CN111653315A - Method for constructing mathematical model for detecting breast cancer in vitro and application thereof - Google Patents

Method for constructing mathematical model for detecting breast cancer in vitro and application thereof Download PDF

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CN111653315A
CN111653315A CN202010482487.7A CN202010482487A CN111653315A CN 111653315 A CN111653315 A CN 111653315A CN 202010482487 A CN202010482487 A CN 202010482487A CN 111653315 A CN111653315 A CN 111653315A
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高俊莉
高俊顺
高金波
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Hangzhou Guangke Ander Biotechnology Co ltd
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Abstract

The application provides a method for constructing a mathematical model for detecting breast cancer in vitro, which comprises the steps of obtaining the concentrations of at least two breast cancer markers from a sample, carrying out logistic regression on the concentration value of each marker, substituting the concentration obtained by detection into the logistic regression model to obtain an analysis result, and carrying out comprehensive breast cancer analysis by using the concentration of each marker and the logistic regression analysis result. The application also provides an application of the method.

Description

Method for constructing mathematical model for detecting breast cancer in vitro and application thereof
Technical Field
The application relates to the technical field of medical diagnosis, in particular to a method for constructing a mathematical model for in-vitro breast cancer detection and application thereof.
Background
The female breast is composed of skin, fibrous tissue, breast glands and fat, and breast cancer is a malignant tumor that occurs in the mammary gland epithelial tissue. Breast cancer occurs in 99% of women and only 1% in men.
Mammary gland is not an important organ for maintaining human body life activity, and the in-situ breast cancer is not fatal; however, the breast cancer cells lose the characteristics of normal cells, and the cells are loosely connected and easily fall off. Once cancer cells are shed, free cancer cells can be disseminated to the whole body along with blood or lymph fluid to form metastasis, which endangers life. At present, breast cancer becomes a common tumor threatening the physical and mental health of women.
The incidence of breast cancer worldwide has been on the rise since the end of the 70 s of the 20 th century. In the United states, 1 woman will have breast cancer in their lifetime. China is not a high-incidence country of breast cancer, but is not optimistic, and the growth rate of the incidence of breast cancer in China is 1-2% higher than that of the high-incidence country in recent years. According to 2009 breast cancer onset data published by the national cancer center and health department disease prevention and control agency 2012, it is shown that: the incidence of breast cancer of women in the national tumor registration area is 1 st of malignant tumors of women, the incidence (thickness) of breast cancer of women is 42.55/10 ten thousand in total nationwide, 51.91/10 ten thousand in cities and 23.12/10 ten thousand in rural areas.
Breast cancer has become a major public health problem in the current society. The global breast cancer mortality rate has shown a decreasing trend since the 90 s of the 20 th century; firstly, the breast cancer screening work is carried out, so that the proportion of early cases is increased; secondly, the development of comprehensive treatment of breast cancer improves the curative effect. Breast cancer has become one of the most effective solid tumors.
In the ideal situation, when a person needs to screen for breast cancer, screening can be performed immediately by an effective means. However, to date, none of the screening approaches is perfect, 100% accurate. The invention provides a multidimensional combined method for diagnosing breast cancer in vitro, which jointly detects protein markers, metabolites, cell-free DNA, cell-free non-coding RNA, autoantibodies, inflammatory factors, growth factors, circulating breast cancer cells, DNA methylation, exosomes and the like related to the breast cancer, and improves the sensitivity and specificity of the breast cancer detection.
Disclosure of Invention
The main objective of the present application is to provide a method for constructing a mathematical model for in vitro breast cancer detection, so as to improve the sensitivity and specificity of clinical breast cancer detection, no marker for breast cancer detection can diagnose breast cancer with very high sensitivity and specificity results, most breast cancers adopt a joint inspection form, but all adopt molecular diagnosis or immunodiagnosis to detect several markers of one type, and detection of various dimensions is not combined, and in order to enhance the prediction accuracy, it is better to combine the detection of both horizontal and vertical directions, and both inside and outside: it is an object of the present invention to combine metabolites, exosomes, molecular diagnostics, immunodiagnosis.
The application provides a method for constructing a mathematical model for detecting breast cancer in vitro, which comprises the steps of obtaining the concentrations of at least two breast cancer markers from a sample, carrying out logistic regression on the concentration value of each marker, substituting the concentration obtained by detection into the logistic regression model to obtain an analysis result, and carrying out comprehensive breast cancer analysis by using the concentration of each marker and the logistic regression analysis result.
Preferably, the breast cancer markers include at least one of the following categories:
breast cancer protein markers, breast cancer-related metabolite markers, breast cancer molecular diagnostic markers, breast cancer autoantibodies, breast cancer-related inflammatory factors and/or growth factors, breast cancer-related exosomes, and breast cancer-related DNA methylation markers.
Preferably, the breast cancer protein marker is selected from any one or more combination of CA15-3, TIMP-1, uPA, PAI, NMP66, TF, OPN, CEACAM6, RARA, Bc1, Bc2, Bc3, CEA, FTL, CSTA, TPT1, IGFBP1, GRM1, GRIK1, H6PD, MDM4, S100A8, CA6, EMT, MET, MUC2, MUC3, MUC4, MUC5, MUC6, IGF-I, Cyclin E;
the breast cancer metabolite marker is selected from the group consisting of acetylspermine, diacetylspermine, sarcosine, purine, glyceride, 5-monophosphate-cytidine/pentadecanoic acid, glycerophosphocholine, phosphocholine, choline, homovanic oxalate, 4-hydroxyphenylacetic acid, 5-oxindoleacetic acid, urea, xanthine (xanthine), glucose-6-phosphate (glucose-6-phosphate), mannose-6-phosphate (mannose-6-phosphate), guanine (guanine), adenine (adenine), formate (formate), histidine (histidine), proline (proline), choline (choline), tyrosine (tyrosine), 3-hydroxybutyrate (3-hydroxybutyrate), lactate (lactate), glutamate (glutamic acid), N-acetylglycine (N-acetyl-glycine), 3-hydroxy-2-methylbutyrate (3-hydroxy-2-butyric acid) Azelaic acid (nonandioic acid), Estrogen Receptor (ER), Progestin Receptor (PR), human epidermal growth factor receptor-2 (Her-2);
the breast cancer molecular diagnostic marker is selected from UHRF1, BRCA1, BRCA2, FGFR2, HER-2/neu, PTEN, SIRT, STAT3, Ki-67, PIK3CA, STK15, Survivin, CCNB1(Cyclin B1), MYBL2, ACTB (B-actin), GAPDH, RPLPO, CTSL2(Cathepsin L2), GUS, TFRC, PGR, BCL2, SCUBE2, GSTM1, CD68, BAG1, miR-21, miR-20a, miR-214, miR-181a, miR-1304, miR-141, miR-200a/c, miR-203, miR-210, miR-375, miR-801, miR-141, miR-200B/c, miR-210, miR-769-3p, miR-376-3 p, miR-3 p-801, miR-3B-409, miR-16 p-409, miR-2/c, miR-1, miR-actin-1, miR-60, miR-B, miR-3 p-409, miR-3B-3, Any one or more of miR-410, 193a-3p, miR-766, miR-563, miR-550, miR-432, miR-548D-5p, miR-33b, miR-1539, miR-155, miR-16, miR-21, miR-210, CA 27-29, cyclin D2, RAR-beta, Twist promoters, RASSF1A, CDH1, GSTP1, BRCA1, p16INK4a, DAPK, APC, DAP-kinase;
the breast cancer autoantibody is selected from: any one or more of CTAG1B, CTAG2, TP53, RNF216, PPHLN1, PIP4K2C, ZBTB16, TAS2R8, WBP2NL, DOK2, PSRC1, MN1, TRIM21, HER2, MUC1, endostatin, p53, p80, S6, PA32, NY-ESO-1, annexin XI-A, malignin;
the breast cancer related inflammatory factors and growth factors are selected from any one or more of CRP, Ch17CEP, sHER2, MAD1L1, IL-6, IL-8, IGF, COX-2, TNF and TGF-beta 1;
the breast cancer related exosome is selected from any one or more of miR-27a, miR-451, miR-21-5p, miR-21, miR-221, TGF-beta 1, HMGB1, CagA, GKN1, UBR2, TRIM3, miR-130a, miR-27a, miR-21-5p, ZFAS1 and ciRS-133;
the breast cancer related DNA methylation marker is selected from any one or more of PITX2P2, APC, GSTP1, RASSF1A, RAR-beta 2, DNMT1, DMAP1, MeCP2, MBD1, MBD2a, MBD2b, MBD3, MLH1, TIMP-3, CDKN2B, CDKN2A, P21WAF1/CIP1, 14-3-3 sigma and RAR beta 2.
Preferably, the formula of the logistic regression is:
Figure BDA0002516060660000041
wherein, Logit (P) is the logistic regression model result of the same or different breast cancer markers, C is the natural constant obtained by regression, alpha is the coefficient of each marker obtained by regression analysis and is a natural number, the concentration i of the marker is the concentration of the marker in the same or different classes, and n is an integer which is more than or equal to 2.
Preferably, the sample to be tested comprises: human or animal tissue, a blood sample, urine, saliva, body fluid, feces.
Preferably, the detection technique comprises one or more of a radiological method, an immunological method, a fluorescence method, a flow fluorescence, a latex turbidimetry, a biochemical method, an enzymatic method, a PCR method, a sequencing method, a hybridization method, a gas chromatography, a liquid chromatography, a chemiluminescence method, a magnetoelectric conversion method, and a photoelectric conversion method.
Preferably, the breast cancer markers are breast cancer protein markers, breast cancer molecular diagnostic markers and breast cancer-related DNA methylation markers, wherein the breast cancer protein markers are CA15-3, TIMP-1, uPA, PAI, NMP66, OPN, CEACAM6, Bc1, IGFBP1, the breast cancer molecular diagnostic markers are UHRF1, BRCA1, BRCA2, 2, STAT3, PIK3CA, MYBL2, GAPDH, RPLPO, BCL2, the breast cancer-related DNA methylation markers are PITX2P2, APC, GSTP1, RASSF1A, RAR- β 2, DNMT1, the concentration values of these markers in the sample are obtained, natural conversion logarithm is performed, and the regression model obtained after removing the non-contributing markers is logistic regression analysis: logit (P) ═ 2.43+1.012 Ln (CA15-3) +0.452 Ln (upa) +0.785 Ln (opn) +0.652 Ln (CEACAM6) +0.741 Ln (IGFBP1) +1.210 Ln (UHRF1) +0.471 Ln (BRCA1) +0.723 Ln (FGFR2) +0.457 Ln (PIK3CA) +0.789 Ln (RASSF1A) +0.354 Ln (pi2P 2) +0.987 Ln (g 541) +0.541 Ln (gs 1), where Ln is the logarithm of natural number.
Another aspect of the present application provides the use of the mathematical model obtained by the method for constructing a mathematical model for in vitro breast cancer detection for predicting the risk of cancer in a subject of a sample, wherein the subject of the sample is considered to be at risk of cancer when the value of the result of computational analysis obtained from the mathematical model is ≧ 2.648.
The application has the following advantages: the breast cancer detection method has the advantages that the breast cancer detection method has different dimensionalities, different types of combination are combined horizontally and longitudinally, the detection is realized both internally and externally, the defects that the detection sensitivity and specificity of one marker or one dimensionality are not high in the market are overcome, the accuracy and the precision of breast cancer diagnosis are greatly improved, the traditional invasive diagnosis such as CT or biopsy puncture can be replaced, the subtype of the breast cancer can be judged, early diagnosis, early screening, auxiliary diagnosis or prognosis observation can be provided at the same time, and good news is brought to patients.
Detailed Description
In order to make the technical solutions in the embodiments of the present application better understood, the technical solutions in the embodiments of the present application are clearly and completely described, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The detection methodology used in the examples may be a commercially available reagent test kit or a self-made kit.
Example 1
The method comprises the steps of testing 11 breast cancer protein marker concentrations (CA15-3, TIMP-1, PAI, NMP66, OPN, CEACAM6, RARA, IGFBP1, GRM1, GRIK1 and S100A8) in a blood sample by using a self-made chemiluminescence method detection kit or a self-made flow-type fluorescence detection kit, testing 13 breast cancer molecular marker concentrations (UHRF1, BRCA1, FGFR2, HER-2/neu, STAT 2, Ki-67, PIK 32, CCNB 2 (Cyclin B2), MYBL2, ACTB (B-actin), CTSL2(Cathepsin L2), CD 2, BAG 2) in the blood sample by using a fluorescence in-situ hybridization method or a sequencing method, and testing 12 breast cancer related DNA methylation RAR marker concentrations (PI2P 2, GSSSTP 2, RASSF 1. beta., RAP 1. DNP 2, DMAKN 2, CDKN2, WAKN 2 and WAKN 2) in the blood sample by using a flow-type fluorescence method detection kit.
Performing logistic regression analysis on the tested concentration of the related marker to obtain Logit (P) ═ constant + lambda 1. multidot. P1+ lambda 2. multidot. P2+ eta 3. multidot. P3+ eta 4. multidot. P4 … …
And testing the concentration of each marker of the unknown blood sample, substituting the concentration into the regression model, and comprehensively diagnosing whether the breast cancer suffers from the breast cancer and the breast cancer risk according to the judgment standard of the calculated logit (P) and the value of the logit (P) of the regression model.
Figure BDA0002516060660000061
Example 2
Testing 6 breast cancer protein marker concentrations in blood samples (CA15-3, TIMP-1, OPN, CEACAM6, CEA, IGFBP1) with a purchased or self-made chemiluminescence method kit, testing 9 breast cancer molecular markers in blood samples (miR-21, miR-20a, miR-214, miR-181a, miR-1304, miR-141, miR-200a/c, miR-203, miR-210) with a fluorescence in situ hybridization method, testing the concentrations of 13 breast cancer autoantibodies in blood samples (CTAG1B, CTAG2, TP53, RNF216, PPHLN1, PIP4K2C, TB16, TAS2R8, WBP2NL, DOK2, PSRC1, MN1, TRIM21) with a purchased or self-made immunofluorescence method, testing 11 breast cancer related exosomes in urine or blood (miR-27a, miR-221-21, miR-451-21, miR-11 breast cancer related exosomes (miR-21, miR-11) in urine or blood) with a flow fluorescence method, TGF- β 1, HMGB1, CagA, GKN1, UBR2, TRIM3), 7 relevant inflammatory and growth factors in urine or blood were detected by flow fluorescence method: (CRP, Ch17CEP, sHER2, MAD1L1, IL-6, TNF, TGF-. beta.1)
Performing logistic regression analysis on the tested concentration of the related marker to obtain Logit (P) ═ constant + lambda 1. multidot. P1+ lambda 2. multidot. P2+ eta 3. multidot. P3+ eta 4. multidot. P4 … …
And testing the concentration of each marker of the unknown blood sample, substituting the concentration into the regression model, and comprehensively diagnosing whether the breast cancer suffers from the breast cancer and the breast cancer risk according to the judgment standard of the calculated logit (P) and the value of the logit (P) of the regression model.
Figure BDA0002516060660000071
Example 3
The breast cancer protein markers are CA15-3, TIMP-1, uPA, PAI, NMP66, OPN, CEACAM6, Bc1 and IGFBP1, the breast cancer molecular diagnostic markers are UHRF1, BRCA1, BRCA2, FGFR2, STAT3, PIK3CA, MYBL2, GAPDH, RPLPO and BCL2, the breast cancer related DNA methylation markers are PITX2P2, APC, GSTP1, RASSF1A, RAR-beta 2 and DNMT1, the concentration values of the markers in a sample are obtained, natural logarithm conversion is carried out, and after a nondeliverizing marker is removed through logistic regression analysis, the obtained regression model is: logit (P) ═ 2.43+1.012 Ln (CA15-3) +0.452 Ln (upa) +0.785 Ln (opn) +0.652 Ln (CEACAM6) +0.741 Ln (IGFBP1) +1.210 Ln (UHRF1) +0.471 Ln (BRCA1) +0.723 Ln (FGFR2) +0.457 Ln (PIK3CA) +0.789 Ln (RASSF1A) +0.354 Ln (pi2P 2) +0.987 Ln (g 541) +0.541 Ln (gs 1), where Ln is the logarithm of natural number.
And testing the concentration of each marker of the unknown blood sample, substituting the concentration into the regression model, and comprehensively diagnosing whether the breast cancer suffers from and the risk of the breast cancer according to the judgment standard of the calculated logit (P) and the value of the logit (P) of the regression model.
Figure BDA0002516060660000081
Through experimental research, the breast cancer detection in a multi-dimensional combination and combination mode has higher sensitivity and specificity compared with single detection of one or more types, the sensitivity can reach 99 percent, and the specificity is 100 percent and is far better than breast cancer diagnosis markers on the market.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (8)

1. A method of constructing a mathematical model for the in vitro detection of breast cancer, the method comprising obtaining the concentrations of at least two breast cancer markers from a sample, performing logistic regression on the concentration values determined for each marker, substituting the detected concentrations into a logistic regression model to obtain an analysis result, and performing a comprehensive breast cancer analysis using the concentration of each marker and the logistic regression analysis result.
2. The method of constructing a mathematical model for the in vitro detection of breast cancer according to claim 1, wherein said breast cancer markers comprise at least one of the following categories:
breast cancer protein markers, breast cancer-related metabolite markers, breast cancer molecular diagnostic markers, breast cancer autoantibodies, breast cancer-related inflammatory factors and/or growth factors, breast cancer-related exosomes, and breast cancer-related DNA methylation markers.
3. The method for constructing a mathematical model for the in vitro detection of breast cancer according to claim 1, wherein the breast cancer protein marker is selected from any one or more of the group consisting of CA15-3, TIMP-1, uPA, PAI, NMP66, TF, OPN, CEACAM6, RARA, Bc1, Bc2, Bc3, CEA, FTL, CSTA, TPT1, IGFBP1, GRM1, GRIK1, H6PD, MDM4, S100a8, CA6, EMT, MET, MUC2, MUC3, MUC4, MUC5, MUC6, IGF-I, Cyclin E;
the breast cancer metabolite marker is selected from the group consisting of acetylspermine, diacetylspermine, sarcosine, purine, glyceride, 5-monophosphate-cytidine/pentadecanoic acid, glycerophosphocholine, phosphocholine, choline, homovanic oxalate, 4-hydroxyphenylacetic acid, 5-oxindoleacetic acid, urea, xanthine (xanthine), glucose-6-phosphate (glucose-6-phosphate), mannose-6-phosphate (mannose-6-phosphate), guanine (guanine), adenine (adenine), formate (formate), histidine (histidine), proline (proline), choline (choline), tyrosine (tyrosine), 3-hydroxybutyrate (3-hydroxybutyrate), lactate (lactate), glutamate (glutamic acid), N-acetylglycine (N-acetyl-glycine), 3-hydroxy-2-methylbutyrate (3-hydroxy-2-butyric acid) Azelaic acid (nonandioic acid), Estrogen Receptor (ER), Progestin Receptor (PR), human epidermal growth factor receptor-2 (Her-2);
the breast cancer molecular diagnostic marker is selected from UHRF1, BRCA1, BRCA2, FGFR2, HER-2/neu, PTEN, SIRT, STAT3, Ki-67, PIK3CA, STK15, Survivin, CCNB1(Cyclin B1), MYBL2, ACTB (B-actin), GAPDH, RPLPO, CTSL2(Cathepsin L2), GUS, TFRC, PGR, BCL2, SCUBE2, GSTM1, CD68, BAG1, miR-21, miR-20a, miR-214, miR-181a, miR-1304, miR-141, miR-200a/c, miR-203, miR-210, miR-375, miR-801, miR-141, miR-200B/c, miR-210, miR-769-3p, miR-376-3 p, miR-3 p-801, miR-3B-409, miR-16 p-409, miR-2/c, miR-1, miR-actin-1, miR-60, miR-B, miR-3 p-409, miR-3B-3, Any one or more of miR-410, 193a-3p, miR-766, miR-563, miR-550, miR-432, miR-548D-5p, miR-33b, miR-1539, miR-155, miR-16, miR-21, miR-210, CA 27-29, cyclin D2, RAR-beta, Twist promoters, RASSF1A, CDH1, GSTP1, BRCA1, p16INK4a, DAPK, APC, DAP-kinase;
the breast cancer autoantibody is selected from: any one or more of CTAG1B, CTAG2, TP53, RNF216, PPHLN1, PIP4K2C, ZBTB16, TAS2R8, WBP2NL, DOK2, PSRC1, MN1, TRIM21, HER2, MUC1, endostatin, p53, p80, S6, PA32, NY-ESO-1, annexin XI-A, malignin;
the breast cancer related inflammatory factors and growth factors are selected from any one or more of CRP, Ch17CEP, sHER2, MAD1L1, IL-6, IL-8, IGF, COX-2, TNF and TGF-beta 1;
the breast cancer related exosome is selected from any one or more of miR-27a, miR-451, miR-21-5p, miR-21, miR-221, TGF-beta 1, HMGB1, CagA, GKN1, UBR2, TRIM3, miR-130a, miR-27a, miR-21-5p, ZFAS1 and ciRS-133;
the breast cancer related DNA methylation marker is selected from any one or more of PITX2P2, APC, GSTP1, RASSF1A, RAR-beta 2, DNMT1, DMAP1, MeCP2, MBD1, MBD2a, MBD2b, MBD3, MLH1, TIMP-3, CDKN2B, CDKN2A, P21WAF1/CIP1, 14-3-3 sigma and RAR beta 2.
4. The method for constructing a mathematical model for the in vitro detection of breast cancer according to claim 3, wherein the logistic regression is formulated as:
Figure FDA0002516060650000021
wherein, Logit (P) is the logistic regression model result of the same or different breast cancer markers, C is the natural constant obtained by regression, alpha is the coefficient of each marker obtained by regression analysis and is a natural number, the concentration i of the marker is the concentration of the marker in the same or different classes, and n is an integer which is more than or equal to 2.
5. The method for constructing a mathematical model for the in vitro detection of breast cancer according to claim 1, wherein the sample to be tested comprises: human or animal tissue, a blood sample, urine, saliva, body fluid, feces.
6. The method for constructing a mathematical model for in vitro breast cancer detection according to claim 1, wherein the detection technique comprises one or more of a radiation method, an immunological method, a fluorescence method, a flow fluorescence, a latex turbidimetry, a biochemical method, an enzymatic method, a PCR method, a sequencing method, a hybridization method, a gas chromatography, a liquid chromatography, a chemiluminescence method, a magnetoelectric conversion method and a photoelectric conversion method.
7. The method for constructing a mathematical model for in vitro breast cancer detection according to claim 1, wherein the breast cancer markers are a breast cancer protein marker, a breast cancer molecular diagnostic marker and a breast cancer-related DNA methylation marker combination, wherein the breast cancer protein marker is CA15-3, TIMP-1, uPA, PAI, NMP66, OPN, CEACAM6, Bc1, IGFBP1, the breast cancer molecular diagnostic marker is UHRF1, BRCA1, BRCA2, FGFR2, STAT3, PIK3CA, MYBL2, GAPDH, RPLPO, BCL2, the breast cancer-related DNA methylation markers are PITX2P2, APC, GSTP1, RASSF1A, RAR- β 2, and mt1, the concentration values of these markers in the sample are obtained, natural logarithm conversion is performed, and analyzed by logistic regression, the model after rejecting the non-contributing markers is: logit (P) ═ 2.43+1.012 Ln (CA15-3) +0.452 Ln (upa) +0.785 Ln (opn) +0.652 Ln (CEACAM6) +0.741 Ln (IGFBP1) +1.210 Ln (UHRF1) +0.471 Ln (BRCA1) +0.723 Ln (FGFR2) +0.457 Ln (PIK3CA) +0.789 Ln (RASSF1A) +0.354 Ln (pi2P 2) +0.987 Ln (g 541) +0.541 Ln (gs 1), where Ln is the logarithm of natural number.
8. Use of a mathematical model obtained by the method of constructing a mathematical model for the in vitro detection of breast cancer according to any one of claims 1 to 7 for predicting the risk of cancer in a subject of a sample, wherein the subject of the sample is considered to be at risk of cancer when the value of the calculated analysis result obtained from the mathematical model is ≥ 2.648.
CN202010482487.7A 2020-05-29 2020-05-29 Method for constructing mathematical model for detecting breast cancer in vitro and application thereof Pending CN111653315A (en)

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Application publication date: 20200911