Application of skin typing marker in risk assessment of occurrence of ARD in radiotherapy after breast cancer operation

Abstract

The invention discloses an application of a skin typing marker in risk assessment of occurrence of ARD in breast cancer postoperative radiotherapy, and belongs to the technical field of biomedicine. From the perspective of skin microbial flora, two skin types with a stable microbial network mode are obtained through identification, and whether ARD will occur after radiotherapy of a patient and the ARD severity degree are evaluated according to the judgment result of the skin types of the patient. The technical scheme of the invention is of great significance to prevention and treatment of ARD in the radiotherapy process after breast cancer surgery.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedicine, and particularly relates to the application of skin typing markers in the risk assessment of acute radiation dermatitis (ARD) occurring after radiotherapy for breast cancer patients after surgery. Background Art

[0002] Radiotherapy (RT) is an important treatment method for breast cancer (BC) in clinical practice. However, statistics show that approximately 95% of patients receiving RT treatment will develop acute radiation dermatitis (ARD) to varying degrees. Especially for breast cancer patients receiving RT treatment after surgery, due to the impairment of their skin thermoregulatory reflex and the increase in skin stretching, the risk and severity of developing ARD will increase. ARD can cause pain, itching, and burning, greatly affecting the quality of life and aesthetics of patients. Severe ARD will lead to treatment interruption or delay. Therefore, for breast cancer patients undergoing surgical treatment, ARD may increase the risk of infection and delay wound healing, increasing the possibility of reconstruction failure. Therefore, ARD is a problem that both medical workers and patients attach great importance to in clinical practice.

[0003] Currently, due to the progress of RT technology (such as intensity-modulated radiotherapy), the incidence rate of ARD has decreased significantly. However, even mild ARD will significantly affect the quality of life and self-image. Therefore, if the risk and severity of ARD occurring in a patient can be evaluated before RT treatment and intervention or preventive treatment can be carried out in advance, the impact of ARD on the patient can be greatly reduced.

[0004] The human skin contains millions of microorganisms, including bacteria and fungi, which constitute the skin microbiota. The skin microbiota is a complex and dynamic multi-kingdom ecosystem. The dysregulation of the skin microbiota is increasingly associated with many skin diseases. The present invention discovers through research that the skin microbiota is related to the risk and severity of ARD occurring after radiotherapy for breast cancer patients after reconstruction surgery. Based on this, the present invention provides a technical solution for skin typing of patients through skin microorganisms and evaluating the risk and severity of ARD occurring in patients based on the skin typing results. Summary of the Invention

[0005] The purpose of the present invention is to reveal the potential relationship between the skin microbiota and ARD, and propose an idea for risk assessment of ARD in breast cancer patients receiving radiotherapy after surgery, that is, to perform skin typing on breast cancer patients planning to receive radiotherapy after surgery according to the skin microbiome characteristics, and then evaluate the risk and severity of ARD occurring in the patient after radiotherapy based on the skin typing results.

[0006] The object of the present invention is achieved by the following technical solutions:

[0007] In a first aspect, the present invention provides the use of a microbial biomarker or a substance for detecting a microbial biomarker in at least one of the following:

[0008] a1) Use in the preparation of a product for skin typing;

[0009] a2) Use in the preparation of a product for predicting the risk of ARD occurrence after breast cancer postoperative radiotherapy;

[0010] a3) Use in the preparation of a product for evaluating the risk level of ARD after breast cancer postoperative radiotherapy.

[0011] In the present invention, the term "breast cancer postoperative radiotherapy" refers to the period when a breast cancer patient undergoes radiotherapy after surgical treatment. The surgery includes radical mastectomy known to those skilled in the art and / or reconstructive surgery after the radical mastectomy. The reconstructive surgery is a plastic surgery for breast loss or deformity, aiming to reconstruct the shape and volume of the breast by implanting a prosthesis, using autologous tissue, or combining both, so as to restore the appearance and symmetry of the breast.

[0012] The product includes but is not limited to reagents, reagent kits, chips, test strips, membrane strips or detection platforms.

[0013] The microbial biomarker includes Cutibacterium and Devosiaceae.

[0014] In some embodiments of the present invention, the microbial biomarker further includes a combination of Corynebacteriaceae and Staphyloccaceae.

[0015] In some embodiments of the present invention, the microbial biomarker further includes a combination of one or both of Burkholderiaceae and Flavobacteriaceae.

[0016] In the present invention, the substance for detecting the microbial biomarker is a substance for detecting the genome of the biomarker in a skin sample, and the substance includes any reagent required for detecting the gene expression of the microbial biomarker by RT-PCR method, RT-qPCR method, biochip detection method, DNA blotting method, high-throughput sequencing method.

[0017] In a specific embodiment of the present invention, the substance for detecting the microbial biomarker is any reagent required for detecting the V3-V4 region of the bacterial 16S rRNA gene by high-throughput sequencing method.

[0018] In some embodiments of the present invention, the reagent includes specific amplification primers for the V3-V4 region of the bacterial 16S rRNA gene.

[0019] In a specific embodiment of the present invention, the amplification primers for the V3-V4 region of the bacterial 16S rRNA gene are 341F and 805R.

[0020] In some embodiments of the present invention, the reagent further includes a bacterial DNA extraction reagent, reagents required for library construction, and reagents required for sequencing.

[0021] The "ARD risk level" as described in the present invention refers to grading the skin reaction according to the revised version of the Radiation Therapy Oncology Group (RTOG) toxicity criteria; RTOG grade 0-1 is defined as no or mild ARD; RTOG grade ≥2 is defined as significant ARD.

[0022] In a specific embodiment of the present invention, when the level (enrichment degree) of Propionibacterium in the skin sample is high, the skin type is C-skin type; when the level (enrichment degree) of Devosia in the skin sample is high, the skin type is D-skin type, indicating that the patient has no ARD or mild ARD after radiotherapy.

[0023] In a specific embodiment of the present invention, the C-skin type further includes enrichment of Corynebacteriaceae and / or Staphylococcaceae.

[0024] In a specific embodiment of the present invention, the D-skin type further includes enrichment of Burkholderiaceae and / or Flavobacteriaceae.

[0025] In a second aspect, the present invention provides a kit, characterized in that the kit includes the substance for detecting microbial markers described in the first aspect of the present invention.

[0026] The substance for detecting microbial markers is a substance for detecting the genome of the marker in the skin sample, and the substance includes any reagent required for detecting the gene expression of the microbial marker by RT-PCR method, RT-qPCR method, biochip detection method, Southern blotting method, high-throughput sequencing method.

[0027] In a specific embodiment of the present invention, the substance for detecting microbial markers is any reagent required for detecting the V3-V4 region of the bacterial 16S rRNA gene by high-throughput sequencing method.

[0028] In some embodiments of the present invention, the reagent includes specific amplification primers for the V3-V4 region of the bacterial 16S rRNA gene.

[0029] In a specific embodiment of the present invention, the primers for amplifying the V3-V4 region of the bacterial 16S rRNA gene are 341F and 805R.

[0030] In some embodiments of the present invention, the reagent further includes a bacterial DNA extraction reagent, reagents required for library construction, and reagents required for sequencing.

[0031] The kit provided by the present invention is used for b1) skin typing; b2) predicting the risk of ARD occurrence after breast cancer radiotherapy; b3) evaluating the risk level of ARD after breast cancer radiotherapy.

[0032] In a third aspect, the present invention provides a skin typing method not for the purpose of disease diagnosis and treatment, characterized in that the method comprises the following steps:

[0033] (1) Obtain a skin sample and detect the levels of the microbial markers described in the first aspect of the present invention in the sample, and the microbial markers include Propionibacterium and Devosia;

[0034] (2) Judge the skin type according to the relative levels of Propionibacterium and Devosia in the sample. When the level of Propionibacterium in the sample is high, the skin type is C-skin type; when the level of Devosia in the skin sample is high, the skin type is D-skin type.

[0035] In some embodiments of the present invention, the microbial markers are a combination of Propionibacterium, Corynebacteriaceae, Staphylococcaceae, and Devosia.

[0036] In some embodiments of the present invention, the microbial markers are a combination of Propionibacterium, Devosia, Burkholderiaceae, and Flavobacteriaceae.

[0037] In some embodiments of the present invention, the microbial markers are a combination of Propionibacterium, Corynebacteriaceae, Staphylococcaceae, Devosia, Burkholderiaceae, and Flavobacteriaceae.

[0038] In the step (1), the present invention detects the gene levels of the microbial marker 16S rRNA gene V3-V4 region by high-throughput sequencing.

[0039] In a fourth aspect, the present invention provides a system for skin typing, characterized in that the system includes an information acquisition module and a diagnosis module;

[0040] The information acquisition module is used to acquire the information of the levels of the microbial markers described in the first aspect of the present invention of the subject;

[0041] The diagnosis module is used to execute the step of judging the skin type of the subject according to the information of the microbial marker levels.

[0042] In a specific embodiment of the present invention, when the level of Propionibacterium in the skin sample is high, the skin type is C-skin type; when the level of Devosia in the skin sample is high, the skin type is D-skin type, indicating that the subject has no ARD or has mild ARD after radiotherapy.

[0043] In a specific embodiment of the present invention, the C-skin type further includes enrichment of Corynebacteriaceae and / or Staphylococcaceae.

[0044] In a specific embodiment of the present invention, the D-skin type further includes enrichment of Burkholderiaceae and / or Flavobacteriaceae.

[0045] The subject is a patient undergoing radiotherapy for breast cancer postoperative reconstruction.

[0046] In a fifth aspect, the present invention provides an application of the skin typing system according to the fourth aspect of the present invention in at least one of the following:

[0047] c1) Application in the preparation of a product for predicting the risk of ARD occurrence after radiotherapy for breast cancer;

[0048] c2) Application in the preparation of a product for evaluating the risk level of ARD after radiotherapy for breast cancer.

[0049] The application includes the following steps:

[0050] (1) Obtain a skin sample and detect the levels of the microbial markers described in the first aspect of the present invention in the sample, and the microbial markers include Propionibacterium and Devosia;

[0051] (2) Determine the skin type according to the relative levels of Propionibacterium and Devosia in the sample. When the level of Propionibacterium in the sample is high, the skin type is C-skin type; when the level of Devosia in the skin sample is high, the skin type is D-skin type;

[0052] (3) When the skin type is C-skin type, further detection is required; when the skin type is D-skin type, it indicates that the patient has no ARD or has mild ARD after radiotherapy.

[0053] In some embodiments of the present invention, the microbial markers are a combination of Propionibacterium, Corynebacteriaceae, Staphylococcaceae, and Devosia.

[0054] In some embodiments of the present invention, the microbial markers are a combination of Propionibacterium, Devosia, Burkholderiaceae, and Flavobacteriaceae.

[0055] In some embodiments of the present invention, the microbial markers are a combination of Propionibacterium, Corynebacteriaceae, Staphylococcaceae, Devosia, Burkholderiaceae, and Flavobacteriaceae.

[0056] In the step (1), the present invention detects the V3-V4 region of the bacterial 16S rRNA gene by high-throughput sequencing.

[0057] Starting from the perspective of skin microbial flora, the present invention identifies two skin types with stable microbial network patterns, and evaluates whether ARD will occur and its severity after radiotherapy for a patient based on the judgment result of the patient's skin type, providing a new idea for preventing ARD in patients undergoing radiotherapy after breast cancer postoperative reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 Left: Principal Coordinates Analysis (PCoA) of Jensen Shannon dissimilarity, clustering 140 samples according to fungi and bacteria of BC patients who received RT after reconstruction surgery. The box plot in the upper left corner shows the relationship between skin type and ARD severity, C-skin type (orange, N = 119): SD = 31, N_MD = 88; D-skin type (purple, N = 21): SD = 0, N_MD = 21. The box plot in the upper right corner shows that there is no significant change in the skin type of samples before and after RT. Right: Relative abundances of two species: Propionibacterium (top) and Devosia (bottom).

[0059] Figure 2 Microbial co-occurrence networks of two skin types, with the left side showing the microbial species enriched in the C-skin type and the right side showing the microbial species enriched in the D-skin type. Each bouquet of a color represents a species. DETAILED DESCRIPTION OF THE INVENTION

[0060] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Apparently, the described embodiments are only part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] Research Methods

[0062] 1. Patient Recruitment

[0063] From January 2021 to September 2022, a total of 103 patients were recruited for this prospective study. The enrolled patients were breast cancer patients aged 18 - 70 years who planned to undergo adjuvant RT after autologous and / or implant-based reconstruction. Patients were excluded if they had received chemotherapy, antibiotics, steroids, or immunosuppressants within four weeks before skin microbiota sampling. Cleaning with antibacterial cleansers was not allowed within 7 days before sample collection. Bathing, shampooing, or moisturizing was not allowed within 24 hours after sample collection. For each enrolled patient, skin microbiota samples were collected sequentially from the treated side and the contralateral healthy side of the patient on the first day of radiotherapy and one day after completion of radiotherapy.

[0064] Without knowledge of the microbiome information, the researchers graded the skin reactions of the patients using a revised version of the Radiation Therapy Oncology Group (RTOG) toxicity criteria weekly during radiotherapy, within 6 weeks after radiotherapy, and at 3, 6, 9, 12, and 24 months after radiotherapy. The enrolled patients were divided into two groups based on the worst score during radiotherapy and within 3 months after radiotherapy: the no ARD or mild ARD subgroup (N_MD, RTOG grade 0 - 1) and the severe ARD subgroup (SD, RTOG grade ≥2). This study has been approved by the institutional ethics committee (2104234 - 7), and all patients have given written informed consent.

[0065] 2. Skin microbiota sampling and DNA extraction

[0066] To obtain sufficient DNA from the skin site, a sterile polyester fiber-tipped swab moistened with 0.15 M NaCl solution was used to wipe a 4 cm 2 skin area 40 times, a negative control of a mock swab was collected, and each sampling was analyzed. DNA was extracted using the cetyltrimethylammonium bromide (CTAB) method, and DNA was quantified using Qubit (Invitrogen, USA).

[0067] 3. PCR amplification and sequencing

[0068] The variable region (V3 - V4) of the bacterial 16S rRNA gene was PCR amplified using the universal primers 341F (5’-CCTACGGGNGGCWGCAG-3’) and 805R (5’-GACTACHVGGGTATCTAATCC-3’) for the bacterial 16S rRNA V3 - V4 region.

[0069] The fungal ITS gene region was PCR amplified using the universal primers ITS1FI2 (5′-GTGARTCATCGAATCTTTG-3′) and ITS2 (5′-TCCTCCGCTTATTGATATGC-3′) for the fungal internal transcribed spacer (ITS).

[0070] The PCR amplification conditions were as follows: pre-denaturation at 98 °C for 30 seconds for 32 cycles, denaturation at 98 °C for 10 seconds, annealing at 54 °C for 30 seconds, and extension at 72 °C for 45 seconds. The final extension step was carried out at 72 °C for 10 minutes.

[0071] Library construction and sequencing: The PCR products were purified using AMPure XT Beads and quantified using Qubit (Invitrogen, USA); the purified PCR products were evaluated using an Agilent 2100 Bioanalyzer and an Illumina library quantification kit (Kapa Biosciences, Woburn, MA, USA), and then pooled and sequenced on an Illumina NovaSeq 6000 (PE250).

[0072] 4, Sequence processing and bioinformatics analysis

[0073] Cutadapt (v1.9) was used to remove the sequencing primers (RRID:SCR_011841) from the raw sequences, and FLASH (v1.2.8) was used to splice and merge each pair of paired-end reads into a longer tag according to the overlap region. The sliding window algorithm in fqtrim (v 0.94) was used to trim low-quality reads (quality score < 20), short reads (< 100 bp), and reads containing more than 5% "N" records. Quality filtering was performed according to fqtrim to obtain high-quality clean tags. The Vsearch software (v2.3.4) was used to filter chimeric sequences. DADA2 was used for denoising to obtain ASV (feature) characteristic sequences and an ASV (feature) abundance table, and singletons ASVs (i.e., ASVs with a total sequence count of only 1 in the entire sample) were removed. Based on the obtained characteristic sequences and characteristic sequence abundances, the sequencing depth was evaluated, and samples with a sequencing depth of less than 40,000 reads were removed. The QIIME2 plugin feature classifier was used for sequence alignment for species annotation, and the alignment databases used were SILVA and NT-16S to obtain taxonomic information and statistically analyze the community composition of each sample at each taxonomic level: kingdom, phylum, class, order, family, genus, species.

[0074] Calculate α and β diversity using QIIME2. To identify taxa enriched with differences between groups, perform high-dimensional class comparison using LEfSe analysis combined with LDA scores to find Biomarkers with statistical differences between groups, that is, species with significant differences between groups. The microbial co-occurrence network is inferred using Spearman's rank correlation and constructed using OmicStudio tools (Spearman rho ≥ 0.5 and P < 0.05, respectively). To predict metagenomic functional content, perform PICRUSt2 (phylogenetic investigation of communities by reconstruction of unobserved states); use the MetaCyc database (RRID:SCR_007778) to predict pathways.

[0075] 5, Determination of skin microbiome skin types

[0076] All samples were clustered using Jensen Shannon distance (JSD). The PAM function was used to cluster the samples, and the Calinski-Harabasz (CH) index was used to determine the optimal number of clusters. PCoA analysis was performed on the typing results of samples from three sites using JSD distance and Bray-Curtis (BC) distance, respectively. A PERMANOVA test based on Bray-Curtis (BC) distance was performed on the skin type results using RRID:SCR_011950.

[0077] 6, Statistical analysis

[0078] Continuous data were analyzed using the Wilcoxon rank sum test, and categorical data were analyzed using the Pearson chi-square test or Fisher's exact test. All statistical analyses were performed using R software, and a two-sided P < 0.05 was considered statistically significant.

[0079] Research results

[0080] 1, Patient characteristics and overall skin microbiome profile

[0081] A total of 412 skin microbiota samples were collected from 103 patients (demographics and clinical characteristics are shown in Table 1), and the samples were taken from the treated side and the contralateral healthy side of the patients before and after radiotherapy. Among them, due to insufficient DNA, 10 and 27 patients were excluded from the bacterial cohort and fungal cohort, respectively. Finally, this study analyzed the skin microbiota samples of 93 patients (348 samples) in the bacterial cohort and 76 patients (277 samples) in the fungal cohort. The primary endpoint of this study was the association between the skin microbiota and the development of grade 2 or higher ARD. The patients were divided into two groups: the no ARD or mild ARD subgroup (N_MD, RTOG grade 0-1) and the severe ARD subgroup (SD, RTOG grade ≥2).

[0082] Table 1 Demographics and clinical characteristics of the enrolled patients

[0083]

[0084]

[0085] All patients were classified as type 4 on the Fitzpatrick skin scale. In the bacterial cohort, N_MD included 69 patients (74.2%; grade 0: 1 patient; grade 1: 68 patients), and SD included 24 patients (25.8%; grade 2: 21 patients; grade 2.5: 1 patient; grade 3: 2 patients). In the fungal cohort, N_MD included 59 patients (77.6%; grade 0: 1 patient; grade 1: 58 patients), and SD included 17 patients (22.4%; grade 2: 16 patients; grade 3: 1 patient). Two patients in the bacterial cohort and one patient in the fungal cohort experienced severe radiation dermatitis (RTOG grade 3), which was characterized by confluent moist desquamation. Most patients were under 40 years old, had a body mass index (BMI) below 24 kg / m 2 , were premenopausal, and received implant-based reconstruction.

[0086] All patients received a prescribed dose of 50 Gy / 25 fractions within 5 weeks. The patients in the N_MD and SD subgroups were similar in terms of age, BMI, planned treatment volume (PTV) of the reconstructed chest wall, menopausal status, reconstruction type, and systemic treatment. This study first evaluated the skin microbiota status of BC patients who received RT after reconstruction surgery by sequencing the bacterial 16S rRNA V3-V4 region and the fungal internal transcribed spacer (ITS).

[0087] 2. Skin microbiota typing during RT

[0088] To explore the impact of RT on the skin microbiota of BC patients and whether these perturbations in the skin microbiota are associated with the severity of ARD, this study analyzed the richness and diversity of the skin microbiota before and after RT. No significant differences were observed, indicating that RT does not cause significant changes in the overall structure of the skin microbiota.

[0089] Furthermore, this study classified skin microbiotypes. First, the PAM method was used to cluster the samples separately, and the CH index was used to determine the optimal number of clusters. The results of the CH index showed that the CH index score was the highest when the number of clusters was 2. All skin microbiota samples (n = 140: 68 before RT and n = 72 after RT) formed two separate clusters, representing two skin types. The PCoA analysis of the typing results of the samples at three sites was performed using the JSD distance and the Bray-Curtis (BC) distance, respectively. The results are as Figure 1 shown. The microorganisms that contributed the most to this classification were Cutibacterium and Devosiaceae, respectively, that is, one type was enriched in Cutibacterium, and the other type was enriched in Devosiaceae. Two skin types were defined according to the species dominance, namely: Cutibacterium (C-skin type) and Devosiaceae (D-skin type).

[0090] 82.3% of the patients maintained the same skin type after radiotherapy, indicating its relative stability. The D-skin type was completely composed of N_MD, while the C-skin type was composed of a mixture of N_MD and SD (P < 0.01)( Figure 1 ). Correlation analysis was performed based on the relative levels of the differential microbiota. The results showed that the microorganisms preferentially appeared in each skin type. For example, Corynebacteriaceae and Staphyloccaceae were enriched in the C-skin type, while the D-skin type was highly diverse, with several characteristic genera, including Burkholderiaceae and Flavobacteriaceae enriched( Figure 2 ). In addition, there was a strong positive correlation between the microorganisms enriched in the same skin type, while there was a strong negative correlation between the microorganisms enriched in different skin types. The results of this analysis indicate that the microorganisms enriched in the same skin type may occupy different ecological niches respectively, forming a stable ecological network with each other, constructing a strong microbial community to resist the colonization of other microorganisms, including potentially pathogenic microorganisms, and there are potential antagonistic interactions between different skin types.

[0091] This study found that the D-skin type was identified as a potential protective skin type, and RT did not significantly change the skin type of patients, mainly because the interrelated skin microbiota within a skin type formed a stable ecological network. Therefore, in clinical practice, the risk and severity of ARD in patients after RT treatment can be evaluated by detecting the skin type of patients. The results of this study are of great significance for the prevention and treatment of ARD during RT treatment for breast cancer postoperative reconstruction.

[0092] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. Use of a microbial biomarker or a substance for detecting a microbial biomarker in at least one of the following: a1) Use in the preparation of a product for skin typing; a2) Use in the preparation of a product for predicting the risk of ARD occurrence after breast cancer postoperative radiotherapy; a3) Use in the preparation of a product for evaluating the ARD risk level after breast cancer postoperative radiotherapy; The microbial biomarker includes Cutibacterium and Devosiaceae.

2. The use according to claim 1, wherein the microbial biomarker further includes Corynebacteriaceae and Staphyloccaceae.

3. The use according to claim 1, wherein the microbial biomarker further includes Burkholderiaceae and Flavobacteriaceae.

4. The application according to any one of claims 1 to 3, characterized in that, The substance for detecting the microbial biomarker is a substance for detecting the genome of the biomarker in a skin sample, and the substance includes any reagent required for detecting the gene expression of the microbial biomarker by RT-PCR method, RT-qPCR method, biochip detection method, Southern blotting method, high-throughput sequencing method.

5. The application according to claim 4, characterized in that, The substance for detecting the microbial biomarker is any reagent required for detecting the V3-V4 region of the bacterial 16S rRNA gene by high-throughput sequencing method, including specific amplification primers for the V3-V4 region of the bacterial 16S rRNA gene, such as 341F and 805R.

6. The application according to claim 5, characterized in that, The substance for detecting the microbial biomarker further includes a bacterial DNA extraction reagent, reagents required for library construction, and reagents required for sequencing.

7. Kit, characterized in that, The kit includes the substance for detecting the microbial biomarker according to any one of claims 1-3.

8. A skin typing method not for the purpose of disease diagnosis and treatment, characterized in that, The method includes the following steps: (1) Obtain a skin sample and detect the level of the microbial biomarker according to any one of claims 1-3 in the sample; (2) Judge the skin type according to the relative levels of Cutibacterium and Devosiaceae in the sample. When the level of Cutibacterium in the sample is high, the skin type is C-skin type; when the level of Devosiaceae in the skin sample is high, the skin type is D-skin type.

9. A system for skin typing, characterized in that, The system includes an information acquisition module and a diagnosis module; The information acquisition module is used to obtain the information on the level of the microbial biomarker according to any one of claims 1-3 of the subject; The diagnosis module is used to perform the step of judging the skin type of the subject according to the microbial biomarker level information; When the level of Cutibacterium in the skin sample is high, the skin type is C-skin type; when the level of Devosiaceae in the skin sample is high, the skin type is D-skin type.

10. Use of the skin typing system according to claim 9 in at least one of the following: c1) Use in the preparation of a product for predicting the risk of ARD occurrence after breast cancer postoperative radiotherapy; c2) Use in the preparation of a product for evaluating the ARD risk level after breast cancer postoperative radiotherapy; The use includes the following steps: (1) Obtain a skin sample and detect the level of the microbial biomarker described in any one of claims 1-3 in the sample, wherein the microbial biomarker includes Propionibacterium and Devosia; (2) Determine the skin type based on the relative levels of Propionibacterium and Devosia in the sample. When the level of Propionibacterium in the sample is high, the skin type is C-skin type; when the level of Devosia in the skin sample is high, the skin type is D-skin type; (3) A skin type of D-skin type indicates that the patient has no ARD or has mild ARD after radiotherapy.

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