Identifying novel biomarkers is crucial for early detection of colorectal cancer (CRC). Saliva, as a noninvasive sample, holds promise for CRC detection. Here, we used Olink proteomics and untargeted metabolomics to analyze saliva samples from CRC patients and healthy controls with the aim of identifying candidate biomarkers in CRC saliva. Univariate and multivariate analyses revealed 16 differentially expressed proteins (DEPs) and 40 differentially accumulated metabolites (DAMs). Pathway enrichment showed DEPs were mainly involved in cancer transcriptional dysregulation, Toll-like receptor signaling, and chemokine signaling. Metabolomics analysis highlighted significant changes in amino acid metabolites, particularly in pathways such as arginine biosynthesis, histidine metabolism, and cysteine and methionine metabolism. Random forest analysis and ELISA validation identified four potential biomarkers: succinate, l-methionine, GZMB, and MMP12. A combined protein-metabolite diagnostic model was developed using logistic regression, achieving an area under the curve of 0.933 (95% CI: 0.871–0.996) for the discovery cohort and 0.969 (95% CI: 0.918–1.000) for the validation cohort, effectively distinguishing CRC patients from healthy individuals. In conclusion, our study identified and validated a panel of noninvasive saliva-based biomarkers that could improve CRC screening and provide new insights into clinical CRC diagnosis.

The plasma proteome is the ultimate target for biomarker discovery. It stores an endless amount of information on the pathophysiological status of a living organism, which is, however, still difficult to comprehensively access. The high complexity of the plasma proteome can be addressed by either a system-wide and unbiased tool such as mass spectrometry (LC–MS/MS) or a highly sensitive targeted immunoassay such as the proximity extension assay (PEA). To address relevant differences and important shared characteristics, we tested the performance of LC–MS/MS in the data-dependent and data-independent acquisition modes and Olink PEA to measure circulating plasma proteins in 173 human plasma samples from a Southern German population-based cohort. We demonstrated the measurement of more than 300 proteins with both LC–MS/MS approaches applied, mainly including high-abundance plasma proteins. By the use of the PEA technology, we measured 728 plasma proteins, covering a broad dynamic range with high sensitivity down to pg/mL concentrations. Then, we quantified 35 overlapping proteins with all three analytical platforms, verifying the reproducibility of data distributions, measurement correlation, and gender-based differential expression. Our work highlights the limitations and the advantages of both targeted and untargeted approaches and proves their complementary strengths. We demonstrated a significant gain in proteome coverage depth and subsequent biological insight by a combination of platformsa promising approach for future biomarker and mechanistic studies.

The maternal inflammatory proteome during pregnancy and its role in predicting the risk of spontaneous preterm birth1. Dataset Overview-------------------This dataset contains cell-free RNA (cfRNA) transcriptomic and Olink inflammation-related proteomic data from maternal blood plasma samples collected during the second trimester of pregnancy from the INSIGHT cohort. The data were generated to support the development of predictive models for spontaneous preterm birth (sPTB) risk < 35 weeks.2. Study Design---------------- Cohort: INSIGHT- Sample type: Maternal blood plasma- Collection window: 16–24 weeks' gestation- Sample size: Total = 138 (sPTB = 46, Term = 92)- Outcomes: Spontaneous preterm birth (

The high-throughput proteomics platform market is booming, driven by advancements in mass spectrometry and growing demand for faster protein analysis in drug discovery and diagnostics. Explore market size, trends, key players (SomaLogic, Olink), and regional insights (North America, Europe, Asia-Pacific) for 2025-2033.

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Between-days comparisons of mean NPX values (+/- 0.95 CI) for each chemokine.

Comparison of chemokine expression levels in dichotomized patient groups.

Abstract Extracellular vesicles (EV) are an emerging biomarker source for diagnosis and prognosis of cardiovascular disease. A protein comparison of plasma EVs in relation to blood plasma and atherosclerotic plaque has not been performed but would provide insight into the origin and content of biomarker sources, and their association with atherosclerotic progression. Using samples of 88 carotid endarterectomy patients in the Athero-Express, 92 proteins (Olink Cardiovascular III panel) were measured in citrate plasma, plasma derived LDL-EVs and atherosclerotic plaque. Proteins were correlated between sources and were related to occurrence of pre-operative stroke and three-year major adverse cardiovascular events (MACE). Plasma and EV proteins correlated moderately on average, but with substantial variability. Plasma and EVs showed little correlation with plaque, suggesting that these circulating biomarkers may not originate from the latter. Plaque (n = 17) contained most differentially-expressed proteins in patients with stroke, opposed to EVs (n = 6) and plasma (n = 5). In contrast, EVs contained most differentially-expressed proteins for MACE (n = 21) compared to plasma (n = 9) and plaque (n = 1). EVs appear to provide additional information about severity and progression of systemic atherosclerosis than can be obtained from plasma or atherosclerotic plaque. Ethical approval for this study (TME/C-01.18) was provided by the Medical Research Ethics Committee of University Medical Center Utrecht, Utrecht, The Netherlands on 10 April 2002, and all research was conducted according to the principles of the Declaration of Helsinki (59th amendment, Seoul 2008) and in accordance with the Dutch Medical Research Involving Human Subjects Act (WMO). Important notice on availability of data There are restrictions on use by commercial parties, and on sharing openly based on (inter)national laws and regulations and the written informed consent. Therefore these data (and additional clinical data) are only available upon discussion and signing a Data Sharing Agreement (see Terms of Access) and within a specially designed UMC Utrecht provided environment.

BackgroundLong-term non-progressors (LTNPs) with HIV infection can naturally control viral replication for up to a decade without antiretroviral therapy (ART), but the underlying mechanisms of this phenomenon remain elusive.MethodsTo investigate the relevant immune and inflammatory factors associated with this natural control mechanism, we collected plasma samples from 16 LTNPs, 14 untreated viral progressors (VPs), 17 successfully ART-treated patients (TPs), and 16 healthy controls (HCs). The OLINK immune response panel and inflammation panel were employed to detect critical proteins, and the plasma neutralizing activity against a global panel of pseudoviruses was assessed using TZM-bl cells.ResultsThe combination of IL17C, IL18, DDX58, and NF2 contributed to discriminating LTNPs and VPs. IL18 and CCL25 were positively associated with CD4+ T cell counts but negatively correlated with viral load. Furthermore, CXCL9 and CXCL10 emerged as potential supplementary diagnostic markers for assessing the efficacy of antiretroviral therapy (ART). Finally, TNFRSF9 displayed positive correlations with neutralization breadth and Geometry Median Titer (GMT) despite the lack of significant differences between LTNPs and VPs.ConclusionIn summary, this study identified a set of biomarkers in HIV-infected individuals at different disease stages. These markers constitute a potential network for immune balance regulation in HIV infection, which is related to the long-term control of HIV by LTNPs. It provides important clues for further exploring the immune regulatory mechanism of HIV.

we valuated the efficacy and safety of the selective JAK1 inhibitor Abrocitinib in the treatment of chronic actinic dermatitis patients for the first time. And propose to use the Clinical Active Score of Chronic Actinic Dermatitis（CAS-CAD）score to assess the disease severity of chronic actinic dermatitis. At the same time, we analyzed the changes of inflammatory protein levels before and after the treatment through the inflammation panel of Olink Proteomics Analysis, providing valuable insights for future research on the mechanism of CAD.