Human disease biomarker panels through systems biology

Lambda Theta Reflectometry: a new technique to measure optical film thickness applied to planar protein arrays

Quantitative protein measurements provide valuable information about biological pathways, immune system functionality, and mechanisms of disease. The most accurate methods for detecting proteins are label-free and preserve native protein binding interactions. Label-free biomolecular interaction analysis includes reflectometry, a group of techniques that detect proteins by measuring the reflectance properties of a thin film on a substrate. Most of these techniques are limited in some way by instrument complexity, sensitivity, or consumable manufacturing requirements. To address these issues, we introduce Lambda Theta Reflectometry (LTR), a new reflectometric technique that measures changes in film thickness by determining the location of null reflectivity as a function of wavelength (lambda) and angle of incidence (theta). The substrate is simultaneously illuminated with a range of angles and wavelengths and reflected light is angularly and spectrally resolved. Our prototype LTR reflectometer can measure SiO2 layer thickness with milli-Ångstrom precision. LTR measurements of Si/SiO2 oxide films are in excellent agreement with spectroscopic ellipsometry for film thicknesses ranging from 1390-1465 A. This technique enables sensitive measurements across a range of biological analyte concentrations without requiring stringent control over probe deposition thickness or substrate manufacturing.

Retinal Biomarkers in Diabetic Retinopathy: From Early Detection to Personalized Treatment

Diabetic retinopathy (DR) is a leading cause of vision loss globally, with early detection and intervention critical to preventing severe outcomes. This narrative review examines the role of retinal biomarkers-molecular and imaging-in improving early diagnosis, tracking disease progression, and advancing personalized treatment for DR. Key biomarkers, such as inflammatory and metabolic markers, imaging findings from optical coherence tomography and fluorescence angiography and genetic markers, provide insights into disease mechanisms, help predict progression, and monitor responses to treatments, like anti-VEGF and corticosteroids. While challenges in standardization and clinical integration remain, these biomarkers hold promise for a precision medicine approach that could transform DR management through early, individualized care.

Brain white matter damage biomarkers

White matter (WM), constituting nearly half of the human brain's mass, is pivotal for the rapid transmission of neural signals across different brain regions, significantly influencing cognitive processes like learning, memory, and problem-solving. The integrity of WM is essential for brain function, and its damage, which can occur due to conditions such as multiple sclerosis (MS), stroke, and traumatic brain injury, results in severe neurological deficits and cognitive decline. The primary objective of this book chapter is to discuss the clinical significance of fluid biomarkers in assessing WM damage within the central nervous system (CNS). It explores the biological underpinnings and pathological changes in WM due to various neurological conditions and details how alterations can be detected and quantified through fluid biomarkers. By examining biomarkers like Myelin Basic Protein (MBP), Neurofilament light chain (NFL), and others, the chapter highlights their role in enhancing diagnostic precision, monitoring disease progression, and guiding therapeutic interventions, thus providing crucial insights into maintaining WM integrity and preventing cognitive and physical disabilities.

Development and validation of a prognostic prediction model for endometrial cancer based on CD8+ T cell infiltration-related genes

Endometrial cancer (EC) is the most common gynecologic malignancy with increasing incidence and mortality. The tumor immune microenvironment significantly impacts cancer prognosis. Weighted Gene Co-Expression Network Analysis (WGCNA) is a systems biology approach that analyzes gene expression data to uncover gene co-expression networks and functional modules. This study aimed to use WGCNA to develop a prognostic prediction model for EC based on immune cell infiltration, and to identify new potential therapeutic targets. WGCNA was performed using the Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma dataset to identify hub modules associated with T-lymphocyte cell infiltration. Prognostic models were developed using LASSO regression based on genes in these hub modules. The Search Tool for the Retrieval of Interacting Genes/Proteins was used for protein-protein interaction network analysis of the hub module. Gene Set Variation Analysis identified differential gene enrichment analysis between high- and low-risk groups. The relationship between the model and microsatellite instability, tumor mutational burden, and immune cell infiltration was analyzed using The Cancer Genome Atlas data. The model's correlation with chemotherapy and immunotherapy resistance was examined using the Genomics of Drug Sensitivity in Cancer and Cancer Immunome Atlas databases. Immunohistochemical staining of EC tissue microarrays was performed to analyze the relationship between the expression of key genes and immune infiltration. The green-yellow module was identified as a hub module, with 4 genes (ARPC1B, BATF, CCL2, and COTL1) linked to CD8+ T cell infiltration. The prognostic model constructed from these genes showed satisfactory predictive efficacy. Differentially expressed genes in high- and low-risk groups were enriched in tumor immunity-related pathways. The model correlated with EC-related phenotypes, indicating its potential to predict immunotherapeutic response. Basic leucine zipper activating transcription factor-like transcription factor(BATF) expression in EC tissues positively correlated with CD8+ T cell infiltration, suggesting BATF's crucial role in EC development and antitumor immunity. The prognostic model comprising ARPC1B, BATF, CCL2, and COTL1 can effectively identify high-risk EC patients and predict their response to immunotherapy, demonstrating significant clinical potential. These genes are implicated in EC development and immune infiltration, with BATF emerging as a potential therapeutic target for EC.

Prediction of Major Adverse Limb Events in Females with Peripheral Artery Disease using Blood-Based Biomarkers and Clinical Features

The objective of this study was to identify a female-specific prognostic biomarker for peripheral artery disease (PAD) and develop a prediction model for 2-year major adverse limb events (MALE). Patients with/without PAD were recruited (n=461). Plasma concentrations of 68 circulating proteins were measured and patients were followed for 2 years. The primary outcome was MALE (composite of vascular intervention, major amputation, or acute/chronic limb threatening ischemia). We trained a random forest model using: 1) clinical characteristics, 2) female-specific PAD biomarker, and 3) clinical characteristics and female-specific PAD biomarker. Galectin-9 was the only protein to be significantly elevated in females compared to males in the discovery/validation analyses. The random forest model achieved the following AUROC's: 0.72 (clinical features), 0.83 (Galectin-9), and 0.86 (clinical features + Galectin-9). We identified Galectin-9 as a female-specific PAD biomarker and developed an accurate prognostic model for 2-year MALE using a combination of clinical features and plasma Galectin-9 levels.

Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression

Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.

Investigating the Prognostic Potential of Plasma ST2 in Patients with Peripheral Artery Disease: Identification and Evaluation

Soluble interleukin 1 receptor-like 1 (ST2) is a circulating protein demonstrated to be associated with cardiovascular diseases; however, it has not been studied as a biomarker for peripheral artery disease (PAD). Using a prospectively recruited cohort of 476 patients (312 with PAD and 164 without PAD), we conducted a prognostic study of PAD using clinical/biomarker data. Plasma concentrations of three circulating proteins [ST2, cytokine-responsive gene-2 (CRG-2), vascular endothelial growth factor (VEGF)] were measured at baseline and the cohort was followed for 2 years. The outcome of interest was a 2-year major adverse limb event (MALE; composite of major amputation, vascular intervention, or acute limb ischemia). Using 10-fold cross-validation, a random forest model was trained using clinical characteristics and plasma ST2 levels. The primary model evaluation metric was the F1 score. Out of the three circulating proteins analyzed, ST2 was the only one that was statistically significantly higher in individuals with PAD compared to patients without PAD (mean concentration in plasma of 9.57 [SD 5.86] vs. 11.39 [SD 6.43] pg/mL, p < 0.001). Over a 2-year period, 28 (9%) patients with PAD experienced MALE. Our predictive model, incorporating clinical features and plasma ST2 levels, achieved an F1 score of 0.713 for forecasting 2-year MALE outcomes. Patients identified as high-risk by this model showed a significantly increased likelihood of developing MALE (HR 1.06, 95% CI 1.02-1.13, p = 0.003). By combining clinical characteristics and plasma ST2 levels, our proposed predictive model offers accurate risk assessment for 2-year MALE in PAD patients. This algorithm supports risk stratification in PAD, guiding clinical decisions regarding further vascular evaluation, specialist referrals, and appropriate medical or surgical interventions, thereby potentially enhancing patient outcomes.

Inflammatory Protein Panel: Exploring Diagnostic Insights for Peripheral Artery Disease Diagnosis in a Cross-Sectional Study

Cytokine-induced neutrophil chemoattractant 1 (CINC-1), a cluster of differentiation 95 (CD95), fractalkine, and T-cell immunoglobulin and mucin domain 1 (TIM-1) are circulating proteins known to be involved in inflammation. While their roles have been studied in neurological conditions and cardiovascular diseases, their potential as peripheral artery disease (PAD) biomarkers remain unexplored. We conducted a cross-sectional diagnostic study using data from 476 recruited patients (164 without PAD and 312 with PAD). Plasma levels of CINC-1, CD95, fractalkine, and TIM-1 were measured at baseline. A PAD diagnosis was established at recruitment based on clinical exams and investigations, defined as an ankle-brachial index < 0.9 or toe-brachial index < 0.67 with absent/diminished pedal pulses. Using 10-fold cross-validation, we trained a random forest algorithm, incorporating clinical characteristics and biomarkers that showed differential expression in PAD versus non-PAD patients to predict a PAD diagnosis. Among the proteins tested, CINC-1, CD95, and fractalkine were elevated in PAD vs. non-PAD patients, forming a 3-biomarker panel. Our predictive model achieved an AUROC of 0.85 for a PAD diagnosis using clinical features and this 3-biomarker panel. By combining the clinical characteristics with these biomarkers, we developed an accurate predictive model for a PAD diagnosis. This algorithm can assist in PAD screening, risk stratification, and guiding clinical decisions regarding further vascular assessment, referrals, and medical/surgical management to potentially improve patient outcomes.

Prediction of Peripheral Artery Disease Prognosis Using Clinical and Inflammatory Biomarker Data

Purpose

Inflammatory biomarkers associated with peripheral artery disease (PAD) have been examined separately; however, an algorithm that includes a panel of inflammatory proteins to inform prognosis of PAD could improve predictive accuracy. We developed predictive models for 2-year PAD-related major adverse limb events (MALE) using clinical/inflammatory biomarker data.

Methods

We conducted a prognostic study using 2 phases (discovery/validation models). The discovery cohort included 100 PAD patients that were propensity-score matched to 100 non-PAD patients. The validation cohort included 365 patients with PAD and 144 patients without PAD (non-matched). Plasma concentrations of 29 inflammatory proteins were determined at recruitment and the cohorts were followed for 2 years. The outcome of interest was 2-year MALE (composite of major amputation, vascular intervention, or acute limb ischemia). A random forest model was trained with 10-fold cross-validation to predict 2-year MALE using the following input features: 1) clinical characteristics, 2) inflammatory biomarkers that were expressed differentially in PAD vs non-PAD patients, and 3) clinical characteristics and inflammatory biomarkers.

Results

The model discovery cohort was well-matched on age, sex, and comorbidities. Of the 29 proteins tested, 5 were elevated in PAD vs non-PAD patients (MMP-7, MMP-10, IL-6, CCL2/MCP-1, and TFPI). For prognosis of 2-year MALE on the validation cohort, our model achieved AUROC 0.63 using clinical features alone and adding inflammatory biomarker levels improved performance to AUROC 0.84.

Conclusion

Using clinical characteristics and inflammatory biomarker data, we developed an accurate predictive model for PAD prognosis.

Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

The Identification and Evaluation of Interleukin-7 as a Myokine Biomarker for Peripheral Artery Disease Prognosis

Background/Objectives: Myokines have been demonstrated to be associated with cardiovascular diseases; however, they have not been studied as biomarkers for peripheral artery disease (PAD). We identified interleukin-7 (IL-7) as a prognostic biomarker for PAD from a panel of myokines and developed predictive models for 2-year major adverse limb events (MALEs) using clinical features and plasma IL-7 levels. Methods: A prognostic study was conducted with a cohort of 476 patients (312 with PAD and 164 without PAD) that were recruited prospectively. Their plasma concentrations of five circulating myokines were measured at recruitment, and the patients were followed for two years. The outcome of interest was two-year MALEs (composite of major amputation, vascular intervention, or acute limb ischemia). Cox proportional hazards analysis was performed to identify IL-7 as the only myokine that was associated with 2-year MALEs. The data were randomly divided into training (70%) and test sets (30%). A random forest model was trained using clinical characteristics (demographics, comorbidities, and medications) and plasma IL-7 levels with 10-fold cross-validation. The primary model evaluation metric was the F1 score. The prognostic model was used to classify patients into low vs. high risk of developing adverse limb events based on the Youden Index. Freedom from MALEs over 2 years was compared between the risk-stratified groups using Cox proportional hazards analysis. Results: Two-year MALEs occurred in 28 (9%) of patients with PAD. IL-7 was the only myokine that was statistically significantly correlated with two-year MALE (HR 1.56 [95% CI 1.12-1.88], p = 0.007). For the prognosis of 2-year MALEs, our model achieved an F1 score of 0.829 using plasma IL-7 levels in combination with clinical features. Patients classified as high-risk by the predictive model were significantly more likely to develop MALEs over a 2-year period (HR 1.66 [95% CI 1.22-1.98], p = 0.006). Conclusions: From a panel of myokines, IL-7 was identified as a prognostic biomarker for PAD. Using a combination of clinical characteristics and plasma IL-7 levels, we propose an accurate predictive model for 2-year MALEs in patients with PAD. Our model may support PAD risk stratification, guiding clinical decisions on additional vascular evaluation, specialist referrals, and medical/surgical management, thereby improving outcomes.

Identification and Evaluation of Blood-Based Biomarkers for Abdominal Aortic Aneurysm

INTRODUCTION

Blood-based biomarkers for abdominal aortic aneurysm (AAA) have been studied individually; however, we considered a panel of proteins to investigate AAA prognosis and its potential to improve predictive accuracy.

MATERIALS AND METHODS

Using a prospectively recruited cohort of patients with/without AAA (n = 452), we conducted a prognostic study to develop a model that accurately predicts AAA outcomes using clinical features and circulating biomarker levels. Serum concentrations of 9 biomarkers were measured at baseline, and the cohort was followed for 2 years. The primary outcome was major adverse aortic event (MAAE; composite of rapid AAA expansion [>0.5 cm/6 months or >1 cm/12 months], AAA intervention, or AAA rupture). Using 10-fold cross-validation, we trained a random forest model to predict 2 year MAAE using (1) clinical characteristics, (2) biomarkers, and (3) clinical characteristics and biomarkers.

RESULTS

Two-year MAAE occurred in 114 (25%) patients. Two proteins were significantly elevated in patients with AAA compared with those without AAA (angiopoietin-2 and aggrecan), composing the protein panel. For predicting 2 year MAAE, our random forest model achieved area under the receiver operating characteristic curve (AUROC) 0.74 using clinical features alone, and the addition of the 2-protein panel improved performance to AUROC 0.86.

CONCLUSIONS

Using a combination of clinical/biomarker data, we developed a model that accurately predicts 2 year MAAE.

A machine learning algorithm for peripheral artery disease prognosis using biomarker data

Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an algorithm that considers a protein panel to inform PAD prognosis may improve predictive accuracy. Biomarker-based prediction models were developed and evaluated using a model development (n = 270) and prospective validation cohort (n = 277). Plasma concentrations of 37 proteins were measured at baseline and the patients were followed for 2 years. The primary outcome was 2-year major adverse limb event (MALE; composite of vascular intervention or major amputation). Of the 37 proteins tested, 6 were differentially expressed in patients with vs. without PAD (ADAMTS13, ICAM-1, ANGPTL3, Alpha 1-microglobulin, GDF15, and endostatin). Using 10-fold cross-validation, we developed a random forest machine learning model that accurately predicts 2-year MALE in a prospective validation cohort of PAD patients using a 6-protein panel (AUROC 0.84). This algorithm can support PAD risk stratification, informing clinical decisions on further vascular evaluation and management.

How far are we from reliable metabolomics-based biomarkers? The often-overlooked importance of addressing inter-individual variability factors

Metabolomics has proven great potential to unravel the molecular basis of diseases. However, most attempts aimed at identifying reliable metabolomics-based biomarkers for diagnosis, prediction, and prognosis of diseases have repeatedly failed because of inconsistent results and unsatisfactory replication in independent cohorts. This review article explores the possible causes behind this reproducibility crisis, with special focus on the role that inter-individual variability factors play in modulating the susceptibility to disease development. Furthermore, we provide future perspectives on the applicability of metabolomics in biomedical research and its translatability into clinical practice.

The thrombus proteome in stroke reveals a key role of the innate immune system and new insights associated with its etiology, severity, and prognosis

BACKGROUND

Nowadays little is known about the molecular profile of the occluding thrombus of patients with ischemic stroke.

OBJECTIVES

To analyze the proteomic profile of thrombi in patients who experienced an ischemic stroke in order to gain insights into disease pathogenesis.

METHODS

Thrombi from an exploratory cohort of patients who experienced a stroke were obtained by thrombectomy and analyzed by sequential window acquisition of all theoretical spectra-mass spectrometry. Unsupervised k-means clustering analysis was performed to stratify patients who experienced a stroke. The proteomic profile was associated with both the neurological function (National Institute of Health Stroke Scale [NIHSS]) and the cerebral involvement (Alberta Stroke Program Early CT Score [ASPECTS]) prior to thrombectomy and the clinical status of patients at 3 months using the modified Rankin Scale. In an independent cohort of 210 patients who experienced a stroke, the potential role of neutrophils in stroke severity was interrogated.

RESULTS

Proteomic analysis identified 580 proteins in thrombi, which were stratified into 4 groups: hemostasis, proteasome and neurological diseases, structural proteins, and innate immune system and neutrophils. The thrombus proteome identified 3 clusters of patients with distinctive severity, prognosis, and etiology of the stroke. A protein signature clearly distinguished atherothrombotic and cardioembolic strokes. Several proteins were significantly correlated with the severity of the stroke (NIHSS and ASPECTS). Functional proteomic analysis highlighted the prominent role of neutrophils in stroke severity. This was in line with the association of neutrophil activation markers and count with NIHSS, ASPECTS, and the modified Rankin Scale score 90 days after the event.

CONCLUSION

The use of sequential window acquisition of all theoretical spectra-mass spectrometry in thrombi from patients who experienced an ischemic stroke has provided new insights into pathways and players involved in its etiology, severity, and prognosis. The prominent role of the innate immune system identified might pave the way for the development of new biomarkers and therapeutic approaches in this disease.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects

Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.

Identification of key genes in colorectal cancer diagnosis by co-expression analysis weighted gene co-expression network analysis

BACKGROUND

The purpose of this study was using bioinformatics tools to identify biomarkers and molecular factors involved in the diagnosis of colorectal cancer, which are effective for the diagnosis and treatment of the disease.

METHODS

We determined differentially expressed genes (DEGs) related to colorectal cancer (CRC) using the data series retrieved from GEO database. Then the weighted gene co-expression network analysis (WGCNA) was conducted to explore co-expression modules related to CRC diagnosis. Next, the relationship between the integrated modules with clinical features such as the stage of CRC was evaluated. Other downstream analyses were performed on selected module genes.

RESULTS

In this study, after performing the WGCNA method, a module named blue module which was more significantly associated with the CRC stage was selected for further evaluation. Afterward, the Protein-protein interaction network through sting software for 154 genes of the blue module was constructed and eight hub genes were identified through the evaluation of constructed network with Cytoscape. Among these eight hub genes, upregulation of MMP9, SERPINH1, COL1A2, COL5A2, COL1A1, SPARC, and COL5A1 in CRC was validated in other microarray and TCGA data. Based on the results of the mRNA-miRNA interaction network, SERPINH1 was found as a target gene of miR-940. Finally, results of the DGIDB database indicated that Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, and Bevacizumab, could be used as a therapeutic agent for targeting the MMP9. Furthermore, Ocriplasmin and Collagenase clostridium histolyticum could target COL1A1, COL1A2, COL5A1, and COL5A2.

CONCLUSION

Taken together, the results of the current study indicated that seven hub genes including COL1A2, COL5A1, COL5A2, SERPINH1, MMP9, SPARC, and COL1A1 which were upregulated in CRC could be used as a diagnostic and progression biomarker of CRC. On the other hand, miR-940 which targets SERPINH1 could be used as a potential biomarker of CRC. More ever, Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, Bevacizumab, Ocriplasmin , and Collagenase clostridium histolyticum were introduced as therapeutic agents for CRC which their therapeutic potential should be evaluated experimentally.

Discovery and replication of blood-based proteomic signature of PTSD in 9/11 responders

Proteomics provides an opportunity to develop biomarkers for the early detection and monitoring of post-traumatic stress disorder (PTSD). However, research to date has been limited by small sample sizes and a lack of replication. This study performed Olink Proseek Multiplex Platform profiling of 81 proteins involved in neurological processes in 936 responders to the 9/11 disaster (mean age at blood draw = 55.41 years (SD = 7.93), 94.1% white, all men). Bivariate correlations and elastic net regressions were used in a discovery subsample to identify concurrent associations between PTSD symptom severity and the profiled proteins, and to create a multiprotein composite score. In hold-out subsamples, nine bivariate associations between PTSD symptoms and differentially expressed proteins were replicated: SKR3, NCAN, BCAN, MSR1, PVR, TNFRSF21, DRAXIN, CLM6, and SCARB2 (|r| = 0.08-0.17, p < 0.05). There were three replicated bivariate associations between lifetime PTSD diagnosis and differentially expressed proteins: SKR3, SIGLEC, and CPM (OR = 1.38-1.50, p < 0.05). The multiprotein composite score retained 38 proteins, including 10/11 proteins that replicated in bivariate tests. The composite score was significantly associated with PTSD symptom severity (β = 0.27, p < 0.001) and PTSD diagnosis (OR = 1.60, 95% CI: 1.17-2.19, p = 0.003) in the hold-out subsample. Overall, these findings suggest that PTSD is characterized by altered expression of several proteins implicated in neurological processes. Replicated associations with TNFRSF21, CLM6, and PVR support the neuroinflammatory signature of PTSD. The multiprotein composite score substantially increased associations with PTSD symptom severity over individual proteins. If generalizable to other populations, the current findings may inform the development of PTSD biomarkers.

Biophysical Reviews- 2021, the year that was

The current issue (volume 13 issue 6, 2021) is a Special Issue jointly dedicated to scientific content presented at the 20th triennial IUPAB Congress that was held in conjunction with both the 45th Annual Meeting of the Brazilian Biophysical Society (Sociedade Brasileira de Biofísica - SBBf) and the 50th Annual Meeting of the Brazilian Society for Biochemistry and Molecular Biology (Sociedade Brasileira de Bioquímica e Biologia Molecular - SBBq). In addition to describing the scientific and nonscientific content arising from the meeting this sub-editorial also provides a look back at some of the high points for Biophysical Reviews in the year 2021 before going on to describe a number of matters of interest to readers of the journal in relation to the coming year of 2022.