基于質(zhì)譜血清多肽組譜圖的管理分析系統(tǒng)構(gòu)建與應(yīng)用研究
發(fā)布時(shí)間：2018-08-15 14:10
【摘要】： 在后基因組時(shí)代,隨著人類和其他模式生物基因組測序的完成以及質(zhì)譜儀器和方法取得的重要突破,蛋白質(zhì)組學(xué)在基礎(chǔ)研究和臨床應(yīng)用等方面取得了巨大進(jìn)展。臨床蛋白質(zhì)組學(xué)是蛋白質(zhì)組學(xué)新近出現(xiàn)的一個(gè)分支學(xué)科,它側(cè)重于蛋白質(zhì)組學(xué)技術(shù)在臨床醫(yī)學(xué)領(lǐng)域的應(yīng)用研究,包括疾病預(yù)防、早期診斷和輔助治療等方面。臨床蛋白組學(xué)涉及多種數(shù)據(jù)類型,血清多肽組譜圖(簡稱血肽圖)是其中比較重要的一種,是基于非凝膠系統(tǒng)的臨床蛋白質(zhì)組學(xué)應(yīng)用研究,其基本原理是通過基質(zhì)輔助激光解吸電離飛行時(shí)間質(zhì)譜(matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, MALDI-TOF/MS)或表面增強(qiáng)激光解吸電離飛行時(shí)間質(zhì)譜( surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, SELDI-TOF/MS)檢測血清中多肽組的精確質(zhì)量數(shù),然后采用生物信息學(xué)方法處理獲得的一種數(shù)據(jù)。通過比較疾病與健康對(duì)照血肽圖的差異,人們可以發(fā)現(xiàn)疾病特異表達(dá)的蛋白或多肽,進(jìn)而有助于在蛋白水平研究疾病的發(fā)生機(jī)制。 血肽圖技術(shù)在生物標(biāo)志物發(fā)現(xiàn)、疾病早期診斷和個(gè)性化治療等領(lǐng)域有著廣泛的應(yīng)用前景。然而血肽圖技術(shù)應(yīng)用于臨床研究過程中必須考慮下列一些因素。首先是樣本選擇對(duì)血肽圖技術(shù)的影響,對(duì)于臨床研究所需要收集的疾病患者和正常對(duì)照人群樣本,要考慮到樣本個(gè)體間差異和個(gè)體內(nèi)差異,正常對(duì)照人群個(gè)體間差異包括年齡、性別、種族、家族史和疾病史等,疾病患者樣本最好包含完整的疾病亞型,收集的信息要盡可能完整,以便滿足構(gòu)建數(shù)學(xué)模型和驗(yàn)證的需要。其次是樣本收集對(duì)血肽圖技術(shù)的影響,這屬于分析前差異,包括樣本收集、存儲(chǔ)和運(yùn)送過程中由于環(huán)境條件差異對(duì)樣本所產(chǎn)生的影響,由于這些差異一般與疾病無關(guān),有可能增加尋找與疾病相關(guān)的差異蛋白質(zhì)或多肽的復(fù)雜性,最終影響血肽圖分析的結(jié)果。最后是儀器分析的差異對(duì)血肽圖技術(shù)的影響,血肽圖技術(shù)需要的質(zhì)譜儀器主要是MALDI-TOF/MS和SELDI-TOF/MS。由于質(zhì)譜實(shí)驗(yàn)過程中存在多種影響因素,質(zhì)譜產(chǎn)生的原始譜圖數(shù)據(jù)包含了大量的噪音信號(hào),必須進(jìn)行預(yù)處理以去除干擾。 鑒于血肽圖具有變量個(gè)數(shù)和樣本數(shù)目均眾多的特點(diǎn),面對(duì)這樣復(fù)雜的數(shù)據(jù),只有通過生物信息學(xué)方法,才能識(shí)別出與疾病密切相關(guān)的一組多肽峰,發(fā)現(xiàn)血肽圖中與疾病相關(guān)的特征信息。然而,現(xiàn)有的數(shù)據(jù)管理與分析工具已經(jīng)無法滿足當(dāng)前的需要,而商業(yè)化軟件由于價(jià)格昂貴,也在一定程度上制約了血肽圖技術(shù)的廣泛應(yīng)用。為此,我們將臨床蛋白質(zhì)組學(xué)與生物信息學(xué)相結(jié)合,開發(fā)了一套基于質(zhì)譜血清多肽組譜圖的管理分析系統(tǒng)BioSunMS。該系統(tǒng)基于ECLIPSE插件架構(gòu),采用JAVA語言開發(fā),具有易于發(fā)布及二次開發(fā),界面友好,跨系統(tǒng)平臺(tái)等特點(diǎn),便于管理臨床樣本、質(zhì)譜譜圖和對(duì)質(zhì)譜譜圖進(jìn)行預(yù)處理和建模分析,從而為相關(guān)研究人員方便快捷地開展疾病分類與分型研究提供幫助,最后,我們以基于肺癌患者血肽圖的樣本分類和分型研究為例說明BioSunMS的功能,具體內(nèi)容如下。 1.血肽圖數(shù)據(jù)庫構(gòu)建 血肽圖數(shù)據(jù)庫主要用來存放正常人以及多種腫瘤(包括肺癌、肝癌、乳腺癌、直腸癌、前列腺癌和白血病等)患者的血清多肽譜、樣本及其臨床相關(guān)信息。該數(shù)據(jù)庫主要包含樣品來源、診斷方法、樣品處理過程、質(zhì)譜檢測方法、血清多肽質(zhì)譜數(shù)據(jù)等內(nèi)容。該數(shù)據(jù)庫主要提供了下列重要功能:血清多肽圖查詢,通過該系統(tǒng),用戶可獲得特定腫瘤的血肽圖的標(biāo)志譜峰及其對(duì)應(yīng)的多肽序列;各種疾病血肽圖數(shù)據(jù)的提交,通過此系統(tǒng),研究人員可以將自己實(shí)驗(yàn)室收集的疾病血肽圖數(shù)據(jù),提交到本數(shù)據(jù)庫中,從而豐富了數(shù)據(jù)庫中的疾病種類;血肽圖疾病信息分析,檢測人員將臨床獲得的血肽圖直接通過本數(shù)據(jù)庫進(jìn)行查詢,從而得到疾病相關(guān)信息。 2.血肽圖數(shù)據(jù)處理與分析的軟件開發(fā) 為了快速準(zhǔn)確地開展以血肽圖數(shù)據(jù)為基礎(chǔ)的腫瘤分類與分型研究,開發(fā)了血多肽數(shù)據(jù)處理與分析模塊。數(shù)據(jù)處理模塊可實(shí)現(xiàn)對(duì)獲得的血肽圖質(zhì)譜數(shù)據(jù)實(shí)現(xiàn)質(zhì)譜圖展示、數(shù)據(jù)導(dǎo)入、導(dǎo)出、格式轉(zhuǎn)化和預(yù)處理等功能。數(shù)據(jù)分析模塊具有對(duì)預(yù)處理后的數(shù)據(jù)進(jìn)行統(tǒng)計(jì)學(xué)分析,找到特征譜峰,建立血肽圖模型,對(duì)盲樣進(jìn)行判別等功能,可實(shí)現(xiàn)快速、自動(dòng)化發(fā)現(xiàn)生物標(biāo)志物等相關(guān)分析。 3.基于血肽圖數(shù)據(jù)的腫瘤分類與分型研究 以支持向量機(jī)(SVM)、主成分分析(PCA)、遺傳算法(GA)、樸素貝葉斯方法(Na?ve Bayes)和偏最小二乘法(PLS)等常用的統(tǒng)計(jì)學(xué)及機(jī)器學(xué)習(xí)方法為工具,以血肽圖數(shù)據(jù)庫中的數(shù)據(jù)為基礎(chǔ),構(gòu)建了基于血肽圖數(shù)據(jù)的腫瘤分類與分型模塊,并提供模型參數(shù)優(yōu)化功能,便于相關(guān)人員開展腫瘤分類與分型研究工作。 4.腫瘤特征性血肽圖模型建立 該研究是與國家儀器分析中心合作開展的。在前期工作中,國家儀器分析中心已經(jīng)完成了1000例健康人群和2000多例肺癌、肝癌、乳腺癌、直腸癌、前列腺癌和白血病等腫瘤患者的血肽圖高分辨質(zhì)譜數(shù)據(jù)采集。在此基礎(chǔ)上,運(yùn)用BioSunMS系統(tǒng)對(duì)數(shù)據(jù)庫中254例肺癌組以及257例正常對(duì)照組的血肽圖進(jìn)行分析。首先,我們以150例肺癌組樣本和150例對(duì)照組樣本的血肽圖數(shù)據(jù)構(gòu)建了訓(xùn)練集,剩余104例肺癌組樣本和107例正常對(duì)照組樣本的血肽圖構(gòu)建了測試集。通過t檢驗(yàn)進(jìn)行變量選擇,以P0.005為標(biāo)準(zhǔn),篩選出74個(gè)特征譜峰。以這些變量為基礎(chǔ),我們采用SVM方法構(gòu)建了肺癌血肽圖的分類模型,并用測試集進(jìn)行了驗(yàn)證。對(duì)于測試集,分類準(zhǔn)確度、敏感性和特異性分別是92.3%,96.3%,94.3%。通過上述分析,我們發(fā)現(xiàn)了一些肺癌特征質(zhì)譜譜峰信息,并以這些譜峰信息為特征,構(gòu)建了基于質(zhì)譜血肽圖的肺癌早期診斷模型,對(duì)肺癌的早期診斷研究進(jìn)行了初步的探索。 綜上所述,該研究構(gòu)建了一個(gè)集質(zhì)譜血清多肽組譜圖的數(shù)據(jù)庫管理和分析為一體的軟件BioSunMS,并應(yīng)用該系統(tǒng)對(duì)肺癌血肽圖數(shù)據(jù)進(jìn)行了初步分析,構(gòu)建了肺癌血肽圖早期診斷模型,為基于質(zhì)譜血肽圖的相關(guān)研究提供了生物信息學(xué)支持。
[Abstract]:In the post-genome era, great progress has been made in basic research and clinical application of proteomics with the completion of human and other model organism genome sequencing and important breakthroughs in mass spectrometry instruments and methods. Clinical proteomics involves a variety of data types. Serum polypeptide profiles (hemopeptide profiles) are one of the most important, and are based on non-gel systems in clinical proteomics applications. The basic principles of these proteomics are universal. Detection of blood by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS) or surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF/MS) The exact mass of the polypeptide group in the serum is then processed using bioinformatics. By comparing the differences between the peptide maps of disease and healthy controls, one can discover disease-specific proteins or polypeptides, and thus help to study the pathogenesis of disease at the protein level.
Hemopeptide mapping has broad application prospects in the fields of biomarker discovery, early diagnosis and individualized treatment. However, the following factors must be taken into account in the application of hemopeptide mapping in clinical research. First, the influence of sample selection on hemopeptide mapping is important for the patients and patients who need to be collected by clinical research institutes. Normal control group samples should take into account individual differences and individual differences. Individual differences between normal control groups include age, sex, race, family history and disease history. Disease patients'samples should contain complete disease subtypes, and the collected information should be as complete as possible to meet the needs of building mathematical models and validation. Secondly, the impact of sample collection on hemopeptide mapping technology is pre-analysis differences, including sample collection, storage and transportation due to environmental differences in the impact of samples, as these differences are generally not related to disease, may increase the complexity of finding disease-related differences in proteins or peptides, and ultimately affect blood Finally, the influence of instrumental analysis on hemopeptide mapping technology is discussed. MALDI-TOF/MS and SELDI-TOF/MS are the main mass spectrometers needed for hemopeptide mapping technology. Due to various factors in the process of mass spectrometry experiment, the original spectrum data produced by mass spectrometry contains a large number of noise signals, which must be pre-processed to remove. Interference.
In view of the characteristics of large number of variables and samples in hemopeptide map, facing such complex data, only through bioinformatics method can we identify a group of peptide peaks closely related to disease and discover the characteristic information related to disease in hemopeptide map. However, the existing data management and analysis tools can not meet the needs of the disease. For this reason, we combine clinical proteomics with bioinformatics to develop a management and analysis system based on mass spectrometry serum peptide profiles, BioSunMS. This system is based on ECLIPSE plug-in architecture, using JAVA. Language development has the characteristics of easy release and secondary development, friendly interface, cross-system platform, easy to manage clinical samples, mass spectrogram and mass spectrogram pretreatment and modeling analysis, so as to facilitate the relevant researchers to carry out disease classification and typing research conveniently and quickly. Finally, we based on lung cancer patients peptide. The sample classification and typing research of the graph illustrate the function of BioSunMS as an example.
1. blood peptide map database construction
Serum peptide profiles, samples and clinical information of normal persons and patients with various tumors (including lung cancer, liver cancer, breast cancer, rectal cancer, prostate cancer, leukemia, etc.) are stored in the hemopeptidase database. The database mainly contains sample sources, diagnostic methods, sample processing, mass spectrometry detection methods, and serum peptide mass spectrometry numbers. The database mainly provides the following important functions: serum peptide map inquiry, through the system, users can obtain the marker spectrum peaks of specific tumors and corresponding peptide sequences; various diseases blood peptide map data submission, through this system, researchers can collect disease blood peptide map data in their laboratory, Submitted to this database, thus enriching the types of diseases in the database; analysis of the disease information of the blood peptide map, the detection personnel will be directly obtained by the clinical blood peptide map query through this database, thus obtaining disease-related information.
Software development of data processing and analysis of blood peptide map 2.
In order to rapidly and accurately carry out tumor classification and typing research based on hemopeptide map data, a data processing and analysis module of hemopeptide map was developed. After statistical analysis of the processed data, the characteristic peaks can be found, the blood peptide map model can be established, and the blind samples can be discriminated.
3. tumor classification and typing based on blood peptide map data
With support vector machine (SVM), principal component analysis (PCA), genetic algorithm (GA), Na? Ve Bayes, partial least squares (PLS) and other commonly used statistical and machine learning methods as tools, tumor classification and typing module based on blood peptide map data was constructed, and model parameters were provided. The optimization function is convenient for relevant personnel to carry out tumor classification and typing research.
Establishment of 4. tumor characteristic blood peptide map model
The study was carried out in collaboration with the National Center for Instrumental Analysis (NIAA). In the previous work, NIAA has completed the collection of high resolution mass spectrometry (HRMS) data from 1000 healthy people and 2000 patients with lung cancer, liver cancer, breast cancer, rectal cancer, prostate cancer and leukemia. The blood peptide maps of 254 lung cancer patients and 257 normal controls were analyzed in the database. Firstly, we constructed the training set from the blood peptide maps of 150 lung cancer patients and 150 control samples. The rest 104 lung cancer patients and 107 normal control samples were used to construct the test set. Seventy-four characteristic peaks were screened out according to the standard of P 0.005. Based on these variables, we constructed the classification model of lung cancer hemopeptide map by SVM and validated it by test set. For test set, the accuracy, sensitivity and specificity of classification were 92.3%, 96.3% and 94.3% respectively. Based on the information of spectral peaks, an early diagnosis model of lung cancer based on mass spectrometric hemopeptide map was constructed, and the early diagnosis of lung cancer was preliminarily explored.
To sum up, a software named BioSunMS, which integrates the database management and analysis of serum peptide profiles of mass spectrometry, was constructed. The system was used to analyze the data of lung cancer hemopeptide profiles, and the early diagnosis model of lung cancer hemopeptide profiles was constructed, which provided bioinformatics support for the related research based on mass spectrometry hemopeptide profiles.
【學(xué)位授予單位】：中國人民解放軍軍事醫(yī)學(xué)科學(xué)院
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2009
【分類號(hào)】：R346
[Abstract]:In the post-genome era, great progress has been made in basic research and clinical application of proteomics with the completion of human and other model organism genome sequencing and important breakthroughs in mass spectrometry instruments and methods. Clinical proteomics involves a variety of data types. Serum polypeptide profiles (hemopeptide profiles) are one of the most important, and are based on non-gel systems in clinical proteomics applications. The basic principles of these proteomics are universal. Detection of blood by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS) or surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF/MS) The exact mass of the polypeptide group in the serum is then processed using bioinformatics. By comparing the differences between the peptide maps of disease and healthy controls, one can discover disease-specific proteins or polypeptides, and thus help to study the pathogenesis of disease at the protein level.
Hemopeptide mapping has broad application prospects in the fields of biomarker discovery, early diagnosis and individualized treatment. However, the following factors must be taken into account in the application of hemopeptide mapping in clinical research. First, the influence of sample selection on hemopeptide mapping is important for the patients and patients who need to be collected by clinical research institutes. Normal control group samples should take into account individual differences and individual differences. Individual differences between normal control groups include age, sex, race, family history and disease history. Disease patients'samples should contain complete disease subtypes, and the collected information should be as complete as possible to meet the needs of building mathematical models and validation. Secondly, the impact of sample collection on hemopeptide mapping technology is pre-analysis differences, including sample collection, storage and transportation due to environmental differences in the impact of samples, as these differences are generally not related to disease, may increase the complexity of finding disease-related differences in proteins or peptides, and ultimately affect blood Finally, the influence of instrumental analysis on hemopeptide mapping technology is discussed. MALDI-TOF/MS and SELDI-TOF/MS are the main mass spectrometers needed for hemopeptide mapping technology. Due to various factors in the process of mass spectrometry experiment, the original spectrum data produced by mass spectrometry contains a large number of noise signals, which must be pre-processed to remove. Interference.
In view of the characteristics of large number of variables and samples in hemopeptide map, facing such complex data, only through bioinformatics method can we identify a group of peptide peaks closely related to disease and discover the characteristic information related to disease in hemopeptide map. However, the existing data management and analysis tools can not meet the needs of the disease. For this reason, we combine clinical proteomics with bioinformatics to develop a management and analysis system based on mass spectrometry serum peptide profiles, BioSunMS. This system is based on ECLIPSE plug-in architecture, using JAVA. Language development has the characteristics of easy release and secondary development, friendly interface, cross-system platform, easy to manage clinical samples, mass spectrogram and mass spectrogram pretreatment and modeling analysis, so as to facilitate the relevant researchers to carry out disease classification and typing research conveniently and quickly. Finally, we based on lung cancer patients peptide. The sample classification and typing research of the graph illustrate the function of BioSunMS as an example.
1. blood peptide map database construction
Serum peptide profiles, samples and clinical information of normal persons and patients with various tumors (including lung cancer, liver cancer, breast cancer, rectal cancer, prostate cancer, leukemia, etc.) are stored in the hemopeptidase database. The database mainly contains sample sources, diagnostic methods, sample processing, mass spectrometry detection methods, and serum peptide mass spectrometry numbers. The database mainly provides the following important functions: serum peptide map inquiry, through the system, users can obtain the marker spectrum peaks of specific tumors and corresponding peptide sequences; various diseases blood peptide map data submission, through this system, researchers can collect disease blood peptide map data in their laboratory, Submitted to this database, thus enriching the types of diseases in the database; analysis of the disease information of the blood peptide map, the detection personnel will be directly obtained by the clinical blood peptide map query through this database, thus obtaining disease-related information.
Software development of data processing and analysis of blood peptide map 2.
In order to rapidly and accurately carry out tumor classification and typing research based on hemopeptide map data, a data processing and analysis module of hemopeptide map was developed. After statistical analysis of the processed data, the characteristic peaks can be found, the blood peptide map model can be established, and the blind samples can be discriminated.
3. tumor classification and typing based on blood peptide map data
With support vector machine (SVM), principal component analysis (PCA), genetic algorithm (GA), Na? Ve Bayes, partial least squares (PLS) and other commonly used statistical and machine learning methods as tools, tumor classification and typing module based on blood peptide map data was constructed, and model parameters were provided. The optimization function is convenient for relevant personnel to carry out tumor classification and typing research.
Establishment of 4. tumor characteristic blood peptide map model
The study was carried out in collaboration with the National Center for Instrumental Analysis (NIAA). In the previous work, NIAA has completed the collection of high resolution mass spectrometry (HRMS) data from 1000 healthy people and 2000 patients with lung cancer, liver cancer, breast cancer, rectal cancer, prostate cancer and leukemia. The blood peptide maps of 254 lung cancer patients and 257 normal controls were analyzed in the database. Firstly, we constructed the training set from the blood peptide maps of 150 lung cancer patients and 150 control samples. The rest 104 lung cancer patients and 107 normal control samples were used to construct the test set. Seventy-four characteristic peaks were screened out according to the standard of P 0.005. Based on these variables, we constructed the classification model of lung cancer hemopeptide map by SVM and validated it by test set. For test set, the accuracy, sensitivity and specificity of classification were 92.3%, 96.3% and 94.3% respectively. Based on the information of spectral peaks, an early diagnosis model of lung cancer based on mass spectrometric hemopeptide map was constructed, and the early diagnosis of lung cancer was preliminarily explored.
To sum up, a software named BioSunMS, which integrates the database management and analysis of serum peptide profiles of mass spectrometry, was constructed. The system was used to analyze the data of lung cancer hemopeptide profiles, and the early diagnosis model of lung cancer hemopeptide profiles was constructed, which provided bioinformatics support for the related research based on mass spectrometry hemopeptide profiles.
【學(xué)位授予單位】：中國人民解放軍軍事醫(yī)學(xué)科學(xué)院
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2009
【分類號(hào)】：R346
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5 中科院院士 吳e，
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