ARID1A regulates colorectal cancer metastasis through the AKT/mTOR/p70S6K pathway

Highlights

• •ARID1A downregulation promotes invasion and metastasis in colorectal cancer.
• •Knockdown of ARID1A is associated with changes in autophagy-related markers in colorectal cancer cells.
• •The AKT/mTOR/p70S6K signaling axis mediates the progression of colorectal cancer induced by ARID1A knockdown.

Abstract

Background

Colorectal cancer (CRC) is the most common malignant tumor of the digestive system. Exploring effective diagnostic markers and therapeutic targets has great significance for the diagnosis and treatment of CRC. ARID1A is frequently mutated in CRC, but its prognostic value for this disease remains controversial. Therefore, our aim was to explore the biological role of ARID1A in CRC and the underlying molecular mechanisms.

Methods

The clinical relevance of ARID1A in CRC was evaluated using integrating multiple biological databases and a clinical cohort. In vitro and in vivo functional assays (CCK-8, colony formation, transwell, and xenograft models) assessed its tumor-modulating effects. Autophagy was examined via transmission electron microscopy and immunofluorescence, while western blotting quantified epithelial-mesenchymal transition, autophagy-related, and signaling pathway proteins.

Results

Based on CRC mutation data from TCGA-COAD and cBioPortal, the ARID1A mutation frequency was 14% and 16%, respectively. Survival analysis showed that the low-ARID1A-expression group had a poorer prognosis than the high-expression group. In vitro and in vivo studies showed that downregulation of ARID1A promotes various malignant biological behaviors of CRC cells, including proliferation, invasion, and metastasis. These phenotypic changes were accompanied by alterations in autophagy-related markers (such as decreased Beclin1 and LC3B, and increased p62) and activation of the AKT/mTOR/p70S6K signaling pathway. In addition, analyses of clinical samples indicated that low ARID1A expression is an independent risk factor for CRC recurrence and metastasis.

Conclusion

In summary, ARID1A has a tumor-suppressive role in CRC. These data suggest that ARID1A expression is a potential biomarker for CRC, which could lead to novel clinical diagnostic and treatment approaches.

Introduction

Currently, colorectal cancer (CRC) is the third most common malignant tumor worldwide. According to global cancer data statistics, in 2020, there were >1.9 million newly diagnosed cases of CRC and 935,000 associated deaths, accounting for approximately one-tenth of all cancer cases and ranking as the second leading cause of cancer-related mortality worldwide [1]. Among digestive tract tumors, CRC is the most common malignancy imposing a huge global disease burden. Although colonoscopy and stool tests have improved early screening rates, unfortunately, many patients are diagnosed at middle or late stages, with 20% already presenting with distant metastases. During the disease course, another 40% of patients are likely to develop distant metastases, primarily due to the distinct biological underpinnings of different CRC subtypes [[2], [3], [4]]. Moreover, CRC can spread via lymphatic, hematogenous, and peritoneal routes, with the most common metastatic sites being the regional lymph nodes, liver, lungs, and peritoneum [5]. CRC occurrence and metastasis is a complex, multifactorial, and multistage process. While comprehensive treatments, including surgery, chemotherapy, targeted therapy, and immunotherapy, have improved survival rates for patients with CRC, some patients fail to benefit from these therapies, possibly due to underlying genomic variations. Therefore, the development of CRC biomarkers might help overcome these limitations.

The switch/sucrose non-fermenting (SWI/SNF) complex is the most commonly mutated chromatin remodeling factor in tumors, with an estimated mutation frequency of 20% in various cancers [6]. The AT-rich interaction domain protein 1A (ARID1A), also known as BAF250a, is a core member of this complex. Its gene is located on chromosome 1 at position 1p35.3 in humans. Furthermore, the encoded protein is primarily localized in the nucleus, with a relative molecular mass of approximately 240 kDa [7]. ARID1A is mutated in various tumors, including ovarian cancer, gastric cancer, breast cancer, CRC, and lung cancer, resulting in the loss of protein expression [[8], [9], [10], [11], [12]]. Although the ARID1A mutation rate is high in patients with CRC, its prognostic value in this context remains controversial [13,14]. One study by Wei et al. found that among 209 primary CRC tumor samples, 54 cases (25.8%) exhibited ARID1A protein loss, and these patients had a significantly higher rate of distant metastasis than those without ARID1A loss [14]. However, some studies have found that ARID1A expression is downregulated in CRC tissues but that this downregulation is not significantly correlated with clinical pathological features or prognosis [13]. Moreover, current research indicates that ARID1A loss promotes CRC cell invasion and metastasis, but the specific mechanisms underlying this effect remain unclear [15]. Therefore, the aim of this study is to explore the biological role of ARID1A in CRC and the molecular mechanisms underlying its effects on invasion and metastasis, intending to provide new insights for clinical diagnosis and treatment.

Materials and methods

Analyses of public datasets

The mutation data for colon adenocarcinoma (COAD) were obtained from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/) and cBioPortal (https://www.cbioportal.org/) databases. The COAD genomic mutation landscape was visualized using the R package “GenVisR” [16]. The GSE17538 dataset was obtained from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) database, comprising data from 232 patients with COAD and six patients with adenoma. The patients with cancer were included in the subsequent analysis. Patients were stratified into high- and low-expression groups according to the median ARID1A expression, and survival analysis was performed using the R package “survival.” To assess the predictive performance of ARID1A expression for 1-, 3-, and 5-year survival in CRC, we constructed time-dependent receiver operating characteristic (ROC) curves based on the Kaplan–Meier method, implemented using the R package “timeROC”. At each prespecified time point (1, 3, and 5 years), sensitivity and 1 − specificity were calculated, and the area under the curve (AUC) was used to quantify the predictive accuracy. Univariate and multivariate analyses were performed to analyze the predictive capability of gene expression and clinicopathological features for prognosis and survival. In parallel, the association between gene expression and CRC prognosis was evaluated using the Kaplan–Meier Plotter (KM plotter) (https://kmplot.com/analysis/index.php?p=background) online tool, with the optimal expression cutoff applied to stratify patients. The “limma” package of R software was used to screen the differentially expressed genes (DEGs) between the ARID1A-high and ARID1A-low groups, and the “ggplot2” and “pheatmap” packages were used to draw the volcano map and heatmap of the DEGs, respectively. The SangerBox (http://sangerbox.com) online tool was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of differential genes from the GEO dataset and co-expressed proteins from the cBioPortal. The correlations between genes were analyzed using the GEPIA2 (http://gepia2.cancer-pku.cn/) database.

Cell culture and construction of stable cells

The CRC cell line HCT116 was purchased from Pricella (Wuhan, China) and cultured in Dulbecco’s modified Eagle’s medium containing 10% fetal bovine serum (FBS) and 1% P/S (100 IU/ml penicillin and 100 IU/ml streptomycin) at 37 °C in a humidified atmosphere of 5% CO₂. The CRC cell line HCT15 was provided by the Cancer Institute of Qingdao University (Qingdao, China), and cultured in RPMI-1640 medium containing 10% FBS and 1% P/S at 37 °C in a humidified atmosphere of 5% CO₂. Control lentivirus sequences and ARID1A-interfering lentivirus sequences were packaged by GeneChem (Shanghai, China). Viral sequences are listed in Supplementary Table 1. The brands and catalog numbers of the reagents required in the experiment are shown in Supplementary Table 2.

Cell proliferation assay

After successfully constructing the stably transfected cell lines, cell suspensions were prepared at a concentration of 3000 cells/100 µL and seeded into a 96-well plate for incubation. At 24, 48, 72, and 96 h, 10 µL of CCK-8 reagent was added to each well and incubated for an additional hour. The absorbance at 450 nm was then measured using a microplate reader (ALLSHENG, AMR-100, Hangzhou, China).

Cell colony-formation assay

Cell suspensions were prepared from the stably transfected cells, and 1000 cells per well were seeded into a 6-well plate and incubated for 10–14 days. Once colonies appeared, the cells were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet and the results were documented by taking photographs of the samples.

Scratch wound-healing assay

The stably transfected cells were seeded into 6-well plates at a density of 1 × 10⁶ cells per well. When the cells reached full confluence, a scratch was made vertically from top to bottom on the bottom of the well using a 200 µL pipette tip. After washing three times, serum-free medium was added. The width of the scratch was documented using an inverted microscope at 0 h and again at 24 or 48 h. Relative wound healing (%) was calculated using the standard formula: [(Wound width at 0 h – Wound width at 24 h) / Wound width at 0 h] × 100%.

Transwell migration and invasion assays

For the migration assay, 5 × 10⁴ cells were seeded in the upper chamber of a transwell chamber. For the invasion assay, the upper chamber was pre-coated with Matrigel, into which 1 × 10⁵ cells were seeded. The subsequent steps were similar to those in the migration assay. Cells in the upper chamber were cultured in serum-free medium, whereas the lower chamber contained complete medium with 10% FBS. After 48 h of incubation, cells that had migrated through the membrane pores were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet, and the number of migrated/invaded cells was observed and recorded using an inverted microscope.

Western blot (WB) analysis

Total protein was extracted from the cells using RIPA lysis buffer containing protease and phosphatase inhibitors, and the protein concentration was determined using the BCA method (Beyotime Biotechnology, Shanghai, China). Equal amounts of protein lysates were separated via sodium dodecyl sulfate polyacrylamide gel electrophoresis, according to molecular weight, and subsequently transferred onto a 0.22 μm nitrocellulose membrane. The membrane was blocked with 5% skim milk dissolved in TBST, followed by incubation with primary and secondary antibodies. Information on the primary antibodies is listed in Supplementary Table 3. According to the manufacturer’s instructions, the results were obtained using ECL reagents, and the expression of the target proteins was assessed using ImageJ software.

Reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR)

Total RNA was extracted using TRIzol reagent, and reverse transcription of the total RNA was performed using the PrimeScript™ RT reagent kit (Takara, RR047A, Takara Bio Inc., Shiga, Japan). The expression of mRNA was detected via RT-qPCR using TB Green® Premix Ex Taq™ II (Takara, RR820A). The primers used in this study are listed in Supplementary Table 4.

Immunofluorescence (IF) assay

The cells were fixed with 4% paraformaldehyde for 20 min, permeabilized with 0.5% Triton X-100 for 20 min, and blocked with 5% BSA at 20 °C for 1 h. The cells were then incubated with the primary antibody at 4 °C overnight, followed by incubation with the secondary antibody at room temperature for 1 h. The nuclei were stained with 4, 6-diamino-2-phenylindole, and the cells were observed under a fluorescence microscope. The antibodies used in the IF assay are listed in Supplementary Table 3.

Transmission electron microscopy (TEM)

The cells were pre-fixed with 2.5% glutaraldehyde and post-fixed with 1% osmium tetroxide, followed by gradient dehydration with ethanol and acetone. Ultrathin slices were stained with uranyl acetate and lead citrate. Observation and imaging were performed using a transmission electron microscope (JEOL, JEM1200, Tokyo, Japan).

In vivo xenograft models

Four-week-old female BALB/c nude mice were acclimatized for 1 week in a specific pathogen-free-grade animal facility. For the subcutaneous xenograft model, HCT116^shControl and HCT116^shARID1A cells were injected subcutaneously into the nude mice (5 × 10⁶ cells/100 μL/mouse). The lengths and widths of the subcutaneous tumors were measured every 2–3 days, and the tumor volume was calculated using the following formula: tumor volume = (length × width²) / 2. After 21 days, the mice were euthanized, and the subcutaneous tumors were excised and weighed. For the lung metastasis model, HCT116^shControl and HCT116^shARID1A cells (5 × 10⁶ cells/100 µL/mouse) were injected into nude mice via the tail vein. After 35 days, the mice were euthanized, and the lungs were excised, fixed in 4% paraformaldehyde, and embedded in paraffin for hematoxylin and eosin staining. The animal experiments were reviewed and approved by the Laboratory Animal Welfare Ethics Committee of Qingdao University (No. 20230914BALB/cNu2420240204055).

Patient samples

This study included 98 patients with CRC who underwent surgery in our hospital from January 2015 to July 2018. Clinical information was collected for each patient, including their sex, age, stage, vascular cancer emboli, and other relevant factors. Slides, for immunohistochemistry (IHC), were collected to assess ARID1A expression, and patients were followed up for progression-free survival (PFS) after surgery. The study protocol was approved by the Ethics Committee of the Affiliated Hospital of Qingdao University (QYFYWZLL29197).

IHC assay

IHC staining was performed according to the previously described steps [17]. All slides were examined and graded by two pathologists. A composite score was calculated based on the “percentage score × intensity score.” The percentage of positive staining was scored as follows: < 5% = 0, 5–25% = 1, 26–50% = 2, 51–75% = 3, 76–100% = 4. The staining intensity was scored as follows: negative = 0, weak = 1, intermediate = 2, and strong = 3. The antibodies used are listed in Supplementary Table 3.

Statistical analysis

Graphs were created using R software (version 4.2.1), Adobe Photoshop (2021), and GraphPad Prism (version 8.0). The Kaplan–Meier method was used to analyze survival differences between high-ARID1A and low-ARID1A expression groups, with the log-rank test employed to determine the significance of these differences. Pearson correlation coefficients were used to analyze the correlations. The experimental data were expressed as means ± standard deviation (SD). Data from cell assays were obtained from at least three independent experiments. For comparisons between two groups, an unpaired Student’s t-test was used. For comparisons involving three or more groups, one-way ANOVA was performed, followed by Tukey’s post hoc test for multiple comparisons. P < 0.05 was considered statistically significant.

Results

ARID1A is frequently mutated in CRC

We first analyzed mutation data using the TCGA-COAD patient cohort, and the ARID1A mutation frequency in CRC was 14% (Fig. 1A). Additionally, among members of the SWI/SNF family, ARID1A was most frequently mutated (Fig. 1B). In the cBioPortal-COAD dataset, the ARID1A mutation frequency was 16%, with truncating mutations being the predominant type (Fig. 1C). Subsequently, we analyzed the relationship between ARID1A mutations and protein expression, revealing that protein expression levels in the ARID1A-mutated group were lower than those in the non-mutated group (Fig. 1D). Next, to explore the relationship between ARID1A and CRC prognosis, we conducted a survival analysis using TCGA – Firehose Legacy cohort in cBioPortal. The result showed that overall survival (OS) was shorter in the ARID1A driver altered group (42.87 vs. 83.18 months), although the difference was not statistically significant (P = 0.946) (Fig. S1A). The disease-free survival was significantly reduced in the driver altered group compared with the wild-type group (25.95 vs. 84.23 months, P = 0.0136) (Fig. S1B). Given that most pathogenic ARID1A mutations result in loss or reduced expression of the ARID1A protein, we further investigated the association between ARID1A expression and prognosis in patients with CRC using the K-M plotter online tool. Overall survival (97 months vs 134.86 months, P = 0.029) and post-progression survival (PPS) (21 months vs 35 months, P < 0.001) were significantly shorter in the low-ARID1A expression group than in the high expression group (Fig. 1E, 1F). In addition, we screened a COAD dataset from the GEO database, with baseline information presented in Table 1. Here, low ARID1A expression was associated with later clinical stages (Table 1), and patients with low ARID1A expression had a poorer OS (low vs high: 67.82 months vs undefined, P = 0.014) (Fig. 1G). Moreover, the ROC curves indicated that ARID1A has good predictive value for the 1-, 3-, and 5-year survival rates of patients with CRC, with AUCs of 0.7511, 0.6904, and 0.6997, respectively (Fig. 1H). Furthermore, univariate and multivariate analyses showed that clinical stage (P < 0.001) and ARID1A expression (P = 0.028) were independent prognostic factors for patients with CRC (Table 2).

Fig. 1.

Mutation frequency of ARID1A in colorectal cancer (CRC) and its association with prognosis. A: Genomic mutation profile of CRC in the TCGA dataset. B: Mutation frequency of SWI/SNF family members in CRC within the TCGA dataset. C: The mutation frequency of ARID1A in CRC within the cBioPortal dataset. D: The relationship between ARID1A mutation and protein expression in the cBioPortal dataset. E: The overall survival (OS) curves of CRC patients with high and low ARID1A expression in the Kaplan-Meier Plotter database. F: The post-progression survival (PPS) curves of CRC patients with high and low ARID1A expression in the Kaplan-Meier Plotter database. G: The OS curves of CRC patients with high and low ARID1A expression in the GEO—COAD dataset. H: The ROC curves for predicting 1-, 3-, and 5-year survival in CRC patients based on ARID1A in the GEO—COAD dataset.

Table 1.

The clinical baseline information of patients in the GEO—COAD dataset.

Clinical parameters	COAD		ARID1A expression		P value
			High	Low
Age	<65	110 (47.4%)	57 (24.6%)	53 (52.8%)	0.6008
	≥65	122 (52.6%)	59 (25.4%)	63 (27.2%)
Sex	Male	122 (52.6%)	66 (28.5%)	56 (24.1%)	0.1901
	Female	110 (47.4%)	50 (21.5%)	60 (25.9%)
Stage	I	28 (12.1%)	19 (8.2%)	9 (3.9%)	0.0441
	II-IV	204 (87.9%)	97 (41.8%)	107 (46.1%)

Table 2.

Univariate and multivariate survival analyses of the GEO—COAD dataset.

Characteristics	Total(N)	Univariate analysis		Multivariate analysis
		Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value
Age	232
<65	116	Reference
≥65	116	1.106 (0.736 - 1.661)	0.628
Stage	232
I	28	Reference		Reference
II	72	1.891 (0.629 - 5.680)	0.256	1.713 (0.570 - 5.148)	0.338
III	76	3.186 (1.101 - 9.220)	0.033	2.731 (0.941 - 7.923)	0.064
IV	56	13.550 (4.779 - 38.420)	< 0.001	12.300 (4.347 - 34.804)	< 0.001
Sex	232
Female	110	Reference
Male	122	1.006 (0.669 - 1.515)	0.975
ARID1A	232
Low	116	Reference		Reference
High	116	0.595 (0.392 - 0.903)	0.015	0.623 (0.409 - 0.951)	0.028

ARID1A knockdown promotes CRC cell growth and proliferation in vitro and in vivo

Considering the aforementioned results, we next investigated the effect of ARID1A suppression on the biological functions of CRC cells. We assessed ARID1A expression across seven CRC cell lines and found relatively high expression in HCT116 and HCT15 cells, whereas expression was absent in RKO cells (Fig. S2A). Accordingly, HCT116 and HCT15 cells were selected for subsequent experiments. Stable ARID1A-knockdown HCT116 and HCT15 cell lines were obtained through lentiviral infection and puromycin selection, and the knockdown efficiency was verified through WB (Fig. S2B) and RT-qPCR (Fig. S2C). CCK-8 results showed that the proliferative capability of cells was significantly increased after the downregulation of ARID1A expression compared to that in the control group (Fig. S3A). Meanwhile, colony-formation assays showed that the downregulation of ARID1A expression significantly enhanced the colony-formation capability of HCT116 and HCT15 cells compared to that in the control group (Fig. S3B).

Furthermore, we used animal experiments to explore the effect of ARID1A on CRC growth in vivo. ARID1A knockdown significantly increased subcutaneous tumor volume (Fig. S3C, D), tumor growth rate (Fig. S3E), and final tumor weight (Fig. S3F) compared with the control group. While control mice exhibited a continuous increase in body weight, the ARID1A-knockdown group began losing weight after day 13 (Fig. S3G), suggesting that the downregulation of ARID1A contributes to cancer cachexia.

ARID1A knockdown promotes CRC cell migration

Metastasis is the leading cause of death in patients with CRC; therefore, we explored the effect of ARID1A on the invasion and migration capabilities of CRC cells. ARID1A knockdown significantly enhanced wound healing (Fig. 2A, B) and transwell migration (Fig. 2C, D) in both HCT116 and HCT15 cells. Epithelial–mesenchymal transition (EMT) is an important molecular mechanism that causes changes in cell invasion and migration capabilities. ARID1A knockdown decreased E-cadherin and increased N-cadherin and vimentin expression (Fig. 2E), indicating induction of epithelial–mesenchymal transition (EMT).

Fig. 2.

The effect of ARID1A on the migration and invasion of colorectal cancer (CRC) cells in vitro and in vivo. A-B: Wound healing assays to assess the effect of ARID1A on the wound healing capability of HCT116 and HCT15 cells. C-D: Transwell assays to assess the effect of ARID1A on the migration and invasion capabilities of HCT116 and HCT15 cells. E: Western Blot experiment to detect the expression of EMT-related marker proteins after the downregulation of ARID1A. F-G: The number of lung metastatic nodules and hematoxylin and eosin staining in the control and ARID1A downregulation groups in the lung metastasis model (n = 5 mice per group). H: Immunohistochemical analysis to detect the expression of E-cadherin in subcutaneous xenografts in nude mice (n = 4 mice per group). The experimental data were expressed as means ± standard deviation (SD). Data from cell assays were obtained from at least three independent experiments. *: P < 0.05; **: P < 0.01; ***: P < 0.001 (unpaired Student’s t-test).

To investigate the effects of ARID1A on CRC invasion and metastasis in vivo, we established a nude mouse lung metastasis model via the tail vein injection of HCT116 cells stably transfected with shNC or shARID1A constructs. Here, the number of lung metastatic tumor nodules in the ARID1A-knockdown group was significantly greater than that in the control group (Fig. 2F,2G). Meanwhile, IHC results showed that the expression of E-cadherin was also correspondingly reduced in the ARID1A-knockdown group (Fig. 2H). These findings further indicate that the downregulation of ARID1A expression promotes the invasion and metastatic capability of CRC cells.

ARID1A knockdown alters autophagy-related markers in CRC cells

Considering the aforementioned results, revealing a high ARID1A mutation frequency in patients with CRC and that low expression was associated with malignant biological behavior and poor prognosis, we further explored the potential mechanisms by which ARID1A influences the biological functions of CRC. Based on the GEO—COAD dataset, DEGs between high- and low-ARID1A-expression groups were screened, and a heatmap was generated (Fig. S4A). Then, a volcano plot of DEGs with a log fold change (|logFC|) > 0.5 was obtained (Fig. S4B). The GO enrichment analysis of DEGs primarily identified pathways related to cell migration and biological adhesion (Fig. 3A). Additionally, the KEGG enrichment analysis indicated that ARID1A mainly functions through pathways such as focal adhesion, ECM receptor interactions, and cell adhesion molecules (Fig. 3B). Moreover, GO and KEGG enrichment analyses of co-expressed proteins in the cBioPortal-COAD dataset revealed the significant enrichment of pathways related to autophagy, wound healing, cell migration, and the mTOR pathway (Fig. 3C, 3D).

Fig. 3.

Potential mechanisms underlying the effects of ARID1A on colorectal cancer (CRC) biology and prognosis. A-B: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes between high and low ARID1A expression groups in the GEO dataset. C-D: GO and KEGG enrichment analysis of co-expressed proteins of ARID1A in the cBioPortal dataset.

Subsequently, a series of experiments was employed to assess the effect of ARID1A on autophagy in CRC cells. WB results showed that expression of the autophagy marker protein LC3B-II and the autophagy regulator Beclin1 was decreased in CRC cells after ARID1A knockdown, whereas expression of the autophagy substrate protein p62 was increased (Fig. 4A). Meanwhile, IF results showed that after the downregulation of ARID1A expression, the LC3B fluorescence signals in HCT116 and HCT15 cells were significantly reduced (Fig. 4B). Furthermore, TEM was performed to observe the relationship between ARID1A expression and the formation of autophagosomes in tumor cells. The number of autophagosomes in the ARID1A-knockdown group was significantly lower than that in the control group in HCT116 and HCT15 cells (Fig. 4C). In addition, in vivo IHC results were consistent, with the ARID1A-knockdown group exhibiting lower Beclin1 expression and higher p62 expression compared to the control group (Fig. 4D). These data suggest that ARID1A downregulation is associated with changes in autophagy-related markers, including reduced Beclin1 and LC3B-II and increased p62.

Fig. 4.

ARID1A regulates autophagy in colorectal cancer (CRC). A: Western Blot experiments to detect the expression of autophagy-related proteins. B: Immunofluorescence to detect the expression of LC3B protein in stable cell lines. C: Transmission electron microscopy to detect the formation of autophagosomes in stable cell lines. D: Immunohistochemical analysis to detect the expression of Beclin1 and p62 in subcutaneous xenografts in nude mice (n = 4 mice per group). The experimental data were expressed as means ± standard deviation (SD). Data from cell assays were obtained from at least three independent experiments. *: P < 0.05; **: P < 0.01; ***: P < 0.001 (unpaired Student’s t-test).

ARID1A promotes CRC metastasis through the AKT/mTOR/p70S6K pathway activation

Previous studies have indicated that autophagy can influence cell invasion and migration [[18], [19], [20]]. In this study, we further explored the relationship between ARID1A-related autophagy and CRC cell invasion and migration. To this end, cells were treated for 24 h with culture media containing either the autophagy inhibitor chloroquine (CQ, 25 μM) or the autophagy activator rapamycin (Rapa, 100 nM) [21], and their migratory and invasive capacities were evaluated; control cells were treated with medium containing the corresponding solvent (DMSO). The migration and invasion capability of cells increased after the addition of CQ to inhibit autophagy, whereas these abilities were suppressed following the addition of rapamycin to induce autophagy (Fig. 5A). Moreover, WB results showed that after autophagy inhibition, expression of the epithelial marker E-cadherin was decreased, whereas expression of the mesenchymal markers N-cadherin and vimentin was increased. In contrast, the opposite changes occurred after autophagy induction (Fig. 5B).

Fig. 5.

The effect of autophagy on the migration and invasion of colorectal cancer (CRC) cells. A: Changes in the migration and invasion abilities of CRC cells after inhibition and activation of autophagy. B: The expression of autophagy- and EMT-related proteins after autophagy inhibition (Cells were cultured in medium containing 25 μM chloroquine (CQ) for 24 h) and activation (Cells were cultured in medium containing 100 nM rapamycin (rapa) for 24 h). The experimental data were expressed as means ± standard deviation (SD). Data from cell assays were obtained from at least three independent experiments. *: P < 0.05; **: P < 0.01; ***: P < 0.001 (one-way ANOVA with Tukey’s post hoc test).

Bioinformatic analysis revealed that ARID1A might exert its effects through the mTOR signaling pathway. Previous studies have also reported the interactions among ARID1A, AKT, and mTOR [[22], [23], [24]]. Thus, we used WB experiments to detect the phosphorylation of AKT, mTOR, and the downstream molecule p70S6K. After ARID1A knockdown, the phosphorylation of AKT, mTOR, and p70S6K in the cells were significantly increased (Fig. 6A), indicating activation of the AKT/mTOR/p70S6K pathway. Subsequently, cells were treated for 24 h with culture media with or without the AKT inhibitor MK-2206 (5 μM) [25] to suppress the aforementioned signaling pathway, and the effects on cell migration and invasion were assessed. The transwell experiment results showed that MK-2206 reversed the increase in cell invasion and migration induced by ARID1A knockdown in HCT116 and HCT15 cells (Fig. 6B). WB results further showed that the phosphorylation of AKT, mTOR, and p70S6K proteins was decreased after the addition of MK-2206, leading to the inhibition of pathway activity. Meanwhile, expression of the autophagy-related proteins Beclin1 and LC3B-II was increased, as was that of the epithelial marker E-cadherin, and expression of the mesenchymal markers N-cadherin and vimentin was decreased (Fig. 6C). Furthermore, in the ARID1A-deficient RKO cell line, we validated the effects of ARID1A on CRC cell invasion and migration. At the protein level, we assessed EMT and autophagy-related markers, as well as alterations in the AKT/mTOR/p70S6K signaling pathway, thereby further substantiating the tumor-suppressive role of ARID1A in CRC (Fig. S5). These results suggest that ARID1A knockdown is associated with activation of the AKT/mTOR/p70S6K pathway and concomitant changes in autophagy-related markers, which potentially promoting to the enhanced invasion and metastasis of CRC cells.

Fig. 6.

The effect of AKT signaling pathway on the migration and invasion of colorectal cancer (CRC) cells. A: Western blot experiments to detect changes in signaling pathways after ARID1A downregulation. B: Transwell assay to assess the effect of AKT inhibitor (Cells were cultured in medium containing 5 μM MK-2206 for 24 h) on the invasion and migration abilities of shARID1A cells. C: Western Blot experiments to detect the expression of EMT- and autophagy-related proteins in the ARID1A downregulation group after adding an AKT inhibitor (Cells were cultured in medium containing 5 μM MK-2206 for 24 h). The experimental data were expressed as means ± standard deviation (SD). Data from cell assays were obtained from at least three independent experiments. *: P < 0.05; **: P < 0.01; ***: P < 0.001; ****: P < 0.001 (A: unpaired Student’s t-test; B, C: one-way ANOVA with Tukey’s post hoc test).

Clinical significance of ARID1A in CRC

Finally, we verified the effect of ARID1A on CRC recurrence and metastasis using clinical samples from patients. Clinical data from 98 patients with CRC who underwent surgical resection were collected and organized, along with pathological slides. Based on ARID1A-IHC staining scores, the patients were divided into high and low expression groups, with 43 cases in the high-expression group and 55 cases in the low-expression group (Fig. 7A). Statistical analysis revealed a significant correlation between ARID1A expression and the clinical stage (P = 0.0013), T stage (P = 0.0186), N stage (P = 0.0063), and vascular cancer emboli (P = 0.0016) (Table 3). Moreover, survival analysis results showed that the PFS in the low-ARID1A-expression group was significantly shorter than that in the high-expression group (46 months vs undefined, P = 0.0009) (Fig. 7B). Based on ROC curves, ARID1A expression was found to have good predictive value for the 1-, 3-, and 5-year PFS of patients with CRC, with AUC values of 0.712, 0.635, and 0.647, respectively. Among these, the predictive value for the 1-year PFS was the best (Fig. 7C). Univariate and multivariate analyses showed that clinical stage (P = 0.002), N stage (P = 0.026), nerve invasion (P = 0.027), vascular cancer emboli (P = 0.029), and ARID1A expression (P < 0.001) were prognostic factors for CRC. Furthermore, the clinical stage (P = 0.01) and ARID1A expression (P = 0.005) were independent prognostic factors for CRC (Table 4). We further analyzed the correlation between the expression of ARID1A and E-cadherin based on the clinical cohort. The results showed a significant positive correlation between ARID1A and E-cadherin at the protein level (R = 0.29, P < 0.01) (Fig. 7D). In addition, compared to that in the high-expression group, the protein expression of E-cadherin in the ARID1A-low-expression group was decreased (Fig. 7E).

Fig. 7.

The relationship between ARID1A and the prognosis of colorectal cancer (CRC) patients in a clinical cohort. A: Immunohistochemical staining of ARID1A in pathological specimens of CRC patients. B: Progression-free survival (PFS) curves for high and low ARID1A expression groups. C: ROC curves for predicting 1-year, 3-year, and 5-year PFS in CRC patients based on ARID1A. D: Heatmap of the correlation between ARID1A and E-cadherin expression in a clinical CRC cohort. E: The expression of E-cadherin in high and low ARID1A expression groups in a clinical CRC cohort. F: The correlation between ARID1A and CDH1 expression in the GEPIA 2 database. G-I: Overall survival (OS), relapse-free survival (RFS) and post-progression survival (PPS) curves for high and low CDH1 expression groups. J: The correlation between ARID1A and Beclin1 expression in the GEPIA 2 database. K-M: Overall survival (OS), relapse-free survival (RFS) and post-progression survival (PPS) curves for high and low Beclin1 expression groups. **: P < 0.01 (unpaired Student’s t-test).

Table 3.

Baseline information of patients in the clinical cohort.

Clinical parameters	COAD		ARID1A expression		P value
			High	Low
Age	<65	63 (64.3%)	26 (26.6%)	37 (37.7%)	0.4903
	≥65	35 (35.7%)	17 (17.3%)	18 (18.4%)
Sex	Male	62 (63.3%)	26 (26.6%)	36 (36.7%)	0.6155
	Female	36 (36.7%)	17 (17.3%)	19 (19.4%)
Clinical stage	I-II	53 (54.1%)	31 (31.7%)	22 (22.4%)	0.0013
	III-IV	45 (45.9%)	12 (12.2%)	33 (33.7%)
T	T1-T2	25 (25.5%)	16 (16.3%)	9 (9.2%)	0.0186
	T3-T4	73 (74.5%)	27 (27.6%)	46 (46.9%)
N	0	58 (59.2%)	32 (32.7%)	26 (26.5%)	0.0063
	1–2	40 (40.8%)	11 (11.2%)	29 (29.6%)
Nerve invasion	Negative	52 (53.1%)	25 (25.5%)	27 (27.6%)	0.3783
	Positive	46 (46.9%)	18 (18.4%)	28 (28.5%)
Vascular cancer embolus	Negative	71 (72.5%)	38 (38.8%)	33 (33.7%)	0.0016
	Positive	27 (27.5%)	5 (5.1%)	22 (22.4%)

Table 4.

The prognostic factors of colorectal cancer were analyzed by univariate and multivariate analysis.

Characteristics		Total(N)	Univariate analysis		Multivariate analysis
			Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value
Sex		98
	Male	62	Reference
	Female	36	1.583 (0.858 - 2.920)	0.142
Age		98
	≥65	35	Reference
	<65	63	0.834 (0.450 - 1.545)	0.565
Clinical stage		98
	I-II	53	Reference		Reference
	III-IV	45	2.711 (1.450 - 5.070)	0.002	5.321 (1.495 - 18.939)	0.010
T		98
	T1-T2	25	Reference
	T3-T4	73	1.756 (0.811 - 3.801)	0.153
N		98
	0	58	Reference		Reference
	1–2	40	1.999 (1.088 - 3.671)	0.026	0.365 (0.119 - 1.121)	0.078
Nerve invasion		98
	Negative	52	Reference		Reference
	Positive	46	2.003 (1.081 - 3.712)	0.027	1.650 (0.841 - 3.236)	0.145
Vascular cancer embolus		98
	Negative	71	Reference		Reference
	Positive	27	2.026 (1.075 - 3.820)	0.029	0.789 (0.360 - 1.728)	0.553
ARID1A		98
	Low	55	Reference		Reference
	High	43	0.298 (0.146 - 0.609)	< 0.001	0.345 (0.163 - 0.730)	0.005

Meanwhile, we also analyzed the correlation between ARID1A and Cadherin 1 (CDH1, encoding the E-cadherin protein), as well as Beclin1, based on the GEPIA 2 database, and conducted a survival analysis using the K-M plotter. The results showed a positive correlation between ARID1A and CDH1 expression at the gene level (R = 0.27, P < 0.001) (Fig. 7F). Survival analysis further showed that compared to patients with high CDH1 expression, those in the low-expression group had a significantly shorter OS (P = 0.0013), relapse-free survival (RFS) (P < 0.001), and PPS (P = 0.0009) (Fig. 7G-I). Similarly, ARID1A was positively correlated with Beclin1 expression (R = 0.26, P < 0.001) (Fig. 7J), and compared to patients with high Beclin1 expression, the low-expression group also had a significantly shorter OS (P = 0.042), RFS (P < 0.001), and PPS (P = 0.0036) (Fig. 7K-M). Taken together, ARID1A expression is associated with CRC metastasis and could serve as a prognostic biomarker for this disease.

Discussion

As the third most common malignancy worldwide, CRC has extremely complex molecular mechanisms underlying its occurrence and progression. The development of next-generation sequencing has provided a deeper understanding of tumor gene mutations and has played an important role in the development of cancer biomarkers. ARID1A serves as a core subunit in the SWI/SNF chromatin remodeling complex, where mutations in its encoding gene frequently result in the loss or reduction of protein levels [26]. In recent years, researchers have detected frequent ARID1A mutations in CRC [27,28]. Moreover, a meta-analysis integrating multiple studies found that the mutation frequency of ARID1A in CRC ranges from 5.8% to 39%. The aggregated analysis revealed that 13.0% of patients with CRC harbor ARID1A mutations, and 11.7% of patients exhibit the loss of ARID1A protein expression [29]. In our study, the frequency of ARID1A mutations in patients with CRC was approximately 16%, exhibiting the highest mutation rate among members of the SWI/SNF complex. Most mutations in ARID1A were truncating mutations, leading to the downregulation of protein levels. These findings are thus consistent with previous research results.

Although increasing evidence suggests that ARID1A mutations or deletions are associated with the clinical characteristics and prognosis of CRC, the specific role of this gene remains controversial across different studies. Research by Lee et al. showed that ARID1A loss was significantly associated with poorly differentiated CRC, microsatellite instability, and lymphovascular invasion; however, no significant correlation was observed with patient OS [30]. In contrast, another study found no significant correlation between ARID1A loss and clinical characteristics or prognosis [13]. These discrepancies prompted us to explore the significance of ARID1A for the prognosis of patients with CRC and its correlation with clinical pathological features. Accordingly, we analyzed the relationship between ARID1A expression and prognosis in patients with CRC using public databases and clinical samples. Our findings suggested that low ARID1A expression is associated with a poorer prognosis in these patients. Moreover, based on a clinical cohort, low ARID1A expression was linked to later stages and vascular cancer emboli. Furthermore, patients with low ARID1A expression experienced earlier recurrence and metastasis compared to those with high expression. Although ARID1A expression was associated with clinical stages based on the GEO dataset, the results are limited by the small sample size and the disparity in the number of patients with early-stage versus late-stage disease. Meanwhile, using our clinical cohort to complement public databases, we analyzed the association of ARID1A expression with PFS and clinical characteristics, supporting the potential prognostic value of ARID1A expression in CRC. One limitation of our clinical analysis is that our results reflect the impact of ARID1A expression on the risk of developing metastases over time rather than its association with pre-existing metastatic disease. Future studies incorporating a larger cohort with diverse M stages would be valuable to further validate the clinical relevance of ARID1A expression in metastatic CRC.

The occurrence and progression of CRC involve complex molecular mechanisms, with recurrence and metastasis being the primary causes of patient mortality. Tumor invasion and metastasis involve a series of biological events; specifically, tumor cells gradually acquire invasive capabilities, directly infiltrating adjacent tissues or invading distant organs through the blood and lymphatic systems, where they ultimately colonize these organs and regaining proliferative activity [31]. Throughout this process, tumor cells require a variety of characteristics to complete each step of the metastatic cascade, including genetic mutations, the activation of oncogenes, and suppression of tumor suppressor genes, leading to uncontrolled proliferation, migration, and invasion [32]. Although EMT is not a prerequisite for tumor metastasis, it is a crucial component for tumor invasion and metastasis, participating in various processes of tumorigenesis, progression, and drug resistance [31]. In this study, following ARID1A knockdown, expression of the epithelial marker E-cadherin was decreased, whereas expression of the mesenchymal markers N-cadherin and vimentin was increased, indicating the occurrence of EMT. This molecular evidence confirms that the downregulation of ARID1A expression promotes EMT in CRC cells, loosening the cell–cell connections within the tumor tissue and facilitating cell migration.

EMT is regulated by multiple signaling pathways, including Wnt, Notch, autophagy, and PI3K/AKT [[33], [34], [35], [36]]. Studies have detected interactions between ARID1A and the AKT, mTOR, and MEK/ERK pathways, as well as autophagy, in various cancers [[17], [37]]. Our bioinformatic analyses suggested that ARID1A might influence the biological functions of CRC through the autophagy and mTOR signaling pathways. As mTOR is a key regulator of autophagy, it might mediate these connections; thus, we further investigated the effect of ARID1A on the AKT/mTOR signaling pathway and autophagy in CRC cells. In this study, ARID1A knockdown in CRC cells resulted in significantly decreased levels of the autophagy-related proteins LC3B-II and Beclin1, accompanied by a reduction in the number of autophagosomes. These findings suggest that ARID1A downregulation is associated with alterations in autophagy-related markers. Inhibition of autophagy with CQ enhanced cell invasion and migration and upregulated EMT markers, whereas activation of autophagy with rapamycin suppressed these abilities. These results indicate that changes in autophagy-related processes might contribute to the enhanced invasive and migratory capacity of CRC cells upon ARID1A knockdown. In CRC, autophagy levels are influenced by multiple factors, including tumor cell-intrinsic genomic alterations, germline genetic background variations, and various signaling pathways. mTOR is a conserved serine/threonine kinase and an important regulator of autophagy, and its activation can inhibit cellular autophagy. Furthermore, when the PI3K/AKT pathway is activated in cells, mTOR is activated through the phosphorylation of TSC1/TSC2, thereby suppressing autophagic activity [38,39]. Our findings suggest that mTOR might act as a mediator linking autophagy markers and EMT in the context of ARID1A-modulated CRC metastasis. One study found that Beclin1, a key autophagy-associated protein and tumor suppressor, is a target of the protein kinase AKT and that the AKT-mediated phosphorylation of Beclin1 enhances its interaction with the cytoskeletal protein vimentin [39]. Thus, in exploring the upstream signaling pathways of autophagy, we further focused on the AKT/mTOR/p70S6K pathway. After the treatment with AKT inhibitors, the activity of AKT and its downstream molecules was suppressed, which also reversed the autophagy-related markers and cell migration induced by ARID1A knockdown. These results suggest that ARID1A knockdown in CRC cells may affect autophagy marker changes, likely through activation of the AKT/mTOR/p70S6K signaling pathway, thereby promoting cell migration and contributing to malignant progression. However, the direct regulatory mechanism of ARID1A on dynamic autophagic activity requires further experimental verification. Finally, this study validated the correlation between ARID1A and CDH1, as well as Beclin1, based on a patient cohort, and analyzed the relationship between CDH1 or Beclin1 and the prognosis of patients with CRC, further demonstrating the clinical value of our findings.

In summary, our study systematically explored the role of ARID1A in CRC, as well as the associated molecular mechanisms, using public databases, in vitro and in vivo experiments, and clinical samples. In this work, we described a correlation between ARID1A downregulation and dysregulated autophagy signature in CRC and characterized its upstream signaling network. Of course, ARID1A might modulate tumor malignant phenotypes via multiple independent mechanisms. For instance, ARID1A loss has been shown to reduce reactive oxygen species (ROS) levels and attenuate KRAS-induced senescence, thereby promoting pancreatic intraepithelial neoplasia [40]. Our findings indicate that ARID1A downregulation is associated with enhanced migratory and invasive capacities of CRC cells, accompanied by concomitant changes in autophagy-related markers downstream of the AKT/mTOR/p70S6K signaling cascade. However, whether ARID1A directly governs dynamic autophagic activity still needs to be elucidated in follow-up investigations. Nevertheless, the AKT/mTOR/p70S6K pathway and autophagy might act as therapeutic vulnerabilities in ARID1A-downregulated CRC and facilitate future translational clinical research (Fig. 8). Admittedly, several limitations of this study should be acknowledged. First, the role of ARID1A in CRC was investigated only at the expression level, further studies using ARID1A-mutant cells are warranted to corroborate these findings. Second, the sample sizes in both the animal experiments and the clinical analyses were relatively small, which might affect statistical power. Thus, independent validation in larger cohorts is required. In addition, although we observed changes in autophagy-related markers following ARID1A modulation, these measurements were primarily based on static endpoints and therefore may not fully reflect dynamic autophagic activity. Ideally, autophagic flux assays would provide a more direct assessment of autophagy dynamics. Future studies incorporating direct measurements of autophagic flux will be necessary to further substantiate the mechanistic link between ARID1A expression and autophagy in CRC.

Fig. 8.

The schematic diagram of mechanism. Downregulation of ARID1A activates the AKT/mTOR/p70S6K signaling pathway, which in turn leads to alterations in autophagy-related markers. These changes are associated with enhanced migration and invasion capabilities of colorectal cancer cells, thereby promoting distant metastasis.

Conclusion

Our study shows that ARID1A is frequently mutated in CRC, with mutations leading to protein loss, and that low ARID1A expression is associated with poor prognosis for patients. Through in vitro and in vivo experiments, we confirmed that the downregulation of ARID1A expression promotes various malignant biological behaviors, including proliferation, invasion, and metastasis in CRC cells, and that it is related to autophagy, which is regulated by the AKT/mTOR/p70S6K signaling pathway. Our research enriches the theoretical foundation for ARID1A expression as a potential biomarker for CRC, providing new insights and references for clinical diagnosis and treatment.

Ethics approval and consent to participate

All experimental protocols were approved by the Ethics Committee of the Affiliated Hospital of Qingdao University. All patients approved participation and publication. The animal experiments were reviewed and approved by the Laboratory Animal Welfare Ethics Committee of Qingdao University.

Consent for publication

Not applicable.

Availability of data and materials

The data acquired for this study are available from the corresponding author upon reasonable request.

Funding

Qilu leader training project to Na Zhou; Natural Science Foundation of Shandong Province to Xiaochun Zhang (ZR2022MH048).

CRediT authorship contribution statement

Yuming Zhang: Writing – original draft, Investigation, Formal analysis, Data curation. Xueqin Duan: Validation, Data curation. Helei Hou: Writing – review & editing, Methodology, Conceptualization. Guanqun Wang: Methodology, Data curation. Qian Lin: Software, Methodology, Formal analysis. Yufeng Li: Resources, Investigation. Hongwei Lan: Writing – review & editing, Investigation. Xingfa Huo: Software, Resources. Fangfang Yang: Validation, Data curation. Guangming Fu: Resources, Data curation. Chuantao Zhang: Writing – review & editing, Validation, Resources. Xiaochun Zhang: Writing – review & editing, Project administration, Funding acquisition. Na Zhou: Writing – review & editing, Funding acquisition, Conceptualization.

Declaration of competing interest

All authors reviewed and approved the manuscript. This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. The study design was approved by the appropriate ethics review board. We confirm that there is no image duplication, image manipulation, or visual plagiarism. The authors declare that they have no competing interests.