression in CRC and corresponding normal tissues from the GSE31737 data set. **(C)** Relative mRNA expression levels of ACE2 in normal colon cells (NCM460) and colon cancer cell lines (SW480, RKO, and Caco-2) was quantified by qRT-PCR, NCM460 acts as the statistical comparison.(D) Evaluation of ACE2 protein expression in CRC tissues and adjacent normal tissues. **(E)** Immunohistochemical analysis of ACE2 in 119 CRC tissues and their adjacent normal tissues. *p < 0.05; ** p < 0.01.

To investigate the ACE2 protein expression in CRC, we used the UALCAN platform, which confirmed a significantly elevated expression of ACE2 protein in CRC tissues compared to normal tissues (Fig. 4D). We also collected 119 tumour samples and corresponding normal samples from postoperative patients with CRC at the Department of Pathology, Xiangya Hospital. Immunohistochemical staining was performed to validate the ACE2 expression, showing its predominant localisation in the cytoplasm with a brownish-yellow appearance in areas of high expression. Among these 119 patients with CRC, the ACE2 expression was significantly higher in tumour tissues compared to normal tissues, demonstrating statistical significance (Fig. 4E). We further analyzed the clinicopathological features associated with ACE2 expression in these 119 patients. According to Table 2, the ACE2 expression was significantly correlated with the T stage (p = 0.011); however, it did not show any significant association with other clinicopathological parameters, including age, gender, N stage, degree of differentiation, tumour diameter, perineural invasion, and venous invasion. Moreover, we utilized the Xiantao platform to evaluate the potential clinical role of ACE2 in CRC, and found that ACE2 expression was significantly higher in CRC stage IV than in stages I, II, and III (Fig. 5A). Furthermore, data retrieved from the CRN database confirmed that ACE2 expression across all pathological stages (I–IV) of colon cancer was elevated compared with that in normal tissues (Table 3).

Table 2.

Association between the expression of ACE2 and clinicopathological parameters of CRC.

Characteristics	N	Low expression of ACE2 (n = 38)	High expression of ACE2 (n = 81)	P value
Pathologic T stage				0.011*
T1&T2	9	6 (15.8%)	3 (3.7%)
T3&T4	110	32 (84.2%)	78 (96.3%)
Pathologic N stage				0.36
N0	60	22 (57.9%)	38 (46.9%)
N1	28	6 (15.8%)	22 (27.2%)
N2	31	10 (26.3%)	21 (25.9%)
Differentiation degree				0.530
Poor	25	7 (18.4%)	18 (22.2%)
Moderate	86	27 (71.1%)	59 (72.8%)
Well	8	4(10.5%)	4(5.0%)
Age				0.376
< 60	65	23 (60.5%)	42 (51.9%)
≥ 60	54	15 (39.5%)	39 (48.1%)
Gender				0.271
Female	62	17 (44.7%)	45 (55.6%)
Male	57	21 (55.3%)	36 (44.4%)
Diameter(cm)				0.078
< 5	58	23 (60.5%)	35 (43.2%)
≥ 5	61	15 (39.5%)	46 (56.8%)
Venous invasion				0.148
No	80	29 (76.3%)	51 (63.0%)
Yes	39	9 (23.7%)	30 (37.0%)
Perineural invasion				0.446
No	92	31 (81.6%)	61 (75.3%)
Yes	27	7 (18.4%)	20 (24.7%)

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Fig. 5 — Correlation between the ACE2 expression and pathological characteristics/prognosis in patients with CRC. **(A)** ACE2 expression across different pathological stages. **(B)** Expression level of ACE2 in CRC patients stratified by different treatment responses, including the Complete Response (CR), Partial Response (PR), Stable Disease (SD) and Progressive Disease (PD) groups. **(C)** Correlation between the ACE2 expression and overall survival (OS) in the GSE106584 dataset. **(D)** Correlation between the ACE2 expression and OS in the GSE28722 dataset. **(E)** Correlation between the ACE2 expression and recurrence-free survival (RFS) in the GSE106584 dataset. **(F)** Correlation between the ACE2 expression and RFS in the GSE28722 dataset.

Table 3.

The expression of ACE2 in TCGA COAD) RNA-seq dataset were analyzed by the Cancer RNASeq Nexus.

	ACE2 (Transcript ID: uc004cxb.2)
Colon adenocarcinoma subset pair	Average expression in cancer	Average expression in normal	Cancer versus Normal p-value
Colon adenocarcinoma––Stage I versus Normal (adjacent normal)	2.18	0.62	p < 0.05
Colon adenocarcinoma––Stage II versus Normal (adjacent normal)	1.05	0.62
Colon adenocarcinoma––Stage IIA versus Normal (adjacent normal)	2.17	0.62
Colon adenocarcinoma––Stage III versus Normal (adjacent normal)	1.38	0.62
Colon adenocarcinoma––Stage IIIA versus Normal (adjacent normal)	2.45	0.62
Colon adenocarcinoma––Stage IIIB versus Normal (adjacent normal)	4.20	0.62
Colon adenocarcinoma––Stage IV versus Normal (adjacent normal)	1.80	0.62

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Note: The Cancer RNASeq Nexus (CRN, http://syslab4.nchu.edu.tw/CRN) is an open resource for intuitive data exploration, providing coding-transcript/lncRNA expression profiles that was contained alternative splicing to support researchers generating new hypotheses in cancer research and personalized medicine.

Furthermore, we investigated the correlation between the level of ACE2 and the therapeutic response in CRC, including the Complete Response (CR), Partial Response (PR), Stable Disease (SD) and Progressive Disease (PD) groups. As shown in Fig. 5B, the level in the CR group was lower than that in the non-CR group (PR + SD+ PD) (p = 0.08). Prognostic analysis was further conducted using two GEO datasets (GSE106584 and GSE28722), both of which demonstrated a significant correlation between high ACE2 expression and shorter OS in patients with CRC (p = 0.0045, p = 0.013, respectively; Fig. 5C-D). Moreover, a higher ACE2 expression was associated with worse recurrence-free survival (p < 0.0001, p = 0.039, respectively; Fig. 5E-F).

Co-expression network, single-cell, and functional analysis of ACE2 in CRC

To explore the biological functions of ACE2 in CRC, we analyzed the ACE2 co-expressed genes using cBioPortal. After the primary screening (|log₂ fold-change| > 0.07, p < 0.05), we identified 226 differentially expressed genes (DEGs) (Supplementary Table 1), including the Mucin 2 (MUC2) and Chloride Channel Accessory 1 (CLCA1) genes, which were most closely associated with ACE2 expression (Fig. 6A). GO and KEGG pathway enrichment analysis of these DEGs indicated involvement in digestion, skeletal system morphogenesis, ion channel activity, and immune-related pathways such as the IL-17 signalling pathway (Fig. 6B).

Fig. 6 — ACE2 co-expression network analysis. **(A)** Co-expression network of ACE2 and associated genes, constructed using the STRING database, Metascape, and Cytoscape software. **(B)** Biological processes, molecular functions, and cellular components analyzed via Xiantao Academic. **(C)** KEGG pathway enrichment of the co-expressed genes, as analyzed using Xiantao Academic.

The analysis of individual cells enables a cellular-level investigation of common diseases, including their aetiology, progression, and therapeutic strategies. In this study, we explored the correlation between ACE2 expression and 14 distinct functional states across various tumour types. Our results revealed a positive association between ACE2 expression and factors such as inflammation, metastasis, and differentiation in CRC, while showing a negative correlation with stemness, quiescence, and the cell cycle (Fig. 7A). Figure 7B illustrates the specific correlation coefficients between ACE2 and these functional states, highlighting a significant positive correlation with CRC metastasis (r = 0.33). Furthermore, Fig. 7C presents a bar chart detailing ACE2 expression across 14 functional states in CRC. In addition, the expression profile of ACE2 at the single-cell level was analyzed (Fig. 7D).

Fig. 7 — ACE2 expression and its association with biological processes in cancer. **(A)** ACE2 expression is linked to various biological processes in multiple cancer types, including angiogenesis, apoptosis, cell cycle regulation, differentiation, DNA damage and repair, epithelial-mesenchymal transition (EMT), hypoxia, inflammation, invasion, metastasis, proliferation, quiescence, and stemness. **(B)** Investigation of the association between the ACE2 expression and biological processes in CRC. **(C)** Heatmap showing the ACE2 expression across different biological processes. **(D)** t-SNE plot of the ACE2 expression distribution in CRC.

tumor.

ACE2 knockdown suppresses the proliferation, and invasion of CRC cells

To explore the impact of ACE2 on the proliferation and invasion of CRC cells, we used specific siRNAs to knockdown the ACE2 expression. qRT-PCR results showed that compared with the si-NC group, ACE2 mRNA expression was significantly downregulated in the si-ACE2#1 and si-ACE2#2 group (p < 0.05), while no significant change was observed in the si-ACE2#3 group (Fig. 8A). After establishing siRNA efficacy, we assessed the effects of ACE2 knockdown in CRC cells. We found that knockdown ACE2 expression significantly inhibited the proliferation of CRC RKO and SW480 cells relative to control cells using the CCK-8 assay (p < 0.05, Fig. 8B). Subsequently, we explored the effects of ACE2 knockdown on the invasiveness of CRC cells. We found that knockdown ACE2 expression significantly inhibited the invasive capacity of CRC RKO and SW480 cells compared to control group cells using transwell matrigel assays (p < 0.05, Fig. 8C and D). To further elucidate the molecular mechanisms by which knockdown of ACE2 expression suppressed proliferation and invasion by CRC cells in vitro, we used qRT-PCR assay to assess mRNA levels of the proliferation markers CCNB1, CCND1, CCNE1, as well as the invasion markers Snail1, β-Catenin, and MMP9 in RKO cells. Knockdown ACE2 expression significantly inhibited expression of CCNB1, CCND1, CCNE1, Snail1, β-Catenin, and MMP9 (p < 0.05, Fig. 8E). Similarly, Western blotting assay also found that knockdown ACE2 expression significantly inhibited expression of CCND1, CCNE1, Snail1 and β-Catenin (p < 0.05, Fig. 8F).These findings suggested that ACE2 plays a significant role in CRC tumorigenesis.

Fig. 8 — Effects of ACE2 knockdown on CRC RKO and SW480 cells proliferation and invasion. After transfection of RKO cells with either si-NC or si-ACE2, **(A)** qRT-PCR analysis were used to detect the transfection efficiency, **(B)** CCK-8 assay were used to detect cell proliferative ability, **(C-D)** transwell matrigel assays were used to detect cell metastatic ability, **(E)** qRT-PCR assay were used to detect the mRNA expression of CCNB1, CCND1, CCNE1, Snail1, β-Catenin, and MMP9, **(F)** Western blotting assay were used to detect the protein expression of CCND1, CCNE1, Snail1 and β-Catenin. * p < 0.05; ** p < 0.01; *** p < 0.001.

Correlation between the ACE2 expression and methylation status in CRC

We also explored ACE2 gene alterations in CRC and found a mutation rate of 10% among 524 patients with CRC (Fig. 9A). Methylation analysis using UALCAN and MethSurv revealed significantly reduced methylation levels of ACE2 in the CRC tissues compared to normal tissues (p < 0.05, Fig. 9B). Furthermore, we treated RKO and SW480 cells with the DNA methyltransferase inhibitor 5‑Aza for 6 days. Then, qRT‑PCR analysis revealed a significant increase in ACE2 mRNA levels in both cell lines following 5‑Aza treatment compared to untreated controls (p < 0.05, Fig. 9C). These results demonstrated that 5‑Azacytidine‑induced DNA demethylation upregulates ACE2 gene expression, which indicated the expression of ACE2 is regulated by epigenetics in CRC cells.

To further explore the relationship between the expression of ACE2 and drug sensitivity of CRC, the GEPIA3 database were be used. The results demonstrated a negative correlation between ACE2 expression and the IC₅₀ (half-maximal inhibitory concentration) of Afatinib. Conversely, a positive correlation was observed between the ACE2 expression and the IC₅₀ of Sphingosine Kinase 1Inhibitor ll, GW-2580, JQ12, ETP-45,835, XMD14-99, Linifanib, Tivozanib, Foretinib, Ponatinib and FTI-277 (Fig. 9D).

Association between the ACE2 expression and immunogenetics in COAD

Growing evidence points to a strong association between immune cell infiltration and the initiation and progression of human tumours³⁷. To further investigate the relationship between ACE2 expression and immune regulation in COAD, we used the TISIDB database to examine the correlation between ACE2 and immune activators, immunosuppressive agents, and major histocompatibility complex (MHC) molecules. The heatmap analysis revealed an inverse correlation between ACE2 expression in COAD and most immune stimulants (Fig. 10A). Among these, C10orf54 (r = −0.361, p = 1.47e-15) and CXCR4 (r = −0.362, p = 1.19e-15) showed the strongest negative correlations (Fig. 10B). Furthermore, the ACE2 expression in COAD was negatively correlated with most immune suppressors (Fig. 10C), such as TGFB1 (r = −0.304, p = 3.55e-11) and HAVCR2 (r = −0.332, p = 2.83e-12) (Fig. 10D). A significant inverse relationship was also observed between the ACE2 expression and MHC molecules (Fig. 10E), with HLA-E (r = −0.295, p = 1.45e-10) exhibiting the strongest correlations (Fig. 10F). These findings suggest that ACE2 may play a role in immune function modulation within the COAD TME and can serve as a marker for the immune-related characteristics of COAD.

Fig. 10 — Association between the ACE2 expression and immune-related genes in CRC. **(A)** The ACE2 expression was associated with immune-stimulatory gene expression. **(B)** Two immune-stimulant factors most negatively correlated with ACE2 in CRC. **(C)** The ACE2 expression was associated with immunosuppressive factors. **(D)** Two immunosuppressive factors most negatively correlated with ACE2 in CRC. **(E)** The ACE2 expression was correlated with the major histocompatibility complex (MHC). **(F)** Two MHC factors most inversely correlated with ACE2 expression in CRC.

Correlation analysis between the ACE2 expression and NK cell infiltration in COAD

NK cells play a dual role in tumour immunity by engaging in cytotoxic activity and immune modulation, exerting innate cytotoxicity against tumour cells. We performed an in-depth analysis to assess the correlation between ACE2 expression and NK cell infiltration in COAD. The ACE2 expression showed a notable inverse correlation with NK cell infiltration (r = −0.252) (Fig. 11A). Additionally, both NK CD56bright and CD56dim cells demonstrated a negative correlation with ACE2 expression, mirroring the general trend seen in NK cells. Figure 11B highlights the significant association between the ACE2 expression and NK cell infiltration in COAD (p < 0.05). We also analyzed the relationship between ACE2 expression and NK cell markers using TIMER data, uncovering a significant inverse correlation with six NK cell markers: KLRD1, KIR2DL1, KIR2DL3, KIR2DL4, KIR3DL1, and KIR3DL2 (Fig. 11C).

Fig. 11 — Relationship between ACE2 and immune Cells in CRC. **(A)** Correlation between the ACE2 expression and various immune cell types in CRC. **(B)** Heatmap showing the ACE2 expression and its association with immune cell populations in CRC. **(C)** Correlation between the ACE2 expression and NK cell markers. **(D)** ACE2 expression levels in 25 CRC tissues cases and adjacent tissues. **(E)** CD94 expression levels in the same CRC samples. **(F)** Scatter plot depicting the correlation between ACE2 and CD94 expression in the 25 CRC samples.

To further validate these results, we analyzed 25 paired colon cancer and adjacent normal tissue samples using qRT-PCR. The results demonstrated significantly higher ACE2 expression in tumours compared to normal tissues (p = 0.0173, Fig. 11D). However, the CD94 (also named as KLRD1) expression displayed the opposite trend (p = 0.0305, Fig. 11E). A subsequent correlation analysis revealed a negative association between ACE2 and CD94 expression (r = −0.4049, p = 0.0446, Fig. 11F). These findings suggest that ACE2 may contribute to anti-tumour immune responses by recruiting immune effector cells, such as CD8 + T cells, through the activity of NK cells, and may serve as a molecular marker for immunotherapy.

Analysis of the ACE2 expression in relation to immunotherapy

The tumour mutational burden (TMB), microsatellite instability (MSI), and neoantigen load (NEO) are known to be associated with anti- tumour immunity and are important predictive factors for the effectiveness of immunotherapy³⁸. We investigated the potential association between the ACE2 expression and these factors. The analysis revealed a significant negative correlation between the ACE2 expression and TMB in COAD (Fig. 12A). The MSI is an important prognostic marker and predictor of adjuvant chemotherapy efficacy in CRC. Our findings revealed a significant inverse correlation between ACE2 and MSI in both COAD and CRC (Fig. 12B). Moreover, the ACE2 expression was negatively correlated with the NEO in most tumors, with the strongest negative correlation observed in COAD (Fig. 12C). We also examined the association between ACE2 expression and the ESTIMATE score, which indicated a strong negative correlation in COAD (Fig. 12D). These findings provide valuable insights into the potential role of ACE2 in COAD development and therapeutic targeting.

Fig. 12 — ACE2 and its association with the tumour mutational burden, microsatellite instability, neoantigen load, and immune checkpoint therapy response. **(A)** Correlation between the ACE2 expression and tumour mutational burden in various cancers. **(B)** Correlation between the ACE2 expression and microsatellite instability. **(C)** Relationship between the ACE2 expression and neoantigen load. **(D)** Radar chart illustrating the association between ACE2 expression and ESTIMATE scores across the cancer types. **(E)** Correlation between ACE2 expression and immunotherapeutic response in Melanoma. **(F)** Correlation between ACE2 expression and immunotherapeutic response in urothelial cancer.

Furthermore, regarding the association between anti-CTLA-4 immunotherapy and ACE2, we further explored the publicly available data on the BEST website. Among them, the data from the Van Allen 2015 cohort served as key evidence, indicating that the expression level of ACE2 in melanoma patients receiving anti-CTLA-4 immunotherapy was correlated with prognosis. As shown in Fig. 12E, Post-treatment, the ACE2 levels were notably reduced in the treatment group compared to the control group. The ROC curve demonstrated improved diagnostic performance following anti-CTLA-4 therapy (AUC = 0.741), and patients with lower ACE2 expression exhibited longer progression-free survival (PFS). Similarly, the analysis of IMvigor210 dataset demonstrated that anti-PDL-1 therapy patients with lower ACE2 expression exhibited longer OS (Fig. 12F). These results suggest that ACE2 may hold significant value in guiding tumor immunotherapy and provide a meaningful direction for subsequent studies on ACE2’s role in CRC immunotherapy.

Discussion

Ang II is a key bioactive peptide in the RAS, known for its vasodilatory effects, blood pressure regulation, and cardiovascular protection. Beyond these, it also modulates critical physiological processes such as insulin secretion, oxidative stress, and inflammatory responses³⁹. Recent studies underscore the pivotal role of ACE2 in tumour progression, metastasis, and therapeutic responses⁴. In thyroid cancer, the ACE/ACE2 ratio is inversely associated with differentiation, where higher-grade tumours exhibit lower ACE/ACE2 ratios¹². In lung cancer, elevated ACE2 expression may inhibit endothelial cell proliferation and tube formation by converting Ang II to Ang-(1–7)⁴⁰. Furthermore, aberrant ACE2 expression in breast cancer can regulate vascular endothelial growth factor (VEGF)-a expression, thereby hindering angiogenesis associated with tumour growth⁷. ACE2 not only influences tumour angiogenesis but also alters the signalling within the TME, impacting tumour cell interactions and progression. For instance, in lung cancer, elevated ACE2 expression modulates E-cadherin expression, reducing metastasis and affecting vimentin expression during epithelial-mesenchymal transition⁴¹. Notably, ACE2 expression is highly upregulated in CRC, with particularly elevated levels observed in CRC lung metastases compared to normal lung tissue⁴². Our study confirmed high ACE2 expression in CRC through bioinformatics analysis and immunohistochemistry, showing a correlation between ACE2 levels and tumour histological subtype and staging. Additionally, ACE2 was associated with poorer OS and recurrence-free survival, suggesting its potential as a prognostic marker in CRC. Meanwhile, in vitro experiments have demonstrated that ACE2 facilitates the proliferation and invasion of CRC cells, which indicate the ACE2 may be served as a potential therapeutic target in CRC.

Immune cells play a crucial role in the TME, contributing to tumour immune evasion and tolerance^43–45. Numerous studies have demonstrated significant differences in immune cell infiltration between colon cancer tissues and adjacent normal tissues, characterised by extensive heterogeneity. ACE2 is an important regulator of the TME. Previous studies have reported a negative correlation between ACE2 expression and immune cell infiltration, particularly with neutrophils and macrophages⁴⁶. Cheng et al.⁴⁷ demonstrated that ACE2 overexpression could suppress VEGF synthesis in the TME, inhibiting tumour invasion and inflammatory responses. In our study, we further explored the relationship between ACE2 expression and immune infiltration in colon cancer, revealing a strong correlation. Specifically, ACE2 exhibited a negative association with CD8 + T cells, neutrophils, and NK cells while showing a positive correlation with CD4 + T cells. Additionally, the observed negative correlation between ACE2 and NK cells infiltration suggests that ACE2 may hinder anti-tumour immunity by reducing NK cells activity. These findings highlight the critical role of ACE2 in modulating immune responses in the TME.

Because the TME plays a critical role in the progression of disease and response to immunotherapy^44,48, we further explore the relationship between the expression of ACE2 and tumour immunotherapy. Cytotoxic T-lymphocyte antigen 4 (CTLA-4) immunotherapy is a therapeutic modality that activates the immune system to attack cancer cells by blocking the receptor for CTLA-4⁴⁹. This receptor exerts an immunosuppressive effect on the surface of T-cells and inhibits T-cell activation by binding to B7 molecules. Anti-CTLA-4 antibodies (e.g. Ipilimumab) inhibit this process, thereby enhancing the T-cell immune response and improving anti-tumor effects. Recent studies have indicated that ACE2 may interact with CTLA-4 in immune regulation. In the tumor microenvironment, alterations in ACE2 expression may influence the function of CTLA-4 and, consequently, the immune escape mechanism. Consequently, investigating the relationship between ACE2 and CTLA-4 represents a promising avenue for research in immunotherapy for colorectal cancer patients. In our study, we revealed that high ACE2 expression in patients who were unresponsive to CTLA-4 therapy was associated with a shorter PFS, and anti-PDL-1 therapy patients with lower ACE2 expression exhibited longer OS. These results indicated that ACE2 may serve as a valuable biomarker for predicting immunotherapy efficacy and prognosis in CRC.

Conclusions

This study provides an important preliminary investigation into the relationship between the ACE2 expression and immune cell infiltration in CRC. We aimed to elucidate the pivotal role of ACE2 in cancer progression and the immune response in patients with CRC. The findings indicate that ACE2 holds potential as a promising biomarker and therapeutic target, offering opportunities for enhanced diagnostic and prognostic strategies that could improve clinical outcomes for patients with CRC.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1^{(28.4KB, xlsx)}

Supplementary Material 2^{(318.7KB, docx)}

Acknowledgements

The authors would like to thank TCGA projects and KEGG platform for providing their platform and contributors for uploading their meaningful datasets.

Abbreviations

ACE2: Angiotensin converting enzyme 2
AngI: Angiotensin I
AngII: Angiotensin II
RAS: Renin-angiotensin system
CCLE: Cancer Cell Line Encyclopedia
TCGA: The Cancer Genome Atlas
PPI: protein protein interaction
GO: Gene ontology
KEGG: Kyoto Encyclopedia of Genes and Genomes
TIMER: Tumor Immune Estimation Resource
TAMs: Tumor-associated macrophages
BEST: The Biomarker exploration of solid tumors
IHC: Immunohistochemistry
qRT-PCR: Quantitative real-time polymerase chain reaction
TME: Tumor microenvironment
GDSC: Cancer Drug Sensitivity Genomics
IC₅₀: Semi-maximum inhibitory concentration
PD-1: Programmed death 1
MSI-H: Microsatellite instability high
MSI-L: Microsatellite instability low

Author contributions

Guoqian Liu and Xiaoqian Yu conducted experimental operations, sample processing, and data analysis, and performed the experiments. All authors participated in writing the paper. Hui Nie and Chunlin Ou conceived and designed the experiments. All authors read and approved the final manuscript.

Funding

This study was supported by the Central South University Innovation-Driven Research Programme (2023CXQD075).

Data availability

The original datasets used in this study are available in the TCGA (https://portal.gdc.cancer.gov/) and Gene Expression Omnibus repository (https://www.ncbi.nlm.nih.gov/geo). The analyzed data sets generated during the research are available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics declarations

This retrospective study was carried out using the opt-out method for the case series of our hospital. We have confirmed that all experiments were conducted in accordance with the relevant guidelines and regulations. This study was approved by the Ethics Committee of Xiangya Hospital (Approval No. 202308166) and was conducted in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was waived by our Institutional Review Board (Medical Ethics Committee of Xiangya Hospital, Central South University) because of the retrospective nature of our study. Research still conducts to comply with ethical norms.

Footnotes

Publisher’s note

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

These authors contributed equally to this work: Guoqian Liu and Xiaoqian Yu.

These authors jointly supervised this work: Chunlin Ou and Hui Nie.

Contributor Information

Hui Nie, Email: nieh0521@163.com.

Chunlin Ou, Email: ouchunlin@csu.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1^{(28.4KB, xlsx)}

Supplementary Material 2^{(318.7KB, docx)}

Data Availability Statement

[CR1] 1."Acharya, K. R. et al. Advances in the structural basis for angiotensin-1 converting enzyme (ACE) inhibitors.Bioscience Reports. 44(8) ,BSR20240130 (2024). [DOI] [PMC free article] [PubMed]

[CR2] 2.Gheblawi, M. et al. Angiotensin-Converting Enzyme 2: SARS-CoV-2 Receptor and Regulator of the Renin-Angiotensin System. Circul. Res.126 (10), 1456–1474 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR3] 3.Domińska, K. Involvement of ACE2/Ang-(1–7)/MAS1 Axis in the Regulation of Ovarian Function in Mammals. International J. Mol. Sciences21(13) 4572 (2020). [DOI] [PMC free article] [PubMed]

[CR4] 4.Rizopoulos, T. et al. Angiotensin-converting enzyme 2 expression in human tumors: Implications for prognosis and therapy (Review). Oncol. Rep.54 (3), 1–18 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR5] 5.Perlot, T. et al. ACE2 – From the renin–angiotensin system to gut microbiota and malnutrition. Microbes Infect.15 (13), 866–873 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR6] 6.Yang, J-K. et al. Binding of SARS coronavirus to its receptor damages islets and causes acute diabetes. Acta Diabetol.47 (3), 193–199 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR7] 7.Zhang, Q. et al. ACE2 inhibits breast cancer angiogenesis via suppressing the VEGFa/VEGFR2/ERK pathway. Journal Experimental & Clin. Cancer Research38(1),173 (2019). [DOI] [PMC free article] [PubMed]

[CR8] 8.Chen, S. et al. Causal linkage between angiotensin-converting enzyme 2 and risk of lung cancer: a bidirectional two-sample Mendelian randomization study. Frontiers Medicine111419612 (2024). [DOI] [PMC free article] [PubMed]

[CR9] 9.Jedrzej Grzegrzolka, K. S. ACE and ACE2 expression in normal and malignant skin lesions. Folia Histochem. Cytobiol50(3 ) 232-8 (2013). [DOI] [PubMed]

[CR10] 10.Xu, J. et al. The ACE2/Angiotensin-(1–7)/Mas Receptor Axis: Pleiotropic Roles in Cancer. Frontiers Physiology8276 (2017). [DOI] [PMC free article] [PubMed]

[CR11] 11.Xu, H. et al. High expression of ACE2 receptor of 2019-nCoV on the epithelial cells of oral mucosa. International J. Oral Science12(1) 8 (2020). [DOI] [PMC free article] [PubMed]

[CR12] 12.Narayan, S. S. et al. Angiotensin converting enzymes ACE and ACE2 in thyroid cancer progression. Neoplasma67 (02), 402–409 (2020). [DOI] [PubMed] [Google Scholar]

[CR13] 13.Bernardi, S. et al. Characterization and significance of ACE2 and Mas receptor in human colon adenocarcinoma. J. Renin-Angiotensin-Aldosterone Syst.13 (1), 202–209 (2011). [DOI] [PubMed] [Google Scholar]

[CR14] 14.Siegel, R. L. et al. Cancer statistics, CA: Cancer J. Clin.75(1), 10–45(2025). [DOI] [PMC free article] [PubMed]

[CR15] 15.Zhou, M. et al. Plasma-derived exosomal tRF-3004a as a diagnostic biomarker for colorectal cancer. Scientific Reports15(1) 45558 (2025). [DOI] [PMC free article] [PubMed]

[CR16] 16.Zhang, Y. et al. Associations between pathological features and risk of metachronous colorectal cancer. Int. J. Cancer. 155 (6), 1023–1032 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR17] 17.Zheng, W. et al. Increasing coverage in cervical and colorectal cancer screening by leveraging attendance at breast cancer screening: A cluster-randomised, crossover trial. PLOS Medicine21(8) ,e1004431 (2024). [DOI] [PMC free article] [PubMed]

[CR18] 18.Hang, D. et al. Development and evaluation of a risk prediction tool for risk-adapted screening of colorectal cancer in China. Cancer Letters597217057 (2024). [DOI] [PubMed]

[CR19] 19.Li, J. et al. A phase IV study to evaluate the safety of fruquintinib in Chinese patients in real-world clinical practice. Oncologist29 (8), e1012–e1019 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR20] 20.Tamraz, M. et al. Optimization of colorectal cancer screening strategies: New insights. World J. Gastroenterol.30 (28), 3361–3366 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR21] 21.Qiao, X. et al. Discovery of novel and potent dual-targeting AXL/HDAC2 inhibitors for colorectal cancer treatment via structure-based pharmacophore modelling, virtual screening, and molecular docking, molecular dynamics simulation studies, and biological evaluation. Journal Enzyme Inhib. Med. Chemistry39(1) 2295241 (2023). [DOI] [PMC free article] [PubMed]

[CR22] 22.Tang, Z. et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res.45 (W1), W98–W102 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR23] 23.Liang, Y. et al. In silico analyses of pan-squamous cell carcinoma unveiled the immunological implications of MRPL13, which had previously been under-recognized. Heliyon10(1) e23582 (2024). [DOI] [PMC free article] [PubMed]

[CR24] 24.Chandrashekar, D. S. et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia19 (8), 649–658 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR25] 25.Li, J-R. et al. Cancer RNA-Seq Nexus: a database of phenotype-specific transcriptome profiling in cancer cells. Nucleic Acids Res.44 (D1), D944–D951 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR26] 26.Yang, X. et al. Comprehensive analysis of prognostic value and immune infiltration of IAPs family in hepatocellular carcinoma. J. Cancer. 14 (15), 2848–2866 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR27] 27.Cerami, E. et al. The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data. Cancer Discov.2 (5), 401–404 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR28] 28.Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res.51 (D1), D638–D646 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR29] 29.Puig, R. R. et al. Network Building with the Cytoscape BioGateway App Explained in Five Use Cases. Current Protocols Bioinformatics72(1) e106 (2020). [DOI] [PubMed]

[CR30] 30.Yuan, H. et al. CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res.47 (D1), D900–D908 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Angiotensin‑converting enzyme 2 as an immune and prognostic biomarker in colorectal cancer

Guoqian Liu

Xiaoqian Yu

Wenying Jiang

Xiaoyun He

Hui Nie

Chunlin Ou

Abstract

Supplementary Information

Introduction

Materials and methods

Expression analysis of ACE2 in pan-cancer

Prognostic analysis of ACE2 in pan-cancer

Enrichment analysis

Functional analysis of ACE2 at the single-cell level

Methylation analysis

Immune infiltration analysis

Tissue samples

Immunohistochemistry (IHC)

Cell culture and transfection

Isolation of RNA from formalin-fixed and paraffin-embedded samples

Quantitative real-time polymerase chain reaction (qRT-PCR)

Table 1.

Transwell invasion assay

5-Azacitidine treatment

CCK-8 cell proliferation assay

Western blot

Statistical analysis

Results

Differential expression of ACE2 in pan‑cancer analysis

Fig. 1.

Fig. 2.

Diagnostic and prognostic value of ACE2 in pan-cancers

Fig. 3.

Association between ACE2 expression and clinicopathological features of CRC

Fig. 4.

Table 2.

Fig. 5.

Table 3.

Co-expression network, single-cell, and functional analysis of ACE2 in CRC

Fig. 6.

Fig. 7.

ACE2 knockdown suppresses the proliferation, and invasion of CRC cells

Fig. 8.

Correlation between the ACE2 expression and methylation status in CRC

Fig. 9.

Association between the ACE2 expression and immunogenetics in COAD

Fig. 10.

Correlation analysis between the ACE2 expression and NK cell infiltration in COAD

Fig. 11.

Analysis of the ACE2 expression in relation to immunotherapy

Fig. 12.

Discussion

Conclusions

Supplementary Information

Acknowledgements

Abbreviations

Author contributions

Funding

Data availability

Declarations

Competing interests

Ethics declarations

Footnotes

Contributor Information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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