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PubMedDate not supplied

Utilizing human blood plasma for proteomic biomarker discovery.

Candidate proteomic biomarker discovery from human plasma holds both incredible clinical potential as well as significant challenges. The dynamic range of proteins within plasma is known to exceed 10(10), and many potential biomarkers are likely present at lower protein abundances. At present, proteomic based MS analyses provide a dynamic range typically not exceeding approximately 10(3) in a single spectrum, and approximately 10(4)-10(6) when combined with on-line separations (e.g., reversed-phase gradient liquid chromatography), and thus are generally insufficient for low level biomarker detection directly from human plasma. This limitation is providing an impetus for the development of experimental methodologies and strategies to increase the possible number of detections within this biofluid. Discussed is the diversity of available approaches currently used by our laboratory and others to utilize human plasma as a viable medium for biomarker discovery. Various separation, depletion, enrichment, and quantitative efforts as well as recent improvements in MS capabilities have resulted in measurable improvements in the detection and identification of lower abundance proteins (by approximately 10-10(2)). Despite these improvements, further advances are needed to provide a basis for discovery of candidate biomarkers at very low levels. Continued development of depletion and enrichment techniques, coupled with improved pre-MS separations (both at the protein and peptide level) holds promise in extending the dynamic range of proteomic analysis.

Biomarkers↗
PubMed2005

[Study of serum proteome biomarkers with relation to the formation of portal vein tumor thrombi in hepatocellular carcinoma patients].

OBJECTIVE: To screen serum proteome biomarkers and establish predictive model with relation to the formation of portal vein tumor thrombi (PVTT) in hepatocellular carcinoma (HCC) patients. METHODS: Serum samples were collected from 135 HCC patients, which were divided, into training set (including 33 HCC patients with PVTT and 62 HCC patients without PVTT) and blind testing set (including 18 HCC patients with PVTT and 22 HCC patients without PVTT). Special serum protein or peptide pattern was determined by SELDI-TOF-MS measurement after treating the sample onto WCX2 protein chip for each case. The obtained data were analyzed by BioMarker Wizard software to screen serum proteome biomarkers with relation to the formation of PVTT, while decision tree classification algorithm and blind validation were determined by Biomarker Patterns Software. RESULTS: Ranging from 1100 to 30 000 at the m/z value, 100 protein features were detected in the serum protein pattern stably. Among them, 6 protein peaks with the m/z value of 3478, 1314, 1744, 1725, 2022 and 3380 were upregulated, 10 proteins peaks with the m/z value of 8901, 9353, 9415, 8773, 2766, 2745, 8697, 7773, 8569 and 1373 were downregulated respectively in the group of HCC with PVTT. The 7 candidate protein peaks with the m/z value of 3478, 2022, 8901, 9415, 8773, 2766 and 2745 were selected to establish predictive model by BPS with a sensitivity of 75.8% (25/33) and specificity of 82.3% (51/62). An accuracy of 87.5% (35/40), sensitivity of 100% (18/18), specificity of 77.3% (17/22), positive predictive value of 78.3% (18/23), and negative predictive value of 100% (17/17) were validated in blind testing set. CONCLUSION: Sixteen candidate proteome biomarkers may be related with the formation of PVTT in HCC patients. Decision tree classification algorithm may have great clinical significance in predicting the formation of PVTT.

Adult↗
PubMed2006

[Preliminary study of serum proteome biomarkers of hypopharyngeal squamous cell carcinoma].

OBJECTIVE: To screen the serum proteome biomarkers of hypopharyngeal squamous cell carcinoma (HSCC) and to establish a predictive model for early detection of HSCC. METHODS: Serum samples were collected from 48 HSCC patients before surgery and 52 age and sex-matched individuals without cancer used as controls. The samples were divided into 2 sets: training set (including 36 HSCC patients and 36 controls) and blind testing set (including 12 HSCC patients and 16 controls). With WCX2 and IMAC3 protein chips, surface-enhanced laser desorption/ionization (SELDI) was used to analyze the serum protein profiling. 72 samples of the training set were analyzed by a decision tree algorithm to be able to differentiate HSCC patients from controls. Double-blind test was used to determine the sensitivity and specificity of the classification model. RESULTS: Ranging from 2000 - 50000 (M/Z), 11 potential biomarkers on WCX2 and 19 biomarkers on IMAC3 protein chip could differentiate HSCC patients from the control set (P < 10(-5)). Among them 4 candidate protein peaks with the m/z values of 7796, 4216, 5927, and 5361 were selected to be used to establish a predictive model by Biomarker Pattern Software. The model separated effectively the HSCC samples from the control samples, achieving a sensitivity of 94.44%, and a specificity of 88.89%. An accuracy of 85.71% (24/28), sensitivity of 91.67% (11/12), specificity of 81.25% (13/16), positive predictive value of 78.57%% (11/14), and negative predictive value of 92.85% (13/14) were validated in the double-blind testing set. CONCLUSION: The SELDI-TOF-MS Protein Chip combined with artificial intelligence classification algorithm helps find serum proteome biomarkers and establish predictive model for early diagnosis of HSCC. This technique has potential for the development of a screening test for the detection of HSCC.

Adult↗
PubMed2026

Identification of Immune Response-Related Proteomic Biomarkers in Moyamoya Disease Using Serum Olink Proteomics.

Moyamoya disease, a rare chronic cerebrovascular disorder, requires invasive digital subtraction angiography (DSA) for diagnosis. This study employed high-throughput proteomics to identify plasma biomarkers for Moyamoya disease diagnosis. We conducted immunopanel analysis using the Olink platform to evaluate 92 immune-related proteins in plasma samples from 88 Moyamoya disease patients and 88 healthy controls. Key proteins were identified through differential expression analysis, GO, and KEGG enrichment analysis. A diagnostic model was constructed using LASSO regression, Boruta algorithm, and machine learning models including random forest and XGBoost. Validation of these proteins was performed using GEO external data sets, followed by prediction of potential therapeutic drugs and molecular docking validation through pharmacogenomic databases. A total of 44 differentially expressed proteins were identified through the Olink immunopanel, with 12 downregulated and 32 upregulated. GO and KEGG analyses revealed significant enrichment of these proteins in innate immune responses and signaling pathways such as NF-kB and MAPK. Through LASSO, random forest, and protein under-area analysis, four potential biomarkers for Moyamoya disease (MGMT, SIT1, PRDX1, TRAF2) were identified. A diagnostic model using these proteins showed the highest AUC value with the XGBoost model. Additionally, TRAF2 and PRDX1 exhibited significant expression differences in Moyamoya disease patients within the GEO data set. Our study revealed the immune landscape of Moyamoya disease, identified four biomarkers, and established a variety of diagnostic models.

Humans↗
PubMed2005

Proteomic biomarkers that predict the clinical success of rescue cerclage.

OBJECTIVE: The origin of incompetent cervix is multifactorial, and the success of rescue cerclage is unpredictable. We tested amniotic fluid from women who were preparing to undergo rescue cerclage for proteomic biomarkers and correlated their presence with clinical outcome. STUDY DESIGN: Amniocentesis was performed to facilitate rescue cerclage in 37 consecutive women with painless dilation (> 2 cm) and no detectable uterine activity for 4 hours (range, 1-24 hours) before cerclage. Thirty-nine consecutive women with a sonographically normal pregnancy and cervix who underwent amniocentesis for chromosomal testing during the same study interval at the same clinical site provided the control samples. A proteomic fingerprint was generated with the discarded sample and the Mass-Restricted score (MR score) for inflammation calculated. Peaks corresponding to free hemoglobin chains were sought as evidence of decidual hemorrhage or intra-amniotic bleeding. RESULTS: Amniocentesis was performed at 23.5 weeks in cerclage (mean dilation, 4 cm) versus 19.5 weeks in control subjects. Cerclage subjects were delivered at 28.8 weeks; control subjects were delivered at 39.2 weeks. Thirty-two of 37 of cerclage subjects (86%) were delivered prematurely. Ten of 37 of cerclage subjects (27%), but no control subject, had a MR score that was indicative of inflammation (P < .001). Hemoglobin peaks were present in 12 of 37 of cerclage subject (32%), but no control subjects. Among cerclage subjects, those with a MR score of 3 to 4 were delivered earlier than those with a MR score of 0 to 2 (P < .001). Women with a MR score of 3 to 4 had a shorter latency period (days from amniocentesis to delivery; 3 days) and a shorter percentage of prolongation (1.8%) than women with a MR score of 0 to 2 (35 days; P < .05; 17.9%; P < .05). Women with hemoglobin had a shorter latency period (6 days) and a shorter percentage of prolongation (3.8%) than women without hemoglobin (38 days; P < .05; 21.8%; P < .05). Hemoglobin was present in 7 of 10 of the cerclage subjects (70%) with a MR score of 3 to 4. Women with both a MR score of 3 to 4 and hemoglobin had the shortest intervals to delivery. CONCLUSION: These findings illustrate 2 pathologic mechanisms that are associated with preterm delivery are also associated with incompetent cervix. Either an intrauterine inflammatory response or decidual hemorrhage predates surgery in one half the women whose condition requires rescue cerclage. The activation of either mechanism predicts cerclage failure.

Adult↗
PubMed2005

Discriminatory proteomic biomarker analysis identifies free hemoglobin in the cerebrospinal fluid of women with severe preeclampsia.

OBJECTIVE: Preeclampsia is an idiopathic multisystem disorder specific to human pregnancy. This study used proteomic analysis of cerebrospinal fluid (CSF) to identify protein biomarkers characteristic of preeclampsia and related to its severity. STUDY DESIGN: CSF was collected from women diagnosed clinically with severe preeclampsia (sPE: n = 7), mild preeclampsia (mPE: n = 8), and normotensive controls (CRL: n = 8). Samples were subjected to proteomic analysis using surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectroscopy. A discriminative proteomic biomarker profile was extracted by applying Mass Restricted analysis, and a Preeclampsia Proteomic Biomarker (PPB) score developed based on the presence or absence of four discriminatory protein peaks in individual CSF SELDI tracings. In-gel tryptic digests, Western blot analysis, on-chip immunoassays, ELISA, and spectral analysis were used to identify the biomarkers composing the PPB score. RESULTS: PPB score distinguished patients with a clinical diagnosis of sPE from mPE and CRLs. (PPB median [range]: sPE: 4 [0-4] vs mPE: 1 [0-1] vs CRL: 0 [0-0]; P < 0.001). PPB scores were unaffected by parity, magnesium seizure prophylaxis, CSF leukocyte counts, and total protein content. Proteomic identification techniques matched the discriminatory protein peaks to the alpha- and beta-hemoglobin chains. ELISA confirmed that women diagnosed clinically with sPE had significantly higher CSF hemoglobin concentrations than women with mPE or CRL (median [range]: sPE: 6.6 [0.0-10.3] microg/mL vs mPE: 0 [0-1.3] microg/mL vs CRL: 0 [0-0] microg/mL; P < 0.001). CONCLUSION: Proteomic analysis of CSF can accurately distinguish sPE from both mPE and CRL. Patients with sPE have nanomolar amounts of free hemoglobin in their CSF. Further studies are needed to confirm these observations and determine their physiologic implications.

Biomarkers↗
PubMed2005

Proteomic biomarker analysis of amniotic fluid for identification of intra-amniotic inflammation.

BACKGROUND: Intra-amniotic inflammation is associated with poor neonatal outcome independent of prematurity. We applied proteomic technology (SELDI: surface-enhanced laser desorption ionisation) to identify the proteomic profile of intra-amniotic inflammation. DESIGN: One hundred and four samples of amniotic fluid were analysed. In stage 1, samples from patients with symptoms of preterm labour and known outcomes were tested to identify the characteristic profile for inflammation. We extracted the profile using a novel, stepwise logical approach comparing SELDI tracings from patients who delivered preterm and had intra-amniotic inflammation in response to infection to the tracings of patients who had symptoms of preterm labour but delivered at term. In stage 2, we applied the algorithm to samples from pregnancies whose outcomes were unknown to the investigators. SETTING: North-American university in collaboration with Ciphergen field demonstration laboratory. SAMPLE: One hundred and four samples of human amniotic fluid from transabdominal amniocentesis. METHODS: SELDI (surface-enhanced laser desorption ionisation) and Mass Restricted analysis, a novel algorithm for extraction of clinical and biological relevant biomarkers from proteomic SELDI tracings. MAIN OUTCOME MEASURE: Presence of intra-amniotic inflammation and/or infection leading to preterm birth. RESULTS: Patients with intra-amniotic inflammation that deliver preterm have a distinctive amniotic fluid proteomic profile of three or four of the following proteins: neutrophil defensins-1 and -2, and calgranulins A and C. Based on the presence or absence of these biomarkers, we devised the mass restricted (MR) score ranging from 0 (all biomarker peaks absent) to 4 (all biomarker peaks present). In stage 1, MR score > 2 had 92.9% sensitivity (95% CI 76.5-98.9) and 91.8% specificity (95% CI 80.4-97.7) for detection of intra-amniotic inflammation. In blind testing (stage 2), MR score > 2 provided 100% specificity and sensitivity (95% CI 100-100). A MR score > 2 was associated with imminent preterm delivery. CONCLUSION: Proteomic analysis of amniotic fluid reveals the presence of biomarkers characteristic of intrauterine inflammation. This methodology may identify the subgroup of patients that might benefit most from interventions to prevent fetal damage in utero.

Adult↗
PubMed2004

Research issues and strategies for genomic and proteomic biomarker discovery and validation: a statistical perspective.

The development and validation of clinically useful biomarkers from high-dimensional genomic and proteomic information pose great research challenges. Present bottlenecks include: that few of the biomarkers showing promise in initial discovery were found to warrant subsequent validation; and biomarker validation is expensive and time consuming. Biomarker evaluation should proceed in an orderly fashion to enhance rigor and efficiency. A molecular profiling approach, although promising, has a high chance of yielding biased results and overfitted models. Specimens from cohorts or intervention trials are essential to eliminate biases. The high cost for biomarker validation motivates some novel study design features, including sequential filtering and DNA pooling. For data analysis, logistic regression (in particular, boosting logistic regression) has features of robustness against model misspecification, and has resistance to model overfitting. Model assessment and cross-validation are critical components of data analysis. Having an independent test set is a vital feature of study design.

Biomarkers, Tumor↗
PubMed2005

Practical proteomic biomarker discovery: taking a step back to leap forward.

There is a pressing need for radically improved proteomic screening methods that allow for earlier diagnosis of disease, for systematic monitoring of physiological responses and for uncovering the fundamental mechanisms of drug action. Recent developments in proteomic technology offer tremendous, yet untapped, potential to yield novel biomarkers that are translatable to routine clinical use. Despite the significant conceptual promise of comparative proteomic profiling as a research platform for biomarker discovery, however, major hurdles remain for practical and clinical implementation. In particular, there is growing recognition that rigorous experimental design principles are urgently required to validate conclusively the unproven methodologies currently being touted. Debate and confusion persist about where the burden of proof lies: statistically, biologically or clinically? Moreover, there is no consensus about what constitutes a meaningful benchmark. An important question is how to achieve a scientifically rigorous, and therefore convincing, proof-of-concept that can be accepted by the field. Key analytical challenges related to these issues that must be addressed by the burgeoning biomarker community are discussed here.

Animals↗
PubMed2007

Cerebrospinal fluid proteomic biomarkers for Alzheimer's disease.

OBJECTIVE: To find a panel of proteins in antemortem cerebrospinal fluid (CSF) that could be used to differentiate between samples from Alzheimer's disease (AD) patients and samples from healthy and neurological control subjects. METHODS: For a test cohort, antemortem CSF proteins from 34 AD and 34 non-AD patients were separated using two-dimensional gel electrophoresis. The resulting protein patterns were analyzed using the random forest multivariate statistical method. Protein spots of interest were identified using tandem time-of-flight mass spectrometry. A validation cohort consisting of CSF from 18 AD patients and 10 non-AD subjects was analyzed in a similar way. RESULTS: Using the test cohort, a panel of 23 spots was identified that could be used to differentiate AD and non-AD gels with a sensitivity of 94%, a specificity of 94%, and a predicted classification error rate of only 5.9%. These proteins are related to the transport of beta-amyloid, the inflammatory response, proteolytic inhibition, and neuronal membrane proteins. The 23 spots separately classified the validation cohort with 90% sensitivity (probable AD subjects), 83% specificity, and a predicted classification error rate of 14% in a blinded analysis. The total observed sensitivity is 93%, the total observed specificity is 90%, and the predicted classification error rate is 8.3%. INTERPRETATION: A panel of possible CSF biomarkers for AD has been identified using proteomic methods.

Alzheimer Disease↗
PubMed2025

Associations of High Attenuation Area-Related Proteomic Biomarkers with Fibrotic or Subpleural Interstitial Lung Abnormalities.

Rationale: High-attenuation area (HAA) is a computed tomography (CT) tool that correlates with lung inflammation and fibrosis. Systemic molecular correlates of HAA (e.g., plasma proteins) may inform biological processes involved in interstitial lung disease. Objectives: To identify plasma proteins that associate with HAA and correlate with a higher probability of developing new-onset fibrotic or subpleural interstitial lung abnormalities (ILAs). Methods: Plasma protein levels were measured using a semiquantitative aptamer-based platform in MESA (the Multi-Ethnic Study of Atherosclerosis; N&#x2009;=&#x2009;5,486) and SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study; N&#x2009;=&#x2009;1,781). Linear regression models identified HAA-associated proteins after adjustment for demographic and socioeconomic factors, CT scanner parameters, study center, and batch. Associations of HAA-related proteins with new-onset fibrotic or subpleural ILAs were examined in MESA participants with ILA assessments on full-lung CT 10 years later. Immunohistochemical staining of select proteins was performed in lung tissue from pulmonary fibrosis cases. Measurements and Main Results: There were 75 proteins detected that were significantly associated with HAA in MESA and SPIROMICS. Gene Ontology analysis of these proteins identified processes involved in immune cell chemotaxis and cellular growth and apoptosis. Seven proteins were associated with a higher probability of new-onset fibrotic or subpleural ILAs in MESA, and two of these, junctional adhesion molecule-like protein and GTP cyclohydrolase 1 feedback regulatory protein, stained in areas of fibrosis in lung tissue from patients with interstitial lung disease. Conclusions: Plasma proteins associated with more HAA are involved in immune and cellular processes and associate with new-onset fibrotic-subpleural ILA.

Humans↗
PubMed2006

Schizophrenia proteomics: biomarkers on the path to laboratory medicine?

Over two million Americans are afflicted with schizophrenia, a debilitating mental health disorder with a unique symptomatic and epidemiological profile. Genomics studies have hinted towards candidate schizophrenia susceptibility chromosomal loci and genes. Modern proteomic tools, particularly mass spectrometry and expression scanning, aim to identify both pathogenic-revealing and diagnostically significant biomarkers. Only a few studies on basic proteomics have been conducted for psychiatric disorders relative to the plethora of cancer specific experiments. One such proteomic utility enables the discovery of proteins and biological marker fingerprinting profiling techniques (SELDI-TOF-MS), and then subjects them to tandem mass spectrometric fragmentation and de novo protein sequencing (MALDI-TOF/TOF-MS) for the accurate identification and characterization of the proteins. Such utilities can explain the pathogenesis of neuro-psychiatric disease, provide more objective testing methods, and further demonstrate a biological basis to mental illness. Although clinical proteomics in schizophrenia have yet to reveal a biomarker with diagnostic specificity, methods that better characterize the disorder using endophenotypes can advance findings. Schizophrenia biomarkers could potentially revolutionize its psychopharmacology, changing it into a more hypothesis and genomic/proteomic-driven science.

Journal Article↗
PubMed2005

Proteomic biomarker discovery in cerebrospinal fluid for neurodegenerative diseases.

The clinical diagnosis of major neurodegenerative disorders, e.g. Alzheimer's disease, Parkinson's disease, and dementia with Lewy body, remains unsatisfactory based on current clinical criteria and limited laboratory investigations. The possibility of identifying multiple novel biomarkers for neurodegenerative diseases, those related to disease pathogeneses in particular, has been greatly enhanced with recent advances in genomics, proteomics, and metabolomics. In this chapter, we will be reviewing a few issues related to proteomic identification of proteins in human cerebrospinal fluid (CSF) as well as unique protein markers that could be used for clinical diagnosis of various neurodegenerative diseases or monitoring their progression. Great attention has been directed to practical considerations and limitations of several major aspects of proteomic analysis of human CSF.

Apolipoproteins B↗
PubMed2006

Proteomic biomarkers for autoimmune disease.

Autoimmune diseases affect 3% of the world population, yet the diagnosis and classification of autoimmune diseases remain based on clinical examination combined with traditional laboratory tests and imaging studies. The development of genomic and proteomic technologies provides an unprecedented ability to identify novel biosignatures to diagnose, classify, and guide therapeutic decision making in patients with autoimmune disease. In this article, we review recent advances in proteomics technologies and their application to autoimmune disease.

Autoantibodies↗
PubMedDate not supplied

Place of pattern in proteomic biomarker discovery.

The role of pattern in biomarker discovery and clinical diagnosis is examined in its historical context. The use of MS-derived pattern is treated as a logical extension of prior applications of non-MS-derived pattern. Criticisms pertaining to specific technology platforms and analytic methodologies are considered separately from the larger issues of pattern utility and deployment in biomarker discovery. We present a hybrid strategy that marries the desirable attributes of high-information content MS pattern with the capability to obtain identity, and explore the key steps in establishing a data analysis pipeline for pattern-based biomarker discovery.

Biomarkers↗