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PubMed2007

Proteomic identification and characterization of bacterial factors associated with Burkholderia cenocepacia survival in a murine host.

Burkholderia cenocepacia is a member of the Burkholderia cepacia complex, a diverse family of Gram-negative bacteria that are serious respiratory pathogens in immunocompromised patients and individuals with cystic fibrosis. To identify putative bacterial virulence determinants, proteomic profiles were compared between two B. cenocepacia isolates that demonstrated differential persistence in a mouse model of pulmonary infection; clinical isolate C1394 is rapidly cleared from the murine lung whereas the strain variant, C1394mp2, persists. Two-dimensional (2D) gel electrophoresis was used to identify candidate proteins involved in B. cenocepacia survival in a susceptible host. The 2D proteome of the persistent isolate (C1394mp2) revealed loss of an alkyl hydroperoxide reductase subunit C (AhpC) protein spot and increased production of flagellin proteins. Loss of AhpC expression in C1394mp2 correlated with enhanced susceptibility to oxidative stress. C1394mp2 expressed increased flagellin production and enhanced swimming motility, traits that were subject to regulation by heat and low pH. Together, these results revealed differential expression and stress regulation of putative virulence determinants associated with B. cenocepacia persistence in a susceptible host.

Animals↗

PubMed2026

Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.

BACKGROUND: COVID-19 has been closely associated with coagulation abnormalities. However, existing biomarkers, including D-dimer and fibrin degradation products (FDP), exhibit limited accuracy in stratifying disease severity and predicting long-term clinical outcomes. OBJECTIVES: This study aimed to use proteomic analysis to identify plasma biomarkers associated with COVID-19 severity and prognosis, and validate their predictive utility for mortality and thromboembolic complications. METHODS: Plasma proteomic profiles were analyzed across three COVID-19 severity classes. Differential expression analysis and functional analysis were performed. Clustering analysis was used to identify proteins correlated with disease severity. Candidate biomarkers were validated in an independent cohort. Predictive performance of the biomarkers for mortality, sepsis and venous thromboembolism was evaluated using bootstrap-corrected ROC analyses and multivariable regression analyses. RESULTS: Proteomic analysis revealed progressive involvement of the coagulation and complement pathway with increasing disease severity. SERPINA1 and CD59 were identified as candidate biomarkers and exhibited significantly higher plasma levels in severe cases. Bootstrap-corrected ROC analyses demonstrated strong predictive performance: SERPINA1 achieved AUCs of 0.775 and 0.924 for 30-day and 12-month mortality, and CD59 achieved AUCs of 0.720 for sepsis; the combined model further improved prediction of 12-month mortality (AUC 0.946) and sepsis (AUC 0.904), outperforming D-dimer and FDP. Multivariable regression confirmed their independent prognostic value. CONCLUSION: This exploratory study identifies SERPINA1 and CD59 as candidate prognostic biomarkers in COVID-19, highlighting the role of coagulation and complement-related pathways in disease severity and warranting further prospective validation.

Humans↗

PubMed2026

Proteomic and machine learning analysis predicts treatment response signatures in Myasthenia Gravis.

BACKGROUND: Myasthenia gravis (MG) is a prototypical antibody-mediated autoimmune disease with variable treatment responses with a need for biomarkers to guide therapeutic decision making. Proteomic profiling, coupled with machine learning, offers a hypothesis-free approach to identify multi-protein signatures associated with treatment response. METHODS: We analyzed sera collected at entry (baseline) from participants in a phase 3 trial randomized trial comparing thymectomy plus prednisone versus prednisone alone, along with matched controls using liquid chromatography-mass spectrometry. We derived disease-specific proteomic signatures and evaluated associations between baseline proteins and 6-month clinical outcomes using multiple machine-learning approaches with internal validation. RESULTS: Baseline serum proteomes distinguished MG from controls, with pathway enrichment implicating complement activation, immunoglobulin production, and T-cell receptor signaling. Distinct protein panels predicted 6-month clinical improvement within each treatment arm. In the thymectomy-plus-prednisone group, models captured non-linear relationships of predictive proteins in contrast with the predominant additive patterns observed in the prednisone-alone group. Predictive proteins were enriched for T-cell signaling and leukocyte trafficking functions, providing insight into treatment-specific biology. CONCLUSIONS: Baseline serum proteomics captures core disease characteristics of MG and predicts short-term clinical response in a treatment-specific manner. While our results require validation in independent cohorts, these findings could enable biomarker-guided selection of thymectomy, refine risk stratification, and furnish mechanistic readouts for future MG trials and clinical care. We aim to conduct future studies using -omic approaches to validate these baseline predictive biomarkers and pathways of treatment response in patients with MG.

Adult↗

PubMed2006

Analysis of neuron-like differentiation of human bone marrow mesenchymal stem cells.

The objective of the study was to evaluate differentiation of human bone marrow mesenchymal stem cells into true or pseudo neurons after treating with chemical induction medium in vitro. The morphological changes were assessed using interference contrast microscopy. Immunocytochemistry and Western blotting were performed using neuronal markers. Further evaluation was conducted with proteomic profiling, DNA microarray analysis and the whole-cell patch clamp test. After three hours of treatment with chemical induction medium, nearly three-fourths of the hMSCs changed to cells with a neuronal phenotype. The results of immunocytochemistry and Western blotting showed a high expression of neuronal markers in these cells at 3 h which decreased at 24 h. The proteomics analysis showed no change of proteins related to neuronal differentiation. DNA microarray showed downregulation of neuron related genes. The patch clamp test was unable to demonstrate any similarity to true neurons. Our findings suggest that neuron-like cells derived from chemical induction of hMSCs are not the genuine neurons as they resemble true neurons phenotypically but are different in genotypic and electrophysiological characteristics.

Biomarkers↗

PubMed2026

Machine learning-based clinical prediction model and multi-omics integration for assessing pancreatic cancer risk in new-onset diabetes.

BACKGROUND: Given that pancreatic cancer (PC) is typically diagnosed at an advanced stage but is often preceded by new-onset diabetes mellitus (NODM), providing a window for early detection, we sought to develop and validate an interpretable machine-learning model integrated with multi-omics profiling to identify early biomarkers of NODM-associated PC. METHODS: In a population-based cohort, individuals with NODM-associated PC and NODM without PC were identified and randomly divided (70:30) into training and validation sets after feature selection. Eight machine learning (ML) classifiers were compared using fivefold cross-validation, and model performance was evaluated in terms of discrimination, calibration, and decision curve–based clinical utility. We evaluated interpretability using the Shapley additive explanations (SHAP) analyses. Mechanistically, Olink proteomic profiling and metabolomics were analyzed through clinical classifications and model-defined risk strata. RESULTS: Categorical boosting achieved the best performance in the independent validation set (AUROC = 0.844). The NODM cohort was stratified into high- (n = 2,362) and low-risk (n = 5,030) groups, and internal validation together with SHAP analyses demonstrated consistent model performance and identified clinically interpretable predictors. Proteomic and metabolomic analyses under clinical and risk-based grouping identified 39 overlapping differentially expressed proteins and 145 overlapping metabolites with enriched across 11 shared KEGG pathways. Cross-platform validation highlighted PLTP, CRTAC1, and ITGAV as serum biomarkers with a strong potential for early NODM-PC detection. CONCLUSIONS: We developed an interpretable ML framework centered on NODM enables practical risk stratification for early PC detection by multi-omics and provides a pathway of ML-based triage followed by biomarker confirmation for earlier detection and diagnosis.

Humans↗

PubMed2004

High-throughput comparative proteome analysis using a quantitative cysteinyl-peptide enrichment technology.

A new quantitative cysteinyl-peptide enrichment technology (QCET) was developed to achieve higher efficiency, greater dynamic range, and higher throughput in quantitative proteomics that use stable-isotope labeling techniques combined with high-resolution liquid chromatography (LC)-mass spectrometry (MS). This approach involves (18)O labeling of tryptic peptides, high-efficiency enrichment of cysteine-containing peptides, and confident protein identification and quantification using the accurate mass and time tag strategy. Proteome profiling of naïve and in vitro-differentiated human mammary epithelial cells using QCET resulted in the identification and quantification of 603 proteins in a single LC-Fourier transform ion cyclotron resonance MS analysis. Advantages of this technology include the following: (1) a simple, highly efficient method for enriching cysteinyl-peptides; (2) a high-throughput strategy suitable for extensive proteome analysis; and (3) improved labeling efficiency for better quantitative measurements. This technology enhances both the functional analysis of biological systems and the detection of potential clinical biomarkers.

Cells, Cultured↗

PubMed2026

Mapping the causal chain from genetic risk variants to lipid dysmetabolism in Parkinson's disease.

The molecular pathways linking genetic variants to Parkinson's disease (PD) onset and progression remain incompletely defined; however, risk alleles in multiple genes, including GBA1, strongly implicate lipid metabolism. To systematically identify causal biomarker signatures, we analysed comprehensive metabolome profiles from blood plasma in 149 PD patients and 150 controls, along with complementary genetic, RNA-sequencing and metabolic data from other available clinical and pathologic cohorts. Using colocalization and summary-data-based Mendelian randomization, we tested whether expression and metabolic quantitative trait loci mediate the association between implicated genetic variants and PD risk. We further integrated differential metabolomics and proteomics from blood and brain to reveal pertinent mechanisms. We show that common PD risk variants at the serine palmitoyltransferase small subunit B (SPTSSB) locus, a key regulator of de novo sphingolipid biosynthesis, are associated with increased SPTSSB brain expression and elevated plasma ceramides. Additional analyses strongly support our hypothesis that a common SPTSSB causal variant is responsible for PD risk as well as the expression and metabolic quantitative trait loci. Multiple sphingolipids and fatty acid derivatives were perturbed in PD, and we identified both unique and shared features with the Alzheimer's disease metabolome. A PD acylcarnitine signature was further replicated in human post-mortem brain tissue, when comparing those with or without preclinical Lewy body pathology. Integrated analysis of complementary brain proteomic profiles revealed dysregulation of mitochondrial processes dependent on acylcarnitines, including fatty acid beta-oxidation, the tricarboxylic acid cycle and oxidative phosphorylation. Our results identify promising biomarkers and reveal a causal chain linking genetic variation to altered gene/protein expression, lipid dysmetabolism, and the manifestation of PD.

Humans↗

PubMed2026

The composition of the periostracum in the razor clam Sinonovacula constricta and the mantle's response to sulfide.

The razor clam Sinonovacula constricta inhabits sulfide-rich intertidal sediments and exhibits remarkable tolerance to this toxicant, yet the role of its periostracum in sulfide adaptation remains poorly understood. In this study, we investigated the composition and structure of the periostracum proteins, and the response of the mantle to sulfide stress. Scanning electron microscopy and energy-dispersive X-ray spectroscopy revealed that the periostracum is approximately 10 μm thick and contains 1.43 wt% sulfur, and proteomic analysis further confirmed the presence of organic sulfur (Cys/Met-rich proteins), suggesting its involvement in sulfur deposition. Using LC-MS/MS, we identified 77 high-confidence proteins from the periostracum, which were classified into six functional categories: enzymes, framework proteins, immune-related proteins, calcium ion-related proteins, other proteins, and proteins with unknown functions. Phylogenetic analyses of representative proteins revealed bivalve-specific evolutionary patterns, with several proteins exclusively present in Bivalvia, such as Unknown protein 2 and 7, which possess signal peptides and low-complexity domains. For the sulfide exposure experiment, razor clams were subjected to three Na2S concentrations (0, 10, and 100 μM). qPCR analysis showed that, compared with the control group, Chitin-binding protein 3 and Tyrosinase were significantly upregulated in the mantle, peaking in the 100 μM group at 48 h (5677.84-fold and 157.20-fold, respectively), whereas Collagen and Cadherin 3 were generally suppressed. This study represents one of the most comprehensive proteomic profiles of the razor clam periostracum and highlights the mantle's potential role in sulfide tolerance, offering insights for sulfur-tolerant aquaculture breeding and bioremediation applications.

Animals↗

PubMed2005

Transcriptome and proteome analysis identifies the pathways that increase hepatic lipid accumulation in zinc-deficient rats.

For identification of the underlying molecular changes in hepatic lipid metabolism in zinc deficiency, rats were force-fed a zinc-deficient diet. Subsequently DNA-microarray and proteome profiling was performed in combination with hepatic lipid analysis. Of 6200 target sequences analyzed, 268 transcripts showed altered expression levels in livers of zinc-deficient rats, with 43 genes thereof related to hepatic lipid metabolism. Northern blot analysis and quantitative real-time RT-PCR were employed to confirm changes in mRNA levels. Proteins involved in lipid metabolism were identified by proteome analysis. Functional gene clusters with uniform changes in transcript levels suggested that the pathways required for lipolysis and mitochondrial as well as peroxisomal fatty acid degradation were downregulated, whereas those needed for de novo fatty acid synthesis and triglyceride assembly were increased. Subsequent enzymatic analysis of liver tissues confirmed an almost 40% greater triacylglycerol concentration in zinc-depleted rats, as well as an altered fatty acid composition of the lipid fraction as determined by gas chromatography. Liver lipids of zinc-deficient rats had significantly greater proportions of cis-9-oleic acid, cis-11-vaccenic acid, caprylic acid, myristic acid, alpha-linolenic acid, and eicosapentaenoic acid, and significantly less stearic and arachidonic acids. These alterations in hepatic metabolism are discussed in the context of changes in mRNA and protein levels of enzymes and transporters responsible for fatty acid metabolism, sequestration, and their transcriptional control.

Animals↗

PubMed2006

Label-free semiquantitative peptide feature profiling of human breast cancer and breast disease sera via two-dimensional liquid chromatography-mass spectrometry.

A label-free semiquantitative peptide feature profiling method was developed in response to challenges associated with analysis of two-dimensional liquid chromatography-tandem mass spectrometry data. One hundred twenty human sera (49 from invasive breast carcinoma patients, 26 from non-invasive breast carcinoma patients, 35 from benign breast disease patients, and 10 from normal controls) were repeatedly analyzed using a standardized two-dimensional liquid chromatography-mass spectrometry method. Data were extracted using the novel semiquantitative peptide feature profiling method, which is based on comparisons of normalized relative ion intensities. Hierarchical cluster analyses and principle component analyses were used to evaluate the predicative capability of the extracted data, and results were promising. Extracted data were also randomly assigned to either a training group (65%) or to a test group (35%) for artificial neural network modeling. Models best identified invasive breast carcinomas (212 predictions, 94% accurate) and benign non-neoplastic breast disease (96 predictions, 81.3% accurate). These results suggest that, after further development, the novel method may be useful for large scale clinical proteomic profiling.

Breast Diseases↗

PubMed2004

Proteomic approaches in endometriosis research.

To date, the quest to develop a noninvasive diagnostic test for endometriosis has mostly concentrated on the levels of cytokines and growth factors that are involved in inflammation, angioneogenesis and tissue remodeling, present in serum, peritoneal fluid, endometrium and endometriotic lesions. As this has not yet translated into the development of such a diagnostic test, proteomic techniques are now being employed to identify proteins that are potential biomarkers for the disease. As proteomics allows the comprehensive analysis of complex fluid and tissue samples with good sensitivity and resolution, it has promise in delivering markers associated with endometriosis. Once identified, the challenge will be in translating these markers into a clinically useful test for endometriosis, as the pathophysiology of this disease is unknown and likely to be complex and multifactorial. Also, with variation between individuals and the influences of steroid hormones during the menstrual cycle, it could be difficult to validate findings relating to a single protein or small groups of proteins differentially expressed in the disease state. Proteomic profiling, using mass spectrometry in combination with sophisticated bioinformatics software to identify protein patterns, may be where a significant clinical diagnostic contribution can be made.

Endometriosis↗

PubMed2026

Proteomics-driven discovery of intervention windows and risk subtypes in osteoporosis: A prospective cohort study.

Given the limited feasibility of population-wide bone mineral density screening and the infrequency of long-term monitoring in healthy individuals, identifying the window for early intervention and the populations to be prioritized for screening is critical. This study aimed to identify intervention windows for osteoporosis and to determine potential high-risk subtypes within the healthy population. Based on proteomic data from 41,408 healthy adults, we conducted the DE-SWAN method to identify change peaks in plasma protein during the pre-diagnostic osteoporosis phase, and employed finite Gaussian mixture model-based clustering to delineate high-risk subtypes of osteoporosis. We identified 122 protein biomarkers significantly associated with osteoporosis risk throughout the follow-up period. Importantly, we identified two critical peaks occurring approximately 10 and 6 years before diagnosis, with the former enriched in immune-related pathways and the latter prominently involving responses to retinoic acid and glucocorticoids. Furthermore, one high-risk subtype for osteoporosis was identified in both males and females, termed the Frailty and Obesity Subtype. This subtype is characterized by a high degree of frailty and obesity, accompanied by a significantly elevated risk of both osteoporosis and fractures. Finally, we developed a predictive model comprising 10 proteins for identifying high-risk subtypes of osteoporosis, which demonstrated better performance than the traditional risk factor model (AUC: 0.743 vs. 0.680). Our findings demonstrate that proteomic profiling can reveal early molecular changes and identify high-risk subtypes years before clinical onset, providing a foundation for screening and precision prevention of osteoporosis.

Proteomics↗

PubMed2006

Amniocentesis results and novel proteomic analysis in a case of occult candidal chorioamnionitis.

We report a case of subclinical Candida albicans chorioamnionitis with mid-trimester cervical dilation diagnosed by amniotic fluid culture and Gram stain. Proteomic analysis of amniotic fluid demonstrated protein biomarkers previously associated only with bacterial intra-amniotic infections. We hypothesize that since amniotic fluid culture of yeast may take days, novel tests such as proteomic profiling may lead to the development of rapid diagnostic tests for fungal intra-amniotic infections.

Adult↗

PubMed2026

Proteomic regulation of anti-proliferative and anti-migratory activity by potent phytochemicals from Pistacia integerrima J.L. Steward Ex Brandis via PI3K, AKT1, and KRAS for Lung Cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the leading cause of mortality worldwide and remains a major therapeutic challenge due to high metastasis, drug resistance and limited treatments. Pistacia integerrima J.L. Steward Ex Brandis (PI) consists of flavonoids, steroids, terpenoids and phenolic compounds reported for pharmacological activities. The efficacy of potent bioactives from P. integerrima may be ascertained employing cytotoxic, antiproliferative, anti-migratory, and anti-metastatic evaluations in A549 NSCLC cells with proteomic profiling, molecular docking, and dynamics simulation study. METHODS AND RESULTS: PI EtAc produced significant dose-dependent cytotoxicity in A549 cells (100 µg/mL, p < 0.0001 in the MTT assay. There was a pronounced decrease in colony formation after treatment with EtAc, with 18.41% (p < 0.002), and markedly. Furthermore. PI EtAC markedly inhibited cell migration emphasized by wound healing and Transwell migration (p < 0.01) assays, indicating reduced metastatic migratory potential. Proteomic analysis demonstrated significant downregulation of Endoglin (CD105), KLK5 and MMP-2, indicating suppression of angiogenic and metastatic signalling pathways in the Human XL Oncology protein array. The interaction of major PI phytochemicals with key NSCLC-associated targets was recorded in Molecular docking, revealing favourable binding affinities of kaempferol, β- sitosterol, luteolin, and quercetin towards several oncogenic targets, including AKT1(- 7.6 kcal/mol), PI3K(- 9.4 kcal/mol), KRAS (- 8.5 kcal/mol) and MMP9 (- 8.1 kcal/mol). Molecular dynamics simulation confirmed the structural stability of the kaempferol -AKT1 complex throughout the 100 ns simulation. CONCLUSION: Pistacia integerrima bioactives exhibited significant anti-proliferative, anti-migratory, and anti-metastatic activities in vitro, which may provide scientific rationale identifying newer promising candidates for NSCLC.

Humans↗

PubMed2025

MEG3 Promoter Methylation and F11 Receptor (F11R) Overexpression Define a High-Risk Subtype of Diabetic Pancreatic Cancer.

Long-standing diabetes mellitus (long-DM) (≧3 years) is associated with worse clinical outcomes in patients with pancreatic ductal adenocarcinoma (PDAC). Emerging evidence suggests that epigenetic alterations may contribute to this association; however, the underlying mechanisms remain largely unclear. This study aimed to elucidate the role of the tumor-suppressive long noncoding RNA maternally expressed gene 3 (MEG3) and related molecules in the development of PDAC with long-DM. A total of 117 patients who underwent surgical resection for PDAC at Hirosaki University Hospital were retrospectively analyzed. Histopathological assessment followed World Health Organization criteria and the Union for International Cancer Control tumor-node-metastasis classification. Promoter methylation of MEG3 was assessed via methylation-specific PCR using formalin-fixed paraffin-embedded tissue. MEG3 expression levels were assessed by real-time quantitative PCR. Additionally, proteomic profiling was performed using liquid chromatography-tandem mass spectrometry on formalin-fixed paraffin-embedded tissue samples. Among the 117 cases with PDAC, patients with long-DM exhibited significantly poorer tumor differentiation and reduced cancer-specific survival. MEG3 promoter methylation was more prevalent in patients with long-DM. MEG3 methylation was correlated with reduced MEG3 expression, increased venous invasion, higher recurrence rates, and worse prognosis. Proteomic analysis and protein structure prediction tool revealed F11 receptor (F11R) as a potential downstream effector of MEG3. F11R protein expression levels were evaluated using semiquantitative immunohistochemistry. Higher F11R expression was observed in patients with long-DM, correlating with poor histologic differentiation and unfavorable outcomes. Patients with PDAC showing simultaneous MEG3 methylation and F11R high expression were more likely to have long-DM, with additive effects of these changes and tumor recurrence. Our results demonstrated that MEG3 and its potential downstream regulator, F11R, could be involved in PDAC progression, particularly in patients with long-DM. The findings underscore the clinical significance of epigenetic regulation in DM-related PDAC, suggesting novel targets, such as MEG3 and F11R, for potential therapeutic intervention.

Humans↗

PubMed2005

Proteomic analysis of formalin-fixed prostate cancer tissue.

Proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissue would enable retrospective biomarker investigations of this vast archive of pathologically characterized clinical samples that exist worldwide. These FFPE tissues are, however, refractory to proteomic investigations utilizing many state of the art methodologies largely due to the high level of covalently cross-linked proteins arising from formalin fixation. A novel tissue microdissection technique has been developed and combined with a method to extract soluble peptides directly from FFPE tissue for mass spectral analysis of prostate cancer (PCa) and benign prostate hyperplasia (BPH). Hundreds of proteins from PCa and BPH tissue were identified, including several known PCa markers such as prostate-specific antigen, prostatic acid phosphatase, and macrophage inhibitory cytokine-1. Quantitative proteomic profiling utilizing stable isotope labeling confirmed similar expression levels of prostate-specific antigen and prostatic acid phosphatase in BPH and PCa cells, whereas the expression of macrophage inhibitory cytokine-1 was found to be greater in PCa as compared with BPH cells.

Amino Acid Sequence↗

PubMed2002

Proteomic analysis of lipopolysaccharide-induced apoptosis in PC12 cells.

We employed rat pheochromocytoma PC12 cells as our model system to identify cellular proteins that accompany Escherichia coli lipopolysaccharide (LPS)-induced apoptosis, based on a proteomic approach. Cell viability tests revealed that naïve PC12 cells underwent cell death in a dose-dependent manner after treatment with LPS. Flow cytometric analysis confirmed that apoptosis was primarily responsible for the observed cell death. Two-dimensional electrophoresis in conjunction with N-terminal sequencing, immunoblot, matrix-assisted laser desorption/ionization-time of flight analysis or computer matching with protein databases further revealed that the LPS-induced apoptosis is accompanied by an augmented level of calreticulin, calcium binding protein 50, endoplasmic reticulum protein 60 (ERP60), heat shock protein 60 (HSP60) or HSP90, and a reduced level of amphoterin, cytochrome c oxidase polypeptide VIa-liver or ERP29. These proteins are associated with endoplasmic reticulum, mitochondria or cell membrane, and are with known or potential roles in apoptosis. Their identification therefore provides an impetus for further delineation of the cellular and molecular basis of apoptotic cell death and sepsis based on proteomic profiling of PC12 cells.

Animals↗

PubMed2004

Circulating nucleic acids and proteomics of plasma/serum: clinical utility.

Circulating tumor-specific nucleic acids have been identified in plasma, serum, and other body fluids from cancer patients with tumors originating in almost any organ site. Polymerase chain reaction provides a highly sensitive and specific technique for the detection of these genetic changes in a limited amount of tissue/fluid. The presence of elevated levels of free DNA/RNA in many medical conditions, malignancy, and infectious processes is being investigated for screening, diagnosis, prognosis, surveillance for occult disease progression, identifying potential therapeutic targets, and monitoring treatment response. Additionally, elevated fetal DNA/RNA in maternal blood is being used to determine gender identity, assess chromosomal abnormalities, and monitor pregnancy-associated complications. Questions remain on the etiology, characteristics, stability, and potential pathologic consequences of cell-free DNA/RNA in the circulation. Nevertheless, nucleic acid-based assays that monitor plasma, serum, and body fluids provide a noninvasive, facile, and practical method for assessing patients. Proteomic profiling may prove complementary to a total functionality approach in providing a comprehensive evaluation of the patient's disease.

Biomarkers, Tumor↗