Metastasis-associated colon cancer 1 drives pancreatic cancer via p53/Notch signaling

Abstract

BACKGROUND

Metastasis-associated colon cancer 1 (MACC1) is known to promote tumor growth and metastasis in several solid malignancies. However, its expression profile, clinical relevance, and underlying mechanisms in pancreatic cancer (PC) remain incompletely defined.

AIM

To systematically evaluate the multiomics characteristics and clinical significance of MACC1 across cancers and to investigate its biological functions and molecular mechanisms in PC.

METHODS

MACC1 expression, DNA methylation, copy number changes, and genetic alterations were analyzed across The Cancer Genome Atlas, Genotype-Tissue Expression, Gene Expression Omnibus, cBioPortal, TIMER2.0, CancerSEA, and single-cell datasets. The diagnostic and prognostic value of MACC1 was assessed using Cox models, Kaplan-Meier analysis, and receiver operating characteristic curves. Functional studies were performed by silencing or overexpressing MACC1 in PANC-1 cell and ASPC-1 cell. Cell proliferation, colony formation, migration, invasion, and apoptosis were evaluated. Enriched pathways were screened using gene set enrichment analyses and confirmed by western blot.

RESULTS

MACC1 was significantly upregulated in PC at both mRNA and protein levels in public datasets and our local cohort. High MACC1 expression was linked to advanced stage, poor differentiation, and worse survival. Multiomics analysis showed that MACC1 alterations were common across cancers and were associated with copy number gain and promoter hypomethylation. Functional assays demonstrated that MACC1 enhanced pancreatic tumor proliferation, colony formation, migration, and invasion while reducing apoptosis. Gene set enrichment analyses, dual luciferase gene assay and western blot confirmed that MACC1 activated p53-related and Notch-related signaling in PC cells.

CONCLUSION

MACC1 is a clinically relevant oncogene in PC. Its overexpression promotes aggressive tumor behavior through activation of p53 and Notch pathways. These findings support MACC1 as a potential diagnostic marker and therapeutic target in PC.

Key Words: Metastasis-associated colon cancer 1; Pancreatic cancer; P53 pathway; Notch pathway; Multiomics; Tumor progression

Core Tip: Metastasis-associated colon cancer 1 (MACC1) is strongly upregulated in pancreatic cancer (PC) and shows consistent alterations across multiomics datasets. In this study, we combined pan-cancer bioinformatic profiling with functional experiments to clarify its role in PC. Silencing MACC1 suppressed proliferation, migration, invasion, and increased apoptosis, while overexpression produced the opposite effects. We also confirmed that MACC1 activates both p53 and Notch signaling. These findings show that MACC1 is not only a useful diagnostic and prognostic marker but also a potential therapeutic target in PC.

INTRODUCTION

Pancreatic cancer (PC) is recognized as the seventh most common cause of cancer death globally, and results in more than 300000 deaths per year. It is estimated that PC will become the second leading cause of tumor-related mortality in the next 10 years[1,2]. The most common malignant PC type is known as pancreatic ductal adenocarcinoma, which is considered as the fourth highest cancer-associated mortality, with > 50000 new cancers cased and death of > 40000 patients alone in the United States in 2017[3]. It has the poorest prognosis among all the gastrointestinal tumors, with a five-year overall survival (OS) rate of 7% in all stages. Among all the other challenges we are facing in PC, lack of sensitive prognostic biomarkers and therapeutic targets are the major problems.

Metastasis-associated colon cancer 1 (MACC1) was first identified in colorectal cancer as its name, is an important regulator for tumor proliferation and metastasis[4]. It has been found that MACC1 encodes hepatocyte growth factor (HGF), regulates mTOR signaling pathway, HGF/Met signaling pathway and accelerates cancer cell to migrate, invade, and metastasize[4-6]. Moreover, six studies have discussed that the overexpression of MACC1 is significantly correlated with the poor clinical outcomes in colorectal carcinoma, hepatocellular carcinoma, lung adenocarcinoma, gastric carcinoma, esophageal cancer, and ovarian cancer[7-12]. However, the deep mechanism of the carcinogenesis of MACC1 in multiple cancers, especially the precise mRNA expression, survival analysis and mechanism of MACC1 in PC patients and PC carcinogenesis remain unclear.

High-throughput technologies (sequencing), such as gene chip-based sequencing and RNA-sequencing, gene-related analyses have been able to identify the significance of target genes from overall gene expression data. Bioinformatics based methods have not been applied for identification of function of MACC1 in multiple cancers, including the PC. Therefore, in the current study, we intended to investigate the clinical value and the pivotal role of MACC1 with targeted signal pathway in multiple cancers, especially in pancreatic carcinogenesis using pan-cancer bioinformatical analysis and experiment validation. The integrative analysis will employ state-of-the-art bioinformatics tools to sift through vast datasets from repositories like The Cancer Genome Atlas (TCGA) described as the Cancer Genome Atlas, as well as Genotype-Tissue Expression (GTEx) described as and the Genotype-Tissue Expression project, identifying patterns of MACC1 expression and its genetic alterations across different cancers. This comprehensive approach is designed to validate the oncogenic role of MACC1 and association of this gene with clinical outcomes, including survival rates and response to therapeutic application. Furthermore, by pinpointing the signaling pathways modulated by MACC1, such as the HGF/Met pathway in pancreatic carcinogenesis, our study aims to illuminate the molecular drivers of MACC1-mediated tumorigenesis. These findings may have great significance in basic research and clinical translational practice and offer a new perspective for the treatment of multiple-cancer patients, especially PC patients.

MATERIALS AND METHODS

Patients and tumor samples

We collected the human PC tumor tissues and adjacent normal control tissues from 25 patients (age range, 45-72 years) between January 2021 and January 2023 at Changhai Hospital. A pair of PC and paracancerous tissue samples from 15 males and 10 females were collected during pancreatectomy with pathologically confirmed diagnosis. After resection, the collected tissues were snap-frozen by using liquid nitrogen and stored at -80 °C until polymerase chain reaction (PCR) assay. The patient’s age, sex, pathology grade, American Joint Committee on Cancer (AJCC) stage, lymphatic metastasis, and distant metastasis, were recorded and analyzed. This study was approved after careful review and consideration by the Ethics Committee of Changhai Hospital (No. CHEC2020-173). In addition, informed and written consent was received from the enrolled patients before the start of the study.

Analysis for molecular expression

MACC1 mRNA expression was determined using real-time reverse transcription-PCR. For this process, first we extracted total RNA from PC samples and paired paracancerous samples through Trizol reagent (Invitrogen, United States) following the guidelines provided by the manufacturer, which was then reverse transcribed into cDNA by using a chain synthesis kit for cDNA (Vazyme, China). For preforming real-time reverse transcription-PCR experiment Light Roche 480 System was used. Amplification, cDNA was done and GAPDH was used as reference primer for all the experiments. The quantification was estimated using the ΔΔCT method. The method of bioinfomatical analysis has been provided in Supplementary material.

Cell culture

PC cell lines from human, including PANC-1 and ASPC-1 were acquired from Zhengzhou University Hospital, First Affiliated Hospital. The PANC-1 cell line authentication has been provided in Supplementary material, and the ASPC-1 cell line authentication has been provided in Supplementary material.

These cells were cultured in a medium known as DMEM using 10% fetal bovine serum (Gibico, United States), 100 U/mL penicillin, and 100 μg/mL streptomycin (Invitrogen, United States) under 5% CO₂ and 1% O₂ at 37 °C. The experiments were repeated three time independently.

Transfection of cell and small interfering RNA

MACC1 small interfering RNA (siRNA) and its negative control (MACC1-NC) were purchased from RiboBio (Guangzhou, China). The siRNA sequences for MACC1 were as follows: (1) Forward, 5′-UUAGGAUGCCCUCAGCUAUCACUGC-3′; and (2) Reverse, 5′-AUUGUUGUAAAGCAGAAGCACUUCC-3′. The siRNA sequences for control were as follows: (1) Forward, 5′-CCAGTTAAGAACGTCCCCAAGCG-3′; and (2) Reverse, 5′-AAGCTTGAGGTCTAGGTAATTTC-3′.

To transfect PC cells, we used Lipofectamine 3000 Reagent (Invitrogen, United States). MACC1 and control vector plasmids were also obtained from RiboBio (Guangzhou, China). We then transfected siRNA (1 μg) into ASPC-1, through Lipofectamine 3000 reagent (Invitrogen, United States), following manufacturer’s guidelines.

Assays based on cell proliferation and adherent colony formation

We used the cell counting Kit8 (DojinDo Japan) for the assessment of cell proliferation. The collection of transfected cells done for the detection of cell viability from plated in 96-well culture plates (2 × 10³ per well). For colony formation assay, the cells were seeded in 35 mm culture plates at 37 °C in 5% CO₂ after transfection (2 × 10³ per well). After 2 weeks, 4% paraformaldehyde and 0.1% crystal violet were used to fix the colony and stained the colony. A countable colony was defined as a cluster containing ≥ 50 cells to exclude small cell aggregate. The rate of colony formation was detected through the number of stained colonies.

Cell migration assay

We performed a trans well assay to evaluate cell migration. 5 × 10⁵ cells were planted on trans well upper chambers with 100 μL Dulbecco’s modified Eagle’s medium (DMEM) [without fetal bovine serum (FBS)] and the lower chambers was infused with 600 μL of DMEM (with 10% FBS, Gibco, United States). After the 24 hours of incubation, methanol and crystal violet were used to fix and stain the cells. The cells on the outside surfaces of the upper chambers were then recorded by microscope (Olympus, Tokyo, Japan) at 100 × magnification.

Cell invasion and cell apoptosis assays

Another trans well assay with Matrigel was performed to find out the cell invasion. The trans well upper chambers coated with Matrigel (Costar, United States) were used to plate PC cells (5 × 105 per chamber). The lower chambers of the trans well were infused with 600 μL of DMEM (with 10% FBS, Gibco, United States). After the incubation for 24 hours, we used methanol, hematoxylin, and eosin to fix and stain cells, and methanol and crystal violet were used to fix and stain the cells. The cells on the outside surfaces of the upper chambers were then recorded by microscope (Olympus, Tokyo, Japan) at 100 × magnification. The apoptosis of dead cells was determined using Apoptosis Kit (KeyGEN, China). We followed the manufacturer’s guidelines on a BD Accuri C6 flow cytometer (BD Bioscience, United States).

Western blot assay

To extract all of the protein from transfected cells, combination of a solution known as RIPA (Beyotime, China) and protease inhibitor cocktail (Roche, United States) was done. In order to measure the concentration, we utilized BCA Protein Assay Kit (Thermo, United States). Following a uniform concentration adjustment, the protein samples were separated using SDS-PAGE (10%) and then transferred to membranes of polyvinylidene fluoride. After blocking, the obtained membrane was co-incubated with a number of antibodies. The antibodies contain: (1) Anti-p53 (Abcam, ab33889, 1:1000); (2) Anti-MACC1 (Abcam, ab226803, 1:1000); (3) Anti-HES1 (Abcam, ab71559, 1:1000); (4) Anti-HEY1 (Abcam, ab22614, 1:1000); (5) Anti-Notch1 (Abcam, ab83232,1:1000); (6) Anti-BAX (Abcam, ab32503, 1:1000); (7) Anti-Bcl-2 (Abcam, ab182858, 1:1000); (8) Anti-GAPDH (Abcam, EPR16891, 1:5000); and (9) Goat anti-Rabbit HRP (Dingguo Changsheng, IH-0011). Finally, fluorescent signals were detected using an ECL chromogenic apparatus (Amersham Imager 600, United States). After the experiment, the relative gray density was calculated and normalized through Image J software (version 1.53).

Statistical analysis

To perform statistical analysis, we used webtools GraphPad 5.0, and R software (version 4.3.0). For bioinformatics, Pearson was used for normal distribution data. The Spearman correlation analysis was used for other data analysis. We used Kruskal-Wallis Rank Sum Test Wilcoxon Rank Sum and Signed Rank Tests between variables to detect differences. Cox and Kaplan-Meier survival analysis were performed with the help of survival package. The Kaplan-Meier method uses log-rank test for significance test, and the “survminer” package to visualize Kaplan-Meier analysis. Relative risks were described using hazard ratios and 95%CI. Receiver operating characteristic (ROC) curve analysis was performed using the “pROC“ package to evaluate gene diagnostic performance. All tests/statistical analyses were bilateral. For experimental validation, results were presented as mean ± SD and analyzed using unpaired Student’s t-test and one-way analysis of variance. The significant values were evaluated based on the P < 0.05.

RESULTS

Aberrant expression of MACC1 among multiple cancer

We performed difference analysis of samples received from TCGA (Figure 1A), and paired analysis of difference (Figure 1B) to determine dysregulation pattern of MACC1. Subsequently, the expression level was obtained for MACC1 from the perspective of PC to further expand the non-cancer sample size by combining TCGA and GTEx and mining these resources (Figure 1C). The distribution pattern of MACC1 expression was visualized by organ map (Figure 1D). We found that MACC1 was found expressed differentially in most types of cancer and depicted a regular and consistent pattern of expression across cancer species analyses, i.e., generally significantly upregulated. The verification of protein levels was based on the CPTAC database (Figure 1E). External verification of mRNA levels was conducted by Gene Expression Omnibus database (Supplementary Figure 1), and the above results were fully verified by regression analysis based on TCGA, TCGA-GTEx, Gene Expression Omnibus and CPTAC logistics (Figure 1F). We can observe that different omics and different databases have good consistency in the trend of data set expression. In fact, the estimated ROC curve showed (Figure 1G) that MACC1 mRNA expression levels had satisfactory sensitivity and specificity for the diagnosis of patients with multiple tumors [area under the curve (AUC) > 0.7]. MACC1 expression was correlated with 5 tumor stages (Supplementary Figure 2), suggesting that it was associated with the progression of these tumors. Interestingly, MACC1 differed across a large number of molecular subtypes (Figure 1H and Supplementary Figure 3).

Figure 1 Expression of metastasis-associated colon cancer 1 among multiple cancer. A: Y-axis showing the metastasis-associated colon cancer 1 (MACC1) the expression levels of mRNA in The Cancer Genome Atlas (TCGA). ^aP < 0.05, ^bP < 0.01, ^cP < 0.001, ^dP < 0.0001; B: As depicted from the figure boxplots showing minimum, and maximum, and median, and quartiles, each point showing one sample only more details are similar to Figure 1A, but group samples are paired. ^aP < 0.05, ^bP < 0.01, ^cP < 0.001, ^dP < 0.0001; C: Depicts the expression level of MACC1 in normal and tumor tissues and these details were collected from two different datasets known as Genotype-Tissue Expression and TCGA. ^aP < 0.05, ^bP < 0.01, ^cP < 0.001, ^dP < 0.0001; D: In various organs the distribution and expression of MACC1; E: It further depicts the validation of different protein levels externally, and this was performed using a CPTAC database as per requirements; F: Here, the Y-axis indicates the levels of genetic expression: Analysis based on logistic regression for TCGA and TCGA-Genotype-Tissue Expression, where red is 1 or greater number, blue shows values between 0 and 1, a white circle denotes no significance, and no collection of relevant data set was depicted by an empty value); G: Diagnostic value of MACC1 for distinguishing different tissues (tumor and normal); H: Wilcoxon rank-sum test showing the differences in MACC1 expression in microsatellite instability. AUC: Area under the curve; GTEx: Genotype-Tissue Expression; MACC1: Metastasis-associated colon cancer 1; TCGA: The Cancer Genome Atlas.

Altered genetic expressions of MACC1 in cancers

We examined genomics data of normal tumor tissues in the cohort of PC from TCGA, including genetic variation, DNA methylation data, somatic copy number alteration (SCNA), and mRNA expression data. The cBioPortal database suggests that MACC1 has a certain frequency of genetic changes in most cancers, with amplification and mutation being the most common types of genetic changes (Figure 2A). Missense mutation is the main type of mutation (Figure 2B). We systematically examined the two-dimensional mutation site structure of MACC1 (Figure 2C). To further investigate the genetic aberration of MACC1 in cancer, we examined the percentage of SCNA, which in general occurs more frequently in most types of different cancer and less frequently in only a very small tumors by numbers (Figure 2D). Clearly, SCNA plays a crucial role in the regulating gene expression in tumors (Figure 2E). We investigated the effect of SCNA on MACC1 mRNA expression by calculating the Spearman correlation. These obtained results have revealed that the MACC1 mRNA expression was significantly correlated with SCNA in different tumors types (Figure 2F). This result suggests that MACC1 copy number abnormalities are common among most of the tumors, and can affect gene expression. We found that MACC1 showed a relatively consistent methylation pattern in the pan-cancer cohort; most tumor tissues showed hypomethylation compared with normal tissues (Figure 2G). In general, MACC1 mRNA expression negatively correlates with DNA methylation as indicated by Figure 2H. Additionally, we calculated the spearman correlation of MACC1 with 10 genomics feature scores, including Aneuploidy Score, Homologous Recombination Defects, Intratumor Heterogeneity, Microsatellite Instability, Nonsilent Mutation Rate, Tumor Ploidy, SNV Neoantigens, Tumor Mutation Burden, Tumor Purity and Silent Mutation Rate (Figure 2I).

Figure 2 Altered genetic expression of metastasis-associated colon cancer 1. A: Metastasis-associated colon cancer 1 (MACC1) frequency of mutations in different types of tumors; B: The details of the mutation information; C: Number of cases and sites of genetic alterations of MACC1 in pancreatic cancer collected from cBioPortal; D: Histogram shows the frequency of somatic copy number alteration for MACC1 in each cancer type; E: The inter-connections between the MACC1 mRNA expression level and altered genetic expression/regulation; F: The Spearman’s correlation between somatic copy number alteration and the expression of MACC1; G: Spearman’s correlation of MACC1 between promoter methylation and transcriptional expression and (red represent positive and blue represent negative correlations); H: Heatmap showing hypermethylated (red) and hypomethylated (blue) MACC1 (Wilcoxon rank-sum test); I: Visualization of spearman correlation coefficients by radar map for MACC1 and 10 genomics features. ^aP < 0.05, ^bP < 0.01, ^cP < 0.001, ^dP < 0.0001. MACC: Metastasis-associated colon cancer.

Correlation between immune infiltration and high MACC1 expression in cancer

The correlation between MACC1 and major histocompatibility complex molecules, immune-activator genes, immunosuppressive genes, and chemokines showed surprising consistency, and an overall positive correlation trend was observed (Figure 3A). It is important to understand that we analyzed the anti-cancer immune status and response to each of the seven steps of the cancer immune cycle as described above. High and low MACC1 expression groups differences were calculated. Clearly, there was better consistency in pan carcinoma, i.e., a higher Tracking Tumor Immunophenotype score (Figure 3B). To elucidate the specific cell types regulated by MACC1 in the tumor microenvironment, we used the TIMER2.0 database to explore the association between MACC1 mRNA expression and immune infiltration levels and stromal cell abundance. These results suggest the involvement of MACC1 in the formation of immune rejection or infiltration of immune cells which plays a key role in immune escape (Figure 3C). The trend of this association is slightly different in different tumors, due to different proportions of immune invasion in different cancers and unique tumor microenvironment. However, the seven types of software (evaluation methods) corroborated each other and confirmed the accuracy of our analysis. In addition, multiple single-cell data sets in the TISCH database consistently described the expression landscape of MACC1 in most tumors, and the results showed that although MACC1 was not strongly expressed in most tumors, it mainly came from malignant cells, confirming the above results of immune infiltration (Figure 3D).

Figure 3 Metastasis-associated colon cancer 1 expression correlates with infiltration of immunological factors. A: The heatmap showed a prominent correlation of expression level of metastasis-associated colon cancer 1 (MACC1) with chemokine, immune-stimulatory, major histocompatibility complex genes, chemokine receptor, and immune-inhibitor; B: The Tracking Tumor Immunophenotype scores differences calculation between low expression MACC1 group and high expression MACC1 group. The correlation between MACC1 and tumor microenvironment feature scores was visualized by radar map, and the overall negative correlation trend was observed; C: Evaluation of the correlation of MACC1 expression with cancer immune infiltration seven software were used; D: Cell sources of MACC1 in pan-cancer at the single-cell level.

Correlation between MACC1 and cancer pathways

MACC1 was negative in a large number of cells, indicating that cell growth was inhibited after MACC1 knockout (Figure 4A), and cell adhesion related pathways were significantly activated in the group with high expression of MACC1 (Figure 4B). In addition, metabolic pathways analysis was carried systematically, which indicated consistency between cancer species. This implies the conservative nature of MACC1 function. We observed that immune and epithelial mesenchymal transformation pathways are often enriched in tumors with high MACC1. Moreover, MACC1 is thought to be associated with tumors and metabolism related disorders. Interestingly, MACC1 inhibits drug metabolism-related processes, which may be related to the sensitivity of chemotherapy (Figure 4C). We analyzed the relationship between 14 cancer markers and 14 tumor-related pathway scores and MACC1, and the main correlation was positive (Figure 4D). The Cancer Proteome Atlas data suggested that CLAUDIN7 had a higher expression intensity in the group with high expression of MACC1 mRNA (Figure 4E) and this finding confirmed constituency with transcriptome results.

Figure 4 The function mediated by metastasis-associated colon cancer 1 and its regulatory mechanism. A: Metastasis-associated colon cancer 1 (MACC1) was negative in a large number of cells, indicating that cell growth was inhibited after MACC1 knockout; B: Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed for differential-expression genes (UP); C: Enrichment differences of MACC1 in 50 HALLMARK and 85 metabolism gene sets; D: MACC1 correlation with 14 tumor-related pathway scores and 14 cancer markers, where main correlation is positive; E: The Cancer Proteome Atlas data suggested that CLAUDIN7 had a higher expression intensity in the group with high expression of MACC1 mRNA.

Clinical relevance of MACC1

The Pan-cancer Atlas of survival analysis showed that all MACC1 was correlated with multiple survival times for multiple cancer types (Figure 5A), and the associations were relatively homogeneous, as MACC1 can be used as a protective factor for different cancers in most cases, especially kidney renal papillary cell carcinoma (KIRP), kidney chromophobe carcinoma, and kidney renal clear cell carcinoma (kidney tumors). A small number of tumors also present as risk factors, suggesting that MACC1 may play different roles in different types of cancers. Notably, MACC1 was associated with four lifetimes in KIRP and pancreatic adenocarcinoma (PAAD). Further information from the atlas of pan-cancer and results based on the analysis (cox survival) of four survival periods are shown in forest maps (Figure 5B-E), and log rank test results of KIRP and PAAD are shown in Kaplan-Meier curves (Figure 5F and G).

Figure 5 Clinical relevance of metastasis-associated colon cancer 1. A: Metastasis-associated colon cancer 1 (MACC1) expression correlation with overall survival, disease-specific survival, disease-free interval and progression-free interval on the basis of univariate Cox regression and Kaplan-Meier models (red shows that MACC1 is a risk factor, and green arrow showed MACC1 is a protective factor); B-E: Cox survival analysis results of four survival periods are shown in forest maps; F and G: Log rank test results of kidney renal papillary cell carcinoma and pancreatic adenocarcinoma are shown in Kaplan-Meier curves inner packages.

Identification of chemicals that interact with MACC1

We performed CMAP analyses to identify potential treatment strategies, which can counteract MACC1-mediated tumor-promoting effects. We established an MACC1-associated gene signature, including the top-most upregulated and the most down-regulated genes, detected by the comparison of patients with low and high MACC1 expression in each cancer type. The similarity scores of 1288 compounds were obtained by comparing MACC1-related characteristics with CMAP gene characteristics using XSum, an optimal feature matching method. W.13 exhibit significantly lower scores in most cancer types, suggesting that they have the potential to inhibit MACC1-mediated cancer-promoting effects (Figure 6A). We analyzed MACC1 expression in a large number of immunotherapies, and it was clear that AUC values were not universally desirable (Figure 6B). However, we observed a skin melanoma cohort with higher MACC1 expression in the response group, a higher proportion of response patients in the MACC1 high expression group, and better sensitivity and specificity were also obtained by ROC curve analysis (Figure 6C-E). In the chemotherapy analysis, we evaluated the association between drug sensitivity and MACC1 expression using 3 different databases (Cancer Therapeutics Response Portal, genomics of drug sensitivity in cancer, and Preservice Research Institute for Science and Mathematics). Obviously, MACC1 is a potential drug resistance gene (Figure 6F-I).

Figure 6 Analysis of resistance to drug based on the expression of metastasis-associated colon cancer 1. A: Analysis for the sensitivity of drug through different databases; B: Cancer Therapeutics Response Portal; C: Genomics of drug sensitivity in cancer; D: Predicting potential compounds to target metastasis-associated colon cancer 1 (MACC1). Visualized the top 5 selected compounds on the basis of analysis through connectivity map, that could potentially target MACC1; E and F: Those with high expression of MACC1 survived better; G: The expression of MACC1 was higher in the complete or partial response group; H: The proportion of complete remission or partial remission group with elevated expression of MACC1; I: The compounds with significance between high expression of MACC1 and low expression of MACC1 in Cancer Therapeutics Response Portal database.

Upregulated expression of MACC1 promoted tumor proliferation, migration, invasion and resist cell apoptosis in PC cell

The upregulated MACC1 in PC is also verified according to our result in clinical samples between cancer tissues and normal tissues (Figure 7A left). The clinicopathologic parameters of these 25 subjects with MACC1 expression level shows upregulated MACC1 was related to poor pathology grade, higher AJCC stage and higher possibility of distant metastasis (P < 0.001; Figure 7A right, Table 1). We selected ASPC-1 for transfection with siRNA and PANC-1 to be transfected with MACC1 plasmid. The results of western blot showed that the transfection in these two cell lines was effective (Figure 7B). Overexpression of MACC1 can promote the proliferation and downregulated MACC1 can restrain the proliferation of PC cell. Moreover, according to the result of Hallmarks in gene set enrichment analyses (GSEA), the related pathway including cell cycle and PC are also enriched in TCGA-PAAD cohort (Figure 7C). The result of clone formation, transwell migration, invasion and flow cytometry indicated upregulated MACC1 can promote tumor migration, invasion and restrain apoptosis in PC cell. Down regulated MACC1 can restrain tumor migration, invasion and predispose to apoptosis in PC cell (Figure 7D and E). Interestingly, some related results of Hallmarks in GSEA term also demonstrated the potential function of MACC1 as a tumor promoter and apoptosis depressor (Figure 7F).

Figure 7 Upregulated expression of metastasis-associated colon cancer 1 promoted tumor proliferation, migration, invasion and resist cell apoptosis in pancreatic cancer cells. A: Left: The mRNA level of metastasis-associated colon cancer 1 in pancreatic cancer tissues and paracancerous tissue from our local cohort. Right: Correlation expression level between expression of metastasis-associated colon cancer 1 and clinical American Joint Committee on Cancer stages or pathological grading from our local cohort; B: Proliferation of PANC-1 and ASPC after transfection was evaluated using cholecystokinin-8 assay. ^aP < 0.05, ^bP < 0.01; C: Some enriched proliferation-related pathway from gene set enrichment analyses were listed; D: The representative images of the colony formation, transwell migration and invasion in transfected PANC-1 and ASPC cells were presented; E: Apoptosis analysis of transfected pancreatic cancer cells were performed by flow cytometry; F: Some enriched related pathway from gene set enrichment analyses were listed. FDR: False discovery rate; KEGG: Kyoto Encyclopedia of Genes and Genomes; MACC1: Metastasis-associated colon cancer 1; NES: Normalized enrichment score.

Table 1 Associations of metastasis-associated colon cancer 1 expression with clinicopathological features in 25 pancreatic cancer patients, mean ± SD.

Clinicopathologic parameters	Number	Metastasis-associated colon cancer 1 expression	P value
Sex
Male	15	2.32 ± 0.52	0.432
Female	10	2.65 ± 0.35
Age
< 60	11	2.59 ± 0.47	0.483
≥ 60	14	2.67 ± 0.53
Pathology grade
G1	4	1.53 ± 0.43	< 0.001
G2	13	1.88 ± 0.32
G3 and G4	8	2.74 ± 0.56
American Joint Committee on Cancer stage
I and II	11	1.67 ± 0.38	< 0.001
III and IV	14	2.51 ± 0.42
Lymphatic metastasis
No	8	2.16 ± 0.34	0.112
Yes	17	2.34 ± 0.57
Distant metastasis
No	11	1.88 ± 0.48	< 0.001
Yes	14	2.70 ± 0.54

MACC1 activates Notch signaling pathway and p53 signaling pathway in PC

To evaluate the exact signaling pathway which MACC1 is participated in, we searched the potential signaling pathway in GSEA results above and some pathways were identified with high correlation (P < 0.05; Figure 8A and B), including p53 signaling pathway (false discovery rate = 0.01), Notch signaling pathway (false discovery rate = 0.02). Dual-luciferase reporter assays with Notch1 or p53 promoter constructs revealed attenuated transcriptional activity of both promoters upon MACC1 knockdown (Figure 8C). After verification through western blot assay, we confirmed that Notch signaling pathway related proteins, including Notch1, Hey1 and Hes1 were downregulated and p53 signaling pathway related protein, including p53 were significantly changed after knockdown of MACC1 expression level in PC cell (Figure 8D). These results indicated that MACC1 plays a crucial role in pancreatic tumorigenesis through p53 and Notch signaling pathway.

Figure 8 Metastasis-associated colon cancer 1 activated Notch and p53 signaling pathway. A and B: Hallmarks of gene set enrichment analyses revealed that the expression level of Metastasis-associated colon cancer 1 significantly correlates with Notch and p53 signaling pathway; C: The luciferase assay showed the effect of Metastasis-associated colon cancer 1 on luciferase activity of Notch1 and p53 promoter in ASPC-1; D: Up: Western blot result about the expression of Notch pathway-related protein, including Notch1, Hey1 and Hes1 in downregulated ASPC-1, and western blot result about the expression of p53 pathway-related protein downregulated in ASPC-1. Down: The gray density about the expression of Notch pathway-related protein, including Notch1, Hey1 and Hes1 in downregulated ASPC-1 (n = 3), and the gray density about the expression of p53 pathway-related protein (n = 3). ^aP < 0.05, ^bP < 0.01. FDR: False discovery rate; KEGG: Kyoto Encyclopedia of Genes and Genomes; MACC1: Metastasis-associated colon cancer 1; NES: Normalized enrichment score; NS: Not significant.

DISCUSSION

MACC1 is a novel oncogene which stimulates cancer cell to migrate and invade in vitro and vivo through HGF/Met pathway[13]. High-expression of MACC1 is associated with poor clinical outcomes in patients suffered from colorectal[14], hepatocellular[15], lung[16], gastric, esophageal[17], and ovarian[18] cancer. A recent meta-analysis has shown that overexpression of MACC1 is distinctly associated with unfavorable OS of patients with multiple solid cancers [hazard ratios = 2.11 (95%CI: 1.59-2.80), P < 0.001][19]. Another study have confirmed that over expression of MACC1 can activate PC cell chemoresistance[20]. Recently, a study reported that the MACC1 can regulate programmed death ligand-1 expression to influence tumor immunity in gastric cancer. Our data provide compelling evidence that MACC1 is overexpressed in PC and is closely associated with poor prognosis. Notably, while MACC1 exerts tumor-promoting effects in both colorectal cancer and PC, highlighting its potential as a pan-cancer oncogenic driver, their core regulatory mechanisms differ significantly: In colorectal cancer, MACC1’s oncogenic role primarily depends on the HGF/Met signaling pathway, whereas in PC, it accelerates tumor progression by activating the p53 and Notch pathways. This tissue-specific regulatory divergence underscores the need for personalized medical strategies. MACC1’s impact extends to the tumor microenvironment. Specifically, it impairs the anti-tumor immune response by inhibiting the recruitment of cytotoxic T lymphocytes and natural killer cells. Furthermore, MACC1 modulates immune cell function and immune checkpoints through the programmed death 1/programmed death ligand-1 pathway, while also downregulating the expression of major histocompatibility complex I molecules on the tumor cell surface[21]. It shows strong potential as a diagnostic biomarker with its diagnostic sensitivity and specificity highlighted by AUC values above 0.7. Its expression correlates with poorer prognosis and increased tumor stage, marking it as a potent therapeutic target, especially given its role in chemoresistance and potential involvement in metabolic dysregulation within tumors.

PC has been reported as the seventh most common mortality causing cancer in men, and 8^th in women[22]. Pancreatic ductal adenocarcinoma is the major subtype of PC which makes up the 85% of the overall PC cases. PC has the poorest prognosis among all the gastrointestinal cancer with a 5-year survival rate of 6%. The mortality rate of PC patients showed no significant improvement since 1997 until 2007[23]. The only treatment option which may provide prolonged survival is the complete surgical resection of the cancerous tissue. However, due to the lack of initial symptoms and high degree of malignancy, concrete diagnosis is made at the advanced stage of PC in most of the patients. At this stage of the diagnosis, most of patients have distance metastases or locoregional extension already[24,25]. In this grim situation, it is an urgent situation for the researchers to identify some sensitive biomarkers for PC to make timely diagnosis, and to find a potential therapeutic target. Many studies have discovered and discussed the relation between p53 and PC[26]. Notably, p53 is a well-characterized tumor suppressor gene that typically induces cell cycle arrest, apoptosis, or senescence to prevent malignant transformation. However, our findings demonstrate that MACC1 activates the p53 signaling pathway to promote PC progression, which seems contradictory to p53’s canonical tumor-suppressive function. This seeming contradiction is readily explained by the existence of gain-of-function (GOF) p53 – an oncogenic variant of p53. Accumulating evidence indicates that mutant p53, which is frequently detected in PC (with a mutation rate exceeding 70%), acquires oncogenic GOF properties that diverge from wild-type p53. Unlike wild-type p53, GOF p53 can promote cell proliferation, migration, invasion, and chemoresistance through interactions with other signaling molecules or transcriptional regulation of oncogenic target genes[27-29]. Notch signaling pathway play an important role in intercellular communication with high evolutionary conservation. Several functions can be regulated through Notch signaling pathway, and some of them are highly related to carcinogenesis including the regulation of tumor stem cell, the mediation of tumor proliferation, invasion and apoptosis[30]. To the best of our knowledge, the modulation between MACC1 and these two cancer-related signaling pathway have not been reported in any type of cancer. Our study for the first time presents these novel findings which is a significant addition to what is already available on this topic, and may provide a clinical perspective for the early diagnosis and related therapy of PC patients.

Another important spotlight of the current study is the prediction of molecular functions and pathways through bioinformatics analysis and genome-wide association study strategy with further verification through wet experiments. Since the first publication in 2005, the genome-wide association study strategy has contributed significantly to the understanding of the mechanisms of cancer[31]. GSEA is one of these important bioinformatics methods, it can facilitate researchers to predict the potential functions and activated pathways of a single gene according to the degree of variation of various genes in high-throughput sequencing profiles of certain cancers compared to normal samples. By using these methods, pathway-level changes could be identified and interpreted, and these results could lead to the orientation of further wet experiments and improve reliability of basic medical science experiment.

In our study, we firstly explore the potential role of MACC1 acted in PC. We predicted several function clusters and activated pathway and some of them have been proved in the results of functional experiment, including the proliferation, migration, invasion, apoptosis phenotype. Additionally, some critical protein in predicted pathways is also verified in western blot assay, including Notch signaling pathway, and p53 signaling pathway. The results of western blot showed that the transfection in these two cell lines were effective. Overexpression of MACC1 can promote the proliferation and downregulated MACC1 can restrain the proliferation of PC cell. Moreover, according to the result of GSEA, the related pathway including cell cycle and PC are also enriched in TCGA-PAAD cohort. The result of clone formation, transwell migration, invasion and flow cytometry indicated upregulated MACC1 can promote tumor migration, invasion and restrain apoptosis in PC cell. Down regulated MACC1 can restrain tumor migration, invasion and predispose to apoptosis in PC cell. Interestingly, some related results of GSEA also demonstrated the potential function of MACC1 as a tumor promoter and apoptosis depressor.

However, this study has some limitations as well as strength. One of the major limitations includes the lack of identification and verification of directly target of MACC1 in this pathway. The further study is needed to clarify the deep mechanism and identify the direct targets of MACC1 in p53 signaling pathway and Notch signaling pathway. The strength of our study includes bioinformatics analysis and hypothesis has been followed and confirmed by the wet experiment using our own cohort.

CONCLUSION

Our study indicated that MACC1 are significantly high-expressed in multiple cancer tissue with remarkable related changes in multi-omics, especially PC and distinctly poorly associated with AJCC stages, grades, OS and progression-free survival in PC. Overexpression of MACC1 could promote malignant phenotype of PC via activating p53 and Notch signaling pathway.