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1 패혈증생존및사망환자혈장에서단백질칩을이용한분석의차이 강원대학교의과대학내과학교실, 1 마취과학교실, 2 생화학교실및강원대학교병원임상의학연구소박명옥, 이희영, 손희정 1, 성지현 2, 이승준, 이성준, 하권수 2, 김우진 Difference in Protein Markers According to the Survival of Sepsis Patients using Protein Chips Myoung Ok Park, M.D., Heui Young Lee, M.D., Hee Jung Son, M.D. 1, Ji Hyun Sung, M.D. 2, Seung Joon Lee, M.D., Sung Joon Lee, M.D., Kwon Soo Ha, M.D. 2, Woo Jin Kim, M.D. Department of Internal Medicine, 1 Department of Anesthesia, and 2 Department of Molecular and Cellular Biochemistry, College of Medicine, Kangwon National University and the Clinical Research Institute of Kangwon National University Hospital Background; Several clinical scoring systems are currently being used to predict the outcome of sepsis, but they all have certain limitations. Therefore, we sought to identify the proteomic biomarkers, with wsing proteomic tools, that differed according to the outcome of sepsis patients. Methods; Upon admission to the ICU, blood samples were obtained from the 16 patients with sepsis who were enrolled in this study. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI TOF MS) was used to identify the markers that could predict the outcome of sepsis. Results; We found six peaks, by using cation and anion chips, that statistically differed between those patients who died and those who survived. Conclusion; The biomarkers we found by using proteomic tools may help predict the prognosis and also plan the treatment of sepsis. (Tuberc Respir Dis 2006; 61: 41-4) Keywords; Sepsis, Treatment outcome, Proteomics. 서 최근수년간패혈증의병태생리학에대한이해와치료에대한발전이있었지만패혈증환자의사망률은여전히높으며 1, 패혈증환자를사망에이르게하는핵심요인은아직잘밝혀지지않고있다 2. 패혈증환자를치료함에있어예후를예측하는것은매우중요하다. Acute physiology and chronic health evaluation (APACHE) II 와 III, simplified acute physiology score (SAPS) II, 그리고 mortality probability model (MPM) II 와같은점수체계들은질병의정도가위중한환자들의생존가능성을평가 론 Address for correspondence : Woo Jin Kim, M.D. Department of Internal Medicine, Hyoja-3-dong, 17-1, Kangwon National University Hospital, Chunchon, , Korea Tel: , Fax: pulmo2@knuh.or.kr Received : Mar Accepted : Jul 하는데는적절하다 3-6. 그러나점수체계는채점방식이복잡하고, 나이 7, 국가간차이 8, 자료수집의오류 9, 입원시기 10, 의료기관의요인 11 에따른차이가있을수있다는문제가있다. SELDI TOF MS (Surface-enhanced laser desorption/ionization time of flight mass spectroscopy) 는환자의혈장의저분자량단백질분포차이를알아보는데유용한도구이다. 최근이방법을이용하여난소암과전립선암의진단과감염성질환의감별진단에도움이되었다는보고들이있었고 12-1, 특정질환에서발현의차이를보이는생물학적지표를찾는데유용한도구로보고되고있다. 그러나, 이방법을이용하여패혈증환자들의혈액에서예후를예측할수있는바이오마커를발굴할수있는지는현재까지연구된바없다. 본연구에서는이에 SELDI TOF MS를이용하여패혈증환자예후에따라차이를보이는프로테오믹마커들을찾기위하여실험을시행하였다. 41

2 MO Park et al. : Proteomic markers for sepsis outcome 1. 대상환자 방 법 System II (Ciphergen) 으로분석하였다. 레이저강도는 170, 측정민감도는 7로 time-of-flight spectra를얻었다. 2004년 3월부터 200년 2월까지강원대학교병원내과중환자실에입원한 16명의성인환자를대상으로연구를시행하였다. 패혈증기준에맞는환자를대상으로중환자실내원시혈장을채취하였다 1. 패혈증기준은감염이있으면서, 체온이 38. C 이상이서나 3.0 C 미만인경우, 맥박수가분당 90회이상인경우, 호흡수가 20 회이상이거나 PaCO2가 32 mmhg 미만이거나기계호흡기필요한경우, 백혈구수치가 12,000/mm 3 이상이거나 4,000/mm 3 미만인경우의네가지조건중두가지이상을만족하는경우로정의하였다. 혈장은분석을진행하기전까지 -20 이하에서보관하였다. 본연구는강원대학교병원 Institutional Review Board (IRB) 의승인을받았다. 4. 통계분석두군간의 peak 세기의차이는 Biomarker Wizard 3.1 (ProteinChip software, (Ciphergen) 를이용하여통계분석하였다. 두군간의나이와 APACHE II score는 Mann-Whitney 검정으로분석하였고, 두군간에유의한차이를보이는 peak의세기와 APACHE II score가관련성이있는지알아보기위해 Spearman s 상관분석 (SPSS version 10.0 프로그램, SPSS, Inc., Chicago, IL, USA) 을사용하여분석하였다. p 값은 0.0이하인경우를통계적으로유의성이있는것으로판정하였다. 결과 2. 혈장의전처리 1. 대상환자의임상적결과 대상환자의혈장을처리하기전에 Montage Albumin Depletion kit (Millipore, Billerica, MA, USA) 을사용하여알부민을제거하였다. 알부민제거후, 완충액 (9.M urea, 2% CHAPS, 1% DTT) 으로혈장을희석하였다. 30분간반응시킨후, 시료를 binding buffer (1:10 v/v, Triton-X 100) 로다시희석하였다. 3. SELDI TOF MS 반응시킨시료를각각 CM10 weak cation exchange chip과 Q10 anion exchange chip을장착한 bioprocessor well (Ciphergen Biosystems, Fremont, CA, USA) 에점적하였다. 시료를점적한후, 칩을한시간동안흔들면서반응시켰다. 칩은 binding buffer 로두번세척하였고, 공기중에서건조시켰다. Sinapinic acid (0% acetonitrile, trifluoroacetic acid) 를각각의칩에점적하였다. 칩은 Protein Biology 패혈증의진단기준을만족하는 16명의환자를대상으로혈장을분석하였다. 환자들의주요원인질환은폐렴과요로감염증이었다. 대상환자중 8명의환자가생존하였고 8명의환자가사망하였다. 평균나이는생존군에서는 71.4세사망군에서는 72.3세였고, 양군간에의미있는차이는없었다. 기저질환은심부전이사망군중 3명에서있었고생존군에서는없었다. 생존군의 APACHE II score의평균값은 18.3 ± 6.3점, Table 1. Characteristics of the subjects (n=8) Mortality group (n=8) Age (mean years) 71.4 (± 9.9) 72.3 (±9.9) Male (%) APACHE Ⅱ score 18.3 (± 6.3) 23.7 (± 6.8) Urinary tract infection (%) 28.6 (2) 44.4(4) Pneumonia (%) 7.1 (4) 66.7 (6) Pressure sore (%) 14.3 (1) 0 (0) Unknown origin (%) 11.1 (1) 0 (0) 42

3 Tuberculosis and Respiratory Diseases Vol. 61. No.1, Jul Table 2. Proteomic biomarkers which showed difference between two groups CM 10 m/z Mortality group p-value 8, ± ± , ± ± , ± ± , ± ± Q10 m/z Mortality group p-value 9, ± ± , ± ± Log Normalized Intensity 3. APACHE II score 와 peak 와의관계 M/ Z APACHE II score와관련성이높은 peak가있는지알아보기위하여, 두군간에차이를보였던 6개의 peak의세기 (intensity) 와 APACHE II score의관련성을분석한결과, 모든 peak에대해유의한상관관계를보이지않았다. Figure 1. Log normalized intensity of plasma samples revealed that intensity of m/z value of 4828 were significantly different between circle (mortality group) and rectangle (survivor group) 사망군에서는 23.7 ± 6.8점이었고 (Table 1) 사망군의 APACHE II score가통계적으로의미있게높았다 (p=0.02). 2. Peak identification CM10 chip에서총 77개의 peak를확인하였고, Q10 chip에서총 69개의 peak를확인하였다. CM10 chip에서통계적으로유의한차이가있는 peak는 4개였다. 8227, 4828, 7778, 10846의 m/z 값에해당하는 peak가사망군에서생존군에비해의미있게낮았다 (Fig. 1, Table 2). Q10 chip에서통계적으로유의한차이가있는 peak는 2개였다 (Table 2). 이들은각각 9439, 1387의 m/z 값을갖는단백질로모두사망군에서의미있게낮았다. 고찰본연구에서패혈증환자의예후에따라통계적으로유의한 6개의 proteomic markers 를발견하였다. 본연구에서는환자들의 APACHE II 점수가예후에따라의미있는차이를보였다. 그런데, 패혈증환자에서이러한생리학적인분류법은임상연구에서흔히사용되지만임상적인적용에일부어려움이있다. 또한, 최근패혈증의새로운치료제들이개발되었거나개발중이고, 어떤환자에게고가의신약을사용을결정하기위해서는패혈증환자의예후와중증도평가가용이한마커가필요하다 16. SELDI -TOF mass spectrometry는생화학표지자 (biomarker) 의확인과생화학분자간상호작용에있어중요한과학기술이다. 최근연구에서단백질체의차이를이용하여진단에도움을받았다는보고가있었다. 수면병환자의혈청에서 23/24 kda, 47 kda 등의단백질이증가함을찾아진단에이용하고자하였으며 14, 또한연구에서는 7772, 3933 질량값을갖는 43

4 MO Park et al. : Proteomic markers for sepsis outcome 단백질의차이로간염의진행을판단하고자하였다 1. 그러나이방법의제한점은단백질을확인할수없다는것이다. 아마도이런단백들은질병의병태생리와관련된물질일가능성이있으며, 이단백질을밝혀내기위해이차원적인젤전기영동으로차이가있는점을찾고그부분을단백분해효소로분해한후, matrix-assisted laser desorption ionization time-of -flight (MALDI-TOF) mass spectrometry 17 으로확인될수있을것이다. 그러나, 현재까지 SELDI -TOF mass spectrometry는대부분단백질의동정없이단백체의차이를신속하게판단할수있는장점을이용한적용으로연구되고있다. 이연구의제한점으로환자군의수가적다는것이다. 많은프로테오믹방법들이재연성이제한된다는단점이있지만, 통합된생물정보학 (bioinformatics) 을기초로더많은환자를대상으로한다면재연성있고믿을만한 biomarker를확인할수있을것이다 18,19. 본연구에서확인된여섯개의펩타이드는모두사망군에서감소되어있었다. 이펩타이드들이모두사망군에서낮았으므로, 이들은패혈증의진행과정에서생성된다량의항염증매개체일가능성이나, 보호효과를가지는물질일가능성이있다. 단백의확인이가능하다면패혈증의병태생리의이해에도도움이될것으로사료된다. 최근 procalcitonin 20 과 TREM-1 (triggering receptor expressed on myeloid cells) 21 이패혈증의진단에도움을준다는내용이발표되었고, 최근 endocan이라는물질이혈액내에증가할수록패혈증환자의중증도와나쁜예후를반영한다는보고가있었다 22. 그러나아직까지는예후를예측할수있는단일생화학표지자는없는실정이다. 본연구에서도각각의펩타이드를 APACHE II 점수와상관관계가있는펩타이드는없었고, 단일단백으로예후를판단하기보다는여러마커의조합으로판단하는것이좋을것으로사료된다. 몇몇악성종양에대하여 proteomic pattern diagnostic의종양표지자에대한접근이이루어졌고최근에는감염성질환에도적용되고있다. 저자들은패혈증의예후를예측할수있는생화학표지자를찾으려하였다. 그러나이번연구에서는적은수의환자 를대상으로하였으므로추가적인생화학표지자확인과실용화를위해서는더많은환자를대상으로한추가적인연구가필요하다하겠다. 결론적으로프로테오믹마커는패혈증환자의예후를예측하고치료계획을세우는데있어유용하게이용될가능성이있을것으로보인다. 요 배경 : 패혈증환자의예후를예측하는데현재사용되고있는임상적채점방식은몇가지제한점이있다. 그래서단백질체학 (proteomics) 기법을사용하여표지자 (proteomic biomarkers) 를찾으려연구를진행하였다. 방법 : 본연구에서는 16명의패혈증환자에게서중환자실에입원하자마자혈장을채취하였다. 패혈증의예후를예측할수있는표지자를찾기위해 Surface-enhanced laser desorption/ionization timeof-flight (SELDI -TOF) mass spectrometry를사용하였다. 결과 : 사망환자와생존환자사이에통계적으로유의한차이가있는 6개의단백표지자를발견하였고이들은패혈증환자의예후예측과치료계획수립에도움이될것으로생각된다. 결론 : 프로테오믹마커는패혈증환자의예후를예측하고치료계획을세우는데있어유용하게이용될가능성이있을것으로사료된다. 약 참고문헌 1. Annane D, Bellisant E, Cavaillon JM. Septic shock. Lancet 20036: Hotchkiss RS, Karl IE. The pathophysiology and treatment of sepsis. N Engl J Med : Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med 19813: Knaus WA, Wagner DP, Draper EA, Zimmerman JE, BergnerM, Bastos PG, et al. The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults. Chest 44

5 Tuberculosis and Respiratory Diseases Vol. 61. No.1, Jul : le Gall JR, Loirat P, Alperovitch A, Glaser P, Granthil C, Mathieu D, et al. A simplified acute physiology score for ICU patients. Crit Care Med : Lemeshow S, Teres D, Avrunin JS, Gage RW. Refining intensive care unit outcome prediction by using changing probabilities of mortality. Crit Care Med : de Rooji SE, Abu-Hanna A, Levi M, de Jonge E. Factors that predict outcome of intensive care treatment in very elderly patients: a review. Crit Care 2009:R Livingston BM, MacKirdy FN, Howie JC, Jones R, Norrie JD. Assessment of the performance of five intensive care scoring models within a large Scottish databases. Crit Care Med : Polderman KH, Thijs LG, Girbes AR. Interobserver variability in the use of APACHE II score. Lancet : Morales IJ, Peters SG, Afessa B. Hospital mortality rate and length of stay in patients admitted at night to the intensive care unit. Crit Care Med : Rosenberg AL, Hofer TP, Strachan C, Watts CM, Hayward RA. Accepting critically ill transfer patients: adverse effect on a referral center s outcome and benchmark measures. Ann Intern Med : Petricoin EF 3rd, Ornstein DK, Paweletz CP, Ardekani A, Hackett PS, Hitt BA, et al. Serum proteomic patterns for detection of prostate cancer. J Natl Cancer Inst 2002;94: Petricoin EF, Ardekani AM, Hitt BA, Levine PJ, Fusaro VA, Steinberg SM, et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet : Papadopoulos MC, Abel PM, Agranoff D, Stich A, Tarelli E, Bell BA, et al. A novel and accurate diagnostic test for human African trypanosomiasis. Lancet : Zhu XD, Zhang WH, Li CL, Xu Y, Liang WJ, Tien P. New serum biomarkers for detection of HBV-induced liver cirrhosis using SELDI protein chip technology. World J Gastroenterol : Burchardi H, Schneider H. Economic aspects of severe sepsis: a review of intensive care unit costs, cost of illnessand cost effectiveness of therapy. Pharmacoeconomics 2004;22: Ahn WS, Park SP, Bae SM, Lee JM, Namkoong SE, Nam GH, et al. Identification of hemoglobin-α and β subunits as potential serum biomarkers for the diagnosis and prognosis of ovarian cancer. Cancer Sci 20096: Ilyin SE, Belkowski SM, Plata-Salaman CR. Biomarker discovery and validation: technologies and integrative approaches. Trends biotechnol 2004;22: Rodland KD. Proteomics and cancer diagnosis: the potential of mass spectrometry. Clin Biochem 2004;37: Christ-Crain M, Jaccard-Stolz D, Bingisser R, Gencay MM, Huber PR, Tamm M, et al. Effect of procalcitonin-guided treatment on antibiotic use and outcome in lower respiratory tract infections: clusterrandomised, single-blinded intervention trial. Lancet 2004;363: Colonna M, Facchetti F. TREM-1: A new player in acute inflammatory responses. JInfect Dis 2003;187: S Scherpereel A, Depontieu F, Grigoriu B, Cavestri B, Tsicopoulos A Gentina T, et al. Endocan, a new endothelial marker in human sepsis. Crit Care Med 2006;34:

012임수진

012임수진 Received : 2012. 11. 27 Reviewed : 2012. 12. 10 Accepted : 2012. 12. 12 A Clinical Study on Effect of Electro-acupuncture Treatment for Low Back Pain and Radicular Pain in Patients Diagnosed with Lumbar