Use of the enoph1 gene

Abstract

The application discloses application of ENOPH1 gene and relates to the technical field of biological medicine. In the application, bioinformatics analysis is adopted to identify key metabolic genes closely related to CRC prognosis, and expression of ENOPH1 in KRAS mutant CRC tissues and cell lines is evaluated. Through in vitro experiments such as CCK8, wound healing experiment and subcutaneous xenotransplant tumor model in vivo, the function of ENOPH1 in KRAS G12D / G13D mutant CRC is verified. The results show that ENOPH1 is highly expressed in KRAS mutant CRC and is controlled by MEK / ERK signal cascade. Through shRNA knockdown of ENOPH1, the proliferation, migration and tumorigenesis of CRC cells can be inhibited, cell apoptosis can be induced, and the response to chemotherapy can be enhanced. The application provides a new strategy and direction for the treatment of colorectal cancer, has extremely high clinical application value, and can bring better treatment effect for patients with intestinal cancer.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, and more specifically, relates to the application of the ENOPH1 gene. Background Technology

[0002] Colorectal cancer (CRC) is a major global health burden, ranking as the third most common cancer and the second leading cause of cancer-related death worldwide. Radical resection remains the primary treatment for CRC; however, approximately 20% of patients have metastases at diagnosis, making curative surgery ineligible. Furthermore, despite advancements in treatment, about 25% of patients with localized disease who undergo curative surgery experience recurrence or metastasis, resulting in a five-year survival rate of less than 10%.

[0003] Activation of the KRAS oncogene is a recognized genetic driver of CRC development and progression. Most oncogenic KRAS mutations occur at codons G12, G13, and Q61, impairing GTPase activity and leading to constitutive activation in cancer. This triggers aberrant downstream signaling and metabolic programs that promote tumor cell proliferation, differentiation, survival, and migration. KRAS mutations are present in approximately 40-50% of CRC patients, with G12D and G13D being the most common subtypes, collectively accounting for nearly half of all KRAS mutations. Due to the lack of a well-defined druggable binding pocket, KRAS has long been considered a challenging target for small molecule drugs. While recent advances have been made in developing inhibitors targeting specific KRAS mutations (such as sotorasib or adagrasib), these drugs have limited clinical efficacy in CRC, with a response rate of approximately 30% as monotherapy against KRAS^G12C-mutant non-small cell lung cancer (NSCLC). KRAS^G12C mutations account for only about 3% of CRC cases, and the response rate to monotherapy in CRC is approximately 10%. More common mutations (such as KRAS^G12D and KRAS^G13D) still lack effective treatment options. Therefore, developing feasible treatment strategies for KRAS-mutant CRC remains a major challenge in oncology, requiring a multi-pronged approach to identify and validate new therapeutic targets.

[0004] Metabolic reprogramming is increasingly recognized as a hallmark of malignant tumors. In CRC, several metabolic genes involved in this reprogramming and closely associated with disease progression have been identified, including HK2, GLUT1 (both involved in glycolysis), IDH1 (a key player in the tricarboxylic acid cycle), and FASN22 (involved in lipid metabolism). Recent studies have shown that KRAS mutations reprogram multiple metabolic processes in CRC cells, including glycolysis, the tricarboxylic acid cycle, and lipid metabolism, to promote tumor cell growth and survival. Metabolic intervention is emerging as a novel potential cancer treatment. Therefore, identifying key metabolic genes regulated by KRAS mutations provides new opportunities for therapeutic intervention in KRAS-mutant CRC. Through bioinformatics analysis, we identified enolase-phosphatase-1 (ENOPH1) as a promising potential metabolic intervention gene closely associated with KRAS signaling and CRC prognosis. ENOPH1 is a recently discovered bifunctional enzyme involved in the methionine rescue pathway, maintaining methionine levels by recovering methylsulfonium groups from methylthioadenosine. However, the role and potential mechanism of ENOPH1 in KRAS mutant CRC have not been explored to date. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, the technical problem to be solved by this application is to provide an application of the ENOPH1 gene, specifically its application as a drug target in the preparation of targeted drugs for the treatment of colorectal cancer.

[0006] The technical solution of this application is as follows:

[0007] Application of the ENOPH1 gene as a drug target in the preparation of targeted drugs for the treatment of colorectal cancer.

[0008] Furthermore, the colorectal cancer described is a KRASG12D / G13D mutant.

[0009] Furthermore, the targeted drug knocks out or silences the ENOPH1 gene in colorectal cancer cells.

[0010] An anti-colorectal cancer drug is an shRNA containing ENOPH1, or an expression vector of shRNA containing ENOPH1, or a viral vector of an shRNA expression vector containing ENOPH1.

[0011] Furthermore, the shRNA is:

[0012] Sh-E1-1: 5'-GCAGAGTCTTTGCAGATGTA-3',

[0013] And / or Sh-E1-2:5'-CCCTTGTGATTTAGAAGATTA-3'.

[0014] Furthermore, the aforementioned anti-colorectal cancer drug is a drug used to enhance the sensitivity of colorectal cancer cells to chemotherapy drugs.

[0015] Furthermore, the chemotherapy drug mentioned is doxorubicin hydrochloride.

[0016] Furthermore, the aforementioned anti-colorectal cancer drug is used to inhibit the proliferation, migration, or invasion of colorectal cancer cells.

[0017] Application of ENOPH1 shRNA, or expression vectors containing ENOPH1 shRNA, or viral vectors containing ENOPH1 shRNA in the preparation of anti-colorectal cancer drugs.

[0018] Furthermore, the sequence of the shRNA is as follows:

[0019] Sh-E1-1: 5'-GCAGAGTCTTTGCAGATGTA-3',

[0020] And / or Sh-E1-2:5'-CCCTTGTGATTTAGAAGATTA-3'.

[0021] Compared with the prior art, the beneficial effects of this application are as follows:

[0022] Bioinformatics analysis was used to identify key metabolic genes closely related to CRC prognosis. The expression of ENOPH1 in KRAS-mutant CRC tissues and cell lines was assessed. The function of ENOPH1 in KRASG12D / G13D-mutant CRC was validated through in vitro experiments (including CCK8, EdU incorporation, and wound healing assays) and in vivo subcutaneous xenograft models. Furthermore, qPCR, Western blotting, ELISA, immunofluorescence, and Seahorse metabolic flux analysis were combined with transcriptomic and metabolomic sequencing data to systematically explore the mechanism of ENOPH1's mediating effect in KRASG12D / G13D-mutant CRC. Results showed that ENOPH1 is highly expressed in KRAS-mutant CRC and is regulated by the MEK / ERK signaling cascade. Knockdown of ENOPH1 using shRNA inhibited CRC cell proliferation, migration, and tumorigenesis, induced apoptosis, and enhanced the response to chemotherapy. Multiomics analysis revealed that ENOPH1 plays a crucial regulatory role in KRAS-mutant CRC. Mechanistically, ENOPH1 regulates the phosphorylation of SMAD2 / 3 and the ubiquitination of SMAD4, thereby affecting the TGF-β / SMAD signaling pathway. Furthermore, ENOPH1 participates in central carbon metabolism, regulating glycolytic activity in KRASG12D / G13D mutant CRC cells, thus promoting tumor progression.

[0023] ENOPH1 enhances glycolysis through TGF-β / SMAD signaling, promoting the progression of KRAS G12D / G13D mutant CRC. Targeting ENOPH1 may offer therapeutic potential for KRAS mutant CRC. This application provides a new strategy and direction for the treatment of colorectal cancer, with extremely high clinical application value, and can bring better treatment outcomes to colorectal cancer patients. Attached Figure Description

[0024] Figure 1-1 The analysis plots above show ENOPH1 identified as a key metabolic gene associated with CRC prognosis; where a. Volcano plots show the differential expression of amino acid metabolism-related genes between CRC tissues and adjacent non-cancerous tissues; b, c. Forest plots of univariate and multivariate Cox regression analyses show metabolic genes associated with CRC prognosis, with hazard ratios and 95% confidence intervals; d. Box plots show the expression levels of ENOPH1, MTAP, SFXN3, and SLC25A15 in colorectal cancer and adjacent normal tissues.

[0025] Figure 1-2 Figure ef shows the analysis of ENOPH1 as a key metabolic gene associated with CRC prognosis; where e. GSVA analysis reveals 22 differentially expressed CRC pathways; f. Pearson correlation analysis of four key metabolic genes (ENOPH1, MTAP, SFXN3, SLC25A15) with the 22 CRC-related pathways identified by GSVA.

[0026] Figure 2-1 Figure a, e shows the analysis of ENOPH1 upregulation by MEK / ERK signaling in KRAS mutant and KRASG13D mutations. Specifically: a. Immunofluorescence staining assesses ENOPH1 expression in KRAS mutant and wild-type CRC tissues; the left image shows a representative immunofluorescence image, and the right image shows quantification of ENOPH1 protein levels (scale bar: 50 μm); b, c. Western blot analysis of the effects of overexpression of KRASWT, KRASG12D, and KRASG13D on p-ERK and ENOPH1 expression in NIH / 3T3 cells; d, e. Effects of KRAS knockdown, 1 μM PD-0325901 treatment for 24 hours, or combined treatment, on p-ERK and ENOPH1 expression in HCT116 cells were assessed by Western blot and qPCR; e. Error bars represent SEM; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001;

[0027] Figure 2-2Figure f1 shows the analysis of ENOPH1 upregulation by MEK / ERK signaling in KRASG12D and KRASG13D mutations. Specifically, f, g. the effects of KRAS knockdown, 1 μM PD-0325901 treatment for 24 hours, or a combination thereof on p-ERK and ENOPH1 expression in DLD1 cells were assessed by Western blot and qPCR; h, i. the effects of KRAS knockdown, 1 μM PD-0325901 treatment for 24 hours, or a combination thereof on p-ERK and ENOPH1 expression in CT26 cells were assessed by Western blot and qPCR; g, i. Error bars represent SEM; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001;

[0028] Figure 3-1 Figure ac shows the analysis of how ENOPH1 knockdown inhibits the proliferation and migration of KRASG12D / G13D CRC cells and promotes apoptosis. Specifically, a. CCK8 assay assessed the effect of ENOPH1 knockdown on the proliferation of DLD1 and CT26 cells; b. Colony formation and 3D spheroidization assays assessed the effect of ENOPH1 knockdown on the proliferation of DLD1 and CT26 cells; c. Wound healing assays detected the effect of ENOPH1 knockdown on the migration of DLD1 and CT26 cells.

[0029] Figure 3-2 Figure showing the effect of ENOPH1 knockdown on apoptosis and DOX-induced antitumor effects in DLD1 and CT26 cells, detected by flow cytometry with PI / Annexin V staining.

[0030] Figure 3-3 Figure ef shows the analysis of how ENOPH1 knockdown inhibits the proliferation and migration of KRASG12D / G13D CRC cells and promotes apoptosis. In Figures e and g, xenograft tumor models were established by subcutaneously seeding DLD1 and CT26 cells (n=5 per group) stably expressing ENOPH1 knockdown or control cells. e is a representative image of the xenograft tumor, and g shows the ENOPH1 knockdown in tumor tissue verified by Western blot analysis. f shows the continuous monitoring and recording of tumor growth time in mice receiving the specified treatment. Note: Data in a and f are expressed as mean ± SEM. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001.

[0031] Figure 4-1 Figure ab shows the analysis of ENOPH1’s key role in CRC through multi-omics analysis; where a. heatmap of DEGs in HCT116 cells after ENOPH1 silencing; b. the top 20 significantly enriched KEGG pathways in transcriptome sequencing analysis.

[0032] Figure 4-2 Figure cd shows the analysis of ENOPH1’s key role in CRC through multi-omics analysis; c. PCA clustering diagram of non-targeted metabolomics data of HCT116 cells after ENOPH1 silencing; d. Heatmap of differential metabolites, showing metabolic changes in HCT116 cells after ENOPH1 silencing.

[0033] Figure 4-3 KEGG pathway enrichment analysis for ENOPH1 knockdown HCT116 cells, highlighting the top 20 significantly enriched pathways.

[0034] Figure 5 Analysis of the upregulation of TGF-β by MEK / ERK signaling mediated by KRAS12D / G13D through ENOPH1; a. KEGG pathway analysis of differentially expressed genes in ENOPH1 knockdown DLD1 cells and control cells; b, c. ELISA detection of TGF-β levels in the supernatant of ENOPH1 knockdown DLD1 and CT26 cells and their respective control cells; d, e. Western blot analysis to assess the effect of specified treatments on TGF-β expression in DLD1 and CT26 cells; f, g. Knockdown of ENOPH1 in NIH / 3T3 cells overexpressing KRASG12D or KRASG13D, and Western blot analysis to determine whether KRASG12D and KRASG13D regulate TGF-β expression through ENOPH1. Note: error bars in b, c. represent SEM; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001;

[0035] Figure 6-1To investigate the regulation of p-SMAD2 / 3 nuclear transport and SMAD4 stability by ENOPH1 to mediate TGF-β / SMAD signaling, the study included: a. Western blot analysis to detect p-SMAD2 and p-SMAD3 levels in the cytoplasm and nuclear components of ENOPH1-knocked and control cells in DLD1 cells; b. Immunoprecipitation of total cell lysates from DLD1 cells (with or without ENOPH1 knockdown) using anti-SMAD4 antibody, followed by Western blot analysis of the lysates with a specified antibody; c. Detection of SMAD4 expression in ENOPH1-knocked and control cells in DLD1 cells using qPCR and Western blot, assessing differences (error bars represent SEM, *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001); d. Treatment of DLD1 cells with or without ENOPH1 knockdown with CHX for a specified time, followed by Western blot analysis. e. Western blot analysis of ubiquitinated SMAD4 in DLD1 cells with or without ENOPH1 knockdown; f. Western blot analysis of p-SMAD2 and p-SMAD3 levels in ENOPH1 knockdown cells with or without TGF-β treatment, using total protein extraction.

[0036] Figure 6-2 Graphs gh show the analysis of ENOPH1 regulating p-SMAD2 / 3 nuclear transport and SMAD4 stability to mediate TGF-β / SMAD signaling; g. Western blot analysis of p-SMAD2 and p-SMAD3 expression in the cytoplasm and nuclear components of ENOPH1 knockdown DLD1 cells treated with or without TGF-β; h. Representative immunofluorescence images of p-SMAD2 / 3 localization in the cytoplasm and nucleus of ENOPH1 knockdown DLD1 cells treated with or without TGF-β, with DAPI staining (scale bar: 10 μm). The TGF-β concentration used in all experiments was 5 ng / mL.

[0037] Figure 7-1 This is a KEGG pathway enrichment analysis diagram of the metabolic pathways associated with enhanced glycolysis and significantly altered metabolites in KRAS-mutant CRC, mediated by TGF-β / SMAD signaling of ENOPH1.

[0038] Figure 7-2Figures b and f show the enhanced glycolysis by ENOPH1 in KRAS-mutant CRC via TGF-β / SMAD signaling. Specifically: b. Heatmap of glycolysis-related genes from RNA-seq data, normalized using Z-scores; c. Quantification of mRNA expression levels of glycolysis-related genes using qPCR in DLD1 cells with and without ENOPH1 knockdown; d. ECAR measurement using a Seahorse XF96 analyzer to assess the effect of ENOPH1 knockdown on metabolic reprogramming in DLD1 cells; e. Non-targeted metabolomics experiments to determine pyruvate levels in DLD1 cells with and without ENOPH1 knockdown; f. ELISA to measure lactate levels in DLD1 cells with and without ENOPH1 knockdown. Note: Error bars in c, e, and f represent SEM; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001.

[0039] Figure 7-3 Figure gj shows the enhanced glycolysis of ENOPH1 in KRAS-mutant CRC via TGF-β / SMAD signaling. g. Expression of glycolysis-related genes in ENOPH1 knockdown DLD1 cells (with or without TGF-β treatment) was detected by qPCR. h. ECAR was measured using a Seahorse XF96 analyzer to assess the role of ENOPH1 in metabolic reprogramming via TGF-β / SMAD2 / 3 signaling. i, j. CCK-8 and 3D spheroid culture experiments were performed in ENOPH1 knockdown DLD1 cells to determine whether cell growth could be restored via TGF-β / SMAD2 / 3 signaling. Note: g, i. Error bars represent SEM; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001.

[0040] Figure 8 A schematic diagram depicting the mechanism of action of ENOPH1 in KRASG12D / G13D mutant colorectal cancer. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention is further described below with reference to specific embodiments. Unless otherwise described in detail, the technical means used in the following embodiments are all conventional means well known to those skilled in the art. Alternatively, they may be carried out according to the kit and product instructions. Unless otherwise specified, the materials and reagents used in the following embodiments are commercially available.

[0042] Example 1

[0043] I. Materials and Methods

[0044] Bioinformatics Analysis

[0045] Transcriptome data (RNA-seq) of colorectal adenocarcinoma were downloaded from the Cancer Genome Atlas (TCGA) database in FPKM format, and corresponding clinical data were obtained, excluding cases lacking follow-up information. Genes with a mean expression level below 0.5 were removed. 448 metabolic genes related to human amino acid metabolism pathways were retrieved from the MsigDB Molecular Characteristics Database (duplicates removed). The "limma" software package was used to identify differentially expressed metabolic genes between cancerous and adjacent non-cancerous tissues, with a selection criterion of Log2 fold change > 1 and p < 0.05. Based on preliminary results, univariate and multivariate Cox regression analyses were performed to identify genes significantly associated with prognosis. The Wilcoxon test was used to assess the expression differences of these genes between cancerous and adjacent tissues, and genes meeting the criteria were designated as candidate genes. Subsequently, gene set variation analysis (GSVA) ​​and pathway quantification were performed using the "clusterProfiler" and "GSVA" software packages, while pathway differences were analyzed using the "limma" software package with the same selection criteria. Pearson correlation analysis was performed between candidate genes and differentially expressed pathways, with a correlation coefficient R>0.2 and a p-value<0.05 as the cutoff value.

[0046] Human colorectal cancer tissue

[0047] Paraffin-embedded cancer tissue samples were obtained from 12 patients with primary colorectal cancer (CRC) at the Affiliated Hospital of Nantong University. The CRC diagnosis was pathologically confirmed; 6 patients carried the KRAS mutation, and the remaining 6 were wild-type KRAS. None of the patients received preoperative intervention. Informed consent was obtained from all patients before the start of this study, and the use of clinical data and human tissue samples was approved by the Ethics Committee of Nantong University (No.: 2024-L054).

[0048] Cell lines and drug treatment

[0049] Patient-derived CRC cell lines HCT116 and DLD1, and mouse-derived CT26 were purchased from the American Type Culture Collection (ATCC). Other cell lines (such as HEK293T and NIH / 3T3) were purchased from Pronosei (Wuhan, China). All cell lines were cultured in DMEM or RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS) or bovine serum (CS) and penicillin-streptomycin under standard conditions (37°C, 5% CO2). In some experiments, cells were treated with 5 ng / mL TGF-β1 (MedChemExpress), 20 μg / mL cyclohexylimide (CHX, Beyotime), 1 μM doxorubicin hydrochloride (DOX, Beyotime), or 1 μM PD-0325901 (MedChemExpress). Stock solutions of TGF-β1 and CHX were prepared with water, and PD-0325901 and DOX were dissolved in dimethyl sulfoxide (DMSO), with an equal volume of DMSO as a control. The stock solutions were filtered through a 0.22 μm filter.

[0050] shRNA, lentiviral transfection and expression plasmids

[0051] Short hairpin RNAs (shRNAs) targeting ENOPH1 (human), Enoph1 (mouse), KRAS (human), Kras (mouse), SMAD2 (human), and SMAD3 (human), along with a control shRNA, were purchased from Generay. The plasmids were transfected into cells at approximately 70% confluence using Lipofectamine 3000 (Thermo Fisher Scientific). The ENOPH1 shRNA primarily consists of:

[0052] Sh-E1-1: 5'-GCAGAGTCTTTGCAGATGTA-3',

[0053] Sh-E1-2: 5'-CCCTTGTGATTTAGAAGATTA-3' (abbreviated as Sh-E1).

[0054] ENOPH1 shRNA was cloned into a puromycin-resistant lentiviral vector (Generay) to induce stable expression. Lipo293 was used for virus preparation. TM HEK293T cells were co-transfected with lentiviral packaging plasmid pMD2.G, envelope plasmid pVSVG, and pLKO.1-Puro plasmid using a transfection reagent (Beyotime). The culture medium was changed after 24 hours, and viral particles were collected at 48 and 72 hours. Cells were infected with recombinant lentivirus at approximately 50% confluence and selected for two weeks with 2 μg / mL puromycin to generate a stable ENOPH1 knockdown CRC cell line. The knockdown efficiency was verified by Western blotting.

[0055] The plasmid and empty vector (pcDNA3.1) expressing wild-type KRAS were purchased from Generay. Using the wild-type KRAS plasmid as a template, Phusion was employed. TM Plasmids expressing KRAS G12D and G13D mutants were amplified using Plus PCR Master Mix (Thermo Fisher Scientific). Unmutated templates were digested with DpnI (NEB), and primer sequences were designed using software. The plasmids were transfected into 70% confluent cells using Lipofectamine 3000.

[0056] In the wound healing assay, transfected DLD1 and CT26 cell lines were aseptically seeded into 6-well plates, ensuring overnight growth to form a monolayer. Once the cells had fully spread and formed a monolayer, a straight scratch was created on the cell layer using a sterile tool (such as a sharp pipette tip or scratcher). The cells were gently washed with PBS to remove any cell debris generated during the scratching process, followed by the addition of serum-free or low-serum culture medium to minimize interference from cell proliferation on the experimental results. An initial photograph was taken immediately after the scratch as a baseline, and subsequent photographs were taken at regular time intervals (e.g., 6 hours, 24 hours, 48 ​​hours) to record the wound healing progress.

[0057] Cell proliferation assay (CCK-8 assay)

[0058] Cells from each group were digested and collected 48 h after transfection, and centrifuged for later use. Cells were resuspended in complete culture medium to adjust the cell density to a uniform size. 100 μL of cell suspension was added to each well, with 6 replicates per group. The cells were gently tapped to distribute them evenly. After the cells adhered (about 6-8 h), a certain amount of CCK-8 reagent was added at 0, 24, 48, 72, and 96 h. The cells were gently tapped and placed in an incubator for 2 h. The absorbance at 450 nm was measured using a microplate reader. The measured data were statistically processed using GraphpadPrism 9 to generate a line graph.

[0059] 3D spherical culture

[0060] CRC cells were cultured into 3D spheroids in 96-well Corning microplates, with 1 × 10⁶ cells seeded per well. 4 Cells were collected by centrifugation at 2000g for 10 minutes and then cultured at 37℃ and 5% CO2. Spherical images were acquired on day 1 and day 7, respectively.

[0061] Apoptosis detection

[0062] Apoptosis levels were assessed using the Annexin V-FITC apoptosis detection kit (Beyotime) and flow cytometry (BD Accuri C6Plus). In short, 5-10 × 10⁻⁶ cells / year.4 Cells were stained with Annexin V-FITC and then with PI, incubated on ice for 10-20 minutes, and analyzed by flow cytometry. Data were processed using FlowJo software.

[0063] RNA extraction and quantitative real-time PCR (qRT-PCR)

[0064] Total RNA was extracted using TRIzol (Thermo Fisher Scientific) and reverse transcribed into cDNA using HiScript IIQ RT SuperMix (Vazyme). The cDNA was then processed using Taq Pro Universal SYBR qPCR Master Mix (Vazyme) in AppliedBiosystems. TM qPCR was performed on a Thermo Fisher Scientific instrument (7500), using β-actin as an internal control, and 2... - The relative gene expression level was calculated using the ΔΔCT method, and the primer sequences were designed using software.

[0065] Protein preparation and Western blot

[0066] Nucleoproteins and cytosolic proteins were extracted using a nuclear protein extraction kit (Solarbio). Total protein was extracted using RIPA buffer (Beyotime) containing protease and phosphatase inhibitors, and quantified using an enhanced BCA protein assay kit (Beyotime). Western blots were performed according to the reagent instructions. In short, proteins were separated by SDS-PAGE and detected using ECL chemiluminescence reagent (NCM Biotech). All antibodies used were commercially available.

[0067] Immunoprecipitation

[0068] Cells were lysed in 1 ml RIPA lysis buffer (MedChemExpress) on a cold shaker for 30 minutes to obtain whole-cell lysate, and 100 μl was used as the input group. For immunoprecipitation, 800 μl of lysis buffer and 1 μg of SMAD4 antibody were incubated overnight at 4°C. The next day, 20 μl of resuspended Protein A / G magnetic beads (MCE) were added, and the mixture was shaken at 4°C for 4 hours. The magnetic beads were collected using a magnetic rack, and the supernatant was carefully discarded. The magnetic beads were washed three times with PBS. Finally, 100 μl of SDS-PAGE loading buffer (Beyotime) was added to the immunoprecipitate, vortexed, and boiled for 10 minutes for immunoblotting to detect immunoprecipitate (IP) and input proteins.

[0069] ELISA

[0070] Collect the supernatant from the six-well plate and use the human TGF-β1 (Lianke) and lactate (Kselida) ELISA kit according to the instructions to detect TGF-β1 and lactate levels.

[0071] Immunofluorescence

[0072] Cells were cultured and processed on 24-well plates. Immunofluorescence staining was performed on cells and paraffin-embedded KRAS wild-type and mutant CRC tissue sections. Samples were fixed with 4% paraformaldehyde, permeabilized with 0.5% Triton X-100, blocked with 5% donkey serum, incubated with primary antibody diluted 1:100, and then stained with CoraLite488-conjugated goat anti-rabbit IgG (H+L) secondary antibody and DAPI. Images were acquired using a Zeiss confocal microscope.

[0073] Transcriptome sequencing analysis

[0074] APTBIO used the Illumina Novaseq 6000 platform to perform RNA-seq on three pairs of ENOPH1 knockdown CRC cells and their control cells. The data were analyzed using DESeq2 software. The screening criteria for differentially expressed genes (DEGs) were p-value <0.05 and Log2 fold change >1 or <-1. Subsequently, the DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.

[0075] Untargeted metabolomics assays were performed using APTBIO to extract metabolites from six pairs of ENOPH1-knockdown CRC cells and their control cells, followed by liquid chromatography-mass spectrometry (LC-MS) analysis. Metabolites were separated using an ACQUIY UPLC BEH Amide column and analyzed by mass spectrometry on a Sciex TripleTOF 6600 mass spectrometer. Data were processed using XCMS software (including baseline correction, peak detection, and peak area integration). Metabolites were annotated and metabolic pathways were enriched using the KEGG database. Enrichment analysis was performed based on Fisher's exact test, with only pathways showing a p-value <0.05 considered significantly altered.

[0076] Metabolic flux analysis

[0077] Extracellular acidification rate (ECAR) was measured using a Seahorse XFe96 extracellular flux analyzer (Agilent). Cells were loaded at 4 × 10⁻⁶. 4 Cells were seeded at a density of cells / well in 96-well plates, and glycolytic stress was tested according to the manufacturer's instructions. After baseline measurement, glucose (25 mM), oligomycin (5 μM), and 2-deoxyglucose (2 mM) were added sequentially to the wells to measure ECAR. ECAR values ​​were normalized to the number of cells per well and analyzed using Seahorse Wave desktop software (Agilent).

[0078] Animal experiments

[0079] Four- to five-week-old female BALB / c nude mice (Jicui Yaokang) were housed under specific pathogen-free conditions and randomly divided into four groups (n=5 per group). All animal experiments were approved by the Animal Ethics Committee of Nantong University. DLD1 (5×10⁻⁶) stably knocked down ENOPH1 was used. 6 (each) and CT26 (3×10) 6 (Number) cells or control cells were subcutaneously injected into the axillary tissue of nude mice to establish a xenograft or syngeneic tumor model. Tumor size was measured every 3 days starting from day 5 post-injection, and tumor volume was calculated using the formula: Tumor volume = length × width² × 0.5. Mice were sacrificed and tumors were collected three weeks later.

[0080] Statistical analysis

[0081] Statistical analysis was performed using R software version 4.0.2 and GraphPad Prism 9.0 software. Student's t-test, one-way or two-way ANOVA was used to compare differences between two or more groups. All data were from at least three independent experiments and are expressed as mean ± standard error (SEM). p < 0.05 was considered statistically significant.

[0082] II. Results

[0083] 1. ENOPH1 is a key metabolic gene associated with the prognosis of CRC patients.

[0084] Transcriptome data and corresponding clinical information from 325 patients with colorectal adenocarcinoma were collected from TCGA. By analyzing the human amino acid metabolism-related gene set (containing 448 genes), 78 significantly differentially expressed metabolism-related genes were identified between CRC tissues and adjacent non-cancerous tissues. Figure 1-1 a). Further univariate and multivariate Cox regression analyses identified four key metabolic genes closely related to CRC prognosis: ENOPH1, MTAP, SFXN3, and SLC25A15 ( Figure 1-1 b, c). The expression levels of these genes in CRC tissues were significantly higher than those in adjacent normal tissues, with ENOPH1 showing the most significant differential expression. Figure 1-1 d). To further understand the biological function of ENOPH1 in CRC, gene set variation analysis (GSVA), pathway quantification, and Pearson correlation analysis were performed, identifying 22 differentially expressed signaling pathways (GSM). Figure 1-2 e). Notably, oncogenic pathways such as KRAS and glycolysis are closely associated with ENOPH1 expression. Figure 1-2 f) provides important clues about the potential role of ENOPH1 in the progress of CRC.

[0085] 2. KRASG12D / G13D-mediated MEK / ERK cascade upregulates ENOPH1 expression.

[0086] Given the close association between ENOPH1 and KRAS signaling, we first detected the expression of ENOPH1 in CRC tissues with different KRAS states using immunofluorescence analysis. The results showed that ENOPH1 expression in the KRAS mutant group was significantly higher than that in the KRAS wild-type group. Figure 2-1 a) This suggests that KRAS mutations may upregulate ENOPH1 expression. Since KRASG12D and KRASG13D are very common and important subtypes of KRAS mutations, KRAS wild-type (KRASWT), KRASG12D, and KRASG13D were introduced into NIH / 3T3 cells to verify the role of ENOPH1 in KRAS mutant cells. The results showed that ENOPH1 was expressed in both KRAS wild-type and mutant cells, but its expression was significantly higher in mutant cells. Figure 2-1 (b, c). Furthermore, compared to KRASWT, KRASG12D and KRASG13D mutations significantly promoted NIH / 3T3 cell proliferation. Knockdown of ENOPH1 inhibited the proliferation of both wild-type and mutant KRAS cells, with a more pronounced inhibitory effect in mutant cells, suggesting that the growth of KRASG12D / G13D mutant cells is more dependent on ENOPH1 expression. In addition, this application found a positive correlation between ENOPH1 expression and p-ERK. Therefore, using CRC cell lines carrying different KRAS mutant subtypes, this application further investigated the relationship between ENOPH1 and KRAS signaling. In HCT116 and DLD1 cells carrying the KRASG13D mutation, silencing the KRAS gene or using the MEK / ERK inhibitor (PD-0325901) significantly reduced ENOPH1 expression, and the combined treatment had a stronger inhibitory effect on ENOPH1 expression. Figure 2-1 d,e, Figure 2-2 f,g). Similar results were also observed in CT26 cells carrying the KRASG12D mutation (f,g). Figure 2-2 These experimental results confirm that ENOPH1 expression is regulated by KRAS / MEK / ERK signaling, suggesting that ENOPH1 may play a key role in KRAS mutation-driven CRC progression.

[0087] 3. ENOPH1 as a potential therapeutic target for KRASG12D / G13D mutant CRC

[0088] Based on the above findings, this application hypothesizes that ENOPH1 may be a potential therapeutic target for KRASG12D / G13D-driven CRC. To verify this, this application used two independent shRNAs to knock down ENOPH1 expression in DLD1 and CT26 cells, and Western blot confirmed that ENOPH1 was effectively knocked down. Cell proliferation experiments showed that knocking down ENOPH1 severely impaired cell proliferation capacity. Figure 3-1 a, b). Wound healing experiments showed that silencing ENOPH1 significantly reduced cell migration ability. Figure 3-1 c). Further analysis showed that ENOPH1 knockdown reduced the expression of epithelial-mesenchymal transition (EMT) markers N-cadherin and vimentin, while increasing the expression of E-cadherin. Flow cytometry confirmed that ENOPH1 knockdown significantly increased apoptosis and enhanced DOX-induced apoptosis. Figure 3-2 In in vivo experiments, injecting DLD1 and CT26 cells with stable ENOPH1 knockdown into nude mice resulted in tumors significantly smaller than those in the corresponding control group, indicating a reduction in tumor growth rate and volume. Figure 3-3 e, f). Western blot confirmed downregulation of ENOPH1 expression in tumor tissue. Figure 3-3 These results indicate that ENOPH1 is a promising therapeutic target for KRASG12D / G13D driven CRC.

[0089] 4. Multi-omics analysis identifies ENOPH1 as a key regulator in CRC cells.

[0090] To understand the functional role of ENOPH1 in KRAS-mutant CRC, this application performed transcriptome sequencing and untargeted metabolomics analysis after ENOPH1 knockdown in DLD1 cells. Differential expression analysis revealed 2,857 differentially expressed genes (DEGs) between ENOPH1-silenced (sh) and wild-type (WT) cells, including 1,628 upregulated genes and 1,229 downregulated genes. Figure 4-1 a). KEGG enrichment analysis showed that these DEGs significantly aggregated in pathways regulating immune-inflammatory responses, metabolic processes, disease progression, and signal transduction. Figure 4-1 b). Further non-targeted metabolomics analysis revealed a significant separation between the sh and WT groups in principal component analysis (PCA), with tight intra-group clustering, demonstrating the robustness and reproducibility of the data. Figure 4-2 c). Comparative metabolite analysis revealed 90 significantly altered metabolites in the sh group compared to the WT group, with 61 metabolites showing decreased synthesis and only 29 showing increased synthesis. Figure 4-2d). KEGG enrichment analysis of these metabolites highlighted several tumor-associated pathways, including the mTOR signaling pathway, the cAMP signaling pathway, and central carbon metabolism in cancer. Figure 4-3 This indicates that ENOPH1 coordinates key oncogenic signaling networks and glycolytic metabolic pathways during CRC progression. Overall, these multi-omics data demonstrate that ENOPH1 is widely involved in key signaling and metabolic processes in CRC development, providing a solid foundation for further mechanistic investigations.

[0091] 5. KRASG12D and KRASG13D promote TGF-β expression via an ENOPH1-dependent pathway.

[0092] To further understand the role of ENOPH1 in the progression of KRASG12D / G13D mutant CRC, this application performed RNA-seq on ENOPH1 knockdown and control cells. KEGG pathway analysis showed that ENOPH1 expression was closely related to the activation of TGF-β signaling. Figure 5 a). ELISA results showed that, compared with the control group, the TGF-β level in the supernatant of ENOPH1 knockdown cells was significantly reduced ( Figure 5 (b, c). Since TGF-β is a key downstream mediator of KRAS mutation-driven invasive growth of CRC, this application hypothesizes that KRAS mutation-activated MEK / ERK signaling enhances TGF-β expression by upregulating ENOPH1. Indeed, silencing KRAS and inhibiting MEK / ERK signaling in DLD1 and CT26 cells reduced ERK phosphorylation and TGF-β expression. Figure 5 (d, e). To determine whether KRASG12D and KRASG13D depend on ENOPH1 to induce TGF-β expression, KRASG12D and KRASG13D were overexpressed in NIH / 3T3 cells. Western blot results showed that both KRASG12D and KRASG13D significantly increased TGF-β expression, while ENOPH1 knockdown reversed this effect. Figure 5 These findings suggest that ENOPH1 may play a key role in mediating KRASG12D / G13D-induced TGF-β signaling activation.

[0093] 6. ENOPH1 mediates TGF-β / SMAD signaling in KRAS-mutant CRC cells.

[0094] Based on previous results and supporting literature, this application hypothesizes that ENOPH1 regulates TGF-β / SMAD signaling in KRAS mutant CRC cells. This application used nucleo-cytoplasmic separation and Western blot to assess the levels of phosphorylated (p)SMAD2 and SMAD3 in DLD1 cells, finding that ENOPH1 knockdown significantly reduced the nuclear levels of these two proteins. Figure 6-1 a). Immunoprecipitation assays confirmed that ENOPH1 knockdown reduced the binding of p-SMAD2 / 3 to SMAD4 ( Figure 6-1 b). Interestingly, ENOPH1 knockdown also reduced SMAD4 protein levels but did not affect SMAD4 mRNA expression, suggesting post-translational regulation. Figure 6-1 c). To verify this, cells were treated with CHX for a specified time, and Western blot showed that ENOPH1 knockdown accelerated SMAD4 degradation. Figure 6-1 d), while ubiquitination experiments showed that ENOPH1 knockdown increased SMAD4 ubiquitination in cells. Figure 6-1 e). Furthermore, treatment with TGF-β restored the levels and nuclear localization of p-SMAD2 / 3 in ENOPH1-knockdown cells. Figure 6-1 f, Figure 6-2 These results indicate that ENOPH1 regulates nuclear transport of p-SMAD2 / 3 and stability of SMAD4, thereby mediating TGF-β / SMAD signaling.

[0095] 7. ENOPH1 enhances glycolytic activity through the TGF-β / SMAD signaling pathway.

[0096] To understand the role of ENOPH1 in metabolic reprogramming of KRAS-mutant CRC cells, this application performed non-targeted metabolomics analysis. KEGG pathway analysis showed significant enrichment of central carbon metabolism (…). Figure 7-1 Glycolysis, including the tricarboxylic acid cycle and the pentose phosphate pathway, are crucial for energy production and biosynthesis in tumor cells. Given the close relationship between ENOPH1 and glycolysis, this application focuses on investigating the effects of ENOPH1 on glycolytic metabolism in KRAS-mutant CRC cells. Transcriptome analysis identified a series of key glycolytic genes regulated by ENOPH1 (such as ALDOA, PFKL, and HK3). Figure 7-2 b) This finding was verified by qPCR. Figure 7-2 c). Seahorse XF experiments further demonstrated that ENOPH1 knockdown significantly reduced extracellular acidification rate (ECAR), indicating decreased glycolytic activity. Figure 7-2 d). Furthermore, metabolomics and ELISA experiments showed that ENOPH1 knockdown significantly reduced pyruvate and lactate levels. Figure 7-2e, f). These findings indicate that ENOPH1 plays a crucial role in the regulation of glycolysis. To confirm that ENOPH1 participates in this process through the TGF-β / SMAD pathway, TGF-β was supplemented in ENOPH1-knockdown DLD1 cells, and expression of glycolysis genes was observed ( Figure 7-3 g) and ECAR level ( Figure 7-3 h) partial recovery. However, silencing SMAD2 / 3 eliminated the TGF-β-induced recovery. Similarly, TGF-β supplementation restored cell proliferation and tumor spheroid formation, but these effects were reversed by SMAD2 / 3 knockdown (h). Figure 7-3 These findings suggest that ENOPH1 enhances glycolytic activity through the TGF-β / SMAD signaling pathway, thereby affecting the proliferation of KRAS mutant CRC cells.

[0097] In summary, KRAS mutation is a core driving event in colorectal cancer (CRC). Although KRAS-induced metabolic reprogramming (such as glycolysis and dependence on methionine metabolism) is widely considered a key oncogenic mechanism, the molecular hubs regulating these processes remain unclear. The function and regulatory network of ENOPH1, a core enzyme in the methionine rescue pathway, in KRAS-mutant CRC are unknown. Through integrated multi-omics analysis, this application reveals for the first time that ENOPH1 is a key target closely related to signal transduction and metabolism in KRAS-mutant CRC, regulating glycolytic metabolism and influencing tumor cell behavior. Therefore, ENOPH1 is a potential therapeutic target for KRASG12D / G13D mutant CRC.

[0098] Through various experiments, this application identified ENOPH1 as a key regulator of glycolytic reprogramming and CRC progression driven by KRASG12D and KRASG13D mutations. Furthermore, it was found that KRASG12D and KRASG13D mutations promote ENOPH1 expression through the MEK / ERK signaling cascade, which in turn enhances glycolysis via the TGF-β / SMAD signaling pathway, thereby promoting the growth of KRAS mutant CRC cells. Figure 8 These findings suggest that ENOPH1 is an important mediator of oncogenic KRAS signaling and may serve as a potential therapeutic target for KRASG12D / G13D mutant CRC, providing a theoretical basis for developing novel therapeutic strategies for this type of cancer.

[0099] In summary, oncogenic KRASG12D / G13D activates the MEK / ERK cascade, thereby upregulating ENOPH1 expression. Subsequently, ENOPH1 enhances the nuclear translocation of phosphorylated SMAD2 / 3 and regulates SMAD4 ubiquitination, promoting the activation of the TGF-β / SMAD signaling pathway. Simultaneously, ENOPH1 drives the transcription of glycolytic genes through activated TGF-β / SMAD signaling, enhancing glycolytic activity to support tumor cell proliferation and migration. This mechanism highlights the potential of targeting ENOPH1 as a therapeutic strategy for KRASG12D / G13D mutant colorectal cancer.

[0100] The above description is illustrative only and not restrictive of the present invention. Those skilled in the art will understand that many modifications, variations or equivalents can be made without departing from the spirit and scope defined by the appended claims, and all such modifications, variations or equivalents will fall within the protection scope of the present invention.

Claims

1. An anti-colorectal cancer drug, characterized in that, The shRNA containing ENOPH1, or an expression vector of shRNA containing ENOPH1, or a viral vector containing an expression vector of shRNA containing ENOPH1, wherein the shRNA is: Sh-E1-1: 5'-GCAGAGTCTTTGCAGATGTA-3', And / or Sh-E1-2:5'- CCCTTGTGATTTAGAAGATTA -3'.

2. The anti-colorectal cancer drug according to claim 1, characterized in that, The aforementioned anti-colorectal cancer drug is used to enhance the sensitivity of colorectal cancer cells to chemotherapy drugs.

3. The anti-colorectal cancer drug according to claim 2, characterized in that, The chemotherapy drug mentioned is doxorubicin hydrochloride.

4. The use of ENOPH1 shRNA, or an expression vector containing ENOPH1 shRNA, or a viral vector containing ENOPH1 shRNA in the preparation of anti-colorectal cancer drugs, wherein the sequence of the shRNA is: Sh-E1-1: 5'-GCAGAGTCTTTGCAGATGTA-3', And / or Sh-E1-2:5'- CCCTTGTGATTTAGAAGATTA -3'.

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