Application of MC1R

Abstract

The invention discloses application of MC1R, particularly relates to application of a reagent for inhibiting MC1R in preparation of a medicine for treating colorectal cancer, and belongs to the field of biological medicine. In-vitro experiments show that over-expression of the MC1R in a colorectal cancer cell line can promote cell proliferation and migration, meanwhile, ferroptosis is inhibited by down-regulating ACSL4 expression, and the reverse effect is generated by knocking down the MC1R. In-vivo experiments also find that both the volume and the weight of a xenotransplantation tumor formed by colorectal cancer cells knocking down the MC1R are reduced, and the MC1R activates a Notch signal channel to cause expression inhibition of ACSL4, so that ferroptosis is inhibited, and cell growth and migration are further blocked; the high expression of MC1R in colorectal cancer is related to poor prognosis and is negatively related to ferroptosis level. Targeting MC1R may provide a new strategy for enhancing colorectal cancer ferroptosis, and provides an important clue for developing colorectal cancer targeted therapy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biological medicine, and relates to the application of MC1R, and in particular to the application of an agent for inhibiting MC1R in the preparation of a drug for treating colorectal cancer. BACKGROUND

[0002] Colorectal cancer (CRC) is a major challenge in the field of global health for decades. According to Globocan 2020 data, it is the third most common cancer and the second leading cause of cancer-related deaths worldwide. Despite significant advances in diagnosis and treatment over the past few decades, the 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 shown to be closely related to the pathogenesis of various malignancies. Ferroptosis, a form of cell death dependent on iron and not triggering apoptosis, has become a key mechanism in the progression of various cancers. This mode of cell death not only inhibits tumor growth, but also enhances the efficacy of immunotherapy and overcomes the resistance of existing anticancer drugs. Its characteristic is the accumulation of lipid reactive oxygen species (ROS) and the destruction of cell membrane integrity. This unique mode of death is different from other forms of cell death such as apoptosis and necrosis due to its dependence on iron metabolism and lipid peroxidation pathways. The initiation of ferroptosis is due to the imbalance of intracellular iron and lipid homeostasis, which ultimately leads to the excessive production of reactive oxygen species (ROS) and the peroxidation of polyunsaturated fatty acids (PUFAs). For example, iron uptake mediated by transferrin receptor (TFRC) of serum transferrin or lactoferrin promotes ferroptosis, while iron efflux mediated by solute carrier family 40 member 1 (SLC40A1) inhibits this process. PUFAs, which are prone to peroxidation during ferroptosis, can destroy the lipid bilayer and damage 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. As a member of the long-chain acyl-CoA synthetase family, ACSL4 can catalyze the combination of free arachidonic acid or adrenic acid with coenzyme A to form AA-CoA or AdA-CoA derivatives, and then LPCAT can promote their esterification into phospholipids, thereby increasing the content of long-chain PUFAs in cell lipids and membranes. ACSL4-mediated lipid metabolism can increase lipid ROS levels under ferroptosis conditions, making cells more susceptible to ferroptosis. The overactivation of ACSL4 is associated with increased lipid peroxidation, which is one of the hallmark features of ferroptosis. As a highly conserved cellular signaling system, the Notch signaling pathway plays a key role in regulating cell fate determination, proliferation and differentiation. From a physiological perspective, this signaling pathway is essential for the normal development and homeostasis of intestinal epithelial cells. Specifically, the Notch signaling pathway regulates the differentiation process of colon goblet cells and stem cells. In human colorectal cancer tissues, the inventors found that the Notch receptor (Notch1) was abnormally activated, and this atypical activation was closely related to the poor prognosis and metastasis risk of patients with colorectal cancer. In addition, active Notch signaling can further promote the metastasis of colorectal cancer by regulating the tumor microenvironment and affecting the expression of epithelial-mesenchymal transition (EMT)-related transcription factors.

[0003] In addition, the Notch signaling pathway promotes colorectal cancer metastasis by regulating the tumor microenvironment and affecting epithelial-mesenchymal transition (EMT)-related transcription factors such as SLUG and SNAIL. Recent studies have shown that the Notch signaling pathway is also involved in the regulation of ferroptosis. In astrocyte-like glioma cells, Notch signal activation can trigger enhanced mitochondrial lipid peroxidation, increased reactive oxygen species (ROS) production, and decreased glutathione levels, thereby increasing cell sensitivity to ferroptosis. However, the specific mechanisms of Notch signaling pathway in ferroptosis remain to be elucidated. A comprehensive understanding of the interactions between these signaling pathways is crucial for developing innovative cancer treatment strategies. Melanocortin 1 receptor (MC1R), as a G protein-coupled receptor, is expressed in human melanocytes and melanoma cells, and plays an important 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, overexpression of MC1R is particularly prominent in metastatic melanoma, especially in clones that have developed resistance to targeted therapy, suggesting that it may be a potential molecular target for treating metastatic melanoma. Inhibition of MC1R is a promising molecular strategy to alleviate drug resistance. In addition, the expression of MC1 receptor in various cell types other than melanocytes, such as keratinocytes, fibroblasts, endothelial cells, and immune system cells, has been widely demonstrated and is believed to be involved in physiological processes such as anti-inflammatory effects, immune responses, burn reactions, collagen synthesis, and scar formation. There is no research on MC1R interfering with the Notch signaling pathway to regulate ferroptosis and colorectal cancer. These findings provide a theoretical basis for the development of new therapeutic targets. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides an application of MC1R in anti-colorectal cancer.

[0005] To solve the above technical problems, the technical solutions of the present application are as follows:

[0006] The present application protects the application of an agent inhibiting MC1R in the preparation of a drug for treating colorectal cancer.

[0007] The present application also protects the application of an agent inhibiting MC1R in combination with an ACSL4 inhibitor in the preparation of a drug for treating colorectal cancer.

[0008] Further, the agent inhibiting MC1R is achieved by activating the Notch signaling pathway to inhibit the expression of ACSL4, thereby inhibiting ferroptosis and further blocking the growth and migration of colorectal cancer cells.

[0009] Further, the agent for inhibiting MC1R is shMC1R, and the nucleotide sequence is shown as SEQ ID NO 2 or 3.

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

[0011] In vitro experiments of the present application show that overexpression of MC1R in colorectal cancer cell lines promotes cell proliferation and migration, while inhibiting ferroptosis by down-regulating ACSL4 expression, and knocking down MC1R has the opposite effect. In vivo experiments also found that the volume and weight of xenograft tumors formed by colorectal cancer cells with knocked down MC1R were reduced, and it was found that MC1R activated the Notch signaling pathway, leading to suppressed ACSL4 expression, thereby inhibiting ferroptosis and further blocking cell growth and migration; high expression of MC1R in colorectal cancer is associated with poor prognosis and is negatively correlated with the level of ferroptosis. Targeting MC1R may provide a new strategy for enhancing colorectal cancer ferroptosis and provide an important clue for the development of targeted therapy for colorectal cancer. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 MC1R is related to colorectal cancer ferroptosis, wherein Figure 1 A volcano plot of differentially expressed genes between colon cancer and normal tissue, Figure 1 B is a WGCNA dendrogram; Figure 1 C correlation matrix presents a module-feature relationship diagram, Figure 1 D is a correlation scatter plot; Figure 1 E is a univariate Cox regression analysis of genes associated with poor prognosis, Figure 1 F is the use of StepCox, CoxBoost, random survival forest (RSF), XGBoost, Boruta, support vector machine (SVM), elastic network (ENet) and Lasso regression algorithms to identify genes associated with colorectal cancer prognosis; Figure 1 G is the top ten ranking of colorectal cancer pre-prognostic genes screened by various algorithms, Figure 1 H is the correlation of the ten genes with the ferroptosis pathway analyzed by ssGSEA algorithm, Figure 1 I is based on TCGA data analysis of MC1R mRNA expression levels in colon adenocarcinoma tumor tissue and normal tissue, Figure 1 J is the comparison of the total survival Kaplan-Meier survival curve between the low MC1R group and the high MC1R group in the TCGA training data set.

[0013] Figure 2 Inhibiting MC1R can inhibit CRC cell proliferation and migration, wherein Figure 2 A and Figure 2B is the mRNA or protein expression level of MC1R in HCT116 and SW620 cells transfected with shNC, shMC1R-1 and shMC1R-2 detected by RT-qPCR or Western blot, respectively; Figure 2 C and Figure 2 D is the proliferation of HCT116 and SW620 cells after MC1R knockdown at different time points detected by CCK-8 method; Figure 2 E is to evaluate the clonogenicity of HCT116 and SW620 cells after MC1R knockdown by clonogenicity experiment, Figure 2 F is to determine the migration ability of HCT116 and SW620 cells after MC1R knockdown by Transwell chamber experiment; Figure 2 G is to show the effect of MC1R on tumor growth in vivo by xenograft tumor model, Figure 2 H and Figure 2 I is the tumor weight and volume measurement results of xenograft model.

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

[0015] Figure 4 MC1R down-regulation inhibits Notch signaling pathway and up-regulates ACSL4 expression, wherein Figure 4 A is the KEGG enrichment analysis of MC1R, Figure 4 B is the comparison of ssGSEA scores of Notch signaling pathway between Low_MC1R and High_MC1R groups; Figure 4 C is the difference in expression level of ferroptosis-related genes between Low_MC1R and High_MC1R groups; Figure 4 D is the correlation analysis of MC1R and ACSL4 mRNA levels in CRC;Figure 4 E and Figure 4 F are the RT-qPCR detection results of NICD, HES1, HEY1, ACSL4 and TFRC after shNC, shMC1R-1 and shMC1R-2 transfection of HCT116 cells and F SW620 cells, respectively; Figure 4 G is the Western blot detection results of NICD, HES1, HEY1, ACSL4 and TFRC in HCT116 and SW620 cells after MC1R knockdown; Figure 4 H is 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 of colorectal cancer cells through ACSL4, wherein Figure 5 A and Figure 5 B are the comparison graphs of mRNA expression levels of MC1R and ACSL4 in HCT116 and SW620 cells detected by RT-qPCR, respectively, Figure 5 C is a graph of Western blot analysis of overexpression of MC1R and ACSL4 at the protein level in HCT116 and SW620 cells; Figure 5 D is the MDA content after overexpression of MC1R and ACSL4 in HCT116 and SW620 cells detected by lipid peroxidation MDA detection kit; Figure 5 E is the intracellular iron ion level after overexpression of MC1R and ACSL4 in HCT116 and SW620 cells measured by iron ion detection kit, Figure 5 F is the glutathione level after overexpression of MC1R and ACSL4 in HCT116 and SW620 cells measured by glutathione detection kit.

[0017] Figure 6 MC1R regulates proliferation and migration of colorectal cancer cells through ACSL4, wherein Figure 6 A and Figure 6 B are the proliferation of HCT116 and SW620 cells at different time points after overexpression of MC1R and ACSL4 detected by CCK-8 method, respectively; Figure 6 C and Figure 6 D are the colony formation ability of HCT116 and SW620 cells after overexpression of MC1R and ACSL4 evaluated by colony formation experiment; Figure 6 E and Figure 6 F are the migration abilities of HCT116 and SW620 cells after overexpression of MC1R and ACSL4 measured by Transwell chamber experiment, respectively;Figure 6 G and Figure 6 H is the association of MC1R with clinical characteristics by univariate forest plot and multivariate Cox regression analysis for colorectal cancer patients, respectively. Figure 6 I is the survival curve graph of predicting colorectal cancer patients. DETAILED DESCRIPTION

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

[0019] EMBODIMENT

[0020] Bioinformatics analysis dataset and preprocessing: The transcriptome data (HTSeq count) and clinical data (including pathological stage, age, gender and prognosis information) were derived from the TCGA-COADREAD database. The dataset contains 426 COADREAD samples and 42 normal tissue samples, and samples without clinical data are excluded. The HTSeq count is used for analysis after log2(TPM+1) conversion, and the differential expression is evaluated by HTSeq counter. Differential expression gene (DEG) detection: DESeq2 R software package is used to identify differential expression genes between two groups, with threshold |log2 fold change|>1 and adjusted P value<0.05. Single sample gene set enrichment analysis (ssGSEA): The ssGSEA algorithm in the "GSVA" software package is used to quantify the enrichment score of ferroptosis-related pathways through the MSigDB database. This method can evaluate the incidence and intensity of ferroptosis response in colorectal cancer samples.

[0021] Weighted gene co-expression network analysis (WGCNA): In this study, the "WGCNA" software package is used to screen key genes related to ferroptosis score based on the up-regulated expression data of TCGA COADREAD cohort. By setting the soft threshold softpower=4 to construct the adjacency matrix, the hub module with the strongest positive Pearson correlation is identified. According to the screening criteria of module member degree (MM)>0.4 and gene significance (GS)>0.3, the final potential ferroptosis-related candidate genes are determined. Independent risk factor identification: First, univariate COX regression analysis is performed on the 234 ferroptosis-related genes screened by WGCNA, and p<0.05 and HR>1 are used as preset thresholds to screen potential prognostic risk factors. Subsequently, the "Mime1" software package is used to test eight machine learning algorithms, including StepCox, CoxBoost, random survival forest (RSF), XGBoost, Boruta, SVM-RFE, elastic network (Enet) and LASSO, and finally the top 10 genes with the highest selection frequency are selected as the best prognostic risk factors.

[0022] Enrichment analysis: Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed using the "clusterProfiler" 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 detailed procedures for establishing and culturing colorectal tumor organoids are as follows: After the tumor tissue is chopped and digested into a single-cell suspension, centrifuge at 200 x g for 5 minutes. Seed 250,000 cells per genotype in 80 μL Matrigel in a 12-well plate to establish organoids. Lentivirus transduction experiments are performed in 500 μL organoid medium with the addition of 4 μg / mL polybrene for 24 hours. After transduction is complete, replace the virus medium with 100 μL fresh Matrigel and regular growth medium containing Y-27632. Screening starts with 2 μg / mL puromycin from day 5 and is maintained with Y-27632 until day 11. Replace the medium every 48 hours during the screening period. From day 11, the organoids are cultured in puromycin-free medium until the end of the culture on day 14.

[0023] Chemical reagent IMR-1 (item number 53101ES08) was purchased from YEASEN company, with a final concentration of 25 μM. Erastin (item number SC0224) was purchased from Biyun Tian company, with a final concentration of 10 μM. In terms of cell culture, human colorectal cancer cell lines HCT116 and SW620 were cultured using DMEM medium (Corning company 10-013-CVR type) added with 10% fetal bovine serum (Ausbian company VS500T type). All cell lines were purchased from the American Type Culture Collection (ATCC). Cells were maintained in a 37°, 5% carbon dioxide incubator.

[0024] Lentivirus shRNA vectors pLenti-MC1R (OE-MC1R), pLenti-ACSL4 (OE-ACSL4) and MC1R shRNA (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: AACAGTCGCGTTTGCGACTGG; shMC1R-1: TCACCATCCTGCTGGGCATTT; shMC1R-2: CACCAGGGCTTTGGCCTTAAA, the sequences of which are shown in SEQ ID NO: 1-3, respectively. Virus preparation and infection experiments used the HEK293T cell line for lentivirus amplification. Viruses were collected 48 and 72 hours after transfection, filtered through a 0.45-μm filter, and infected target cells in the presence of 8 μg / ml polybrene. The infected cell lines were then selected with appropriate antibiotics.

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

[0026] Quantitative real-time fluorescence quantitative PCR (RT-qPCR) Total RNA extraction used TRIzol reagent (Thermo, Cat# 15596026CN), and cDNA synthesis used HiScript II Q Select RT SuperMix reagent (Vazyme, Cat# R233-01). RT-qPCR experiments used Taq Pro Universal SYBR qPCR Master Mix (Vazyme, Cat# Q712-02) for three repeated detections. The relative expression of mRNA was quantified by 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'-CGTCGGCGCTTCTCAATTATTC-3';

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

[0033] TFRC-R: 5'-CAGTTTCTCCGACAACTTTCTCT-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 sequences are shown as SEQ ID NOs: 4-17.

[0040] Western blot analysis and antibody usage: Whole cell lysates were prepared using RIPA buffer (Thermo, Cat# 89901) containing complete protease inhibitors. Proteins were separated by 7.5-15% SDS-PAGE and transferred to PVDF membranes, followed by incubation with the following primary antibodies: NICD (1: 1000, Proteintech, Cat# 20687-1-AP), HEY1 (1: 1000, Proteintech, Cat# 19929-1-AP), HES1 (1: 1000, abcam, Cat# ab108937), TFRC (1: 10000, Proteintech, Cat# 10084-2-AP), and GAPDH (1: 30000, Proteintech, Cat# 60004-1-Ig), and HRP-conjugated secondary antibodies.

[0041] Cell proliferation assay CCK-8 assay kit (MeiRunSheng, Cat# MA0218-Feb-191) was used to determine colorectal cancer cell viability according to the manufacturer’s instruction. Cells were seeded at a density of 2000 cells per well in 100 μL culture medium and incubated at different time points. After the addition of 10 μL CCK-8 reagent, cells were incubated at 37 °C for 2 hours. Subsequently, the absorbance was measured at 450 nm wavelength using a microplate reader. Cell-free blank controls were set up for background correction and the final cell viability was normalized by comparison with the control group. Colony formation assay was performed by seeding cells at a density of 1000 cells per well in a six-well plate. After 14 days of incubation, cells were washed with PBS and fixed with 1 mL 4% paraformaldehyde solution for 30 minutes. Cells were then stained with 0.1% crystal violet for about 20 minutes. After several washes and air-drying, the stained area was analyzed using ImageJ software. The number of colonies containing more than 50 cells was counted under a microscope and photographed.

[0042] Transwell cell migration assay was performed using a 24-well plate containing 8 μm polyethylene terephthalate membranes (Corning, Cat# 3422). HCT116 and SW620 cells (1 x 10 5 cells per well) were placed in the upper chamber in serum-free DMEM medium and DMEM medium containing 10% fetal bovine serum was added to the lower chamber. The experiment was performed in a 37 °C, 5% carbon dioxide humidified incubator for 24 hours. After the non-migrated cells were removed with a cotton swab, they were fixed with 4% paraformaldehyde for 30 minutes and stained with 0.1% crystal violet for 20 minutes before being photographed. The stained area was analyzed using ImageJ software. The migration ability was quantified by counting the number of cells in each field (five randomly selected fields per membrane).

[0043] Lipid peroxidation malondialdehyde (MDA) assay was performed using an MDA assay kit from Biyun Tian (Cat# S0131S). After preparing the working solution, 1 x 10 6 cells were resuspended in 100 μL extract and sonicated before being centrifuged at 12,000 rpm for 10 minutes at 4 °C. The supernatant was collected and cooled in an ice bath. After adding 200 μL MDA assay reagent mixture, the mixture was heated in a 100 °C water bath for 15 minutes. After cooling to room temperature and centrifugation at 1000 g for 10 minutes, 200 μL supernatant was transferred to a 96-well plate and the absorbance was measured at 532 nm wavelength using a microplate reader.

[0044] Cell Fe 2+ detection method In this experiment, a cell Fe 2+ detection kit produced by mlbio company was used to determine the Fe 2+ content according to the manufacturer’s instruction. The specific steps are as follows: after preparing the working solution, 1 x 10 6Cells were resuspended in 450 μL extraction solution, and then were broken by ultrasonic wave. After centrifugation at 4 °C, 5000 rpm for 10 min, the supernatant was collected and placed on ice. Then, 1.2 mL FeAssay Buffer was added, and the mixture was incubated at 37 °C for 10 min. Finally, the absorbance value was measured at 562 nm using a microplate reader.

[0045] The intracellular glutathione (GSH) was quantitatively detected using a GSH quantitative kit (Anaspec, USA, item number ADS-W-G001). After preparing the working solution according to the instructions, 5 x 10 6 Cells were resuspended in 1 mL extraction solution, and then were broken by ultrasonic wave. After centrifugation at 4 °C, 12,000 rpm for 15 min, the supernatant was collected and placed on ice. Then, the sample was added to a 96-well plate at a density of 20 μL / well, and the working solution was added according to the instructions. After mixing and vortexing, the absorbance value was measured at 412 nm after incubation for 5 min. 7 All animal experiments 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 the double-blind protocol, and 1 x 10 2 HCT-116 colorectal cancer cells. The tumor volume (calculated formula: L x W 3 / 2, where L represents length and W represents width) and body weight were monitored every 3 days for 23 consecutive days. To ensure ethical standards and ensure data validity, the experimental animals were euthanized when the tumor volume exceeded 2000 mm

[0046] The experimental results are as follows:

[0047] MC1R is highly expressed in colorectal cancer and is negatively correlated with ferroptosis. To explore the mechanism of ferroptosis in colorectal cancer, differential gene expression analysis showed that there were 2,615 significantly dysregulated genes between colorectal cancer tissues and normal tissues (|log2FC| > 1, corrected p < 0.05) (A). Whole-genome co-expression network analysis revealed 13 unique gene modules, of which the pink module (containing 234 genes) was most significantly associated with ferroptosis (B-D). Single-factor Cox regression analysis identified 40 prognosis-related genes, which were refined to 10 high-confidence candidate genes after screening by various machine learning algorithms (StepCox, RSF, SVM, etc.) (E-G). ssGSEA correlation analysis found that four genes (TRIP6, TSPEAR, MC1R, TLX1) could inhibit ferroptosis, of which MC1R showed the strongest negative correlation (H). Figure 6 Figure 1 Figure 1 Figure 1 ​​​H). Given that MC1R is highly associated with ferroptosis and has a clear role as a cell surface receptor in cancer, while other candidate genes (TRIP6, TSPEAR, TLX1) have lower relevance or are not obviously linked to colorectal cancer biology, we chose MC1R for further study. TCGA-COAD data confirmed that MC1R expression in colorectal cancer tissues was higher than that in normal controls Figure 1 I). Survival analysis showed that high MC1R expression was significantly associated with a significantly shortened overall survival of colorectal cancer patients Figure 1 J), highlighting its potential as a prognostic biomarker and therapeutic target.

[0048] MC1R significantly enhances the proliferation and migration ability of colorectal cancer cells. To explore the functional role of MC1R in colorectal cancer, the inventors intervened in HCT116 and SW620 cells through shRNA-mediated knockdown technology. RT-qPCR and Western blot detection confirmed that the mRNA and protein levels of MC1R were significantly reduced Figure 1 A, Figure 2 B). Functional experiments showed that CCK-8 detection found that MC1R knockdown significantly inhibited the proliferation of colorectal cancer cells Figure 2 C, Figure 2 D); Transwell migration experiments showed that its colony formation ability was impaired Figure 2 E), and the migration ability was also significantly decreased Figure 2 F). To verify the specificity, the shRNA-resistant MC1R construct was reintroduced into MC1R-knockdown cells. The re-expression of MC1R restored the cell proliferation ability, confirming the observed phenotype to be MC1R-dependent. To extend these findings to in vivo and clinically relevant models, the volume and weight of xenograft tumors formed after implanting MC1R-knockdown cells into nude mice were significantly reduced compared to the control group Figure 2 G-I). Further verification in patient-derived three-dimensional colorectal cancer organoids showed that knockdown of MC1R inhibited organoid growth, resulting in a decrease in the number of tumors and a reduction in tumor volume Figure 2 J). The comprehensive data showed that MC1R deletion could delay the progression of colorectal cancer in vitro, in vivo, and ex vivo models, highlighting its key role in maintaining tumor growth and metastatic potential.

[0049] MC1R significantly inhibits the level of ferroptosis in colorectal cancer cells. To explore the role of MC1R in the regulation of ferroptosis in colorectal cancer, single-sample gene set enrichment analysis (ssGSEA) found that high expression of MC1R was negatively correlated with the activity of ferroptosis-related pathways Figure 2 A, Figure 3B), suggesting that MC1R might have a role in inhibiting ferroptosis. Functional validation in MC1R-knocked-down HCT116 and SW620 cells showed significantly elevated ferroptosis markers: enhanced lipid peroxidation mediated by malondialdehyde (MDA), increased intracellular iron content, and decreased glutathione (GSH) levels compared with the control group Figure 3 C-E). In contrast, MC1R-overexpressing cells showed resistance to erastin, a ferroptosis inducer, and no significant ferroptotic cell death was observed after treatment Figure 3 F). Notably, flow cytometry confirmed that knocking down MC1R did not change the rate of apoptosis, ruling out the possibility of apoptosis as an interfering factor. The integrated data suggest that MC1R deletion promotes ferroptosis by enhancing lipid peroxidation, iron accumulation, and antioxidant depletion, while its overexpression confers ferroptosis resistance to cells. Therefore, MC1R plays a negative regulatory role in the ferroptosis process in colorectal cancer cells.

[0050] MC1R inhibits ferroptosis in colorectal cancer cells through the Notch signaling pathway. KEGG pathway analysis showed that MC1R is functionally associated with fatty acid metabolism and the Notch signaling pathway Figure 3 A). Given that ferroptosis is driven by iron-dependent lipid peroxidation, the association of MC1R with fatty acid metabolism suggests that it may play a regulatory role in this cell death pathway. Notably, MC1R is linked to the Notch signaling pathway, which is crucial for colorectal cancer progression by regulating cell proliferation and differentiation. ssGSEA analysis confirmed that MC1R expression was significantly positively correlated with Notch pathway activity in the colorectal cancer patient cohort Figure 4 B). Differential gene expression analysis further revealed that MC1R is associated with key ferroptosis regulators Figure 4 C). While MC1R expression was negatively correlated with SLC7A11, a component of the glutathione synthesis system, no association with GPX4 was observed Figure 4 C). Notably, knocking down MC1R in colorectal cancer cells did not change the protein levels of SLC7A11 or GPX4 (Figure S1B), indicating that MC1R does not regulate ferroptosis through the glutathione pathway. In contrast, MC1R was significantly negatively correlated with ACSL4, a lipid peroxidation driver, and TFRC, an iron transporter Figure 4 C, D), suggesting that lipid metabolism and iron homeostasis are likely the main mechanisms of action.

[0051] To dissect this axial regulatory mechanism, the inventors performed pathway-specific changes analysis on MC1R-knocked down 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 upregulated genes related to ferroptosis (ACSL4, TFRC) Figure 4 E-G). The significant increase of ACSL4 at both transcriptional and translational levels highlighted lipid peroxidation as the dominant ferroptosis subprogram regulated by MC1R. To verify the causality, the study found that MC1R overexpression (OE-MC1R) activated the Notch signaling pathway (manifested as increased expression of NICD, HES1, HEY1) and suppressed ACSL4 expression. In contrast, the Notch inhibitor IMR-1 could prevent Maml1 from being recruited to the Notch ternary complex (NTC) on the chromatin, thereby inhibiting the transcription of Notch target genes and restoring ACSL4 levels. Notably, the combination of OE-MC1R and IMR-1 could reverse the ACSL4 suppression phenomenon induced by MC1R overexpression Figure 4 H), which directly revealed the mechanism by which MC1R regulated ferroptosis - it transregulates ACSL4 through the Notch signaling pathway. The results showed that MC1R suppressed ACSL4 expression by activating the Notch signaling pathway, thereby inhibiting 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 explore the role of MC1R in ferroptosis, four groups of experiments were performed: control (OE-Vector), OE-MC1R, OE-ACSL4, and OE-MC1R+OE-ACSL4. RT-qPCR and Western blot detection confirmed the successful overexpression of MC1R and ACSL4 Figure 4 A-C). MC1R overexpression reduced the levels of ferroptosis markers (malondialdehyde, iron ions) 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 the marker levels returned to the control group level Figure 5 D-F). The data showed that MC1R inhibited ferroptosis by downregulating ACSL4 expression, and ACSL4 was a key downstream mediator of MC1R in CRC.

[0053] MC1R affects colorectal cancer cell proliferation and migration 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: control group (OE-Vector), overexpression of MC1R group (OE-MC1R), overexpression of ACSL4 group (OE-ACSL4), and double overexpression group (OE-MC1R+OE-ACSL4). The experimental results showed that overexpression of MC1R (OE-MC1R) significantly promoted cell proliferation (determined by CCK-8 detection method), while overexpression of ACSL4 (OE-ACSL4) inhibited cell proliferation. When MC1R and ACSL4 were overexpressed simultaneously, the level of cell proliferation returned to that of the control group Figure 5 A、 Figure 6 B). In the migration experiment, the colony formation ability of the MC1R overexpression group was enhanced, while that of the ACSL4 overexpression group was weakened, but the colony formation ability of the double overexpression group was comparable to that of the control group Figure 6 C、 Figure 6 D). Migration experiments showed that the migration ability of the MC1R overexpression group was enhanced (evaluated by Transwell experiment), while the ACSL4 overexpression group inhibited migration. The double overexpression group reversed the effect of MC1R in promoting migration Figure 6 E、 Figure 6 F). These results showed that MC1R can enhance the proliferation and migration ability of colorectal cancer cells, while ACSL4 has an antagonistic effect. The phenotype restoration observed in the double overexpression group confirmed that ACSL4 is a key downstream mediator of MC1R in regulating the growth and movement 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) of colorectal cancer (CRC) Figure 6 G). Subsequent multivariate analysis further confirmed that age, M stage, pathological stage and MC1R expression were independent prognostic factors Figure 6 H), establishing MC1R as a new biomarker for prognosis of colorectal cancer. To translate these findings into clinical applications, the inventors developed a nomogram model integrating MC1R expression and key clinical parameters to predict the survival probability at 1 year, 2 years and 3 years Figure 6 Figure 6 Figure 6 I). The importance of MC1R in risk stratification and personalized treatment plans provides a quantitative tool for optimizing the prognosis evaluation and treatment strategy of CRC patients.

Claims

1. Use of reagents that inhibit MC1R in the preparation of drugs for the treatment of colorectal cancer.

2. Use of an MC1R-inhibiting agent in combination with an ACSL4 inhibitor in the preparation of a drug for the treatment of colorectal cancer.

3. The use according to claim 1 or 2, characterized in that: The reagent that inhibits MC1R activates the Notch signaling pathway to inhibit ACSL4 expression, thereby inhibiting ferroptosis and blocking the growth and migration of colorectal cancer cells.

4. The use according to any one of claim 13, characterized in that: The reagent for inhibiting MC1R is shMC1R, and its nucleotide sequence is shown in SEQ ID NO: 2 or 3.

Images