KR20160104330A - Device for diagnosing ovarian cancer and Method for providing information on diagnosing ovarian cancer - Google Patents

Abstract

The present invention relates to a device for diagnosing ovarian cancer, and to a method for providing ovarian cancer diagnosis information. The device for diagnosing ovarian cancer according to the present invention comprises: an input unit for inputting mass spectrum data of a low mass ion detected from a biological sample; and a diagnosing unit for determining ovarian cancer diagnosis information with respect to the mass spectrum data inputted by the input unit by using a first ovarian cancer diagnosing low mass ion group. The first ovarian cancer diagnosing low mass ion group includes at least one low mass ion among 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024,175.1585, 188.8544, 190.7493, 230.0197, and 709.7879 m/z (an error range is 0.1%).

Description

Technical Field [0001] The present invention relates to an ovarian cancer diagnostic apparatus and a method for providing ovarian cancer diagnostic information,

The present invention relates to an ovarian cancer diagnostic apparatus and a method for providing diagnosis information of ovarian cancer.

Cancer is a disease in which cells infiltrate infinitely and interfere with normal cell function. It is a disease of lung, gastric cancer, breast cancer (BRC), colorectal cancer (CRC) and ovarian cancer OVC), and the like, but they can occur in virtually any tissue.

Early diagnosis of cancer was based on external changes of the biotissue due to the growth of cancer cells. Recently, however, diagnosis using biomolecules such as blood, glycocyte, DNA, And detection has been attempted. However, the most commonly used cancer diagnosis method is a tissue sample obtained through biopsy or an image diagnosis.

Among them, biopsy results in great pain for the patient, which is not only costly but also takes a long time to diagnose. In addition, when the patient is actually cancerous, there is a risk that cancer metastasis may be induced during the biopsy, and in the case where a tissue sample can not be obtained through biopsy, It is impossible to diagnose the disease until the removal of the disease.

In the diagnosis using imaging, cancer is judged based on X-ray image, nuclear magnetic resonance (NMR) image obtained using a contrast agent having a disease target substance, and the like. However, there is a possibility of misdiagnosis according to the skill of the clinician or the reader, and this image diagnosis has a disadvantage that it depends greatly on the precision of the device for obtaining the image. Furthermore, even the most precise apparatus can not detect tumors of a few mm or less, which is difficult to detect in the early stages of onset. In addition, in order to obtain an image, a patient or a disease-capable person is exposed to electromagnetic waves of high energy capable of causing mutation of a gene, so that it can not only cause another disease but also has a disadvantage in that the number of diagnosis through imaging is limited .

On the other hand, recent biostatistical analysis of low-mass ions extracted from biological samples suggests a method of diagnosing cancer.

Korean Patent Publication No. 2012-0015978 (published on February 22, 2012) Korean Patent Laid-Open Publication No. 2013-0079988 (published on July 11, 2013)

The object of the present invention is to provide a device for diagnosing ovarian cancer and a method for providing diagnosis information of ovarian cancer through biostatistical analysis of low mass ions extracted from biological sample.

It is an object of the present invention to provide an ovarian cancer diagnosis apparatus comprising an input unit for inputting mass spectrum data of low mass ions detected in a biological sample; And a diagnostic unit for determining ovarian cancer diagnostic information on the mass spectral data input from the input unit using a low mass ion group for first ovarian cancer diagnosis, wherein the first low-mass ion group for ovarian cancer diagnosis is 21.1873, 37.0311, At least one low mass ion among the m / z (where the error range is ± 0.1%) is achieved by the use of a low-mass ion such as sodium hydroxide, sodium hydroxide, sodium hydroxide, potassium hydroxide,

The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z At least three low mass ions.

The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z At least five low mass ions.

Wherein the diagnostic unit further comprises a low mass ion group for the second ovarian cancer diagnosis to determine ovarian cancer diagnostic information for the mass spectral data and the low mass ion group for the second ovarian cancer diagnosis comprises 496.5220, 524.5837 and 1466.7073 m / z (With an error range of +/- 0.1%).

The second group of low mass ions for ovarian cancer diagnosis may include 496.5220, 524.5837 and 1466.7073 m / z (with an error range of +/- 0.1%).

It is an object of the present invention to provide an ovarian cancer diagnostic apparatus comprising: a diagnostic unit for determining ovarian cancer diagnostic information on mass spectral data of low mass ions detected in a biological sample using a low mass ion group for first ovarian cancer diagnosis; And an output unit for outputting a diagnosis result of the diagnosis unit, wherein the first low-mass ion group for ovarian cancer diagnosis is 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z (with an error range of +/- 0.1%).

The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z At least three low mass ions.

The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z At least five low mass ions.

Wherein the diagnostic unit further comprises a low mass ion group for the second ovarian cancer diagnosis to determine ovarian cancer diagnostic information for the mass spectral data and the low mass ion group for the second ovarian cancer diagnosis comprises 496.5220, 524.5837 and 1466.7073 m / z (With an error range of +/- 0.1%).

The second group of low mass ions for ovarian cancer diagnosis may include 496.5220, 524.5837 and 1466.7073 m / z (with an error range of +/- 0.1%).

It is an object of the present invention to provide a method for diagnosing ovarian cancer, comprising: obtaining mass spectral data of a low mass ion in a biological sample; And providing ovarian cancer diagnosis information on the mass spectral data using the low mass ion group for the first ovarian cancer diagnosis, wherein the low mass ion group for the first ovarian cancer diagnosis is 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z (with an error range of +/- 0.1%).

The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z At least three low mass ions.

The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z At least five low mass ions.

The ovarian cancer diagnostic information providing step may further include a low mass ion group for diagnosing ovarian cancer to provide ovarian cancer diagnosis information to the mass spectral data, and the low mass ion group for the second ovarian cancer diagnosis is 496.5220, 524.5837 And 1466.7073 m / z (with an error range of +/- 0.1%).

The second group of low mass ions for ovarian cancer diagnosis may include 496.5220, 524.5837 and 1466.7073 m / z (with an error range of +/- 0.1%).

According to the present invention, a method for diagnosing ovarian cancer and a method for providing ovarian cancer diagnostic information by biostatistical analysis of low-mass ions extracted from a biological sample are presented.

1 is a block diagram of an ovarian cancer diagnosis apparatus according to an embodiment of the present invention,
FIG. 2 is a discriminant score distribution diagram of a reference mass spectrum according to principal component analysis-based linear discriminant analysis (PCA-DA)
3 is a discrimination score distribution diagram of the reference mass spectrum calculated by the first group candidate,
4 shows the diagnostic result of the training set A ₁ using the first group,
FIG. 5 shows the diagnostic result of the training set A ₂ using the first group,
6 shows the diagnostic result of the verification set B using the first group,
FIG. 7 shows the diagnostic result of the training set A ₁ using the first group and the second group,
FIG. 8 shows the diagnostic result of the training set A ₂ using the first group and the second group,
9 shows the diagnostic result of the verification set B using the first group and the second group.

In the present invention, the term "biological sample" refers to whole blood, serum, plasma, urine, stool, sputum, saliva, Extracts, in vitro cell cultures, and the like. In the examples described below, sera from patients or non-patients was used as a biological sample.

In the present invention, the mass to charge ratio (m / z) measured by a MALDI-TOF (matrix assisted laser desorption / ionization-time of fight) mass spectrometer includes an error range of "± 0.1%". This is because some errors may occur in the mass value measurement depending on the experimental environment, and the larger the mass value, the larger the error becomes.

In the present invention, "low mass ion" means an ion having a mass value obtained using a MALDI-TOF mass spectrometer or the like, which is smaller than 1500 m / z. In some cases, some of the diagnostic low-mass ions have a higher mass value than this range, but most of them fall within this range, so even in this case we try to use the name low-mass ion. That is, the limit of 1500 m / z is used as a rough value rather than a definite value.

In the present invention, each low-mass ion is expressed as a mass value of the corresponding low-mass ion measured in a MALDI-TOF mass spectrometer. In the present invention, the mass value measured by a MALDI-TOF mass spectrometer is a value obtained in a positive mode of a MALDI-TOF mass spectrometer. In the present invention, the mass value is the value of the H + adduct form of each low mass ion measured by a MALDI-TOF mass spectrometer, and the mass value of the actual low mass ion is as small as 1 m / z.

In the present invention, "mass intensity" refers to a value measured by a MALDI-TOF mass spectrometer and has a correlation with the amount of low mass ions.

In the present invention, "normalization" refers to matching the range of data or making the distribution similar, and may be normalized using mean, median, Various known methods may be applied in some cases. In this embodiment, the subtotal of the mass intensities of each sample is obtained, and the average of the subscales of each sample is obtained, and normalized by multiplying each mass intensity by the sample magnification factor so that the subtotal of the mass intensities of each sample coincides with this average . That is, after normalization as above, the subtotal of the mass intensities for each sample becomes equal.

In the present invention, "Pareto scaling" refers to subtracting the mean value of each mass ion from each normalized mass intensity, and then dividing by the square root of the standard deviation. In a more general scaling method, autoscaling completely eliminates the size information of the data by dividing it by the standard deviation, whereas in Pareto scaling, avoiding the amplification of noise by partially preserving the size information of the data There is an advantage that it can be.

In the present invention, a "weighting factor" refers to a factor that adjusts the numerical magnitude of data after multiplying the weight by proportional to the significance in terms of statistical significance. (factor loading) is an example of a weight.

In the present invention, a process of deriving a candidate for a low mass ion group for diagnosis of a first ovarian cancer (hereinafter referred to as "first group candidate") from a raw low mass ion group of a biological sample (hereinafter referred to as a " (Hereinafter referred to as " first group ") and / or a low-mass ion group for diagnosis of second ovarian cancer (hereinafter referred to as" second group & The process of judging the positive / negative can be found in another application of the present applicant, for example, Korean Patent Publication No. 2013-0079988.

BEST MODE FOR CARRYING OUT THE INVENTION The present invention will be described in more detail with reference to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

FIG. 1 shows an ovarian cancer diagnosis apparatus according to an embodiment of the present invention.

The input unit 100 inputs mass spectral data (hereinafter referred to as "diagnosis object data") of low-mass ions detected by the biological sample. The input unit 100 may perform a preprocessing process such as sorting, normalization, and / or scaling on the data to be diagnosed, or input the data to be diagnosed to the input unit 100 after the preprocessing process. In addition, the data to be diagnosed may include only data corresponding to the first group or data corresponding to the first group and the second group, as the case may be.

It is possible to input a plurality of diagnostic object data to the input unit 100 for one patient.

The diagnosis unit 200 generates ovarian cancer diagnostic information for the diagnostic target data using only the first group or both the first group and the second group. That is, the diagnosis unit 200 determines the ovarian cancer positive or ovarian cancer negative with respect to the data to be diagnosed.

The diagnosis unit 200 determines whether the ovarian cancer is positive or negative by using data corresponding to the first group and the second group included in the inputted diagnostic object data. For example, the discrimination score is derived using the scaled mass intensity and the weight of the low-mass ions corresponding to the first group and the second group, and judging whether the ovarian cancer is positive or negative according to a predetermined criterion for the discrimination score can do.

The low mass ions corresponding to the first group can be selected from among 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z. At least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, and at least eleven of the thirteen low mass ions Or at least 12 can be used. It is also possible to use all 13 low mass ions.

The low mass ions corresponding to the second group can be selected from 496.5220, 524.5837 and 1466.7073 m / z. At least one, or at least two, or all three of these three low mass ions can be used. Specifically, 496.5220 m / z corresponds to LPC (lysophosphatidylcholine) 16: 0, 524.5837 m / z corresponds to LPC (lysophosphatidylcholine) 18: 0, and 1466.7073 m / z corresponds to LPC Corresponds to the fibrinogen alpha chain.

The second group of low-mass ions are very low in mass (P value) when analyzed in the blood using Triple-TOF mass spectrometry, and are low mass ions whose quantitative difference is clear when compared to the normal control group.

Hereinafter, a method of deriving the first group will be described.

The first group is selected from the first group candidates. The process of deriving the first group candidates from the original group will be described first.

The mass spectrum can be repeatedly measured for one sample. In the example illustrated hereafter, mass spectra were measured six times for one sample. The mass spectra were selected six times for the samples selected as the training set, and then the mass spectrum showing the best discrimination performance among the six was selected as the reference mass spectrum by applying the PCA-DA analysis. The factor load value obtained by the PCA-DA analysis for this reference mass spectrum is the weight of each low mass ion.

Next, we selected mass ions whose absolute value of the value obtained by multiplying the Pareto-scaled mass intensity by each weight of each sample in the reference mass spectrum is greater than 0.1. We selected the P mass ions, which are common to more than 50% of the total samples constituting the reference mass spectrum among the mass ions selected for each sample, as the first group candidate.

Next, a process of deriving the first group from the first group candidate will be described.

In this procedure, the first group of low mass ions are selected based on the diagnostic capability of low mass ion combinations that can be composed of the first group candidates rather than individual low mass ions. In this case, the diagnostic performance refers to the diagnostic performance of the training set with the new training set (group A ₂ ) added to the training set (group A ₁ ) used when selecting the first group candidate and calculating the weight of the low mass ion. We have thus extended the training set to mitigate the overfitting phenomenon. For the training set A ₁ , the discriminant score was calculated by averaging the remaining five mass spectrums not selected as the reference mass spectrum among the mass spectra measured six times, and the mass spectrum measured six times for the training set A ₂ And the discriminant score was calculated by averaging the five selected mass spectra. In order to reduce the inevitable random error in the experiment of measuring the mass spectrum, the diagnostic performance was evaluated based on the average discrimination score.

In the first step, the diagnostic performance of all combinations ( _P C ₂ and _P C ₃ ) that can consist of two or three low mass ions among the P first group candidates is compared and the sensitivity plus specificity value Select a large combination. When there is more than one combination of sensitivity plus specificity, the combination with the lowest number of low mass ions is selected. When the number of low-mass ions is the same, the Fisher's discriminant ratio is selected to be the largest.

The process is repeated with the first group candidate remaining after subtracting the two or three low mass ions derived, and this process is repeated until only one low mass ion remains or none remains in the first group candidate.

In the second step, the combinations with two or three low mass ions constituted in the first step are regarded as one low mass ion and the same process as the first step is repeated.

In other words, the low mass ion combination formed in the previous step is regarded as one low mass ion in the next step and the same process is repeated. The seed set is chosen as the lowest mass ion combination with the highest sensitivity plus specificity, regardless of the stage of the low mass ion combination.

If the S low mass ions are selected as the seed set, the first group candidate will remain R (= P - S). ( _R C ₁ , _R C ₂ , and _R C ₃ ) that can consist of one, two, or three low-mass ions among the remaining Group 1 candidates are added to the seed set and their diagnostic performance The seed set is updated if the combination with the best diagnostic performance is better than the diagnostic performance of the original seed set.

Update the seed set and repeat this process with the remaining first group candidates. The last set of seed sets that have undergone this repetition process is selected as the final group for diagnosing ovarian cancer, that is, the first group.

Example-Deriving Group 1

Hereinafter, a process of deriving the first group using the above-described method will be described.

A total of 1,184 female serum samples were used. The sample number and age information for each patient group are shown in Table 1.

Sample information 1 Count age Average ± Standard Deviation range Control 276 50.3 ± 10.7 19 - 86 OVC  89 55.5 ± 10.3 27 - 77 CRC 237 63.4 ± 11.8 30 - 87 GC 139 59.1 ± 13.8 28 - 86 BUT  83 45.8 ±  8.2 25 - 70 BOT  71 42.0 ± 13.4 20 - 83 PCL  88 42.1 ± 11.9 22 - 79 BRC  93 47.8 ±  9.2 29 - 69 BBT  65 45.4 ± 10.0 19 - 62 UCC  33 50.7 ± 14.8 26 - 82 EMC  10 53.6 ±  7.9 40 - 66

In Table 1, Control is the normal control, BUT is the benign uterine tumor, BOT is the benign ovarian tumor, PCL is the precancerous cervical lesion, BBT is the benign breast UCC refers to uterine cervical cancer, and EMC refers to endometrial cancer.

Information on how many samples were collected at each hospital is shown in Table 2.

 Sample information 2 Count hospital Count hospital Control 193 National Cancer Center GC 139 Donga University Hospital  83 Donga University Hospital BUT  83 Lee Dae Mok Dong Hospital OVC   8 Seoul National University Hospital BOT  71  22 Samsung Seoul Hospital PCL  88  31 National Cancer Center BRC  93 National Cancer Center  28 Lee Dae Mok Dong Hospital BBT  65 CRC  74 National Cancer Center UCC  33 Lee Dae Mok Dong Hospital  32 Counter hospital EMC  10 131 Donga University Hospital

The samples were roughly divided into triplicate and training set and verification set as shown in Table 3. As shown in Table 3, when the low mass ion discrimination formula consisting of the remaining samples was not used as the training set because the number of cervical cancer and endometrial cancer samples was not so large, Respectively.

 Training set and verification set Sum Training set Verification set Set A ₁ Set A ₂ Set B Control 276 92 92 92 OVC  89 30 30 29 CRC 237 79 79 79 GC 139 47 46 46 BUT  83 27 28 28 BOT  71 23 24 24 PCL  88 30 29 29 BRC  93 31 31 31 BBT  65 22 21 22 UCC  33 33 EMC  10 10

Table 4-14 summarizes the sample information for each patient group.

Normal control sample information Set A ₁ Sample
no. Age
year Sample
no. Age
year Sample
no. Age
year Sample
no. Age
year A001 37 A024 49 A047 40 A070 63 A002 55 A025 52 A048 86 A071 38 A003 54 A026 52 A049 41 A072 50 A004 49 A027 55 A050 62 A073 52 A005 47 A028 70 A051 47 A074 33 A006 46 A029 37 A052 35 A075 57 A007 63 A030 55 A053 55 A076 59 A008 60 A031 54 A054 36 A077 66 A009 49 A032 49 A055 53 A078 48 A010 61 A033 47 A056 48 A079 59 A011 69 A034 46 A057 56 A080 64 A012 70 A035 63 A058 44 A081 46 A013 50 A036 60 A059 46 A082 61 A014 37 A037 49 A060 54 A083 28 A015 38 A038 61 A061 47 A084 40 A016 54 A039 41 A062 35 A085 31 A017 50 A040 54 A063 43 A086 53 A018 56 A041 47 A064 57 A087 30 A019 54 A042 45 A065 60 A088 36 A020 57 A043 52 A066 60 A089 32 A021 69 A044 43 A067 45 A090 52 A022 59 A045 56 A068 48 A091 49 A023 54 A046 57 A069 64 A092 34 Set A ₂ Sample
no. Age
year Sample
no. Age
year Sample
no. Age
year Sample
no. Age
year A093 57 A116 38 A139 39 A162 50 A094 69 A117 54 A140 38 A163 55 A095 59 A118 50 A141 50 A164 56 A096 54 A119 56 A142 52 A165 52 A097 49 A120 54 A143 58 A166 43 A098 52 A121 57 A144 44 A167 38 A099 52 A122 69 A145 42 A168 67 A100 55 A123 59 A146 61 A169 42 A101 70 A124 54 A147 36 A170 49 A102 37 A125 49 A148 61 A171 55 A103 55 A126 52 A149 31 A172 32 A104 54 A127 52 A150 55 A173 52 A105 49 A128 55 A151 46 A174 32 A106 47 A129 70 A152 55 A175 46 A107 46 A130 50 A153 64 A176 42 A108 63 A131 59 A154 33 A177 68 A109 60 A132 55 A155 56 A178 57 A110 49 A133 35 A156 51 A179 43 A111 61 A134 50 A157 51 A180 43 A112 69 A135 43 A158 48 A181 43 A113 70 A136 43 A159 32 A182 68 A114 50 A137 44 A160 57 A183 36 A115 37 A138 49 A161 63 A184 54 Set B Sample
no. Age
year Sample
no. Age
year Sample
no. Age
year Sample
no. Age
year B001 69 B024 46 B047 30 B070 46 B002 70 B025 63 B048 52 B071 65 B003 50 B026 60 B049 62 B072 38 B004 37 B027 49 B050 33 B073 33 B005 38 B028 61 B051 46 B074 35 B006 54 B029 69 B052 57 B075 63 B007 50 B030 70 B053 38 B076 75 B008 56 B031 50 B054 47 B077 47 B009 54 B032 37 B055 43 B078 63 B010 57 B033 38 B056 51 B079 41 B011 69 B034 54 B057 30 B080 49 B012 59 B035 50 B058 41 B081 62 B013 54 B036 56 B059 48 B082 28 B014 49 B037 54 B060 42 B083 51 B015 52 B038 47 B061 34 B084 48 B016 52 B039 40 B062 38 B085 52 B017 55 B040 35 B063 45 B086 50 B018 70 B041 57 B064 39 B087 43 B019 37 B042 48 B065 42 B088 36 B020 55 B043 59 B066 51 B089 54 B021 54 B044 46 B067 36 B090 27 B022 49 B045 52 B068 30 B091 19 B023 47 B046 40 B069 43 B092 73

Ovarian cancer patient sample information Set A ₁ Sample
no. Age
year Stage Histology A001 52 IIA Endometrioid adenocarcinoma A002 63 IV Papillary serous adenocarcinoma A003 55 IA Malignant Brenner tumor A004 50 IC Clear cell carcinoma A005 44 IIIC Papillary serous adenocarcinoma A006 53 IIIB Serous carcinoma A007 77 IIIC Transitional cell carcinoma A008 57 IIIC Clear cell carcinoma A009 51 IIB Serous carcinoma A010 74 IV Serous carcinoma A011 56 IIIC Serous carcinoma A012 50 IIIC Serous carcinoma A013 54 IIIC Serous carcinoma A014 66 IIIC Serous carcinoma A015 55 IA Clear cell carcinoma A016 58 IIIC Serous carcinoma A017 42 IIIC Serous carcinoma A018 44 IIA Clear cell carcinoma A019 54 IIIC Serous carcinoma A020 34 IA Serous carcinoma A021 41 IA Clear cell carcinoma A022 61 IIIC Serous carcinoma A023 65 IIIC Endometrioid carcinoma A024 52 IIIC Serous carcinoma A025 53 IC Serous carcinoma A026 57 IIIC Metastatic papillary serous carcinoma A027 57 IV Papillary serous carcinoma A028 48 IIIA2 Clear cell carcinoma A029 55 IIIC Papillary serous carcinoma A030 52 IV Serous adenocarcinoma Set A ₂ Sample
no. Age
year Stage Histology A031 73 IIIC Serous adenocarcinoma A032 47 IIIC Papillary serous adenocarcinoma A033 58 IIC Serous adenocarcinoma A034 57 IB Mixed cell adenocarcinoma A035 68 IA Mucinous carcinoma A036 67 IIIC Serous carcinoma A037 74 IIIC Serous carcinoma A038 67 IV Serous carcinoma A039 57 IIIC Serous carcinoma A040 57 IIIC Serous carcinoma A041 61 IIIA Endometrioid carcinoma A042 58 IIA Serous carcinoma A043 55 IV Serous carcinoma A044 51 IIIC Serous carcinoma A045 57 IV Serous carcinoma A046 52 IC Serous carcinoma A047 27 IA Mucinous carcinoma A048 52 IA Mucinous carcinoma A049 41 IIIC Serous carcinoma A050 46 IIIC Serous carcinoma A051 69 IIIC Serous carcinoma A052 57 IIIC Serous carcinoma A053 62 IIIC Serous carcinoma A054 73 IIIB Serous carcinoma A055 71 IIB Serous carcinoma A056 61 IC1 Serous carcinoma A057 60 IIIC Papillary serous carcinoma A058 51 IIIC Papillary serous carcinoma A059 41 IVB Metastatic papillary serous carcinoma A060 68 IIIC Serous carcinoma Set B Sample
no. Age
year Stage Histology B001 72 IIIC Serous adenocarcinoma B002 54 IIIC Papillary serous adenocarcinoma B003 40 IC Papillary serous adenocarcinoma B004 74 IIB Transitional cell carcinoma B005 47 IV Serous adenocarcinoma B006 67 IA Mucinous carcinoma B007 49 IIIC Serous carcinoma B008 59 IIIC Serous carcinoma B009 46 IIA Endometrioid carcinoma B010 44 IV Mucinous carcinoma B011 49 IIIC Serous carcinoma B012 44 IIIC Serous carcinoma B013 53 IIIC Serous carcinoma B014 52 IIIC Serous carcinoma B015 49 IA Endometrioid carcinoma B016 64 IIA Serous carcinoma B017 55 IA Serous carcinoma B018 52 IIIC Endometrioid carcinoma B019 43 IC Serous carcinoma B020 49 IIIC Serous carcinoma B021 37 IIIC Serous carcinoma B022 54 IIIA Serous carcinoma B023 48 IIIC Serous carcinoma B024 76 IIIB Mucinous carcinoma B025 75 IIIA Serous carcinoma B026 45 IC1 Clear cell carcinoma B027 51 IIIC Serous carcinoma B028 56 IIIC Papillary serous carcinoma B029 67 IV Serous carcinoma

Adenocarcinoma (MAC: mucinous adenocarcinoma) Set A ₁ Sample no. Age
year Stage Location Cell type Sample no. Age
year Stage Location Cell type A001 48 IIB T-colon AC A041 58 IIIB Colon A002 50 IIIA Rectum AC A042 56 IIIB Rectum A003 73 IIIA Rectum AC A043 67 IIA Colon A004 70 I A-colon AC A044 70 IIA Colon A005 41 I HF-colon AC A045 53 IIA Colon A006 51 IIIB A-colon AC A046 47 I Colon A007 33 IIIB S-colon AC A047 70 IIA Colon A008 58 I RS-colon A048 58 IIA Colon A009 53 IIIB HF-colon AC A049 71 IIA Colon A010 58 I S-colon AC A050 80 IIIB Colon A011 82 IIIB Rectum AC A051 77 IIA Colon A012 44 IIIB S-colon AC A052 73 IVB Colon A013 70 IIIB S-colon AC A053 72 I Rectum A014 84 IIB S-colon AC A054 50 I Rectum A015 79 0 S-colon AC A055 71 IIA Rectum A016 59 IIIB Rectum AC A056 56 IIIA Colon A017 48 I Rectum A057 56 IIIB Colon A018 52 IIIC S-colon AC A058 56 IIA Colon A019 53 IIA RS-colon AC A059 55 I Rectum A020 78 I Rectum AC A060 75 IIIB Colon A021 43 IIA RS-colon MAC A061 71 I Colon A022 66 IIA S-colon AC A062 69 I Rectum A023 73 IIA HF-colon AC A063 63 IIIB Colon A024 68 S-colon AC A064 74 IVB Rectum A025 74 IVA Rectum AC A065 71 IIIA Colon A026 55 IIIB RS-colon AC A066 66 IIA Colon A027 53 I D-colon AC A067 73 IIB Colon A028 74 I Rectum AC A068 78 IIA Colon A029 80 IIIB S-colon AC A069 63 IIA Rectum A030 82 IIIB Rectum AC A070 73 I Colon A031 53 IIIC S-colon AC A071 72 I Colon A032 46 IIIB RS-colon AC A072 55 IIIB Rectum A033 81 IIA D-colon AC A073 47 I Colon A034 56 IIIB S-colon MAC A074 71 IIA Colon A035 75 I A-colon AC A075 43 I Rectum A036 50 IIA S-colon AC A076 55 IIIA Colon A037 71 IIIA Colon A077 55 I Rectum A038 67 IIIB Colon A078 74 IIA Rectum A039 51 IIIA Rectum A079 71 I Colon A040 79 I Colon Set A ₂ Sample no. Age
year Stage Location Cell type Sample no. Age
year Stage Location Cell type A080 71 I RS-colon A120 41 I Colon A081 45 IIA S-colon AC A121 68 I Rectum A082 36 IIIB S-colon AC A122 76 II Colon A083 73 IIA S-colon AC A123 72 I Rectum A084 86 IIA Rectum AC A124 47 I Colon A085 68 IIIB A-colon AC A125 52 IIA Colon A086 71 I S-colon AC A126 40 IIIC Colon A087 84 IIIB A-colon AC A127 62 IIIC Colon A088 69 IIA Rectum AC A128 72 IIIA Rectum A089 39 IIA Rectum AC A129 50 IIIB Colon A090 42 IIIB A-colon AC A130 68 IIA Colon A091 49 IIIA A-colon AC A131 62 IIIA Rectum A092 47 IIIC S-colon AC A132 75 IIIA Colon A093 78 IIIB RS-colon AC A133 56 I Rectum A094 65 I Rectum AC A134 52 IIA Rectum A095 54 I S-colon AC A135 65 I Rectum A096 59 I S-colon A136 74 IIIB Rectum A097 78 IIA S-colon AC A137 81 II Colon A098 72 IIIB Rectum AC A138 70 IIIC Colon A099 54 IIIB S-colon AC A139 76 IIA Colon A100 73 I A-colon AC A140 59 IVA Colon A101 54 IVA A-colon AC A141 70 IIIB Rectum A102 73 IIIB T-colon AC A142 65 III Colon A103 69 I A-colon AC A143 56 IIIC Colon A104 62 IIIB A-colon A144 63 II Colon A105 79 IIA RS-colon AC A145 51 I Rectum A106 57 IIIC A-colon AC A146 30 II Rectum A107 66 IIA S-colon AC A147 65 I Colon A108 65 IIIB Rectum AC A148 46 II Rectum A109 48 IIIC RS-colon AC A149 56 I Colon A110 63 I Rectum AC A150 62 IIIC Colon A111 55 IIIB RS-colon AC A151 78 I Colon A112 60 I S-colon AC A152 49 IIA Colon A113 46 IIIC Rectum AC A153 55 IIC Colon A114 38 I S-colon AC A154 78 IIIB Rectum A115 77 II Rectum A155 85 IIA Colon A116 82 II Colon A156 73 I Rectum A117 76 IVA Colon A157 76 IIA Rectum A118 74 IIC Colon A158 63 I Colon A119 72 I Colon

 Gastric cancer patient sample information Set A ₁ Sample no. Age
year Stage Sample no. Age
year Stage Sample no. Age
year Stage A001 62 IA A017 73 IA A033 62 IA A002 67 IA A018 59 IB A034 54 IA A003 64 IB A019 81 IV A035 67 IA A004 41 IB A020 86 IV A036 64 IA A005 81 IIIB A021 45 IV A037 82 IIA A006 79 IA A022 60 IIB A038 52 IV A007 42 IA A023 81 IA A039 35 IA A008 42 IB A024 70 IA A040 44 IA A009 54 IA A025 55 IA A041 40 IA A010 45 IIIC A026 54 IA A042 66 IA A011 56 IA A027 56 IA A043 38 IV A012 61 IA A028 72 IIIA A044 74 IA A013 70 IA A029 76 IA A045 77 IA A014 72 IA A030 82 IIA A046 55 IIIB A015 64 IA A031 49 IIIA A047 56 0 A016 36 IA A032 35 IIB Set A ₂ Sample no. Age
year Stage Sample no. Age
year Stage Sample no. Age
year Stage A048 62 IA A064 60 IIIB A079 42 IA A049 65 IA A065 60 IA A080 46 IB A050 54 IA A066 63 IA A081 59 IA A051 58 IIB A067 48 IA A082 69 IA A052 40 IA A068 64 IB A083 60 IB A053 74 IV A069 75 IIIA A084 46 IA A054 77 IV A070 80 IA A085 42 IA A055 28 IA A071 72 IA A086 77 IIA A056 70 IIIB A072 53 IA A087 69 IIIB A057 38 IV A073 70 IA A088 79 IB A058 72 IA A074 57 IB A089 52 IIB A059 62 IA A075 50 IA A090 50 IV A060 67 IA A076 40 IV A091 62 IA A061 58 IIIA A077 71 IA A092 63 IA A062 75 IA A078 48 IIB A093 41 IA A063 68 IA Set B Sample no. Age
year Stage Sample no. Age
year Stage Sample no. Age
year Stage B001 66 IV B017 78 IA B032 74 IIA B002 75 IB B018 37 IA B033 81 IB B003 65 IA B019 57 IA B034 58 IA B004 71 IA B020 58 IA B035 69 IA B005 68 IA B021 42 IA B036 46 IA B006 43 IA B022 56 IIIB B037 77 IA B007 50 IA B023 50 IA B038 76 IA B008 59 IA B024 53 IIIB B039 51 IA B009 63 IA B025 57 IA B040 35 IA B010 66 IA B026 39 IA B041 49 IA B011 30 IA B027 63 IV B042 57 IA B012 47 IA B028 45 IA B043 81 IIIA B013 39 IA B029 58 IV B044 36 IIIB B014 76 IIA B030 41 IIIA B045 33 IV B015 71 IV B031 76 IIB B046 63 0 B016 56 IA

Uterine benign tumor patient sample information Set A ₁ Sample
no. Age
year Pathology Sample
no. Age
year Pathology A001 41 Leiomyoma A015 34 Leiomyoma A002 52 Adenomyosis A016 51 Leiomyoma A003 42 Adenomyosis A017 42 Leiomyoma A004 32 Leiomyoma A018 65 Leiomyoma A005 41 Leiomyoma A019 47 Leiomyoma A006 45 Leiomyoma A020 44 Leiomyoma A007 51 Leiomyoma A021 39 Leiomyoma A008 42 Leiomyoma A022 38 Leiomyoma A009 49 Leiomyoma A023 32 Leiomyoma A010 39 Leiomyoma A024 45 Leiomyoma A011 48 Leiomyoma A025 47 Leiomyoma A012 38 Leiomyoma A026 35 Leiomyoma A013 57 Leiomyoma A027 45 Leiomyoma A014 41 Leiomyoma Set A ₂ Sample
no. Age
year Pathology Sample
no. Age
year Pathology A028 70 Leiomyoma A042 48 Leiomyoma A029 56 Leiomyoma A043 42 Leiomyoma A030 50 Leiomyoma A044 51 Leiomyoma A031 48 Adenomyosis A045 48 Leiomyoma A032 51 Leiomyoma A046 51 Leiomyoma A033 47 Leiomyoma A047 48 Leiomyoma A034 50 Leiomyoma A048 45 Leiomyoma A035 47 Leiomyoma A049 56 Adenomyosis A036 67 Leiomyoma A050 40 Leiomyoma A037 57 Leiomyoma A051 44 Leiomyoma A038 44 Leiomyoma A052 44 Leiomyoma A039 29 Leiomyoma A053 47 Leiomyoma A040 25 Leiomyoma A054 45 Leiomyoma A041 69 Leiomyoma A055 40 Leiomyoma Set B Sample
no. Age
year Pathology Sample
no. Age
year Pathology B001 42 Leiomyoma B015 47 Endometrial polyp B002 36 Leiomyoma B016 32 Leiomyoma B003 50 Leiomyoma B017 50 Leiomyoma B004 48 Adenomyosis B018 56 Leiomyoma B005 41 Adenomyosis B019 46 Endometrial polyp B006 46 Chronic inflammation with squamous metaplasia B020 49 Leiomyoma B007 39 Leiomyoma B021 39 Leiomyoma B008 46 Leiomyoma B022 36 Leiomyoma B009 43 Leiomyoma B023 48 Leiomyoma B010 40 Leiomyoma B024 59 Leiomyoma B011 50 Leiomyoma B025 44 Leiomyoma B012 50 Leiomyoma B026 40 Leiomyoma B013 49 Leiomyoma B027 47 Leiomyoma B014 39 Leiomyoma B028 45 Leiomyoma

Ovarian Benign Tumor Patient Sample Information Set A ₁ Sample
no. Age
year Pathology A001 45 Chronic salpingitis A002 26 Cystic teratoma A003 62 Serous cystadenoma A004 53 Chronic salpingitis A005 45 Mucinous cystadenoma A006 36 Endometriosis A007 38 Acute serositis, Chronic endometritis, Cervicitis A008 29 Endometriosis A009 25 Mature cystic teratoma A010 54 Fibrothecoma A011 57 Mucinous cystadenoma A012 41 Serous cystadenoma A013 36 Endometriotic cyst A014 23 Mature cystic teratoma A015 51 Endometriotic cyst A016 42 Serous cystadenofibroma A017 33 Endometriotic cyst A018 31 Endometriotic cyst A019 30 Mature cystic teratoma A020 49 Endometriotic cyst A021 56 Serous cystadenoma A022 26 Mature cystic teratoma A023 29 Endometriosis Set A ₂ Sample
no. Age
year Pathology A024 45 Mucinous cystadenoma A025 73 Solitary fibrous tumor A026 43 Endometriosis A027 42 Endometriosis A028 52 Pseudomyxoma peritonei, Small serous cystadenoma A029 62 Serous cystadenoma A030 55 Serous cystadenoma A031 36 Endometriotic cyst A032 29 Cystic teratoma A033 24 Endometriotic cyst A034 36 Endometriosis A035 24 Multilocular peritoneal inclusion cyst A036 45 Endometriosis A037 42 Endometriosis A038 36 Serous cystadenoma A039 43 Serous cystadenoma A040 34 Mucinous cystadenoma A041 54 Mucinous cystadenoma A042 30 Serous cystadenofibroma A043 43 Endometriosis A044 30 Mature cystic teratoma A045 72 Simple mesothelial cyst A046 25 Mature cystic teratoma A047 42 Endometriosis Set B Sample
no. Age
year Pathology B001 55 Mature cystic teratoma B002 83 Serous cystadenoma B003 41 Endometriosis B004 39 Endometriosis B005 71 Abscess B006 49 Endometriosis B007 48 Paratubal cyst B008 49 Adenofibroma, Paratubal cyst B009 34 Endometriosis B010 43 Ovarian and adnexal tissue with endometriosis B011 45 Endometriosis B012 21 Serous papillary adenofibroma B013 32 Endometriotic cyst B014 60 Serous cystadenoma B015 31 Mature cystic teratoma B016 41 Endometriosis B017 58 Serous cystadenoma B018 31 Endometriosis with focal degenerative atypia B019 52 Simple cyst with atrophic ovary B020 26 Mature cystic teratoma B021 42 Endometriotic cyst B022 34 Endometriosis B023 46 Serous cystadenoma B024 20 Cystic teratoma

Cervical cancer precancerous lesion patient sample information Set A ₁ Sample
no. Age
year Pathology A001 40 Adenocarcinoma in situ A002 44 Cervical intraepithelial neoplasia III A003 50 Carcinoma in situ A004 71 Cervical intraepithelial neoplasia I A005 47 Atypical squamous cells of undetermined significance A006 29 Cervical intraepithelial neoplasia I A007 46 Cervical intraepithelial neoplasia I A008 54 Atypical squamous cells, can not rule out high-grade squamous intraepithelial lesion A009 33 Cervical intraepithelial neoplasia III A010 50 Carcinoma in situ A011 71 Cervical intraepithelial neoplasia II A012 42 Cervical intraepithelial neoplasia III A013 36 Cervical intraepithelial neoplasia III A014 45 Cervical intraepithelial neoplasia I A015 52 Cervical intraepithelial neoplasia I A016 64 Cervical intraepithelial neoplasia II A017 45 Cervical intraepithelial neoplasia I A018 40 Cervical intraepithelial neoplasia II A019 48 Cervical intraepithelial neoplasia I A020 34 Cervical intraepithelial neoplasia III A021 50 Cervical intraepithelial neoplasia III A022 33 Cervical intraepithelial neoplasia II A023 42 Carcinoma in situ A024 70 Cervical intraepithelial neoplasia III A025 74 Carcinoma in situ A026 31 Cervical intraepithelial neoplasia II A027 30 Cervical intraepithelial neoplasia III A028 39 Cervical intraepithelial neoplasia II A029 48 Cervical intraepithelial neoplasia III A030 41 Carcinoma in situ Set A ₂ Sample
no. Age
year Pathology A031 32 Cervical intraepithelial neoplasia II A032 52 Cervical intraepithelial neoplasia I-II A033 45 Cervical intraepithelial neoplasia II A034 51 Cervical intraepithelial neoplasia III A035 41 Cervical intraepithelial neoplasia II-III A036 32 Cervical intraepithelial neoplasia III A037 36 Cervical intraepithelial neoplasia II A038 29 Cervical intraepithelial neoplasia III A039 32 Cervical intraepithelial neoplasia III A040 36 Carcinoma in situ A041 33 Cervical intraepithelial neoplasia III A042 34 Cervical intraepithelial neoplasia I A043 40 Carcinoma in situ A044 47 Cervical intraepithelial neoplasia I A045 46 Cervical intraepithelial neoplasia I A046 42 Cervical intraepithelial neoplasia I A047 31 Cervical intraepithelial neoplasia I A048 60 Carcinoma in situ A049 47 Cervical intraepithelial neoplasia II A050 36 Cervical intraepithelial neoplasia III A051 31 Cervical intraepithelial neoplasia III A052 25 Cervical intraepithelial neoplasia III A053 44 Carcinoma in situ A054 37 Cervical intraepithelial neoplasia III A055 44 Cervical intraepithelial neoplasia II A056 42 Cervical intraepithelial neoplasia III A057 28 Cervical intraepithelial neoplasia III A058 49 Cervical intraepithelial neoplasia I A059 51 Carcinoma in situ Set B Sample
no. Age
year Pathology B001 34 Cervical intraepithelial neoplasia III B002 58 Cervical intraepithelial neoplasia III B003 26 Cervical intraepithelial neoplasia III B004 41 Carcinoma in situ B005 39 Cervical intraepithelial neoplasia II B006 41 Adenocarcinoma in situ B007 36 Cervical intraepithelial neoplasia III B008 51 Cervical intraepithelial neoplasia I B009 32 Cervical intraepithelial neoplasia III B010 54 Cervical intraepithelial neoplasia II-III B011 25 Cervical intraepithelial neoplasia II B012 79 Cervical intraepithelial neoplasia II B013 31 Cervical intraepithelial neoplasia II B014 33 Cervical intraepithelial neoplasia II B015 30 Adenocarcinoma in situ B016 48 Carcinoma in situ B017 22 Carcinoma in situ B018 24 Cervical intraepithelial neoplasia III B019 37 Cervical intraepithelial neoplasia III B020 58 Cervical intraepithelial neoplasia III B021 39 Cervical intraepithelial neoplasia II B022 48 Cervical intraepithelial neoplasia II B023 38 Cervical intraepithelial neoplasia I B024 27 Cervical intraepithelial neoplasia III B025 40 Cervical intraepithelial neoplasia I B026 30 Carcinoma in situ B027 29 Carcinoma in situ B028 49 Cervical intraepithelial neoplasia II B029 53 Cervical intraepithelial neoplasia I

Breast cancer patient sample information Set A ₁ Sample no. Age
year Stage Sample no. Age
year Stage Sample no. Age
year Stage A001 52  IIA A012 45  IB A022 50  IIA A002 52  IIA A013 42  IA A023 46  IIA A003 40  IA A014 41  IA A024 57  IIA A004 40  IA A015 67  IA A025 38  IIA A005 51  IIA A016 40  IIB A026 40  IIIA A006 64  IA A017 68  IA A027 52  IIA A007 57  IA A018 53  IA A028 50 0 A008 69 0 A019 41 0 A029 47 0 A009 33  IIA A020 49  IA A030 47  IIIA A010 44  IA A021 42  IIA A031 42  IIIA A011 29  IA Set A ₂ Sample no. Age
year Stage Sample no. Age
year Stage Sample no. Age
year Stage A032 58  IIB A043 57  IIB A053 37  IIIA A033 51  IIA A044 33 0 A054 69  IIB A034 46  IIA A045 40  IA A055 49  IIA A035 41  IIB A046 42 0 A056 54  IIA A036 45  IA A047 52  IA A057 39  IA A037 35  IB A048 40  IIIA A058 44  IA A038 41  IIB A049 67  IA A059 69  IA A039 56  IA A050 43  IIB A060 30  IA A040 55  IA A051 50  IA A061 38  IIA A041 41  IIB A052 46  IIIA A062 41 0 A042 52  IA Set B Sample no. Age
year Stage Sample no. Age
year Stage Sample no. Age
year Stage B001 46  IA B012 57  IA B022 51  IIA B002 50  IA B013 50  IA B023 51  IIA B003 55  IA B014 44  IIA B024 48  IIB B004 66  IA B015 46  IIA B025 47  IIB B005 42  IIA B016 42  IA B026 64  IIIC B006 45  IA B017 59  IA B027 52 0 B007 33  IB B018 41  IA B028 31  IIIA B008 51  IIA B019 51  IIA B029 37 0 B009 57  IA B020 54  IA B030 43 0 B010 52  IIA B021 46  IIA B031 42 0 B011 46  IA

Breast-positive tumor patient sample information Set A ₁ Sample
no. Age
year Sample
no. Age
year Sample
no. Age
year Sample
no. Age
year A001 42 A007 62 A013 40 A018 21 A002 62 A008 19 A014 55 A019 41 A003 22 A009 42 A015 54 A020 46 A004 47 A010 42 A016 52 A021 49 A005 55 A011 46 A017 48 A022 48 A006 56 A012 39 Set A ₂ Sample
no. Age
year Sample
no. Age
year Sample
no. Age
year Sample
no. Age
year A023 58 A029 55 A034 38 A039 46 A024 22 A030 45 A035 49 A040 32 A025 42 A031 53 A036 33 A041 54 A026 46 A032 48 A037 36 A042 52 A027 27 A033 39 A038 32 A043 50 A028 39 Set B Sample
no. Age
year Sample
no. Age
year Sample
no. Age
year Sample
no. Age
year B001 49 B007 46 B013 41 B018 61 B002 53 B008 38 B014 46 B019 40 B003 42 B009 41 B015 54 B020 46 B004 46 B010 38 B016 42 B021 59 B005 53 B011 54 B017 52 B022 60 B006 59 B012 47

Cervical cancer patient sample information Set B Sample
no. Age
year Stage Pathology B001 26 IIB Squamous cell carcinoma B002 32 IB2 Squamous cell carcinoma B003 71 IIB Squamous cell carcinoma B004 34 IA1 Microinvasive squamous cell carcinoma B005 50 IB1 Adenocarcinoma B006 52 IB Squamous cell carcinoma B007 55 IB1 Adenosquamous carcinoma B008 45 IIB B009 40 I Endocervical adenocarcinoma B010 43 IB1 Squamous cell carcinoma B011 40 IB1 Squamous cell carcinoma B012 43 IB1 Squamous cell carcinoma B013 40 IV Squamous cell carcinoma B014 45 I Squamous cell carcinoma B015 46 IB1 Adenosquamous carcinoma B016 64 IB1 Squamous cell carcinoma B017 44 IB1 Mucinous adenocarcinoma B018 43 IIB Squamouscellcarcinoma B019 44 IA1 Squamous cell carcinoma B020 47 IIB Squamous cell carcinoma B021 44 IA1 Microinvasive squamous cell carcinoma B022 30 IV Adenosquamouscellcarcinoma B023 51 IB1 Squamous cell carcinoma B024 50 IB1 Squamous cell carcinoma B025 67 IB1 Squamous cell carcinoma B026 70 IIA Squamous cell carcinoma B027 74 IIB Squamous cell carcinoma B028 74 IIB Squamous cell carcinoma B029 73 IB1 Squamous cell carcinoma B030 82 IIB Squamous cell carcinoma B031 49 IIIB Squamous cell carcinoma B032 72 IIB Microinvasive papillary squamous cell carcinoma B033 32 IA1 Microinvasive squamous cell carcinoma

Endometrial cancer patient sample information Set B Sample
no. Age
year Stage Pathology B001 58 IIIC Endometrioid adenocarcinoma B002 66  IA Endometrioid adenocarcinoma B003 49  IA Endometrioid adenocarcinoma B004 64  IIIC2 Metastatic carcinoma B005 54  IA Endometrioid adenocarcinoma B006 52  IA Endometrioid adenocarcinoma B007 54 IA Endometrioidadenocarcinoma B008 54 IA Endometrioid adenocarcinoma B009 40 IA Endometrioid adenocarcinoma B010 45 IA Endometrioid adenocarcinoma

The results of applying PCA-DA to the reference mass spectrum of training set A ₁ are shown in FIG. In FIG. 2, DS denotes a discriminant score. When the discrimination score 0 point (horizontal dotted line) is used as reference, low specificity is obtained depending on the patient group. However, when the discrimination score threshold is changed to the horizontal solid line of FIG. 2, the discrimination result on the reference mass spectrum shows that both the sensitivity and the specificity It can be seen that it is very excellent.

Through the process of deriving the first group candidates, 176 first group candidates were selected. FIG. 3 shows a result of discrimination of the reference mass spectrum with only 176 first group candidates. As shown in FIG. 2, which is discriminated by 10,000 low-mass ions, not only the sensitivity and specificity were maintained, but the range of the discrimination score did not change much even though only low-mass ions which were not 2% of the total low-mass ions were used You can see the sound.

Using the method described above, thirteen first groups were derived from the first group candidates.

Example - ovarian cancer determination - use of group 1

The results of the determination using 13 low-mass ions of the first group for training sets A ₁ and A ₂ are shown in FIGS. 4 and 5, respectively.

The diagnostic performance of the training set A ₁ and A ₂ is shown in Table 15, and it can be confirmed that the sensitivity and the specificity are excellent.

FIG. 6 shows the result of verifying the verification set B using 13 low-mass ions of the first group.

Table 16 shows the diagnostic performance of the verification set B, and it can be confirmed that the sensitivity and specificity are also excellent for the verification set.

Example - Ovarian cancer determination - use of first group and second group

The results of the determination using 13 low-mass ions of the first group and three low-mass ions of the second group for training sets A ₁ and A ₂ are shown in FIGS. 7 and 8, respectively.

The diagnostic performance of the training set A ₁ and A ₂ is shown in Table 17, and it can be confirmed that the sensitivity and the specificity are excellent. When only the first group was used, the sample with a positive discrimination score was judged to be ovarian cancer positive, while the sample with negative discrimination score was judged negative to ovarian cancer. That is, the threshold value of the discrimination score for determining whether ovarian cancer was positive / negative was 0 point. When the first group and the second group are used, the threshold value of the discrimination score (the horizontal solid line in FIGS. 7 and 8) is determined so that the diagnostic performance for the training set is maximized. The threshold value of the discrimination score thus selected is applied to the judgment of the verification set as it is.

FIG. 9 shows the result of verifying the verification set B using thirteen low-mass ions of the first group and three low-mass ions of the second group.

The diagnostic performance for the assertion set B is shown in Table 18, and it can be confirmed that the sensitivity and specificity are also excellent for the asserted set. Particularly, high specificity was also shown for 13 cervical cancer and endometrial cancer, which were not included in the training group when only 13 low - mass ions of group 1 were used and when compared.

The above-described embodiments are illustrative of the present invention, and the present invention is not limited thereto. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the appended claims.

Claims (15)

In an ovarian cancer diagnosis apparatus,
An input unit for inputting mass spectral data of low mass ions detected in the biological sample;
And a diagnosis unit for determining ovarian cancer diagnosis information on the mass spectral data input from the input unit using a low mass ion group for first ovarian cancer diagnosis,
The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z And at least one low-mass ion. The method according to claim 1,
The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z And at least three low-mass ions. The method according to claim 1,
The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z And at least five low-mass ions. 4. The method according to any one of claims 1 to 3,
Wherein the diagnostic unit further uses a low mass ion group for diagnosing a second ovarian cancer to determine ovarian cancer diagnostic information for the mass spectral data,
Wherein the second ovarian cancer diagnostic low mass ion group comprises at least one low mass ion among 496.5220, 524.5837, and 1466.7073 m / z (with an error range of +/- 0.1%). 5. The method of claim 4,
Wherein the second low-mass ion group for diagnosing ovarian cancer comprises 496.5220, 524.5837, and 1466.7073 m / z (with an error range of +/- 0.1%). In an ovarian cancer diagnosis apparatus,
A diagnostic unit for diagnosing ovarian cancer diagnosis information on mass spectral data of low mass ions detected in the biological sample using a low mass ion group for first ovarian cancer diagnosis;
And an output unit for outputting a diagnosis result of the diagnosis unit,
The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z And at least one low-mass ion. The method according to claim 6,
The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z And at least three low-mass ions. The method according to claim 6,
The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z And at least five low-mass ions. 9. The method according to any one of claims 6 to 8,
Wherein the diagnostic unit further uses a low mass ion group for diagnosing a second ovarian cancer to determine ovarian cancer diagnostic information for the mass spectral data,
Wherein the second ovarian cancer diagnostic low mass ion group comprises at least one low mass ion among 496.5220, 524.5837, and 1466.7073 m / z (with an error range of +/- 0.1%). 10. The method of claim 9,
Wherein the second low-mass ion group for diagnosing ovarian cancer comprises 496.5220, 524.5837, and 1466.7073 m / z (with an error range of +/- 0.1%). A method for providing information for ovarian cancer diagnosis,
Obtaining mass spectral data of low mass ions in a biological sample;
And providing ovarian cancer diagnostic information on the mass spectral data using a low mass ion group for first ovarian cancer diagnosis,
The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z And at least one low-mass ion. 12. The method of claim 11,
The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z Wherein the diagnostic information comprises at least three low-mass ions. 12. The method of claim 11,
The low mass ion group for diagnosis of the first ovarian cancer was found to be in the range of 21.1873, 37.0311, 37.5142, 37.7989, 38.6222, 84.8716, 147.1669, 171.4024, 175.1585, 188.8544, 190.7493, 230.0197 and 709.7879 m / z Wherein the diagnostic information comprises at least five low-mass ions. 14. The method according to any one of claims 11 to 13,
The ovarian cancer diagnosis information providing step may further include a low mass ion group for the second ovarian cancer diagnosis to provide ovarian cancer diagnosis information on the mass spectral data,
Wherein the second low-mass ion group for ovarian cancer diagnosis comprises at least one low mass ion among 496.5220, 524.5837 and 1466.7073 m / z (with an error range of +/- 0.1%). 15. The method of claim 14,
Wherein the second low-mass ion group for diagnosing ovarian cancer comprises 496.5220, 524.5837, and 1466.7073 m / z (with an error range of +/- 0.1%).

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