Biomarker for predicting response to dietary intervention in postmenopausal breast cancer patient

Abstract

The present invention relates to a biomarker for predicting a response to dietary intervention in a postmenopausal breast cancer patient, and provides a biomarker combination for predicting a response to dietary intervention in a postmenopausal obese or overweight breast cancer patient, the biomarker combination comprising one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 or proteins thereof.

Description

Biomarkers for Predicting Response to Dietary Therapy in Postmenopausal Breast Cancer Patients

[0001] The present invention relates to a biomarker for predicting the response to dietary therapy in postmenopausal breast cancer patients.

[0002] Breast cancer is the most commonly diagnosed malignant tumor in women worldwide, with over 3 million new cases and more than 1 million deaths projected annually by 2040. Approximately 70–80% of newly diagnosed breast cancer patients are reported to be hormone receptor-positive; accordingly, adjuvant hormone therapy is applied as the standard treatment for patients with estrogen receptor (ER)-positive breast cancer. While this treatment is effective in significantly reducing the recurrence rate in the early stages, the risk of recurrence and death persists for at least 20 years after diagnosis.

[0003] In particular, obesity has been reported to be closely associated with an increased risk of recurrence and death during aromatase inhibitor treatment in postmenopausal ER-positive breast cancer patients. On the other hand, lifestyle improvements such as weight loss, increased physical activity, and a healthy diet are known to improve the health of breast cancer survivors and have a positive effect on biomarkers related to the risk and prognosis of breast cancer.

[0004] The Mediterranean diet (MD) is characterized by an abundant intake of fruits, vegetables, whole grains, nuts, legumes, and olive oil, while consuming less red meat or processed meat. It has been reported to have beneficial effects on various chronic diseases, including obesity, cardiovascular disease, and various types of cancer. Furthermore, the Mediterranean diet is known to improve overall health and potentially lower the risk of recurrence and death from breast cancer after menopause.

[0005] The background description of the invention is provided to facilitate a better understanding of the present invention. The matters described in the background description should not be construed as an acknowledgment that they exist as prior art.

[0006] Meanwhile, the biological mechanisms by which the Mediterranean diet affects breast cancer patients have not yet been clearly elucidated, and conventional analytical methods, such as single omics analysis, have limitations in that they are insufficient to understand the effects of dietary therapy on gene expression from the perspective of a holistic biological network.

[0007] Therefore, in order to predict the effects of dietary therapy in postmenopausal breast cancer patients and establish personalized nutritional strategies, it is necessary to discover molecular-level biomarkers related to dietary response and develop technologies capable of predicting dietary response based on them. In particular, a multi-omics-based approach capable of integratively analyzing various biological layers such as gene expression, metabolites, and the microbiome is required.

[0008] Accordingly, the inventors of the present invention applied a Korean-tailored Mediterranean diet developed in a form suitable for the dietary habits of Koreans to conduct an 8-week randomized parallel-group clinical trial on postmenopausal obese or overweight breast cancer patients, and by integrating and analyzing clinical indicators and transcriptome data before and after dietary therapy, identified a gene network module functionally linked to the improvement of metabolic health by the Mediterranean diet, and through this, discovered a biomarker useful for predicting the response to dietary therapy.

[0009] Accordingly, the problem that the present invention aims to solve is to provide a combination of biomarkers for predicting the response to diet in postmenopausal obese or overweight breast cancer patients comprising one or more genes or proteins thereof selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1.

[0010] In addition, another problem that the present invention aims to solve is to provide a composition for predicting the response to diet in postmenopausal obese or overweight breast cancer patients comprising a preparation that measures the level of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 or the protein expression level thereof.

[0011] In addition, another problem that the present invention aims to solve is to provide a method for providing information to predict the response to dietary therapy in postmenopausal obese or overweight breast cancer patients.

[0012] The problems of the present invention are not limited to those mentioned above, and other unmentioned problems will be clearly understood by those skilled in the art from the description below.

[0013] To solve the problem described above, a combination of biomarkers for predicting the response to diet in postmenopausal obese or overweight breast cancer patients is provided, comprising one or more genes or proteins thereof selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 according to one embodiment of the present invention.

[0014] According to a feature of the present invention, the biomarker may be a gene expression marker or a protein expression marker.

[0015] According to another feature of the present invention, the breast cancer patient may be a patient who has completed initial cancer treatment and is receiving maintenance therapy.

[0016] According to another feature of the present invention, the response to the diet may be a dietary response to metabolic health indicators.

[0017] According to another feature of the present invention, the metabolic health indicator may be at least one selected from the group consisting of body weight, BMI, waist circumference, triglycerides, and insulin resistance.

[0018] In order to solve the problem described above, a composition for predicting the response to dietary therapy in postmenopausal obese or overweight breast cancer patients is provided, comprising a preparation for measuring the level of one or more genes or proteins selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 according to another embodiment of the present invention.

[0019] According to a feature of the present invention, the agent for measuring the gene level may be any one selected from the group consisting of an antisense oligonucleotide that specifically binds to a gene, a primer pair, and a probe.

[0020] According to another feature of the present invention, the measurement at the gene level may be performed by one or more methods selected from the group consisting of reverse transcriptase polymerase chain reaction (RT-PCR), competitive reverse transcriptase polymerase chain reaction (competitive RT-PCR), real-time polymerase chain reaction (real-time RT-PCR), real-time quantitative RT-PCR, RNase protection method, Northern blotting, and DNA chips.

[0021] According to another feature of the present invention, the agent for measuring the protein level may be any one selected from the group consisting of an oligopeptide that specifically binds to a protein, a monoclonal antibody, a polyclonal antibody, a chimeric antibody, a ligand, a PNA, and an aptamer.

[0022] According to another feature of the present invention, the measurement of the protein level may be performed by one or more methods selected from the group consisting of Western blot, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, fluorescence-activated cell sorter (FACS), and protein chip.

[0023] According to another feature of the present invention, the breast cancer patient may be a patient diagnosed with breast cancer stage 1, stage 2, or stage 3.

[0024] According to another feature of the present invention, the breast cancer patient may be a patient who has completed initial cancer treatment and is receiving maintenance therapy.

[0025] According to another feature of the present invention, the cancer treatment may include at least one of breast surgery, adjuvant chemotherapy, and radiation therapy.

[0026] According to another feature of the present invention, the maintenance therapy may be an adjuvant hormone therapy.

[0027] According to another feature of the present invention, the response to the diet may be a dietary response to metabolic health indicators.

[0028] According to another feature of the present invention, the metabolic health indicator may be at least one selected from the group consisting of body weight, BMI, waist circumference, triglycerides, and insulin resistance.

[0029] In order to solve the problem described above, a method for providing information to predict the response to dietary therapy for a postmenopausal obese or overweight breast cancer patient is provided according to another embodiment of the present invention.

[0030] Specifically, the method comprises the steps of: measuring the level of expression of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 from a biological sample isolated from a breast cancer patient before the start of diet therapy; measuring the level of expression of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 from a biological sample isolated from a breast cancer patient after the end of diet therapy; and comparing the level of expression of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 or their proteins before and after diet therapy.

[0031] According to a feature of the present invention, the biological sample may be selected from the group consisting of whole blood, plasma, serum, urine, feces, saliva, tears, cerebrospinal fluid, cells, and tissue specimens.

[0032] According to another feature of the present invention, the method may further include the step of predicting a high clinical responsiveness to a diet when the expression level of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 or the protein thereof is increased.

[0033] According to another feature of the present invention, the breast cancer patient may be a patient who has completed initial cancer treatment and is receiving maintenance therapy.

[0034] According to another feature of the present invention, the response to the diet may be a dietary response to metabolic health indicators.

[0035] The biomarker combination according to the present invention can predict the responsiveness to dietary therapy in postmenopausal breast cancer patients based on a close correlation with HOMA-IR, insulin, TG, LDL, MUFA intake, etc., and can be usefully applied in the field of establishing personalized nutritional strategies and preventing and managing metabolic diseases.

[0036] In addition, the present invention enables effective dietary intervention by predicting in advance the response to diet therapy, particularly regarding metabolic health indicators, using a combination of biomarkers, thereby inducing weight control and improvement of metabolism-related indicators in breast cancer survivors.

[0037] In addition, obesity and metabolic abnormalities are known to be major factors that increase the risk of recurrence and death in postmenopausal breast cancer patients, and the present invention can contribute to improving the long-term survival rate of postmenopausal breast cancer patients by optimizing dietary therapy.

[0038] The effects according to the present invention are not limited to those exemplified above, and various other effects are included in this specification.

[0039] FIG. 1 briefly illustrates the clinical trial and in-depth phenotypic analysis process regarding the effects of a Korean-style Mediterranean diet according to one embodiment of the present invention on postmenopausal obese or overweight breast cancer patients.

[0040] FIG. 2 illustrates, exemplarily, the composition of a lunch box (one serving) configured according to the MEDi-PoB diet standard according to one embodiment of the present invention.

[0041] FIG. 3 illustrates an exemplary Korean version of the MD-adherence questionnaire for evaluating Mediterranean diet (MD) adherence according to one embodiment of the present invention.

[0042] FIG. 4a illustrates a heatmap visualizing changes in nutrient intake following dietary intervention of a Korean-style Mediterranean diet according to one embodiment of the present invention.

[0043] FIG. 4b illustrates the results of principal component analysis (PCA) on nutrient intake following a Korean-style Mediterranean diet intervention according to one embodiment of the present invention.

[0044] FIG. 5a illustrates a heatmap visualizing the change in major clinical indicators of a study subject following a Korean-style Mediterranean diet intervention according to one embodiment of the present invention.

[0045] FIG. 5b illustrates the results of principal component analysis (PCA) on the change in major clinical indicators of a study subject following a Korean-style Mediterranean diet intervention according to one embodiment of the present invention.

[0046] Figure 6 illustrates the distribution of a whole blood transcriptome according to three scaling methods (count, TRM, RPKM) according to one embodiment of the present invention.

[0047] FIGS. 7a to 7d illustrate the results of differential expression gene analysis in human peripheral blood mononuclear cells following dietary intervention of a Korean-style Mediterranean diet according to one embodiment of the present invention.

[0048] FIG. 8 is a matrix plot showing the change in gene expression for 22 gene modules between a Korean Mediterranean diet and a control diet according to one embodiment of the present invention.

[0049] Figure 9a illustrates the results of visualizing changes in expression by module before and after dietary intervention of the Korean-style Mediterranean diet according to one embodiment of the present invention.

[0050] FIG. 9b illustrates the statistical comparison results of differential expression (DE) between the Korean Mediterranean diet intervention group and the control group according to one embodiment of the present invention.

[0051] FIG. 9c illustrates the results of visualizing the change in the differential co-expression (DcoE) pattern within the module according to the Korean Mediterranean diet intervention in one embodiment of the present invention.

[0052] FIG. 10a briefly illustrates the process of an animal experiment to evaluate the effects of a Korean Mediterranean diet in an animal model according to one embodiment of the present invention.

[0053] FIG. 10b illustrates the results of histological analysis of mouse liver and white adipose tissue following Mediterranean diet intervention according to one embodiment of the present invention.

[0054] FIG. 10c illustrates the results of oral glucose tolerance and insulin tolerance tests in mice following Mediterranean diet intervention according to one embodiment of the present invention.

[0055] FIG. 10d illustrates the results of a metabolic profile analysis of a mouse following Mediterranean diet intervention according to one embodiment of the present invention.

[0056] FIG. 10e illustrates the results of a hierarchical cluster analysis based on the metabolic profile of a mouse following Mediterranean diet intervention according to one embodiment of the present invention.

[0057] FIG. 10f illustrates the results of transcriptome profile analysis of a mouse following Mediterranean diet intervention according to one embodiment of the present invention.

[0058] FIG. 10g illustrates the results of a correlation analysis between changes in mouse transcriptomes and changes in human blood transcriptomes according to one embodiment of the present invention.

[0059] FIG. 11a illustrates the results of a gene network analysis of a dark red module following dietary intervention according to one embodiment of the present invention.

[0060] FIG. 11b illustrates the results of a gene network analysis of a grey60 module according to dietary intervention in one embodiment of the present invention.

[0061] FIG. 11c illustrates the results of a gene network analysis of a blue module according to dietary intervention in one embodiment of the present invention.

[0062] FIG. 11d illustrates the results of a correlation analysis between genes of the dark red module and clinical and nutritional indicators according to dietary intervention in one embodiment of the present invention.

[0063] FIG. 11e illustrates the results of a correlation analysis between genes of the grey60 module and clinical and nutritional indicators according to dietary intervention in one embodiment of the present invention.

[0064] FIG. 11f illustrates the results of a correlation analysis between the genes of the blue module and clinical and nutritional indicators according to dietary intervention in one embodiment of the present invention.

[0065] You can do it.

[0066] The above primer or probe can be chemically synthesized using a phosphoramidite solid support synthesis method or other widely known methods. Additionally, the primer or probe can be modified in various ways according to methods known in the art, to the extent that hybridization with the target mRNA is not interfered with. Examples of such modifications include methylation, capping, substitution with one or more homologues of natural nucleotides, and modifications between nucleotides, such as uncharged linkages (e.g., methyl phosphonate, phosphotriester, phosphoroamidate, carbamate, etc.) or charged linkages (e.g., phosphorothioate, phosphorodithioate, etc.), and the binding of fluorescent or enzymatic labeling materials.

[0067] In the present invention, "the level is increased" means that something that was not previously detected is detected, or that the amount detected is relatively higher than the normal level. To a person skilled in the art, the meaning of the opposite term can be understood as having the opposite meaning in accordance with the above definition.

[0068] The term "Mediterranean Diet (MD)" as used in this invention refers to a diet derived from the traditional dietary lifestyle of the Mediterranean region, consisting primarily of plant-based foods (fruits, vegetables, grains, legumes, etc.) and main ingredients such as olive oil, fish, and seafood, which are rich in unsaturated fats. This diet includes moderate amounts of dairy products and poultry, while the consumption of red meat is limited. Additionally, moderate consumption of wine is recommended. The Mediterranean diet is known to have health benefits such as promoting cardiovascular health, weight management, reducing inflammation, and preventing metabolic syndrome.

[0069] The term “Korean-style Mediterranean diet” used in this invention refers to a diet based on the nutritional principles of the traditional Mediterranean diet, while reflecting the food culture and characteristics of Korean ingredients. While the conventional Mediterranean diet consists of olive oil, tomatoes, basil, seafood, whole grains, nuts, and wine, and is prepared through raw consumption or simple cooking processes, the Korean-style Mediterranean diet consists of ingredients commonly used in Korea, such as oats, brown rice, sweet potatoes, burdock, hijiki, wild chives, and kimchi, and is characterized by being prepared by applying cooking methods such as steaming, baking, and stir-frying.

[0070] The inventors confirmed through a dietary intervention study targeting postmenopausal overweight or obese breast cancer survivors that patients with high responsiveness to metabolic health indicators regarding diet showed significant activation of specific gene modules after dietary intervention, and that the LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 genes included in said gene modules were found to be involved in biological pathways related to mitochondrial function regulation, inflammation suppression, maintenance of genomic stability, inhibition of cell proliferation, and regulation of immune responses, respectively.

[0071] According to the present invention, the expression levels of the genes show a significant correlation with HOMA-IR, insulin, triglycerides (TG), LDL, MUFA intake, etc., and this combination of genes has significant diagnostic value as a biological indicator for predicting dietary responsiveness or evaluating metabolic health status in postmenopausal breast cancer patients.

[0072] In one aspect, the present invention relates to a combination of biomarkers for predicting the response to diet in postmenopausal obese or overweight breast cancer patients comprising one or more genes or proteins thereof selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1.

[0073] In one embodiment of the present invention, the LARP1B gene may be located on human chromosome 4, preferably on human chromosome 4 128,060,789-128,222,926 (Gene ID: 55132).

[0074] In one embodiment of the present invention, the UGP2 gene may be located on human chromosome 2, preferably at human chromosome 2 63,840,969-63,891,560 (Gene ID: 7360).

[0075] In one embodiment of the present invention, the ACADM gene may be located on human chromosome 1, preferably on human chromosome 1 75,724,709-75,763,679 (Gene ID: 34).

[0076] In one embodiment of the present invention, the LPAR5 gene may be located on human chromosome 12, preferably located at human chromosome 12 6,618,835-6,635,959 (Genome Reference Consortium: GRCh38.p14, Gene ID: 57121). In this case, the LPAR5 gene may be encoded by or transcribed from a sequence complementary to a nucleic acid sequence located in the region.

[0077] In one embodiment of the present invention, the MPIG6B gene may be located on human chromosome 6, preferably at 31,720,296-31,726,714 on human chromosome 6 (Gene ID: 80739). In this case, due to the high polymorphism of the region, the MPIG6B gene may have multiple alternate loci (ALT-loci) within the reference sequence. The present invention is interpreted to include all known genomic sequence variants comprising such primary assembly and ALT-loci sequences.

[0078] In one embodiment of the present invention, the ANP32B gene may be located on human chromosome 9, and preferably may be located at human chromosome 97,983,341-98,015,943 (Gene ID: 10541).

[0079] In one embodiment of the present invention, the MKLN1 gene may be located on human chromosome 7, and preferably may be located at human chromosome 7 131,110,094-131,496,632 (Gene ID: 4289).

[0080] In one embodiment of the present invention, the biomarker may be a gene expression marker or a protein expression marker, but is not limited thereto.

[0081] In one embodiment of the present invention, the breast cancer patient may be a patient who has completed initial cancer treatment and is receiving maintenance therapy, but is not limited thereto.

[0082] In one embodiment of the present invention, the response to the diet may be a dietary response to a metabolic health indicator, but is not limited thereto.

[0083] In one embodiment of the present invention, the metabolic health indicator may be at least one selected from the group consisting of body weight, BMI, waist circumference, triglycerides, and insulin resistance, but is not limited thereto.

[0084] In one aspect, the present invention relates to a composition for predicting the response to diet in postmenopausal obese or overweight breast cancer patients comprising a preparation for measuring the level of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 or the protein expression level thereof.

[0085] In one embodiment of the present invention, the agent for measuring the gene level may be a agent for confirming the presence and degree of expression of mRNA, and for measuring the amount of mRNA. For this purpose, an analysis method may be used, for example, reverse transcription polymerase chain reaction (RT-PCR), competitive reverse transcription polymerase chain reaction, real-time reverse transcription polymerase chain reaction, RNase protection assay, Northern blotting, DNA chip, etc., and may be an antisense oligonucleotide, primer pair, or probe that specifically binds to the mRNA.

[0086] The above antisense oligonucleotide may refer to an oligomer having a backbone between nucleotide base sequences and subunits, wherein the antisense oligomer hybridizes with a target sequence within RNA via Watson-Crick base pairing, thereby allowing the formation of a mRNA and RNA:oligomer heterodimer within the target sequence. The oligomer may have exact or approximate sequence complementarity with respect to the target sequence. This antisense oligomer may block or inhibit the translation of mRNA and alter the processing of mRNA to produce splice variants of mRNA.

[0087] The primer may refer to a short nucleic acid sequence having a short free 3-terminal hydroxyl group, capable of forming base pairs with a complementary template, and functioning as a starting point for template strand replication. The primer may initiate DNA synthesis in the presence of reagents for a polymerization reaction (i.e., DNA polymerase or reverse transcriptase) and four different nucleoside triphosphates at an appropriate buffer solution and temperature.

[0088] The above probe may refer to a nucleic acid fragment, such as RNA or DNA, ranging from a few bases to hundreds of bases in length, capable of forming a specific binding with mRNA, and is labeled so that the presence or absence of a specific mRNA can be confirmed. The probe may be constructed in the form of an oligonucleotide probe, a single-stranded DNA probe, a double-stranded DNA probe, an RNA probe, etc. The selection of a suitable probe and hybridization conditions may be modified based on those known in the art.

[0089] The above antisense oligonucleotides, primers, or probes, etc., can be chemically synthesized using the phosphoramidite solid support method or other widely known methods. These nucleic acid sequences can also be modified using many means known in the art. Examples of such modifications include methylation, capping, substitution with one or more homologues of natural nucleotides, and modification between nucleotides (e.g., modification to uncharged linkages such as methylphosphonates, phosphotriesters, phosphoramidates, carbamates, or charged linkages such as phosphorothioates, phosphorodithioates).

[0090] In one embodiment of the present invention, the measurement at the gene level may be performed by one or more methods selected from the group consisting of reverse transcriptase polymerase chain reaction (RT-PCR), competitive reverse transcriptase polymerase chain reaction (competitive RT-PCR), real-time polymerase chain reaction (real-time RT-PCR), real-time quantitative RT-PCR, RNase protection method, Northern blotting, and DNA chips, but is not limited thereto.

[0091] In one embodiment of the present invention, the preparation for measuring the protein level may be a preparation for confirming the presence and degree of expression of the protein, and may be for measuring the amount of protein. As an analytical method for this purpose, Western blot, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, fluorescence-activated cell sorter (FACS), protein chip, etc. may be used, and may be an oligopeptide, monoclonal antibody, polyclonal antibody, chimeric antibody, ligand, PNA, or aptamer that specifically binds to the protein.

[0092] The above antibody is a term known in the art and refers to a specific protein molecule directed toward an antigenic site. Each gene can be cloned into an expression vector according to conventional methods to obtain a protein encoded by the marker gene, and can be manufactured from the obtained protein by conventional methods. The form of the above antibody is not particularly limited and may be a monoclonal antibody, a polyclonal antibody, or a chimeric antibody, and optionally may include special antibodies such as humanized antibodies.

[0093] The above-mentioned aptamer refers to single-stranded DNA (ssDNA) or RNA that has high specificity and affinity for a specific substance. Aptamers have very high affinity for a specific substance, are stable, can be synthesized by a relatively simple method, can be modified in various ways to increase binding strength, and can target cells, proteins, and even small organic substances. Therefore, their specificity and stability are much higher than those of antibodies that have already been developed.

[0094] In one embodiment of the present invention, the breast cancer patient may be a patient diagnosed with breast cancer stage 1, stage 2, or stage 3, but is not limited thereto.

[0095] In one embodiment of the present invention, the breast cancer patient may be a patient who has completed initial cancer treatment and is receiving maintenance therapy, but is not limited thereto.

[0096] In one embodiment of the present invention, the cancer treatment may include at least one of breast surgery, adjuvant chemotherapy, and radiation therapy, but is not limited thereto.

[0097] In one embodiment of the present invention, the maintenance therapy is not particularly limited and may include any treatment method known in the art to suppress cancer recurrence or progression and sustain the therapeutic effect after initial treatment (surgery, chemotherapy, radiation therapy, etc.), and preferably may be an adjuvant hormone therapy, but is not limited thereto.

[0098] In one embodiment of the present invention, the response to the diet may be a dietary response to a metabolic health indicator, but is not limited thereto.

[0099] In one embodiment of the present invention, the metabolic health indicator may be at least one selected from the group consisting of body weight, BMI, waist circumference, triglycerides, and insulin resistance, but is not limited thereto.

[0100] In one aspect, the present invention relates to a method for providing information to predict the response to diet in postmenopausal obese or overweight breast cancer patients.

[0101] Specifically, the method comprises the steps of: measuring the level of expression of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 from a biological sample isolated from a breast cancer patient before the start of diet therapy; measuring the level of expression of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 from a biological sample isolated from a breast cancer patient after the end of diet therapy; and comparing the level of expression of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 or their proteins before and after diet therapy.

[0102] In one embodiment of the present invention, the biological sample may be selected from the group consisting of whole blood, plasma, serum, urine, feces, saliva, tears, cerebrospinal fluid, cells, and tissue specimens, but is not limited thereto.

[0103] In one embodiment of the present invention, the method may further include, but is not limited to, a step of predicting a high clinical responsiveness to a diet when the expression level of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 or the protein thereof is increased.

[0104] In one embodiment of the present invention, the breast cancer patient may be a patient who has completed initial cancer treatment and is receiving maintenance therapy, but is not limited thereto.

[0105] In one embodiment of the present invention, the response to the diet may be a dietary response to a metabolic health indicator, but is not limited thereto.

[0106] The following examples will be explained in more detail. However, these examples are intended to illustrate one or more specific examples, and the scope of the present invention is not limited to these examples.

[0107] Example 1: Design of a Randomized Parallel-Group Clinical Trial

[0108] 1-1. Selection of Study Participants

[0109]

[0110] In the clinical trial, study subjects were randomly assigned to either the treatment group or the control group only once, and remained in their assigned group to receive treatment. The study protocol was approved by the Institutional Review Board (IRB) of Gangnam Severance Hospital, and screening and data collection were conducted after obtaining prior written consent from all participants. This trial was registered on ClinicalTrials.gov, operated by the National Library of Medicine (NLM), with registration number NCT04045392.

[0111] The study participants were postmenopausal breast cancer patients who were diagnosed with stage I-III breast cancer, had completed cancer treatment (surgery alone, or surgery combined with adjuvant chemotherapy or radiation therapy), and were currently receiving adjuvant hormone therapy; they were obese or overweight (body mass index [BMI] ≥ 23.0 kg / m²) and were recruited from August 2019 to June 2020 at Gangnam Severance Hospital (Seoul, South Korea).

[0112] Patients with cancer recurrence or metastasis, patients who have lost more than 5 kg in the last 3 months, patients with hypothyroidism, patients with liver or kidney disease, patients taking anti-obesity drugs, patients being treated with systemic corticosteroids, and patients with a history of food allergies (seafood, fish, nuts, eggs, meat, tomatoes, wheat, etc.) were excluded from this study.

[0113] Based on the above exclusion criteria, 2 of the 80 patients initially recruited were excluded, and finally 78 patients were registered.

[0114] The baseline clinical characteristics of the 78 registered patients are shown in Table 1.

[0115] [Table 1]

[0116]

[0117] 1-2. Research Protocol

[0118] FIG. 1 briefly illustrates the clinical trial and in-depth phenotypic analysis process regarding the effects of a Korean-style Mediterranean diet according to one embodiment of the present invention on postmenopausal obese or overweight breast cancer patients.

[0119] Referring to Figure 1, a total of 80 people underwent screening, and among them, 78 breast cancer patients who met the eligibility criteria were enrolled in this study. After randomization at a 1:1 ratio, 39 people were assigned to the MEDi-PoB dietary intervention group and 39 people to the standard diet intervention group (control group). Randomization was performed using a central computerized generation system set to a block size of 4. In the MEDi-PoB dietary intervention group, 3 people withdrew their consent and 1 person dropped out of follow-up. In the standard diet intervention group, 3 people withdrew their consent. Finally, 35 people in the MEDi-PoB dietary intervention group and 36 people in the standard diet intervention group were included in the study.

[0120] Afterward, 25 people were randomly selected from each group, and RNA was extracted from their blood samples.

[0121] 1-3. Dietary Intervention of the Korean Mediterranean Diet

[0122] Figure 2 illustrates an exemplary composition of a lunch box (one serving) configured according to the MEDi-PoB diet standards.

[0123] During the 8-week intervention period, total calorie intake was limited to 1,500 kcal per day, and the study participants received lunch boxes at home 5 times a week, consisting of 2 meals per day (1 menu per meal). Nutrition education was provided for meals other than the 2 daily lunch boxes, and guidelines were presented and they were instructed to comply with them.

[0124] The nutrient ratios per serving of the Korean Mediterranean diet (MEDi-PoB diet) were set as follows: carbohydrates at 50-55% of total calories, protein at 20-25%, and fat at 30-35%; the specific nutrient intake standards were as follows: less than 500 kcal, and a ω-ω ratio of 1:4 to 1:8. The MEDi-PoB diet included 15 g of olive oil (equivalent to 3 servings), 1.5 servings of fruit, 4 servings of vegetables, 1 serving of nuts, and 3.5 servings of fish or meat; red wine was excluded from the diet to consider its potential impact on breast cancer patients. Adherence to the Mediterranean diet was assessed using the Korean version of the Mediterranean Diet Compliance Assessment Tool (K-MEDAS), developed and validated by our research team (see Figure 3).

[0125] 1-4. Dietary Intervention of General Diet

[0126] The control group was instructed to maintain their usual dietary habits while keeping their total daily caloric intake below 1,500 kcal. Carbohydrates, protein, fat, ω-6 fatty acids, and ω-3 fatty acids were set to account for 55-65%, 7-20%, 15-30%, 4-10%, and ≤1% of the total caloric intake, respectively. In addition, daily cholesterol intake was limited to a maximum of 300 mg.

[0127] Example 2: Changes in Nutrient Intake After Mediterranean Diet

[0128] To determine changes in nutritional status after the Korean Mediterranean diet, the dietary intake of the subjects in the MEDi-PoB diet intervention group and the general diet intervention group was investigated.

[0129] To assess nutritional status, both groups were instructed to record their daily dietary habits using a smartphone application, and changes in nutrient intake between the two groups before and after the 8-week intervention were analyzed using an application-based self-reporting method. Nutrient and caloric intake were tracked and analyzed based on the National Standard Food Composition Table.

[0130] Mediterranean Diet (MD) adherence was assessed twice during the study period (before and after dietary intervention) using the Korean version of the MD-adherence questionnaire, and the specific contents of the questionnaire are shown in Figure 3 below. This questionnaire consists of a total of 14 items regarding the consumption habits and frequency of various foods, including perilla oil or olive oil, vegetables, fruits, red meat, butter and margarine, carbonated beverages, wine, beans and tofu, fish and seafood, snacks, nuts, poultry, and whole grains. Each item is awarded 1 point if it meets the criteria, with a maximum score of 14 points. The educational goal is set to achieve a score of 10 or higher, which signifies high MD adherence.

[0131] Changes in dietary intake following the MEDi-PoB intervention were presented as mean ± standard deviation for each nutrient based on the difference between pre- and post-intervention periods. The intervention effect was evaluated by calculating the least squares mean (LSMean) and 95% confidence interval (CI), for which a linear mixed-effects model was applied. Considering the repeated measures data, the individual identifier (ID) was included as a random intercept effect to account for within-individual correlations, while time (pre-intervention vs. post-intervention) was set as a fixed effect. Additionally, major confounding variables such as age, sex, body mass index (BMI), alcohol consumption, smoking status, physical activity level, and total energy intake were included as covariates for adjustment. Statistical significance was determined to be significant if the p-value for a two-sided test was less than 0.05. All statistical analyses were performed using R software version 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria, http: / www.R-project.org).

[0132] As a result, as shown in Figure 4a and Table 2, significant changes in nutrient intake were observed in the MEDi-PoB dietary intervention group. Specifically, protein (%), monounsaturated fatty acid (MUFA %), polyunsaturated fatty acid (PUFA %), omega-3 and omega-6 fatty acid intake increased, and the MUFA / SFA ratio improved, while carbohydrates (CHO %), trans fat (%), and dietary cholesterol (mg / day) decreased significantly. In contrast, no significant changes in nutrient intake were observed in the control group (normal diet intervention group).

[0133] Next, Principal Component Analysis (PCA) was performed to visualize nutrient intake patterns according to dietary conditions. As a result, as shown in Figure 4b, both the MEDi-PoB dietary intervention group and the control group showed a converged pattern, and no clear difference was observed between the two diets.

[0134] [Table 2]

[0135]

[0136] Data are expressed as mean ± standard error. *The P-value indicates that P < 0.05 when compared to the baseline value by the Wilcoxon signed-rank test. The P-value was calculated using an independent samples t-test between the two groups.

[0137] Example 3: Measurement of changes in body and biochemical indicators after Mediterranean diet

[0138] To evaluate the effects of the Korean Mediterranean diet provided in the present invention on postmenopausal obese or overactive breast cancer patients, the levels of anthropometric measurements and biochemical indicators of the MEDi-PoB diet intervention group and the general diet intervention group were measured.

[0139] Specifically, clinical and biochemical analyses were performed at a total of two visits (before the diet and after the diet). Weight and height were measured using an automatic height analyzer (BSM 330; Biospace, Seoul, Korea) while wearing light clothing. Body Mass Index (BMI) was calculated by dividing weight (kg) by the square of height (m²). For body composition analysis, skeletal muscle mass, body fat mass, and body fat percentage were measured using a bioelectrical impedance analyzer (ACCUNIQ BC720; Selvas Healthcare, Daejeon, Korea).

[0140] Fasting blood samples were collected before the intervention and after 8 weeks of intervention, following an 8-hour fast. Leukocyte counts were quantified using an XN-9000 blood analyzer (Sysmex, IL, USA). Fasting blood glucose, high-sensitivity C-reactive protein, total cholesterol, triglycerides, HDL cholesterol, and LDL cholesterol were measured using an ADVIA 1650 Clinical Chemistry system (Siemens Medical Solutions, Tarrytown, NY, USA). Fasting insulin was measured using an electrochemiluminescence immunoassay with an Elecsys 2010 instrument (Roche, Indianapolis, IN, USA). Insulin resistance was estimated using the HOMA-IR method according to the following formula.

[0141] HOMA-IR = Fasting Insulin (μIU / mL) × Fasting Blood Glucose (mg / dL) / 405.

[0142] The 24-hour dietary recall method was used to assess nutritional status. Physical activity levels were calculated as metabolic equivalent hours per week (MET-hours / week) using the Godin Leisure-Time Exercise Questionnaire.

[0143] Figure 5 shows a heatmap visualizing the changes in major clinical indicators of study subjects following the Korean-style Mediterranean diet intervention.

[0144] As a result, as shown in Figure 5 and Table 3, the MEDi-PoB dietary intervention group showed a statistically significantly greater reduction in body weight, BMI, waist circumference, and body fat percentage compared to the control group (normal diet intervention group).

[0145] Specifically, the mean changes in the MEDi-PoB dietary intervention group were body weight (-3.0±2.1 kg vs. -0.5±1.8 kg, p<0.001) and BMI (-1.3±0.9 kg / m² 2 vs. -0.2±0.8 kg / m 2 A more pronounced decrease was observed in the MEDi-PoB dietary intervention group compared to the control group in waist circumference (-5.0±4.0 cm vs. -0.7±3.3 cm, p<0.001), and body fat percentage (-1.6±3.4% vs. +0.5±2.8%, p=0.020). In addition, triglyceride levels decreased by -31.7±65.1 mg / dL in the MEDi-PoB dietary intervention group, whereas they increased by +5.9±59.5 mg / dL in the control group, showing a significant difference (p=0.038) between the two groups.

[0146] This means that the MEDi-PoB diet improved the metabolic health of postmenopausal obese or overweight breast cancer patients.

[0147] Next, Principal Component Analysis (PCA) was performed to visualize the patterns of clinical variables according to dietary conditions. As a result, as shown in Figure 5b, the MEDi-PoB dietary intervention group showed more distinct clustering than the control group in clinical-medical status, but no clear difference was observed between the two groups.

[0148] [Table 3]

[0149]

[0150] Data are expressed as mean ± standard error. *The P-value indicates that P < 0.05 when compared to the baseline value by the Wilcoxon signed-rank test. The P-value was calculated using an independent samples t-test between the two groups.

[0151] Example 3: Discovery of candidate biomarkers for predicting response to diet

[0152] To investigate changes in gene expression induced by dietary intervention with the MEDi-PoB diet, transcriptome profiling was performed on human peripheral blood mononuclear cells, and count, transcripts per kilobase million (TPM), and mapped reads per kilobase per million (RPKM) distributions were compared to characterize RNA-seq data (see Fig. 6). To visualize transcriptome patterns according to dietary conditions, top variance genes were selected and hierarchical clustering analysis was performed, and differential expression gene analysis (DE analysis) was performed using DESeq2 based on counts.

[0153] RNA was extracted using QIAzol lysis reagent (Qiagen, Hilden, Germany) and then subjected to column purification using the RNeasy mini kit (Qiagen) according to the manufacturer's instructions. Genomic DNA was removed from the purified RNA by treating it with DNase I (New England Biolabs, Ipswich, MA, USA). RNA concentration and integrity were measured using the Qubit™RNA HS Assay Kit (Cat # Q32855) and the Agilent 2100 Bioanalyzer (Santa Clara, CA, USA), and it was confirmed that the RNA Integrity Number was 8 or higher. The cDNA library was constructed using the Illumina TruSeq RNA library kit (Illumina Inc., San Diego, CA, USA) with 1 μg of total RNA as the starting material. The library was sequenced into 150 bp paired-end reads per sample using Illumina Novaseq600® after 15 PCR amplifications, using a single S2 flowcell (Teragen, Seoul, Korea).

[0154] Differential Expression Gene Analysis (DEG) was performed using DESeq2, a negative binomial distribution-based algorithm. DESeq2 is suitable for analyzing transcriptome data that generally exhibit a left-biased distribution and utilizes count-based gene expression data to calculate the log-transformed fold change and the corresponding standard error of each gene between two conditions. Genes with a false positive detection rate (FDR) correction p-value of 0.05 or less were defined as differential expression genes (DEGs).

[0155] Hypergeometric tests were performed to identify biological pathways abundant in the candidate gene list (DEGs and module genes, etc.). Biological pathway information was collected from the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. If the FDR-corrected p-value of the hypergeometric test results between the two gene lists was 0.05 or less, the corresponding gene set was considered statistically significantly abundant.

[0156] Next, DEGs exhibiting similar expression patterns were clustered using the k-means algorithm. Hypergeometric tests were used to quantitatively evaluate the associations between biological pathways and candidate genes collected from the GO and KEGG databases.

[0157] As a result, as shown in Figures 7a to 7d, hierarchical clustering analysis of the top 30 genes with the highest variance between samples was able to distinguish between the pre- and post-diet interventions, but no clear clustering was observed between the MEDi-PoB diet intervention group and the control group (Figure 7a). Transcriptome changes following the MEDi-PoB diet showed a trend similar to the pattern of change observed in the control group after the diet intervention (Figure 7b). Furthermore, the MEDi-PoB diet intervention induced changes across the blood transcriptome by achieving a balance between the upregulation and downregulation of gene expression (Figure 7b), and similarly, significant changes in gene expression were observed in the control group. Differentially expressed genes (DEGs, n = 7,439) in the MEDi-PoB diet intervention group were presumed to be associated with major metabolic pathways such as insulin signaling, mTOR signaling, p53 signaling, the cell cycle, and oxidative phosphorylation.

[0158] In particular, changes in expression were observed for 1,665 genes only after the MEDi-PoB diet (Fig. 7c), and hypergeometric analysis results showed that these genes were significantly abundant in various functional pathways, such as cancer-related signaling, immune response, protein degradation, and RNA metabolism, and were mainly concentrated in mitochondrial-related pathways (Fig. 7d). Accordingly, the above 1,665 genes were selected as candidate biomarkers for predicting the response to diet in postmenopausal obese or overweight breast cancer patients receiving maintenance therapy after cancer treatment.

[0159] Example 4: Changes in gene module connectivity mediated by MEDi-PoB diet

[0160] Next, among the 1665 candidate biomarkers discovered in Example 3, changes in module connectivity following MEDi-PoB dietary intervention were measured to identify biomarkers highly associated with dietary responses to metabolic health indicators.

[0161] 4-1. Co-expression Network Analysis and Module Construction

[0162] Co-expression network analysis was performed to identify functionally co-expressed gene modules affected by the MEDi-PoB diet.

[0163] In this analysis, if two genes are functionally associated, they were considered to exhibit similar gene expression patterns. Blood samples [100 (Nall) = 50 (baseline) + 25 (control diet) + 25 (MEDi-PoB diet)] obtained from all participants were analyzed, and genes (n = 18,414) belonging to the top 50 percentile based on expression variance and mean were analyzed to present functionally associated genes in the form of a co-expression network.

[0164] Figure 8 illustrates a matrix plot comparing changes in gene expression across 22 gene modules between the MEDi-PoB diet and the control diet. The first row (Module) of the top matrix plot represents the 22 modules in various colors, while the second row (MED) and fourth row (CN) represent genes whose expression has changed significantly compared to the corresponding baseline groups in the MEDi-PoB diet intervention group and the control group (CD), respectively. Each line corresponds to a single gene, and the colors are displayed differently according to the trend of change in the Fold Change (FC) value. The third row (MED_ext) and fifth row (CN_ext) represent genes that have been narrowed down (selected) from the genes located in the second and fourth rows, respectively, based on the criterion of an absolute FC value of 0.5.

[0165] Specifically, to select MEDi-PoB diet-related gene modules, a batch-effect removal integrated dataset was used to remove the influence of various time points (i.e., baseline and after dietary intervention) and each intervention (MEDi-PoB diet and control diet), thereby constructing a generalized co-expression network independent of clinical phenotype or specific dietary intervention. A correlation matrix was constructed using biweight midcorrelation for all possible gene pairs, and subsequently, this correlation matrix was transformed into an adjacency matrix by applying a soft-threshold power value of 11.

[0166] Modules containing highly interrelated genes were detected based on hierarchical clustering. A major limitation of hierarchical clustering is determining the optimal number of modules or clusters. To address this, a correlation matrix based on Biweight midcorrelation was calculated when constructing a co-expression network. A total of 60 simulations were performed by applying various hyperparameters, such as soft power values, deepSplit parameters (DS), minimum module size (minClusterSize), dendrogram cut height (DCOR), and pamStage. The settings were optimized to include a sufficient number of gene sets (modules) capable of reproducibility at least 30 times. Among these, cases reproducible in more than half of the replicates were selected to derive 22 final modules (Fig. 8a), each containing 80 to 1163 genes.

[0167] 4-2. Selection of MEDi-PoB Diet-Related Gene Modules

[0168] MEDi-PoB diet-related gene modules were identified by applying differential expression (DE) and differential co-expression analysis (DcoE).

[0169] In the first step (DE analysis), the eigengene values ​​of each module were compared before and after dietary intervention. Changes in gene expression were observed in all modules (n = 22) following the MEDi-PoB diet (Fig. 9a). For the differential analysis summary statistics of the MEDi-PoB diet, false-positive results were removed using a paired t-test to compare with the control diet (Fig. 9b). Of the 22 modules, 14 showed changes following the MEDi-PoB diet; among them, 6 modules (turquoise, green, green-yellow, red, salmon, dark turquoise) showed a downward trend in expression, while 8 modules (blue, magenta, light yellow, yellow, light green, dark red, tan, grey60) showed an upward trend (Bottom of Fig. 8).

[0170] In the second step (DcoE analysis), the co-expression patterns of genes within each module were evaluated according to the two interventions. All modules (n = 14) selected in the first step showed differential co-expression patterns before and after the MEDi-PoB diet (based on t-statistics, Fig. 9c). The t-test in this step was used to compare changes in the biweight midcorrelation coefficients (correlation coefficients between genes) of genes within each module between the two interventions. Specifically, the correlations of gene pairs within each module were calculated, and LOC (decreased connectivity) or GOC (increased connectivity) was determined based on the change in mean connectivity. As a result, 10 modules showed significant changes in the MEDi-PoB diet compared to the control diet (Fig. 9c). However, four modules—turquoise, green, green-yellow, and dark turquoise—were excluded as false positives because the results of the DE analysis and the DcoE analysis were inconsistent.

[0171] Considering both DE and DcoE analyses, the red module showed loss of connectivity (LOC) as genes were downregulated. This indicates that the MEDi-PoB diet inactivated the genes in this module, resulting in reduced interactions between them. The dark red, blue, gray60, light yellow, and yellow modules showed increased connectivity (GOC) as genes were upregulated. This indicates that the MEDi-PoB diet activated the genes in these modules, along with strengthening the functional associations between these genes.

[0172] In conclusion, six gene modules were finally selected, one of which is a down-regulatory module with LOC (red), and five are up-regulatory modules with GOC (dark red, blue, gray60, light yellow, yellow).

[0173] Example 5: Effects of the Korean Mediterranean Diet in an Animal Model

[0174] 5-1. Preparation of Animal Models

[0175] Five-week-old male C57BL / 6J mice were purchased from Animals & Plants Co., Ltd. (Seoul, Korea) and housed under rearing conditions (22 ± 2°C, humidity 50 ± 10%, 12-hour cycle (light / dark)), with free access to food and water. After a one-week acclimatization period, mice were randomly assigned to a diet group based on body weight. At week 30, mice were euthanized, and blood was collected via cardiac puncture. Excised tissues (liver and visceral fat) were flash-frozen in liquid nitrogen and stored at Invitrogen (Waltham, MA, USA, Qiagen) at -80°C until further analysis.

[0176] All animal experiment procedures were approved by the Yonsei University Animal Ethics Committee (Approval No. 2021-0094), and all experiments were conducted in compliance with relevant guidelines and regulations. Specific methods used in the mouse study included glucose tolerance testing, insulin tolerance testing, serum analysis, diet, micro-CT imaging acquisition, histological analysis of liver and white adipose tissue, RNA extraction, and sequencing.

[0177] 5-2. Design of Animal Experiments

[0178] Referring to Fig. 10a, after a one-week acclimatization period, mice were randomly assigned to the following dietary protocols: (1) feed diet (CHOW: 10% calories from fat, n=10); (2) high-fat diet (HFD: 60% calories from fat, n=10); (3) Mediterranean diet (MD, n=10). The Mediterranean diet (MD) used in the animal experiment was designed to replicate the traditional human Mediterranean diet as closely as possible based on the product D12052702 from Research Diets, and micronutrients included a mineral mix, a vitamin mix, calcium, potassium, biotin, choline, and red wine extract. The total fiber content was 50.8 g, accounting for 5.9% of the total. The composition of fatty acids reflects the characteristics of a Mediterranean diet by having a ratio of saturated fat, monounsaturated fat, and polyunsaturated fat of 23.3%, 60.1%, and 15.9%, respectively, with total omega-6 and omega-3 of 18.1g and 8.5g, respectively, in a ratio of 2.1:1, with more than 60% monounsaturated fat and an n-6:n-3 ratio of 2:1.

[0179] Mice were fed either CHOW or HFD, and body weight and blood glucose levels were measured every 4 weeks in the evening after an 8-hour fast. At week 14, after measuring fasting blood glucose and the oral glucose tolerance test (GTT), HFD mice were randomly divided into two groups. One group continued on a 60 kcal% high-fat diet (HFD), while the other group began MD for 16 weeks. Feed intake and mouse body weight were measured at the same time each week.

[0180] 5-3. Histological Analysis and Abdominal Fat Changes

[0181] After administering three dietary interventions for 30 weeks according to the study protocol described in Example 5-2, mice were anesthetized by inhaling 2% isoflurane with oxygen and scanned using a Quantum GX2 micro-CT (Perkin Elmer Inc, Waltham, MA, USA). The system was controlled by Analyze 14.0 (AnalyzeDirect Inc., Overland Park, KS, USA), and abdominal fat images were processed after acquisition.

[0182] Hepatic and white adipose tissues were fixed in 10% formalin, embedded in paraffin, and then dehydrated in alcohol stepwise. 5 μm thick tissue sections were prepared, stained with hematoxylin-eosin (H&E; Sigma-Aldrich, St. Louis, MO, USA), and observed at 100x magnification under an optical microscope (Olympus, BX43, Tokyo, Japan).

[0183] As a result, as shown in Figure 10b, the highest number of fat droplets was observed in the HFD group, followed by the MD group and the CHOW group. The size of the fat cells decreased most significantly in the CHOW group, followed by the MD group and the HFD group. After 30 weeks, the amount of abdominal fat measured by micro-CT was highest in the HFD group, followed by the MD group and the CHOW group.

[0184] 5-4. Oral Glucose Tolerance Test and Insulin Tolerance Test

[0185] In all experimental groups, fasting blood glucose was measured weekly after an 8-hour fast starting from the initiation of the MD diet. For the oral glucose tolerance test (GTT), glucose (2 g / kg, Sigma-Aldrich, St. Louis, MO, USA) was administered orally after a 16-hour fast. Blood glucose levels were measured at 0, 30, 60, 90, and 120 minutes using blood drawn from the tail vein with an Accu-Chek Performa® blood glucose meter (Roche, Dubai, UAE). The area under the curve (AUC) at 0 minutes was calculated using GraphPad Prism (GraphPad Software, Boston, MA, USA).

[0186] Insulin resistance testing (ITT) was performed 2 weeks after oral GTT measurement. Mice were fasted for 8 hours prior to the test. Insulin (Roche, Dubai, UAE) was administered intraperitoneally at a dose of 0.5 U / kg body weight. Blood glucose levels were measured at 0, 30, 60, 90, and 120 minutes using an Accu-Chek Performa® blood glucose meter (Roche, Dubai, UAE) with blood collected from the tail vein. During the procedure, mice were closely monitored for signs of hypoglycemia and demonstrated tolerance until the end of the study. Blood was collected via cardiac puncture, and serum was prepared by centrifuging at 10,000 ×g for 5 minutes in a microcentrifuge tube. Serum samples were stored at -80°C for subsequent analysis. Serum glucose was measured using an analysis kit (BioVision Systems, Hayward, CA, USA) according to the manufacturer's protocol. Insulin was analyzed using an insulin ELISA kit (Morinaga, Yokohama, Japan).

[0187] As a result, as shown in Figure 10c, plasma glucose levels in the HFD-treated group increased significantly compared to the CHOW-treated group in the 14-week GTT. In the 30-week GTT as well, plasma glucose levels were highest in the HFD-treated group, followed by the MD-treated group and the CHOW-treated group. In the 30-week ITT, insulin resistance was observed in the HFD-treated group, followed by the MD-treated group and the CHOW-treated group.

[0188] 5-5. Metabolic Profile Analysis

[0189] Hierarchical cluster analysis was performed using metabolic profiles and clinical variables such as body weight, liver function indicators (AST, ALT), lipids (TC, TG), blood glucose, insulin, and HOMA-IR according to the MD dietary intervention in the MD benefit group before and after the MD dietary intervention.

[0190] The experimental group measured body weight weekly during the 30-week experimental period. At the end of the 30-week period, blood samples were collected to measure serum AST and ALT levels to evaluate liver function, and total cholesterol (TC) levels were analyzed to confirm the lipid profile. Biochemical analysis was performed using an AST / ALT assay kit and a cholesterol E kit (WAKO, Japan).

[0191] After measuring body weight, liver enzymes (AST, ALT), lipid indices (TC, TG, HDL, LDL), blood glucose, insulin, and HOMA-IR in each experimental group, hierarchical clustering was performed based on Z-score normalized data. Through this, metabolic response patterns according to dietary conditions were visualized.

[0192] As a result, as shown in Figures 10d and 10e, the MD-fed group showed more pronounced weight loss compared to the HFD-fed group, and improvements were observed in liver function (decrease in AST and ALT) and lipid profile (decrease in total cholesterol) (Figure 10d). Similarly, the results of hierarchical cluster analysis revealed distinct metabolic patterns based on dietary response differences in body weight, liver indicators, lipid profile, glucose, insulin, and HOMA-IR (Figure 10e).

[0193] These results indicate that the MD diet significantly altered the gene expression patterns of liver tissue induced by the HFD diet.

[0194] 5-6. Transcriptome Profile Analysis

[0195] According to the research protocol described in Example 5-2, three dietary interventions were administered to 30 mice: CHOW for 30 weeks (CHOW feeding group), HFD for 30 weeks (HFD feeding group), and MD for 16 weeks after HFD for 14 weeks (MD feeding group). Then, three mice were randomly selected from each experimental group, livers were collected, and RNA was extracted using the same method described in Example 3. The RNA-seq data was normalized using the RPKM method to generate transcriptome profiles.

[0196] Differential expression analysis was performed to evaluate differences in gene expression between the Mediterranean (MD) and high-fat (HFD) diets. The differential expression analysis utilized DESeq2 (negative binomial model), and genes with significant expression changes (DEGs) were selected based on a FDR < 0.05 criterion.

[0197] As a result, it was confirmed that genes related to mitochondrial function, energy metabolism, and inflammation regulation were upregulated in the MD-supplied group.

[0198] Next, principal component analysis (PCA) was performed to visualize transcriptome patterns according to dietary conditions.

[0199] As a result, as shown in Fig. 10f, the MD diet group showed a distinct separation at the transcriptome level compared to the HFD diet group (Fig. 10f), which visually confirmed that the Mediterranean diet (MD) altered metabolic abnormalities induced by the high-fat diet (HFD) at the transcriptome level.

[0200] Next, cross-species correlation analysis was performed to evaluate the correlation of gene expression change patterns between mouse liver transcripts (MD vs HFD) and human blood transcripts (MEDi-PoB vs CD).

[0201] As a result, as shown in Figure 10g, the FC values ​​between the two species were found to be significantly correlated, confirming that the effects of the MEDi-PoB diet are preserved in humans and mice.

[0202] Example 6: Selection of Biomarkers for Predicting Response to Dietary Therapy

[0203] Using the results of the mouse liver tissue transcriptome profile analysis of Examples 5-6, DcoE analysis was performed on the red, blue, light yellow, yellow, dark red, and gray60 modules selected through DE analysis and DcoE analysis using human transcriptome data in the same manner as in Example 4-2, and the co-expression pattern between genes within the modules was evaluated.

[0204] Specifically, in the DcoE analysis, the correlation coefficients between genes were calculated for each experimental group (CHOW, HFD, and MD groups) using the Pearson correlation coefficients (PCCs) of all gene pairs within each module, and the mean PCC for each module (Mean PCC) was used as an indicator of connectivity. PCC represents the similarity of expression patterns between genes, and a higher value indicates stronger co-expression of genes within a module. Next, to compare PCC differences between dietary conditions, a t-test was performed on the PCC distribution, and the p-value was calculated.

[0205] As a result, as shown in Table 4, among the modules that showed significant connectivity changes by Mediterranean diet intervention in humans, the modules in which the co-expression pattern was significantly restored by Mediterranean diet in mice were the dark red, grey60, and blue modules.

[0206] [Table 4]

[0207]

[0208] Figures 11a to 11c illustrate the results of gene network analysis of dark red, grey60, and blue modules following MEDi-PoB diet mediation.

[0209] Gene network analysis was performed on each module of the dark red, grey60, and blue modules, which were identified in both humans and mice through cross-species validation, to examine changes in gene interactions and connectivity within the modules.

[0210] The major hub genes of the dark red module include TRAPPC6B (trafficking protein particle complex subunit 6B), LARP1B (La ribonucleoprotein 1B), UBE2Q2 (ubiquitin conjugating enzyme E2 Q2), UGP2 (UDP-glucose pyrophosphorylase 2), APOOL (apolipoprotein O-like), TCEAL8 (transcription elongation factor A-like 8), ACADM (acyl-CoA dehydrogenase medium chain), ORC4 (origin recognition complex subunit 4), LACTB2 (lactamase beta 2), ACP1 (acid phosphatase 1), and WASHC3 (WASH complex subunit 3), and the major hub genes of the gray60 module include ZNF292 (zinc finger protein 292), TAOK3 (TAO kinase 3), ANP32B (acidic nuclear phosphoprotein 32 family member B), and MKLN1 It includes (muskelin 1), DCUN1D1 (defective in cullin neddylation 1 domain containing 1), and SLF1 (SMC5 / 6 complex localization factor 1), and the major genes of the blue module include SSX2IP (SSX family member 2 interacting protein), F13A1 (coagulation factor XIII A chain), TMCC2 (transmembrane and coiled-coil domain family 2), TUBB1 (tubulin beta 1 class VI), and LPAR5 (lysophosphatidic acid receptor 5),SYNM (synemin), MPIG6B (megakaryocyte and platelet inhibitory receptor G6b), POLE2 (DNA polymerase epsilon subunit 2), TRHDE (thyrotropin releasing hormone degrading enzyme), INPP4B (inositol polyphosphate-4-phosphatase type II B), ZC3H12C (zinc finger CCCH-type containing 12C) and GPR21 (G Contains protein-coupled receptor 21).

[0211] To analyze the interactions between the aforementioned major genes included in the gene modules (dark red, grey60, and blue) altered by the MEDi-PoB diet, the Pearson correlation coefficient (PCC) was calculated between the expression values ​​of each gene pair. Networks were constructed by defining the existence of interactions (edges) between two genes as PCC values ​​exceeding 0.5–0.7, and the degree of connectivity (degree, the number of edges connecting a specific node (gene) to another node) of each node was calculated. Subsequently, a heatmap was created to visualize the change in the degree of connectivity (Δ degree) of each node (gene) by comparing gene networks between two dietary conditions (MEDi-POB vs. control; human, MEDi-POB vs. normal diet; human, and MEDi-POB vs. HFD; mouse). Colors represent the change in the degree of connectivity between the two gene networks and were scaled to have a range of +10 to -10 relative to the change in the degree of connectivity of each gene. Red hues indicate an increase in the degree of connectivity, while green hues indicate a decrease.

[0212] As a result, as shown in Figures 11a to 11c, after MEDi-PoB diet mediation, the interaction strength between genes included in the dark red module increased, and a Gain-of-Connectivity (GOC) phenomenon was observed in which inter-gene connectivity was strengthened. Gain-of-Connectivity (GOC), in which inter-gene interactions increased after MEDi-PoB diet mediation, was also confirmed in the grey60 and blue modules.

[0213] In particular, significantly more interactions (edges) were observed in the LARP1B, UGP2, and ACADM genes for the Dark red module, the LPAR5 and MPIG6B genes for the Blue module, and the ANP32B and MKLN1 genes for the Grey60 module compared to other genes in both humans and mice.

[0214] Figures 11d to 11f illustrate the results of correlation analysis between genes in the dark red, grey60, and blue modules and clinical and nutritional indicators following MEDi-PoB dietary intervention. Positive correlations (PCC>0) were visualized as red curves, and negative correlations (PCC<0) as blue curves.

[0215] To confirm functional associations through correlation analysis between genes within modules and clinical and nutritional variables, correlations were analyzed between genes included in gene modules (dark red, grey60, and blue) with confirmed interspecies conservedness and clinical and nutritional indicators using human data.

[0216] Accordingly, the SETDB2, ACADM, VPS37A, SIRT5, UBE2Q2, TRAPPC6B, KLHL8, NEK1, ORC4, DCAF6, and TARS3 genes of the dark red module; the PITPNB, VPS13A, ND2, ND6, PHF20L1, USP48, SACM1L, USP33, POLK, and GOSR1 genes of the gray 60 module; and the MAX, FNBP1L, TRAPPC1, ITGB3BP, ANGPT1, RANBP9, NDUFA6, NEK7, and NCOA4 genes of the blue module were selected for analysis. The Pearson correlation coefficients (PCCs) were calculated between each gene expression value and clinical indicators (BMI, TC, TG, LDL, HDL, insulin, HOMA-IR, WBC, etc.) and nutritional intake indicators (MUFA, MUFA / SFA ratio, CHO, total energy intake, etc.), and based on this, The positive or negative correlation between genes and indicators was visualized.

[0217] As a result, as shown in Figures 11d to 11f, all three modules showed that different genes had significant correlations with various clinical indicators and nutritional intake indicators.

[0218] Synthesizing the above results, the MEDi-PoB diet improved the metabolic health of postmenopausal obese or overweight breast cancer patients, and it was confirmed that the dark red, grey60, and blue modules were significantly activated by the MEDi-PoB dietary intervention, and the genes included in the gene modules showed significant correlations with various metabolism-related clinical indicators.

[0219] These results suggest that the activation of gene networks related to mitochondrial function regulation, inflammation regulation, genomic stability, cellular stress response, and immune regulation is closely associated with the improvement of metabolic health following dietary intervention.

[0220] Since the expression levels of these genes show significant correlations with HOMA-IR, insulin, triglycerides (TG), LDL, MUFA intake, and Mediterranean diet score (MDS), the gene combinations included in the dark red, grey60, and blue modules suggest that they can be used as a biomarker panel to predict responsiveness to dietary interventions in postmenopausal obese or overweight breast cancer patients.

[0221] Therefore, the seven genes (LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1) corresponding to genes in which the number of interactions (edges) differed by five or more in both humans and mice in the results of the gene network comparison analysis between the two dietary conditions based on whether or not Mediterranean diet intervention was performed were selected as biomarkers for predicting the response to dietary therapy in postmenopausal obese or overweight breast cancer patients receiving maintenance therapy after cancer treatment.

[0222]

[0223] [National R&D projects that supported this invention]

[0224] [Project ID] 5005886

[0225] [Assignment No.] 2021-31-0670

[0226] [Ministry Name] Ministry of Agriculture, Food and Rural Affairs

[0227] [Project Management (Specialized) Agency Name] Korea Institute of Planning and Evaluation for Food, Agriculture and Forestry Technology

[0228] [Research Project Name] 2021 High Value-Added Food Technology Development Project

[0229] [Research Project Title] Research and Development of Customized Meal Management Solutions and Home Meal Service Based on Dietary Management Demand by Target Group

[0230] [Name of Project Performing Organization] S-Food Co., Ltd.

[0231] [Research Period] April 1, 2021 ~ December 31, 2025

[0232]

[0233] [Project ID] 1345363075

[0234] [Assignment No.] 2018R1D1A1B07049223

[0235] [Ministry Name] Ministry of Education

[0236] [Name of Project Management (Specialized) Agency] National Research Foundation of Korea

[0237] [Research Project Name] Individual Basic Research (Ministry of Education)

[0238] [Project Title] Verification of Metabolic Efficacy and Discovery of Metabolic Biomarkers After Application of Mediterranean Diet in Breast Cancer Patients

[0239] [Name of Project Performing Organization] Yonsei University College of Medicine

[0240] [Research Period] 2018.06.01 ~ 2023.05.03

Claims

1. A combination of biomarkers for predicting the response to diet in postmenopausal obese or overweight breast cancer patients, comprising one or more genes or proteins thereof selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1.

2. In Paragraph 1, The above biomarker is a combination of biomarkers for predicting the response to diet in postmenopausal obese or overweight breast cancer patients, the biomarker being a gene expression marker or a protein expression marker.

3. In Paragraph 1, A combination of biomarkers for predicting the response to diet in postmenopausal obese or overweight breast cancer patients who have completed initial cancer treatment and are receiving maintenance therapy.

4. In Paragraph 1, A combination of biomarkers for predicting the response to a diet in postmenopausal obese or overweight breast cancer patients, characterized in that the response to the diet is a dietary response to metabolic health indicators.

5. In Paragraph 4, A combination of biomarkers for predicting the response to diet in postmenopausal obese or overweight breast cancer patients, wherein the above metabolic health indicator is at least one selected from the group consisting of body weight, BMI, waist circumference, triglycerides, and insulin resistance.

6. A composition for predicting the response to diet in postmenopausal obese or overweight breast cancer patients, comprising a preparation for measuring the level of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 or the protein expression level thereof.

7. In Paragraph 6, A composition for predicting the response to diet in postmenopausal obese or overweight breast cancer patients, wherein the agent for measuring the gene level is any one selected from the group consisting of an antisense oligonucleotide that specifically binds to a gene, a primer pair, and a probe.

8. In Paragraph 7, A composition for predicting the response to dietary therapy in postmenopausal obese or overweight breast cancer patients, wherein the above-mentioned gene-level measurement is performed by one or more methods selected from the group consisting of reverse transcriptase polymerase chain reaction (RT-PCR), competitive reverse transcriptase polymerase chain reaction (competitive RT-PCR), real-time polymerase chain reaction (real-time RT-PCR), real-time quantitative RT-PCR, RNase protection method, Northern blotting, and DNA chips.

9. In Paragraph 6, A composition for predicting the response to a diet in postmenopausal obese or overweight breast cancer patients, wherein the preparation for measuring the protein level is any one selected from the group consisting of oligopeptides that specifically bind to proteins, monoclonal antibodies, polyclonal antibodies, chimeric antibodies, ligands, PNAs, and aptamers.

10. In Paragraph 9, A composition for predicting the response to dietary therapy in postmenopausal obese or overweight breast cancer patients, wherein the measurement of the protein level is performed by one or more methods selected from the group consisting of Western blot, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, fluorescence activated cell sorter (FACS), and protein chip.

11. In Paragraph 6, A composition for predicting the response to diet in postmenopausal obese or overweight breast cancer patients, the breast cancer patient being a patient diagnosed with stage 1, 2, or 3 breast cancer.

12. In Paragraph 6, A composition for predicting the response to dietary therapy in postmenopausal obese or overweight breast cancer patients, the breast cancer patients who have completed initial cancer treatment and are receiving maintenance therapy.

13. In Paragraph 12, A composition for predicting the response to a diet for postmenopausal obese or overweight breast cancer patients, wherein the above cancer treatment comprises at least one of breast surgery, adjuvant chemotherapy, and radiation therapy.

14. In Paragraph 12, The above maintenance therapy is an adjuvant hormone therapy, a composition for predicting the response to a diet in postmenopausal obese or overweight breast cancer patients.

15. In Paragraph 6, A composition for predicting the response to a diet in postmenopausal obese or overweight breast cancer patients, characterized in that the response to the above diet is a dietary response to metabolic health indicators.

16. In Paragraph 15, A composition for predicting the response to diet in postmenopausal obese or overweight breast cancer patients, wherein the above metabolic health indicator is at least one selected from the group consisting of body weight, BMI, waist circumference, triglycerides, and insulin resistance.

17. A step of measuring the expression level of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1, or their proteins, from a biological sample isolated from a breast cancer patient prior to the start of dietary therapy, A step of measuring the expression level of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1, or their proteins, from biological samples isolated from breast cancer patients after the completion of diet therapy, and A method for providing information to predict the response to diet in postmenopausal obese or overweight breast cancer patients, comprising the step of comparing the expression levels of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1, or their proteins, before and after diet.

18. In Paragraph 17, A method for providing information to predict the response to diet in postmenopausal obese or overweight breast cancer patients, wherein the biological sample is selected from the group consisting of whole blood, plasma, serum, urine, stool, saliva, tears, cerebrospinal fluid, cell and tissue specimens.

19. In Paragraph 17, A method for providing information to predict the response to diet in postmenopausal obese or overweight breast cancer patients, comprising further a step of predicting a high clinical response to diet when the expression level of one or more genes selected from the group consisting of LARP1B, UGP2, ACADM, LPAR5, MPIG6B, ANP32B, and MKLN1 or their proteins is increased.

20. In Paragraph 17, A method for providing information to predict the response to dietary therapy in postmenopausal obese or overweight breast cancer patients, the breast cancer patients mentioned above, who have completed initial cancer treatment and are receiving maintenance therapy.

21. In Paragraph 17, A method for providing information to predict the response to a diet in postmenopausal obese or overweight breast cancer patients, characterized in that the response to the above diet is a dietary response to metabolic health indicators.

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