KR102279107B1 - Method for providing information for diagnosis in patients with pancreatic cancer - Google Patents

Method for providing information for diagnosis in patients with pancreatic cancer Download PDF

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KR102279107B1
KR102279107B1 KR1020190114969A KR20190114969A KR102279107B1 KR 102279107 B1 KR102279107 B1 KR 102279107B1 KR 1020190114969 A KR1020190114969 A KR 1020190114969A KR 20190114969 A KR20190114969 A KR 20190114969A KR 102279107 B1 KR102279107 B1 KR 102279107B1
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phosphatidylcholine
carnitine
alkyl
pancreatic cancer
acyl
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강창무
박민수
이상국
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연세대학교 산학협력단
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Abstract

개체의 췌장암을 진단하기 위한 정보를 제공하는 방법 및 췌장암을 진단하는 키트에 관한 것이다. 일 양상에 따른 개체의 췌장암을 진단하기 위한 정보를 제공하는 방법 및 이를 이용한 키트에 따르면, 정상인 혹은 다른 질환을 앓는 환자로부터 일정 대사체 만으로 높은 정도의 특이도 및 진단도로 경제적으로 췌장암을 효과적으로 진단할 수 있어 췌장암 환자 맞춤형 치료가 가능하다.It relates to a method for providing information for diagnosing pancreatic cancer in an individual and a kit for diagnosing pancreatic cancer. According to a method for providing information for diagnosing pancreatic cancer in an individual according to an aspect and a kit using the same, it is possible to economically effectively diagnose pancreatic cancer with a high degree of specificity and diagnostic degree only with a certain metabolite from a normal person or a patient suffering from another disease. This allows for customized treatment for pancreatic cancer patients.

Description

췌장암을 진단하기 위한 정보를 제공하는 방법{Method for providing information for diagnosis in patients with pancreatic cancer}Method for providing information for diagnosis in patients with pancreatic cancer

개체의 췌장암을 진단하기 위한 정보를 제공하는 방법 및 췌장암을 진단하기 위한 키트 등에 관한 것이다.It relates to a method for providing information for diagnosing pancreatic cancer in an individual, a kit for diagnosing pancreatic cancer, and the like.

췌장암은 위장관에서 발생하는 가장 치명적인 암 중 하나로, 다른 암에 비해 치료 성적이 극히 불량하여 생존기간이 14개월에 불과하다. 2008년 국가 암 등록 통계에 의하면 췌장암은 전체 암 종 중 발생분율 9위(10만명당 8.7명 발생)를 차지한 반면, 사망분율 5위를 차지하고 있다. 우리나라 전체 췌장암 환자의 5년 생존율은 평균 7.6%로서, 종양 의학의 지속적인 발전에 힘입어 전체 암 환자의 생존율은 꾸준히 증가추세를 보임에도 불구하고, 다른 암과 다르게 췌장암의 생존율은 지난 20여 년간 거의 향상되지 않았다. 2030년 췌장암이 암 관련 사망의 두 번째 원인이 될 것으로 예측되고 있다. Pancreatic cancer is one of the most lethal cancers occurring in the gastrointestinal tract, and the survival time is only 14 months due to the extremely poor treatment results compared to other cancers. According to the 2008 national cancer registration statistics, pancreatic cancer ranks ninth in incidence (8.7 cases per 100,000) among all cancers, while it ranks fifth in mortality. The 5-year survival rate of all pancreatic cancer patients in Korea is an average of 7.6%, and despite the steady increase in the survival rate of all cancer patients thanks to the continuous development of oncology, the survival rate of pancreatic cancer, unlike other cancers, has been almost unchanged for the past 20 years. did not improve Pancreatic cancer is predicted to become the second leading cause of cancer-related deaths by 2030.

따라서, 상기 개시한 바와 같은 사망 위험도가 높은 것은 대부분 췌장암이 진행된 후에 발견되기 때문이며, 발견 당시 수술 절제가 가능한 경우가 20% 이내이고, 육안으로 보기에 완전히 절제되었다 하더라도 미세 전이에 의해 재발율이 높고 생존율 향상이 적으며, 항암제 및 방사선 치료에 대한 반응이 낮기 때문이다. 따라서 생존율을 향상시킬 수 있는 가장 중요한 방법은 증상이 없거나 비특이적일 때 조기 발견하여 수술하는 것이다. 그러나 췌장은 후복막에 다른 장기들에 둘러 싸여져 있고, 초기에 증상이 거의 없어 조기 진단이 매우 어렵다. 이에 췌장암을 우수한 민감도와 특이도로 간편하고 경제적으로 진단하기 위한 췌장암 진단에 대한 연구가 필요한 실정이다.Therefore, the high risk of death as described above is mostly because it is discovered after pancreatic cancer has progressed, and surgical resection is possible within 20% of the time at the time of discovery, and even if it is completely resected visually, the recurrence rate is high due to micrometastasis, and the survival rate is high. This is because the improvement is small and the response to chemotherapy and radiation therapy is low. Therefore, the most important way to improve the survival rate is early detection and surgery when there are no symptoms or non-specific symptoms. However, the pancreas is surrounded by other organs in the retroperitoneum, and early diagnosis is very difficult because there are few symptoms in the early stages. Accordingly, there is a need for a study on the diagnosis of pancreatic cancer to diagnose pancreatic cancer simply and economically with excellent sensitivity and specificity.

일 양상은 개체의 췌장암을 진단하기 위한 정보를 제공하는 방법에 관한 것이다.One aspect relates to a method of providing information for diagnosing pancreatic cancer in a subject.

다른 양상은 개체의 췌장암을 진단하기 위한 키트에 관한 것이다. Another aspect relates to a kit for diagnosing pancreatic cancer in a subject.

다른 양상은 상기 방법을 실행하기 위한 컴퓨터 프로그램을 기록한 컴퓨터로 판독 가능한 기록 매체에 관한 것이다.Another aspect relates to a computer-readable recording medium storing a computer program for executing the method.

또 다른 양상은 개체의 췌장암을 진단하기 위한 키트에 관한 것이다.Another aspect relates to a kit for diagnosing pancreatic cancer in a subject.

또 다른 양상은 Ala, Asn, Asp, Cit, Glu, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Thr, Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC (C4-OH), C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10:2, C12-DC, C14, C16-OH, C16:1-OH, lysoPC a C16:0, lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42:2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38:4, PC ae C40:1, PC ae C40:4, PC ae C40:5, PC ae C42:3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6, SM (OH) C14:1, SM (OH) C16:1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0, SM C16:1, SM C18:0, SM C18:1, SM C20:2, SM C24:0, SM C24:1, SM C26:0, SM C26:1, 또는 이의 조합에서 선택된 변수를 수신하는 수신부; 상기 변수에 대한 예측 점수를 산출하는 점수 산출부; 및 상기 예측 점수를 기초로 췌장암 진단 확률을 산출하는 확률 산출부를 포함하는 췌장암 진단장치에 관한 것이다.Another aspect is Ala, Asn, Asp, Cit, Glu, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Thr, Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC ( C4-OH), C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10:2, C12-DC, C14, C16-OH, C16:1-OH, lysoPC a C16:0, lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42 :2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38:4, PC ae C40:1, PC ae C40:4, PC ae C40:5 , PC ae C42:3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6, SM (OH) C14:1, SM (OH) C16 :1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0, SM C16:1, SM C18:0, SM C18:1, SM C20: 2, a receiver for receiving a variable selected from SM C24:0, SM C24:1, SM C26:0, SM C26:1, or a combination thereof; a score calculation unit for calculating a predicted score for the variable; and a probability calculator for calculating a pancreatic cancer diagnosis probability based on the predicted score.

본 발명은 개체로부터 분리된 혈액 시료에서 췌장암 환자만을 높은 정도의 특이도 및 정확도로 진단할 수 있는 방법을 발견하기 위한 연구 결과, 대사체만으로 췌장암 환자를 우수하게 진단할 수 있는 모델을 확립하고 이를 검증함으로써 개체의 췌장암을 진단하기 위한 정보를 제공하는 방법을 발명하였다.The present invention establishes a model capable of excellently diagnosing pancreatic cancer patients only with metabolites as a result of a study to discover a method for diagnosing only pancreatic cancer patients with high degree of specificity and accuracy from blood samples isolated from individuals. A method for providing information for diagnosing an individual's pancreatic cancer by verification was invented.

일 양상은 개체의 췌장암을 진단하기 위한 정보를 제공하는 방법을 제공한다.One aspect provides a method of providing information for diagnosing pancreatic cancer in a subject.

본 명세서에서 "췌장암"은 가장 흔하고 중요한 악성종양인 췌관 관세포암(pancreatic ductal adenocarcinoma)만을 포함하는 것이며, 또한 1기, 2기 등과 같이 분류되는 암의 진행단계에 따른 췌장암을 포함할 수 있다.As used herein, "pancreatic cancer" includes only pancreatic ductal adenocarcinoma, which is the most common and important malignancy, and may also include pancreatic cancer according to the stage of cancer classified as stage I or II.

명세서에서 용어 "대사체"는 생체 기원의 시료로부터 수득한 대사물질을 말하며 바람직하게, 상기 대사체를 수득할 수 있는 생체 기원의 시료는 전혈, 혈장, 혈청, 혈소판일 수 있고, 더욱 바람직하게는 혈장일 수 있다. 상기 대사체는 대사 및 대사 과정에 의해 생산된 물질 또는 생물학적 효소 및 분자에 의한 화학적 대사작용으로 발생한 물질 등을 포함할 수 있다. As used herein, the term "metabolite" refers to a metabolite obtained from a sample of biological origin, and preferably, a sample of biological origin from which the metabolite can be obtained may be whole blood, plasma, serum, platelets, and more preferably It may be plasma. The metabolite may include a substance produced by metabolism and metabolic processes or a substance generated by chemical metabolism by biological enzymes and molecules.

상기 방법은 개체의 생물학적 시료에서 n개의 대사체 시료를 수득하는 단계; 수득된 대사체 시료에서 n개의 대사체 수준을 측정하는 단계; 측정된 n개의 대사체 수준을 정규화 하여 x값을 도출하는 단계; 상기 x값을 각각 지정된 대사체 상수와 곱하여 n개의 대사체 상수의 합에 회귀계수를 더한 a값을 도출하는 단계; 및 상기 도출된 a 값을 하기 수학식에 대입하여 개체가 췌장암 진단 예측 확률(y)을 계산하는 단계를 포함하는 방법을 포함할 수 있다. The method includes obtaining n samples of metabolites from a biological sample of the subject; measuring the level of n metabolites in the obtained metabolite sample; deriving an x value by normalizing the measured n metabolite levels; deriving a value a by adding a regression coefficient to the sum of n metabolite constants by multiplying the x value by a specified metabolite constant; and calculating a predictive probability (y) of an individual's diagnosis of pancreatic cancer by substituting the derived value of a into the following equation.

[수학식][Equation]

Figure 112019095542593-pat00001
Figure 112019095542593-pat00001

상기 방법에 있어서, n은 1 내지 200일 수 있으나, 구체적으로 n은 70일 수 있다. 본 명세서에서는 구체적인 실시예를 통하여 70개의 대사체만으로 90%이상의 높은 정확도, 민감도 및 특이도로 효과적인 췌장암 진단이 가능한 것을 확인하였다. In the above method, n may be 1 to 200, specifically, n may be 70. In the present specification, through specific examples, it was confirmed that effective pancreatic cancer diagnosis with high accuracy, sensitivity and specificity of over 90% is possible only with 70 metabolites.

상기 방법에 있어서, 측정된 대사체의 수준인 x 값은 사용된 대사체들의 상호 간 축척을 동등하게 비교하기 위하여 평균 값을 제하고, 이를 표준 편차 값으로 나누어 정규화한 값을 사용할 수 있으며, 이는 각 대사체의 평균과의 차이와 표준편차의 보정을 통해서 정규화한 된 것을 의미할 수 있다. In the method, the x value, which is the level of the measured metabolite, subtracts the average value in order to compare the mutual scales of the metabolites used, and divides it by the standard deviation value. It may mean normalized through the correction of the difference with the mean of each metabolite and the standard deviation.

상기 방법에 있어서, 상기 회귀 계수는 부분 최소제곱법 및 희소 부분 최소 제곱법으로 이루어진 군으로부터 선택되는 한 가지 이상의 회귀분석 방법으로 회귀 분석을 수행하여 계산하는 것일 수 있다. In the method, the regression coefficient may be calculated by performing regression analysis using one or more regression analysis methods selected from the group consisting of a partial least squares method and a sparse partial least squares method.

상기 방법에 있어서 대사체는 상기 방법에 있어서 대사체는 Alanine (Ala), Asparagine (Asn), Aspartate (Asp), Citrulline (Cit), Glutamate (Glu), Histidine (His), Isoleucine (Ile), Leucine (Leu), Lysine (Lys), Methionine (Met), Ornithine (Orn), Phenylalanine (Phe), Proline (Pro), Threonine (Thr), Tryptophan (Trp), Tryosine (Tyr), Valine (Val), Creatinine, Serotonin, Carnitine (C0), Propionyl-L-carnitine (C3), Malonyl-L-carnitine/Hydroxybutyryl-L-carnitine (C3-DC (C4-OH)), Hydroxypropionyl-L-carnitine (C3-OH), Propenyl-L-carnitine (C3:1), Butenyl-L-carnitine (C4:1), Methylglutaryl-L-carnitine (C5-M-DC), Pimelyl-L-carnitine (C7-DC), Octanoyl-L-carnitine (C8), Decadienyl-L-carnitine (C10:2), Dodecanedioyl-L-carnitine (C12-DC), Tetradecanoyl-L-carnitine (C14), Hydroxyhexadecanoyl-L-carnitine (C16-OH), Hydroxyhexadecenoyl-L-carnitine (C16:1-OH), lysoPhosphatidylcholine acyl C16:0 (lysoPC a C16:0), lysoPhosphatidylcholine acyl C18:0 (lysoPC a C18:0), lysoPhosphatidylcholine acyl C18:2 (lysoPC a C18:2), Phosphatidylcholine diacyl C26:0 (PC aa C26:0), Phosphatidylcholine diacyl C28:1 (PC aa C28:1), Phosphatidylcholine diacyl C30:2 (PC aa C30:2), Phosphatidylcholine diacyl C40:4 (PC aa C40:4), Phosphatidylcholine diacyl C42:0 (PC aa C42:0), Phosphatidylcholine diacyl C42:2 (PC aa C42:2), Phosphatidylcholine acyl-alkyl C30:0 (PC ae C30:0), Phosphatidylcholine acyl-alkyl C30:2 (PC ae C30:2), Phosphatidylcholine acyl-alkyl C32:1 (PC ae C32:1), Phosphatidylcholine acyl-alkyl C38:2 (PC ae C38:2), Phosphatidylcholine acyl-alkyl C38:4 (PC ae C38:4), Phosphatidylcholine acyl-alkyl C40:1 (PC ae C40:1), Phosphatidylcholine acyl-alkyl C40:4 (PC ae C40:4), Phosphatidylcholine acyl-alkyl C40:5 (PC ae C40:5), Phosphatidylcholine acyl-alkyl C42:3 (PC ae C42:3), Phosphatidylcholine acyl-alkyl C42:4 (PC ae C42:4), Phosphatidylcholine acyl-alkyl C42:5 (PC ae C42:5), Phosphatidylcholine acyl-alkyl C44:4 (PC ae C44:4), Phosphatidylcholine acyl-alkyl C44:5 (PC ae C44:5), Phosphatidylcholine acyl-alkyl C44:6 (PC ae C44:6), Hydroxysphingomyeline C14:1 (SM (OH) C14:1), Hydroxysphingomyeline C16:1 (SM (OH) C16:1), Hydroxysphingomyeline C22:1 (SM (OH) C22:1), Hydroxysphingomyeline C22:2 (SM (OH) C22:2), Hydroxysphingomyeline C24:1 (SM (OH) C24:1), Sphingomyeline C16:0 (SM C16:0), Sphingomyeline C16:1 (SM C16:1), Sphingomyeline C18:0 (SM C18:0), Sphingomyeline C18:1 (SM C18:1), Sphingomyeline C20:2 (SM C20:2), Sphingomyeline C24:0 (SM C24:0), Sphingomyeline C24:1 (SM C24:1), Sphingomyeline C26:0 (SM C26:0), Sphingomyeline C26:1 (SM C26:1) 또는 이의 조합일 수 있다. In the method, the metabolite is Alanine (Ala), Asparagine (Asn), Aspartate (Asp), Citrulline (Cit), Glutamate (Glu), Histidine (His), Isoleucine (Ile), Leucine in the method (Leu), Lysine (Lys), Methionine (Met), Ornithine (Orn), Phenylalanine (Phe), Proline (Pro), Threonine (Thr), Tryptophan (Trp), Tryosine (Tyr), Valine (Val), Creatinine , Serotonin, Carnitine (C0), Propionyl-L-carnitine (C3), Malonyl-L-carnitine/Hydroxybutyryl-L-carnitine (C3-DC (C4-OH)), Hydroxypropionyl-L-carnitine (C3-OH), Propenyl-L-carnitine (C3:1), Butenyl-L-carnitine (C4:1), Methylglutaryl-L-carnitine (C5-M-DC), Pimelyl-L-carnitine (C7-DC), Octanoyl-L- Carnitine (C8), Decadienyl-L-carnitine (C10:2), Dodecanedioyl-L-carnitine (C12-DC), Tetradecanoyl-L-carnitine (C14), Hydroxyhexadecanoyl-L-carnitine (C16-OH), Hydroxyhexadecenoyl-L -carnitine (C16:1-OH), lysoPhosphatidylcholine acyl C16:0 (lysoPC a C16:0), lysoPhosphatidylcholine acyl C18:0 (lysoPC a C18:0), lysoPhosphatidylcholine acyl C18:2 (lysoPC a C18:2), Phosphatid ne diacyl C26:0 (PC aa C26:0), Phosphatidylcholine diacyl C28:1 (PC aa C28:1), Phosphatidylcholine diacyl C30:2 (PC aa C30:2), Phosphatidylcholine diacyl C40:4 (PC aa C40:4) ), Phosphatidylcholine diacyl C42:0 (PC aa C42:0), Phosphatidylcholine diacyl C42:2 (PC aa C42:2), Phosphatidylcholine acyl-alkyl C30:0 (PC ae C30:0), Phosphatidylcholine acyl-alkyl C30:2 (PC ae C30:2), Phosphatidylcholine acyl-alkyl C32:1 (PC ae C32:1), Phosphatidylcholine acyl-alkyl C38:2 (PC ae C38:2), Phosphatidylcholine acyl-alkyl C38:4 (PC ae C38: 4), Phosphatidylcholine acyl-alkyl C40:1 (PC ae C40:1), Phosphatidylcholine acyl-alkyl C40:4 (PC ae C40:4), Phosphatidylcholine acyl-alkyl C40:5 (PC ae C40:5), Phosphatidylcholine acyl -alkyl C42:3 (PC ae C42:3), Phosphatidylcholine acyl-alkyl C42:4 (PC ae C42:4), Phosphatidylcholine acyl-alkyl C42:5 (PC ae C42:5), Phosphatidylcholine acyl-alkyl C44:4 (PC ae C44:4), Phosphatidylcholine acyl-alkyl C44:5 (PC ae C44:5), Phosphatidylcholine acyl-alkyl C44:6 (PC ae C44:6), H ydroxysphingomyeline C14:1 (SM (OH) C14:1), Hydroxysphingomyeline C16:1 (SM (OH) C16:1), Hydroxysphingomyeline C22:1 (SM (OH) C22:1), Hydroxysphingomyeline C22:2 (SM (OH) ) C22:2), Hydroxysphingomyeline C24:1 (SM (OH) C24:1), Sphingomyeline C16:0 (SM C16:0), Sphingomyeline C16:1 (SM C16:1), Sphingomyeline C18:0 (SM C18: 0), Sphingomyeline C18:1 (SM C18:1), Sphingomyeline C20:2 (SM C20:2), Sphingomyeline C24:0 (SM C24:0), Sphingomyeline C24:1 (SM C24:1), Sphingomyeline C26: 0 (SM C26:0), Sphingomyeline C26:1 (SM C26:1), or a combination thereof.

상기 방법에 있어서, 생물학적 시료는 혈액, 혈장, 혈소판, 혈청, 또는 이들의 조합일 수 있다. In the method, the biological sample may be blood, plasma, platelets, serum, or a combination thereof.

본 명세서에서 용어 "수준 측정"이란 췌장암을 진단하기 위하여 생물학적 시료에서의 대사체 발현 수준을 확인하는 과정으로, 본 발명의 일 구현예에 따르면 상기 기재된 70개이상의 대사체의 수준을 확인할 수 있다. 또한 상기 방법에 있어서, 상기 대사체의 수준을 측정하는 단계는 크로마토그래피/질량 분석법을 이용하여 수행되는 것일 수 있으나, 이에 한정되는 것은 아니다.As used herein, the term "level measurement" refers to a process of confirming the expression level of a metabolite in a biological sample for diagnosing pancreatic cancer, and according to an embodiment of the present invention, the level of the above-described 70 or more metabolites can be confirmed. In addition, in the method, the step of measuring the level of the metabolite may be performed using chromatography/mass spectrometry, but is not limited thereto.

본 명세서에서 용어 "진단"은 병리 상태의 존재 또는 특징을 확인하는 것을 의미한다. 본 발명의 목적상, 췌장암의 발병 여부를 확인하는 것이다.As used herein, the term “diagnosis” refers to ascertaining the presence or characteristics of a pathological condition. For the purpose of the present invention, it is to determine whether the occurrence of pancreatic cancer.

다른 양상은 상기 방법을 실행하기 위한 컴퓨터 프로그램을 기록한 컴퓨터로 판독 가능한 기록 매체를 제공한다.Another aspect provides a computer-readable recording medium storing a computer program for executing the method.

또 다른 양상은 개체의 췌장암을 진단하기 위한 키트를 제공한다.Another aspect provides a kit for diagnosing pancreatic cancer in a subject.

또 다른 양상은 Ala, Asn, Asp, Cit, Glu, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Thr, Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC (C4-OH), C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10:2, C12-DC, C14, C16-OH, C16:1-OH, lysoPC a C16:0, lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42:2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38:4, PC ae C40:1, PC ae C40:4, PC ae C40:5, PC ae C42:3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6, SM (OH) C14:1, SM (OH) C16:1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0, SM C16:1, SM C18:0, SM C18:1, SM C20:2, SM C24:0, SM C24:1, SM C26:0, SM C26:1, 또는 이의 조합에서 선택된 변수를 수신하는 수신부; 상기 변수에 대한 예측 점수를 산출하는 점수 산출부; 및 상기 예측 점수를 기초로 췌장암 진단 확률을 산출하는 확률 산출부를 포함하는 췌장암 진단장치를 제공한다. Another aspect is Ala, Asn, Asp, Cit, Glu, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Thr, Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC ( C4-OH), C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10:2, C12-DC, C14, C16-OH, C16:1-OH, lysoPC a C16:0, lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42 :2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38:4, PC ae C40:1, PC ae C40:4, PC ae C40:5 , PC ae C42:3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6, SM (OH) C14:1, SM (OH) C16 :1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0, SM C16:1, SM C18:0, SM C18:1, SM C20: 2, a receiver for receiving a variable selected from SM C24:0, SM C24:1, SM C26:0, SM C26:1, or a combination thereof; a score calculation unit for calculating a predicted score for the variable; and a probability calculator for calculating a pancreatic cancer diagnosis probability based on the predicted score.

상기 키트는 대사체 수준을 측정하는 제제를 포함하고, 상기 대사체는 Ala, Asn, Asp, Cit, Glu, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Thr, Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC (C4-OH), C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10:2, C12-DC, C14, C16-OH, C16:1-OH, lysoPC a C16:0, lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42:2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38:4, PC ae C40:1, PC ae C40:4, PC ae C40:5, PC ae C42:3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6, SM (OH) C14:1, SM (OH) C16:1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0, SM C16:1, SM C18:0, SM C18:1, SM C20:2, SM C24:0, SM C24:1, SM C26:0, SM C26:1, 또는 이의 조합일 수 있다.The kit comprises an agent for determining the level of a metabolite, wherein the metabolite is Ala, Asn, Asp, Cit, Glu, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Thr, Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC (C4-OH), C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10:2, C12-DC , C14, C16-OH, C16:1-OH, lysoPC a C16:0, lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42:2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38:4, PC ae C40 :1, PC ae C40:4, PC ae C40:5, PC ae C42:3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6 , SM (OH) C14:1, SM (OH) C16:1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0, SM C16:1 , SM C18:0, SM C18:1, SM C20:2, SM C24:0, SM C24:1, SM C26:0, SM C26:1, or a combination thereof.

상기 유도체의 대사체, 대사체의 수준, 이의 측정은 전술한 바와 같다.The metabolite of the derivative, the level of the metabolite, and its measurement are the same as described above.

상기 키트는 췌장암 진단에 필요한 시료를 더 포함할 수 있다. The kit may further include a sample necessary for diagnosing pancreatic cancer.

일 구체예에 있어서, 상기 키트는 크로마토그래피 키트일 수 있으나, 이에 제한되는 것은 아니다.In one embodiment, the kit may be a chromatography kit, but is not limited thereto.

상기 키트는 상기 대사체 수준을 측정하기 위한 제제, 장치, 및 알고리즘이 내장된 컴퓨터를 포함할 수 있고, 상기 알고리즘을 통해 상기 대사체의 수준 측정 결과를 췌장암의 진단과 연관시키는 것인, 키트에 관한 것일 수 있다. The kit may include a computer having a built-in agent, device, and algorithm for measuring the level of the metabolite, and correlating the result of measuring the level of the metabolite with the diagnosis of pancreatic cancer through the algorithm. may be about

상기 알고리즘은 췌장암 진단 예측 확률(y)을 계산하는 단계를 포함하는 췌장암 진단 예측 확률 모델에 의할 수 있다.The algorithm may be based on a pancreatic cancer diagnosis prediction probability model including calculating a pancreatic cancer diagnosis prediction probability (y).

구체적으로, 상기 키트는 대사체, 예를 들어 Ala, Asn, Asp, Cit, Glu, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Thr, Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC (C4-OH), C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10:2, C12-DC, C14, C16-OH, C16:1-OH, lysoPC a C16:0, lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42:2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38:4, PC ae C40:1, PC ae C40:4, PC ae C40:5, PC ae C42:3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6, SM (OH) C14:1, SM (OH) C16:1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0, SM C16:1, SM C18:0, SM C18:1, SM C20:2, SM C24:0, SM C24:1, SM C26:0, SM C26:1, 또는 이의 조합에서 선택된 변수를 수신하는 수신부; 상기 변수에 대한 예측 점수를 산출하는 점수 산출부; 및 상기 예측 점수를 기초로 췌장암 진단 확률을 산출하는 확률 산출부를 포함한다.Specifically, the kit may contain metabolites such as Ala, Asn, Asp, Cit, Glu, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Thr, Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC (C4-OH), C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10:2, C12-DC, C14, C16-OH , C16:1-OH, lysoPC a C16:0, lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42:2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38:4, PC ae C40:1, PC ae C40 :4, PC ae C40:5, PC ae C42:3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6, SM (OH) C14 :1, SM (OH) C16:1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0, SM C16:1, SM C18:0, a receiver for receiving a variable selected from SM C18:1, SM C20:2, SM C24:0, SM C24:1, SM C26:0, SM C26:1, or a combination thereof; a score calculation unit for calculating a predicted score for the variable; and a probability calculator for calculating a pancreatic cancer diagnosis probability based on the prediction score.

상기 장치는 상기 수신부, 점수 산출부, 및 확률 산출부 중 적어도 하나와 연결된 출력부를 더 포함할 수 있다.The apparatus may further include an output unit connected to at least one of the receiving unit, the score calculating unit, and the probability calculating unit.

상기 출력부는 상기 변수를 입력할 수 있는 모델을 출력하는 것일 수 있다.The output unit may output a model capable of inputting the variable.

상기 장치는 웹 페이지, 어플리케이션 등을 구동하기 위한 장치로서, 예를 들어 컴퓨팅 디바이스, 모바일 디바이스, 서버 등을 포함 할 수 있다. 상기 장치는 프로세서, 저장부, 메모리, 입력부 및 출력부의 구성요소를 포함할 수 있으며, 수신부, 점수 산출부 및 확률 산출부는 장치의 구성요소들을 통해 구현될 수 있다. 서버로 구현되는 경우, 췌장암 진단 예측 장치는 산출된 값들을 출력부를 갖는 다른 디바이스로 전송하도록 구동될 수도 있다. The apparatus is an apparatus for driving a web page, an application, and the like, and may include, for example, a computing device, a mobile device, a server, and the like. The device may include components of a processor, a storage unit, a memory, an input unit, and an output unit, and the receiving unit, the score calculation unit, and the probability calculation unit may be implemented through the components of the device. When implemented as a server, the pancreatic cancer diagnosis prediction apparatus may be driven to transmit the calculated values to another device having an output unit.

구체적으로, 상기 수신부는 대사체, 예를 들어 Ala, Asn, Asp, Cit, Glu, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Thr, Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC (C4-OH), C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10:2, C12-DC, C14, C16-OH, C16:1-OH, lysoPC a C16:0, lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42:2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38:4, PC ae C40:1, PC ae C40:4, PC ae C40:5, PC ae C42:3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6, SM (OH) C14:1, SM (OH) C16:1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0, SM C16:1, SM C18:0, SM C18:1, SM C20:2, SM C24:0, SM C24:1, SM C26:0, SM C26:1, 또는 이의 조합 수준 각각에 대한 변수를 수신한다.Specifically, the receiver includes metabolites such as Ala, Asn, Asp, Cit, Glu, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Thr, Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC (C4-OH), C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10:2, C12-DC, C14, C16-OH , C16:1-OH, lysoPC a C16:0, lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42:2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38:4, PC ae C40:1, PC ae C40 :4, PC ae C40:5, PC ae C42:3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6, SM (OH) C14 :1, SM (OH) C16:1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0, SM C16:1, SM C18:0, Receive a variable for each level of SM C18:1, SM C20:2, SM C24:0, SM C24:1, SM C26:0, SM C26:1, or a combination thereof.

상기 수신부에 출력부가 연결될 수 있고, 상기 출력부는 변수를 입력할 수 있는 입력 모델을 시각적으로 출력할 수 있다. An output unit may be connected to the receiver, and the output unit may visually output an input model capable of inputting a variable.

상기 점수 산출부는 상기 수신부에서 수신한 변수에 대한 예측 점수를 산출한다. 구체적으로, 점수 산출부는 상기 변수들마다 미리 결정된 값을 매칭시킬 수 있다. 예를 들어, 상기 매칭은 최저점과 최고점을 가지는 점수선의 적어도 일부에 각 변수의 측정값 범위가 매칭된 모델을 사용하여 수행될 수 있다. The score calculator calculates a predicted score for the variable received by the receiver. Specifically, the score calculator may match a predetermined value for each of the variables. For example, the matching may be performed using a model in which a range of measured values of each variable is matched to at least a part of a score line having a lowest point and a highest point.

상기 확률 산출부는 상기 점수 산출부에서 매칭된 각 변수에 대한 예측 점수를 수신하여 이를 기초로 췌장암 진단 예측 확률을 산출한다. 구체적으로, 확률 산출부는 상기 변수에 대한 예측 점수를 모두 합산하여 총점을 얻고, 총점과 미리 결정된 생존율을 매칭시킬 수 있다. 예를 들어, 상기 매칭은 췌장암 환자 진단 확률과 각 변수에 대한 예측 점수를 합산한 총점이 매칭된 모델을 사용하여 수행될 수 있다. The probability calculator receives the predicted scores for each variable matched by the score calculator and calculates a predictive probability of diagnosing pancreatic cancer based thereon. Specifically, the probability calculator may obtain a total score by summing all predicted scores for the variables, and may match the total score with a predetermined survival rate. For example, the matching may be performed using a model in which a total score obtained by adding up a diagnosis probability of a pancreatic cancer patient and a predicted score for each variable is matched.

상기 모델은 췌장암 진단 예측 확률 모델에 의하여 결정될 수 있다. The model may be determined by a pancreatic cancer diagnosis predictive probability model.

상기 키트에는 알고리즘이 사용될 수 있는 있으며, 이로 제한하는 것은 아니나, 부분 최소제곱법 및 희소 부분 최소 제곱법으로 이루어진 군으로부터 선택되는 한 가지 이상이 선택될 수 있다.Algorithms may be used in the kit, and although not limited thereto, at least one selected from the group consisting of a partial least squares method and a sparse partial least squares method may be selected.

상기 부분 최소 제곱법(partial least squares: PLS)은 변수들 간의 다양한 관계성을 지닌 원 자료 대신 상관 관계가 없는 성분 집합들로 설명 변수들을 줄이고, 이러한 성분들을 가지고 최소 제곱법을 수행하는 방법이다. PLS는 변수들 간의 상관성이 높은 공선적 관계에 있거나 설명 변수의 수가 관측된 대상자의 수보다 더 많으며, 이로 인해 기존의 최소 제곱법을 사용하지 못하거나 표준 오차가 높은 계수를 생성할 경우에 효율성이 높아지는 것으로 알려져 있다. 설명 변수 간에 상관 관계를 갖고 있는 유전체 자료의 분석이나 물리 화학 속성 사이에 대한 관련 연구에서 활용도가 높은 분석 기법이다. PLS는 주성분분석(principal component analysis; PCA)과 유사한 방식으로 성분 집합을 구성한다. 변수들로부터 주성분과 유사한 잠재변수들을 찾은 다음 잠재변수들을 통해 선형 결합하게 되는데, 이 때 잠재변수들은 서로 직교하게 되어 다중공선성(multicollinearity)의 문제로부터 벗어날 수 있어 대사체와 같이 서로 상관도가 높은 변수들에 대해서 효과적으로 추정할 수 있다. PLS가 PCA와 서로 다른 점은 PCA는 설명변수 간의 분산구조를 고려한다면, PLS는 종속변수와 극대화한 상관관계를 갖도록 하는 방향으로 성분을 구성하게 되는 것으로 알려져 있다. The partial least squares method (PLS) is a method of reducing explanatory variables to non-correlated component sets instead of raw data having various relationships between variables, and performing the least squares method with these components. PLS has a high collinear relationship between variables, or the number of explanatory variables is greater than the number of observed subjects, so the efficiency is inefficient when the conventional least squares method cannot be used or when generating coefficients with high standard error. known to increase. It is an analysis technique that is highly utilized in the analysis of genomic data that has a correlation between explanatory variables or in related studies on physical and chemical properties. PLS constructs a set of components in a manner similar to principal component analysis (PCA). After finding the latent variables similar to the main component from the variables, they are linearly combined through the latent variables. At this time, the latent variables become orthogonal to each other, so that the problem of multicollinearity can be avoided, so that the latent variables have high correlation with each other like metabolites. Variables can be estimated effectively. The difference between PLS and PCA is that if PCA considers the structure of variance between explanatory variables, it is known that PLS composes components in a way that maximizes correlation with dependent variables.

PLS는 예측 모형을 개발하는데 중요성을 부여하고 반응 변수를 설명하는데 필요하지 않은 설명 변수들은 제거하지 않는다. 하지만 본 명세서에서 사용하는 희소 부분 최소 제곱법(sparse partial least squares : SPLS)는 반응 변수에 미치는 영향이 미비한 설명 변수들은 제거한 후 일정 수준 이상의 영향을 갖는 변수들만 선택하여 모형에 추가하는 것으로, 이로 인해 모형 수립 시에는 모든 설명 변수들을 필요로 하지만 모형 수립 이후에는 선택된 변수들만 가지고도 예측이 가능하므로 활용도 측면에서 장점을 지닌 것으로 알려져 있다.PLS attaches importance to developing predictive models and does not remove explanatory variables that are not necessary to explain the response variables. However, the sparse partial least squares (SPLS) method used in this specification removes explanatory variables that have little effect on the response variable, then selects only variables having an influence of a certain level or more and adds them to the model. All explanatory variables are required when establishing a model, but it is known that it has an advantage in terms of utilization because prediction is possible only with selected variables after model establishment.

본 명세서에서 달리 정의되지 않은 용어들은 본 발명이 속하는 기술분야에서 통상적으로 사용되는 의미를 갖는 것이다.Terms not otherwise defined herein have meanings commonly used in the art to which the present invention pertains.

일 양상에 따른 개체의 췌장암을 진단하기 위한 정보를 제공하는 방법 및 이를 이용한 키트에 따르면, 정상인 혹은 다른 질환을 앓는 환자로부터 얻은 혈액에서의 일정 대사체 수치 만으로 높은 정도의 특이도 및 진단도로 경제적으로 췌장암을 효과적으로 진단할 수 있어 췌장암 환자 맞춤형 치료가 가능하다.According to a method for providing information for diagnosing pancreatic cancer in an individual according to an aspect and a kit using the same, only a certain metabolite level in blood obtained from a normal person or a patient suffering from another disease is economically with a high degree of specificity and diagnostic degree It can effectively diagnose pancreatic cancer, enabling customized treatment for pancreatic cancer patients.

이하 실시예를 통하여 보다 상세하게 설명한다. 그러나, 이들 실시예는 하나 이상의 구체예를 예시적으로 설명하기 위한 것으로 본 발명의 범위가 이들 실시예에 한정되는 것은 아니다. Hereinafter, it will be described in more detail through examples. However, these examples are for illustrative purposes of one or more embodiments, and the scope of the present invention is not limited to these examples.

실시예 1. 췌장암의 진단을 위하여 혈액시료에서 혈액 대사체의 검출 및 수준 측정Example 1. Detection and Level Measurement of Blood Metabolites in Blood Samples for Diagnosis of Pancreatic Cancer

정상인, 췌장암을 앓고 있는 환자 및 기타 다양한 암 및 질환을 앓고 있는 환자를 314명의 혈액 시료를 총 3번에 걸쳐 수집하고, 하기 실험을 진행하였다. 본 실험은 세브란스 병원의 기관 검토의원회로부터 승인받았다. 환자가 앓고 있는 질환의 종류 및 이에 해당되는 환자의 수는 하기 표 1에 나타내었다. Blood samples from 314 normal subjects, patients suffering from pancreatic cancer, and patients suffering from various other cancers and diseases were collected three times in total, and the following experiment was performed. This study was approved by the Institutional Review Board of Severance Hospital. The types of diseases suffered by the patients and the number of patients corresponding to them are shown in Table 1 below.

1회차 (N=157)Round 1 (N=157) 2회차 (N=82)Round 2 (N=82) 3회차 (N=75)3 rounds (N=75) GroupGroup NN GroupGroup NN GroupGroup NN 췌장암pancreatic cancer 5757 정상normal 3838 유방암breast cancer 1010 담낭암gallbladder cancer 2020 폐렴Pneumonia 1212 간세포암hepatocellular carcinoma 88 정상normal 2020 패혈증blood poisoning 3232 폐선암lung adenocarcinoma 55 췌장낭성종양(SPN&SCN&IPMN&MCN)Pancreatic Cystic Tumor (SPN&SCN&IPMN&MCN) 2020 폐편평상피세포암lung squamous cell carcinoma 55 바터 팽대부암ampulla of barter 1010 만성 췌장염chronic pancreatitis 2929 대장암colorectal cancer 1010 췌장 신경내분비종양Pancreatic Neuroendocrine Tumor 99 위암stomach cancer 1010 감상선 유두암Sentimental gland papillary cancer 99 담관암cholangiocarcinoma 1010

낭종성 양성종양인 장액성 낭성 종양(serous cystadenoma; SCN), 점액성 낭성 종양((mucinous cystic neoplasm;MCN), 췌관 내 유두상 점액 종양(intraductal papillary mucinous neoplasm, IPMN), 고형 가유두상 종양(solid pseudopapillary tumor, SPN)Serous cystadenoma (SCN), mucinous cystic neoplasm (MCN), intraductal papillary mucinous neoplasm (IPMN), solid pseudopapillary tumor pseudopapillary tumor (SPN)

상기 표 1에 나타난 바와 같이, 57명의 췌장암 환자와 정상인 및 다른 질환 환자들(257명)의 혈액 시료로부터 대사체를 검출하고, 이를 고성능 액체 크로마토그래피 (HPLC)-텐덤(tandem) 질량 분석기와 Absolute IDQTM p180 키트 (BIOCRATES Life Sciences AG, Innsbruck, Austria)를 사용, 표적화된 대사체학(metabolomics) 접근법을 기반으로 분석하였다. 대사체는 21개의 아미노산, 21개의 생체 아민(biogenic amines), 40개의 아실카르니틴 (Cx:y), 93개의 글리세로인지질[14개의 리소포스파티딜콜린 (lyso PCx:y) 및 79개의 포스파티딜콜린 (PC aa x:y 또는 PC ae x:y)], 15개의 스핑고지질 (SMx:y 또는 SM (OH)x:y) 및 1개의 헥소오스이다. Cx:y는 지질 측쇄 배열을 나타내며, 여기서 x는 측쇄에서 탄소 수를 나타내고, y는 불포화 사슬의 수를 나타낸다. 시료 준비 및 분석 절차는 3 가지 품질 관리 물질의 동시 측정과 함께 제조업체의 지침에 따라 수행되었다. 시료를 Agilent 1290 시리즈 HPLC와 결합된 QTRAP 5500 질량 분석기 (SCIEX, Woodlands Central, Singapore)로 분석하였다. 그러나 상기 대사체 중 10개 대사체는 주요한 비율로 검출되지 않아 통계분석에 사용하지 않았고, 최종적으로 총 178개의 대사체를 통계분석에 사용하였으나, 최종적으로 효과적으로 췌장암 진단을 가능케 하는 70종의 대사체를 선정하고, 이를 통한 췌장암 진단을 위한 모델을 정립하였다. As shown in Table 1 above, metabolites were detected from blood samples of 57 pancreatic cancer patients, normal people and patients with other diseases (257 patients), and the metabolites were detected using high-performance liquid chromatography (HPLC)-tandem mass spectrometry and absolute The analysis was based on a targeted metabolomics approach using the IDQTM p180 kit (BIOCRATES Life Sciences AG, Innsbruck, Austria). The metabolite consists of 21 amino acids, 21 biogenic amines, 40 acylcarnitines (Cx:y), 93 glycerophospholipids [14 lysophosphatidylcholine (lyso PCx:y) and 79 phosphatidylcholine (PC aa x) :y or PC ae x:y)], 15 sphingolipids (SMx:y or SM (OH)x:y) and 1 hexose. Cx:y represents the lipid side chain arrangement, where x represents the number of carbons in the side chain and y represents the number of unsaturated chains. Sample preparation and analysis procedures were performed according to the manufacturer's instructions with simultaneous measurements of three quality control substances. Samples were analyzed on a QTRAP 5500 mass spectrometer (SCIEX, Woodlands Central, Singapore) coupled with an Agilent 1290 series HPLC. However, 10 metabolites among the metabolites were not detected in a major ratio, so they were not used for statistical analysis. Finally, a total of 178 metabolites were used for statistical analysis, but 70 metabolites that enable effective pancreatic cancer diagnosis. was selected, and a model for diagnosing pancreatic cancer was established.

실시예 2. 췌장암의 진단을 위한 모델의 확립과 회귀 계수의 계산Example 2. Establishment of a model for the diagnosis of pancreatic cancer and calculation of regression coefficients

췌장암을 효과적으로 진단하기 위한 총 70종의 대사체를 활용한 췌장암 진단 모델을 수립하기 위하여 희소 부분 최소 제곱법(sparse partial least squares: SPLS)을 활용하였으며, 이의 회귀계수를 계산하였다. To establish a pancreatic cancer diagnostic model using a total of 70 metabolites for effectively diagnosing pancreatic cancer, sparse partial least squares (SPLS) was used, and its regression coefficient was calculated.

부분 최소 제곱법(partial least squares: PLS, Wold 1966)은 변수들 간의 다양한 관계성을 지닌 원 자료 대신 상관 관계가 없는 성분 집합들로 설명 변수들을 줄이고, 이러한 성분들을 가지고 최소 제곱법을 수행하는 방법으로, 전체 대사체의 회귀계수를 설정하여, 췌장암 환자를 효과적으로 진단하기 위하여 부분 최소 제곱법을 하였고, 희소 부분 최소 제곱법(sparse partial least squares: SPLS)는 반응 변수에 미치는 영향이 미비한 설명 변수들은 제거한 후 일정 수준 이상의 영향을 갖는 변수들만 선택하여 모형에 추가하게 되므로, SPLS를 수행하기 위해서 Chung & Keles (2010)가 개발한 분류 알고리즘을 사용하여, 총 70개 대사체의 회귀 계수를 위해서 아래의 식을 사용하였다.Partial least squares (PLS, Wold 1966) is a method of reducing explanatory variables to uncorrelated component sets instead of raw data with various relationships between variables, and performing the least squares method with these components In order to effectively diagnose pancreatic cancer patients, the partial least squares method was performed by setting the regression coefficients of all metabolites, and the sparse partial least squares method (SPLS) was used for explanatory variables that had little effect on response variables. After removal, only variables with influence above a certain level are selected and added to the model. For SPLS, the classification algorithm developed by Chung & Keles (2010) is used to calculate the regression coefficients of a total of 70 metabolites. expression was used.

부분 최소 제곱법(partial least squares: PLS, Wold 1966)은 변수들 간의 다양한 관계성을 지닌 원 자료 대신 상관 관계가 없는 성분 집합들로 설명 변수들을 줄이고, 이러한 성분들을 가지고 최소 제곱법을 수행하는 방법으로, 전체 대사체의 회귀계수를 설정하여, 췌장암 환자를 효과적으로 진단하기 위하여 부분 최소 제곱법을 하였고, 희소 부분 최소 제곱법(sparse partial least squares: SPLS)는 반응 변수에 미치는 영향이 미비한 설명 변수들은 제거한 후 일정 수준 이상의 영향을 갖는 변수들만 선택하여 모형에 추가하게 되므로, SPLS를 수행하기 위해서 Chung & Keles (2010)가 개발한 분류 알고리즘을 사용하여, 총 70개 대사체의 회귀 계수(

Figure 112019095542593-pat00002
)를 위해서 아래의 식을 사용하였다.Partial least squares (PLS, Wold 1966) is a method of reducing explanatory variables to uncorrelated component sets instead of raw data with various relationships between variables, and performing the least squares method with these components In order to effectively diagnose pancreatic cancer patients, the partial least squares method was performed by setting the regression coefficients of all metabolites, and the sparse partial least squares method (SPLS) was used for explanatory variables that had little effect on response variables. After removal, only variables with influence above a certain level are selected and added to the model. In order to perform SPLS, the classification algorithm developed by Chung & Keles (2010) was used to calculate the regression coefficients of a total of 70 metabolites (
Figure 112019095542593-pat00002
), the following formula was used.

Figure 112019095542593-pat00003
Figure 112019095542593-pat00003

여기서 X는 독립변수, 즉 대사체를 의미하며 종속변수(=Y)와 독립변수는 잠재변수 T와 각각의 계수행렬(Q, P)의 곱과 오차항(F, E)의 합으로 다음과 같이 구성하였다.Here, X means an independent variable, that is, a metabolite, and the dependent variable (=Y) and the independent variable are the product of the latent variable T and each coefficient matrix ( Q , P ) and the error term ( F , E ), as follows. composed.

Figure 112019095542593-pat00004
Figure 112019095542593-pat00004

Figure 112019095542593-pat00005
Figure 112019095542593-pat00005

잠재변수 T는 독립변수 X와 K-차원의 방향 벡터

Figure 112019095542593-pat00006
들을 갖는 방향벡터(direction vector)들로 구성된 행렬 W로 아래와 식과 같이 구성된다. The latent variable T is the independent variable X and K -dimensional direction vectors
Figure 112019095542593-pat00006
A matrix W composed of direction vectors having

Figure 112019095542593-pat00007
Figure 112019095542593-pat00007

여기서

Figure 112019095542593-pat00008
은 자료의 총 대상자 수이고
Figure 112019095542593-pat00009
는 총 변수 수이다. 본 모델에서는 최적의 K를 찾기 위해서 5-fold 교차검증(cross validation)을 사용하였다.here
Figure 112019095542593-pat00008
is the total number of subjects in the data
Figure 112019095542593-pat00009
is the total number of variables. In this model, 5-fold cross validation was used to find the optimal K.

잠재변수를 사용한 최소제곱 추정량을

Figure 112019095542593-pat00010
라고 정의할 수 있으며,
Figure 112019095542593-pat00011
의 관계성을 이용하여 최종적으로 선형계수는 다음과 같이 추정된다.Least-squares estimator using latent variables
Figure 112019095542593-pat00010
can be defined as
Figure 112019095542593-pat00011
Finally, the linear coefficient is estimated as follows.

Figure 112019095542593-pat00012
(1)
Figure 112019095542593-pat00012
(One)

SPLS는 PLS와 다르게 사용되는 독립변수의 개수를 줄이는 차원축소 과정을 거치므로, 본 모델에서는 다음과 같은 최적화 문제를 풀어 모형에 선정할 최적의 변수 개수를 선택하여 최종적인 선형계수를 추정하였다.Since SPLS undergoes a dimensionality reduction process that reduces the number of independent variables used differently from PLS, this model solves the following optimization problem to select the optimal number of variables to select for the model and estimate the final linear coefficient.

Figure 112019095542593-pat00013
(2)
Figure 112019095542593-pat00013
(2)

여기서

Figure 112019095542593-pat00014
는 방향벡터이며,
Figure 112019095542593-pat00015
는 방향벡터의 대체(surrogate)벡터로 정의한다. 또한,
Figure 112019095542593-pat00016
과
Figure 112019095542593-pat00017
는 L1-노름(norm)과 L2-노름(norm)을 각각 조절하는 모수(parameter) 값이고,
Figure 112019095542593-pat00018
는 희소성을 조절하는 모수이다.here
Figure 112019095542593-pat00014
is the direction vector,
Figure 112019095542593-pat00015
is defined as a surrogate vector of the direction vector. Also,
Figure 112019095542593-pat00016
and
Figure 112019095542593-pat00017
is a parameter value that controls L1-norm and L2-norm, respectively,
Figure 112019095542593-pat00018
is a parameter that controls sparsity.

아울러, 본 연구에서 사용된 종속변수, 즉 회귀 계수는 연속형 자료가 아닌 이분형 자료이므로, logit 연결 함수를 통해서 일반화하여 추정하여 사용하였다. In addition, the dependent variable used in this study, that is, the regression coefficient, is a dichotomous data, not a continuous data, so it was generalized and estimated through the logit link function.

상기 분류 모형 수립 시 사용된 대사체들은 상호 간의 축척을 동등하게 비교하고자 각 구성물질의 평균 차이와 표준편차의 보정을 통해서 정규화 하여 사용하였다. 아울러, 상기 정규화한 70개의 대사체의 측정값을 지정되어 있는 각각의 대사체 상수와 곱하고, 70개 대사체의 측정값과 대사체 상수와 곱한 값을 모두 더하고, 상기 모델로 구한 회귀 계수를 더한 값(a)를 도출하였다. 이에, 도출된 a값을 하기 수학식에 대입하여, 개체가 췌장암으로 진단될 확률을 구하였다.The metabolites used in establishing the classification model were normalized through correction of the average difference and standard deviation of each constituent in order to compare the scales of each other equally. In addition, the measured values of the normalized 70 metabolites are multiplied by the designated metabolite constants, the values obtained by multiplying the measured values of the 70 metabolites and the metabolite constants are all added, and the regression coefficient obtained by the model is added. The value (a) was derived. Accordingly, by substituting the derived value of a into the following equation, the probability of an individual being diagnosed with pancreatic cancer was obtained.

[수학식][Equation]

Figure 112019095542593-pat00019
Figure 112019095542593-pat00019

아울러, (1)과 (2)의 식을 통해서 선발되어 추정된 상기 70개 대사체에 대한 각각의 지정된 상수값은 하기 표 2에 나타내었다.In addition, each designated constant value for the 70 metabolites selected and estimated through the formulas (1) and (2) is shown in Table 2 below.

대사체metabolite 지정된 상수 값Specified constant value 대사체metabolite 지정된 상수 값Specified constant value 대사체metabolite 지정된 상수 값Specified constant value AlaAla 0.6762802230.676280223 C14C14 0.453091490.45309149 SM..OH..C24.1SM..OH..C24.1 -0.65556429-0.65556429 AsnAsn -0.685587685589795-0.685587685589795 C16.OHC16.OH 0.371697830.37169783 SM.C16.0SM.C16.0 -0.24091768-0.24091768 AspAsp 0.00327504410366050.0032750441036605 C16.1.OHC16.1.OH 0.45300830.4530083 SM.C16.1SM.C16.1 -0.64612395-0.64612395 Citcit -0.383529257098579-0.383529257098579 lysoPC.a.C16.0lysoPC.a.C16.0 -0.53395448-0.53395448 SM.C18.0SM.C18.0 0.339333320.33933332 GluGlu 0.1858517149471750.185851714947175 lysoPC.a.C18.0lysoPC.a.C18.0 -0.45752599-0.45752599 SM.C18.1SM.C18.1 -0.19860643-0.19860643 HisHis -1.73313668962738-1.73313668962738 lysoPC.a.C18.2lysoPC.a.C18.2 -1.36244982-1.36244982 SM.C20.2SM.C20.2 -0.46066381-0.46066381 IleIle -0.665183886269878-0.665183886269878 PC.aa.C26.0PC.aa.C26.0 1.840412251.84041225 SM.C24.0SM.C24.0 -0.69101574-0.69101574 LeuLeu -0.251864533741008-0.251864533741008 PC.aa.C28.1PC.aa.C28.1 -0.50069967-0.50069967 SM.C24.1SM.C24.1 -0.19607839-0.19607839 LysLys 0.415704570.41570457 PC.aa.C30.2PC.aa.C30.2 1.761195581.76119558 SM.C26.0SM.C26.0 -0.88196312-0.88196312 MetMet -0.49400673-0.49400673 PC.aa.C40.4PC.aa.C40.4 0.521734270.52173427 SM.C26.1SM.C26.1 -0.17657509-0.17657509 OrnOrn -0.4925206-0.4925206 PC.aa.C42.0PC.aa.C42.0 0.281869930.28186993 InterceptIntercept -10.4432713170571-10.4432713170571 PhePhe -0.36327781-0.36327781 PC.aa.C42.2PC.aa.C42.2 -0.10879668-0.10879668 ProPro -0.82328267-0.82328267 PC.ae.C30.0PC.ae.C30.0 -0.6777565-0.6777565 ThrThr -0.63381162-0.63381162 PC.ae.C30.2PC.ae.C30.2 0.301288480.30128848 TrpTrp -0.10139695-0.10139695 PC.ae.C32.1PC.ae.C32.1 1.908190021.90819002 TyrTyr -0.62060258-0.62060258 PC.ae.C38.2PC.ae.C38.2 -0.20792852-0.20792852 ValVal 0.277409280.27740928 PC.ae.C38.4PC.ae.C38.4 0.035389890.03538989 CreatinineCreatinine -0.40429159-0.40429159 PC.ae.C40.1PC.ae.C40.1 -0.16489253-0.16489253 SerotoninSerotonin 1.238771771.23877177 PC.ae.C40.4PC.ae.C40.4 0.005057620.00505762 C0C0 -0.69693727-0.69693727 PC.ae.C40.5PC.ae.C40.5 0.420265350.42026535 C3C3 -1.04096984-1.04096984 PC.ae.C42.3PC.ae.C42.3 0.248015680.24801568 C3.DC..C4.OH.C3.DC..C4.OH. 0.013666210.01366621 PC.ae.C42.4PC.ae.C42.4 -0.48295906-0.48295906 C3.OHC3.OH -0.52241362-0.52241362 PC.ae.C42.5PC.ae.C42.5 0.034255260.03425526 C3.1C3.1 0.69159820.6915982 PC.ae.C44.4PC.ae.C44.4 -0.62184863-0.62184863 C4.1C4.1 -0.23422694-0.23422694 PC.ae.C44.5PC.ae.C44.5 -0.52755386-0.52755386 C5.M.DCC5.M.DC 1.005597871.00559787 PC.ae.C44.6PC.ae.C44.6 -0.17685685-0.17685685 C7.DCC7.DC 0.664698370.66469837 SM..OH..C14.1SM..OH..C14.1 -0.09675213-0.09675213 C8C8 -0.11625818-0.11625818 SM..OH..C16.1SM..OH..C16.1 0.081379130.08137913 C10.2C10.2 0.167342880.16734288 SM..OH..C22.1SM..OH..C22.1 -0.76483204-0.76483204 C12.DCC12.DC -0.54757817-0.54757817 SM..OH..C22.2SM..OH..C22.2 -0.48694676-0.48694676

실시예 3. 췌장암의 진단을 위한 모델의 검증Example 3. Validation of a model for the diagnosis of pancreatic cancer

상기 실시예 2에서 확립한 모델을 검증하기 위하여, 분석 도구를 R package, version 3.5.1 (http://www.R-project.org)로 하고, 부트스트랩(Bootstrapping) 1,000번을 통한 internal-validation을 수행하였으며, 오차 행렬(confusion matrix) 결과를 통해 분류능력을 평가하는 도구로 활용하였다.In order to verify the model established in Example 2, the analysis tool is R package, version 3.5.1 (http://www.R-project.org), and internal- through 1000 bootstrap Validation was performed, and the result of the confusion matrix was used as a tool to evaluate classification ability.

상기 1000번의 부트스트랩 결과를 확인한 결과, 평균적으로 정확도가 0.98(0.952, 1)이었으며, 평가자가 다른 경우에 우연히 일치할 확률을 제외한 나머지의 확률로 일치성 확인 척도인 kappa 값이 0.933(0.826, 1)이었고, 민감도가 0.959(0.922, 0.988)이었고, 특이도가 0.992(0.973, 1)인 것을 확인하였다(괄호 안은 95% 신뢰구간). 밸런스 된 정확도(Balanced Accuracy)는 0.9484인 것을 확인하였다. As a result of checking the 1000 bootstrap results, the average accuracy was 0.98 (0.952, 1), and the kappa value, which is the concordance confirmation scale, was 0.933 (0.826, 1) with the remaining probabilities excluding the probability of coincidence when the evaluator was different. ), the sensitivity was 0.959 (0.922, 0.988), and the specificity was 0.992 (0.973, 1) (95% confidence interval in parentheses). It was confirmed that the Balanced Accuracy was 0.9484.

따라서, 1000번의 부트스트랩 결과를 통하여, 평균적으로 0.95가 넘는 높은 정확도, 민감도, 특이도가 확인되어 본 확률 확인모델이 효과적인 췌장암 진단이 가능함을 확인하였다. Therefore, through the 1000 bootstrap results, high accuracy, sensitivity, and specificity of over 0.95 were confirmed on average, confirming that this probability confirmation model can effectively diagnose pancreatic cancer.

실험예 1.Experimental Example 1. 췌장암의 진단을 가능하게 하는 모델의 검증Validation of a model that enables the diagnosis of pancreatic cancer

1.1 178개의 대사체를 통한 모델의 검증1.1 Validation of the model through 178 metabolites

정상인, 췌장암을 앓고 있는 환자 및 기타 다양한 암 및 질환을 앓고 있는 환자 314명의 혈액 시료를 총 3번에 걸쳐 수집하고, 이의 대사체 178개를 활용하여, 검증을 수행하였다. Blood samples from 314 normal subjects, patients with pancreatic cancer, and patients suffering from various other cancers and diseases were collected three times in total, and 178 metabolites thereof were used for validation.

시료sample 00 1One 예측prediction 00 256256 1One 1One 1One 5656

상기 표 3에서 나타난 바와 같이, 95% 신뢰구간 (0.9772, 0.9992)에서 정확도가 0.9936 이었으며, 평가자가 다른 경우에 우연히 일치할 확률을 제외한 나머지의 확률로 일치성 확인 척도인 kappa 값이 0.9786 이었고, 민감도가 0.9825 (0.922, 0.988)이었고, 특이도가 0. 9961이며, 밸런스 된 정확도(Balanced Accuracy)는 0. 9893인 것을 확인하였다. As shown in Table 3 above, the accuracy was 0.9936 in the 95% confidence interval (0.9772, 0.9992), and the kappa value, which is the concordance confirmation scale, was 0.9786 with the remaining probability except for the probability of coincidence in other cases by the rater, and the sensitivity was was 0.9825 (0.922, 0.988), the specificity was 0.9991, and it was confirmed that the Balanced Accuracy was 0.9893.

따라서, 평균적으로 0.95가 넘는 높은 정확도, 민감도, 특이도가 확인되어 본 확률 확인모델이 효과적으로 췌장암 진단이 가능함을 확인하였다. Therefore, high accuracy, sensitivity, and specificity of over 0.95 were confirmed on average, confirming that this probability confirmation model can effectively diagnose pancreatic cancer.

1.2 70개의 대사체를 통한 모델의 검증1.2 Validation of the model through 70 metabolites

정상인, 췌장암을 앓고 있는 환자 및 기타 다양한 암 및 질환을 앓고 있는 환자 314명의 혈액 시료를 총 3번에 걸쳐 수집하고, 이의 대사체 중 70개를 활용하여, 검증을 수행하였다. Blood samples from 314 normal subjects, patients with pancreatic cancer, and patients with various other cancers and diseases were collected three times in total, and 70 of their metabolites were used for validation.

시료sample 00 1One 예측prediction 00 253253 55 1One 44 5252

상기 표 3에서 나타난 바와 같이, 95% 신뢰구간 (0.9463, 0.9868)에서 정확도가 0.9713이었으며, 평가자가 다른 경우에 우연히 일치할 확률을 제외한 나머지의 확률로 일치성 확인 척도인 kappa 값이 0.9029 이었고, 민감도가 0.9123이었고, 특이도가 0.9844이며, 밸런스 된 정확도(Balanced Accuracy)는 0. 9484인 것을 확인하였다. As shown in Table 3 above, the accuracy was 0.9713 in the 95% confidence interval (0.9463, 0.9868), and the kappa value, the concordance check scale, was 0.9029 with the remaining probability except for the probability of coincidence in other cases by the rater, and the sensitivity was 0.9123, the specificity was 0.9844, and it was confirmed that the Balanced Accuracy was 0.9484.

따라서, 평균적으로 90%가 넘는 높은 정확도, 민감도, 특이도가 확인되어 본 확률 확인모델이 178개의 전체 대사체가 아닌 70개의 대사체만으로도 높은 정도의 정확도, 민감도 및 특이도가 확인되어 시간 및 경제적으로 우수한 췌장암 진단을 가능케 하는 진단모델임을 확인할 수 있었다. Therefore, on average, high accuracy, sensitivity, and specificity of over 90% were confirmed, and this probability confirmation model confirmed a high degree of accuracy, sensitivity and specificity with only 70 metabolites instead of 178 all metabolites, saving time and economy. It was confirmed that it is a diagnostic model that enables excellent pancreatic cancer diagnosis.

Claims (11)

하기 단계를 포함하는 개체의 췌장암을 진단하기 위한 정보를 제공하는 방법으로서,
개체의 생물학적 시료에서 70개의 대사체 시료를 수득하는 단계;
수득된 대사체 시료에서 70개의 대사체 수준을 측정하는 단계;
측정된 70개의 대사체 수준에서 각각 대사체의 평균 값을 제하고 남은 값을 표준 편차 값으로 나누고 정규화 하여 x값을 도출하는 단계;
상기 x값을 각각 하기 표 2의 지정된 대사체 상수(coefficient)와 곱하여 각각 70개의 대사체 상수의 합을 구하여 회귀계수인 -10.4432713170571를 더한 a값을 도출하는 단계; 및
상기 도출된 a 값을 하기 수학식에 대입하여 개체가 췌장암 진단 예측 확률(y)을 계산하는 단계를 포함하며,
상기 대사체는 Alanine (Ala), Asparagine (Asn), Aspartate (Asp), Citrulline (Cit), Glutamate (Glu), Histidine (His), Isoleucine (Ile), Leucine (Leu), Lysine (Lys), Methionine (Met), Ornithine (Orn), Phenylalanine (Phe), Proline (Pro), Threonine (Thr), Tryptophan (Trp), Tryosine (Tyr), Valine (Val), Creatinine, Serotonin, Carnitine (C0), Propionyl-L-carnitine (C3), Malonyl-L-carnitine/Hydroxybutyryl-L-carnitine (C3-DC (C4-OH)), Hydroxypropionyl-L-carnitine (C3-OH), Propenyl-L-carnitine (C3:1), Butenyl-L-carnitine (C4:1), Methylglutaryl-L-carnitine (C5-M-DC), Pimelyl-L-carnitine (C7-DC), Octanoyl-L-carnitine (C8), Decadienyl-L-carnitine (C10:2), Dodecanedioyl-L-carnitine (C12-DC), Tetradecanoyl-L-carnitine (C14), Hydroxyhexadecanoyl-L-carnitine (C16-OH), Hydroxyhexadecenoyl-L-carnitine (C16:1-OH), lysoPhosphatidylcholine acyl C16:0 (lysoPC a C16:0), lysoPhosphatidylcholine acyl C18:0 (lysoPC a C18:0), lysoPhosphatidylcholine acyl C18:2 (lysoPC a C18:2), Phosphatidylcholine diacyl C26:0 (PC aa C26:0), Phosphatidylcholine diacyl C28:1 (PC aa C28:1), Phosphatidylcholine diacyl C30:2 (PC aa C30:2), Phosphatidylcholine diacyl C40:4 (PC aa C40:4), Phosphatidylcholine diacyl C42:0 (PC aa C42:0), Phosphatidylcholine diacyl C42:2 (PC aa C42:2), Phosphatidylcholine acyl-alkyl C30:0 (PC ae C30:0), Phosphatidylcholine acyl-alkyl C30:2 (PC ae C30:2), Phosphatidylcholine acyl-alkyl C32:1 (PC ae C32:1), Phosphatidylcholine acyl-alkyl C38:2 (PC ae C38:2), Phosphatidylcholine acyl-alkyl C38:4 (PC ae C38:4), Phosphatidylcholine acyl-alkyl C40:1 (PC ae C40:1), Phosphatidylcholine acyl-alkyl C40:4 (PC ae C40:4), Phosphatidylcholine acyl-alkyl C40:5 (PC ae C40:5), Phosphatidylcholine acyl-alkyl C42:3 (PC ae C42:3), Phosphatidylcholine acyl-alkyl C42:4 (PC ae C42:4), Phosphatidylcholine acyl-alkyl C42:5 (PC ae C42:5), Phosphatidylcholine acyl-alkyl C44:4 (PC ae C44:4), Phosphatidylcholine acyl-alkyl C44:5 (PC ae C44:5), Phosphatidylcholine acyl-alkyl C44:6 (PC ae C44:6), Hydroxysphingomyeline C14:1 (SM (OH) C14:1), Hydroxysphingomyeline C16:1 (SM (OH) C16:1), Hydroxysphingomyeline C22:1 (SM (OH) C22:1), Hydroxysphingomyeline C22:2 (SM (OH) C22:2), Hydroxysphingomyeline C24:1 (SM (OH) C24:1), Sphingomyeline C16:0 (SM C16:0), Sphingomyeline C16:1 (SM C16:1), Sphingomyeline C18:0 (SM C18:0), Sphingomyeline C18:1 (SM C18:1), Sphingomyeline C20:2 (SM C20:2), Sphingomyeline C24:0 (SM C24:0), Sphingomyeline C24:1 (SM C24:1), Sphingomyeline C26:0 (SM C26:0), 및 Sphingomyeline C26:1 (SM C26:1)인 것인 방법.
[수학식]
Figure 112021045671808-pat00021

[표 2]
Figure 112021045671808-pat00022
A method of providing information for diagnosing pancreatic cancer in an individual comprising the steps of:
obtaining 70 metabolite samples from the subject's biological sample;
measuring the level of 70 metabolites in the obtained metabolite sample;
deriving an x value by subtracting the average value of each metabolite from the 70 measured metabolite levels, dividing the remaining value by a standard deviation value, and normalizing the value;
multiplying the x value by the specified metabolite constant of Table 2 below to obtain a sum of 70 metabolite constants, respectively, and deriving a value a plus a regression coefficient of -10.4432713170571; and
Comprising the step of substituting the derived value of a into the following equation to calculate the predictive probability (y) of the individual pancreatic cancer diagnosis,
The metabolites are Alanine (Ala), Asparagine (Asn), Aspartate (Asp), Citrulline (Cit), Glutamate (Glu), Histidine (His), Isoleucine (Ile), Leucine (Leu), Lysine (Lys), Methionine (Met), Ornithine (Orn), Phenylalanine (Phe), Proline (Pro), Threonine (Thr), Tryptophan (Trp), Tryosine (Tyr), Valine (Val), Creatinine, Serotonin, Carnitine (C0), Propionyl- L-carnitine (C3), Malonyl-L-carnitine/Hydroxybutyryl-L-carnitine (C3-DC (C4-OH)), Hydroxypropionyl-L-carnitine (C3-OH), Propenyl-L-carnitine (C3:1) , Butenyl-L-carnitine (C4:1), Methylglutaryl-L-carnitine (C5-M-DC), Pimelyl-L-carnitine (C7-DC), Octanoyl-L-carnitine (C8), Decadienyl-L-carnitine (C10:2), Dodecanedioyl-L-carnitine (C12-DC), Tetradecanoyl-L-carnitine (C14), Hydroxyhexadecanoyl-L-carnitine (C16-OH), Hydroxyhexadecenoyl-L-carnitine (C16:1-OH), lysoPhosphatidylcholine acyl C16:0 (lysoPC a C16:0), lysoPhosphatidylcholine acyl C18:0 (lysoPC a C18:0), lysoPhosphatidylcholine acyl C18:2 (lysoPC a C18:2), Phosphatidylcholine (PC aa C18:2), Phosphatidylcholine diacyl C26:0 26:0), Phosphatidylcholine diacyl C28:1 (PC aa C28:1), Phosphatidylcholine diacyl C30:2 (PC aa C30:2), Phosphatidylcholine diacyl C40:4 (PC aa C40:4), Phosphatidylcholine diacyl C42:0 ( PC aa C42:0), Phosphatidylcholine diacyl C42:2 (PC aa C42:2), Phosphatidylcholine acyl-alkyl C30:0 (PC ae C30:0), Phosphatidylcholine acyl-alkyl C30:2 (PC ae C30:2), Phosphatidylcholine acyl-alkyl C32:1 (PC ae C32:1), Phosphatidylcholine acyl-alkyl C38:2 (PC ae C38:2), Phosphatidylcholine acyl-alkyl C38:4 (PC ae C38:4), Phosphatidylcholine acyl-alkyl C40 :1 (PC ae C40:1), Phosphatidylcholine acyl-alkyl C40:4 (PC ae C40:4), Phosphatidylcholine acyl-alkyl C40:5 (PC ae C40:5), Phosphatidylcholine acyl-alkyl C42:3 (PC ae C42:3), Phosphatidylcholine acyl-alkyl C42:4 (PC ae C42:4), Phosphatidylcholine acyl-alkyl C42:5 (PC ae C42:5), Phosphatidylcholine acyl-alkyl C44:4 (PC ae C44:4), Phosphatidylcholine acyl-alkyl C44:5 (PC ae C44:5), Phosphatidylcholine acyl-alkyl C44:6 (PC ae C44:6), Hydroxysphingomyeline C14 :1 (SM (OH) C14:1), Hydroxysphingomyeline C16:1 (SM (OH) C16:1), Hydroxysphingomyeline C22:1 (SM (OH) C22:1), Hydroxysphingomyeline C22:2 (SM (OH) C22) :2), Hydroxysphingomyeline C24:1 (SM (OH) C24:1), Sphingomyeline C16:0 (SM C16:0), Sphingomyeline C16:1 (SM C16:1), Sphingomyeline C18:0 (SM C18:0) , Sphingomyeline C18:1 (SM C18:1), Sphingomyeline C20:2 (SM C20:2), Sphingomyeline C24:0 (SM C24:0), Sphingomyeline C24:1 (SM C24:1), Sphingomyeline C26:0 ( SM C26:0), and Sphingomyeline C26:1 (SM C26:1).
[Equation]
Figure 112021045671808-pat00021

[Table 2]
Figure 112021045671808-pat00022
 삭제delete 삭제delete 삭제delete 청구항 1에 있어서, 상기 생물학적 시료는 혈액, 혈장, 혈소판, 혈청, 또는 이들의 조합인 것인 방법.The method of claim 1 , wherein the biological sample is blood, plasma, platelets, serum, or a combination thereof. 청구항 1에 있어서, 상기 회귀계수는 부분 최소제곱법 및 희소 부분 최소 제곱법으로 이루어진 군으로부터 선택되는 한 가지 이상의 회귀분석 방법으로 회귀 분석을 수행하여 계산하는 것인 방법.The method according to claim 1, wherein the regression coefficient is calculated by performing regression analysis using one or more regression analysis methods selected from the group consisting of a partial least squares method and a sparse partial least squares method. 청구항 1에 있어서, 상기 대사체의 수준을 측정하는 단계는 크로마토그래피/질량 분석법을 이용하여 수행되는 것인 방법.The method according to claim 1, wherein the step of measuring the level of the metabolite is performed using chromatography/mass spectrometry. 청구항 1, 5 내지 7 중 어느 한 항에 따른 방법을 실행하기 위한 컴퓨터 프로그램을 기록한 컴퓨터로 판독 가능한 기록 매체.A computer-readable recording medium in which a computer program for executing the method according to any one of claims 1, 5 to 7 is recorded. 청구항 1, 5 내지 7 중 어느 한 항에 따른 방법에 의해 개체의 췌장암을 진단하기 위한 키트.A kit for diagnosing pancreatic cancer in an individual by the method according to any one of claims 1, 5 to 7. 청구항 9에 있어서, 상기 키트는 대사체 수준을 측정하는 제제를 포함하고, 상기 대사체는 Ala, Asn, Asp, Cit, Glu, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Thr, Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC (C4-OH), C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10:2, C12-DC, C14, C16-OH, C16:1-OH, lysoPC a C16:0, lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42:2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38:4, PC ae C40:1, PC ae C40:4, PC ae C40:5, PC ae C42:3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6, SM (OH) C14:1, SM (OH) C16:1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0, SM C16:1, SM C18:0, SM C18:1, SM C20:2, SM C24:0, SM C24:1, SM C26:0, 및 SM C26:1의 조합인 것인 키트.10. The method of claim 9, wherein the kit comprises an agent for measuring the level of a metabolite, wherein the metabolite is Ala, Asn, Asp, Cit, Glu, His, He, Leu, Lys, Met, Orn, Phe, Pro, Thr , Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC (C4-OH), C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10: 2, C12-DC, C14, C16-OH, C16:1-OH, lysoPC a C16:0, lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42:2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38: 4, PC ae C40:1, PC ae C40:4, PC ae C40:5, PC ae C42:3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6, SM (OH) C14:1, SM (OH) C16:1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0 , SM C16:1, SM C18:0, SM C18:1, SM C20:2, SM C24:0, SM C24:1, SM C26:0, and SM C26:1. Ala, Asn, Asp, Cit, Glu, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Thr, Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC (C4-OH), C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10:2, C12-DC, C14, C16-OH, C16:1-OH, lysoPC a C16:0, lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42:2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38:4, PC ae C40:1, PC ae C40:4, PC ae C40:5, PC ae C42:3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6, SM (OH) C14:1, SM (OH) C16:1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0, SM C16:1, SM C18:0, SM C18:1, SM C20:2, SM C24:0, SM C24:1, SM C26:0, 및 SM C26:1의 조합에서 선택된 변수를 수신하는 수신부; 상기 변수에 대한 예측 점수를 산출하는 점수 산출부; 및 상기 예측 점수를 기초로 췌장암 진단 확률을 산출하는 확률 산출부를 포함하는, 청구항 1, 5 내지 7 중 어느 한 항에 따른 방법에 의해 개체의 췌장암을 진단하기 위한 장치.Ala, Asn, Asp, Cit, Glu, His, Ile, Leu, Lys, Met, Orn, Phe, Pro, Thr, Trp, Tyr, Val, Creatinine, Serotonin, C0, C3, C3-DC (C4-OH) , C3-OH, C3:1, C4:1, C5-M-DC, C7-DC, C8, C10:2, C12-DC, C14, C16-OH, C16:1-OH, lysoPC a C16:0 , lysoPC a C18:0, lysoPC a C18:2, PC aa C26:0, PC aa C28:1, PC aa C30:2, PC aa C40:4, PC aa C42:0, PC aa C42:2, PC ae C30:0, PC ae C30:2, PC ae C32:1, PC ae C38:2, PC ae C38:4, PC ae C40:1, PC ae C40:4, PC ae C40:5, PC ae C42 :3, PC ae C42:4, PC ae C42:5, PC ae C44:4, PC ae C44:5, PC ae C44:6, SM (OH) C14:1, SM (OH) C16:1, SM (OH) C22:1, SM (OH) C22:2, SM (OH) C24:1, SM C16:0, SM C16:1, SM C18:0, SM C18:1, SM C20:2, SM C24 a receiver for receiving a variable selected from a combination of :0, SM C24:1, SM C26:0, and SM C26:1; a score calculation unit for calculating a predicted score for the variable; and a probability calculator for calculating a pancreatic cancer diagnosis probability based on the prediction score. An apparatus for diagnosing pancreatic cancer in an individual by the method according to any one of claims 1, 5 to 7.
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