Minimally invasive skin condition diagnostic kit including microneedle patch

Abstract

A minimally invasive skin condition diagnostic kit according to one embodiment of the present invention includes: an apparatus capable of quantifying the expression level of RNA genes from a test subject extracted from the skin of a subject; and a microneedle patch having a plurality of microneedles made of biodegradable polymer hyaluronic acid and having a solid structure. The microneedle patch is applied to the skin of a subject, maintained for a predetermined period of time, and then separated. The test subject from the subject's skin or an extract thereof adsorbed on the microneedle surfaces of the microneedle patch is quantitatively analyzed by the apparatus. The apparatus quantifies the expression level of each skin condition-related RNA biomarker gene to be diagnosed from the test subject from the subject's skin adsorbed on the microneedle surfaces of the microneedle patch or an extract thereof, and each skin condition is diagnosed based on the quantified value.

Description

[Technical Field]

[0001] The present invention relates to a minimally invasive skin condition diagnostic kit comprising a microneedle patch. [Background technology]

[0002] This invention claims priority from the seven Korean patent applications specified below. All technical contents disclosed in the detailed description of the invention, claims and drawings of the seven Korean patent applications specified below shall be deemed to be incorporated into the specification of this invention.

[0003] Korean Patent Application No. 10-2020-0100151 (Name of invention: Minimally invasive skin wrinkle or skin elasticity diagnostic kit including microneedle patch) Korean Patent Application No. 10-2020-0100165 (Title of invention: Minimally invasive skin moisture assessment kit including microneedle patch and biomarker for assessing skin moisture level) Korean Patent Application No. 10-2020-0100168 (Title of invention: Minimally invasive skin pigmentation assessment kit including microneedle patch and biomarker for assessing skin pigmentation level) Korean Patent Application No. 10-2020-0100579 (Title of invention: Minimally invasive skin oil level assessment kit including microneedle patch and biomarker for assessing skin oil level) Korean Patent Application No. 10-2020-0101117 (Title of invention: Minimally invasive flushing-sensitive skin evaluation kit including microneedle patch and biomarker for flushing-sensitive skin evaluation) Korean Patent Application No. 10-2020-0101124 (Title of invention: Minimally invasive subjective irritant sensitive skin degree assessment kit including microneedle patch and biomarker for subjective irritant sensitive skin degree assessment) Korean Patent Application No. 10-2020-0100561 (Title of invention: Minimally invasive cosmetic acne prediction kit including microneedle patch and biomarker for predicting cosmetic acne) The present invention is directed to accurate diagnosis, measurement, or evaluation of various skin conditions. The skin conditions addressed by the present invention are the following seven in total:

[0004] -Skin aging (wrinkles or skin elasticity) -Skin moisturizing -Skin pigmentation -Skin oil - Flushing sensitive skin -Irritating sensitive skin - Cosmetic acne Below we will describe in detail what techniques are used to diagnose, measure or evaluate each of the seven skin conditions mentioned above, and what problems the inventor(s) have identified with such techniques currently in use.

[0005] <Skin aging> All living things begin to age from birth, and skin also undergoes aging. The causes of skin aging can be divided into intrinsic factors that progress with age and extrinsic factors that progress with long-term exposure to environmental factors such as sunlight, cold, wind, and smog.

[0006] As skin ages, the content of collagen, elastin, and hyaluronic acid, the main components of the extracellular matrix in the dermis, and the number of fibroblasts decrease, resulting in wrinkles, loss of elasticity, and sagging skin. The decrease in the extracellular matrix in the dermis is caused by matrix metalloproteinase (MMP) enzymes such as collagenase (MMP-1) and gelatinase (MMP-2, MMP-9), and the activity and expression of these enzymes actually increase significantly in aging skin.

[0007] The skin barrier is the stratum corneum, the final stage of differentiation in the epidermal layer, summarized as "Brick and Mortar." Bricks consist of denucleated keratinocytes, various intracellular proteins, and degradation products containing natural moisturizing factors, connected by a membrane (cornified envelope). Mortar refers to intercellular lipids composed primarily of ceramides, cholesterol, and fatty acids, and has a multilayer structure. As aging progresses, approximately 75% of skin becomes dry. This is due to age-related impairments in factors important for maintaining the skin barrier and performing its basic functions (stratum corneum lipid content, epidermal pH). In aged skin, the number of lamellar lamellae decreases, the secretion of lamellar bodies decreases, and lipid metabolism abnormalities occur, including lipid reduction in the stratum corneum. In addition, the function of enzymes that limit the production rates of ceramides, cholesterol, and fatty acids decreases, resulting in a significant decrease in cholesterol synthesis, resulting in a decrease in overall lipid content. The expression of the cornified envelope and natural moisturizing factors also decreases.

[0008] Appropriate efficacy evaluation is necessary to measure the state of skin aging, discover new mechanisms of skin aging, determine the effectiveness of treatments, predict prognosis, and develop new treatments. Due to a complete ban on the distribution and sale of cosmetics that have been tested on animals, cosmetics manufactured using ingredients that have been tested on animals, and imported cosmetics that have been tested on animals or made using ingredients that have been tested on animals, research into the mechanisms of skin aging and the effectiveness of cosmetics currently relies on efficacy evaluations using human-applied equipment, tape stripping, skin tissue biopsies, or ex vivo efficacy evaluations using 3D skin.

[0009] As of the filing date of the present invention, various techniques described below are in use as methods for measuring skin aging using a human-applied device.

[0010] - A method to measure only the morphological parts of the skin using PRIMOS equipment - Using a Cutometer to measure the average value of repeated skin contraction and relaxation using negative pressure - Using Tewameter equipment to measure changes in transepidermal water loss (TEWL) related to abnormalities in the skin barrier function - Using a Corneometer to measure changes in water content using water conductivity etc. - How to measure the acidity of the skin surface using Skin-pHmeter equipment However, the above-mentioned methods are difficult to use in studying the specific mechanisms of skin aging and do not provide a basis for scientifically explaining changes in specific factors, particularly in the extracellular matrix, including intradermal collagen, which is the most important target of skin aging.

[0011] Recently, a non-invasive stripping method using tape has been proposed, in which tape is applied to the affected area and then removed to analyze the skin test object attached to the tape. However, while the main cause of skin aging is changes in the dermis, such as degeneration of collagen and elastic fibers, the material attached to the tape is limited to the stratum corneum and therefore cannot reflect changes in the dermis.

[0012] To overcome these drawbacks, skin biopsies are used, but they require the skilled technique of the surgeon and are a highly invasive method, which causes pain and discomfort for the patient, and leaves scars at the biopsy site after the test. For these reasons, it is difficult to perform skin biopsies before and after each use of a test drug in an actual human efficacy evaluation test, which significantly reduces patient compliance.

[0013] Ex vivo testing using 3D artificial skin provides the most advanced structure that mimics the skin, including all the cells of the epidermal and dermal layers of the actual human body. However, it is expensive and does not include structures such as blood vessels, hair follicles, and subcutaneous fat that exist in real skin. Therefore, it cannot be said to reflect the changes in real skin and cannot completely replace a skin biopsy.

[0014] Therefore, currently, there is no alternative to animal experiments or skin biopsies in research into the mechanisms of skin aging or in research into the effectiveness of anti-skin aging agents. For this reason, there is a strong demand for the development of a non-invasive method that can actually measure changes in the extracellular matrix, including dermal collagen.

[0015] <Skin moisturizing> The skin acts as a protective barrier against external physical and chemical stimuli and maintains moisture. The skin barrier is located in the stratum corneum of the epidermis, which is composed of various layers, and provides primary physical defense against external stimuli. It is a multilayered structure formed by the tight binding of keratinocytes and intercellular lipids. The skin barrier can be summarized as "bricks and mortar." The bricks, which are mainly keratinocytes, consist of keratinocytes with lost nuclei, various intracellular proteins, degradation products containing natural moisturizing factors, and the membrane (cornified envelope) connected to these. Mortar refers to the intercellular lipids, which are mainly composed of ceramides, cholesterol, and fatty acids. This prevents moisture loss and maintains moisture. The stratum corneum is formed through the proliferation and differentiation of keratinocytes and is periodically shed by the action of various proteolytic enzymes to maintain homeostasis.

[0016] Dry skin is a skin condition caused by an increase in keratin, which causes the skin surface to dry out and feel tight, itchy, etc. Dry skin can be caused by a variety of factors, including a decrease in natural moisturizing factors, stratum corneum lipids, and sebum, and abnormal shedding of the stratum corneum, due to endogenous factors such as skin diseases like atopic dermatitis and psoriasis, and exogenous factors such as dry environments, windy weather conditions, chemicals like detergents and organic solvents, excessive bathing and washing, ultraviolet rays, and physical irritation. Therefore, it is important to use an appropriate moisturizer according to the cause and manage your lifestyle.

[0017] Therefore, it is necessary to understand the skin's moisture status, study the mechanisms of skin moisture disorders, and conduct appropriate efficacy evaluations to develop moisturizers and skin care products that can correct or treat such disorders.With the complete ban on the distribution and sale of cosmetics that have been tested on animals, cosmetics manufactured using ingredients that have been tested on animals, and imported cosmetics that have been tested on animals or made using ingredients that have been tested on animals, currently, research into the mechanisms of skin moisture and the effectiveness of cosmetics are conducted using efficacy evaluations using human-applied equipment, tape stripping, skin tissue biopsies, or ex vivo efficacy evaluations using 3D skin.

[0018] Human-applied devices for measuring skin moisture include the Corneometer, Tewameter, and Skin-pHmeter. The Corneometer measures skin moisture by taking advantage of the tendency for capacitance, the electrical charge stored in the body, to be proportional to moisture content. It measures moisture content within 30-40 μm of the stratum corneum. The higher the moisture content, the higher the reading. The Tewameter measures transepidermal water loss (TEWL), which is closely related to damage to the skin barrier. When the skin barrier is damaged, more water is lost by evaporation from the epidermis. The device senses the water evaporating from the epidermis and provides a reading; a higher reading indicates greater loss. The Skin-pHmeter measures skin acidity by measuring the hydrogen ion concentration on the skin surface, allowing you to determine whether the acidity deviates from the normal slightly acidic range (pH 4.5-5.5) for skin.

[0019] Such body-applied devices are very sensitive and are greatly affected by the measurement environment and the person using them. Therefore, reliable results can only be obtained by a skilled person using expensive equipment exposed to constant temperature and dehumidification conditions for a certain period of time. Furthermore, since this method simply measures only skin moisture content, transepidermal water loss, and skin pH, it can grasp the degree of skin dryness, but when the level of skin moisture is abnormal due to various causes, it is difficult to identify the mechanism behind the abnormality or develop treatments or management methods for it.

[0020] The non-invasive stripping method using tape, in which tape is applied to the affected area and then removed to analyze the skin test object attached to the tape, only measures the stratum corneum, which is the top layer of the skin epidermis, and therefore cannot obtain specific information on changes related to moisture retention that exist in the granular layer below the stratum corneum, the basal cell layer, and the dermis layer.

[0021] To overcome these drawbacks, skin biopsies are used, but they require the skilled technique of the surgeon and are a highly invasive method, which causes pain and discomfort for the patient, and leaves scars at the biopsy site after the test. For these reasons, it is difficult to actually perform skin biopsies before and after each use of a test drug in a human body to evaluate its effectiveness, and patient compliance is very low.

[0022] Although ex vivo testing using 3D artificial skin provides the most advanced structure that mimics human skin, including cells from the epidermal and dermal layers, it is not only expensive, but also does not contain structures such as blood vessels, hair follicles, and subcutaneous fat that exist in real skin, nor the various immune cells that exist in the epidermis and dermis, and therefore cannot completely replace skin biopsies, as changes in real skin may differ.

[0023] Therefore, currently, there is no method that can replace animal experiments and skin biopsies in research into the mechanism of skin moisturization or research into the effectiveness of moisturizers. Therefore, a non-invasive method that can measure changes in all layers of the epidermis and the dermis is needed.

[0024] <Skin pigmentation> Melanin is a phenolic polymer found in nature, a complex of black pigment and protein. Melanin has the ability to block a certain amount of UV rays, maintaining skin temperature and protecting it from UV rays. Melanin is also an important factor in determining a person's skin color. Melanin in the skin is produced by melanocytes, and melanin expression genes differ depending on the race, which regulates the amount of melanocytes and determines skin color.

[0025] When the skin is exposed to ultraviolet rays, keratinocytes secrete melanocyte-stimulating hormone, which binds to the melanocortin 1 receptor (MC1R) of melanocytes and acts on the nucleus of melanocytes, causing the expression of enzymes such as tyrosinase and TYRP1, TYRP2, and the start of melanin production.

[0026] The process of melanin production is as follows: First, melanosomes, organelles within melanocytes, are produced. The amino acid tyrosine is oxidized in the melanosomes to DOPA, which is then oxidized to DOPA-quinone with the help of an oxidizing enzyme called tyrosinase. Subsequently, autoxidation occurs to form 5,6-dihydroxyindol and indol-5,6-quinone, ultimately producing dark brown melanin. Large quantities of melanosomes then travel through the dendrites of melanocytes to the surrounding keratinocytes where they are degraded, resulting in the accumulation of melanin in the keratinocytes, which gives skin its color.

[0027] Classification of skin types by skin color began in 1975 when American Fitzpatrick first classified Caucasian skin into four types to determine the initial treatment dose for psoriasis. Two more types were later added, defining Caucasians as type I, II, III, and IV, Asians with brown skin as type V, and Blacks as type VI. However, as many researchers have discovered that Asians also have a variety of skin types, various errors have occurred, and the current classification is based on skin reaction to solar ultraviolet rays, ranging from type I, which always burns in the sun, to type VI, which only tans without burning.

[0028] Appropriate efficacy evaluation is necessary to evaluate or measure skin pigmentation status, discover new causes and mechanisms of skin pigmentation changes from various skin pigmentation disorders, determine the effectiveness of skin pigmentation treatments, predict prognosis, and develop new skin pigmentation treatments. However, due to a complete ban on the distribution and sale of cosmetics that have been tested on animals, cosmetics manufactured using ingredients that have been tested on animals, and imported cosmetics that have been tested on animals or made using ingredients that have been tested on animals, research into the mechanisms of skin pigmentation and the effectiveness evaluation of cosmetics are currently being conducted using efficacy evaluations using human-applied equipment, tape stripping, skin tissue biopsies, or ex vivo efficacy evaluations using 3D skin.

[0029] Methods for measuring skin pigmentation using human-applied devices include a Mexameter, which uses the principle of absorption to measure the skin absorption rate of melanin and hemoglobin, the main factors that determine skin color, and a Spectrophotometer, which measures the spectral reflectance of skin color and calculates skin brightness and saturation using tristimulus values. However, these methods only measure skin color and flushing, making them difficult to use for studying the specific mechanisms of skin pigmentation. Furthermore, they cannot scientifically explain changes in specific factors, and do not provide specific information on changes in melanogenesis, the most important target of pigmentation.

[0030] A non-invasive stripping method using tape has been proposed, in which tape is applied to the affected area and then removed to analyze the skin test material attached to the tape. However, while the main mechanism of skin pigmentation is melanogenesis, which is caused by changes in melanocytes in the basal layer, the material attached to the tape is limited to the stratum corneum, making it difficult to reflect changes in the underlying epidermal layers (granular layer, epithelial cell layer, basal cells, etc.) or dermis layer.

[0031] To overcome these drawbacks, skin biopsies are used, but they require the skilled technique of the surgeon and are a highly invasive method, which causes pain and discomfort for the patient, and leaves scars at the biopsy site after the test. For these reasons, it is difficult to actually perform skin biopsies before and after each use of a test drug in a human body to evaluate its effectiveness, and patient compliance is very low.

[0032] Although ex vivo testing using 3D artificial skin provides the most advanced structure that mimics human skin, including cells from the epidermal and dermal layers, it is not only expensive, but also does not contain structures such as blood vessels, hair follicles, and subcutaneous fat that exist in real skin, nor the various immune cells that exist in the epidermis and dermis, and therefore cannot completely replace skin biopsies, as changes in real skin may differ.

[0033] Therefore, currently, there is no alternative to animal experiments or skin biopsies in research into the mechanisms of skin pigmentation or in research into the effectiveness of pigmentation treatments. Therefore, a non-invasive method that can measure changes in all layers of the epidermis is needed.

[0034] <Skin oil> Sebum, which is responsible for skin oil, is a substance secreted by the sebaceous glands and is composed mainly of triglycerides, wax esters, and squalene. Sebaceous glands are secretory glands attached to hair and located in the dermis layer. The secreted sebum covers the skin surface and prevents moisture from escaping. It also neutralizes alkali and has antibacterial properties, making it an important factor in preventing skin dryness and delaying skin aging.

[0035] Therefore, healthy skin cannot be achieved without maintaining an appropriate level of sebum secretion. However, excessive sebum secretion caused by male hormones, hyperkeratosis of hair follicle walls, inflammation caused by acne bacteria, stress, and other environmental and genetic factors can clog pores and cause skin problems, including acne and seborrheic dermatitis.

[0036] Sebum secretion varies depending on age and gender, with low secretion in infancy, increasing in adolescence, and then decreasing as we age. Furthermore, sebum secretion is more prevalent in men than in women due to hormones. Generally, the opposite of dry skin is oily skin, where sebum secretion is high. The forehead, between the eyebrows, and nose, where sebaceous glands are primarily found, are called the T-zone. The area extending from both cheeks to the chin is called the U-zone, and generally, sebaceous glands are less prevalent in this area. Skin in the U-zone is dry, while the T-zone is oily, and is sometimes classified as combination. For oily skin, it's important to choose the right cleanser and moisturizer.

[0037] Understanding the state of skin oil and appropriate efficacy evaluation methods are necessary for studying the mechanisms of skin oil secretion disorders and hypersecretion, as well as for developing cleansing and therapeutic agents and skin care methods that can correct or treat skin oil disorders.

[0038] Meanwhile, due to the complete ban on the distribution and sale of cosmetics that have been tested on animals, cosmetics manufactured using ingredients that have been tested on animals, and imported cosmetics that have been tested on animals or made using ingredients that have been tested on animals, research into the mechanisms of skin oil disorders and excessive secretion, as well as the evaluation of the effectiveness of related functional cosmetics, is currently being carried out using efficacy evaluations using human-applied equipment, tape stripping, skin tissue biopsies, or ex vivo efficacy evaluations using 3D skin.

[0039] The Sebumeter from Courage+Khazaka Electronics is a device that can be applied to the human body to measure skin oil content. It measures the amount of sebum absorbed after contacting a 0.1mm thick sebum collection tape with the skin, and measures the amount of oil by the principle that when the tape becomes transparent due to sebum, light transmittance increases.

[0040] Such human-applied devices are sensitive and are greatly affected by the measurement environment and the person using them, and reliable results can only be obtained by an experienced person exposed to constant temperature and dehumidification conditions for a certain period of time. Furthermore, this method simply measures only the sebum secreted on the skin surface, so it is not suitable for use in studying the specific mechanisms of skin oil secretion. Furthermore, it cannot scientifically explain changes in specific factors and does not provide specific data on changes in the sebaceous glands in the dermis.

[0041] Non-invasive stripping methods using tape, in which tape is applied to the affected area and the skin test object attached to the tape is analyzed, are also limited to collecting test objects from the stratum corneum, so they cannot reflect changes in the dermis layer related to skin oils.

[0042] To overcome these drawbacks, skin biopsies are used, but they require the skilled technique of the surgeon and are a highly invasive method, which causes pain and discomfort for the patient, and leaves scars at the biopsy site after the test. For these reasons, it is difficult to actually perform skin biopsies before and after each use of a test drug in a human body to evaluate its effectiveness, and patient compliance is very low.

[0043] Although ex vivo testing using 3D artificial skin provides the most advanced structure that mimics human skin, including cells from the epidermal and dermal layers, it is not only expensive, but also does not contain structures such as blood vessels, hair follicles, and subcutaneous fat that exist in real skin, nor the various immune cells that exist in the epidermis and dermis, and therefore cannot completely replace skin biopsies, as changes in real skin may differ.

[0044] Therefore, currently, there is no alternative to animal experiments or skin biopsies in research into the state and mechanism of skin oil secretion, or in research into the effectiveness of skin oil. Therefore, a non-invasive method that can measure changes in the dermis layer of the skin is needed.

[0045] <Flushing sensitive skin> Sensitive skin, one of the four categories of Baumann's skin types, can be further classified into four types: acne-prone sensitive skin, stinging-prone sensitive skin, allergic-prone sensitive skin, and rosacea-prone sensitive skin. Individuals may be sensitive to only one of these types, or may have multiple sensitivities. In a Baumann skin type questionnaire survey of 27,485 Americans, 73% of respondents had sensitive skin, and 49% had the rosacea-prone sensitive skin type (Baumann L., Dermatol Clin. 2008:26(3):359-373).

[0046] Flushing is a condition in which the skin turns red, occurring mainly in the central part of the face due to the dilation of capillaries, and can be caused by a variety of factors, including hormonal imbalance during menopause, sensitivity due to a weakened skin barrier, and steroid side effects. It may occur temporarily due to external stimuli, but if it becomes chronic, it can lead to a chronic inflammatory disease called rosacea.

[0047] Sensitive flushing skin is skin that becomes red and hot when applying cosmetics containing certain ingredients, and is sometimes accompanied by inflammatory papules and dilated capillaries. If you have sensitive flushing skin, it is best to minimize sudden changes in facial temperature and use facial cleansers, moisturizers, and UV protection products that contain anti-inflammatory ingredients, such as aloe vera, argan oil, bisabolol, caffeine, chamomile, cucumber, green tea, witch hazel, niacinamide, and salicylic acid.

[0048] Therefore, proper efficacy evaluation is necessary to discover new mechanisms of sensitive redness, predict the occurrence of sensitive redness in response to various cosmetic ingredients, and develop new treatments.With the complete ban on the distribution and sale of cosmetics that have been tested on animals, cosmetics manufactured using ingredients that have been tested on animals, and imported cosmetics that have been tested on animals or made using ingredients that have been tested on animals, research into the mechanisms of sensitive redness and the evaluation of the effectiveness of cosmetics are currently being conducted using efficacy evaluations using human-applied equipment and ex vivo efficacy evaluations using 3D skin.

[0049] The human-applied devices for measuring reddening sensitive skin include the Mexameter (Courage+Khazaka Electronic) and the Spectrophotometer. The Mexameter simultaneously measures melanin levels and hemoglobin levels, which indicate erythema. It is a spectroscopic device that measures the reflectance of light emitted from a probe using three different wavelength bands (568nm, 660nm, 880nm) that correspond to melanin and erythema, and provides the amount of erythema as an Erythema Index (EI). The Spectrophotometer is a device that measures the spectral reflectance of object color and is based on the L CIE color system. * , a * , b * Calculate the color with L * means brightness, and a * is redness, b * indicates yellowness, and the closer it is to 0, the more achromatic the color is, and the opposite means that the color is more chromatic.

[0050] Such human-applied devices are sensitive and are greatly affected by the measurement environment and the person taking the measurement, so reliable results can only be obtained by an experienced user under constant temperature and dehumidified conditions. Furthermore, since they simply measure the color of the skin surface, they are insufficient for use in studying the specific mechanisms of flushing-sensitive skin, and are unable to scientifically explain changes in specific factors. Furthermore, they do not provide specific information on the capillaries present in the dermis, which are important in flushing.

[0051] Ex vivo testing using 3D artificial skin provides the most advanced structure that mimics human skin, including cells from the epidermal and dermal layers. However, it is not only expensive, but also does not contain structures such as blood vessels, hair follicles, and subcutaneous fat present in real skin, nor the various immune cells present in the epidermis and dermis, so changes in real skin may differ.

[0052] Therefore, a new test that can predict flushing-sensitive skin is needed so that treatments and cosmetics suitable for flushing-sensitive skin can be used.

[0053] <Irritant sensitive skin> Sensitive skin is skin that reacts sensitively to irritants, stress, environmental factors, etc., resulting in irritation and irritation reactions. Sensitive skin can be defined from both subjective and objective perspectives. Skin that is sensitive to subjective stimuli causes subjective symptoms such as burning and itching felt by the individual without objective symptoms of inflammation. Skin that is sensitive to objective reactions causes objective symptoms such as erythema, wheals, and blisters, which are observed by specialists.

[0054] The mechanism behind the development of sensitive skin is highly complex, with various mechanisms known to be involved, including increased neurosensory signaling, increased immune response, and weakened skin barriers. Among the causative factors for sensitive skin, intrinsic factors include genetics, overwork, stress, and lack of sleep, while extrinsic factors include cosmetics, detergents, ultraviolet rays, chemicals, and the environment. In a Baumann Skin Type Questionnaire survey conducted on 27,485 patients in the United States, 73% of patients were observed to be sensitive, and 15% of those had subjectively irritant-sensitive skin.

[0055] Appropriate efficacy evaluation is necessary to measure the degree of subjectively sensitive skin, discover new mechanisms of subjectively sensitive skin, conduct predictive tests for prognosis, and develop topical agents or cosmetics that can prevent subjectively sensitive skin.With the complete ban on the distribution and sale of cosmetics that have been tested on animals, cosmetics manufactured using ingredients that have been tested on animals, and imported cosmetics that have been tested on animals or made using ingredients that have been tested on animals, current methods for researching the mechanisms of subjectively sensitive skin and assessing individual sensitivity can only be performed using subjective measurements that determine the degree of skin irritation based on the test subject's opinion, objective measurements that observe through visual observation by a specialist, or skin tissue biopsies.

[0056] Subjective methods for determining the level of skin irritation based on the subject's opinion include the lactic acid sting test and a burning sensation or pain induction test using a chloroform and methanol mixture. Objective methods include applying SLS, DMSO, ammonium hydroxide, or sodium hydroxide to the skin and observing changes in the skin, such as erythema and blisters. However, there is no standardized test method for diagnosing subjectively irritating sensitive skin. The lactic acid sting test, which has been cited in numerous studies, relies on the subjective feeling of the test subject after application of lactic acid, resulting in limited reproducibility and accuracy, making it difficult to objectively quantify. Therefore, a reproducible, objective method for diagnosing irritation that eliminates subjective evaluation is needed.

[0057] To overcome these drawbacks, skin biopsies are used, but they require the skilled technique of the surgeon and are a highly invasive method, which causes pain and discomfort for the patient, and leaves scars at the biopsy site after the test. For these reasons, it is difficult to actually perform skin biopsies before and after each use of a test drug in a human body to evaluate its effectiveness, and patient compliance is very low.

[0058] Therefore, there is currently no method to objectively evaluate the degree of sensitive skin other than the lactic acid prick test for studying the mechanism of subjectively sensitive skin or for developing topical agents or cosmetics to prevent subjectively sensitive skin. It is necessary to discover biomarkers that are specifically expressed in subjectively sensitive skin and develop a method or kit to non-invasively evaluate the degree of subjectively sensitive skin using these biomarkers.

[0059] <Cosmetic acne> There are four types of sensitive skin: acne type, which causes acne lesions such as papules and pustules; rosacea type, which causes repeated flushing and hot flashes; irritation type, which causes a tingling sensation; and allergy type, which causes symptoms such as erythema and itching when in contact with allergens.

[0060] Acne is an inflammatory skin disease that occurs in the pilosebaceous unit and is common among adolescents and young adults, with a prevalence of 80%. Factors that contribute to acne include hyperkeratinization of hair follicles, inflammatory responses, increased sebum secretion, and colonization by Proprionibacterium acnes. A variety of clinical symptoms result from a combination of factors, including environmental factors. Acne manifests as non-inflammatory lesions, such as open comedones (known as blackheads) and closed comedones (known as whiteheads), as well as inflammatory lesions, such as papules, pustules, and nodules. In mild acne, comedones are the primary lesion, while papules and pustules predominate in more severe cases, and nodules and pseudocysts are the primary lesions in more severe cases.

[0061] Cosmetics have a significant impact on acne that develops after puberty. In other words, some patients often develop acne-like rashes after using cosmetics; these lesions are known as cosmetic acne. In a Baumann skin type questionnaire survey conducted on 27,485 patients in the United States, 73% of patients were observed to be sensitive, and 58% of those were observed to have cosmetic acne. The acne-inducing potential of cosmetics is due to the comedogenic potential induced by follicular keratin plugs and the formation of papulopus-pus bullae.

[0062] While results can vary depending on the testing technique, concentrations of isopropyl myristate and petrolatum below 10% induce early comedones in rabbit ears but papulopus in humans. Emulsifiers such as sodium lauryl sulfate produce purulent material in proportion to their volume. Because comedogenic ingredients such as isopropyl myristate, isopropyl palmitate, and coconut oil can cause cosmetic acne, sensitive skin prone to cosmetic acne should be treated with products free of these ingredients. However, the exact mechanism behind cosmetic acne development remains unclear, and no test has been established to predict whether sensitive skin will develop cosmetic acne.

[0063] Therefore, new, appropriate evaluation methods are needed to uncover new mechanisms of cosmetic acne, predict the effects of various cosmetic ingredients on acne, and develop new treatments. Due to a complete ban on the distribution and sale of cosmetics tested on animals, cosmetics manufactured using ingredients tested on animals, and imported cosmetics tested on animals or made using ingredients tested on animals, current methods for researching the mechanisms of acne and evaluating personal cosmetic acne include visual observation by dermatologists and testing methods based on the acne efficacy evaluation variables specified in the Ministry of Food and Drug Safety guidelines. Recently, after animal testing on cosmetics has been discontinued, use tests have been conducted, and human tests have been conducted using a method in which cosmetics are applied occlusively to the back for four weeks and then used to check for microcomedone formation using super glue or cationic polymer surface biopsies.

[0064] The Korea Food and Drug Safety (MFDS) guidelines for testing efficacy variables for acne include measuring the type and number of lesions on the test subject's face before and after the application of a test substance and recording the grade on a scale of 1 to 5, or measuring the number of lesions and calculating a Michaelson's Acne Severity Index (ASI) value. However, these methods have limitations, as they are determined based on the tester's subjective standards and cannot be objectively quantified. Furthermore, since they only record the current state of acne visible to the naked eye, they cannot scientifically explain the specific mechanisms of cosmetic acne development or changes in specific factors, making them far from being a complete testing method. While ex vivo testing using 3D artificial skin offers the most advanced structure mimicking real human skin, including all cells from the epidermal and dermal layers, it is expensive and does not include structures such as blood vessels, hair follicles, and subcutaneous fat present in real skin, and therefore may differ from real skin changes. Therefore, in order to select treatments and cosmetics suitable for skin that frequently develops acne-like rashes due to the use of cosmetics after puberty, an appropriate cosmetic acne development prediction test is necessary.

[0065] We have discussed in detail the problems with the seven technologies currently in use for measuring and evaluating skin conditions as of the filing date. The inventor(s) use microneedles in the diagnosis, measurement, or evaluation of skin conditions in the present invention. Microneedles are tiny needles less than 1 mm long that penetrate the stratum corneum with minimal invasiveness, creating tiny holes in the skin to effectively deliver drugs. Recently, they have been used not only for the delivery of drugs and bioactive substances, but also for obtaining test subjects from within the body and predicting and diagnosing diseases. In particular, microneedles can be used to collect test subjects, such as body fluids or blood, through the microholes formed by the microneedles for biomarker detection and disease prediction. For example, hollow microneedles have capillaries inside the needles, and are used to extract blood through the capillaries after attachment to the skin. While they offer the advantage of relatively safe blood extraction, minimizing patient discomfort and enabling the detection of blood glucose and cholesterol or biomarkers, they also have the potential for side effects, such as bleeding, if the needles break within the skin.

[0066] Swellable microneedles are attached to the skin without adhesive by absorbing tissue fluid within the skin and swelling to seal the puncture site, and are used to detect, monitor, and predict disease by separating the absorbed bodily fluids. However, because the patch must be attached for a long time to absorb the bodily fluids required for analysis, there is a limitation in that it is difficult to maintain stable patch attachment due to various environmental factors.

[0067] The dissolving microneedles used in the present invention are manufactured by mixing an active ingredient with a polymeric material that dissolves in the body and then solidifying it into a microneedle. When such dissolving microneedles are attached to the skin, they efficiently dissolve in body fluids, allowing for easy drug delivery into the skin. Furthermore, because dissolving microneedles are not made of metal or plastic, they are safe and do not break inside the skin. The present applicant has developed the present invention to utilize dissolving microneedles in new applications, such as the collection of initial skin test subjects (RNA, DNA, protein) using dissolving microneedles; RNA microarray analysis using these test subjects; the discovery of cosmetic acne biomarkers based on the analysis results; and the prediction of cosmetic acne and the evaluation of the effectiveness of treatments and cosmetics using these biomarkers.

[0068] Recently, research into analyzing skin types has been actively conducted through direct-to-consumer (DTC) services, which provide information on skin-related genetic DNA single nucleotide polymorphisms (SNPs). This service typically involves collecting oral epithelium and analyzing DNA sequences. The DNA sequence is compared to normal individuals to determine whether specific gene sequences differ from normal individuals and indicate risk. While this method of skin genetic testing is noninvasive, it has limitations. Because DNA is identical and unchanging in all cells, it cannot reflect actual skin conditions, which vary due to various environmental factors. Even if a person is born with a specific SNP, SNP analysis cannot reflect the results if it is regenerated through postnatal care or treatment. These characteristics make SNP analysis difficult to apply to the discovery of new active ingredients, mechanistic research, and efficacy evaluation. Furthermore, current SNP analysis using oral epithelium does not accurately reflect actual skin conditions because the test sample is collected from oral epithelium rather than the skin. On the other hand, RNA, which can be translated into proteins that function in actual cells and shows specific levels of expression in each cell, can reflect actual skin characteristics that vary due to various factors. Therefore, observing the expression at the RNA and protein levels in actual skin cells is essential for accurate analysis of skin conditions.

[0069] Unlike DNA genome analysis, RNA transcript analysis can detect genes that are specifically expressed in skin cells or whose expression changes in response to environmental conditions such as drug exposure. It can also identify differences in gene expression levels between test and control groups, making it applicable to efficacy evaluations that require before-and-after comparisons, mechanistic studies, and the discovery of candidate substances. Furthermore, microarray technology allows for the comparison of the expression of over 40,000 genes in a single sample, making it extremely useful for disease prediction and the discovery of therapeutic biomarkers. However, as mentioned above, there are limitations to non-invasive methods for collecting RNA and proteins. The present inventors have come up with this invention to propose a method that overcomes these limitations. Summary of the Invention [Problem to be solved by the invention]

[0070] The present invention aims to solve the problems of conventional methods for diagnosing, measuring, or evaluating skin conditions, as described above. Among conventional methods, for example, tissue biopsy requires the surgeon's skilled technique, is painful to the patient, leading to rejection, and leaves a scar at the biopsy site after the test. Another conventional method, tape stripping, has the disadvantage that the substance attached to the tape is limited to the stratum corneum, so it can only be used as a supplementary testing method and does not provide complete results. Furthermore, conventional SNP arrays use the DNA of the human test subject, but DNA, which is determined and remains unchanged from birth, has the disadvantage that it is difficult to reflect actual skin conditions, which change due to various environmental factors.

[0071] The object of the present invention is to provide a new minimally invasive skin condition diagnostic kit that is less painful and leaves no scars compared to existing tissue biopsies, making it more convenient for patients to use, and that can improve the reliability of analysis results compared to existing tape stripping by collecting skin factors inside the stratum corneum, and that can reflect actual skin conditions that change due to various environmental factors.

[0072] It is also an object of the present invention to provide a biomarker for measuring skin conditions using a skin test subject as a biological sample.

[0073] In addition to achieving the above-mentioned object, the present invention also aims to provide the following:

[0074] -Skin condition prediction composition - Skin condition prediction test method and skin type classification method -Methods for screening candidate substances for induction or suppression of various skin conditions and evaluating the efficacy of therapeutic agents -An alternative method for evaluating the efficacy of general cosmetics, functional cosmetics, medical devices, pharmaceuticals, etc. for improving skin conditions by applying them to the human body instead of animal testing. [Means for solving the problem]

[0075] A minimally invasive skin condition diagnostic kit according to one embodiment of the present invention includes: an apparatus capable of quantifying the expression level of RNA genes from a test subject extracted from the skin of a subject; and a microneedle patch having a plurality of microneedles made of biodegradable polymer hyaluronic acid and having a solid structure. The microneedle patch is applied to the skin of a subject, maintained for a predetermined period of time, and then separated. The test subject from the subject's skin or an extract thereof adsorbed on the microneedle surfaces of the microneedle patch is quantitatively analyzed by the apparatus. The apparatus quantifies the expression level of each skin condition-related RNA biomarker gene to be diagnosed from the test subject from the subject's skin adsorbed on the microneedle surfaces of the microneedle patch or an extract thereof, and each skin condition is diagnosed based on the quantified value.

[0076] When the skin condition to be diagnosed is skin aging, two types of biomarker genes are used: collagen / elasticity-related RNA biomarker genes and skin barrier function / moisture synthesis-related RNA biomarker genes. The collagen / elasticity-related biomarkers include one or more of COL1A1, COL3A1, FN1, GSTA3, PON1, PINK1, COL4A4, and MMP8, and the skin barrier function / moisture synthesis-related biomarkers include one or more of IVL, HAS2, HAS3, AQP3, CERS6, CLDN1, SLC9A1, TGM1, SPINK5, KLF4, LCE1A, LCE1B, LCE1F, LCE2A, BGN, and AZGP1.

[0077] When the skin condition to be diagnosed is skin moisturization, any one or more of CDSN, FLG, FLG2, LOR, KLF4, KRT10, LCE1A, LCE2A, LCE2B, LCE2C, SMPD3, CDH1, ITGB4, IVL, SPINK5, CLDN1, AQP3, BGN, HAS3, TGM1, CLDN7, CERS3, CLDN4, and KRT1 can be used as skin moisturization-related biomarkers.

[0078] When the skin condition to be diagnosed is skin pigmentation, any one or more of CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MLANA, PAX3, SOX10, TFAP2A, TYR, TYRP1, MC1R, F2RL1, and CLDN1 can be used as skin pigmentation-related biomarkers.

[0079] When the skin condition to be diagnosed is skin oil, any one or more of MFAP2, IGF1, HSD11B1, GPAM, CPT1C, AR, MPZL3, AQP3, SREBF2, and HSD17B2 can be used as skin oil-related biomarkers.

[0080] When the skin condition to be diagnosed is flush-sensitive skin, any one or more of COL3A1, TAC1, KLK5, CAMP, MMP9, TRPA1, IL13RA1, HSD3B1, CXCR4, ANGPT2, CXCL2, CXCR5, and PSMB9 can be used as flush-sensitive skin-related biomarkers.

[0081] When the skin condition to be diagnosed is subjectively irritant-sensitive skin, any one or more of IVL, LOR, FLG, FLG2, PGF, CYR61, HLA-B, IGHA1, MMP3, RBP4, and G0S2 can be used as subjectively irritant-sensitive skin-related biomarkers.

[0082] When the skin condition to be diagnosed is cosmetic acne, any one or more of MMP3, MMP12, CCR1, AKR1B10, THY1, and IL-6 can be used as cosmetic acne-related biomarkers.

[0083] In addition, the diagnostic kit for skin conditions according to an embodiment of the present invention may further include a device for extracting RNA from a test object taken from the skin of a subject. The RNA obtained by the RNA extraction device may be provided to the quantification device after undergoing an amplification process.

[0084] Additionally, additional components may be further provided in the minimally invasive skin condition diagnostic kit of the present invention. [Effects of the Invention]

[0085] The unique minimally invasive method for obtaining skin test subjects according to the present invention has revealed the usefulness of biomarkers for diagnosing various skin conditions. Biomarkers useful for diagnosing, assessing, or measuring various skin conditions will be described in detail in the Examples below.

[0086] These biomarkers can be used to measure the mRNA or protein levels of one or more genes. Since it has been confirmed that the levels of these genes or proteins increase or decrease in each subject, measuring these levels can be useful for measuring and evaluating skin conditions.

[0087] According to the present invention, the problems of conventional skin type diagnosis methods using SNP arrays that use the DNA of human test subjects can be resolved. Conventional SNP arrays use the DNA of human test subjects, but DNA, which is determined at birth and remains unchanged, has the disadvantage of being difficult to reflect actual skin conditions, which change due to various environmental factors. Furthermore, according to the present invention, the problems of conventional skin aging diagnosis methods using human-applied instrument measurements, tape stripping, tissue biopsy, and 3D artificial skin ex vivo testing methods can be resolved.

[0088] Conventional human body-applied instrumental measurements are a method of measuring the surface of the skin in a cross-sectional manner, and therefore have the disadvantage that they are difficult to use in studying specific mechanisms or to scientifically explain changes in specific factors.

[0089] Conventional tape stripping has the drawback that it can only be used as a supplementary testing method and cannot provide complete results because the substance attached to the tape is limited to the stratum corneum.

[0090] Conventional tissue biopsies have the drawbacks of requiring skilled techniques from the surgeon, causing discomfort to the patient due to the pain involved, and leaving scars at the biopsy site after the test.

[0091] Ex vivo tests using conventional 3D artificial skin are not only expensive, but also cannot perfectly replace skin biopsies because they do not include structures such as blood vessels, hair follicles, and subcutaneous fat that exist in real skin.

[0092] According to the present invention, a new minimally invasive skin condition measurement kit and a biomarker for measuring skin condition can be provided, which is less painful and leaves no scars compared to existing tissue biopsies, making it more convenient for patients to use, and which can improve the reliability of analysis results compared to existing tape stripping by collecting skin factors inside the stratum corneum.

[0093] By utilizing the kit or biomarker provided by the present invention, it is possible to develop and screen substances and treatments for inducing or suppressing specific skin conditions more efficiently than conventional techniques, and to provide accurate information on each individual's skin type, which can be used to scientifically classify individual skin types and develop customized cosmetics.

[0094] Furthermore, the present invention provides a new method for evaluating efficacy of human application that can replace animal testing in the development of general cosmetics, functional cosmetics, medical devices, and pharmaceuticals for improving various skin conditions. [Brief explanation of the drawings]

[0095] [Figure 1] 1 is a diagram illustrating a prior art skin biopsy method. [Figure 2] 1 is a diagram conceptually illustrating a skin biopsy method according to the present invention. [Figure 3] This is a high-resolution photograph taken in connection with measuring crow's feet wrinkles. [Figure 4] Figures 4 and 5 are diagrams illustrating the microarray procedures introduced in the manual for the GeneChip Human Gene 2.0 ST Array (Affymetrix) platform. [Figure 5] Figures 4 and 5 are diagrams illustrating the microarray procedures introduced in the manual for the GeneChip Human Gene 2.0 ST Array (Affymetrix) platform. [Figure 6]1 is a diagram illustrating the results of gene alignment performed using the R 4.0.0 program. [Figure 7] This table lists selected genes related to collagen and elasticity among the causes of skin aging. [Figure 8] This table lists selected genes related to skin barrier function and moisture synthesis among the causes of skin aging. [Figure 9] 9 to 12 show the results of heatmap analysis for visually comparing the expression levels of candidate skin aging biomarkers selected through RNA microarray analysis. [Figure 10] 9 to 12 show the results of heatmap analysis for visually comparing the expression levels of candidate skin aging biomarkers selected through RNA microarray analysis. [Figure 11] 9 to 12 show the results of heatmap analysis for visually comparing the expression levels of candidate skin aging biomarkers selected through RNA microarray analysis. [Figure 12] 9 to 12 show the results of heatmap analysis for visually comparing the expression levels of candidate skin aging biomarkers selected through RNA microarray analysis. [Figure 13] Figures 13 to 20 show the results of bivariate correlation analysis to confirm the correlation between candidate skin aging biomarkers selected through RNA microarray analysis and the measurement results of human-applied devices (PRIMOS, Cutometer, Corneometer, Tewameter, and Skin-pHmeter). [Figure 14] Figures 13 to 20 show the results of bivariate correlation analysis to confirm the correlation between candidate skin aging biomarkers selected through RNA microarray analysis and the measurement results of human-applied devices (PRIMOS, Cutometer, Corneometer, Tewameter, and Skin-pHmeter). [Figure 15]Figures 13 to 20 show the results of bivariate correlation analysis to confirm the correlation between candidate skin aging biomarkers selected through RNA microarray analysis and the measurement results of human-applied devices (PRIMOS, Cutometer, Corneometer, Tewameter, and Skin-pHmeter). [Figure 16] Figures 13 to 20 show the results of bivariate correlation analysis to confirm the correlation between candidate skin aging biomarkers selected through RNA microarray analysis and the measurement results of human-applied devices (PRIMOS, Cutometer, Corneometer, Tewameter, and Skin-pHmeter). [Figure 17] Figures 13 to 20 show the results of bivariate correlation analysis to confirm the correlation between candidate skin aging biomarkers selected through RNA microarray analysis and the measurement results of human-applied devices (PRIMOS, Cutometer, Corneometer, Tewameter, and Skin-pHmeter). [Figure 18] Figures 13 to 20 show the results of bivariate correlation analysis to confirm the correlation between candidate skin aging biomarkers selected through RNA microarray analysis and the measurement results of human-applied devices (PRIMOS, Cutometer, Corneometer, Tewameter, and Skin-pHmeter). [Figure 19] Figures 13 to 20 show the results of bivariate correlation analysis to confirm the correlation between candidate skin aging biomarkers selected through RNA microarray analysis and the measurement results of human-applied devices (PRIMOS, Cutometer, Corneometer, Tewameter, and Skin-pHmeter). [Figure 20]Figures 13 to 20 show the results of bivariate correlation analysis to confirm the correlation between candidate skin aging biomarkers selected through RNA microarray analysis and the measurement results of human-applied devices (PRIMOS, Cutometer, Corneometer, Tewameter, and Skin-pHmeter). [Figure 21] This is a high-resolution photo taken using PRIMOS equipment. [Figure 22] The figure shows the results of measuring the roughness of wrinkles around the corners of the eyes using PRIMOS equipment. [Figure 23] The results of measuring skin elasticity using Cutometer equipment are shown below. [Figure 24] The results of measuring the water content using a Corneometer are shown in the figure. [Figure 25] 1 is a table showing candidate biomarkers for evaluating the effectiveness of collagen / elasticity-related skin aging. [Figure 26] 1 is a table showing candidate biomarkers for evaluating the effectiveness of skin barrier function / moisture synthesis-related skin aging. [Figure 27] A heatmap showing the expression of candidate collagen / elasticity-related biomarkers for assessing skin aging efficacy is shown, based on the results of RNA microarray analysis of test subjects who underwent skin aging efficacy assessment. [Figure 28] A heatmap showing the expression of candidate biomarkers for assessing skin barrier function / moisture synthesis-related skin aging efficacy is shown. [Figure 29] 29 to 32 show the results of bivariate correlation analysis performed to confirm the correlation between candidate biomarkers for assessing skin aging efficacy selected through RNA microarray analysis and the measurement results using human-applied devices (PRIMOS, Cutometer, and Corneometer). [Figure 30]29 to 32 show the results of bivariate correlation analysis performed to confirm the correlation between candidate biomarkers for assessing skin aging efficacy selected through RNA microarray analysis and the measurement results using human-applied devices (PRIMOS, Cutometer, and Corneometer). [Figure 31] 29 to 32 show the results of bivariate correlation analysis performed to confirm the correlation between candidate biomarkers for assessing skin aging efficacy selected through RNA microarray analysis and the measurement results using human-applied devices (PRIMOS, Cutometer, and Corneometer). [Figure 32] 29 to 32 show the results of bivariate correlation analysis performed to confirm the correlation between candidate biomarkers for assessing skin aging efficacy selected through RNA microarray analysis and the measurement results using human-applied devices (PRIMOS, Cutometer, and Corneometer). [Figure 33] 1 is a diagram illustrating gene alignment results. [Figure 34] This is a table summarizing the gene expression values ​​confirmed as a result of microarray testing of genes identified through literature research as being related to skin moisturizing. [Figure 35] 35 to 38 show the results of heatmap analysis for visually comparing the expression levels of candidate skin moisturizing biomarkers selected through RNA microarray analysis. [Figure 36] 35 to 38 show the results of heatmap analysis for visually comparing the expression levels of candidate skin moisturizing biomarkers selected through RNA microarray analysis. [Figure 37] 35 to 38 show the results of heatmap analysis for visually comparing the expression levels of candidate skin moisturizing biomarkers selected through RNA microarray analysis. [Figure 38]35 to 38 show the results of heatmap analysis for visually comparing the expression levels of candidate skin moisturizing biomarkers selected through RNA microarray analysis. [Figure 39] Figures 39 to 45 show the results of bivariate correlation analysis to confirm the correlation between candidate skin moisture biomarkers selected through RNA microarray analysis and the results of measurements using human-applied devices (Corneometer, Tewameter, and Skin-pHmeter). [Figure 40] Figures 39 to 45 show the results of bivariate correlation analysis to confirm the correlation between candidate skin moisture biomarkers selected through RNA microarray analysis and the results of measurements using human-applied devices (Corneometer, Tewameter, and Skin-pHmeter). [Figure 41] Figures 39 to 45 show the results of bivariate correlation analysis to confirm the correlation between candidate skin moisture biomarkers selected through RNA microarray analysis and the results of measurements using human-applied devices (Corneometer, Tewameter, and Skin-pHmeter). [Figure 42] Figures 39 to 45 show the results of bivariate correlation analysis to confirm the correlation between candidate skin moisture biomarkers selected through RNA microarray analysis and the results of measurements using human-applied devices (Corneometer, Tewameter, and Skin-pHmeter). [Figure 43] Figures 39 to 45 show the results of bivariate correlation analysis to confirm the correlation between candidate skin moisture biomarkers selected through RNA microarray analysis and the results of measurements using human-applied devices (Corneometer, Tewameter, and Skin-pHmeter). [Figure 44]Figures 39 to 45 show the results of bivariate correlation analysis to confirm the correlation between candidate skin moisture biomarkers selected through RNA microarray analysis and the results of measurements using human-applied devices (Corneometer, Tewameter, and Skin-pHmeter). [Figure 45] Figures 39 to 45 show the results of bivariate correlation analysis to confirm the correlation between candidate skin moisture biomarkers selected through RNA microarray analysis and the results of measurements using human-applied devices (Corneometer, Tewameter, and Skin-pHmeter). [Figure 46] The results of the skin moisturizing efficacy evaluation are shown in the figure. [Figure 47] This is a table showing gene expression values ​​of genes selected through RNA microarray analysis to evaluate skin moisturizing efficacy, which showed the same tendency as the skin moisturizing (moisture content / moisture loss / skin pH) biomarkers in Figures 35 to 38. [Figure 48] 1 is a graph for visually comparing and analyzing the expression levels of candidate biomarkers for evaluating skin moisturizing efficacy selected through RNA microarray analysis for evaluating skin moisturizing efficacy. [Figure 49] 1 is a diagram illustrating gene alignment results. [Figure 50] This is a table summarizing gene expression values, which are the results of microarray testing of genes identified through literature research as being related to skin pigmentation. [Figure 51] 51 and 52 show the results of heatmap analysis for visually comparing the expression levels of candidate skin pigmentation biomarkers selected through RNA microarray analysis. [Figure 52] 51 and 52 show the results of heatmap analysis for visually comparing the expression levels of candidate skin pigmentation biomarkers selected through RNA microarray analysis. [Figure 53]Figures 53 to 58 show the results of bivariate correlation analysis to confirm the correlation between the CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MC2R, MLANA, PAX3, SOX10, TFAP2A, TYR, and TYRP1 genes, which are candidate skin pigmentation-related biomarkers selected through RNA microarray analysis, and the measurement results using human-applied devices (Spectrophotometer and Mexameter). [Figure 54] Figures 53 to 58 show the results of bivariate correlation analysis to confirm the correlation between the CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MC2R, MLANA, PAX3, SOX10, TFAP2A, TYR, and TYRP1 genes, which are candidate skin pigmentation-related biomarkers selected through RNA microarray analysis, and the measurement results using human-applied devices (Spectrophotometer and Mexameter). [Figure 55] Figures 53 to 58 show the results of bivariate correlation analysis to confirm the correlation between the CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MC2R, MLANA, PAX3, SOX10, TFAP2A, TYR, and TYRP1 genes, which are candidate skin pigmentation-related biomarkers selected through RNA microarray analysis, and the measurement results using human-applied devices (Spectrophotometer and Mexameter). [Figure 56] Figures 53 to 58 show the results of bivariate correlation analysis to confirm the correlation between the CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MC2R, MLANA, PAX3, SOX10, TFAP2A, TYR, and TYRP1 genes, which are candidate skin pigmentation-related biomarkers selected through RNA microarray analysis, and the measurement results using human-applied devices (Spectrophotometer and Mexameter). [Figure 57]Figures 53 to 58 show the results of bivariate correlation analysis to confirm the correlation between the CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MC2R, MLANA, PAX3, SOX10, TFAP2A, TYR, and TYRP1 genes, which are candidate skin pigmentation-related biomarkers selected through RNA microarray analysis, and the measurement results using human-applied devices (Spectrophotometer and Mexameter). [Figure 58] Figures 53 to 58 show the results of bivariate correlation analysis to confirm the correlation between the CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MC2R, MLANA, PAX3, SOX10, TFAP2A, TYR, and TYRP1 genes, which are candidate skin pigmentation-related biomarkers selected through RNA microarray analysis, and the measurement results using human-applied devices (Spectrophotometer and Mexameter). [Figure 59] Figures 59 and 60 show the results of skin color measurement using a spectrophotometer and melanin measurement at the corners of the eyes using a mexameter, respectively, in the skin whitening efficacy evaluation. [Figure 60] Figures 59 and 60 show the results of skin color measurement using a spectrophotometer and melanin measurement at the corners of the eyes using a mexameter, respectively, in the skin whitening efficacy evaluation. [Figure 61] 1 is a table listing candidate biomarkers for skin whitening efficacy. [Figure 62] 1 shows the results of heatmap analysis for visually comparing the expression levels of candidate biomarkers for evaluating the efficacy of skin whitening selected through RNA microarray analysis for evaluating the efficacy of skin whitening. [Figure 63] 63 and 64 show the results of analyzing the correlation between candidate biomarkers for skin whitening efficacy selected through RNA microarray analysis of test subjects undergoing skin whitening efficacy evaluation and the results of measurements using a human-applied device. [Figure 64] 63 and 64 show the results of analyzing the correlation between candidate biomarkers for skin whitening efficacy selected through RNA microarray analysis of test subjects undergoing skin whitening efficacy evaluation and the results of measurements using a human-applied device. [Figure 65] 1 is a diagram illustrating gene alignment results. [Figure 66] This is a table summarizing gene expression values, which are the results of microarray testing of genes identified through literature research as being related to skin oil. [Figure 67] The results of heatmap analysis were performed to visually compare and analyze the expression levels of candidate skin oil biomarkers selected through RNA microarray analysis. [Figure 68] Figures 68 and 69 show the results of bivariate correlation analysis to confirm the correlation between candidate genes for skin oil-related biomarkers selected through RNA microarray analysis and the results of measurements using a human-applied device (Sebumeter). [Figure 69] Figures 68 and 69 show the results of bivariate correlation analysis to confirm the correlation between candidate genes for skin oil-related biomarkers selected through RNA microarray analysis and the results of measurements using a human-applied device (Sebumeter). [Figure 70] 1 is a diagram illustrating gene alignment results. [Figure 71] This is a table summarizing gene expression values, which are the results of microarray testing of genes identified through literature research as being related to sensitive skin with flushing. [Figure 72] The results of heatmap analysis were performed to visually compare and analyze the expression levels of candidate biomarkers for sensitive flushing skin selected through RNA microarray analysis. [Figure 73]Figures 73 to 75 show the results of bivariate correlation analysis to confirm the correlation between candidate genes for reddening sensitive skin-related biomarkers selected through RNA microarray analysis and the results of measurements using human-applied devices (Mexameter, Spectrophotometer). [Figure 74] Figures 73 to 75 show the results of bivariate correlation analysis to confirm the correlation between candidate genes for reddening sensitive skin-related biomarkers selected through RNA microarray analysis and the results of measurements using human-applied devices (Mexameter, Spectrophotometer). [Figure 75] Figures 73 to 75 show the results of bivariate correlation analysis to confirm the correlation between candidate genes for reddening sensitive skin-related biomarkers selected through RNA microarray analysis and the results of measurements using human-applied devices (Mexameter, Spectrophotometer). [Figure 76] 1 is a diagram illustrating a lactate prick test method. [Figure 77] 1 is a diagram illustrating gene alignment results. [Figure 78] This is a table summarizing gene expression values, which are the results of microarray testing of genes identified through literature research as being related to subjectively irritating sensitive skin. [Figure 79] 79 and 80 show the results of heatmap analysis for visually comparing the expression levels of candidate biomarkers for subjectively sensitive skin irritation selected through RNA microarray analysis. [Figure 80] 79 and 80 show the results of heatmap analysis for visually comparing the expression levels of candidate biomarkers for subjectively sensitive skin irritation selected through RNA microarray analysis. [Figure 81]Figures 81 to 84 show the results of bivariate correlation analysis to confirm the correlation between candidate genes for subjectively sensitive skin-related biomarkers selected through RNA microarray analysis and lactic acid sting test scores. [Figure 82] Figures 81 to 84 show the results of bivariate correlation analysis to confirm the correlation between candidate genes for subjectively sensitive skin-related biomarkers selected through RNA microarray analysis and lactic acid sting test scores. [Figure 83] Figures 81 to 84 show the results of bivariate correlation analysis to confirm the correlation between candidate genes for subjectively sensitive skin-related biomarkers selected through RNA microarray analysis and lactic acid sting test scores. [Figure 84] Figures 81 to 84 show the results of bivariate correlation analysis to confirm the correlation between candidate genes for subjectively sensitive skin-related biomarkers selected through RNA microarray analysis and lactic acid sting test scores. [Figure 85] 1 is a diagram illustrating the results of gene alignment performed using the R 4.0.0 program. [Figure 86] This is a table summarizing the gene expression values ​​confirmed as a result of microarray testing of genes identified as being related to acne through literature research. [Figure 87] A total of 10 test subjects were selected, consisting of 5 test subjects with high cosmetic acne activity based on the physical findings of a dermatologist and 5 control subjects with low cosmetic acne activity and a lactic acid sting test score of 0.5 or less. This is a diagram showing the results of heatmap analysis to visually compare and analyze the expression levels of candidate cosmetic acne biomarkers selected through RNA microarray analysis. [Figure 88] 88 to 94 show the results of an analysis of the correlation between the expression level of each gene and physical findings. [Figure 89] 88 to 94 show the results of an analysis of the correlation between the expression level of each gene and physical findings. [Figure 90] 88 to 94 show the results of an analysis of the correlation between the expression level of each gene and physical findings. [Figure 91] 88 to 94 show the results of an analysis of the correlation between the expression level of each gene and physical findings. [Figure 92] 88 to 94 show the results of an analysis of the correlation between the expression level of each gene and physical findings. [Figure 93] 88 to 94 show the results of an analysis of the correlation between the expression level of each gene and physical findings. [Figure 94] 88 to 94 show the results of an analysis of the correlation between the expression level of each gene and physical findings. [Figure 95] 95 to 98 show the results of analyzing the correlation between the expression level of each gene and the oiliness score evaluated by the naked eye. [Figure 96] 95 to 98 show the results of analyzing the correlation between the expression level of each gene and the oiliness score evaluated by the naked eye. [Figure 97] 95 to 98 show the results of analyzing the correlation between the expression level of each gene and the oiliness score evaluated by the naked eye. [Figure 98] 95 to 98 show the results of analyzing the correlation between the expression level of each gene and the oiliness score evaluated by the naked eye. [Figure 99] 99 to 101 are diagrams showing the results of analyzing the correlation between the expression level of each gene and the measurement results of a human-applied device (Sebumeter). [Figure 100] 99 to 101 are diagrams showing the results of analyzing the correlation between the expression level of each gene and the measurement results of a human-applied device (Sebumeter). [Figure 101] 99 to 101 are diagrams showing the results of analyzing the correlation between the expression level of each gene and the measurement results of a human-applied device (Sebumeter). DETAILED DESCRIPTION OF THE INVENTION

[0096] The following detailed description of the present invention refers to the accompanying drawings, which show, by way of example, specific embodiments in which the present invention may be practiced. These embodiments are described in detail to enable those skilled in the art to fully practice the present invention. It should be understood that the various embodiments of the present invention, although different from one another, are not necessarily mutually exclusive. The following detailed description is not to be taken in a limiting sense, and the scope of the present invention should be understood to encompass the scope of the appended claims and all equivalents thereto.

[0097] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, various preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily practice the present invention.

[0098] FIG. 1 is a diagram illustrating a skin biopsy method according to the prior art, and FIG. 2 is a diagram conceptually illustrating a skin biopsy method according to the present invention.

[0099] FIG. 2 is a diagram conceptually illustrating the skin biopsy method according to the present invention.

[0100] The upper left portion of Figure 2 illustrates a microneedle patch known in the art at the time of the filing of this application. The lower left portion of Figure 2 illustrates such a microneedle patch applied to the skin, more specifically, the microneedles of the patch penetrating into the dermis layer of the skin. The right portion of Figure 2 conceptually illustrates a representative technical concept of the present invention in which a microneedle patch is applied to a subject and then a biopsy is performed using intradermal proteins attached to the microneedles of the patch. The term "bio-mining" used here refers to the extraction of intradermal proteins from a subject for biopsy.

[0101] The biodegradable polymer hyaluronic acid microneedle patch used as a means for collecting skin test subjects in this invention has a solid structure and is manufactured using a droplet extension (DEN) process, but this should not be construed as excluding the use of microneedle patches manufactured using other methods, such as a molding method. Regardless of the manufacturing method, microneedle patches made of the same material and structure are considered to be roughly equivalent in terms of their ability to collect skin test subjects.

[0102] The present inventors conducted a clinical trial to discover biomarkers useful for measuring and evaluating various skin conditions. Clinical trial subjects were selected as follows:

[0103] Clinical trial subject selection The inventors conducted experiments on 33 healthy subjects aged 20 to 70 in order to discover biomarkers associated with each skin condition and develop diagnostic methods and diagnostic kits for each skin condition using these biomarkers. The clinical characteristics of the test subjects are summarized in Table 1 below.

[0104] [Table 1]

[0105] The following subjects were excluded from the study: 1 (circled number 1) subjects who did not voluntarily agree to this study after IRB approval and therefore did not fill out a consent form; 2 (circled number 2) subjects who did not agree to donate human-derived materials; 3 (circled number 3) pregnant or lactating women and women of childbearing potential who did not agree to the contraceptive method specified in the protocol; 4 (circled number 4) subjects who used steroid-containing skin preparations for the treatment of skin diseases for more than one month; 5 (circled number 5) subjects who had used oral steroids, oral antibiotics, or immunosuppressants within the past month; 6 (circled number 6) patients with signs of infection (fungal, bacterial, or viral infection) at the target lesion site; 7 (circled number 7) subjects receiving ultraviolet light therapy; 8 (circled number 8) subjects with serious systemic illnesses; 9 (circled number 9) subjects who could not read and understand the consent form (e.g., illiterate); and 10 (circled number 10) subjects who were otherwise deemed unsuitable for the study by the study director.

[0106] The details of the subsequent clinical trial progress will be described below for each skin condition diagnosed, evaluated, or measured by the present invention.

[0107] <Skin aging> Classification of test subjects according to the degree of skin aging, medical interview with a specialist, evaluation of human-applied equipment, lactic acid sting test and collection of skin test subjects After washing their faces, the test subjects were left in a constant temperature and dehumidified condition for 30 minutes, after which they were given a questionnaire evaluation and interviewed by a dermatologist. Measurements were then taken of wrinkles around the eyes, skin elasticity, moisture content, transepidermal water loss, and skin acidity under normal facial expressions. Specifically, wrinkles around the eyes were measured using PRIMOS CR Skin elasticity was measured using a Cutometer Dual MPA 580, moisture content using a Corneometer, transepidermal water loss using a Tewameter, and skin acidity using a Skin-pHmeter. Regarding the measurement of crow's feet wrinkles, please refer to the high-resolution photograph in Figure 3. Crow's feet wrinkles are measured using the roughness (Ra) value, skin elasticity using the R2 value, moisture content using the AU value, and transepidermal water loss using g / h / m. 2 Skin acidity was quantified by pH value.

[0108] The physical findings of the test subjects' crow's feet were visually assessed by a dermatologist based on the visual evaluation criteria of the revised (draft) Guidelines for Testing Methods for Cosmetic Labeling and Advertising Verification.

[0109] Table 2 shows the results of skin aging evaluation using a human-applied device on five people in their 20s (control group) and five people in their 50s or older (test group) selected from the total test subjects.

[0110] [Table 2]

[0111] Additionally, the microneedle patch was attached to the face for 15 minutes, and then the skin test specimens were collected.

[0112] RNA collection from skin test subjects Skin samples obtained from test subjects were placed in an RNeasy Plus Mini Kit (Qiagen), and high-purity RNA was extracted using the kit. After that, the quality control (QC) of the RNA samples was confirmed using NanoDrop and 2100 Bioanalyzer equipment, followed by RNA microarray analysis.

[0113] RNA microarray analysis The RNA isolated by the above method was subjected to flexible and sensitive pretreatment using the GeneChip WT Pico Kit (Applied Biosystems), which amplifies a small amount (100 pg) of total RNA with cRNA. The amplified RNA was subjected to microarraying according to the GeneChip Human Gene 2.0 ST Array (Affymetrix) platform manual. The amplification process is described in more detail below.

[0114] The isolated RNA was amplified using the GeneChip WT Pico Kit (Applied Biosystems) according to the manufacturer's protocol. First-strand cDNA was synthesized using mRNA as a template, along with a T7 promoter sequence. RNA was simultaneously digested with DNA polymerase and RNase H to synthesize single-stranded cDNA with the 3' adaptor. Second-stranded cDNA, which served as a template, was synthesized using Taq DNA polymerase and an adaptor-specific primer. Antisense RNA (cRNA) was then synthesized and amplified by in vitro transcription (IVT) using T7 RNA polymerase. The resulting antisense cRNA was purified using antisense cRNA purification beads and used as a template for reverse transcription to generate single-stranded cDNA in the sense direction. The sense cDNA was then fragmented and biotin-labeled with TdT (Terminal deoxynucleotidyl transferase) using the GeneChip WT Terminal Labeling Kit (Affymetrix). Approximately 5.5 μg of labeled DNA target was subjected to microarray analysis according to the GeneChip Human 2.0 ST Array (Affymetrix) platform manual.

[0115] Figures 4 and 5 illustrate the microarray procedures introduced in the platform's manual. Table 3 below summarizes the RNA microarray analysis protocol for the platform.

[0116] [Table 3]

[0117] The overall data was then summarized and normalized using the Robust Multi-Average (RMA) method in Affymetrix Power Tools (APT), and results were organized at the gene level for differentially expressed gene (DEG) analysis. Statistical significance of the data was analyzed using independent t-tests and fold change. False discovery rates (FDRs) were adjusted for p-values ​​using the Benjamini-Hochberg algorithm, and hierarchical cluster analysis of DEG sets was performed using linkage and Euclidean distance. All data analysis and visualization to identify differential gene expression were performed using the R 4.0.0 program.

[0118] Screening candidate skin aging biomarkers through bioinformatics analysis and significant correlation analysis After the RNA microarray test was completed, gene alignment was performed using the R 4.0.0 program to identify differences in gene expression between the test and control groups. Figure 6 illustrates the gene alignment results. The gene alignment in Figure 6 is also known as heatmap analysis. Heatmap analysis is a combination of the words "heat" and "map," and is an analytical method that displays a variety of information, which can be expressed in color, as a heat distribution diagram on an image. In the heatmap analysis described herein, red indicates higher gene expression values, and blue indicates lower gene expression values. Figure 6 shows that when several hundred genes were analyzed, specific differences in the expression levels of many genes were observed between the test and control groups.

[0119] The inventors then conducted a literature search to identify genes related to collagen and elasticity among the causes of skin aging (Figure 7), and genes related to barrier function and moisture synthesis among the causes of skin aging (Figure 8).The inventors then compiled the gene expression values ​​confirmed by microarray testing of the genes identified through the literature search as being related to skin aging in the table shown in the figure.

[0120] Gene expression values ​​obtained by microarray analysis between a test group with advanced skin aging and a control group without advanced skin aging must be differentiated to be useful as a biomarker for skin aging. To determine this, the inventors used fold change and p-value. Fold change indicates how many times higher or lower the measured value in the test group is compared to the measured value in the control group, and p-value indicates whether the values ​​between the test and control groups show a statistically significant difference in statistical analysis. The normalized signal values ​​(expression values) for each sample obtained through RNA microarray analysis are shown in the tables of Figures 7 and 8, and fold change is the value calculated as a 1:1 expression difference using the normalized signal values ​​between the test and control groups. The p-value is a value between 0 and 1 that indicates the degree to which data is consistent with the null hypothesis; a value less than approximately 0.05 indicates that the null hypothesis is rejected.

[0121] As a result, among the collagen / elasticity-related skin aging genes, genes with a fold change of 1.2-fold or -1.2-fold or greater or a p-value of 0.05 or less were identified as COL3A1, FN1, GSTA3, and PINK1, while among the skin barrier function / moisture synthesis-related skin aging genes with a fold change of 1.2-fold or -1.2-fold or greater or a p-value of 0.05 or less were identified as IVL, HAS3, AQP3, CERS6, CLDN1, SLC9A1, TGM1, SPINK5, KLF4, BGN, LCE1A, LCE1B, LCE1F, and LCE2A.

[0122] Figures 9 to 12 are heatmaps used to visually compare and analyze the expression levels of candidate skin aging biomarkers selected through the RNA microarray analysis described above. Figure 9 is a heatmap showing the expression of candidate skin aging biomarkers related to elasticity / collagen based on the results of RNA microarray analysis of 20 test subjects (5 subjects in their 50s or older test group and 5 subjects in their 20s control group), while Figure 10 is a heatmap showing the expression of candidate skin aging biomarkers related to elasticity / collagen based on the results of RNA microarray analysis of 20 test subjects (5 subjects in each age group: 20s, 30s, 40s, and 50s or older). Figure 11 is a heatmap showing the expression of candidate skin aging biomarkers related to skin barrier function / moisture synthesis based on the results of RNA microarray analysis of 20 test subjects (5 subjects in each age group: 20s, 30s, 40s, and 50s or older).

[0123] As in Figure 6, the heatmap analysis in Figures 9 to 12 also shows that red indicates higher gene expression values, and blue indicates lower gene expression values. From the experimental results in Figures 9 to 12, it was visually confirmed that the expression of specific genes selected through literature research was higher in the test group than in the control group, as indicated by red.

[0124] Bivariate correlation analysis was performed to confirm the correlation between the candidate skin aging biomarkers selected through the RNA microarray analysis and the results of measurements using human-applied devices (PRIMOS, Cutometer, Corneometer, Tewameter, and Skin-pHmeter). The results are shown in Figures 13 to 20.

[0125] Figures 13 and 14 show the correlation between candidate collagen / elasticity-related skin aging biomarkers selected through RNA microarray analysis of a total of 20 test subjects (5 subjects in their 50s or older in the test group and 5 subjects in their 20s in the control group), and age and the results of measurements using a human-applied device. Figures 15 and 16 show the correlation between candidate collagen / elasticity-related skin aging biomarkers selected through RNA microarray analysis of a total of 20 test subjects (5 subjects in each age group: 20s, 30s, 40s, and 50s or older in their ..., respectively).

[0126] As a result, in the RNA microarray analysis of a total of 10 test subjects (5 test subjects over 50 years old and 5 control subjects in their 20s), the FN1, GSTA3, and PINK1 genes were confirmed to show a statistically significant negative correlation with the test subjects' age, the COL1A1, COL3A1, FN1, GSTA3, PON1, and PINK1 genes were confirmed to show a statistically significant negative correlation with the results of measurements using the PRIMOS device to measure crow's feet wrinkles, and the PINK1 gene was confirmed to show a statistically significant positive correlation with the results of measurements using the Cutometer device to measure skin elasticity. Although the names of each gene are omitted in Figure 14, the above description of Figure 14 is given starting from the top left of Figure 14. That is, the results for the FN1, GSTA3, and PINK1 genes, which showed a statistically significant negative correlation with the test subject's age, are shown in the first column of Figure 14, starting from the left; the results for the COL1A1, COL3A1, FN1, GSTA3, PON1, and PINK1 genes, which showed a statistically significant negative correlation with the measurement results for crow's feet using the PRIMOS device, which measured wrinkles around the eyes, are shown in the middle column of Figure 14, starting from the left; and the PINK1 gene, which showed a statistically significant positive correlation with the measurement results for skin elasticity using the Cutometer device, is shown in the last column of Figure 14. This also applies to scatter plots showing the correlation between multiple genes where the gene names are omitted from the graph, as in Figure 14. That is, the correlations will be described starting from the top left of the scatter plot.

[0127] In addition, RNA microarray analysis of 20 test subjects (5 subjects in each age group: 20s, 30s, 40s, and 50s and older) confirmed that the COL1A1, FN1, GSTA3, and PINK1 genes showed a statistically significant negative correlation with the test subjects' age. The FN1, GSTA3, and PINK1 genes also showed a statistically significant negative correlation with the results of measurements using the PRRMOS device, which measures crow's feet, and the GSTA3 and PINK1 genes showed a positive correlation with the results of measurements using the Cutometer device, which measures skin elasticity.

[0128] Therefore, when the test subjects' RNA microarray analysis results and the human-applied instrument measurement results were comprehensively analyzed, a total of six genes, COL1A1, COL3A1, FN1, GSTA3, PON1, and PINK1, were selected as collagen / elasticity-related skin aging biomarkers.

[0129] Figures 17 and 18 show the correlation between candidate skin barrier function / moisture content-related skin aging biomarkers selected through RNA microarray analysis of a total of 20 test subjects (5 subjects in their 50s or older in the test group and 5 subjects in their 20s in the control group), and age and the results of measurements using a human-applied device. Figures 19 and 20 show the correlation between candidate skin barrier function / moisture content-related skin aging biomarkers selected through RNA microarray analysis of a total of 20 test subjects (5 subjects in each age group: 20s, 30s, 40s, and 50s or older).

[0130] As a result, RNA microarray analysis of 10 test subjects (5 in their 50s or older in the test group and 5 in their 20s in the control group) confirmed that the IVL, CERS6, SPINK5, and KLF4 genes showed a significant negative correlation with the test subjects' age, the IVL, HAS2, and HAS3 genes showed a statistically significant negative correlation with the results of measurements using the PRIMOS device to measure crow's feet wrinkles, and the IVL, TGM1, SPINK5, and KLF4 genes showed a statistically significant positive correlation with the results of measurements using the Cutometer device to measure skin elasticity.In addition, the KLF4 gene showed a positive correlation with the results of measurements using the Corneometer device to measure moisture content.

[0131] In addition, RNA microarray analysis of 20 test subjects (five subjects per age group: 20s, 30s, 40s, and 50s and older) confirmed that the IVL, CERS6, and TGM1 genes showed a significant negative correlation with the test subjects' age. The BGN gene showed a statistically significant negative correlation with measurements using the PRIMOS device, which measures crow's feet, and the IVL, TGM1, SPINK5, and KLF4 genes showed a statistically significant positive correlation with measurements using the Cutometer device, which measures skin elasticity. Additionally, the CERS6 and KLF4 genes showed a statistically significant positive correlation with measurements using the Corneometer device, which measures moisture content, and the KLF4 gene showed a statistically significant negative correlation with measurements using the Tewameter device, which measures transepidermal water loss.

[0132] Therefore, after comprehensively analyzing the results of the RNA microarray analysis of the test subjects and the results of measurements using human-applied devices, a total of 15 genes were selected as skin barrier function / moisture synthesis-related skin aging biomarkers: IVL, HAS2, HAS3, AQP3, CERS6, CLDN1, SLC9A1, TGM1, SPINK5, KLF4, LCE1A, LCE1B, LCE1F, LCE2A, and BGN.

[0133] Identification of collagen / elasticity and skin barrier function / moisture synthesis-related skin aging biomarkers through skin aging efficacy evaluation The selected collagen / elasticity and skin barrier function / moisture synthesis-related skin aging biomarkers were used to measure and diagnose the degree of skin aging, and a skin aging efficacy evaluation was conducted to screen for effective ingredients or anti-aging agents related to actual skin aging, evaluate skin aging before and after the use of general cosmetics, functional cosmetics, medical devices, pharmaceuticals, etc., and to verify whether they can be used as an alternative to animal testing.

[0134] Specifically, two test subjects aged 60 or older were asked to apply tretinoin cream (Stiva AR Cream, 0.025%) to the corners of their eyes for four weeks. At week 0 and week 4, high-resolution photographs were taken using PRIMOS equipment (Figure 21) and the roughness of wrinkles at the corners of the eyes was measured (Table 4, Figure 22). Skin elasticity was measured using Cutometer equipment (Table 5, Figure 23). Moisture content was measured using Corneometer equipment (Table 6, Figure 24). A microneedle patch was attached to the corners of the eyes for 15 minutes, and the skin test subjects were then harvested, RNA extracted, and RNA microarray was performed.

[0135] [Table 4]

[0136] [Table 5]

[0137] [Table 6]

[0138] To confirm gene expression differences due to the efficacy of skin aging evaluation after the RNA microarray test, gene alignment was performed using the R 4.0.0 program. Genes showing the same trends were selected from the collagen / elasticity-related genes selected through literature research and the collagen / elasticity-related skin aging biomarkers selected above (COL1A1, COL3A1, FN1, GSTA3, PON1, and PINK1) and listed in the table of Figure 25. Genes showing the same patterns were selected from the skin barrier function / moisture synthesis-related genes selected through literature research and the skin barrier function / moisture synthesis-related skin aging biomarkers selected above (IVL, HAS2, HAS3, AQP3, CERS6, CLDN1, SLC9A1, TGM1, SPINK5, KLF4, LCE1A, LCE1B, LCE1F, LCE2A, and BGN) and listed in the table of Figure 26.

[0139] Among the collagen / elasticity-related skin aging genes listed in the table of FIG. 25, the gene with a fold change of 1.2-fold or -1.2-fold or more or a p-value of 0.05 or less was identified as GSTA3, and among the skin barrier function / moisture synthesis-related skin aging genes listed in the table of FIG. 26, the genes with a fold change of 1.2-fold or -1.2-fold or more or a p-value of 0.05 or less were identified as LEC1A, LCE1F, LCE2A, LOR, AQP3, AQP10, BGN, HAS2, and HAS3.

[0140] Figures 27 and 28 are diagrams illustrating the results of heatmap analysis performed to visually compare and analyze the expression levels of candidate biomarkers for assessing skin aging efficacy selected through the RNA microarray analysis for assessing skin aging efficacy. Figure 27 is a heatmap showing the expression of candidate collagen / elasticity-related biomarkers for assessing skin aging efficacy using the results of RNA microarray analysis of test subjects who underwent skin aging efficacy assessment, and Figure 28 is a heatmap showing the expression of candidate skin barrier function / moisture synthesis-related biomarkers for assessing skin aging efficacy.

[0141] The inventors performed bivariate correlation analysis to confirm the correlation between candidate biomarkers for assessing skin aging efficacy selected through the RNA microarray analysis for assessing skin aging efficacy and the results of measurements using human-applied instruments (PRIMOS, Cutometer, and Corneometer). The results are shown in Figures 29 to 32. Figures 29 and 30 show the correlation between candidate collagen / elasticity-related biomarkers for assessing skin aging efficacy selected through RNA microarray analysis of test subjects who underwent skin aging efficacy assessment and the results of measurements using human-applied instruments. As a result, after 4 weeks of tretinoin cream application compared to week 0, the collagen / elasticity-related skin aging biomarkers COL4A4, FN1, and PINK1 genes were found to have a statistically significant negative correlation with the results of measurements using PRIMOS, which measured crow's feet wrinkles, and the COL4A4 and MMP8 genes were found to have a positive or negative correlation with the results of measurements using Corneometer, which measured moisture content. Therefore, when the skin aging and skin aging efficacy assessments were comprehensively analyzed, the collagen / elasticity-related biomarkers for assessing skin aging efficacy were selected as a total of eight genes, including the skin aging biomarkers (COL1A1, COL3A1, FN1, GSTA3, PON1, and PINK1), COL4A4, and MMP8. Furthermore, from the perspective of accurate and effective diagnosis of skin conditions, the inventor(s) discovered that it is advantageous to include COL1A1, COL3A1 FN1, and PINK1 among these genes in the biomarker group.

[0142] Figures 31 and 32 show the correlation between candidate biomarkers for skin barrier function / moisture synthesis-related skin aging efficacy, selected through RNA microarray analysis of test subjects undergoing skin aging efficacy evaluation, and the results of measurements using human-applied devices. As a result, after 4 weeks of tretinoin cream application compared to week 0, the AQP3 and HAS3 genes, which are skin barrier function / moisture synthesis-related skin aging biomarkers, showed a statistically significant negative correlation with the PRIMOS measurement results, which measured crow's feet wrinkles. The LCE1A, AZGP1, and BGN genes showed a positive correlation with the Cutometer measurement results, which measured skin elasticity, and the LCE1B, HAS2, and HAS3 genes showed a positive correlation with the Corneometer measurement results, which measured moisture content.

[0143] Therefore, when the skin aging and skin aging efficacy assessments were comprehensively analyzed, biomarkers for assessing skin barrier function / moisture synthesis-related skin aging efficacy were selected as the skin aging biomarkers (IVL, HAS2, HAS3, AQP3, CERS6, CLDN1, SLC9A1, TGM1, SPINK5, KLF4, LCE1A, LCE1B, LCE1F, LCE2A, and BGN) and AZGP1, a total of 16 genes. Furthermore, from the perspective of accurate and effective diagnosis of skin conditions, the inventor(s) discovered that it is advantageous to include IVL, HAS3, AQP3, BGN, and AZGP1 among these genes in the biomarker group.

[0144] In summary, the collagen / elasticity-related skin aging biomarkers were selected as COL1A1, COL3A1, FN1, GSTA3, PON1, PINK1, COL4A4, and MMP8, while the skin barrier function / moisture synthesis-related skin aging biomarkers were selected as IVL, HAS2, HAS3, AQP3, CERS6, CLDN1, SLC9A1, TGM1, SPINK5, KLF4, LCE1A, LCE1B, LCE1F, LCE2A, BGN, and AZGP1. These biomarkers can be used for skin aging diagnostic methods or kits; development and screening of skin aging inducers / inhibitors and anti-aging agents; provision of individual skin type information; development of customized cosmetics; and evaluation of the effectiveness of animal-based skin aging substitutes.

[0145] <Skin moisturizing> Classification of test subjects according to skin moisture (moisture content / moisture loss / skin pH), medical interview with a specialist, evaluation of the device applied to the human body, and collection of skin test specimens After washing their faces, the test subjects were left in a constant temperature and dehumidified condition for 30 minutes, after which they were given a questionnaire and interviewed by a dermatologist. The test subjects also had their moisture content, transepidermal water loss, and skin pH measured on both cheeks. Specifically, moisture content was measured using a Corneometer CM825, transepidermal water loss using a Tewameter TM300, and skin pH using a Skin-pHmeter PH905. Moisture content was measured in arbitrary units (AU), and transepidermal water loss was measured in g / h / m. 2 The pH value was analyzed, and the skin pH value was analyzed.

[0146] The physical findings regarding skin moisturization of the test subjects were based on relevant literature (Int J Dermatol. 2017 Yoshida-Amano et al.) and were evaluated by visual inspection by a dermatologist.

[0147] Table 7 shows the results of evaluating the skin moisturizing level of all test subjects, divided into a control group of 5 subjects who were not dry and a test group of 5 subjects who were dry, based on the results of the measurements using the three human-applied devices.

[0148] [Table 7]

[0149] Additionally, the microneedle patch was attached to the face for 15 minutes, and then the skin test specimens were collected.

[0150] RNA collection from skin test subjects Skin samples obtained from test subjects were placed in an RNeasy Plus Mini Kit (Qiagen), and high-purity RNA was extracted using the kit. After that, the quality control (QC) of the RNA samples was confirmed using NanoDrop and 2100 Bioanalyzer equipment, followed by RNA microarray analysis.

[0151] RNA microarray analysis The RNA isolated by the above method was subjected to flexible and sensitive pretreatment using the GeneChip WT Pico Kit (Applied Biosystems), which amplifies small amounts (100 pg) of total RNA with cRNA. The RNA amplified by the above method was subjected to microarray analysis according to the GeneChip Human Gene 2.0 ST Array (Affymetrix) platform manual. The microarray procedure described in the platform manual is illustrated in Figures 4 and 5. The inventors performed microarray analysis using the same procedure. The RNA microarray analysis protocol for this platform is the same as that summarized in Table 3.

[0152] The overall data was then summarized and normalized using the Robust Multi-Average (RMA) method in Affymetrix Power Tools (APT), and results were organized at the gene level for differentially expressed gene (DEG) analysis. Statistical significance of the data was analyzed using independent t-tests and fold change. False discovery rates (FDRs) were adjusted for p-values ​​using the Benjamini-Hochberg algorithm, and hierarchical cluster analysis of DEG sets was performed using linkage and Euclidean distance. All data analysis and visualization to identify differential gene expression were performed using the R 4.0.0 program.

[0153] Screening of candidate biomarkers for skin moisture (water content / water loss / skin pH) through bioinformatics analysis and significant correlation analysis After the RNA microarray experiment, gene alignment was performed using the R 4.0.0 program to identify differences in gene expression between the test and control groups. Figure 33 illustrates the gene alignment results. The gene alignment in Figure 33 is also called heatmap analysis. Heatmap analysis is a combination of the words "heat" and "map," and is an analytical method that displays a variety of information, which can be expressed in color, as a heat distribution diagram on an image. In the heatmap analysis described herein, red indicates higher gene expression values, and blue indicates lower gene expression values. Figure 33 shows that when several hundred genes were analyzed, specific differences in the expression levels of many genes were observed between the test and control groups.

[0154] The inventors then conducted a literature search to select genes related to skin moisturization (moisture content / moisture loss / skin pH) (Figure 34). The inventors then compiled the gene expression values ​​confirmed as a result of microarray testing of genes identified as related to skin moisturization through the literature search in the table shown in the figure.

[0155] The gene expression values ​​obtained by microarray analysis between the control group, which had good skin moisture levels, and the test group, which had poor skin moisture levels, must be differentiated to be useful as biomarkers for skin moisture levels. To determine this, the inventors used fold change and p-value. Fold change indicates how many times higher or lower the measured value in the test group is compared to the measured value in the control group, and p-value indicates whether the values ​​between the test and control groups show a statistically significant difference in statistical analysis. The normalized signal values ​​(expression values) for each sample obtained through RNA microarray analysis are shown in the table in Figure 34, and fold change is the value calculated by calculating the 1:1 expression difference using the normalized signal values ​​between the test and control groups. The p-value is a value between 0 and 1 that indicates the degree to which the data is consistent with the null hypothesis, and a value less than approximately 0.05 indicates that the null hypothesis is rejected.

[0156] As a result, among the genes related to skin moisturization (moisture content / moisture loss / skin pH), the genes with a fold change of 1.2-fold or -1.2-fold or greater or a p-value of 0.05 or less were identified as CDSN, FLG, FLG2, LOR, KLF4, KRT10, LCE1A, LCE2A, LCE2B, LCE2C, SMPD3, CDH1, ITGB4, IVL, SPINK5, CLDN1, AQP3, BGN, HAS3, TGM1, CLDN7, CERS3, CLDN4, and KRT1.

[0157] Figures 35 to 38 are diagrams showing the results of heatmap analysis to visually compare and analyze the expression levels of candidate skin moisturizing biomarkers selected through the RNA microarray analysis described above. Figure 35 is a heatmap showing the expression of candidate skin moisturizing biomarkers after analyzing a total of 10 subjects, including five subjects in the test group with dry skin and five subjects in the control group without dry skin. Figure 36 is a heatmap showing the expression of candidate skin moisturizing biomarkers after analyzing a total of 15 subjects after dividing the moisture content results into sections based solely on Corneometer measurement results. Figure 37 is a heatmap showing the expression of candidate skin moisturizing biomarkers after analyzing a total of 15 subjects after dividing the transepidermal water loss results into sections based solely on Tewameter measurement results. Figure 38 is a heatmap showing the expression of candidate skin moisturizing biomarkers after analyzing a total of 13 subjects after dividing the skin pH results into sections based solely on Skin-pHmeter measurement results.

[0158] As in Figure 33, in the heatmap analysis of Figures 35 to 38, red indicates higher gene expression values, and blue indicates lower gene expression values. From the experimental results of Figures 35 to 38, it was visually confirmed that the expression of specific genes selected through literature research was lower in the dry test group compared to the control group with good skin moisture, as indicated by blue.

[0159] Bivariate correlation analysis was performed to confirm the correlation between the candidate skin moisture biomarkers selected through the RNA microarray analysis and the results of measurements using human-applied devices (Corneometer, Tewameter, and Skin-pHmeter). The results are shown in Figures 39 to 43.

[0160] Figure 39 is a table showing the correlation analysis values ​​between candidate genes for skin moisture biomarkers and human-applied devices. Figure 40 is a table summarizing the analysis values ​​in Figure 39. Figure 41 shows a scatter plot of genes with a significant correlation with Corneometer measurements. From this, it was confirmed that the FLG, FLG2, LOR, KLF4, CDH1, ITGB4, SPINK5, TGM1, CLDN7, and CLDN4 genes showed a statistically significant positive correlation with moisture content (Corneometer). Figure 42 shows a scatter plot of genes with a significant correlation with Tewameter measurements. From this, it was confirmed that the FLG, FLG2, LOR, KLF4, KRT10, LCE1A, CDH1, ITGB4, SPINK5, CLDN1, BGN, TGM1, and CLDN7 genes showed a statistically significant negative correlation with transepidermal water loss (Tewameter). Figure 43 shows a scatter plot of genes with a significant correlation with Skin-pHmeter measurements. From this, it was confirmed that the CDSN, KLF4, CDH1, CLDN1, AQP3, and CLDN7 genes showed a statistically significant negative correlation with skin pH (Skin-pHmeter). Figures 44 and 45 are diagrams analyzing the correlation between candidate genes for skin moisturizing biomarkers and each human-applied device (Corneometer, Tewameter, Skin-pHmeter) based on the results displayed in Figures 36, 37, and 38.As a result, the SMPD3, CDSN, KLF4, FLG, KRT10, CDH1, FLG2, ITGB4, IVL, SPINK5, CLDN1, AQP3, TGM1, CLDN7, CLDN4, and KRT1 genes showed a statistically significant positive correlation with water content (Corneometer). 1. The AQP3, BGN, TGM1, CLDN7, CERS3, and CLDN4 genes showed a statistically significant negative correlation with transepidermal water loss (Tewameter), and the SMPD3, CDSN, KLF4, LOR, FLG, KRT10, LCE2B, LCE1A, CDH1, FLG2, SPINK5, CLDN1, AQP3, TGM1, and KRT1 genes showed a statistically significant negative correlation with skin pH (Skin-pHmeter).

[0161] Validation of skin moisture (water content / water loss / skin pH) biomarkers and selection of candidate biomarkers The skin moisturizing efficacy evaluation was conducted to verify whether the skin moisturizing (moisture content / moisture loss / skin pH) biomarkers selected using the above method can be used to evaluate the level of skin moisturizing (moisture content / moisture loss / skin pH), screen for effective ingredients or moisturizers related to actual skin moisturizing (moisture content / moisture loss / skin pH), evaluate the skin moisturizing (moisture content / moisture loss / skin pH) condition before and after use of general cosmetics, functional cosmetics, medical devices, pharmaceuticals, etc., and ultimately to replace animal testing.

[0162] Specifically, one test subject (test group) with dry skin who had not used a skin moisturizer for more than a month applied Zelloid Intensive Cream MD to the face for two weeks. At weeks 0 and 2, moisture content, transepidermal water loss, and skin pH were measured. A microneedle patch was attached to the measurement site for 15 minutes, and the skin specimen was harvested. RNA was extracted and subjected to RNA microarray. Compared to week 0, the test subject's skin dryness was alleviated after two weeks of Zelloid Intensive Cream MD application. Specifically, moisture content increased, transepidermal water loss decreased, and skin pH decreased. The specific measurement results are summarized in Tables 8 to 10 and Figure 46 below.

[0163] [Table 8]

[0164] [Table 9]

[0165] [Table 10]

[0166] After the RNA microarray test was completed, gene alignment was performed using the R 4.0.0 program to confirm differences in gene expression due to the evaluation of skin moisturizing efficacy. Genes showing the same tendency were selected from the selected skin moisturizing (moisture content / moisture loss / skin pH) biomarkers and classified into the table in Figure 47.

[0167] Among the skin moisturizing biomarker genes shown in the table of Figure 47, 10 genes were identified as having a fold change of 1.2-fold or -1.2-fold or more: CDSN, FLG, FLG2, LOR, KRT10, LCE1A, LCE2A, LCE2B, LCE2C, and SMPD3.

[0168] FIG. 48 is a graph for visually comparing and analyzing the expression levels of candidate biomarkers for evaluating the effectiveness of skin moisturizing selected through the RNA microarray analysis for evaluating the effectiveness of skin moisturizing.

[0169] Finally, the biomarkers for skin moisture (water content / water loss / skin pH) were selected as CDSN, FLG, FLG2, LOR, KLF4, KRT10, LCE1A, LCE2A, LCE2B, LCE2C, SMPD3, CDH1, ITGB4, IVL, SPINK5, CLDN1, AQP3, BGN, HAS3, TGM1, CLDN7, CERS3, CLDN4, and KRT1. Furthermore, from the viewpoint of accurate and effective diagnosis of skin conditions, the present inventors have found that it is advantageous to include FLG, AQP3, and LOR among these genes in the biomarker group.

[0170] The above research results of the inventors are expected to provide very useful tools for the development of methods or kits for evaluating the level of skin moisture, the development and screening of substances that induce or inhibit skin moisture, the provision of skin type information to individuals, the development of customized cosmetics, and the evaluation of the effectiveness of skin moisture as an alternative to animal testing.

[0171] <Skin pigmentation> Classification of test subjects based on skin pigmentation, medical interview with a specialist, evaluation of human-applied devices, and collection of skin test specimens After washing their faces, the test subjects were left in a constant temperature and dehumidified environment for 30 minutes, and then a questionnaire evaluation and a dermatologist interview were conducted. Skin color and melanin were measured using human-applied equipment. Specifically, skin color was measured using a spectrophotometer and melanin was measured using a melanometer. Skin color was analyzed using the ITA° value, and melanin was analyzed using the melanin index value.

[0172] The physical findings of the test subjects' skin color were visually assessed by a dermatologist based on the Fitzpatrick scale.

[0173] Table 11 summarizes the criteria for dividing the test subjects into test and control groups. The test group consisted of all test subjects who were Fitzpatrick type V with an ITA° value of 28 or less and had dark skin. The control group consisted of all test subjects who were Fitzpatrick type II with an ITA° value of 41 or more and had light skin. After selecting the test and control groups, skin pigmentation was evaluated using a human-applied device.

[0174] [Table 11]

[0175] Additionally, the microneedle patch was attached to the face for 15 minutes, and then the skin test specimens were collected.

[0176] RNA collection from skin test subjects Skin samples obtained from test subjects were placed in an RNeasy Plus Mini Kit (Qiagen), and high-purity RNA was extracted using the kit. After that, the quality control (QC) of the RNA samples was confirmed using NanoDrop and 2100 Bioanalyzer equipment, followed by RNA microarray analysis.

[0177] RNA microarray analysis The RNA isolated by the above method was subjected to flexible and sensitive sample pretreatment using the GeneChip WT Pico Kit (Applied Biosystems), which amplifies small amounts (100 pg) of total RNA with cRNA. The RNA amplified by the above method was subjected to microarray analysis according to the GeneChip Human Gene 2.0 ST Array (Affymetrix) platform manual. The microarray procedure described in the platform manual is illustrated in Figures 4 and 5. The inventors performed microarray analysis using the same procedure. The RNA microarray analysis protocol for this platform is the same as that summarized in Table 3.

[0178] The overall data was then summarized and normalized using the Robust Multi-Average (RMA) method in Affymetrix Power Tools (APT), and results were organized at the gene level for differentially expressed gene (DEG) analysis. Statistical significance of the data was analyzed using independent t-tests and fold change. False discovery rates (FDRs) were adjusted for p-values ​​using the Benjamini-Hochberg algorithm, and hierarchical cluster analysis of DEG sets was performed using linkage and Euclidean distance. All data analysis and visualization to identify differential gene expression were performed using the R 4.0.0 program.

[0179] Screening candidate skin pigmentation biomarkers through bioinformatics analysis and significant correlation analysis After the RNA microarray experiment, gene alignment was performed using the R 4.0.0 program to identify differences in gene expression between the test and control groups. Figure 49 illustrates the gene alignment results. The gene alignment in Figure 49 is also called heatmap analysis. Heatmap analysis is a combination of the words "heat" and "map," and is an analytical method that displays a variety of information, which can be expressed in color, as a heat distribution diagram on an image. In the heatmap analysis described herein, red indicates higher gene expression values, and blue indicates lower gene expression values. Figure 49 shows that when several hundred genes were analyzed, specific differences in the expression levels of many genes were found between the test and control groups.

[0180] The present inventors then conducted a literature search to identify genes related to skin pigmentation and melanogenesis (Figure 50). The present inventors then performed a microarray test on the genes identified through the literature search as being related to skin pigmentation. The resulting gene expression values ​​are summarized in the table of Figure 50.

[0181] Gene expression values ​​obtained by microarray analysis between a test group with severe skin pigmentation and a control group with less severe skin pigmentation must be differentiated to be useful as a biomarker for the degree of skin pigmentation. To determine this, the inventors used fold change and p-value. Fold change indicates how many times higher or lower the measured value in the test group is compared to the measured value in the control group, and p-value indicates whether the values ​​between the test and control groups show a statistically significant difference in statistical analysis. The normalized signal values ​​(expression values) for each sample obtained through RNA microarray analysis are shown in the table in Figure 50, and fold change is the value calculated by calculating the 1:1 expression difference using the normalized signal values ​​between the test and control groups. The p-value is a value between 0 and 1 that indicates the degree to which the data is compatible with the null hypothesis, and a value less than approximately 0.05 indicates that the null hypothesis is rejected.

[0182] As a result, the genes related to skin pigmentation that showed a fold change of 1.2-fold or -1.2-fold or more or a p-value of 0.05 or less were identified as CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MC2R, MLANA, PAX3, SOX10, TFAP2A, TYR, and TYRP1.

[0183] Figures 51 and 52 show the results of heatmap analysis conducted to visually compare the expression levels of candidate skin pigmentation biomarkers selected through the RNA microarray analysis described above. Figure 51 shows the results of heatmap analysis for a total of 10 subjects: five in the test group with an ITA° value of 28 or less and five in the control group with an ITA° value of 41 or more. Figure 52 shows a heatmap showing the expression levels of candidate skin pigmentation biomarkers using the results of RNA microarray analysis after selecting a total of 19 test subjects: five with an ITA° value of 28 or less, seven with an ITA° value of 29-40, and seven with an ITA° value of 41 or more.

[0184] As in Figure 49, the heatmap analysis in Figures 51 and 52 also shows that red indicates higher gene expression values, and blue indicates lower gene expression values. From the experimental results in Figures 51 and 52, it was visually confirmed that the expression of specific genes selected through literature research was higher in the test group than in the control group, as indicated by red.

[0185] Bivariate correlation analysis was performed to confirm the correlation between the skin pigmentation-related biomarker candidates CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MC2R, MLANA, PAX3, SOX10, TFAP2A, TYR, and TYRP1 genes selected through the RNA microarray analysis and the results of measurements using human-applied devices (Spectrophotometer and Mexameter). The results are shown in Figures 53 to 58.

[0186] Figures 53 to 55 show the results of analyzing the correlation between candidate skin pigmentation biomarkers selected through RNA microarray analysis and the results of measurement using a human-applied device after selecting a total of 10 test subjects: five in the test group with an ITA° value of 28 or less and five in the control group with an ITA° value of 41 or more. Figures 56 to 58 show the results of analyzing the correlation between candidate skin pigmentation biomarkers selected through RNA microarray analysis and the results of measurement using a human-applied device after selecting a total of 19 test subjects: five with an ITA° value of 28 or less, seven with an ITA° value of 29 to 40, and seven with an ITA° value of 41 or more.

[0187] As a result, RNA microarray analysis of a total of 10 test subjects (5 test subjects with ITA° values ​​of 28 or less and 5 control subjects with ITA° values ​​of 41 or more) confirmed that the CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MLANA, SOX10, TFAP2A, and TYR genes showed a statistically significant positive or negative correlation with the results of measurements using a Spectrophotometer that measured skin color, and that the CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MLANA, PAX3, and SOX10 genes showed a statistically significant negative or positive correlation with the results of measurements using a Mexameter that measured melanin in the corners of the eyes.

[0188] In addition, RNA microarray analysis of a total of 19 test subjects (5 subjects with ITA° below 28, 7 subjects with ITA° between 29 and 40, and 7 subjects with ITA° above 41) confirmed that the CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MLANA, PAX3, SOX10, and TYR genes showed a statistically significant positive or negative correlation with the results of measurements using a Spectrophotometer that measured skin color, and that the CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MLANA, PAX3, SOX10, and TYRP1 genes showed a statistically significant negative or positive correlation with the results of measurements using a Mexameter that measured melanin in the corners of the eyes.

[0189] Therefore, after comprehensively analyzing the results of the RNA microarray analysis of the test subjects and the results of the human-applied instrument measurements, a total of 13 genes were selected as skin pigmentation biomarkers: CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MLANA, PAX3, SOX10, TFAP2A, TYR, and TYRP1.

[0190] Selection of skin pigmentation biomarkers through skin whitening efficacy evaluation The skin pigmentation biomarkers selected in the above-mentioned manner were used to measure and diagnose the degree of skin pigmentation, and also to screen for effective ingredients or whitening agents related to actual skin pigmentation or whitening, evaluate skin pigmentation or whitening before and after the use of general cosmetics, functional cosmetics, medical devices, pharmaceuticals, etc., and furthermore, to verify whether they can be used as an alternative to animal testing.

[0191] Specifically, two test subjects with symptoms in the corners of the eyes were asked to apply Trilstrum Cream (Kolmar, Korea) to the corners of the eyes for two weeks. At week 0 and week 2, skin color was measured using a spectrophotometer (Table 12, Figure 59), and melanin in the corners of the eyes was measured using a mexameter (Table 13, Figure 60). After applying a microneedle patch to the corners of the eyes for 15 minutes, the skin test subjects were obtained, and RNA was extracted for RNA microarray.

[0192] [Table 12]

[0193] [Table 13]

[0194] After the RNA microarray test was completed, gene alignment was performed using the R 4.0.0 program to confirm differences in gene expression due to the evaluation of skin whitening efficacy. Genes showing the same trends were selected and classified from the skin pigmentation- or melanogenesis-related genes selected through literature review and the skin pigmentation-related biomarkers selected using the method described above (CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MLANA, PAX3, SOX10, TFAP2A, TYR, and TYRP1). Figure 61 shows the classification results in a table listing candidate biomarkers for skin whitening efficacy.

[0195] Among the skin pigmentation or melanogenesis-related genes selected through literature research, the genes with a fold change of 1.2-fold or -1.2-fold or greater or a p-value of 0.05 or less were identified as MC1R, F2RL1, CLDN1, DSG1, and GSTP1.

[0196] FIG. 62 illustrates the results of heatmap analysis for visually comparing and analyzing the expression levels of candidate biomarkers for evaluating the skin whitening efficacy selected through the RNA microarray analysis for evaluating the skin whitening efficacy.

[0197] The inventors performed bivariate correlation analysis to confirm the correlation between candidate biomarkers for skin whitening efficacy evaluation selected through the aforementioned RNA microarray analysis for skin whitening efficacy evaluation and the results of measurements using human-applied instruments (Spectrophotometer and Mexameter). Figures 63 and 64 show the results of an analysis of the correlation between candidate biomarkers for skin whitening efficacy selected through RNA microarray analysis and the results of measurements using human-applied instruments in test subjects who underwent a skin whitening efficacy evaluation. The experimental results in Figures 63 and 64 show that after applying Trilstola Cream (a combination of tretinoin, hydroquinone, and a steroid) for two weeks, compared to week 0, the TFAP2A and TYRP1 genes showed a statistically significant negative correlation with the results of measurements using a Spectrophotometer, which measured skin color. Additionally, the TFAP2A gene showed a positive correlation with the results of measurements using a Mexameter, which measured melanin in the corners of the eyes.

[0198] When the inventors comprehensively analyzed the results of the skin pigmentation and skin whitening efficacy evaluation experiments described above, a total of 16 genes, including CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MLANA, PAX3, SOX10, TFAP2A, TYR, TYRP1, MC1R, F2RL1, and CLDN1, were selected as the broadest range of skin pigmentation biomarkers. Furthermore, the inventors discovered that, from the perspective of accurate and effective diagnosis of skin conditions, it is advantageous to include MLANA, TYRP1, and GPNMB among these genes in the biomarker group.

[0199] By utilizing these selection results, it will be possible to quantitatively evaluate the level of skin pigmentation or skin whitening using one or more of the 16 RNA genes as biomarkers.Furthermore, the inventors' research results are expected to provide a very useful tool for developing a method or evaluation kit for evaluating the level of skin pigmentation, developing and screening substances that induce or inhibit skin pigmentation, providing individual skin type information, developing customized cosmetics, and evaluating the effectiveness of skin pigmentation as an alternative to animal testing.

[0200] <Skin oil> Classification of test subjects based on skin oil content, medical interview with a specialist, evaluation of human-applied equipment, and collection of skin test subjects After washing their faces, the subjects were left in a constant temperature and dehumidified condition for 30 minutes, after which they were given a questionnaire and interviewed by a dermatologist. The oil content of the skin on both nostrils was measured. Specifically, the Sebumeter SM815 was used to measure the oil content, and the results were expressed as μg / cm. 2 The values ​​were analyzed.

[0201] The physical findings of the test subjects' skin oil content were provided by visual assessment by a dermatologist based on relevant literature (Reprod Biol Endocrinol. 2017 Homburg et al.).

[0202] Table 14 summarizes the results of the skin oil evaluation, dividing the total test subjects into five control subjects with low skin oiliness and five test subjects with high skin oiliness.

[0203] [Table 14]

[0204] Additionally, the microneedle patch was attached to the face for 15 minutes, and then the skin test specimens were collected.

[0205] RNA collection from skin test subjects Skin samples obtained from test subjects were placed in an RNeasy Plus Mini Kit (Qiagen), and high-purity RNA was extracted using the kit. After that, the quality control (QC) of the RNA samples was confirmed using NanoDrop and 2100 Bioanalyzer equipment, followed by RNA microarray analysis.

[0206] RNA microarray analysis The RNA isolated using the above method was subjected to flexible and sensitive sample pretreatment using the GeneChip WT Pico Kit (Applied Biosystems), which amplifies small amounts (100 pg) of total RNA using cRNA. The RNA amplified using the above method was subjected to microarray analysis according to the GeneChip Human Gene 2.0 ST Array (Affymetrix) platform manual. The microarray procedure described in the platform manual is illustrated in Figures 4 and 5. The inventors performed microarray analysis using the same procedure. The RNA microarray analysis protocol for this platform is the same as that summarized in Table 3.

[0207] The overall data was then summarized and normalized using the Robust Multi-Average (RMA) method in Affymetrix Power Tools (APT), and results were organized at the gene level for differentially expressed gene (DEG) analysis. Statistical significance of the data was analyzed using independent t-tests and fold change. False discovery rates (FDRs) were adjusted for p-values ​​using the Benjamini-Hochberg algorithm, and hierarchical cluster analysis of DEG sets was performed using linkage and Euclidean distance. All data analysis and visualization to identify differential gene expression were performed using the R 4.0.0 program.

[0208] Screening candidate skin oil biomarkers through bioinformatics analysis and significant correlation analysis After the RNA microarray experiment, gene alignment was performed using the R 4.0.0 program to identify differences in gene expression between the test and control groups. Figure 65 illustrates the gene alignment results. The gene alignment in Figure 65 is also called heatmap analysis. Heatmap analysis is a combination of the words "heat" and "map," and is an analytical method that displays a variety of information, expressed in color, as a heat distribution diagram on an image. In the heatmap analysis described herein, red indicates higher gene expression values, while blue indicates lower gene expression values. Figure 65 shows that when several hundred genes were analyzed, specific differences in the expression levels of many genes were observed between the test and control groups.

[0209] The present inventors then conducted a literature search to identify genes related to skin oil (Figure 66). The present inventors then conducted a microarray test on the genes identified through the literature search as being related to skin oil. The resulting gene expression values ​​are summarized in the table of Figure 66.

[0210] Gene expression values ​​obtained by microarray analysis between a test group with high skin oiliness and a control group with low skin oiliness must be differentiated to be useful as a biomarker for skin oiliness. To determine this, the inventors used fold change and p-value. Fold change indicates how many times higher or lower the measured value in the test group is compared to the measured value in the control group, and p-value indicates whether the values ​​between the test and control groups show a statistically significant difference in statistical analysis. The normalized signal values ​​(expression values) for each sample obtained through RNA microarray analysis are shown in the table in Figure 66, and fold change is the value calculated by calculating the 1:1 expression difference using the normalized signal values ​​between the test and control groups. The p-value is a value between 0 and 1 that indicates the degree to which the data is compatible with the null hypothesis, and a value less than approximately 0.05 indicates that the null hypothesis is rejected.

[0211] As a result, the genes related to skin oil content that showed a fold change of 1.2 or -1.2 times or more or a p-value of 0.05 or less were identified as MFAP2, IGF1, HSD11B1, GPAM, CPT1C, AR, MPZL3, AQP3, SREBF2, and HSD17B2.

[0212] Figure 67 shows the results of heatmap analysis conducted to visually compare and analyze the expression levels of candidate skin oil biomarkers selected through the RNA microarray analysis described above. As with Figure 65, in the heatmap analysis results of Figure 67, red indicates a higher gene expression value, and blue indicates a lower gene expression value. The results of Figure 67 visually confirmed that the expression of specific genes selected through literature research in the test group was shown to be higher (red) or lower (blue) compared to the control group.

[0213] Bivariate correlation analysis was performed to confirm the correlation between the MFAP2, IGF1, HSD11B1, GPAM, CPT1C, AR, MPZL3, AQP3, SREBF2, and HSD17B2 genes, which are candidate skin oil-related biomarkers selected through the RNA microarray analysis, and the results of measurements using a human-applied device (Sebumeter). The results are shown in Figures 68 and 69.

[0214] As a result of the bivariate correlation analysis experiments related to Figures 68 and 69, the inventors confirmed that the MFAP2, GPAM, and IGF1 genes showed a statistically significant positive correlation with skin oil content (measured by Sebumeter), and that the MPZL3 and AQP3 genes showed a statistically significant negative correlation with skin oil content (measured by Sebumeter).

[0215] When the results of the skin oil evaluation experiments conducted by the present inventors as described above were comprehensively analyzed, a total of 10 genes, namely MFAP2, AQP3, SREBF2, HSD17B2, GPAM, CPT1C, AR, IGF1, HSD11B1, and MPZL3, could be selected as the broadest range of skin oil biomarkers. Furthermore, from the perspective of accurate and effective diagnosis of skin conditions, the present inventors have discovered that it is advantageous to include MPZL3, GPAM, and IGF1 among these genes in the biomarker group.

[0216] By utilizing these selection results, it will be possible to quantitatively evaluate the level of skin oiliness using one or more of the 10 RNA genes as biomarkers.Furthermore, the inventors' research results are expected to provide a very useful tool for developing skin oiliness evaluation methods or evaluation kits, developing and screening skin oiliness inducers or inhibitors, providing individual skin type information, developing customized cosmetics, and evaluating the effectiveness of skin oiliness as an alternative to animal testing.

[0217] <Flushing sensitive skin> Classification of test subjects based on flushing sensitive skin, medical interview with specialists, evaluation of human-applied devices, lactic acid sting test and collection of skin test subjects After washing their faces, the test subjects were left in a constant temperature and dehumidified environment for 30 minutes, after which they were given a questionnaire evaluation and interviewed by a dermatologist. Both cheeks were measured using a human-applied device, and subjective irritation sensitivity was assessed using a lactic acid sting test.

[0218] Specifically, the amount of erythema was measured using the Mexameter MX18 equipment and the EI (Erythema Index) value was analyzed. In addition, the skin redness was measured using the Spectrophotometer (CM-700d) equipment and a * The values ​​were analyzed. Subjective sensitivity to irritation was also assessed using the 5% lactic acid sting test according to the Frosch & Kligman method (1977). 50 μl of 5% lactic acid and DW were each dispensed into the nasolabial folds on both sides, and the test subjects were asked to directly evaluate the intensity of tingling, burning, and itching at 0, 2.5, 5, and 8 minutes using a 4-point scale (0: no sting, 1: slight sting, 2: moderate sting, 3: severe sting). The higher the numerical value for the intensity of irritation, the greater the sensitivity. In this test, the difference between 5% lactic acid and DW was analyzed and calculated using the following formula:

[0219] Score = (total lactic acid value - total DW value) / 12 (4 sections * 3 items) Table 15 summarizes the results of classifying the total test subjects into five control subjects with low erythema and redness and no subjective sensitivity to irritation, and five test subjects with high erythema and redness and no subjective sensitivity to irritation.

[0220] [Table 15]

[0221] Additionally, the microstructure patch was applied to the face for 15 minutes before skin test specimens were taken.

[0222] RNA collection from skin test subjects Skin samples obtained from test subjects were placed in an RNeasy Plus Mini Kit (Qiagen), and high-purity RNA was extracted using the kit. After that, the quality control (QC) of the RNA samples was confirmed using NanoDrop and 2100 Bioanalyzer equipment, followed by RNA microarray analysis.

[0223] RNA microarray analysis The RNA isolated using the above method was subjected to flexible and sensitive sample pretreatment using the GeneChip WT Pico Kit (Applied Biosystems), which amplifies small amounts (100 pg) of total RNA using cRNA. The RNA amplified using the above method was subjected to microarray analysis according to the GeneChip Human Gene 2.0 ST Array (Affymetrix) platform manual. The microarray procedure described in the platform manual is illustrated in Figures 4 and 5. The inventors performed microarray analysis using the same procedure. The RNA microarray analysis protocol for this platform is the same as that summarized in Table 3.

[0224] The overall data was then summarized and normalized using the Robust Multi-Average (RMA) method in Affymetrix Power Tools (APT), and results were organized at the gene level for differentially expressed gene (DEG) analysis. Statistical significance of the data was analyzed using independent t-tests and fold change. False discovery rates (FDRs) were adjusted for p-values ​​using the Benjamini-Hochberg algorithm, and hierarchical cluster analysis of DEG sets was performed using linkage and Euclidean distance. All data analysis and visualization to identify differential gene expression were performed using the R 4.0.0 program.

[0225] Screening candidate biomarkers for sensitive flushing skin through bioinformatics analysis and significant correlation analysis After the RNA microarray test was completed, gene alignment was performed using the R 4.0.0 program to identify differences in gene expression between the test and control groups. Figure 70 illustrates the gene alignment results. The gene alignment in Figure 70 is also called heatmap analysis. Heatmap analysis is a combination of the words "heat" and "map," and is an analytical method that displays a variety of information, which can be expressed in color, as a heat distribution diagram on an image. In the heatmap analysis described herein, red indicates higher gene expression values, and blue indicates lower gene expression values. Figure 70 shows that when several hundred genes were analyzed, specific differences in the expression levels of many genes were observed between the test and control groups.

[0226] The present inventors then conducted a literature search to identify genes associated with sensitive redness-prone skin (Figure 71). The present inventors then conducted a microarray test on the genes identified through the literature search as being associated with sensitive redness-prone skin. The resulting gene expression values ​​are summarized in the table of Figure 71.

[0227] Gene expression values ​​obtained by microarray analysis between the test group with high flushing sensitivity and the control group without high flushing sensitivity must be differentiated to be useful as biomarkers for flushing-sensitive skin. To determine this, the inventors used fold change and p-value. Fold change indicates how many times higher or lower the measured value in the test group is compared to the measured value in the control group, and p-value indicates whether the values ​​between the test and control groups show a statistically significant difference in statistical analysis. The normalized signal values ​​(expression values) for each sample obtained through RNA microarray analysis are shown in the table in Figure 71, and fold change is the value calculated by calculating the 1:1 expression difference using the normalized signal values ​​between the test and control groups. The p-value is a value between 0 and 1 that indicates the degree to which the data is compatible with the null hypothesis, and a value less than approximately 0.05 indicates that the null hypothesis is rejected.

[0228] As a result, genes related to flushing-sensitive skin with a fold change of 1.2-fold or -1.2-fold or greater, or a p-value of 0.05 or less, were identified as COL3A1, TAC1, KLK5, CAMP, MMP9, TRPA1, IL13RA1, HSD3B1, CXCR4, ANGPT2, CXCL2, CXCR5, and PSMB9.

[0229] Figure 72 shows the results of heatmap analysis conducted to visually compare the expression levels of candidate biomarkers for flush-sensitive skin selected through the RNA microarray analysis described above. As in Figure 70, in the heatmap analysis results in Figure 72, red indicates a higher gene expression value, and blue indicates a lower gene expression value.

[0230] Bivariate correlation analysis was performed to confirm the correlation between the candidate biomarkers related to flushing-sensitive skin, selected through the RNA microarray analysis, including COL3A1, TAC1, KLK5, CAMP, MMP9, TRPA1, IL13RA1, HSD3B1, CXCR4, ANGPT2, CXCL2, CXCR5, and PSMB9, and the results of measurements using human-applied devices (Mexameter, Spectrophotometer). The results are shown in Figures 73 to 75.

[0231] FIG. 73 is a table summarizing correlation values ​​between candidate genes for sensitive flushing skin biomarkers and devices applied to the human body.

[0232] Figure 74 shows a scatter plot of genes that have a significant correlation with Mexameter measurements. As can be seen from the results in Figure 74, the TAC1, CAMP, and MMP9 genes were confirmed to show a statistically significant positive correlation with the amount of skin erythema (Mexameter).

[0233] Figure 75 illustrates a scatter plot of genes that have significant correlations with Spectrophotometer measurements. As can be seen from the results in Figure 75, TAC1, MMP9, and ANGPT2 genes correlate significantly with skin redness (Spectrophotometer, a * It was confirmed that there was a statistically significant positive correlation with the saturation value.

[0234] When the inventors comprehensively analyzed the results of the flush-sensitive skin evaluation experiments conducted as described above, a total of 13 genes, including COL3A1, TAC1, KLK5, CAMP, MMP9, TRPA1, IL13RA1, HSD3B1, CXCR4, ANGPT2, CXCL2, CXCR5, and PSMB9, could be selected as the broadest range of flush-sensitive skin biomarkers. Furthermore, from the perspective of accurate and effective skin condition diagnosis, the inventors discovered that it is advantageous to include MMP9, TAC1, and CAMP among these genes in the biomarker group.

[0235] By utilizing these selection results, it will be possible to quantitatively evaluate flush-sensitive skin using one or more of the 13 RNA genes as biomarkers.Furthermore, the inventors' research results are expected to provide very useful tools for developing methods or kits for evaluating flush-sensitive skin, developing and screening substances that induce or inhibit flush-sensitive skin, providing individual skin type information, developing customized cosmetics, and evaluating the effectiveness of flush-sensitive skin as an alternative to animal testing.

[0236] <Irritant sensitive skin> Classification of test subjects based on subjective irritation and sensitive skin, medical interview with a specialist, lactic acid sting test and collection of skin test specimens After washing their faces, the test subjects were left in a constant temperature and dehumidified condition for 30 minutes, after which they were given a questionnaire evaluation and interviewed by a dermatologist. They were also given a subjective evaluation of the degree of irritation and sensitivity of their skin using lactic acid.

[0237] Specifically, sensitivity was assessed using a 5% lactic acid sting test according to the Frosch & Kligman method (1977). Figure 76 illustrates the lactic acid sting test. Fifty microliters each of 5% lactic acid and DW were dispensed into the nasolabial folds on both sides. At 0, 2.5, 5, and 8 minutes, the subject was asked to directly evaluate the intensity of tingling, burning, and itching on a 4-point scale (0: no sting, 1: slight sting, 2: moderate sting, 3: severe sting). A higher numerical value for the intensity of irritation indicates greater sensitivity. In this test, the difference between 5% lactic acid and DW was analyzed and calculated using the following formula:

[0238] Score = (total lactic acid value - total DW value) / 12 (4 sections * 3 items) Table 16 summarizes the results of selecting test subjects (test group) with subjectively sensitive skin to irritation, who had a lactic acid sting test score of 0.5 or higher, from among all test subjects, and test subjects (control group) with no subjectively sensitive skin to irritation, who had a score of 0.

[0239] [Table 16]

[0240] Additionally, the microneedle patch was attached to the face for 15 minutes, and then the skin test specimens were collected.

[0241] RNA collection from skin test subjects Skin samples obtained from test subjects were placed in an RNeasy Plus Mini Kit (Qiagen), and high-purity RNA was extracted using the kit. After that, the quality control (QC) of the RNA samples was confirmed using NanoDrop and 2100 Bioanalyzer equipment, followed by RNA microarray analysis.

[0242] RNA microarray analysis The RNA isolated using the above method was subjected to flexible and sensitive sample pretreatment using the GeneChip WT Pico Kit (Applied Biosystems), which amplifies small amounts (100 pg) of total RNA using cRNA. The RNA amplified using the above method was subjected to microarray analysis according to the GeneChip Human Gene 2.0 ST Array (Affymetrix) platform manual. The microarray procedure described in the platform manual is illustrated in Figures 4 and 5. The inventors performed microarray analysis using the same procedure. The RNA microarray analysis protocol for this platform is the same as that summarized in Table 3.

[0243] The overall data was then summarized and normalized using the Robust Multi-Average (RMA) method in Affymetrix Power Tools (APT), and results were organized at the gene level for differentially expressed gene (DEG) analysis. Statistical significance of the data was analyzed using independent t-tests and fold change. False discovery rates (FDRs) were adjusted for p-values ​​using the Benjamini-Hochberg algorithm, and hierarchical cluster analysis of DEG sets was performed using linkage and Euclidean distance. All data analysis and visualization to identify differential gene expression were performed using the R 4.0.0 program.

[0244] Screening candidate biomarkers for subjective irritation-sensitive skin through bioinformatics analysis and significant correlation analysis After the RNA microarray experiment, gene alignment was performed using the R 4.0.0 program to identify differences in gene expression between the test and control groups. Figure 77 illustrates the gene alignment results. The gene alignment in Figure 77 is also called heatmap analysis. Heatmap analysis is a combination of the words "heat" and "map," and is an analytical method that displays a variety of information, which can be expressed in color, as a heat distribution diagram on an image. In the heatmap analysis described herein, red indicates higher gene expression values, and blue indicates lower gene expression values. Figure 77 shows that when several hundred genes were analyzed, specific differences in the expression levels of many genes were observed between the test and control groups.

[0245] The present inventors then conducted a literature search to identify genes associated with subjectively sensitive skin (Figure 78). The present inventors then conducted a microarray test on the genes identified through the literature search as being associated with subjectively sensitive skin. The resulting gene expression values ​​are summarized in the table of Figure 78.

[0246] Gene expression values ​​obtained by microarray analysis between the test group with a high level of subjectively sensitive skin and the control group with a low level of subjectively sensitive skin must be differentiated to be useful as biomarkers for subjectively sensitive skin. To determine this, the inventors used fold change and p-value. Fold change indicates how many times higher or lower the measured value in the test group is compared to the measured value in the control group, and p-value indicates whether the values ​​between the test and control groups show a statistically significant difference in statistical analysis. The normalized signal values ​​(expression values) for each sample obtained through RNA microarray analysis are shown in the table in Figure 8, and fold change is the value calculated by calculating the 1:1 expression difference using the normalized signal values ​​between the test and control groups. The p-value is a value between 0 and 1 that indicates the degree to which the data is consistent with the null hypothesis, and a value less than approximately 0.05 indicates that the null hypothesis is rejected.

[0247] As a result, genes related to subjective irritant sensitive skin with a fold change of 1.2 or -1.2 times or more or a p-value of 0.05 or less were identified as IVL, LOR, FLG, FLG2, PGF, CYR61, HLA-B, IGHA1, MMP3, RBP4, and G0S2.

[0248] Figures 79 and 80 show heatmap analyses conducted to visually compare and analyze the expression levels of candidate subjectively sensitive skin biomarkers selected through the RNA microarray analysis described above. Figure 79 is a heatmap showing the expression of candidate subjectively sensitive skin biomarkers using the results of RNA microarray analysis after selecting a total of 10 test subjects: five test subjects with a lactic acid sting test score of 0.5 or higher and five control subjects with a score of 0. Figure 80 is a heatmap showing the expression of candidate subjectively sensitive skin biomarkers using the average RNA microarray values ​​for each score after classifying all 33 test subjects by lactic acid sting test score. As with Figure 77, in the heatmap analysis results of Figures 79 and 80, red indicates higher gene expression levels, and blue indicates lower gene expression levels.

[0249] Bivariate correlation analysis was performed to confirm the correlation between the IVL, LOR, FLG, FLG2, PGF, CYR61, HLA-B, IGHA1, MMP3, RBP4, and GOS2 genes, which were candidates for subjectively sensitive skin-related biomarkers selected through the RNA microarray analysis, and the respective lactic acid sting test scores. The results are shown in Figures 81 to 84.

[0250] As a result, the CYR61, RBP4, and PGF genes in the RNA microarray analysis of 10 test subjects (5 subjects in the test group with subjective irritation who scored 0.5 or higher in the lactic acid sting test and 5 subjects in the control group with no subjective irritation who scored 0) showed a statistically significant positive or negative correlation with the lactic acid sting test score, while the LOR, PTGS2, PGF, and HLA-B genes in the RNA microarray analysis of 33 test subjects showed a statistically significant negative or positive correlation with the lactic acid sting test score. Therefore, by comprehensively analyzing the test subjects' RNA microarray analysis results and the lactic acid sting test, the inventors selected a total of 11 genes, including IVL, LOR, FLG, FLG2, PGF, CYR61, HLA-B, IGHA1, MMP3, RBP4, and G0S2, as subjective irritation-sensitive skin biomarkers. Furthermore, from the viewpoint of accurate and effective diagnosis of skin conditions, the present inventors have discovered that it is advantageous to include PGF, RBP4 and CYR61 of these genes in the biomarker group.

[0251] By utilizing these selection results, it will be possible to quantitatively evaluate the degree of subjective irritation-sensitive skin using one or more of the 11 RNA genes as biomarkers.Furthermore, the inventors' research results are expected to provide a very useful tool for the development of subjective irritation-sensitive skin evaluation methods or evaluation kits, the development and screening of substances that induce or suppress subjective irritation-sensitive skin, the provision of individual skin type information, the development of customized cosmetics, and the evaluation of subjective irritation-sensitive skin efficacy as an alternative to animal testing.

[0252] <Cosmetic acne> Classification of test subjects according to the severity of cosmetic acne, medical interview with a specialist, evaluation of human-applied devices, lactic acid sting test and collection of skin test subjects After washing their faces, the test subjects were left in a constant temperature and dehumidified condition for 30 minutes, after which they were given a questionnaire evaluation and interviewed by a dermatologist. The amount of skin oil was measured using a human-applied device, and the skin irritation sensitivity was evaluated through a lactic acid sting test.

[0253] Specifically, the amount of skin oil was measured using Sebumeter SM815 equipment at both nostrils and measured in μg / cm 2 The values ​​were analyzed. Sensitivity was assessed using the 5% lactic acid sting test according to the Frosch & Kligman method (1977). 50 μl of each of 5% lactic acid and DW was dispensed into the nasolabial folds on both sides, and the test subjects were asked to directly evaluate the intensity of tingling, burning, and itching at 0, 2.5, 5, and 8 minutes using a 4-point scale (0: no sting, 1: slight sting, 2: moderate sting, 3: severe sting). The higher the numerical value for the intensity of irritation, the more sensitive the sensitivity. In this test, the difference between 5% lactic acid and DW was analyzed and calculated using the following formula:

[0254] Score = (total lactic acid value - total DW value) / 12 (4 sections * 3 items) The physical findings of the test subjects regarding cosmetic acne were based on the relevant literature (Korean Acne Severity System, Journal of the Korean Society of Dermatology: Vol. 24, No. 10, 2004), and the physical findings regarding skin oiliness were based on the relevant literature (Reprod Biol Endocrinol. 2017 Homburg et al.), and were provided by dermatologists after visual assessment.

[0255] From all test subjects, test subjects with cosmetic acne (test group) and test subjects without cosmetic acne and with a lactic acid sting test score of 0.5 or less (control group) were selected through expert visual assessment. Specific details regarding the test group and control group are summarized in Table 17 below.

[0256] [Table 17]

[0257] Additionally, the microneedle patch was attached to the face for 15 minutes, and then the skin test specimens were collected.

[0258] RNA collection from skin test subjects Skin samples obtained from test subjects were placed in an RNeasy Plus Mini Kit (Qiagen), and high-purity RNA was extracted using the kit. After that, the quality control (QC) of the RNA samples was confirmed using NanoDrop and 2100 Bioanalyzer equipment, followed by RNA microarray analysis.

[0259] RNA microarray analysis The RNA isolated using the above method was subjected to flexible and sensitive sample pretreatment using the GeneChip WT Pico Kit (Applied Biosystems), which amplifies small amounts (100 pg) of total RNA using cRNA. The RNA amplified using the above method was subjected to microarray analysis according to the GeneChip Human Gene 2.0 ST Array (Affymetrix) platform manual. The microarray procedure described in the platform manual is illustrated in Figures 4 and 5. The inventors performed microarray analysis using the same procedure. The RNA microarray analysis protocol for this platform is the same as that summarized in Table 3.

[0260] The overall data was then summarized and normalized using the Robust Multi-Average (RMA) method in Affymetrix Power Tools (APT), and results were organized at the gene level for differentially expressed gene (DEG) analysis. Statistical significance of the data was analyzed using independent t-tests and fold change. False discovery rates (FDRs) were adjusted for p-values ​​using the Benjamini-Hochberg algorithm, and hierarchical cluster analysis of DEG sets was performed using linkage and Euclidean distance. All data analysis and visualization to identify differential gene expression were performed using the R 4.0.0 program.

[0261] Screening candidate acne biomarkers through bioinformatics analysis and significant correlation analysis After the RNA microarray experiment, gene alignment was performed using the R 4.0.0 program to identify differences in gene expression between the test and control groups. Figure 85 illustrates the gene alignment results. The gene alignment in Figure 85 is also called heatmap analysis. Heatmap analysis is a combination of the words "heat" and "map," and is an analytical method that displays a variety of information, which can be expressed in color, as a heat distribution diagram on an image. In the heatmap analysis described herein, red indicates higher gene expression values, and blue indicates lower gene expression values. Figure 85 shows that when several hundred genes were analyzed, specific differences in the expression levels of many genes were observed between the test and control groups.

[0262] The present inventors then conducted a literature search to identify genes related to cosmetic acne. The present inventors compiled the gene expression values ​​confirmed as a result of microarray testing of genes identified as related to cosmetic acne through the literature search in the table of Figure 86.

[0263] Gene expression values ​​obtained by microarray analysis between a test group with high cosmetic acne activity and a control group with low cosmetic acne activity must be differentially differentiated in order to be useful as biomarkers for predicting cosmetic acne. To determine this, the inventors used fold change and p-value. Fold change is a value calculated using the normalized signal values ​​between the test and control groups to determine the 1:1 expression difference, indicating how many times higher or lower the measured value in the test group is compared to the measured value in the control group. p-value is a value between 0 and 1 that expresses the degree to which data is compatible with the null hypothesis; a value less than approximately 0.05 indicates that the null hypothesis is rejected. In statistical analysis, it indicates whether the values ​​between the test and control groups show a statistically significant difference.

[0264] As a result, among the cosmetic acne-related genes shown in the table of Figure 86, genes with a fold change of 1.2-fold or -1.2-fold or more or a p-value of 0.05 or less were identified as MMP3, MMP12, CCR1, AKR1B10, THY1, and IL-6.

[0265] Figure 87 shows the results of a heatmap analysis conducted to visually compare and analyze the expression levels of candidate cosmetic acne biomarkers selected through the RNA microarray analysis results. A total of 10 test subjects were selected: five test subjects with high cosmetic acne activity based on the physical findings of a dermatologist, and five control subjects with low cosmetic acne activity and a lactic acid sting test score of 0.5 or less. As with Figure 84, the heatmap analysis in Figure 87 indicates that red indicates higher gene expression levels, and blue indicates lower gene expression levels. The experimental results in Figure 87 visually confirmed that the expression of specific genes selected through literature research was higher in the test group than in the control group, as indicated by red.

[0266] Bivariate correlation analysis was performed to confirm the correlation between the MMP3, MMP12, CCR1, AKR1B10, THY1, and IL-6 genes, which were selected as candidate cosmetic acne biomarkers through the RNA microarray analysis, and the presence / absence of acne and the macroscopic assessment of oiliness by dermatologists' physical examination, as well as the Sebumeter. The results are shown in Figures 88 to 101.

[0267] The inventors analyzed the correlation between candidate cosmetic acne biomarkers selected through RNA microarray analysis of a total of 10 test subjects, including five test subjects with cosmetic acne confirmed through physical examinations and five control subjects with no cosmetic acne and a lactic acid sting test score of 0.5 or less, and the scores based on the presence or absence of acne (present: +1 point, absent: 0 point), oiliness scores based on visual evaluation (0-5 points), and measurements using a human-applied device (Sebumeter).

[0268] As a result, it was confirmed that the MMP3, MMP12, CCR1, THY1, and IL6 genes showed a statistically significant positive correlation with the physical findings of whether or not cosmetic acne was present (see Figures 88 to 94). Figures 88 and 89 are tables showing the results of the correlation analysis with physical findings. Figures 90 to 94 are scatter plots of the five genes showing the significant positive correlation.

[0269] Furthermore, it was confirmed that the MMP12 and CCR1 genes showed a statistically significant positive correlation with the oily skin score determined by visual assessment (see Figures 95 to 98). Figures 95 and 96 are tables showing the results of the correlation analysis with the oily skin score determined by visual assessment. Figures 97 and 98 are scatter plots of the two genes showing the significant positive correlation.

[0270] We also confirmed that the CCR1 gene exhibits a statistically significant positive correlation with the results of measurements using a human-applied device (Sebumeter) (see Figures 99 to 101). Figures 99 and 100 are tables showing the results of the correlation analysis with the results of measurements using a human-applied device (Sebumeter). Figure 101 is a scatter plot of one gene that exhibits the significant positive correlation.

[0271] When the results of the cosmetic acne evaluation experiments conducted by the present inventors were comprehensively analyzed as described above, a total of six genes, MMP3, MMP12, CCR1, AKR1B10, THY1, and IL-6, could be selected as the broadest range of cosmetic acne biomarkers. Furthermore, from the perspective of accurate and effective skin condition diagnosis, the present inventors discovered that it is advantageous to include MMP3, MMP12, and CCR1 among these genes in the biomarker group.

[0272] By utilizing these selection results, it will be possible to quantitatively predict cosmetic acne using one or more of the six RNA genes as biomarkers.Furthermore, the inventors' research results are expected to provide a very useful tool for developing a method or kit for predicting cosmetic acne, developing and screening cosmetic acne inducers or inhibitors and treatments, providing individual skin type information, developing customized cosmetics, and evaluating the effectiveness of cosmetic acne treatments as an alternative to animal testing.

[0273] In the above examples, we have described a method for quantifying the expression level of each RNA gene by extracting RNA from a skin test specimen extracted from a subject's skin using a microneedle and then providing the extracted RNA to an RNA microarray quantitative analysis device. However, this invention should not be construed as being limited to such a specific quantification method. It should be understood that any method capable of quantifying the expression level of RNA using a subject's skin test specimen falls within the scope of the present invention. For example, a protein level measurement method such as an ELISA method may be applied. The expression level of each RNA gene may be quantified by identifying the type of protein produced by the expression of the corresponding RNA biomarker and quantifying the protein. That is, the quantification device or kit used in carrying out the present invention may be an ELISA, RT-PCR kit, RNA chip, DNA chip, or protein chip kit. The protein level measurement may be performed using one or more methods selected from the group consisting of ELISA, Western blotting, magnetic bead-antibody immunoprecipitation, immunohistochemistry, and mass spectrometry. Furthermore, the mRNA level may be measured by one or more methods selected from RT-PCR, competitive RT-PCR, real-time RT-PCR, RNase Protection Assay (RPA), Northern blotting, and DNA chip.

[0274] The present invention is characterized by the use of specific RNA biomarkers in a non-invasive manner using microneedles to measure and evaluate various skin conditions, and while both RNA-level and protein-level testing methods can be used to detect specific RNA biomarkers, the use of RNA-level testing methods offers the following advantages over protein-level testing methods: The advantage of RNA-level testing methods can be summarized as being that by applying a technology to amplify RNA samples with low detectable amounts compared to proteins, a much larger number of biomarkers can be detected efficiently and accurately from a small amount of sample.

[0275] One of the biggest practical differences between protein-level and RNA-level testing methods for observing or quantifying the expression of specific biomarkers is the ability to observe or quantify a large number of biomarkers with very small amounts of protein compared to protein testing. Proteome analysis is suitable for simultaneously observing changes in various biomarkers at the protein level, but it requires a large amount of sample. Protein extraction from skin tissue, in particular, requires approximately 10 times the required sample volume, as the yield is less than 10%. Specifically, proteome analysis requires at least 1–2 mg of protein sample, while a 3 mm punch skin tissue biopsy extracts approximately 0.15 mg of protein. This means that approximately 10 3 mm punch skin tissue biopsies are required to perform proteome analysis.

[0276] However, given the various limitations of tissue biopsies, performing 10 3mm punches is practically impossible. Protein extraction using microneedle patches also requires the attachment of more than 20 microneedle patches, as each patch extracts approximately 0.06 mg of protein, making proteome analysis impossible, making it difficult to apply to the skin. Even if proteins are extracted from samples in this way, proteome analysis detects only about 650 biomarkers, which is only 1-2% of the biomarkers detected through RNA microarray analysis (more than 40,000), making it less efficient.

[0277] Other analytical methods have limitations. Even compared to ELISA, the most sensitive protein analysis method, the 0.06 mg of protein extracted from one microneedle patch is only enough to detect changes in the expression of one biomarker when analyzed three times. Additionally, ELISA has the disadvantages of only being able to detect one biomarker per kit, potential interference from solvents, and significant variation between measurements due to the numerous steps required by the experimenter. Unlike RNA, proteins have a three-dimensional structure and cannot be amplified. This makes it difficult to overcome the sample collection limitations and apply it to simultaneously analyze changes in multiple biomarkers for skin type, prognosis and diagnosis of skin diseases, or the effectiveness of new material treatments.

[0278] To solve these problems, the present invention applies RNA to the analysis. For a specific protein to be synthesized, the gene portion involved in the synthesis of that protein is replicated in the form of RNA from DNA present in the cell nucleus (transcription), and this RNA is then transported to the cytoplasm, where it is used as a template to link amino acids corresponding to each base sequence, ultimately synthesizing (translating) the protein. The use of RNA, which is a precursor of protein synthesis and is genetic material that reflects the expression level of actual skin proteins, which changes depending on various environmental factors, allows for more efficient analysis.

[0279] However, because RNA has a lower yield and higher instability than protein, research was required to determine optimal conditions for its use in analysis. It was only through the present invention that it was confirmed that RNA biomarkers can be reliably identified from minute amounts of skin test subjects obtained using a microneedle patch. Prior to the present invention, the industry had previously recognized that RNA has a lower yield and higher instability than protein, making it impossible to identify RNA biomarkers from minute amounts of test subjects obtained using a microneedle patch to diagnose specific skin conditions or diseases.

[0280] The inventors first confirmed that RNA biomarkers can be efficiently and accurately detected from minute amounts of test subject obtained using a microneedle patch by undergoing a process of amplifying RNA to ensure that the minute amount of RNA is in an amount suitable for performing analysis, as described in detail in the above examples.

[0281] When converted to the same units, the amount of protein extracted from one microneedle patch was approximately 60 ng / μL, while the amount of RNA was approximately 10 ng / μL, a 1 / 6th of the original amount. Although the amount is much smaller than the protein amount, RNA amplification technology was applied to overcome this, making RNA microarray analysis possible. RNA amplification technology uses the polymerase reaction principle to amplify 10 ng of RNA by approximately 550 times, to 5,500 ng. In this way, changes in the expression of approximately 40,000 biomarkers can be confirmed from the skin test sample extracted from one microneedle patch. The multiple RNA biomarkers selected in the above examples to measure, evaluate, or diagnose each skin condition can be detected very easily and accurately.

[0282] However, when using a microneedle patch to acquire skin test subjects, it is impossible to accurately detect the expression of multiple protein biomarkers, such as nine, using the amount obtained from a single microneedle patch. Multiple microneedle patches must be used, and the process of acquiring test subjects from a single microneedle patch must be repeated multiple times. As mentioned above, using the ELISA method requires multiple kits because each kit can only detect one biomarker. Even then, interference from solvents can occur, and the experimenter must perform numerous steps, resulting in significant variation between measurements.

[0283] Although the present invention has been described above using examples and drawings in which specific details such as specific components are limited, these are merely provided to facilitate a more general understanding of the present invention, and the present invention is not limited to the above examples. Those skilled in the art to which the present invention pertains may attempt various modifications and variations from such descriptions.

[0284] Therefore, the concept of the present invention should not be limited to the above-described embodiments, and it can be said that not only the scope of the claims below but also all modifications equivalent to or similar to the scope of the claims fall within the scope of the concept of the present invention.

Claims

1. A microneedle patch comprising a plurality of microneedles made of biodegradable polymer hyaluronic acid and having a solid structure; an RNA extraction device for extracting RNA genes from the test subject attached to the microneedle patch on the skin of the subject; The method includes a quantification device capable of quantifying the expression level of the extracted RNA gene, The microneedle patch is applied to the skin of the subject, and after being maintained for a predetermined time and then separated, RNA obtained by the RNA extraction device from the test subject from the skin of the subject that is adsorbed on the surface of the microneedles of the microneedle patch is subjected to an amplification process and provided to the quantification device for quantitative analysis, and the quantification device quantifies the expression level of collagen / elasticity-related RNA biomarker genes or the expression level of skin barrier function / moisture synthesis-related RNA biomarker genes, and skin aging is diagnosed based on the quantified value. The collagen / elasticity-related biomarkers include any one or more of COL1A1, COL3A1, FN1, GSTA3, PON1, PINK1, COL4A4, and MMP8, and the skin barrier function / moisture synthesis-related biomarkers include any one or more of IVL, HAS2, HAS3, AQP3, CERS6, CLDN1, SLC9A1, TGM1, SPINK5, KLF4, LCE1A, LCE1B, LCE1F, LCE2A, BGN, and AZGP1.

2. the collagen / elasticity-related biomarkers include COL1A1, COL3A1, FN1, and PINK1; The minimally invasive diagnostic kit for skin aging status according to claim 1, wherein the skin barrier function / water synthesis related biomarkers include IVL, HAS3, AQP3, BGN and AZGP1.

3. A microneedle patch comprising a plurality of microneedles made of biodegradable polymer hyaluronic acid and having a solid structure; an RNA extraction device for extracting RNA genes from the test subject attached to the microneedle patch on the skin of the subject; The method includes a quantification device capable of quantifying the expression level of the extracted RNA gene, The microneedle patch is applied to the skin of the subject, and after being maintained for a predetermined time, the microneedle patch is separated. The RNA obtained by the RNA extraction device from the test subject adsorbed on the surface of the microneedles of the microneedle patch is amplified and provided to the quantification device for quantitative analysis. The quantification device quantifies the expression level of RNA biomarker genes related to skin moisturization, and the skin moisturization level is evaluated based on the quantification value. The skin moisturizing level diagnostic kit includes one or more of the following biomarkers: CDSN, FLG, FLG2, LOR, KLF4, KRT10, LCE1A, LCE2A, LCE2B, LCE2C, SMPD3, CDH1, ITGB4, IVL, SPINK5, CLDN1, AQP3, BGN, HAS3, TGM1, CLDN7, CERS3, CLDN4, and KRT1.

4. The minimally invasive diagnostic kit for determining skin hydration status according to claim 3, wherein the skin hydration-related biomarkers include FLG, AQP3, and LOR.

5. A microneedle patch comprising a plurality of microneedles made of biodegradable polymer hyaluronic acid and having a solid structure; an RNA extraction device for extracting RNA genes from the test subject attached to the microneedle patch on the skin of the subject; The method includes a quantification device capable of quantifying the expression level of the extracted RNA gene, The microneedle patch is applied to the skin of the subject, and after being maintained for a predetermined time, the microneedle patch is separated. The RNA obtained by the RNA extraction device from the test subject adsorbed on the surface of the microneedles of the microneedle patch is amplified and provided to the quantification device for quantitative analysis. The quantification device quantifies the expression level of the skin pigmentation-related RNA biomarker gene, and the degree of skin pigmentation is evaluated based on the quantified value. The skin pigmentation-related biomarkers include any one or more of CAT, CLU, DSG1, GPNMB, GPX4, GSTM2, GSTP1, MLANA, PAX3, SOX10, TFAP2A, TYR, TYRP1, MC1R, F2RL1 and CLDN1. A minimally invasive diagnostic kit for skin pigmentation status.

6. The minimally invasive diagnostic kit for skin pigmentation status according to claim 5, wherein the skin pigmentation-related biomarkers include MLANA, TYRP1 and GPNMB.

7. A microneedle patch comprising a plurality of microneedles made of biodegradable polymer hyaluronic acid and having a solid structure; an RNA extraction device for extracting RNA genes from the test subject attached to the microneedle patch on the skin of the subject; The method includes a quantification device capable of quantifying the expression level of the extracted RNA gene, The microneedle patch is applied to the skin of the subject, and after being maintained for a predetermined time, the microneedle patch is separated. The RNA obtained by the RNA extraction device from the test subject adsorbed on the surface of the microneedles of the microneedle patch is amplified and provided to the quantification device for quantitative analysis. The quantification device quantifies the expression level of skin oil-related RNA biomarker genes, and the skin oil level is evaluated based on the quantified value. The skin oil level diagnostic kit includes one or more of the skin oil level biomarkers MFAP2, IGF1, HSD11B1, GPAM, CPT1C, AR, MPZL3, AQP3, SREBF2, and HSD17B2.

8. The minimally invasive diagnostic kit for skin oiliness status according to claim 7, wherein the skin oil-related biomarkers include MPZL3, GPAM, and IGF1.

9. A microneedle patch comprising a plurality of microneedles made of biodegradable polymer hyaluronic acid and having a solid structure; an RNA extraction device for extracting RNA genes from the test subject attached to the microneedle patch on the skin of the subject; The method includes a quantification device capable of quantifying the expression level of the extracted RNA gene, The microneedle patch is applied to the skin of the subject, and after being maintained for a predetermined time, the microneedle patch is separated. The RNA obtained by the RNA extraction device from the test subject from the skin of the subject that is adsorbed on the surface of the microneedles of the microneedle patch is amplified and provided to the quantification device for quantitative analysis. The quantification device quantifies the expression level of RNA biomarker genes related to flushing-sensitive skin, and flushing-sensitive skin is evaluated based on the quantified value. The flush-sensitive skin-related biomarkers include any one or more of COL3A1, TAC1, KLK5, CAMP, MMP9, TRPA1, IL13RA1, HSD3B1, CXCR4, ANGPT2, CXCL2, CXCR5, and PSMB9.

10. The minimally invasive diagnostic kit for flushing-sensitive skin conditions according to claim 9, wherein the flushing-sensitive skin-related biomarkers include MMP9, TAC1 and CAMP.

11. A microneedle patch comprising a plurality of microneedles made of biodegradable polymer hyaluronic acid and having a solid structure; an RNA extraction device for extracting RNA genes from the test subject attached to the microneedle patch on the skin of the subject; The method includes a quantification device capable of quantifying the expression level of the extracted RNA gene, The microneedle patch is applied to the skin of the subject, and after being maintained for a predetermined time, the microneedle patch is separated. The RNA obtained by the RNA extraction device from the test subject adsorbed on the surface of the microneedles of the microneedle patch is amplified and provided to the quantification device for quantitative analysis. The quantification device quantifies the expression level of RNA biomarker genes related to subjectively sensitive skin to irritation, and the subjective level of sensitive skin to irritation is evaluated based on the quantified value. The subjectively irritant sensitive skin-related biomarkers include any one or more of IVL, LOR, FLG, FLG2, PGF, CYR61, HLA-B, IGHA1, MMP3, RBP4, and GOS2.

12. The minimally invasive diagnostic kit for subjectively sensitive skin condition according to claim 11, wherein the subjectively sensitive skin-related biomarkers include PGF, RBP4 and CYR61.

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