Anti-cancer peptides and uses thereof

Abstract

This invention relates to the field of biotechnology, specifically disclosing anticancer peptides and their applications. The anticancer peptides of this invention comprise amino acid sequences as shown in any one of SEQ ID NO. 1-39, or amino acid sequences with greater than or equal to 90% homology to any one of SEQ ID NO. 1-39, or fragments of amino acid sequences as shown in any one of SEQ ID NO. 1-39, or fragments of amino acid sequences with greater than or equal to 90% homology to any one of SEQ ID NO. 1-39. This invention provides novel anticancer peptides, offering a new method for cancer treatment.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and more specifically, to anticancer peptides and their applications. Background Technology

[0002] The contribution of the gut microbiome to host health and disease development is increasingly recognized, and gut microbiome dysbiosis has been shown to contribute to metabolic disorders and the development of autoimmune diseases. Gut microbiota dysbiosis is also associated with gastrointestinal cancers, particularly colorectal cancer (CRC). Recently, gut microbiome dysbiosis has been shown to contribute to non-gastrointestinal tumors, including breast and prostate cancer, among others. Most research on the gut microbiome and its association with cancer has focused on pro-inflammatory gut species, such as *Escherichia coli*, and oncogenic molecules, such as colibacillin; however, the gut microbiome can also interfere with cancer treatment by metabolizing drugs or affecting the host's immune response. Therefore, interventions that modulate the gut microbiome to treat cancer are currently being explored with promising results. Fecal microbiota transplantation has been shown to improve response rates to anti-PD-1 immune checkpoint therapy, and in animal models, it has also shown efficacy against Rnf. 5- / - Transplantation of mouse intestinal feces into germ-free mice can inhibit tumor development.

[0003] The influence of the gut microbiome on host health and cancer development is mediated through a highly diverse range of macromolecules and small molecule metabolites. Indeed, the high phylogenetic diversity within the gut microbiome generates a vast array of different macromolecules, including polysaccharides, lipopolysaccharides, and peptidoglycans—products of complex enzymatic reactions—as well as peptides, proteins, and regulatory RNAs encoded by genomic sequences. A growing number of these molecules have been identified as having therapeutic potential. For example, indole-3-lactic acid, produced by *Lactobacillus bile acidophilus*, inhibits the growth of CRC both in vitro and in vivo; aloin, a metabolite produced by *Lactobacillus aloe*, has recently been shown to inhibit cancer development by disrupting reactive oxygen species in cancer cells. However, a large number of functional small proteins and peptides remain underdescribed in their roles in human diseases. The characteristics of these molecules, particularly their varying lengths and low sequence similarity, make the discovery of disease-related molecules challenging, but recent deep learning methods have facilitated their discovery.

[0004] High-throughput analysis using deep learning to mine metagenomic data has proven effective in identifying antimicrobial peptides (AMPs), which hold promise as potential anti-infective drugs. This suggests that similar functional macromolecules, such as anticancer peptides (ACPs), can be found in metagenomics. ACPs are peptides that can inhibit cancer and have long been considered effective therapeutic agents. As early as 1985, the US FDA approved the ACP leuprorelin for multiple cancer indications. Despite decades of research and established relevance, the number of clinically available ACPs remains limited: eight are approved by the US FDA and three by the European Medicines Association (EMA). The promise of these molecules in developing new cancer treatments makes the discovery of novel, potent ACPs a key focus of oncology research.

[0005] One strategy is to improve existing ACPs using chemical methods, such as including non-natural amino acids or D or L isomers of synthetic ACPs to enhance their pharmacological properties. Unfortunately, the empirical discovery of novel ACPs is highly time- and resource-intensive, thus requiring more efficient methods. Last year, many studies explored the utility of machine learning and deep neural networks in predicting ACPs. However, to date, these studies have only been conducted in silicon computing and are not yet fully developed to identify novel ACPs from the large-scale and ever-expanding proteomic and genomic data collected for the gut microbiome. Therefore, further research is needed in the development of ACPs to discover new ones. Summary of the Invention

[0006] One of the purposes of the invention is to provide novel peptides that can effectively fight cancer.

[0007] The gut microbiome plays a vital role in regulating host health and disease and contains a vast library of functional molecules with translational potential. Anticancer peptides (ACPs) are ideal for developing novel cancer therapies, but their discovery is limited by their high dependence on experimental methods. This invention expands the functional peptide discovery paradigm by leveraging the overlap between discovered ACPs and antimicrobial peptides (AMPs). By combining established AMP prediction with the mining of data from healthy and cancer patients in metagenomic populations, it enables the development of novel ACPs.

[0008] This invention employs a validated high-throughput microbiome mining process to discover novel antimicrobial cytokines (ACPs) in metagenomic datasets. Then, differential analysis of gut microbiome data from multiple CRC patient cohorts and matched healthy individuals is used to link the identified ACPs to cancer phenotypes. See the schematic diagram of the research workflow. Figure 1As shown in Figure a, compared to single-center prediction processes that rely solely on prediction models, multi-center prediction processes require one or more data analysis steps to remove a large number of false positives obtained from the prediction model. This can simply and effectively increase the proportion of true positives in the final prediction. As shown in Figure b, this invention combines the predicted potential ACPs with metagenomic cohort analysis for screening new ACPs.

[0009] This invention experimentally validated a total of 40 candidate peptides (which were reduced or depleted in the gut microbiome of CRC patients but enriched in healthy individuals), identifying 39 of them as functional anticancer peptides (ACPs). These potential ACPs (pACPs) exhibit potent potency (inhibitory activity against at least one cancer cell line), demonstrate strong anticancer activity in multiple cancer cell lines, and significantly reduce tumor size in a subcutaneous model of CRC. Although the newly discovered pACPs differ significantly from known published ACPs in both major and secondary structures, experimental data indicate that these pACPs can kill cancer cells. Among them, two of the most promising peptides showed effective tumor suppression in a mouse xenograft cancer model without any detectable toxicity. Structurally, these two peptides possess unusual secondary structures that potentially disrupt cell membranes to inhibit cancer cells. This invention expands upon current knowledge of ACPs and demonstrates that the method combining metagenomic data analysis and metagenomic data mining (referred to as "multicenter" mining) can effectively identify novel, biologically relevant peptides from the gut microbiome, facilitating further qualitative analysis and development of new functional peptides and providing more treatment options for CRC and other types of cancer.

[0010] Specifically, the technical solution of the present invention is as follows:

[0011] In a first aspect, the present invention provides an anticancer peptide comprising an amino acid sequence as shown in any one of SEQ ID NO. 1-39, or comprising an amino acid sequence having a similarity of 90% or more to an amino acid sequence shown in any one of SEQ ID NO. 1-39, or comprising a fragment of an amino acid sequence as shown in any one of SEQ ID NO. 1-39 (i.e., a truncated amino acid sequence based on an amino acid sequence shown in any one of SEQ ID NO. 1-39), or comprising an amino acid sequence having a similarity of 90% or more to a fragment of an amino acid sequence shown in any one of SEQ ID NO. 1-39.

[0012] In some embodiments, the anticancer peptide is an amino acid sequence as shown in any one of SEQ ID NO. 1-39, or an amino acid sequence with a similarity of 90% or more to any one of SEQ ID NO. 1-39, or a fragment of an amino acid sequence as shown in any one of SEQ ID NO. 1-39, or an amino acid sequence with a similarity of 90% or more to any one of SEQ ID NO. 1-39.

[0013] Preferably, the anticancer peptide of the present invention comprises an amino acid sequence as shown in SEQ ID NO. 23 or 24, or comprises an amino acid sequence having a similarity of 90% or more to the amino acid sequence shown in SEQ ID NO. 23 or 24, or comprises a fragment of an amino acid sequence as shown in SEQ ID NO. 23 or 24, or comprises an amino acid sequence having a similarity of 90% or more to the amino acid sequence shown in SEQ ID NO. 23 or 24.

[0014] In some embodiments, the anticancer peptide is an amino acid sequence as shown in SEQ ID NO. 23 or 24, or an amino acid sequence with a similarity of 90% or more to the amino acid sequence shown in SEQ ID NO. 23 or 24, or a fragment of an amino acid sequence as shown in SEQ ID NO. 23 or 24, or an amino acid sequence with a similarity of 90% or more to the amino acid sequence shown in SEQ ID NO. 23 or 24.

[0015] In this invention, a sequence similarity greater than 90% refers to two sequences containing differences in amino acid substitutions, elongations, or truncations, but with differences not exceeding 90%, and amino acid sequences with a similarity greater than or equal to 90% still exhibiting anticancer effects. For example, the sequence shown in SEQ ID No. 38 (pACP253) and the sequence shown in SEQ ID No. 28 (pACP2160) in this invention have 5 more amino acids at the N-terminus than the latter, and their sequence similarity is 90%, both exhibiting anticancer effects.

[0016] In some embodiments, the anticancer peptides of the present invention comprise chemical modifications on amino acids.

[0017] Secondly, the present invention provides a DNA sequence encoding the aforementioned anticancer peptide.

[0018] Thirdly, the present invention provides biological materials containing the above-mentioned DNA sequence, said biological materials including expression cassettes, vectors or host cells.

[0019] Fourthly, the present invention provides the application of the above-mentioned anticancer peptides, DNA sequences, or biological materials in the preparation of anticancer agents.

[0020] Fifthly, the present invention provides an anticancer preparation comprising the above-mentioned anticancer peptide, or further comprising a pharmaceutically acceptable carrier.

[0021] In this invention, "pharmaceutically acceptable" means, within a reasonable medical judgment, compounds, materials, compositions, and / or dosage forms that are suitable for contact with human and animal tissues without excessive toxicity, irritation, allergic reactions, or other problems or complications, and have a reasonable benefit / risk ratio.

[0022] "Pharmaceutically acceptable carrier" refers to a pharmaceutically acceptable material, composition, or medium, such as a liquid or solid filler, diluent, excipient, formulation aid (e.g., lubricant, talc, magnesium stearate, calcium stearate, zinc stearate, or stearic acid), or a solvent encapsulation material involved in carrying or transporting the peptides of the present invention from one organ or part of the body to another organ or part of the body. Each carrier must be "acceptable," meaning it is compatible with other components of the formulation and harmless to the patient.

[0023] The anticancer preparation of the present invention may also contain wetting agents, emulsifiers and lubricants, as well as colorants, releasing agents, coating agents, sweeteners, flavoring agents and aroma agents, preservatives and antioxidants, etc.

[0024] The anticancer preparation of the present invention may also contain other active ingredients that can exert anticancer effects.

[0025] The anticancer agents of the present invention can be used for the treatment of cancers, including solid tumors and hematologic malignancies.

[0026] Preferably, the cancer includes, but is not limited to, rectal cancer, breast cancer, or melanoma.

[0027] The beneficial effects of this invention are at least as follows:

[0028] This invention provides 39 new ACPs through a novel development method, which have inhibitory effects on at least one cancer cell line, thus expanding the treatment options for cancer. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the research workflow. In this diagram, a is a comparison of the multi-center prediction method and the single-center prediction method that relies solely on the prediction model; b is a schematic diagram of the research workflow of this invention.

[0030] Figure 2This describes the ACP screening process of the present invention. Figure a shows the overlap between ACPs and AMPs collected from public databases; figure b shows the composition of the metagenomic cohort of cancer patients in the public database; figure c shows the enrichment of pACPs in healthy controls and information on their source cohorts. The upper figure shows the significant enrichment (-log10(fdr)) of the 39 peptides successfully synthesized in this invention, and the lower figure shows the comparison of enrichment levels (fold change) between healthy individuals and CRC patients. Each bar represents a synthesized ACP, and different colors represent different enrichment levels in healthy individuals. CRC1: PRJDB27928; CRC2: PRJDB6070; CRC3: PRJNA397219; CRC4: PRJDB7774 (number of the public dataset in EBI); figure d provides an overview of the peptide filtering process and the number of peptides retained at each step of the data analysis.

[0031] Figure 3 This study validates potential anticancer peptides in vitro and in vivo. Table a shows the preliminary screening results for anticancer activity. Single cell lines were treated with ACPs or PBS (control). Survival rates (circle size) were calculated as the ratio of survival rates for each ACP and PBS. Gray indicates positive anticancer activity, and the circle size represents the relative survival rate of cancer cell lines. Table b shows the statistical results of the number of cancer cell lines significantly inhibited by each pACP. Table c shows the experimental protocol of pACPs in mouse models. Table d shows the inhibitory effects of two selected anticancer peptides on tumor growth. The bar graph shows the tumor size during the experiment. The statistical results of tumor volume increase in mice treated with pACP1780 peptide are shown as diagonal rectangles, and the statistical results of tumor volume increase in mice treated with pACP2283 peptide are shown as vertical rectangles. * indicates P < 0.05, and *** indicates P < 0.01.

[0032] Figure 4 The results of in vivo and in vitro toxicity tests of the anticancer peptides pACP1780 and pACP2283 of the present invention are shown. Figures a and b show the changes in body weight and survival rate of tumor-free mice after intraperitoneal injection of pACP1780 and pACP2283, respectively. In the figures, light gray circles represent the peptide injection group, and black circles represent the PBS injection control group. Statistical analysis was performed using Dunnett's multiple comparison test. * indicates adjusted P < 0.05, * indicates adjusted P < 0.01, and ns indicates no significant difference. Figures c and d show the inhibitory effects of these two peptides on the HEK293 cell line after overnight co-culture. The dashed line in the figure represents the activity threshold; data above this line indicate no inhibitory effect was observed. Detailed Implementation

[0033] The preferred embodiments of the present invention will now be described in detail with reference to specific examples. It should be understood that the following examples are given for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art can make various modifications and substitutions to the present invention without departing from its spirit and essence.

[0034] Unless otherwise specified, the experimental methods used in the following examples are conventional methods. Unless otherwise specified, the materials and reagents used in the following examples are commercially available.

[0035] The peptides used in this invention were synthesized by Royo Biotech using solid-phase peptide synthesis, and their accurate molecular weights were determined by mass spectrometry. The purity of all peptides was determined by high-performance liquid chromatography, and the purity of all peptides was greater than 90%.

[0036] In this article, terms used in the singular also include the plural, and vice versa, as appropriate.

[0037] Example 1

[0038] According to reports, ACPs can be divided into two main categories based on their targets: one category inhibits cancer cells and bacteria, and the other category inhibits cancer cells, bacteria, and normal cells. Therefore, this invention utilizes the CancerPPD and LAMP2 databases to compare the associations between known ACPs and AMPs. CancerPPD specifically collects ACPs; it currently contains 3491 ACPs, of which 421 are composed entirely of natural amino acids (AAs). LAMP2 specifically collects AMPs, and it currently annotates 1327 peptides as having anticancer activity, the majority of which (1297 peptides) are composed of natural AAs. Repeated portions between these 421 and 1297 peptides were removed, and then 1480 unique ACPs were identified. Of these unique ACPs, 1134 also possess antibacterial activity (76.6%, see [link to relevant documentation]). Figure 2 (a) Based on the significant overlap between ACPs and AMPs, this invention tested a previously established deep learning-based AMP prediction pipeline, demonstrating that the methods initially specified for AMP discovery also effectively recalled published ACPs. This invention then excluded peptides used for model building from 1,480 unique ACPs, leaving 1,279. The prediction model previously designed for predicting AMPs also recalled 1,033 ACPs from the remaining 1,279, achieving a recall rate of 80.77%. Therefore, this invention utilizes meta-analysis of metagenomic populations to mine pACPs.

[0039] This invention employs metagenomic data analysis to filter potential peptides, assuming a significant association between peptide abundance and certain phenotypes. For example, for the detection of AMPs, the network hypothesizes that functional AMPs will be negatively correlated with bacterial abundance; in this case, it is hypothesized that ACPs will be associated with the absence of cancer. By employing a second "mining center," the overall success rate of discovering functional peptide subsets from a large reservoir can theoretically be improved. To this end, this invention collected cancer-related metagenomic studies from GMRepo and selected a total of 6 metagenomic cohorts of cancer patients, all colorectal cancer patients, CRC patients, see [link to relevant documentation]. Figure 2 In the b group, only the metagenomic cohort containing CRC and healthy controls was retained for subsequent analysis; in addition, two separate cohorts were manually obtained, yielding a total of 496 CRC patients and 509 healthy individuals.

[0040] ACP data were downloaded from CancerPPD and LAMP2, two websites covering the majority of ACPs (as of September 13, 2020). This invention first excluded all ACP sequences containing non-natural amino acids from these two databases and then merged all sequences, ultimately yielding 1279 unique ACPs. To calculate the relative abundance of potential ACPs, this invention collected data from a cohort of gut microbiota in CRC patients downloaded from NCBI up to March 2021. After removing cohorts containing only 16S rRNA data, eight other cohorts from various independent studies across Eurasia, containing 1005 samples (496 CRC patients and 509 healthy controls), were included. The potential ACPs (pACPs) sequence set was derived from 2,349 peptides with potential antimicrobial activity and encoding evidence obtained from previous studies.

[0041] To establish a differentially distributed peptide set across multiple cohorts of CRC patients and healthy subjects, this invention investigated the association between peptides in the gut metagenomics of the CRC dataset and the presence or absence of CRC. The relative abundance of 2,349 peptides was calculated for each of the eight cohorts in the CRC dataset. This revealed the presence of 1,757 sequences in the CRC samples. The invention then analyzed the differences in peptide abundance between patients and healthy controls (see [link to pACPs enrichment in healthy controls and information on their source cohorts]). Figure 2 In step c), 867 peptides were identified that showed significant differences in expression between CRC patients and healthy controls (FDR < 0.05). These peptides were then filtered based on folding changes between groups. This invention used a cutoff value of logarithmic fold change greater than 2, prioritizing the 315 peptides with the most significant differences between groups. Among these, 225 peptides were significantly enriched in the healthy control samples; of these 225 peptides, 48 ​​had a relative abundance > 2 * 102. -4These peptides were selected as the subjects for subsequent experimental verification (see...). Figure 2 (d)

[0042] Specifically, pACPs analysis in the gut metagenome: To obtain pACPs abundance information in each metagenomic sample, this invention uses PALADIN software (version 1.4.0) to align pACPs sequences with metagenomic sequences, and then uses SAMtools (version 1.7) to calculate the abundance of pACPs (the SAMtools functions used include "sorting", "indexing" and "idxstats"), and calculates its coverage per million sequences.

[0043] Distribution analysis of ACPs in different groups: The abundance differences of pACPs were analyzed using the wiloxon rank-sum test in the R package stats (version 3.4.4) using the wilox.test function. To control for false discovery rate, the p-values ​​were adjusted using the p.adjust function in the final step (the p-value adjustment method is the Benjamini and Hochberg method or its alias FDR). The threshold for the adjusted FDR value was defined as 0.05, and the logarithmic change (Log2FC) was calculated in R and the threshold was set to 2.

[0044] Example 2

[0045] In this embodiment, 39 pACPs (see Table 1) of the experimental verification targets obtained in Example 1 above were synthesized using conventional chemical methods in the art, so as to characterize their potential anticancer efficacy in vitro or in vivo.

[0046] Table 1

[0047]

[0048]

[0049] First, in this embodiment, 39 pACPs were initially screened in vitro in 16 different cancer cell lines, including human CRC cell lines (HT29, Caco-2, HCT116) and mouse cell line CT26, as well as 12 other cancer models (see details). Figure 3 a) In this context, different cell line groups were selected to test the effectiveness of pACPs in different genetic backgrounds.

[0050] After each cell line reached 60% confluence, 39 candidate peptides were incubated with each cancer cell line at a concentration of 25 μM pACPs for 24 hours, while the complete medium was replaced with maintenance medium containing only 1% FBS (a PBS group was set up as a control). The viability of each cell line was then assessed using an MTT assay kit according to the manufacturer's instructions. Cell growth was then calculated, and candidate peptides with a growth inhibition rate greater than or equal to 20% were identified as effective pACPs with anticancer activity.

[0051] In short, cancer cells were cultured in their respective media and then treated with pACPs at a final concentration of 25 μM (peptides with a minimum effective inhibitory concentration >25 μM were considered non-ACPs). Cancer cell viability was measured using the MTT assay after exposure to pACPs. All pACPs induced at least 20% inhibition in at least one cell line (see [link to relevant documentation]). Figure 3 (a) Of the 39 validated pACPs, 31 effectively inhibited the proliferation of 1 to 4 cell lines, and 5 pACPs effectively inhibited the proliferation of more than half of the test cell lines. Therefore, this embodiment identifies pACPs with very high anti-cancer efficacy in various cancer cell lines.

[0052] Example 3

[0053] Among the pACPs validated in Example 2, pACP2283 and pACP1780 showed the strongest potency and efficacy against almost all tested cell lines (see Example 2). Figure 3 (b) Therefore, in order to examine the transformation relevance of the method of the present invention, this embodiment further selected these two pACPs for in vivo and in vitro studies.

[0054] Animal experiments:

[0055] Six-week-old female nude mice, provided by Beijing HFK Biosciences Co., Ltd., were acclimatized to the laboratory environment one week prior to the experiment. All experiments were conducted to minimize animal discomfort, distress, and pain. Mice were housed in cages of up to five, living in groups under a 12-hour light-dark cycle, with a constant temperature of 21-22°C and humidity of 55±5%, provided with standard rodent feed and water. All procedures were approved by the Ethics Committee of the Institute of Microbiology, Chinese Academy of Sciences (SQIMCAS2021005).

[0056] Explant tumor model:

[0057] The mouse colorectal cancer cell line CT26 was cultured in Roswell Park Memorial Institute (RPMI)-1640 medium (Gibco) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin / streptomycin (Gibco) at 37°C and 5% CO2. To induce tumor formation, 5 × 10⁶ cells were cultured. 6 / ml CT26 cells were injected into the right forelimb of each mouse. All mice were housed in the same environment with ample food and water. Mice were observed daily for survival and tumor progression throughout the study. Mice were randomly assigned to three groups and received different treatments. All groups received CT26 cells on day -5. On day 0, ACPs (pACP1780: 0.51 mg / kg and pACP2283: 0.80 mg / kg) were injected, while the control group received the same volume of PBS. On day 2, ACPs were injected again, while the control group received the same volume of PBS. See the experimental diagram below. Figure 3 c in the text.

[0058] Specifically, this embodiment first determines the CC of pACP2283 in CT26 cells. 50 The concentration of pACP2283 was 10.56 μM, while that of pACP1780 was 6.23 μM. Subcutaneous tumors were then established in athymic nude mice using CT26 cells. Animals were randomly selected to receive two intratumoral injections on days 0 and 2: 0.80 mg / kg pACP2283, 0.51 mg / kg pACP1780, or a PBS control. The dosage of pACPs was based on the concentration of each compound in CT26 cells (CC). 50 The tumor size was calculated as 5 times larger. Tumor size was monitored daily for six days. Treatment with any pACP significantly reduced tumor size compared to the control group (see [reference]). Figure 3 The d value (P value < 0.05) was not observed, and there were no obvious signs of acute toxicity.

[0059] Then, this embodiment investigated the safety of the two pACPs in vivo and in vitro. Specifically, HEK293 cell lines were first treated with different concentrations of the two pACPs. Specifically, after the HEK293 cells reached 60% confluence, they were incubated for 24 hours with the two peptides at a maximum concentration of 100 μM, after seven consecutive half-diluted pACPs. Simultaneously, the complete culture medium was replaced with maintenance medium containing only 1% FBS (a PBS group was set up as a control). Then, the viability of each cell line was assessed using an MTT assay kit according to the manufacturer's instructions. The HEK293 cell line is an embryonic stem cell-derived cell line representing non-cancer cells. This invention found that these two pACPs did not show toxicity to HEK293 cells (see [link to relevant documentation]). Figure 4(c and d in the text). Furthermore, acute toxicity experiments were conducted in tumor-free mice by injecting progressively higher concentrations of pACPs into the abdomen. After injection of pACPs to 50 mg / kg body weight, the mice did not exhibit obvious disease symptoms or experience weight loss in the following days. Although after injection of 50 mg / kg body weight (pACP1780 is CC...) 50 493.5 times that of pACP2283, which is CC 50 (311 times that of the previous year), the mice's rate of weight gain decreased slightly, but gradually recovered over the next 5 days (see [reference]). Figure 4 (a and b in the original text). This could be explained by the fact that the concentration of pACPs was too high, causing a disturbance in the mice's homeostasis, but the disturbance was short-lived. Overall, pACP2283 and pACP1780 did not show toxicity to HEK293 cells, nor did they show any signs of acute toxicity in tumor-free mice.

[0060] Discovering and utilizing functional molecules from the human gut microbiome is a promising strategy for developing new therapies. This invention focuses on a method for efficiently identifying novel pACPs from human microbiome datasets and validates its ability to identify novel peptides with anticancer activity. Previous computational methods have largely focused on predictions based on existing ACPs; however, the need for discovery in large and rapidly growing biological datasets remains unmet. This invention utilizes existing databases to demonstrate a significant overlap between known ACPs and known AMPs, thus reusing a peptide prediction process developed by the inventors' team to discover new pACPs. This invention queries gut microbiome datasets from CRC patients and matched healthy controls, linking the presence of ACPs to the presence or absence of cancer, assuming that the abundance of true ACPs would be negatively correlated with the presence of cancer. Further validation confirmed the anticancer activity of 39 identified pACPs in vitro, demonstrating the effectiveness of the multi-center mining strategy (i.e., combining peptide prediction methods with metagenomic analysis of specific phenotypes) used in this invention for ACP discovery. Furthermore, the newly discovered ACPs showed very low sequence identity with known ACPs, and the secondary structures of the two most efficient pACPs were verified to be dominated by β-parallelism.

[0061] Although almost all the newly discovered pACPs in this invention have been found to kill some cancer cells, their anti-cancer effects vary considerably. In in vitro screening of 16 cancer cell line models, five pACPs showed inhibitory effects on more than half of the tested cell lines. Furthermore, two pACPs effectively inhibited almost all cell lines, and in CT26 culture, these pACPs exhibited considerably high potency (CC). 50(Below 11 μM). Importantly, although this invention identified pACPs from the human gut microbiome and prioritized them based on a dataset of CRC patients, several of these pACPs (including two of the most potent pACPs) effectively inhibited breast cancer cell lines, including well-known refractory triple-negative breast cancer cells. Furthermore, these two novel pACPs showed no inhibitory effect in non-cancerous HEK293 cells, indicating significant selectivity of the candidate ACPs discovered in this invention.

[0062] Compared to the carrier control, two intratumoral injections of the two most effective pACPs significantly shrunk subcutaneous tumors derived from CT26 cells implanted in athymic nude mice, and both pACPs were well-tolerated. Further improvements in pACP delivery, such as the use of cancer-targeting nanoparticles, could expand the therapeutic index of these agents.

[0063] In summary, this invention provides a multi-center paradigm for discovering functional peptides from metagenomic data, which can effectively identify pACPs. This method links the presence of peptides to cancer phenotypes by querying publicly available datasets, forming an additional "mining center." Utilizing existing metagenomic databases, this invention identifies dozens of new pACPs, representing a significant supplement to the currently known set of ACPs, and provides evidence that this technique can identify pACPs capable of inhibiting tumors in vivo. This method can be further extended to replace the prediction method used in the first center with any functionally similar approach, such as other existing prediction procedures (without requiring the development of new predictors), HMM methods (using a hidden Markov model to construct a Pfam-like strategy to identify target proteins), and any method capable of identifying target molecules. Moreover, for the second center, the characteristics of the target molecules are further segmented in more detail; as in this study, a series of peptide-related analyses based on metagenomic cohorts are performed. Furthermore, the accumulation of proteomics and transcriptomics knowledge is necessary to fully unlock the potential of the gut microbiome in mining functional peptides, such as anti-cancer, anti-aging, and antimicrobial peptides. In addition to peptides, future target molecules can be expanded to include proteins and nucleic acids (genes or functional RNA).

[0064] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. An anticancer peptide, characterized in that, The amino acid sequence of the anticancer peptide is shown in SEQ ID NO.

24.

2. A DNA molecule encoding the anticancer peptide of claim 1.

3. A biomaterial containing the DNA molecule of claim 2, characterized in that, The biomaterials include expression cassettes, vectors, or host cells.

4. The use of the anticancer peptide or the DNA molecule of claim 2 or the biomaterial of claim 3 in the preparation of an anticancer agent; the amino acid sequence of the anticancer peptide is shown in SEQ ID NO. 23 or 24; the anticancer agent is used for the treatment of rectal cancer, breast cancer, cervical cancer or melanoma.

5. An anticancer agent, characterized in that, It contains an anticancer peptide, the amino acid sequence of which is shown in SEQ ID NO.

24.

6. The anticancer agent according to claim 5, characterized in that, It also includes pharmaceutically acceptable carriers.

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