ENO2 in progression and treatment of colon adenocarcinoma: integrative bioinformatics analysis on non-apoptotic cell death

Abstract

Colon adenocarcinoma (COAD) is one of the most common types of cancer. The interconnection between non-apoptotic cell death and COAD has not been adequately addressed. In our study, an integrative bioinformatics analysis was performed to explore non-apoptotic cell death-related biomarkers in COAD. ENO2 was determined as a potent biomarker for prognosis, drug response, immunity, and immunotherapy prediction. We used EdU and RT-qPCR assays to test our hypothesis and investigate how the ENO2 gene may influence or regulate cancer-related processes. ENO2 was expected to be a potential target in COAD.

Introduction

Colon adenocarcinoma (COAD) is one of the most common types of cancer, affecting both men and women. Risk factors for developing COAD include older age (the majority of cases occur in individuals over 50), inflammatory bowel diseases like Crohn’s disease or ulcerative colitis, family history of colorectal cancer or polyps, lifestyle factors such as a diet high in red meat, obesity, physical inactivity, and smoking [1]. Treatment for COAD depends on the stage of the disease at the time of diagnosis but often involves a combination of surgery, radiation therapy, and chemotherapy. With early detection and appropriate treatment, COAD is highly treatable, with a 5 year survival rate of over 90% for localized cancers [2]. Therefore, finding novel biomarkers for early detection of COAD is significant.

Cell death is a fundamental biological process in development, homeostasis, and disease [3]. While apoptosis, or programmed cell death, has been extensively studied, other forms of cell death do not fit the classical apoptotic pathway. These alternative cell death mechanisms are collectively known as non-apoptotic cell death. Non-apoptotic cell death encompasses various cellular demise pathways morphologically and mechanistically distinct from apoptosis [4]. Non-apoptotic cell death mechanisms, such as necrosis, necroptosis, pyroptosis, and ferroptosis, have been increasingly recognized for their involvement in cancer development, treatment, and resistance. However, the interconnection between non-apoptotic cell death and COAD has not been adequately addressed.

In our study, an integrative bioinformatics analysis was performed to explore non-apoptotic cell death-related biomarkers in COAD. ENO2 was determined as a potent biomarker for prognosis, drug response, immunity, and immunotherapy prediction. We used various laboratory-based experimental approaches to test our hypothesis and investigate how the ENO2 gene may influence or regulate cancer-related processes.

Materials and methods

Bioinformatics analysis

We sourced COAD samples from the TCGA database [5]. Using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm [6], we calculated the enrichment score for non-apoptotic cell death [7]. We employed Weighted Gene Co-expression Network Analysis (WGCNA) for weighted correlation network analysis to pinpoint the most correlated gene module of the death enrichment score [8]. Further, the module genes were extracted as the input for univariate Cox regression to ascertain their prognostic values. Besides, Random Survival Forest [9] and Least Absolute Shrinkage and Selection Operator (LASSO) regression [10] were applied for dimensional reduction of these prognostic genes. Drug sensitivity to ENO2 was predicted using oncoPredict [11]. The immune characteristics of ENO2 and its role in immunotherapy were predicted using algorithms like MCPcounter [12], ssGSEA [13], and TIMER [14].

MTT cell proliferation assay

The COLO 320DM colon adenocarcinoma cells were treated with an empty vector or Flag-ENO2 for 6, 24, and 48 h. Following the administration of the treatment, a solution of MTT (20 μl, 5 mg/ml in 1 × PBS) was added to each well and incubated for four hours in a cell culture incubator. Following removing the media from each well, 100 μl of DMSO was added to facilitate the dissolution of the formazan crystals. Absorbance was quantified at 570 nm on a microplate reader with a reference wavelength of 630 nm. Three independent experiments were conducted.

EdU assay

The cells were incubated with the 5‐ethynyl‐2′‐deoxyuridine (EdU) (Beyotime) for a period of two hours. The distribution of EdU + cells (green) and DAPI + cells (blue) was imaged. The percentage of EDU positive cells was calculated as the ratio of the number of EdU + cells to the number of DAPI + cells. The data were analysed based on at least three biological replicates.

RT-PCR

Total RNA was extracted from cultured cells usingTRIzol reagent (Takara Bio) and reverse transcribed using First Strand cDNA synthesis Kit (Yeason) in accordance with the manufacturer's instructions. Subsequently, the templates were amplified utilising SYBR Green (Yeason) in order to quantify mRNA levels. The relative target mRNA expression was calculated using the 2^–ΔΔCt method. The primer sequences utilised are presented in Table 1.

Table 1.

Primer sequences for RT-PCR

Gene		Primer sequence (5′–3′)
GAPDH	F	GGAGCGAGATCCCTCCAAAAT
	R	GGCTGTTGTCATACTTCTCATGG
CYCLIN D1	F	GCTGCGAAGTGGAAACCATC
	R	CCTCCTTCTGCACACATTTGAA
CYCLIN B1	F	AATAAGGCGAAGATCAACATGGC
	R	TTTGTTACCAATGTCCCCAAGAG
Ki67	F	ACGCCTGGTTACTATCAAAAGG
	R	CAGACCCATTTACTTGTGTTGGA

Statistical analysis

The normally distributed variables between the two groups and the numerous groups were compared using the Student’s t-test and one-way analysis of variance (ANOVA), respectively. The non-normally distributed data between the two groups and the numerous groups were compared using the Wilcoxon and Kruskal–Wallis tests, respectively.

Results

Developing the death enrichment score

We developed a novel approach by combining gene sets representing 11 distinct non-apoptotic cell death mechanisms to calculate an integrated death enrichment score. As shown in Fig. 1, our analyses uncovered a significant positive correlation between this comprehensive death score and the enrichment of specific non-apoptotic death pathways, such as pyroptosis, ferroptosis, and necroptosis.

Fig. 1.

The enrichment score for pathways leading to non-apoptotic death. The relationship between death enrichment score and 11 different forms of non-apoptotic cell death

Gene module analysis and machine learning

Employing WGCNA, as depicted in Fig. 2A, B, we identified a specific gene module containing 672 genes (the blue module, shown in Fig. 2C) closely associated with the computed death enrichment score, demonstrating significant correlations (Fig. 2D). The genes within this blue module were then used as input for a univariate Cox regression analysis with the threshold of P value < 0.05 (screening to 26 genes) (Fig. 3A). To reduce the dimensionality of these gene-level data, we applied both Random Forest and LASSO regression techniques (screening to 4 genes), which came to the most potent ENO2 (Fig. 3B, C). Both of these methods consistently highlighted ENO2 as one of the most potent predictors of survival among the genes in the blue module.

Fig. 2.

WGCNA on death enrichment score. A. Scale-free topology model for optimal soft threshold. B. Cluster dendrogram for gene modules related to death enrichment score. C. The correlation between gene modules and death enrichment score. D. The correlation between gene significance and module membership in the blue module

Fig. 3.

Machine learning for dimension reduction of module genes. A. Univariate Cox regression analysis on module genes from the blue module. B. Random Survival Forest for the most potent prognostic genes. C. LASSO regression for the most potent prognostic genes

ENO2 is a hazard and prognostic biomarker

ENO2 has significantly higher expression in tumor tissues than normal tissues (Fig. 4A). Varied survival outcomes in ENO2-related groups were observed in COAD patients (Fig. 4B). Patients with higher ENO2 expression levels had different survival profiles compared to those with lower ENO2 expression. ENO2 also impacted the drug sensitivity of COAD patients, with lower IC50 values for drugs like Vinblastine, Cisplatin, Dasatinib, Leflunomide, Entospletinib, Eg5, Fludarabine, AZD8186, AMG-319 in the high ENO2 group (Fig. 4C). These findings suggest that the overexpression of ENO2 in COAD tumors may sensitize the cancer cells to certain chemotherapies and targeted therapies, potentially leading to improved treatment response for patients with high ENO2-expressing tumors.

Fig. 4.

Prognostic value of ENO2. A. ENO2 expression in tumor and normal tissues. B. Survival curves based on ENO2-stratified groups. C. IC50 of nine predicted chemotherapy drugs in ENO2-stratified groups

Immunological implications of ENO2

ENO2 positively correlated with microenvironment scores, immune cells (CD4 T cells, CD8 T cells, B cells, MDSCs, Neutrophils, NK cells, Fibroblasts, etc.), and modulators (CD274, VTCN1, CD276, CD80, CD28, ICOSLG, etc.) (Fig. 5A, B). These findings indicate that ENO2 overexpression in COAD tumors is closely linked to the composition and immunological characteristics of the tumor microenvironment. This relationship may have important implications for understanding the underlying biology of COAD and exploring potential immunotherapeutic strategies targeting the ENO2-related tumor microenvironment.

Fig. 5.

Immune features of ENO2. A. The correlation between ENO2 and immune cells. B. The correlation between ENO2 and immune modulators

ENO2 promotes colon cancer cell proliferation in vitro

The potential for proliferation of COLO 320DM colon adenocarcinoma cells treated with a Flag-ENO2 plasmid was evaluated using an MTT assay. As shown in Fig. 6A, the proliferation potential of COLO 320DM cells significantly increased time-dependent after overexpression of ENO2. Furthermore, the EdU incorporation assay demonstrated that the overexpression of ENO2 resulted in an increased percentage of EdU + COLO 320DM cells (Fig. 6B, C). The expression of Cyclin D1 protein, which positively regulates the transition from the G1 to S phase, significantly increased upon ENO overexpression (Fig. 6D). The mRNA level of Cyclin B1, a G2/M-phase control cyclin, was found to be slightly elevated following the overexpression of ENO2 (Fig. 6E). Furthermore, Ki67 (an indicator of proliferation) was upregulated after ENO overexpression (Fig. 6F). Thus, overexpression of ENO2 promotes the growth of colon adenocarcinoma cells.

Fig. 6.

In vitro validation on ENO2. A. MTT assay testing the cell proliferation ability of ENO2. B. EdU assay testing the cell proliferation ability of ENO2. C. Statistical analysis on EdU assay. D-F. RT-qPCR assay testing the RNA expression of CYCLIN D1, CYCLIN B1, and Ki67

Discussion

This study presents a novel and comprehensive approach to examining non-apoptotic cell death pathways in the context of cancer. By integrating gene sets representing 11 distinct non-apoptotic cell death mechanisms, the authors calculated a composite "death enrichment score" that provided valuable insights. The strong positive correlations observed between this integrated score and the enrichment of specific non-apoptotic death modalities, such as pyroptosis, ferroptosis, and necroptosis, highlight the utility of this innovative methodology. This multifaceted assessment of non-apoptotic cell death allows a more holistic understanding of how these diverse pathways may collectively influence cancer biology. The application of WGCNA enabled the identification of a specific gene module (the blue module) that was closely associated with the computed death enrichment score. The significant correlations observed between this module and the death score underscore the biological relevance of this gene set. By using the blue module genes as input for further statistical analyses, the authors uncovered the potential prognostic significance of these genes, with the ENO2 gene emerging as a prominent candidate. The subsequent dimensionality reduction techniques, including Random Forest and LASSO regression, further corroborated the role of ENO2 as a key player in the context of non-apoptotic cell death and cancer progression.

ENO2, also known as neuron-specific enolase (NSE), is a glycolytic enzyme that catalyzes the conversion of 2-phosphoglycerate to phosphoenolpyruvate during glycolysis [15]. ENO2 is often upregulated in various types of cancer [16], including neuroendocrine tumors [17], small-cell lung cancer [18], and breast cancer [19]. In cancer cells, increased expression of ENO2 can promote several hallmarks of cancer, such as enhanced glycolytic metabolism, cell proliferation, and survival [15]. ENO2 overexpression has been associated with more aggressive tumor phenotypes, increased metastatic potential, and poor patient prognosis in some cancer types. The mechanisms by which ENO2 contributes to cancer progression are not fully elucidated. It may involve its role in facilitating glycolytic energy production, which can support the rapid growth and division of cancer cells [20]. ENO2 may also have non-metabolic functions, such as modulating signaling pathways or interacting with other proteins to promote cancer cell survival and invasion [21]. In colorectal adenocarcinoma (COAD), the dysregulation of metabolic pathways, including glycolysis, is a hallmark of tumor development and progression. As a rate-limiting enzyme in glycolysis, the overexpression of ENO2 can contribute to the metabolic reprogramming of COAD cells, enabling them to sustain their high energy demands and proliferative capacity. Beyond its role in cellular metabolism, emerging evidence suggests that ENO2 may also influence the tumor microenvironment and immune responses in COAD. ENO2 has been implicated in modulating the expression of immune checkpoint molecules and the recruitment of specific immune cell populations, potentially shaping the immunological landscape of the COAD tumor. Therefore, this study aims to comprehensively investigate the clinical and biological significance of ENO2 in COAD, examining its association with patient survival, drug sensitivity, and the tumor microenvironment. Understanding the multifaceted role of ENO2 in COAD pathogenesis may unveil new opportunities for targeted therapies and immunotherapeutic strategies.

In our study, ENO2 is upregulated in colorectal cancer, a common characteristic observed in many types of cancer. The increased expression of ENO2 in the tumor samples compared to normal tissues indicates that ENO2 may play an important role in the cancer phenotype and tumor progression. Besides, the level of ENO2 expression or the presence of ENO2-related factors can influence the survival outcomes of COAD patients. Specific details on how the patients were categorized into different ENO2-related groups and the corresponding survival differences would be needed to interpret this finding further. Our in vitro validation proved that overexpression of ENO2 in COLO 320DM colon adenocarcinoma cells promotes their proliferation and cell cycle progression. The increased expression of key cell cycle regulators, such as Cyclin D1 and Cyclin B1, and the upregulation of the proliferation marker Ki67, suggest that ENO2 overexpression can enhance the proliferative capacity of colon cancer cells.

Targeting ENO2 or its associated metabolic pathways has been explored as a potential therapeutic strategy in some cancers, though clinical applications are still under investigation [22]. In our study, high expression of ENO2 in COAD tumors may sensitize the cancer cells to certain therapeutic agents, such as chemotherapies (e.g., Vinblastine, Cisplatin, Fludarabine) and targeted therapies (e.g., Dasatinib, Leflunomide, Entospletinib, AZD8186, AMG-319). The enhanced drug sensitivity observed in the high ENO2 group could be attributed to the metabolic alterations or other cellular changes associated with increased ENO2 expression in the cancer cells. This information may have implications for personalized treatment approaches, where ENO2 expression levels could guide the selection of more effective drug regimens for COAD patients. Further research is needed to fully understand the complex role of ENO2 in the various aspects of cancer biology and to evaluate its utility as a biomarker or therapeutic target.

Emerging evidence suggests that ENO2 plays a role in cancer immune evasion. Cancer cells can release ENO2 into the tumor microenvironment, where it can interact with and inhibit the function of immune cells, such as T cells and natural killer (NK) cells. ENO2 can inhibit the activation and proliferation of T cells, reducing their ability to mount an effective anti-tumor immune response [15]. Besides, ENO2 can interfere with the cytotoxic function of macrophages, limiting their capacity to recognize and destroy cancer cells [23]. ENO2 can also contribute to the expansion and activation of MDSCs, which are known to suppress anti-tumor immune responses [24]. In line with these findings, our study proved that higher expression of ENO2 is linked to increased infiltration or activation of these components in the tumor microenvironment. This relationship may have important implications for understanding the role of ENO2 in shaping the immune landscape of the tumor and potentially influencing the overall immune response and tumor progression. The modulators, such as CD274 (PD-L1), VTCN1 (B7-H4), and CD276 (B7-H3), are known to play critical roles in immune checkpoint regulation and can influence the ability of the immune system to recognize and attack the tumor cells. The immunosuppressive tumor microenvironment in COAD presents a significant challenge for effective immunotherapy. COAD tumors are often characterized by low tumor mutational burden, poor antigen presentation, and an immunosuppressive cellular landscape, which can limit the activity of checkpoint inhibitors. The positive correlation between ENO2 and these modulators suggests that ENO2 expression may be associated with the upregulation or activation of these immune checkpoint molecules, which could potentially contribute to the tumor cells' evasion of the immune system.

Overall, these findings provide insights into the complex interactions between the ENO2 gene and COAD, highlighting the potential involvement of ENO2 in promoting tumor growth, affecting drug sensitivity, shaping the immune landscape, and influencing the overall immune response against the tumor. Further investigation into the mechanistic links between ENO2 and the oncogenesis of COAD could yield valuable information for understanding tumor biology and potentially identifying therapeutic targets.

Author contributions

JT led the manuscript writing. JT and WW collected and analyzed the data, with GT supervising the study. All authors reviewed and approved the final manuscript.

Data availability

Data used in this work can be acquired from the TCGA and GEO databases.

Declarations

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.