Applications of MC1R

Abstract

The application discloses application of MC1R, in particular, application of an agent for inhibiting MC1R in preparation of a drug for treating colorectal cancer, and belongs to the field of biological medicines. In vitro experiments show that overexpression of MC1R in a colorectal cancer cell line promotes cell proliferation and migration, and inhibits ferroptosis by down-regulating ACSL4 expression, while knockdown of MC1R has an opposite effect. In vivo experiments also find that xenograft tumor volume and weight formed by colorectal cancer cells with knockdown of MC1R are reduced, it is found that MC1R activates a Notch signal pathway, causes ACSL4 expression to be inhibited, thereby inhibiting ferroptosis and further blocking cell growth and migration; high expression of MC1R in colorectal cancer is related to poor prognosis, and is negatively correlated with the ferroptosis level. Targeting MC1R can provide a new strategy for enhancing ferroptosis of colorectal cancer, and provides an important clue for development of targeted therapy for colorectal cancer.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to the application of MC1R, and more specifically to the application of reagents that inhibit MC1R in the preparation of drugs for treating colorectal cancer. Background Technology

[0002] Colorectal cancer (CRC) is a long-standing and significant challenge in global health. According to Globocan data from 2020, it ranks as the third most common malignancy worldwide and the second leading cause of cancer-related deaths. Despite significant advancements in diagnosis and treatment over the past few decades, recurrence and metastasis rates remain high, highlighting the urgent need to develop more effective treatment strategies. Abnormal iron metabolism in the tumor microenvironment has been proven to be closely related to the pathogenesis of various malignancies. Ferraphobia, an iron-dependent form of cell death that does not induce apoptosis, has become a key mechanism in the progression of many cancers. This mode of cell death can not only inhibit tumor growth but may also enhance the efficacy of immunotherapy and overcome the resistance limitations of existing anticancer drugs. Characterized by the accumulation of lipid reactive oxygen species (ROS) and the disruption of cell membrane integrity, this unique mode of death differs from other cell death modes such as apoptosis and necrosis due to its dependence on iron metabolism and lipid peroxidation pathways. Ferraphobia is initiated by intracellular iron and lipid homeostasis dysregulation, ultimately leading to excessive ROS production and the peroxidation of polyunsaturated fatty acids (PUFAs). For example, iron uptake by serum transferrin or lactoferrin mediated by transferrin receptor (TFRC) promotes ferroptosis, while iron efflux mediated by solute carrier family 40 member 1 (SLC40A1) inhibits this process. PUFAs, which are highly susceptible to peroxidation during ferroptosis, can disrupt the lipid bilayer and impair membrane function. Enzymes such as long-chain fatty acid-CoA ligase 4 (ACSL4) and lysophosphatidyltransferase 3 (LPCAT3) are crucial for the biosynthesis and remodeling of PUFAs in the cell membrane. ACSL4, a member of the long-chain acyl-CoA synthase family, catalyzes the binding of free arachidonic acid or adrenaline to coenzyme A to form AA-CoA or AdA-CoA derivatives. Subsequently, LPCAT promotes esterification into phospholipids, thereby increasing the content of long-chain PUFAs in cellular lipids and membranes. ACSL4-mediated lipid metabolism under ferroptotic conditions increases lipid ROS levels, making cells more susceptible to ferroptosis. Overactivation of ACSL4 is associated with enhanced lipid peroxidation, a hallmark of ferroptosis. As a highly conserved cell signaling system, the Notch signaling pathway plays a crucial role in regulating cell fate determination, proliferation, and differentiation. Physiologically, this pathway is essential for the normal development and homeostasis of intestinal epithelial cells. Specifically, the Notch signaling pathway regulates the differentiation of colonic goblet cells and stem cells. In human colorectal cancer tissues, the inventors discovered atypical activation of the Notch receptor (Notch1), which is closely associated with poor prognosis and metastatic risk in colorectal cancer patients. Furthermore, active Notch signaling further promotes the metastatic process of colorectal cancer by modulating the tumor microenvironment and affecting the expression of epithelial-mesenchymal transition (EMT)-related transcription factors.

[0003] Furthermore, the Notch signaling pathway promotes colorectal cancer metastasis by regulating the tumor microenvironment and influencing epithelial-mesenchymal transition (EMT)-related transcription factors such as SLUG and SNAIL. Recent research indicates that the Notch signaling pathway also participates in regulating ferroptosis. In astrocytoid glioma cells, Notch signaling activation leads to enhanced mitochondrial lipid peroxidation, increased reactive oxygen species (ROS) production, and decreased glutathione levels, thereby increasing cellular sensitivity to ferroptosis. However, the specific mechanisms by which the Notch signaling pathway plays a role in ferroptosis remain to be elucidated. A comprehensive understanding of the interactions between these signaling pathways is crucial for developing innovative cancer treatment strategies. The melanocorticoid 1 receptor (MC1R), a G protein-coupled receptor expressed in human melanocytes and melanoma cells, plays a vital role in determining skin and hair pigmentation and is associated with melanoma pathogenesis. In melanoma, MC1R signaling may promote tumor immune escape and treatment resistance. Notably, MC1R overexpression is particularly pronounced in metastatic melanoma, especially in clones that have developed resistance to targeted therapies, suggesting it may be a potential molecular target for treating metastatic melanoma. Inhibiting MC1R is a promising molecular strategy for alleviating drug resistance. Furthermore, the expression of the MC1 receptor in various cell types outside of melanocytes (such as keratinocytes, fibroblasts, endothelial cells, and immune system cells) has been widely confirmed, and it is believed to be involved in physiological processes such as anti-inflammatory effects, immune responses, burn reactions, collagen synthesis, and scar formation. A search revealed no studies on MC1R interfering with the Notch signaling pathway to regulate ferroptosis and thus modulate colorectal cancer; these findings provide a theoretical basis for developing novel therapeutic targets. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an application of MC1R in the treatment of colorectal cancer.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] This invention protects the use of reagents that inhibit MC1R in the preparation of drugs for treating colorectal cancer.

[0007] This invention also protects the use of reagents that inhibit MC1R in combination with ACSL4 inhibitors in the preparation of drugs for treating colorectal cancer.

[0008] Furthermore, the reagent that inhibits MC1R suppresses ACSL4 expression by activating the Notch signaling pathway, thereby inhibiting ferroptosis and blocking the growth and migration of colorectal cancer cells.

[0009] Furthermore, the reagent for inhibiting MC1R is shMC1R, whose nucleotide sequence is shown in SEQ ID NO2 or 3.

[0010] Compared with the prior art, the present invention has the following beneficial effects:

[0011] In vitro experiments of this invention demonstrate that overexpression of MC1R in colorectal cancer cell lines promotes cell proliferation and migration while inhibiting ferroptosis by downregulating ACSL4 expression, while knockdown of MC1R produces the opposite effect. In vivo experiments also revealed that xenografts formed from MC1R-knockdown colorectal cancer cells had reduced volume and weight. MC1R was found to activate the Notch signaling pathway, leading to suppressed ACSL4 expression, thereby inhibiting ferroptosis and subsequently blocking cell growth and migration. High MC1R expression in colorectal cancer is associated with poor prognosis and is negatively correlated with ferroptosis levels. Targeting MC1R may provide a new strategy for enhancing ferroptosis in colorectal cancer and offers important clues for developing targeted therapies for colorectal cancer. Attached Figure Description

[0012] Figure 1 MC1R is associated with ferromorulism in colorectal cancer, among which Figure 1 A volcano diagram of differentially expressed genes between colon cancer and normal tissues. Figure 1 B is the WGCNA tree diagram; Figure 1 C-correlation matrix presents a module-feature relationship graph. Figure 1 D is the relevant scatter plot; Figure 1 E represents the gene map associated with poor prognosis in univariate Cox regression analysis. Figure 1 F is to use algorithms such as StepCox, CoxBoost, Random Survival Forest (RSF), XGBoost, Boruta, Support Vector Machine (SVM), Elastic Network (ENet), and Lasso Regression to identify genes related to the prognosis of colorectal cancer; Figure 1 G represents the ranking of the top ten prognostic genes for colorectal cancer, as determined by various algorithms. Figure 1 H represents the analysis of the association between ten genes and the ferroptosis pathway using the ssGSEA algorithm. Figure 1 I analyzed the MC1R mRNA expression levels in colon adenocarcinoma tumor tissues and normal tissues based on TCGA data. Figure 1 J represents the Kaplan-Meier survival curves of the low MC1R group and the high MC1R group in the TCGA training dataset.

[0013] Figure 2 Inhibiting MC1R can suppress CRC cell proliferation and migration, among which... Figure 2 A and Figure 2B represents the mRNA or protein expression level of MC1R in HCT116 and SW620 cells transfected with shNC, shMC1R-1, and shMC1R-2, respectively, detected by RT-qPCR or Western blot. Figure 2 C and Figure 2 D. The CCK-8 assay was used to detect the proliferation of HCT116 and SW620 cells at different time points after MC1R knockdown. Figure 2 E assessed the colony-forming ability of HCT116 and SW620 cells after MC1R knockdown using a colony formation assay. Figure 2 F represents the migration ability of HCT116 and SW620 cells after MC1R knockdown was determined using a Transwell chamber assay. Figure 2 G represents a xenograft tumor model demonstrating the effect of MC1R on tumor growth in vivo. Figure 2 H and Figure 2 Tumor weight and volume measurements in the xenograft model.

[0014] Figure 3 The effect of MC1R on ferroptosis in colorectal cancer cells. Figure 3 A and Figure 3 B represents the single-sample gene set enrichment analysis (ssGSEA) of ferroptosis-related pathways in the Low_MC1R and High_MC1R groups, respectively. Figure 3 C represents the MDA content in HCT116 and SW620 cells transfected with shNC, shMC1R-1, and shMC1R-2, as detected using a lipid peroxidation MDA assay kit. Figure 3 D represents the intracellular iron ion levels in HCT116 and SW620 cells after MC1R knockdown, as determined using an iron ion detection kit. Figure 3 E used a glutathione assay kit to detect glutathione (GSH) levels in HCT116 and SW620 cells after MC1R knockdown; Figure 3 F represents the effect of MC1R and Erastin (10 μM) on the viability of HCT116 and SW620 cells.

[0015] Figure 4 MC1R downregulates the Notch signaling pathway and upregulates ACSL4 expression, among which Figure 4 A represents the KEGG enrichment analysis of MC1R. Figure 4 B is a comparison of the ssGSEA scores of the Notch signal pathway between the Low_MC1R group and the High_MC1R group; Figure 4 C represents the difference in expression levels of ferroptosis-related genes between the Low_MC1R and High_MC1R groups; Figure 4 D represents the correlation analysis between MC1R and ACSL4 mRNA levels in CRC; Figure 4 E and Figure 4 F represents the RT-qPCR results of NICD, HES1, HEY1, ACSL4, and TFRC in HCT116 cells and F SW620 cells after transfection with shNC, shMC1R-1, and shMC1R-2, respectively. Figure 4 G represents the Western blot results of NICD, HES1, HEY1, ACSL4, and TFRC in HCT116 and SW620 cells after MC1R knockdown; Figure 4 Figure H shows the Western blot analysis results of NICD, HES1, HEY1, and ACSL4 in HCT116 and SW620 cells after OE-MC1R transfection or IMR-1 treatment.

[0016] Figure 5 MC1R affects ferroptosis in colorectal cancer cells via ACSL4, whereby... Figure 5 A and Figure 5 Figure B shows a comparison of the mRNA expression levels of MC1R and ACSL4 in HCT116 and SW620 cells, detected by RT-qPCR. Figure 5 C is a Western blot diagram showing the overexpression of MC1R and ACSL4 at the protein level in HCT116 and SW620 cells; Figure 5 D represents the MDA content in HCT116 and SW620 cells after MC1R and ACSL4 overexpression, as detected by a lipid peroxidation MDA assay kit. Figure 5 E represents the determination of intracellular iron levels in HCT116 and SW620 cells after MC1R and ACSL4 overexpression using an iron ion detection kit. Figure 5 F used a glutathione assay kit to determine the glutathione levels in HCT116 and SW620 cells after overexpression of MC1R and ACSL4.

[0017] Figure 6 MC1R regulates the proliferation and migration of colorectal cancer cells through ACSL4, among which... Figure 6 A and Figure 6 B represents the proliferation of HCT116 and SW620 cells at different time points after MC1R and ACSL4 overexpression, as detected by the CCK-8 assay. Figure 6 C and Figure 6 D is a diagram showing the clonogenic capacity of HCT116 and SW620 cells after overexpression of MC1R and ACSL4, as evaluated by a clonogenic assay. Figure 6 E and Figure 6 F represents the migration ability of HCT116 and SW620 cells after MC1R and ACSL4 overexpression, as determined by Transwell chamber assay. Figure 6 G and Figure 6 H represents the association between MC1R and clinical characteristics in colorectal cancer patients, demonstrated by univariate forest plots and multivariate Cox regression analyses. Figure 6 I represents a survival curve for predicting colorectal cancer patients. Detailed Implementation

[0018] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but the present invention is not limited to the following technical solutions.

[0019] Example

[0020] Bioinformatics Analysis Dataset and Preprocessing: Transcriptome data (HTSeq counts) and clinical data (including pathological stage, age, sex, and prognostic information) were obtained from the TCGA-COADREAD database. This dataset contains 426 COADREAD samples and 42 normal tissue samples; samples without clinical data were excluded. HTSeq counts were transformed using log2(TPM+1) before analysis, and differential expression was assessed using HTSeq counters. Differentially Expressed Genes (DEGs) Detection: The DESeq2 R software package was used to identify differentially expressed genes between the two groups, with a threshold of |log2-fold change| > 1 and an adjusted P-value < 0.05. Single-Sample Gene Set Enrichment Analysis (ssGSEA): The ssGSEA algorithm in the "GSVA" software package was used to quantify the enrichment scores of ferroptosis-related pathways using the MSigDB database. This method can assess the incidence and intensity of ferroptosis responses in colorectal cancer samples.

[0021] Weighted Gene Co-expression Network Analysis (WGCNA): This study used the WGCNA software package to screen key genes associated with ferroptosis scores based on upregulated expression data from the TCGACOADREAD cohort. An adjacency matrix was constructed by setting a soft threshold of softpower = 4, identifying hub modules with the strongest positive Pearson correlation. Based on the screening criteria of module membership (MM) > 0.4 and gene significance (GS) > 0.3, potential ferroptosis-related candidate genes were finally determined. Independent Risk Factor Identification: First, univariate Cox regression analysis was performed on the 234 ferroptosis-related genes screened by WGCNA, using p < 0.05 and HR > 1 as preset thresholds to screen potential prognostic risk factors. Subsequently, the Mime1 software package was used to test eight machine learning algorithms, including StepCox, CoxBoost, Random Survival Forest (RSF), XGBoost, Boruta, SVM-RFE, Elastic Network (Enet), and LASSO. The top 10 genes with the highest selection frequency were finally selected as the best prognostic risk factors.

[0022] Enrichment analysis: Gene ontology (G0) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the "clusterProfiler" software package. Statistical significance criteria were set as adj. P-value < 0.05 and adj. q-value < 0.05. Colorectal cancer patient-derived organoid culture: The specific procedures for establishing and culturing colorectal tumor organoids are as follows: Tumor tissue was minced and digested into a single-cell suspension, centrifuged at 200×g for 5 minutes. 250,000 cells per genotype were seeded into 80 μL of Matrigel in 12-well plates to establish organoids. Lentiviral transduction experiments were performed in 500 μL of organoid culture medium supplemented with 4 μg / mL polybrene for 24 hours. After transduction, the viral medium was replaced with 100 μL of fresh Matrigel and standard growth medium containing Y-27632. Screening was initiated on day 5 using 2 μg / mL puromycin and maintained with Y-27632 until day 11. The culture medium was changed every 48 hours during the screening period. From day 11 onwards, the organoids were cultured in puromycin-free medium until the culture was completed on day 14.

[0023] The chemical reagent IMR-1 (catalog number 53101ES08) was purchased from YEASEN at a final concentration of 25 μM. Erastin (catalog number SC0224) was purchased from Beyotime Biotechnology at a final concentration of 10 μM. For cell culture, the human colorectal cancer cell lines HCT116 and SW620 were cultured in DMEM medium (Corning 10-013-CVR) supplemented with 10% fetal bovine serum (Ausbian VS500T). All cell lines were purchased from the American Type Culture Collection (ATCC). Cells were cultured at 37°C in a 5% CO2 incubator.

[0024] Lentiviral shRNA vectors pLenti-MC1R (OE-MC1R), pLenti-ACSL4 (OE-ACSL4), and MC1RshRNAs (shMC1R-1 and shMC1R-2) were constructed using standard molecular biology techniques. All plasmids were sequenced to verify their effectiveness. The target sequences are as follows: control shRNA: AACAGTGCGTTTGCGACTGG; shMC1R-1: TCACCATCCTGCTGGGCATTT; shMC1R-2: CACCAGGGCTTTGGCCTTAAA, as shown in SEQ ID NO: 1-3, respectively. Lentiviral amplification was performed using the HEK293T cell line for virus preparation and infection experiments. Viruses were collected 48 and 72 hours after transfection, filtered through a 0.45 μm filter, and then used to infect target cells in the presence of 8 μg / ml polybrene. Infected cell lines were subsequently screened using appropriate antibiotics.

[0025] Western blot analysis and antibody detection: Whole-cell lysates were collected using RIPA lysis buffer (Thermo Fisher Scientific, catalog number 89901) containing a complete protease inhibitor. Proteins were separated by 7.5%–15% SDS-PAGE electrophoresis and then transferred to PVDF membranes. The membranes were incubated with the following primary antibodies: NICD (1:1000, Proteintech, catalog number 20687-1-AP), HEY1 (1:1000, Proteintech, catalog number 19929-1-AP), HES1 (1:1000, abcam, catalog number ab108937), TFRC (1:10000, Proteintech, catalog number 10084-2-AP), ACSL4 (1:5000, Proteintech, catalog number 22401-1-AP), and GAPDH (1:30000, Proteintech, catalog number 60004-1-Ig), followed by color development with HRP-labeled secondary antibodies.

[0026] Total RNA was extracted using TRIzol reagent (Thermo Fisher Scientific, catalog number 15596026CN) for quantitative real-time quantitative PCR (RT-qPCR), and cDNA was synthesized using HiScript II Q Select RT SuperMix reagent (Vazyme, catalog number R233-01). RT-qPCR experiments were performed in triplicate using Taq Pro Universal SYBR qPCR Master Mix (Vazyme, catalog number Q712-02). The relative expression level of mRNA was quantified using the 2-ΔΔCt method. The primer sequences used were as follows: NOTCH1-F: 5′-TGGACCAGATTGGGGAGTTC-3′;

[0027] NOTCH1-R: 5′-GCACACTCGTCTGTGTTGAC-3′;

[0028] HES1-F: 5′-CCTGTCATCCCCGTCTACAC-3′;

[0029] HES1-R: 5′-CACATGGAGTCCGCCGTAA-3′;

[0030] HEY1-F: 5′-GTTCGGCTCTAGGTTCCATGT-3′;

[0031] HEY1-R: 5′-CGTCGGCGCTTTCAATTATTC-3′;

[0032] TFRC-F: 5′-GGCTACTTGGGCTATTGTAAAGG-3′;

[0033] TFRC-R: 5′-CAGTTTTCTCCGACAACTTTCTCT-3′;

[0034] MC1R-F: 5′-GCGGCTGCATCTTCAAGAAC-3′;

[0035] MC1R-R: 5′-GAGCATGTCAGCACCTCCTT-3′;

[0036] ACSL4-F: 5′-ATTGGTGGACAGAACATCTC-3′;

[0037] ACSL4-R: 5′-CTCCTGCTTGTAACTTCACT-3′;

[0038] GAPDH-F: 5′-TCCATGACAACTTTGGTATC-3′;

[0039] GAPDH-R: 5′-CAGGGATGATGTTCTGGA-3′; the sequence is shown in SEQ ID NO: 4-17.

[0040] Western blot analysis and antibody use: Whole-cell lysate was prepared using RIPA buffer (Thermo Fisher Scientific, catalog number 89901) containing a complete protease inhibitor. Proteins were separated by 7.5–15% SDS-PAGE electrophoresis and transferred to PVDF membranes, then incubated sequentially with the following primary antibodies: NICD (1:1000, Proteintech, catalog number 20687-1-AP), HEY1 (1:1000, Proteintech, catalog number 19929-1-AP), HES1 (1:1000, abcam, catalog number ab108937), TFRC (1:10000, Proteintech, catalog number 10084-2-AP), and GAPDH (1:30000, Proteintech, 60004-1-Ig), as well as HRP-conjugated secondary antibody.

[0041] The CCK-8 assay kit (Meirun Biotechnology, catalog number MA0218-Feb-191) was used to determine the viability of colorectal cancer cells according to the manufacturer's instructions. Cells were seeded at a density of 2000 cells per well in 100 μL of culture medium and incubated at different time points. After adding 10 μL of CCK-8 reagent, the cells were incubated at 37°C for 2 hours, and then the absorbance was measured at 450 nm using a microplate reader. A cell-free blank control was included for background correction, and cell viability was standardized by comparison with the control group. For colony formation assays, cells were seeded at a density of 1000 cells per well in six-well plates and cultured for 14 days. After washing with PBS, the cells were fixed with 1 mL of 4% paraformaldehyde solution for 30 minutes, and then stained with 0.1% crystal violet for approximately 20 minutes. After multiple washes and drying, the stained areas were analyzed using ImageJ software. The number of colonies containing more than 50 cells was counted under a microscope and photographed.

[0042] Transwell cell migration assays were performed using 24-well plates (containing an 8 μm polyethylene terephthalate membrane, Corning Incorporated, catalog number 3422). HCT116 and SW620 cells were loaded (1 × 10⁶ cells per well). 5 Cells were placed in serum-free DMEM medium, with the upper chamber containing DMEM medium containing 10% fetal bovine serum in the lower chamber. The experiment was conducted at 37°C and 5% CO2 humidity for 24 hours. After removing unmigrated cells with cotton swabs, the cells were fixed with 4% paraformaldehyde for 30 minutes, stained with 0.1% crystal violet for 20 minutes, and photographed. The stained areas were analyzed using ImageJ software. Migration ability was quantified by counting the number of cells in each field of view (five fields of view were randomly selected from each membrane).

[0043] Lipid malondialdehyde (MDA) was detected using the MDA detection kit from Beyotime Biotechnology Co., Ltd. (catalog number S0131S). After preparing the working solution, 1×10 6 One cell was resuspended in 100 μL of extract, sonicated, and then centrifuged at 12,000 rpm for 10 minutes at 4°C. The supernatant was collected, cooled on ice, and 200 μL of MDA assay reagent was added. The mixture was then heated in a 100°C water bath for 15 minutes. After cooling to room temperature, the mixture was centrifuged at 1000 g for 10 minutes. 200 μL of the supernatant was transferred to a 96-well plate, and the absorbance was measured at 532 nm using a microplate reader.

[0044] Cellular Fe 2+ Detection Method: This experiment used cell Fe2+ produced by MLbio. 2+ The test kit was used to determine Fe according to the instructions. 2+ Content. The specific steps are as follows: After preparing the working solution, take 1×10... 6One cell was resuspended in 450 μL of extraction buffer, sonicated, and centrifuged at 5000 rpm for 10 minutes at 4°C. The supernatant was collected and placed on ice, then 1.2 mL of FeAssayBuffer was added, mixed thoroughly, and incubated at 37°C for 10 minutes. Finally, the absorbance was measured at 562 nm using a microplate reader.

[0045] Intracellular glutathione (GSH) levels were measured using a GSH quantification kit (Ansberg Biotech, Inc., catalog number ADS-W-G001). After preparing the working solution according to the manufacturer's instructions, 5 × 10⁻⁶ cells were used. 6 One cell was suspended in 1 mL of extraction solution, sonicated, and centrifuged at 12,000 rpm for 15 minutes at 4°C. The supernatant was collected and stored on ice. The sample was added to a 96-well plate at a density of 20 μL / well, and working solution was added according to the instructions. After mixing thoroughly, the mixture was vortexed and incubated for 5 minutes. The absorbance was measured at 412 nm. All animal experiments for the subcutaneous tumor formation experiment in nude mice were approved by the Medical Ethics Committee of Wuhan University of Technology (BME-2025-2-15). Ten 4-5 week old BALB / c nude mice were randomly divided into two groups according to a double-blind protocol. The mice were injected subcutaneously into the posterior abdominal wall with 1×10⁻⁶ cells. 7 HCT-116 colorectal cancer cells. Tumor volume was monitored every 3 days for 23 consecutive days (calculated using the formula L×W). 2 / 2, where L represents length and W represents width) and weight changes. To ensure ethical compliance and data validity, tumors exceeding 2000 mm² were excluded. 3 The laboratory animals were euthanized at that time.

[0046] The experimental results are as follows:

[0047] High expression of MC1R in colorectal cancer is associated with poor prognosis and is negatively correlated with ferroptosis. To investigate the mechanism of ferroptosis in colorectal cancer, differential gene expression analysis revealed 2,615 significantly dysregulated genes between colorectal cancer tissues and normal tissues (|log2FC|>1, corrected p<0.05). Figure 1 A). Genome-wide co-expression network analysis revealed 13 unique gene modules, among which the pink module (containing 234 genes) was most significantly associated with ferroptosis. Figure 1 BD). Univariate Cox regression analysis identified 40 prognostic-related genes, which were then refined into 10 high-confidence candidate genes after screening using various machine learning algorithms (StepCox, RSF, SVM, etc.). Figure 1 EG). SSGSEA correlation analysis revealed that four genes (TRIP6, TSPEAR, MC1R, and TLX1) could inhibit ferroptosis, with MC1R showing the strongest negative correlation. Figure 1H). Given that MC1R is highly associated with ferroptosis and plays a clear role as a cell surface receptor in cancer, while other candidate genes (TRIP6, TSPEAR, TLX1) have lower correlations or no obvious biological association with colorectal cancer, we chose MC1R for in-depth research. TCGA-COAD data confirmed that the expression level of MC1R in colorectal cancer tissues was higher than that in the normal control group. Figure 1 I). Survival analysis showed that high MC1R expression was significantly associated with shortened overall survival in colorectal cancer patients. Figure 1 J), highlighting its potential as a prognostic biomarker and therapeutic target.

[0048] MC1R significantly enhances the proliferation and migration of colorectal cancer cells. To investigate the functional role of MC1R in colorectal cancer, the inventors intervened in HCT116 and SW620 cells using shRNA-mediated knockdown technology. RT-qPCR and Western blot analysis confirmed that both MC1R mRNA and protein levels were significantly reduced. Figure 2 A, Figure 2 B). Functional experiments showed that MC1R knockdown significantly inhibited the proliferation of colorectal cancer cells, as detected by the CCK-8 assay. Figure 2 C Figure 2 D); Transwell migration experiments showed that its colony-forming ability was impaired. Figure 2 E), migration ability also decreased significantly ( Figure 2 F). To verify specificity, the shRNA-resistant MC1R construct was reintroduced into MC1R knockdown cells. Reexpression of MC1R restored cell proliferation, confirming that the observed phenotype was MC1R-dependent. To extend these findings to in vivo and clinically relevant models, implantation of MC1R-knockdown cells into nude mice resulted in significantly reduced xenograft tumor volume and weight compared to the control group. Figure 2 GI). Further validation in patient-derived three-dimensional colorectal cancer organoids showed that knockdown of MC1R inhibited organoid growth, resulting in a reduction in tumor number and size. Figure 2 J). The combined data show that MC1R deficiency can delay the progression of colorectal cancer in vitro, in vivo and in vitro models, highlighting its key role in maintaining tumor growth and metastatic potential.

[0049] MC1R significantly inhibited ferroptosis in colorectal cancer cells. To investigate the role of MC1R in the regulation of ferroptosis in colorectal cancer, single-sample gene set enrichment analysis (ssGSEA) revealed that high expression of MC1R was negatively correlated with the activity of ferroptosis-related pathways. Figure 3 A, Figure 3B) suggests that MC1R may have an inhibitory effect on ferroptosis. Functional validation in HCT116 and SW620 cells with knocked-down MC1R showed significantly elevated ferroptosis markers compared to the control group: enhanced malondialdehyde (MDA)-mediated lipid peroxidation, increased intracellular iron content, and decreased glutathione (GSH) levels. Figure 3 Conversely, cells overexpressing MC1R exhibited resistance to the ferroptosis inducer errasstatin, and no significant ferroptosis was observed after treatment. Figure 3 F). Notably, flow cytometry confirmed that knockdown of MC1R did not alter the apoptosis rate, ruling out apoptosis as a confounding factor. The combined data indicate that MC1R deficiency promotes ferroptosis by enhancing lipid peroxidation, iron accumulation, and antioxidant depletion, while its overexpression confers ferroptosis resistance. Therefore, MC1R plays a negative regulatory role in ferroptosis in colorectal cancer cells.

[0050] MC1R inhibits ferroptosis in colorectal cancer cells via the Notch signaling pathway. KEGG pathway analysis showed that MC1R is functionally associated with fatty acid metabolism and the Notch signaling pathway. Figure 4 A). Given that ferroptosis is driven by iron-dependent lipid peroxidation, the association between MC1R and fatty acid metabolism suggests that MC1R may play a regulatory role in this cell death pathway. Notably, MC1R is linked to the Notch signaling pathway—a pathway crucial for colorectal cancer progression by regulating cell proliferation and differentiation. ssGSEA analysis confirmed a significant positive correlation between MC1R expression and Notch pathway activity in a cohort of colorectal cancer patients. Figure 4 B). Differential gene expression analysis further revealed an association between MC1R and a key regulator of ferroptosis. Figure 4 C). Although MC1R expression was negatively correlated with SLC7A11, a component of the glutathione synthesis system, no association was observed with GPX4. Figure 4 C). Notably, knocking down MC1R in colorectal cancer cells did not alter the protein levels of SLC7A11 or GPX4 (Figure S1B), indicating that MC1R does not regulate ferroptosis via the glutathione pathway. Instead, MC1R showed a significant negative correlation with ACSL4 (a lipid peroxidation driver) and TFRC (a ferrotransferrin). Figure 4 (C, D) This suggests that lipid metabolism and iron homeostasis may be the main mechanisms of action.

[0051] To elucidate this axial regulatory mechanism, the inventors performed pathway-specific changes analysis on MC1R knockdown HCT116 and SW620 cells. RT-qPCR and Western blot results showed that MC1R deletion significantly reduced the mRNA and protein levels of Notch signaling pathway components (NOTCH1, HES1, HEY1), while upregulating genes related to ferroptosis (ACSL4, TFRC). Figure 4 EG). The significant increase in ACSL4 at both transcriptional and translational levels highlights lipid peroxidation as the dominant ferroptosis subprogramme regulated by MC1R. To verify the causal relationship, the study found that MC1R overexpression (OE-MC1R) activates the Notch signaling pathway (manifested as increased expression levels of NICD, HES1, and HEY1) and inhibits ACSL4 expression. Conversely, the Notch inhibitor IMR-1 prevents Maml1 from being recruited to the Notch ternary complex (NTC) on chromatin, thereby inhibiting the transcription of Notch target genes and restoring ACSL4 levels. Notably, the combined use of OE-MC1R and IMR-1 can reverse the ACSL4 inhibition induced by MC1R overexpression. Figure 4 This discovery directly reveals the mechanism by which MC1R regulates ferroptosis—its transcriptional regulation of ACSL4 through the Notch signaling pathway. The results show that MC1R inhibits ACSL4 expression by activating the Notch signaling pathway, thereby suppressing ferroptosis in colorectal cancer. This finding not only highlights the dual role of MC1R in driving oncogenic pathways but also reveals its unique function in regulating cell death mechanisms.

[0052] MC1R inhibits ferroptosis in colorectal cancer cells by regulating ACSL4 expression. To investigate the role of MC1R in ferroptosis, we conducted four experiments: control group (OE-Vector), OE-MC1R, OE-ACSL4, and OE-MC1R+OE-ACSL4. RT-qPCR and Western blot analysis confirmed successful overexpression of MC1R and ACSL4. Figure 5 AC). MC1R overexpression reduced ferroptosis markers (malondialdehyde, iron) levels and increased glutathione (GSH), while ACSL4 overexpression had the opposite effect. In the co-overexpression group (OE-MC1R+OE-ACSL4), these effects were completely offset, and marker levels returned to control levels. Figure 5 Data indicate that MC1R inhibits ferroptosis by downregulating ACSL4 expression, and ACSL4 is a key downstream mediator in CRC.

[0053] MC1R affects the proliferation and migration of colorectal cancer cells by regulating ACSL4 expression. To investigate the role of MC1R in the growth and migration of colorectal cancer cells, the inventors set up four experimental groups: a control group (OE-Vector), an MC1R overexpression group (OE-MC1R), an ACSL4 overexpression group (OE-ACSL4), and a dual overexpression group (OE-MC1R+OE-ACSL4). The results showed that MC1R overexpression (OE-MC1R) significantly promoted cell proliferation (as detected by CCK-8 assay), while ACSL4 overexpression (OE-ACSL4) inhibited cell proliferation. When both MC1R and ACSL4 were simultaneously overexpressed, the cell proliferation level recovered to a level comparable to the control group. Figure 6 A, Figure 6 B). In the migration assay, the colony-forming ability was enhanced in the MC1R overexpression group and weakened in the ACSL4 overexpression group, but the colony-forming ability of the dual overexpression group was comparable to that of the control group. Figure 6 C Figure 6 D). Migration assays showed that cell migration was enhanced in the MC1R overexpression group (as assessed by Transwell assay), while migration was inhibited in the ACSL4 overexpression group. The dual overexpression group reversed the migratory-promoting effect of MC1R. Figure 6 E, Figure 6 These results indicate that MC1R enhances the proliferation and migration of colorectal cancer cells, while ACSL4 has an antagonistic effect. The phenotypic recovery observed in the double overexpression group confirms that ACSL4 is a key downstream mediator of MC1R in regulating the growth and motility of colorectal cancer cells.

[0054] Univariate Cox regression analysis showed that age, M stage, N stage, pathological stage, T stage, and MC1R expression were important predictors of overall survival (OS) in colorectal cancer (CRC). Figure 6 G). Subsequent multivariate analysis further confirmed that age, M stage, pathological stage, and MC1R expression were all independent prognostic factors. Figure 6 H) established MC1R as a novel prognostic biomarker for colorectal cancer. To translate these findings into clinical applications, the inventors developed a nomogram model integrating MC1R expression with key clinical parameters to predict 1-year, 2-year, and 3-year survival probabilities. Figure 6 I). The importance of MC1R in risk stratification and personalized treatment planning provides a quantitative tool for optimizing prognostic assessment and treatment strategies for CRC patients.

Claims

1. Use of an agent that inhibits MC1R in the manufacture of a medicament for the treatment of colorectal cancer, characterized in that: The agent for inhibiting MC1R is shMC1R, and the nucleotide sequence thereof is shown as SEQ ID NO: 2 or 3. The agent for inhibiting MC1R is shMC1R, and the nucleotide sequence thereof is shown as SEQ ID NO: 2 or 3.

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