Application of ENOPH1 gene

Abstract

The invention discloses application of an ENOPH1 gene, and relates to the technical field of biological medicines. According to the application, a key metabolic gene closely related to CRC prognosis is analyzed and identified by adopting bioinformatics, and the expression of ENOPH1 in KRAS mutant CRC tissues and cell lines is evaluated. The function of the ENOPH1 in the KRASG12D / G13D mutant CRC is verified through CCK8, a wound healing experiment, an in-vivo subcutaneous xenotransplantation tumor model and other in-vitro experiments. The result shows that the ENOPH1 is highly expressed in the KRAS mutant CRC and is subjected to MEK / ERK signal cascade regulation and control. The ENOPH1 is knocked down through shRNA, so that CRC cell proliferation, migration and tumorigenesis can be inhibited, cell apoptosis is induced, and the reaction to chemotherapy is enhanced. The application provides a new strategy and direction for treatment of colorectal cancer, has extremely high clinical application value, and can bring a better treatment effect for patients with colorectal cancer.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of biological medicine, more specifically, relates to the application of ENOPH1 gene. BACKGROUND

[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 main treatment for CRC, however, approximately 20% of patients have metastasis at the time of diagnosis and cannot undergo curative surgery. In addition, despite advances in treatment, approximately 25% of patients with localized disease who undergo curative surgery will develop recurrence or metastasis, with a five-year survival rate of less than 10%.

[0003] Activation of the KRAS oncogene is a well-recognized genetic driver of CRC development. Most oncogenic KRAS mutations occur at G12, G13 and Q61 codons, 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. Approximately 40-50% of CRC patients harbor KRAS mutations, among which G12D and G13D are very common subtypes, together accounting for nearly half of all KRAS mutations. Due to the lack of a clear druggable binding pocket, KRAS has long been considered a challenging target for small molecule drugs. Although recent progress has been made in developing inhibitors targeting specific KRAS mutations (such as sotorasib or adagrasib), these drugs as monotherapy have a response rate of about 30% for KRAS^G12C mutant non-small cell lung cancer (NSCLC), but have limited clinical efficacy in CRC. KRAS^G12C mutations account for only about 3% of CRC cases, and the monotherapy response rate in CRC is about 10%. More prevalent mutations such as KRAS^G12D and KRAS^G13D still lack effective treatment options. Therefore, developing viable treatment strategies for KRAS mutant CRC remains a major challenge in the field of oncology, requiring a multi-pronged approach to identify and validate new therapeutic targets.

[0004] Metabolic reprogramming is increasingly recognized as a hallmark of malignancy. In CRC, multiple metabolic genes involved in this reprogramming and closely related to 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 becoming a new potential cancer treatment method. 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 related to KRAS signaling and CRC prognosis. ENOPH1 is a recently discovered bifunctional enzyme involved in the methionine salvage pathway, which maintains methionine levels by recycling methylthio groups from methylthioadenosine. However, so far, the role of ENOPH1 in KRAS mutant CRC and its underlying mechanisms have not been explored. SUMMARY

[0005] To solve the above-mentioned problems in the prior art, the technical problem to be solved by the present application is to provide an application of an ENOPH1 gene, specifically an application as a drug target in the preparation of a targeted drug for treating colorectal cancer.

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

[0007] The application of ENOPH1 gene as a drug target in the preparation of a targeted drug for treating colorectal cancer.

[0008] Further, the colorectal cancer is KRAS G12D / G13D mutant.

[0009] Further, 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 containing shRNA containing ENOPH1 or a viral vector containing an expression vector containing shRNA containing ENOPH1.

[0011] Further, the shRNA is:

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

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

[0014] Further, the anti-colorectal cancer drug is a drug for enhancing the sensitivity of colorectal cancer cells to a chemotherapeutic drug.

[0015] Further, the chemotherapeutic drug is doxorubicin hydrochloride.

[0016] Further, the anti-colorectal cancer drug is a drug for inhibiting the proliferation activity, migration ability or invasion ability of colorectal cancer cells.

[0017] The shRNA of ENOPH1, or the expression vector containing the shRNA of ENOPH1, or the viral vector containing the shRNA of ENOPH1 is used for preparing an anti-colorectal cancer drug.

[0018] Further, the sequence of the shRNA is:

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

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

[0021] Compared with the prior art, the application has the following beneficial effects:

[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 evaluated. The function of ENOPH1 in KRASG12D / G13D mutant CRC was verified through in vitro experiments (including CCK8, EdU incorporation and wound healing experiments) and in vivo subcutaneous xenotransplant tumor models. In addition, qPCR, Western blotting, ELISA, immunofluorescence and Seahorse metabolic flux analysis were combined with transcriptome and metabolome sequencing data to systematically mine the mechanism of ENOPH1-mediated effects in KRASG12D / G13D mutant CRC. The results showed that ENOPH1 was highly expressed in KRAS mutant CRC and was regulated by the MEK / ERK signaling cascade. Knockdown of ENOPH1 by shRNA could inhibit CRC cell proliferation, migration and tumorigenesis, induce apoptosis, and enhance the response to chemotherapy. Multi-omics analysis showed that ENOPH1 played a key regulatory role in KRAS mutant CRC. Mechanistically, ENOPH1 regulated the phosphorylation of SMAD2 / 3 and the ubiquitination of SMAD4, thereby affecting the TGF-β / SMAD signaling pathway. In addition, ENOPH1 was involved in central carbon metabolism and regulated the glycolytic activity of KRASG12D / G13D mutant CRC cells, thereby promoting tumor progression.

[0023] ENOPH1 enhances glycolysis through TGF-β / SMAD signaling and promotes the progression of KRASG12D / G13D mutant CRC. Targeting ENOPH1 may provide therapeutic potential for KRAS mutant CRC. The present application provides a new strategy and direction for the treatment of colorectal cancer, which has extremely high clinical application value and can bring better treatment effect for patients with intestinal cancer. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1-1 Figures a-d are analysis graphs for ENOPH1 being identified as a key metabolic gene associated with CRC prognosis; wherein a. volcano plot showing the differential expression of amino acid metabolism-related genes in CRC tissues and adjacent non-cancerous tissues; b, c. forest plots of univariate and multivariate COX regression analysis showing metabolic genes associated with CRC prognosis, with hazard ratio attached to 95% confidence interval; d. box plot showing the expression levels of ENOPH1, MTAP, SFXN3 and SLC25A15 in colorectal cancer and adjacent normal tissues;

[0025] Figure 1-2 Figures e-f are analysis graphs for ENOPH1 being identified as a key metabolic gene associated with CRC prognosis; wherein e. GSVA analysis showing 22 CRC-related differential pathways; f. Pearson correlation analysis of four key metabolic genes (ENOPH1, MTAP, SFXN3, SLC25A15) and 22 CRC-related pathways identified by GSVA;

[0026] Figure 2-1 Figures a-e are analysis graphs for KRASG12D and KRASG13D mutations upregulating ENOPH1 through MEK / ERK signaling; wherein a. immunofluorescence staining to evaluate the expression of ENOPH1 in KRAS mutant and wild-type CRC tissues, the left panel is a representative immunofluorescence image, and the right panel is a quantitative analysis 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. HCT116 cells were subjected to KRAS knockdown, 1 μM PD-0325901 treatment for 24 hours or combined treatment, and the effects on p-ERK and ENOPH1 expression were evaluated by Western blot and qPCR; e. error bars represent SEM; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001;

[0027] Figure 2-2Figures f-i are graphs showing ENOPH1 upregulation by MEK / ERK signaling for KRAS G12D and KRAS G13D mutations; wherein f, g. DLD1 cells were subjected to KRAS knockdown, 1 mM PD-0325901 treatment for 24 hours or combination treatment, the effects on p-ERK and ENOPH1 expression were evaluated by Western blot and qPCR; h, i. CT26 cells were subjected to KRAS knockdown, 1 mM PD-0325901 treatment for 24 hours or combination treatment, the effects on p-ERK and ENOPH1 expression were evaluated 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 Figures a-c are graphs showing ENOPH1 knockdown inhibits KRAS G12D / G13D CRC cell proliferation and migration and promotes apoptosis; wherein a. CCK8 assay to evaluate the effects of ENOPH1 knockdown on the proliferation ability of DLD1 and CT26 cells; b. Colony formation and 3D spheroid assay to evaluate the effects of ENOPH1 knockdown on the proliferation ability of DLD1 and CT26 cells; c. Wound healing assay to detect the effects of ENOPH1 knockdown on the migration of DLD1 and CT26 cells;

[0029] Figure 3-2 Figure is a graph showing the effects of ENOPH1 knockdown on DLD1 and CT26 cell apoptosis and DOX-induced anti-tumor effects using flow cytometry with PI / Annexin V staining;

[0030] Figure 3-3 Figures e-f are graphs showing ENOPH1 knockdown inhibits KRAS G12D / G13D CRC cell proliferation and migration and promotes apoptosis; wherein e, g. Xenograft tumor models were established by subcutaneously inoculating DLD1 and CT26 cells stably expressing ENOPH1 knockdown or control cells (n = 5 per group), e is representative images of xenograft tumors, g is verification of ENOPH1 knockdown in tumor tissues by Western blot analysis; f. Tumor growth time course of mice receiving the indicated treatments was continuously monitored and recorded; Note: a, f. Data are presented as mean ± SEM, *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001;

[0031] Figure 4-1 Figures a-b are graphs showing multi-omics analysis reveals the key role of ENOPH1 in CRC; wherein a. Heatmap of DEGs in HCT116 cells after ENOPH1 silencing; b. Top 20 significantly enriched KEGG pathways in transcriptome sequencing analysis;

[0032] Figure 4-2 Figures c-d; wherein, c. PCA clustering plot of HCT116 cells non-targeted metabolomics data after ENOPH1 silencing; d. Heatmap of differential metabolites showing metabolic changes in HCT116 cells after ENOPH1 silencing;

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

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

[0035] Figure 6-1Figure 6. ENOPH1 modulates p-SMAD2 / 3 nuclear translocation and SMAD4 stability to mediate the TGF-b / SMAD signaling graph; where a. Western blot analysis of p-SMAD2 and p-SMAD3 levels in cytoplasmic and nuclear fractions of DLD1 cells with ENOPH1 knockdown versus control cells; b. Immunoprecipitation of total cell lysates of DLD1 cells (with or without ENOPH1 knockdown) using anti-SMAD4 antibody followed by Western blot analysis of the precipitates with the indicated antibodies; c. SMAD4 expression in DLD1 cells with ENOPH1 knockdown versus control cells was detected using qPCR and Western blot, differences were assessed (error bars represent SEM, *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001); d. DLD1 cells with or without ENOPH1 knockdown were treated with CHX for the indicated times, then SMAD4 levels were analyzed by Western blot; e. Western blot analysis of immunoprecipitated ubiquitinated SMAD4 in DLD1 cells with or without ENOPH1 knockdown; f. Total proteins were extracted from ENOPH1 knockdown DLD1 cells treated or not with TGF-b, p-SMAD2 and p-SMAD3 levels were analyzed by Western blot;

[0036] Figure 6-2 Figure 6. ENOPH1 modulates p-SMAD2 / 3 nuclear translocation and SMAD4 stability to mediate the TGF-b / SMAD signaling graph; where a. Western blot analysis of p-SMAD2 and p-SMAD3 levels in cytoplasmic and nuclear fractions of DLD1 cells with ENOPH1 knockdown versus control cells; b. Immunoprecipitation of total cell lysates of DLD1 cells (with or without ENOPH1 knockdown) using anti-SMAD4 antibody followed by Western blot analysis of the precipitates with the indicated antibodies; c. SMAD4 expression in DLD1 cells with ENOPH1 knockdown versus control cells was detected using qPCR and Western blot, differences were assessed (error bars represent SEM, *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001); d. DLD1 cells with or without ENOPH1 knockdown were treated with CHX for the indicated times, then SMAD4 levels were analyzed by Western blot; e. Western blot analysis of immunoprecipitated ubiquitinated SMAD4 in DLD1 cells with or without ENOPH1 knockdown; f. Total proteins were extracted from ENOPH1 knockdown DLD1 cells treated or not with TGF-b, p-SMAD2 and p-SMAD3 levels were analyzed by Western blot;

[0037] Figure 7-1 Figure 7. KEGG pathway enrichment analysis of ENOPH1 in KRAS mutant-driven CRC through TGF-b / SMAD signaling to enhance glycolysis and metabolic pathways related to significantly altered metabolites;

[0038] Figure 7-2Figures b-f depict ENOPH1 enhances glycolysis in KRAS mutant-driven CRC through TGF-b / SMAD signaling. b. Heatmap of glycolysis-related genes from RNA-seq data, using Z-score standardization. c. mRNA expression levels of glycolysis-related genes were quantified using qPCR in DLD1 cells with or without ENOPH1 knockdown. d. Seahorse XF96 analyzer was used to measure ECAR to assess the effect of ENOPH1 knockdown on metabolic reprogramming of DLD1 cells. e. Non-targeted metabolomics experiment was used to determine pyruvate levels in DLD1 cells with or without ENOPH1 knockdown. f. ELISA was used to measure lactate levels in DLD1 cells with or without ENOPH1 knockdown. Note: Error bars in c, e, f represent SEM, *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001.

[0039] Figure 7-3 Figures g-j depict ENOPH1 enhances glycolysis in KRAS mutant-driven CRC through TGF-b / SMAD signaling. g. Expression of glycolysis-related genes was detected by qPCR in ENOPH1 knockdown DLD1 cells with or without TGF-b treatment. h. Seahorse XF96 analyzer was used to measure ECAR to assess the role of ENOPH1 in metabolic reprogramming through TGF-b / 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 by TGF-b / SMAD2 / 3 signaling. Note: Error bars in g, i represent SEM, *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001.

[0040] Figure 8 Figure depicts a schematic diagram of the mechanism of action of ENOPH1 in KRAS G12D / G13D mutant colorectal cancer. DETAILED DESCRIPTION

[0041] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described below with specific examples. If not specifically described in the following examples, the technical means used are all conventional means well known to those skilled in the art. Or according to the instructions of the kit and product. The materials, reagents, etc. used in the following examples, if not specifically described, can be obtained from commercial channels.

[0042] Example 1

[0043] I. Materials and methods

[0044] Bioinformatics analysis

[0045] Transcriptomic data (RNA-seq) of colon adenocarcinoma were downloaded from The Cancer Genome Atlas (TCGA) database in the format of FPKM, and the corresponding clinical data were obtained, excluding cases with lack of follow-up information. Genes with average expression level lower than 0.5 were removed. 448 metabolic genes related to human amino acid metabolic pathways were retrieved from the Molecular Signature Database (MsigDB) (removing duplicates). The differentially expressed metabolic genes between cancer tissues and adjacent non-cancer tissues were identified using the “limma” package, with the screening criteria of Log2 fold change (Log2FoldChange) > 1 and p value < 0.05. Based on the preliminary results, univariate and multivariate Cox regression analysis were performed to identify genes significantly associated with prognosis. Wilcoxon test was used to evaluate the expression difference of the above genes between cancer tissues and adjacent tissues, and the 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” packages, while the “limma” package was used to analyze the differential pathways with the same screening criteria. Pearson correlation analysis of candidate genes and differentially expressed pathways was performed, with the correlation coefficient R > 0.2 and p value < 0.05 as the cutoff.

[0046] Human colorectal cancer tissues

[0047] Twelve paraffin-embedded cancer tissue samples from patients with primary colorectal cancer (CRC) were obtained from the Affiliated Hospital of Nantong University. The diagnosis of CRC was confirmed by pathology, of which 6 patients carried KRAS mutations and the remaining 6 were wild-type KRAS. All patients had not received preoperative intervention. Informed consent was obtained from all patients before the start of the 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, DLD1 and mouse-derived CT26 were purchased from American Type Culture Collection (ATCC), other cell lines (e.g. HEK293T and NIH / 3T3) were purchased from Pologen (Wuhan, China). All cell lines were cultured in DMEM or RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS) or calf 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 cycloheximide (CHX, Biyun Tian), 1 μM doxorubicin hydrochloride (DOX, Biyun Tian) or 1 μM PD-0325901 (MedChemExpress). Stock solutions of TGF-β1 and CHX were prepared in water, and PD-0325901 and DOX were dissolved in dimethyl sulfoxide (DMSO), with an equal volume of DMSO as control. Stock solutions were filtered through 0.22 μm filters.

[0050] shRNA, lentivirus transfection and expression plasmid

[0051] Short hairpin RNAs (shRNAs) targeting ENOPH1 (human), Enoph1 (mouse), KRAS (human), Kras (mouse), SMAD2 (human) and SMAD3 (human) and control shRNA were purchased from Generay. Plasmids were transfected into approximately 70% confluent cells using Lipofectamine 3000 (Thermo Fisher). ENOPH1 shRNAs mainly included:

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

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

[0054] ENOPH1 shRNAs were cloned into puromycin-resistant lentivirus vectors (Generay) for stable expression induction. For virus preparation, lentivirus packaging plasmids pMD2.G, envelope plasmid pVSVG and pLKO.1-Puro plasmid were co-transfected into HEK293T cells using Lipo293 TM transfection reagent (Biyun Tian). After 24 hours, the medium was replaced, and virus particles were collected at 48 and 72 hours, respectively. Cells were infected with recombinant lentivirus at approximately 50% confluence, and selected with 2 μg / mL puromycin for two weeks to generate stable ENOPH1-knocked-down CRC cell lines, and the knockdown efficiency was verified by Western blotting.

[0055] Plasmids expressing wild-type KRAS and empty vector (pcDNA3.1) were purchased from Generay. Plasmids expressing KRAS G12D and G13D mutants were amplified using Phusion TM Plus PCR Master Mix (Thermo) with wild-type KRAS plasmid as template and Dpnl (NEB) to digest the unmutated template. Primers were designed using software. Plasmids were transfected into 70% confluent cells using Lipofectamine 3000.

[0056] Wound healing experiment. After transfection, DLD1 and CT26 cell lines were inoculated into 6-well plates under sterile conditions to ensure that the cells could grow overnight to form a monolayer. When the cells were fully spread and formed a monolayer, a straight line scratch was made on the cell layer using sterile tools such as sharp gun heads or scratchers. The cells were gently washed with PBS to remove cell debris that may have been generated during the scratching process, and then serum-free or low-serum medium was added to reduce the interference of cell proliferation on the experimental results. Immediately after scratching, the first photo was taken as the baseline, and then the photo was taken again at certain time intervals (such as 6 hours, 24 hours, 48 hours) to record the wound healing.

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

[0058] Cells in each group were collected 48h after transfection and centrifuged; the cells were resuspended with complete medium, and the cell density was adjusted to a consistent size; 100μL of cell suspension was added to each well, with 6 replicate wells for each group, and the 96-well plate was gently tapped to distribute the cells evenly; after the cells adhered (about 6-8h), a certain amount of CCK-8 reagent was added at 0, 24, 48, 72, 96h, the 96-well plate was gently tapped, and then placed in the incubator for 2h before being removed and detected on the microplate reader at 450nm; the measured data were statistically processed using Graphpad Prism 9, and a line graph was drawn.

[0059] 3D spheroid culture

[0060] CRC cells were cultured as 3D spheroids in 96-well spheroid microplates (Corning) with 1x10 4 cells per well, and the suspension cells were collected by centrifugation at 2000g for 10 minutes, and cultured at 37°C, 5% CO2. Spheroid images were taken on day 1 and day 7, respectively.

[0061] Apoptosis detection

[0062] Annexin V-FITC apoptosis detection kit (Bi Yun Tian) and flow cytometry (BD Accuri C6 Plus) were used to evaluate the level of apoptosis. Briefly, 5-10x104 Cells were stained with Annexin V-FITC and then stained with PI, incubated on ice for 10-20 minutes, analyzed by flow cytometry, and data were processed by FlowJo software.

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

[0064] Total RNA was extracted using TRIzol (Thermo) and reverse-transcribed into cDNA by HiScript II Q RT SuperMix (Vazyme). qPCR was performed on an Applied Biosystems 7500 instrument (Thermo) using Taq Pro Universal SYBR qPCR Master Mix (Vazyme) with β-actin as an internal control, and the relative gene expression level was calculated using the 2 TM ΔΔCT method, and the primer sequences were designed using software. - ΔΔCT method, and the primer sequences were designed using software.

[0065] Protein preparation and Western blot

[0066] Nuclear and cytoplasmic proteins were extracted using a nuclear protein extraction kit (Solarbio), and total proteins were extracted using RIPA buffer (Bi Yun Tian) containing protease and phosphatase inhibitors. The proteins were quantified using an enhanced BCA protein assay kit (Bi Yun Tian). Western blot was performed according to the manufacturer's instructions. Briefly, the proteins were separated by SDS-PAGE, and then detected using ECL chemiluminescence reagent (NCM Biotech). All the antibodies used were commercially available.

[0067] Co-immunoprecipitation

[0068] Cells were lysed in 1 ml of RIPA lysis buffer (MedChemExpress) on a cold shaker for 30 minutes to obtain whole cell lysate. 100 μl was taken as the input group. For co- immunoprecipitation, 800 μl of lysate was incubated with 1 μg of SMAD4 antibody at 4°C overnight. 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 stand and the supernatant was carefully aspirated. The magnetic beads were washed three times with PBS. Finally, 100 μl of SDS-PAGE loading buffer (Bi Yun Tian) was added to the immunoprecipitate, vortexed, and boiled for 10 minutes. Immunoblotting was performed to detect the immunoprecipitate (IP) and input proteins.

[0069] ELISA

[0070] Supernatants in six-well plates were collected and TGF-β1 and lactate levels were measured using human TGF-β1 (R&D Systems) and lactate (Kselida) ELISA kits according to the manufacturer’s instructions.

[0071] Immunofluorescence

[0072] Cells were cultured and treated on 24-well plate coverslips and 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 antibodies at 1:100 dilution, followed by staining with CoraLite 488-conjugated goat anti-rabbit IgG (H+L) secondary antibody and DAPI, and images were acquired using a Zeiss confocal microscope.

[0073] Transcriptomic sequencing analysis

[0074] RNA-seq was performed by APTBIO using Illumina Novaseq 6000 platform on three pairs of ENOPH1 knockdown CRC cells and their control cells, and data were analyzed by DESeq2 software, with the screening criteria of differentially expressed genes (DEGs) being p-value < 0.05 and Log2 fold change > 1 or <-1, followed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs.

[0075] Metabolomics analysis

[0076] Metabolic flux analysis

[0077] Extracellular acidification rate (ECAR) was measured using Seahorse XFe96 Extracellular Flux Analyzer (Agilent). Cells were seeded in 96-well plates at a density of 4 x 10 4 cells / well, and glycolysis stress test was performed according to the manufacturer’s instructions. After baseline measurement, glucose (25 mM), oligomycin (5 mM) and 2-deoxyglucose (2 mM) were sequentially added to the wells to measure ECAR, and 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. KRAS G12D / G13D-mediated MEK / ERK cascade upregulates ENOPH1 expression

[0086] Given the close association of ENOPH1 with KRAS signaling, the expression of ENOPH1 in CRC tissues with different KRAS status was first examined by immunofluorescence analysis. The results showed that the ENOPH1 expression in KRAS mutant group was significantly higher than that in KRAS wild-type group Figure 2-1 a), suggesting that KRAS mutation might upregulate ENOPH1 expression. Since KRAS G12D and KRAS G13D are very common and important subtypes of KRAS mutations, KRAS wild-type (KRASWT), KRAS G12D and KRAS G13D 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 the expression was significantly higher in mutant cells Figure 2-1 b,c). In addition, KRAS G12D and KRAS G13D mutations significantly promoted the proliferation of NIH / 3T3 cells compared with KRASWT. After knockdown of ENOPH1, the proliferation of KRAS wild-type and mutant cells was inhibited, and the inhibitory effect was more obvious in mutant cells, suggesting that the growth of KRAS G12D / G13D mutant cells was more dependent on ENOPH1 expression. In addition, it was found that ENOPH1 expression was positively correlated with p-ERK. Therefore, using CRC cell lines carrying different KRAS mutant subtypes, the relationship between ENOPH1 and KRAS signaling was further understood. In HCT116 and DLD1 cells carrying KRAS G13D mutation, silencing KRAS gene or using MEK / ERK inhibitor (PD-0325901) significantly reduced ENOPH1 expression, and the inhibitory effect on ENOPH1 expression was stronger with combined treatment Figure 2-1 d,e, Figure 2-2 f,g). Similar results were also observed in CT26 cells carrying KRAS G12D mutation Figure 2-2 h,i). These experimental results confirmed that ENOPH1 expression was regulated by KRAS / MEK / ERK signaling, suggesting that ENOPH1 might play a key role in the progression of CRC driven by KRAS mutation.

[0087] 3. ENOPH1 as a potential therapeutic target for KRAS G12D / G13D mutant CRC

[0088] Based on the above findings, the present application hypothesizes that ENOPH1 can be a potential therapeutic target for KRAS G12D / G13D-driven CRC. To verify this, the present application uses two independent shRNAs to knock down ENOPH1 expression in DLD1 and CT26 cells, and Western blot confirms that ENOPH1 is effectively knocked down. Cell proliferation experiments show that knocking down ENOPH1 severely impairs the cell proliferation ability of DLD1 and CT26 cells Figure 3-1 a,b) Wound healing experiments show that silencing ENOPH1 significantly reduces the cell migration ability of DLD1 and CT26 cells Figure 3-1 c) Further analysis shows that ENOPH1 knockdown reduces the expression of epithelial-mesenchymal transition (EMT) markers N-cadherin and vimentin, while increasing the expression of E-cadherin. Flow cytometry confirms that ENOPH1 knockdown significantly increases apoptosis and enhances DOX-induced apoptosis Figure 3-2 ) In in vivo experiments, DLD1 and CT26 cells stably knocked down ENOPH1 were injected into nude mice, and the tumors formed were significantly smaller than the corresponding control group, showing reduced tumor growth rate and volume Figure 3-3 e,f) Western blot confirms that ENOPH1 expression is down-regulated in tumor tissues Figure 3-3 g) These results suggest that ENOPH1 is a promising therapeutic target for treating KRAS G12D / G13D-driven CRC.

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

[0090] To understand the functional role of ENOPH1 in KRAS mutant CRC, the present application performed transcriptome sequencing and non-targeted metabolomics analysis after knocking down ENOPH1 in DLD1 cells. Differential expression analysis showed that there were 2,857 differentially expressed genes (DEGs) between ENOPH1 silenced (sh) and wild-type (WT) cells, including 1,628 up-regulated genes and 1,229 down-regulated genes Figure 4-1 a) KEGG enrichment analysis showed that these DEGs were significantly clustered in pathways regulating immune-inflammatory response, metabolic processes, disease progression, and signal transduction Figure 4-1 b) Non-targeted metabolomics analysis further showed that there was a clear separation between the sh and WT groups in principal component analysis (PCA), with tight clustering within groups, demonstrating the robustness and reproducibility of the data Figure 4-2 c) Metabolite comparison analysis found that there were 90 significantly changed metabolites in the sh group relative to the WT group, of which 61 metabolites were reduced in synthesis and only 29 metabolites were increased Figure 4-2d). KEGG enrichment analysis of these metabolites highlighted several tumor-related pathways, including the mTOR signaling pathway, the cAMP signaling pathway, and central carbon metabolism in cancer Figure 4-3 ), indicating that ENOPH1 coordinates key oncogenic signaling networks and glycolytic metabolic pathways during CRC progression. Overall, these multi-omics data suggest that ENOPH1 is extensively involved in key signaling and metabolic processes in CRC development, providing a solid foundation for subsequent mechanistic exploration.

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

[0092] To further understand the role of ENOPH1 in KRASG12D / G13D mutant CRC progression, the present 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 the level of TGF-β in the supernatant of ENOPH1 knockdown cells was significantly reduced compared with the control group Figure 5 b,c). Since TGF-β is a key downstream mediator of KRAS mutation-driven invasive growth of CRC, the present application hypothesized that the MEK / ERK signal activated by KRAS mutation enhances TGF-β expression by upregulating ENOPH1. In fact, silencing KRAS and inhibiting the MEK / ERK signal 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, and Western blot results showed that both KRASG12D and KRASG13D significantly increased TGF-β expression, while ENOPH1 knockdown reversed this effect Figure 5 f,g). 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 supportive literature, this application hypothesized that ENOPH1 modulates TGF-β / SMAD signaling in KRAS mutant CRC cells. This application evaluated the levels of phosphorylated (p) SMAD2 and SMAD3 in DLD1 cells using nuclear-cytoplasmic fractionation and Western blot, and found that the nuclear levels of both proteins were significantly reduced upon ENOPH1 knockdown Figure 6-1 a) Immunoprecipitation experiments 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 the existence of post-translational regulation Figure 6-1 c) To verify this, cells were treated with CHX for specified times, and Western blot showed that ENOPH1 knockdown accelerated the degradation of SMAD4 Figure 6-1 d), while ubiquitination experiments indicated that ubiquitination of SMAD4 was increased in ENOPH1 knockdown cells Figure 6-1 e) Furthermore, treatment of cells with TGF-β restored the levels and nuclear localization of p-SMAD2 / 3 in ENOPH1 knockdown cells Figure 6-1 f, Figure 6-2 g, h) These results suggest that ENOPH1 modulates the nuclear translocation of p-SMAD2 / 3 and the 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 the metabolic reprogramming of KRAS mutant CRC cells, this application performed a non-targeted metabolomics analysis. KEGG pathway analysis showed significant enrichment of central carbon metabolism Figure 7-1 ), including glycolysis, tricarboxylic acid cycle, and pentose phosphate pathway, which are critical for energy production and biosynthetic demand of tumor cells. Given the close association of ENOPH1 with glycolysis, this application focused on the impact of ENOPH1 on glycolytic metabolism of KRAS mutant CRC cells. Transcriptomic analysis identified a series of key glycolytic genes (e.g. ALDOA, PFKL, HK3) regulated by ENOPH1 Figure 7-2 b), which was verified by qPCR Figure 7-2 c). Seahorse XF experiments further showed that ENOPH1 knockdown significantly reduced extracellular acidification rate (ECAR), indicating decreased glycolytic activity Figure 7-2 d). In addition, metabolomics and ELISA experiments showed that pyruvate and lactate levels were significantly decreased upon ENOPH1 knockdown Figure 7-2e,f). These findings suggest that ENOPH1 plays a key role in the regulation of glycolysis. To confirm that ENOPH1 is involved in this process through the TGF-β / SMAD pathway, TGF-β was supplemented in ENOPH1-knocked-down DLD1 cells, and it was observed that the expression of glycolytic genes ( Figure 7-3 g) and ECAR levels ( Figure 7-3 h) were partially restored. However, silencing SMAD2 / 3 abolished the TGF-β-induced restoration. Likewise, TGF-β supplementation restored cell proliferation and tumor spheroid formation, but these effects were reversed by SMAD2 / 3 knockdown ( Figure 7-3 i,j). 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 driver event in colorectal cancer (CRC). Although KRAS-induced metabolic reprogramming, such as glycolysis and dependence on methionine metabolism, is widely recognized as a key oncogenic mechanism, the molecular hub that regulates these processes remains unclear. ENOPH1, as a core enzyme in the methionine salvage pathway, its function and regulatory network in KRAS-mutant CRC are unknown. By integrating multi-omics analysis, the present application first reveals that ENOPH1 is a key target closely related to signaling and metabolism in KRAS-mutant CRC, which regulates glycolytic metabolism and affects tumor cell behavior. Therefore, ENOPH1 is a potential therapeutic target for KRASG12D / G13D-mutant CRC.

[0098] Through a variety of experiments, the present application identifies that ENOPH1 is a key regulator of glycolytic reprogramming and CRC progression driven by KRASG12Dand KRASG13Dmutations. And it is found that KRASG12Dand KRASG13Dmutations promote ENOPH1 expression through the MEK / ERK signaling cascade, and then enhance glycolysis through 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 the development of new therapeutic strategies for this type of cancer.

[0099] In summary, oncogenic KRAS G12D / G13D activates the MEK / ERK cascade, leading to upregulation of ENOPH1 expression. Subsequently, ENOPH1 enhances nuclear translocation of phosphorylated SMAD2 / 3 and regulates ubiquitination of SMAD4, facilitating activation of the TGF-β / SMAD signaling pathway. Meanwhile, ENOPH1 drives transcription of glycolytic genes through activated TGF-β / SMAD signaling, enhancing glycolytic activity to support tumor cell proliferation and migration. This mechanistic pathway highlights the potential of targeting ENOPH1 as a therapeutic strategy for KRAS G12D / G13D mutant colorectal cancer.

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

Claims

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

2. The application according to claim 1, characterized in that, The colorectal cancer mentioned is the KRASG12D / G13D mutant type.

3. The application according to claim 1, characterized in that, The targeted drug knocks out or silences the ENOPH1 gene in colorectal cancer cells.

4. An anti-colorectal cancer drug, characterized in that, shRNA containing ENOPH1, or expression vectors of shRNA containing ENOPH1, or viral vectors of shRNA expression vectors containing ENOPH1.

5. The anti-colorectal cancer drug according to claim 4, characterized in that, The shRNA mentioned is: Sh-E1-1: 5'-GCAGAGTCTTTGCAGATGTA-3', And / or Sh-E1-2:5'- CCCTTGTGATTTAGAAGATTA -3'.

6. The anti-colorectal cancer drug according to claim 5, characterized in that, The aforementioned anti-colorectal cancer drug is used to enhance the sensitivity of colorectal cancer cells to chemotherapy drugs.

7. The anti-colorectal cancer drug according to claim 6, characterized in that, The chemotherapy drug mentioned is doxorubicin hydrochloride.

8. The application of ENOPH1 shRNA, or expression vectors containing ENOPH1 shRNA, or viral vectors containing ENOPH1 shRNA in the preparation of anti-colorectal cancer drugs.

9. The application according to claim 8, characterized in that, The sequence of the shRNA is as follows: Sh-E1-1: 5'-GCAGAGTCTTTGCAGATGTA-3', And / or Sh-E1-2:5'- CCCTTGTGATTTAGAAGATTA -3'.

Images