Discovery Of Serum Biomarkers Research Articles

P-707 Discovery of novel serum biomarkers for premature ovarian insufficiency through machine-learning bioinformatic analysis

Abstract Study question Can we develop predictive, practical biomarkers for premature ovarian insufficiency (POI) from normal ovaries and human blood, tested and validated by machine-learning (ML) algorithm? Summary answer Applying random forest and XGBoost algorithms, we pinpointed 60 significant genes from ovarian tissues; transcriptome analysis identified RPM1 as the most significant biomarker. What is known already Accelerated ovarian aging has been suggested as one of the possible underlying mechanisms of POI; previous literature has focused on identifying its cause in genetic realm, commonly adopting first-generation sequencing (Sanger) and next-generation sequencing (NGS). However, such powerful tools are still limited in accurately depicting the complete transcript information of ovarian tissue. Moreover, theoretical value of such disease-predictive gene candidates needs to be validated using human tissues and blood, and finally, underlying mechanisms should be confirmed with ideal animal model and cell line. Study design, size, duration To overcome the existing limitations and maximize clinical relevance of disease-predictive gene analysis and theoretical biomarker exploration, the current study incorporated the transcriptome expression data from both young and old ovaries obtained from the Genotype-Tissue Expression (GTEx) database. Using supervised machine learning techniques, potential marker genes were identified, and a meaningful predictive index was subsequently developed using human plasma samples of control and POI women with and without pregnancy history. Participants/materials, setting, methods The performance evaluation of two ML models were achieved using the area under receiver operating characteristic curve (AUROC), accuracy, kappa, and F1 score, among various age groups. The expression profiles of final candidate marker genes were analyzed within the GTEx datasets, in relation to their correlation pattern with age. Finally, the potential transcriptome biomarkers were quantitatively evaluated using human blood samples from 92 controls and 108 POI women visiting a tertiary center in 2023. Main results and the role of chance We found 41 significant genes using the random forest algorithm and 19 significant genes using the XGboost algorithm. We then tested the ML-characterized 7 final candidate biomarkers in human blood samples (all p < 0.05). The AUROC and interventional predictive index results demonstrated the potential of RPM1 as a predictive marker with AUROC of 0.784, comparable to the AUROC of anti-mullerian hormone (AMH) as 0.659 and inhibin B (INHB) as 0.646. Limitations, reasons for caution To avoid potential biases, RPMs should be tested in a larger, multinational cohort with different genetic and geographic backgrounds, possibly incorporating Mendelian randomization method to estimate the causal effect of RPMs on POI. Finally, to ultimately confirm their therapeutic potential, preclinical studies should be considered using RPM recombinant proteins. Wider implications of the findings Our results proposed that predictive biomarkers for POI could be associated with premature, accelerated ovarian aging of the disease mechanism, and these biomarkers can be developed as evaluation metrics for the efficacy, progress, and prognosis of interventions and treatment modalities assigned to POI women seeking fertility. Trial registration number 202302240001

Deregulation in adult IgA vasculitis skin as the basis for the discovery of novel serum biomarkers

IntroductionImmunoglobulin A vasculitis (IgAV) in adults has a variable disease course, with patients often developing gastrointestinal and renal involvement and thus contributing to higher mortality. Due to understudied molecular mechanisms in IgAV currently used biomarkers for IgAV visceral involvement are largely lacking. Our aim was to search for potential serum biomarkers based on the skin transcriptomic signature.MethodsRNA sequencing analysis was conducted on skin biopsies collected from 6 treatment-naïve patients (3 skin only and 3 renal involvement) and 3 healthy controls (HC) to get insight into deregulated processes at the transcriptomic level. 15 analytes were selected and measured based on the transcriptome analysis (adiponectin, lipopolysaccharide binding protein (LBP), matrix metalloproteinase-1 (MMP1), C-C motif chemokine ligand (CCL) 19, kallikrein-5, CCL3, leptin, C-X-C motif chemokine ligand (CXCL) 5, osteopontin, interleukin (IL)-15, CXCL10, angiopoietin-like 4 (ANGPTL4), SERPIN A12/vaspin, IL-18 and fatty acid-binding protein 4 (FABP4)) in sera of 59 IgAV and 22 HC. Machine learning was used to assess the ability of the analytes to predict IgAV and its organ involvement.ResultsBased on the gene expression levels in the skin, we were able to differentiate between IgAV patients and HC using principal component analysis (PCA) and a sample-to-sample distance matrix. Differential expression analysis revealed 49 differentially expressed genes (DEGs) in all IgAV patient’s vs. HC. Patients with renal involvement had more DEGs than patients with skin involvement only (507 vs. 46 DEGs) as compared to HC, suggesting different skin signatures. Major dysregulated processes in patients with renal involvement were lipid metabolism, acute inflammatory response, and extracellular matrix (ECM)-related processes. 11 of 15 analytes selected based on affected processes in IgAV skin (osteopontin, LBP, ANGPTL4, IL-15, FABP4, CCL19, kallikrein-5, CCL3, leptin, IL-18 and MMP1) were significantly higher (p-adj < 0.05) in IgAV serum as compared to HC. Prediction models utilizing measured analytes showed high potential for predicting adult IgAV.ConclusionSkin transcriptomic data revealed deregulations in lipid metabolism and acute inflammatory response, reflected also in serum analyte measurements. LBP, among others, could serve as a potential biomarker of renal complications, while adiponectin and CXCL10 could indicate gastrointestinal involvement.

Discovery and Validation of Potential Serum Biomarkers for Heart Failure by Untargeted Metabolomics

Detection of biomarkers was extremely important for the early diagnosis, prognosis, and therapy optimization of diseases. The purpose of this study was to investigate the differences in serum metabolites between patients with heart failure (HF) and healthy control (HC) and to diagnose HF qualitatively. In this study, serum samples from 83 patients with HF and 35 HCs were used as the research subjects for untargeted metabolomic analysis using ultraperformance liquid chromatography combined with quadrupole‐time of flight mass spectrometry (UPLC‐QTOF/MS) technology. Potential biomarkers were screened and validated using the orthogonal partial least squares discriminant analysis (OPLS‐DA), random forest (RF), binary logistic regression (BLR), and receiver operating characteristic (ROC) analysis. The results indicated that a total of 43 metabolites were considered as differentially expressed metabolites (DEMs). Among these DEMs, glycodeoxycholate was identified as a specific biomarker of HF. A ROC curve analysis for HC versus HF discrimination showed an area under the ROC curve (AUC) of 0.9853 (95% CI: 0.9859–1.0000), a sensitivity of 95%, and a specificity of 100%. Hence, glycodeoxycholate might serve as a potential biomarker for HF. Furthermore, the amino acid metabolism was screened as the most significantly altered pathway in patients with HF. By identifying serum biomarkers and analyzing metabolic pathways, our study provided opportunities to enhance the understanding of the pathogenesis and early diagnosis of HF.

Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma

Early diagnosis of hepatocellular carcinoma (HCC) lacks highly sensitive and specific protein biomarkers. Here, we describe a staged mass spectrometry (MS)-based discovery-verification-validation proteomics workflow to explore serum proteomic biomarkers for HCC early diagnosis in 1002 individuals. Machine learning model determined as P4 panel (HABP2, CD163, AFP and PIVKA-II) clearly distinguish HCC from liver cirrhosis (LC, AUC 0.979, sensitivity 0.925, specificity 0.915) and healthy individuals (HC, AUC 0.992, sensitivity 0.975, specificity 1.000) in an independent validation cohort, outperforming existing clinical prediction strategies. Furthermore, the P4 panel can accurately predict LC to HCC conversion (AUC 0.890, sensitivity 0.909, specificity 0.877) with predicting HCC at a median of 11.4 months prior to imaging in prospective external validation cohorts (No.: Keshen 2018_005_02 and NCT03588442). These results suggest that proteomics-driven serum biomarker discovery provides a valuable reference for the liquid biopsy, and has great potential to improve early diagnosis of HCC.

Discovery of novel serum biomarkers for diagnosing and predicting postmenopausal osteoporosis patients by 4D-label free protein omics.

We aimed to identify protein biomarkers that could rapidly and accurately diagnose osteoporosis patients (OPs) using a highly sensitive proteomic immunoassay. Four-dimensional (4D) label-free proteomics analysis was performed to determine the differentially expressed proteins in serum collected from 10 postmenopausal osteoporosis patients and 6 non-osteoporosis patients. The ELISA method was used to select the predicted proteins for verification. Serum was taken from 36 postmenopausal osteoporosis patients and 36 healthy individuals from normal postmenopausal women. Receiver operating characteristic (ROC) curves were used to determine the diagnostic potential of this method. We validated the expression of these six proteins using ELISA. The CDH1, IGFBP2, and VWF of osteoporosis patients were significantly higher than those of the normal group. PNP was significantly lower than that in the normal group. And using ROC curve calculation, serum CDH1 had a cut-off of 3.78 ng/mL with a sensitivity of 84.4%, and PNP had a cut-off of 944.32 ng/mL with 88.9% sensitivity. These outcomes suggest that serum-level CHD1 and PNP have the potential power as effective indicators for the diagnosis of PMOP. Our results suggest that CHD1 and PNP might be associated with the pathogenesis of OP and would be helpful in diagnosing OP. Therefore, CHD1 and PNP may act as potential key markers in OP.

Discovery of novel glioma serum biomarkers by proximity extension assay

BackgroundGliomas are among the most malignant tumors, with a very poor prognosis. Early diagnosis is highly desirable since it can help implement more effective treatments for smaller tumors, which have not yet extensively metastasized. Improving early diagnosis may facilitate access of patients to clinical trials and prepare them for the future availability of new disease-modifying treatments.MethodsWe analyzed retrospective samples collected at diagnosis (before therapy initiation), with PEA (Olink Proteomics), quantifying about 3000 proteins. We utilized 30 plasmas from gliomas (20 glioblastomas, 5 anaplastic astrocytomas, 5 anaplastic oligodendrogliomas) and 20 meningiomas (as controls). We then analyzed the data to identify proteins which either alone, or in combination, could discriminate gliomas from meningiomas, or correlate with clinical and molecular alterations.ResultsWe identified 8 plasma proteins which were increased in gliomas vs. meningiomas (GFAP, NEFL, EDDM3B, PROK1, MMP3, CTRL, GP2, SPINT3) and 4 proteins which were decreased in gliomas vs. meningiomas (FABP4, ALDH3A1, IL-12B and OXT). Partition algorithms and logistic regression algorithms with two biomarkers (GFAP and FABP4) achieved sensitivity of 83% and 93% at 100% and 90% specificity, respectively. The strongest single marker was GFAP with an area under the ROC curve (AUC) of 0.86. The AUC for the GFAP-FABP4 combination was 0.98.ConclusionPEA is a powerful new proteomic technology for biomarker discovery. GFAP and a handful of other plasma biomarkers may be useful for early glioma detection and probably, prognosis.StatementDetecting gliomas as early as possible is highly desirable since it can significantly improve the chances of effective treatments. Reliable glioma biomarkers can timely inform glioma patients about the efficacy of their prescribed treatment. Our results reveal some novel putative glioma markers that may prove valuable, when used alone or in combination, towards improved clinical care of gliomas. In order to better appreciate the potential usefulness of these markers, their performance needs to be further validated in a larger cohort of samples.

An experimental workflow for enrichment of low abundant proteins from human serum for the discovery of serum biomarkers.

Serum contains proteins that possess important information about diseases and their progression. Unfortunately, these proteins, which carry the information in the serum are in low abundance and are masked by other serum proteins that are in high abundance. Such masking prevents their identification and quantification. Therefore, removal of high abundance proteins is required to enrich, identify, and quantify the low abundance proteins. Immunodepletion methods are often used for this purpose, but there are limitations in their use because of off-target effects and high costs. Here we presented a robust, reproducible and cost-effective experimental workflow to remove immunoglobulins and albumin from serum with high efficiency. The workflow did not suffer from such limitations and enabled identification of 681 low abundance proteins that were otherwise undetectable in the serum. The identified low abundance proteins belonged to 21 different protein classes, namely the immunity-related proteins, modulators of protein-binding activity, and protein-modifying enzymes. They also played roles in various metabolic events, such as integrin signalling, inflammation-mediated signalling, and cadherin signalling. The presented workflow can be adapted to remove abundant proteins from other types of biological material and to provide considerable enrichment for low-abundance proteins.

Cancer Serum Atlas-Supported Precise Pan-Targeted Proteomics Enable Multicancer Detection.

The wide dynamic range of serum proteome restrained discovery of clinically interested proteins in large cohort studies. Herein, we presented a high-sensitivity, high-throughput, and precise pan-targeted serum proteomic strategy for highly efficient cancer serum proteomic research and biomarker discovery. We constructed a resource of over 2000 cancer-secreted proteins, and the standard MS assays and spectra of at least one synthetic unique peptide per protein were acquired and documented (Cancer Serum Atlas, www.cancerserumatlas.com). Then, the standard peptide-anchored parallel reaction monitoring (SPA-PRM) method was developed with support of the Cancer Serum Atlas, achieving precise quantification of cancer-secreted proteins with high throughput and sensitivity. We directly quantified 325 cancer-related serum proteins in 288 serums of four cancer types (liver, stomach, lung, breast) and controls with the pan-targeted strategy and discovered considerable potential biomarker benefits for early detection of cancer. Finally, a proteomic-based multicancer detection model was built, demonstrating high sensitivity (87.2%) and specificity (100%), with 73.8% localization accuracy for an independent test set. In conclusion, the Cancer Serum Atlas provides a wide range of potential biomarkers that serve as targets and standard assays for systematic and highly efficient serological studies of cancer. The Cancer Serum Atlas-supported pan-targeted proteomic strategy enables highly efficient biomarker discovery and multicancer detection and thus can be a powerful tool for liquid biopsy.

Discovery and Validation of Potential Serum Biomarkers with Pro-Inflammatory and DNA Damage Activities in Ulcerative Colitis: A Comprehensive Untargeted Metabolomic Study.

Ulcerative colitis is a type of non-specific inflammatory bowel disease with unclear etiology. It is considered a progressive disease with risks of bowel motility disorders, anorectal dysfunction, and even colorectal cancer. Commonly used diagnostic markers have poor specificity and cannot predict the development of ulcerative colitis. In this study, 77 serum samples (31 patients, 46 healthy controls) were analyzed using high performance liquid chromatography-quadrupole time-of-flight mass spectrometry and 31 metabolites with significant level changes were found, revealing the relationship of ulcerative colitis to disturbed glutathione metabolism and caffeine metabolism. In addition, pyroglutamic acid, a biomarker of cervical cancer and gastric cancer, was identified with elevated levels in the serum of ulcerative colitis patients. The role of pyroglutamic acid was further analyzed, and the results indicated its positive correlation with the upregulation of inflammatory factors and increased levels of phosphorylated histone H2AX (γH2AX) in IEC-6 cells, which are related to DNA damage. All these results suggest that pyroglutamic acid is not only a biomarker for distinguishing ulcerative colitis status, but that it is also a potential effective metabolite that promotes the transformation of ulcerative colitis to colorectal cancer.

First trimester serum biomarker discovery study for early onset, preterm onset and preeclampsia at term

IntroductionPreeclampsia (PE) is a heterogeneous syndrome during pregnancy and postpartum and it is subdivided in this study into early onset (<34 weeks), preterm onset (34–37 weeks) and PE at term (>37 weeks). First trimester models currently lack a sufficient power to predict PE, but inclusion of biochemical markers shows an improvement of their predictive power. The aim of this study was to perform a biomarker discovery study in order to find possible novel first trimester biomarkers for each PE subtype. Further, our findings were related to available literature and the possible role of the proteins in the development of preeclampsia was discussed. MethodsIn this study, 9 early onset (<34 weeks), 8 preterm onset (34–37 weeks), 6 PE at term (>37 weeks) and 23 control samples were drawn between 11 and 14 weeks gestational age. Serum samples were prepared for liquid chromatography mass spectrometry analysis and protein data were exported for statistical analyses. All differentially expressed proteins were further evaluated by searching literature in MEDLINE, Embase and Web of science and differential expression of two proteins, which were not yet associated with PE, was verified through enzyme-linked immunosorbent assay (ELISA). ResultsAfter statistical analysis, six, four and eight proteins were differently expressed in early onset, preterm onset and PE at term, respectively. After exclusion of antibody fragments, only nine proteins remained. Seven out of these nine proteins were already in literature associated with preeclampsia and only three of them were described as differentially expressed in the first trimester or early second trimester of preeclamptic pregnancies. Differential expression of Apolipoprotein D (ApoD), which was not yet associated with PE, was confirmed by ELISA in both early and preterm onset PE in the first trimester. DiscussionIn this study, two main observations were made. First, some of the differentially expressed proteins have a role in the same biological pathway, such as the acute phase response or endometrium receptivity, and their differential expression was observed in all three PE subtypes. This observation supports the hypothesis that classification of PE could be more accurate when subtyping is based on the etiology and/or phenotype instead of the arbitrary parameter gestational age at onset or delivery. Second, seven differential expressed proteins were already associated in literature with preeclampsia, but this association was for only three of them observed in the first trimester. In addition, ApoD was not yet associated with PE in other studies and, moreover, its differential expression was confirmed by ELISA. Therefore the predictive power of these proteins in the first trimester is worth evaluating in a larger and more heterogeneous cohort.

An in vitro model for discovery of osteoclast specific biomarkers towards identification of racehorses at risk for catastrophic fractures.

Focal bone microcracks with osteoclast recruitment and bone lysis, may reduce fracture resistance in racehorses. As current imaging does not detect all horses at risk for fracture, the discovery of novel serum biomarkers of bone resorption or osteoclast activity could potentially address this unmet clinical need. The biology of equine osteoclasts on their natural substrate, equine bone, has never been studied in vitro and may permit identification of specific biomarkers of their activity. (1) Establish osteoclast cultures on equine bone, (2) Measure biomarkers (tartrate resistant acid phosphatase isoform 5b [TRACP-5b] and C-terminal telopeptide of type I collagen [CTX-I]) in vitro and (3) Study the effects of inflammation. In vitro experiments. Haematopoietic stem cells, from five equine sternal bone marrow aspirates, were differentiated into osteoclasts and cultured either alone or on equine bone slices, with or without a pro-inflammatory stimulus (IL-1β or LPS). CTX-I and TRACP-5b were immunoassayed in the media. Osteoclast numbers and bone resorption area were assessed. TRACP-5b increased over time in osteoclast cultures without bone (p < 0.0001) and correlated with osteoclast number (r=0.63, p < 0.001). CTX-I and TRACP-5b increased with time for cultures with bone (p=0.002; p=0.02 respectively), correlated with each other (r=0.64, p < 0.002) and correlated with bone resorption (r=0.85, p < 0.001; r=0.82, p < 0.001 respectively). Inflammation had no measurable effects. Specimen numbers limited. Equine osteoclasts were successfully cultured on equine bone slices and their bone resorption quantified. TRACP-5b was shown to be a biomarker of equine osteoclast number and bone resorption for the first time; CTX-I was also confirmed to be a biomarker of equine bone resorption in vitro. This robust equine specific in vitro assay will help the study of osteoclast biology.

Biological connotation of four traditional Chinese medicine syndromes of rheumatoid arthritis based on "disease-syndrome-symptom" association network

The present study explored the biological connotation of traditional Chinese medicine(TCM) syndromes of rheumatoid arthritis(RA) from the &quot;disease-syndrome-symptom&quot; association network. RA patients with four TCM syndromes(dampness-heat obstruction, phlegm-stasis obstruction, Qi-blood deficiency, and liver and kidney deficiency), three for each type, were assigned as the RA TCM syndrome group, and three healthy volunteers as the normal control group. The differential gene sets of four syndromes were screened out through transcriptome expression profiling and bioinformatics mining. The relevant gene sets of syndrome-related clinical symptoms were collected from TCMIP v2.0(http://www.tcmip.cn/). The &quot;disease-syndrome-symptom&quot; association networks of four RA syndromes were established by using the intersection genes of syndrome-related differential genes and symptom-related genes, and the key network target genes of each syndrome were screened out and the corresponding biological functions were mined through topological feature calculation and enrichment analysis. The genes associated with clinical symptoms such as vasculitis, joint pain, and fever in the damp-heat obstruction syndrome ranked the top, and the key network target genes of this syndrome were most significantly enriched in the pathways related to material and energy metabolism and thermal reaction biological processes. The clinical symptom-related genes of the phlegm-stasis obstruction syndrome were most significantly enriched in the pathways related to &quot;immunity-inflammation&quot;, nervous system regulation, and sensory response. The clinical symptoms such as hypoglycemia, hypotension, weight loss, palpitation, and arrhythmia in Qi-blood deficiency syndrome were predominant, and its key network target genes were most significantly enriched in the pathways related to the nervous system and &quot;immunity-inflammation&quot; response. The abnormal symptoms in the liver and kidney in the liver and kidney deficiency syndrome were commonly seen, and its key network target genes were most significantly enriched in the &quot;immunity-inflammation&quot; regulatory pathways, and liver and kidney development and metabolic response. In conclusion, the differences and connections of the biological basis between different TCM syndromes of RA are in line with the theoretical interpretation of TCM on the etiology and pathogenesis of RA. This study summarized the objective essence of syndromes to a certain extent from the &quot;disease-syndrome-symptom&quot; association network and is expected to provide a theoretical basis for the discovery of serum biomarkers of RA syndromes.

Discovery of Novel Serum Biomarkers for Diagnosing and Predicting the Postmenopausal Osteoporosis Patients by 4D-Label Free Protein Omics

Discovery of Novel Serum Biomarkers for Diagnosing and Predicting the Postmenopausal Osteoporosis Patients by 4D-Label Free Protein Omics

Rapid sample preparation workflow based on enzymatic nanoreactors for potential serum biomarker discovery in pancreatic cancer

Mass spectrometry (MS)-based proteomics have been extensively applied in clinical practice to discover potential protein and peptide biomarkers. However, the traditional sample pretreatment workflow remains labor-intensive and time-consuming, which limits the application of MS-based proteomic biomarker discovery studies in a high throughput manner. In the current work, we improved the previously reported procedure of the simple and rapid sample preparation methods (RSP) by introducing macroporous ordered siliceous foams (MOSF), namely RSP-MOSF. With the aid of MOSF, we further reduced the digestion time to 10 min, facilitating the whole sample handling process within 30 min. Combining with 30 min direct data independent acquisition (DIA) of LC-MS/MS, we accomplished a serum sample analysis in 1 h. Comparing with the RSP method, the performance of protein and peptide identification, quantitation, as well as the reproducibility of RSP-MOSF is comparable or even outperformed the RSP method. We further applied this workflow to analyze serum samples for potential candidate biomarker discovery of pancreatic cancer. Overall, 576 serum proteins were detected with 41 proteins significantly changed, which could serve as potential biomarkers for pancreatic cancer. Additionally, we evaluated the performance of RSP-MOSF method in a 96-well plate format which demonstrated an excellent reproducibility of the analysis. These results indicated that RSP-MOSF method had the potential to be applied on an automatic platform for further scaled analysis.

Discovery of serum biomarkers for diagnosis of tuberculosis by NMR metabolomics including cross-validation with a second cohort

BackgroundTuberculosis (TB) is a disease with worldwide presence and a major cause of death in several developing countries. Current diagnostic methodologies often lack specificity and sensitivity, whereas a long time is needed to obtain a conclusive result. MethodsIn an effort to develop better diagnostic methods, this study aimed at the discovery of a biomarker signature for TB diagnosis using a Nuclear Magnetic Resonance based metabolomics approach. In this study, we acquired 1H NMR spectra of blood serum samples of groups of healthy subjects, individuals with latent TB and of patients with pulmonary and extra-pulmonary TB. The resulting data were treated with uni- and multivariate statistical analysis. ResultsSix metabolites (inosine, hypoxanthine, mannose, asparagine, aspartate and glutamate) were validated by an independent cohort, all of them related with metabolic processes described as associated with TB infection. ConclusionThe findings of the study are according with the WHO Target Product Profile recommendations for a triage test to rule-out active TB.

Serum biomarker discovery related to pathogenesis in acute coronary syndrome by proteomic approach.

Acute coronary syndrome (ACS) results from inadequate supply of blood flow from the coronary arteries to the heart or ischemia. ACS has an extremely high morbidity and mortality. The levels of biomarkers currently used for detection of ACS also increase in response to myocardial necrosis and other diseases and are not elevated immediately after symptoms appear, thus limiting their diagnostic capacity. Therefore, we aimed to discover new ACS diagnostic biomarkers with high sensitivity and specificity that are specifically related to ACS pathogenesis. Sera from 50 patients with ACS and healthy controls (discovery cohort) each were analyzed using mass spectrometry (MS) to identify differentially expressed proteins, and protein candidates were evaluated as ACS biomarkers in 120 people in each group (validation cohort). α-1-acid glycoprotein 1 (AGP1), complement C5 (C5), leucine-rich α-2-glycoprotein (LRG), and vitronectin (VN) were identified as biomarkers whose levels increase and gelsolin (GSN) as a biomarker whose levels decrease in patients with ACS. We concluded that these biomarkers are associated with the pathogenesis of ACS and can predict the onset of ACS prior to the appearance of necrotic biomarkers.

Research frontiers in precision therapy for liver cancer

Hepatocellular carcinoma (HCC), a fatal malignancy, is highly prevalent in China. Recently, tremendous achievements have been made in HCC, both in the clinical management and the understanding of cancer biology. The discovery and application of novel serum biomarkers, such as proteins, miRNAs, and cfDNA (cell-free circulating DNA) methylation will facilitate the early management of HCC. Improvements in radiological and computational technology have made the practice of precision liver surgery a true reality. The emergence of novel molecular targeted therapies, immunotherapy and their combinations has significantly prolonged patient survival rate, but the therapeutic breakthroughs are still needed. Integrated multi-omics analysis has broadened our understanding of HCC pathogenesis, providing an unprecedented molecular characterization of HCC and revealing new therapeutic opportunities. Technological innovations and revolutions such as single-cell sequencing, genome editing, and innovative drug research and development will greatly accelerate our understanding of HCC biology and bring new therapeutic opportunities. Nevertheless, HCC is a highly heterogeneous and treatment-refractory malignancy, therefore deep insights into the biology of the disease and novel therapies for HCC remain an unmet medical need.

Protein-protein correlations based variable dimension expansion algorithm for high efficient serum biomarker discovery

In this study, we constructed a high specific and efficient serum biomarker discovery pipeline. We utilized dysregulated proteins identified in primary tissue and potentially secreted into the blood as biomarker candidates. The scheduled multiple reaction monitoring method was performed to accurately quantify and verify these candidates directly in serum, thus circumventing the effects of high-abundance proteins. We then generated new variables through assigning values to protein-protein correlations to extend the dimensionality of the dataset (PPC-VDE), and the specificity of disease classification. We successfully applied this pipeline for biomarker discovery of dilated cardiomyopathy and achieved 88.6% accurate classification of dilated cardiomyopathy, ischemic cardiomyopathy and healthy controls with machine learning. This pipeline is straightforward for biomarker discovery in broad clinical field.

Discovery and comparison of serum biomarkers for diabetes mellitus and metabolic syndrome based on UPLC-Q-TOF/MS

IntroductionDiabetes mellitus (DM) and metabolic syndrome (MetS) are systemic metabolic disorders, which have risk factors for diabetic cardiovascular and cerebral microvascular disease. It is very important to screen the metabolic biomarkers between DM and MetS patients, which can make patients benefit to a greater extent and prevent the occurrence of disease in advance. ObjectivesDiabetes mellitus (DM) and metabolic syndrome are a complex, chronic illness with a pronounced impact on the quality of life of many people. However, understanding the metabolic changes in patients and identifying high-risk individuals is crucial for prevention and disease management strategies. MethodsIn this study, a nontargeted metabolomics approach based on UPLC-Q-TOF/MS was used to find the differential metabolites in serum samples from patients with DM and MetS. ResultsMetabonomic analysis reveals metabolic differences between DM and HC with significant differences more than 60 metabolites. While, more than 65 metabolites have significant differences between MetS and HC. The independent disturbed pathway in the DM group was the FoxO signaling pathway. The independent disturbed pathways in the MetS group were the alpha-linolenic acid metabolism, glycerophospholipid metabolism and pyrimidine metabolism.The independent disturbed metabolites and the logistic regression result showed that betaine, alpha-linolenic acid, d-mannose, l-glutamine and methylmalonic acid can be used as a combinatorial biomarker to distinguish DM from healthy control. L-isoleucine, l-glutamine, PC(16:0/16:0), alpha-d-glucose, ketoisocaproic acid, d-mannose, uridine can be used as a combinatorial biomarker in MetS. ConclusionOur findings, on one hand, provide critical insight into the pathological mechanism of DM and MetS. On the other hand, supply a combinatorial biomarker to aid the diagnosis of diseases in clinical usage.

Novel potential biomarkers for the diagnosis and monitoring of patients with ulcerative colitis.

Unambiguously, great progress has been achieved in the unraveling of more pathological pathways implicated in the development and progression of ulcerative colitis during the last decades. Novel effective drugs that have augmented the management armamentarium have been developed alongside this growing comprehension of the disease, rendering mucosal healing not only a feasible but the optimal goal of every therapy. Clinical evaluation, colonoscopy and biomarkers are the tools used by practitioners for the diagnosis and assessment of the status of the disease in order to achieve clinical remission and mucosal healing for their patients. Among these tools, colonoscopy is the gold method for the cause but is still an invasive, high-cost procedure with possible adverse events such as perforation. While clinical evaluation entails much subjectivity, biomarkers are objective, easily reproducible, non-invasive, cheap and potent surrogate tools of mucosal inflammation. Unfortunately, the well-established, currently in use serum biomarkers, such as C-reactive protein, erythrocyte sedimentation rate and others, do not display sufficiently acceptable sensitivity and specificity rates for the diagnosis of ulcerative colitis and, most importantly, do not represent precisely the mucosal inflammation status of the disease. Therefore, the discovery of new serum biomarkers has been the cause of several studies attempting to discover an "optimal" serum biomarker during the recent years. After thorough research, collection and examination of current data, this review focuses on and selectively presents promising, potential, novel serum biomarkers of ulcerative colitis as they are indicated by studies on the patient over the last years.