Discriminatory Biomarkers Research Articles

Artificial neural network risk prediction of chronic obstructive pulmonary disease (COPD) exacerbations using urine biomarkers

BackgroundChronic obstructive pulmonary disease (COPD) exacerbations cause considerable morbidity and mortality. We sought to identify a panel of urine biomarkers that can distinguish between stable and exacerbation states and predict risk of future exacerbations.MethodsA retrospective discovery study was done measuring 35 biomarkers implicated in COPD pathogenesis in paired urine samples from 55 COPD subjects during stable and exacerbation states. A logistic regression model combining the 10 most discriminatory biomarkers in distinguishing between stable and exacerbation states was developed as a near-patient dipstick test with an opto-electronic reader. This biomarker panel was tested in a prospective study of 105 COPD subjects who undertook daily home urine testing over 6 months. The regression model was validated in paired samples from 26 individuals out of 105. An artificial neural network (ANN) using the urine biomarkers from 85 out of 105 subjects was developed and tested as a clinical decision tool to predict risk of an exacerbation.ResultsThe 10-biomarker panel (NGAL, TIMP1, CRP, fibrinogen, CC16, fMLP, TIMP2, A1AT, B2M, and MMP8) was able to distinguish exacerbationversusstable state in the discovery study (ROC with an AUC 0.84 [95% CI, 0.76 to 0.92; p <0.01]), and validation study (AUC 0.81 [95% CI; 0.70 to 0.92, p<0.01]). The ANN model predicted an exacerbation within a 13-day window frame with an AUC 0.89 (95% CI 0.89–0.90) and identified an exacerbation median (interquartile range) 7 (5 to 9) days prior to clinical diagnosis.ConclusionWe identified a panel of biomarkers that can distinguish between stable and exacerbation state and using an artificial neural network model, it can predict exacerbations before symptoms occur.

Investigating the mechanism of supraspinatus tendinopathy induced by type 2 diabetes mellitus in rats using untargeted metabolomics analysis

ObjectiveTo assess the mechanism of supraspinatus tendinopathy induced by type 2 diabetes mellitus (T2DM) in rats using untargeted metabolomics analysis.MethodsThe liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics approach was used to screen tendon biomarkers of supraspinatus tendinopathy in rats with T2DM. Seventy-eight Sprague-Dawley rats were divided into normal group (NG) and T2DM groups. Rats in T2DM groups were divided into 12-week (T2DM-12w), and 24-week (T2DM-24w) subgroups according to the time point of the establishment of the T2DM rat model. Histological evaluation (modified Bonar score) and biomechanical testing were used to analyze the adverse effects of type 2 diabetes on the tendon of the supraspinatus muscle in rats.Three comparable groups were set up, including T2DM-12w group vs. NG, T2DM-24w group vs. NG, and T2DM-24w group vs. T2DM-12w group. Differentially expressed metabolites (DEMs) in the supraspinatus tendons in the three groups of rats were analyzed using LC-MS, and data were analyzed using multivariate statistical methods to screen potential biomarkers. The DEMs included in the intersection of the three groups were identified as those associated with the development of diabetic supraspinatus tendinopathy, and trend analysis and pathway topology analysis were performed.ResultsWith the progression of diabetes, the tendinopathy of the supracinatus muscle of diabetic rats gradually intensified, mainly manifested as inflammatory reactions, disordered collagen fibers, fat infiltration, and increased modified Bonar score. The intersection of DEMs among the three comparable groups was resulted in the identification of 10 key DEMs, in which melezitose and raffinose showed a continuous increasing trend with the prolongation of disease course. By pathway topology analysis, 10 DEMs (P < 0.01) were mainly associated with the pathways of galactose metabolism, which could be involved in the development of diabetes-induced supraspinatus tendinopathy.ConclusionT2DM causes tendinopathy of the supraspinatus muscle in rats. 10 key DEMs obtained by untargeted metabolomics assay suggested that the development of diabetes-induced supraspinatus tendinopathy was associated with changes in metabolic pathways, such as galactose metabolism. melezitose and raffinose hold promise as a biomarker for disease discrimination and/or disease indication in diabetic supraspinatus tendinopathy.

In Search of Relevant Urinary Biomarkers for Thyroid Papillary Carcinoma and Benign Thyroid Nodule Differentiation, Targeting Metabolic Profiles and Pathways via UHPLC-QTOF-ESI+-MS Analysis.

Identification of specific urine metabolic profiles for patients diagnosed with papillary thyroid carcinoma (TC) vs. benign nodules (B) to identify specific biomarkers and altered pathways compared to those of healthy controls (C). Patient urine samples were collected, before surgery and after a histological confirmation of TC (n = 30) and B (n = 30), in parallel with sample collection from healthy controls (n = 20). The untargeted and semi-targeted metabolomic protocols were applied using UPLC-QTOF-ESI+-MS analysis, and the statistical analysis was performed using the Metaboanalyst 6.0 platform. The results for the blood biomarkers, previously published, were compared with the data obtained from urine sampling using the Venny algorithm and multivariate statistics. Partial least squares discrimination, including VIP values, random forest graphs, and heatmaps (p < 0.05), together with biomarker analysis (AUROC ranking) and pathway analysis, suggested a specific model for the urinary metabolic profile and pathway alterations in TC and B vs. C, based on 190 identified metabolites in urine that were compared with the serum metabolites. By semi-targeted metabolomics, 10 classes of metabolites, considered putative biomarkers, were found to be responsible for specific alterations in the metabolic pathways, from polar molecules to lipids. Specific biomarkers for discrimination were identified in each class of metabolites that were either upregulated or downregulated when compared to those of the controls. The lipidomic window was the most relevant for identifying biomarkers related to thyroid cancer and benign conditions, since this study detected a stronger involvement of lipids and selenium-related molecules for metabolic discrimination.

Borderline personality disorder and post-traumatic stress disorder in adolescents: protocol for a comparative study of borderline personality disorder with and without comorbid post-traumatic stress disorder (BORDERSTRESS-ADO)

BackgroundBorderline Personality Disorder (BPD) is a prevalent and debilitating psychiatric condition often accompanied by Post-Traumatic Stress Disorder (PTSD), with a substantial prevalence of trauma history among affected individuals. The clinical, cognitive, and cerebral parallels shared with PTSD suggest a trauma-related etiology for BPD. Studies consistently demonstrate a reduction in hippocampal volume in individuals with BPD, echoing findings in PTSD. However, the interpretation of this shared neurobiological profile remains contentious, with ongoing debates regarding the independence of these pathologies or the potential exacerbation of diminished hippocampal volume in BPD due to concurrent PTSD. Differential impacts on hippocampal subfields across both disorders may further complicate interpretation, suggesting the volume of hippocampal subfields as a potential discriminant biomarker. This study aims to characterize the multidimensional specific and shared profiles of BPD and PTSD-related alterations, with a particular emphasis on hippocampal subfields during adolescence, a crucial period in BPD development.MethodsThis study focuses on female adolescents, who are more prevalent in the BPD population. Participants are categorized into three groups: BPD, BPD with comorbid PTSD, and a control group of matched healthy individuals. Data collection encompasses clinical, cognitive, and neuroimaging domains commonly affected in both disorders, utilizing various imaging markers (including gray matter macrostructure, white matter microstructural integrity, and regional functional connectivity).DiscussionThis study examines adolescent BPD with and without comorbid PTSD on clinical, neuroimaging, and cognitive levels. It is the first to use a comprehensive multi-modal approach within the same sample. Additionally, it uniquely explores hippocampal subfield volume differences in adolescents. Analysis of the relationship between the investigated domains and the effects of PTSD comorbidity will elucidate specific and shared alteration profiles in both disorders.Trial registrationIDRCB number 2019-A00366-51 / clinicaltrials.gov ID: NCT0485274. Registered on 21/04/2021.

A comprehensive in silico analysis and experimental validation of miRNAs capable of discriminating between lung adenocarcinoma and squamous cell carcinoma.

Accurate differentiation between lung adenocarcinoma (AC) and lung squamous cell carcinoma (SCC) is crucial owing to their distinct therapeutic approaches. MicroRNAs (miRNAs) exhibit variable expression across subtypes, making them promising biomarkers for discrimination. This study aimed to identify miRNAs with robust discriminatory potential between AC and SCC and elucidate their clinical significance. MiRNA expression profiles for AC and SCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Differential expression analysis and supervised machine learning methods (Support Vector Machine, Decision trees and Naïve Bayes) were employed. Clinical significance was assessed through receiver operating characteristic (ROC) curve analysis, survival analysis, and correlation with clinicopathological features. Validation was conducted using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Furthermore, signaling pathway and gene ontology enrichment analyses were conducted to unveil biological functions. Five miRNAs (miR-205-3p, miR-205-5p, miR-944, miR-375 and miR-326) emerged as potential discriminative markers. The combination of miR-944 and miR-326 yielded an impressive area under the curve of 0.985. RT-qPCR validation confirmed their biomarker potential. miR-326 and miR-375 were identified as prognostic factors in AC, while miR-326 and miR-944 correlated significantly with survival outcomes in SCC. Additionally, exploration of signaling pathways implicated their involvement in key pathways including PI3K-Akt, MAPK, FoxO, and Ras. This study enhances our understanding of miRNAs as discriminative markers between AC and SCC, shedding light on their role as prognostic indicators and their association with clinicopathological characteristics. Moreover, it highlights their potential involvement in signaling pathways crucial in non-small cell lung cancer pathogenesis.

Vascular Cytokines and Atherosclerosis: Differential Serum Levels of TRAIL, IL-18, and OPG in Obstructive Coronary Artery Disease.

The risk-factor-based prediction of atherosclerotic coronary artery disease (CAD) remains suboptimal, particularly in the absence of any of the standard modifiable cardiovascular risk factors (SMuRFs), making the discovery of biomarkers that correlate with atherosclerosis burden critically important. We hypothesized that cytokines and receptors associated with inflammation in CAD-tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), interleukin-18 (IL-18), and osteoprotegerin (OPG)-would be independently associated with CAD. To determine this, we measured the serum biomarker levels of 993 participants from the BioHEART study who had CT coronary angiograms that were scored for severity of stenosis and plaque composition. We found that the quartiles of TRAIL, OPG, and IL-18 were significantly associated with disease scores, and that the IL-18/TRAIL and OPG/TRAIL ratios demonstrated significant differences between no CAD vs. STEMI whereas only the OPG/TRAIL ratio showed differences between no CAD and obstructive CAD (stenosis > 50%). However, these associations did not persist after adjustment for age, sex, SMuRFs, and a family history of CAD. In conclusion, TRAIL, IL-18, and OPG and the derived ratios of IL-18/TRAIL and OPG/TRAIL demonstrate significant associations with raw disease scores and risk factors, but these markers are not discriminatory biomarkers for the prediction of CAD when incorporated into multi-variable risk models.

The influence of soil salinization, induced by the backwater effect of the Yellow River, on microbial community dynamics and ecosystem functioning in arid regions

Irrigation practices and groundwater levels are critical factors contributing to soil salinization in arid and semi-arid regions. However, the impact of soil salinization resulting from Yellow River water irrigation and recharge on microbial communities and their functions in the Huinong District has not been thoroughly documented. In this study, high-throughput sequencing technology was employed to analyze the diversity, composition, and structure of bacterial and fungal communities across a gradient of salinized soils. The results indicated that the alpha diversity of bacterial communities was significantly higher in slightly saline soils compared to highly saline soils. Soil salinization notably influenced the composition of both bacterial and fungal communities. Highly salinized soils were enriched with bacterial taxa such as Halomonas, Salinimicrobium, Pseudomonas, Solibacillus, and Kocuria, as well as fungal taxa including Emericellopsis, Alternaria, and Podospora. In these highly saline soils, bacterial taxa associated with iron respiration, sulfur respiration, and hydrocarbon degradation were more prevalent, whereas fungal taxa linked to functions such as soil animal pathogens, arbuscular mycorrhizal symbiosis, endophytes, dung saprotrophy, leaf saprotrophy, soil saprotrophy, fungal parasitism, and plant pathogenicity were less abundant. Random forest analysis identified nine bacterial and eighteen fungal taxa as potential biomarkers for salinity discrimination in saline soils. Symbiotic network analysis further revealed that soil salinization pressure reduced the overall complexity and stability of bacterial and fungal communities. Additionally, bacterial community assembly showed a tendency shift from stochastic to deterministic processes in response to increasing salinity, while fungal community assembly remained dominated by deterministic processes. provide robust evidence that soil salinity is a major inhibitor of soil biogeochemical processes in the Huinong District and plays a critical role in shaping bacterial and fungal communities, their symbiotic networks, and their assembly processes.

A comparative study between a power and a connectivity sEEG biomarker for seizure-onset zone identification in temporal lobe epilepsy

Background:Ictal stereo-encephalography (sEEG) biomarkers for seizure onset zone (SOZ) localization can be classified depending on whether they target abnormalities in signal power or functional connectivity between signals, and they may depend on the frequency band and time window at which they are estimated. New method:This work aimed to compare and optimize the performance of a power and a connectivity-based biomarker to identify SOZ contacts from ictal sEEG recordings. To do so, we used a previously introduced power-based measure, the normalized mean activation (nMA), which quantifies the ictal average power activation. Similarly, we defined the normalized mean strength (nMS), to quantify the ictal mean functional connectivity of every contact with the rest. The optimal frequency bands and time windows were selected based on optimizing AUC and F2-score. Results:The analysis was performed on a dataset of 67 seizures from 10 patients with pharmacoresistant temporal lobe epilepsy. Our results suggest that the power-based biomarker generally performs better for the detection of SOZ than the connectivity-based one. However, an equivalent performance level can be achieved when both biomarkers are independently optimized. Optimal performance was achieved in the beta and lower-gamma range for the power biomarker and in the lower- and higher-gamma range for connectivity, both using a 20 or 30 s period after seizure onset. Conclusions:The results of this study highlight the importance of this optimization step over frequency and time windows when comparing different SOZ discrimination biomarkers. This information should be considered when training SOZ classifiers on retrospective patients’ data for clinical applications.

Heat Shock Protein 70 Constitutes a Promising Novel Biomarker in Differential Diagnosis between Takotsubo Syndrome and Non-ST-Segment Elevation Myocardial Infarction.

(1) Background: Due to similar clinical presentation and a lack of specific biomarkers, initial differentiation between Takotsubo syndrome (TTS) and non-ST-segment elevation myocardial infarction (NSTEMI) remains challenging in daily practice. Heat Shock Protein 70 (HSP70) is a novel biomarker that is recognized for its potential in the diagnosis and differentiation of cardiovascular conditions. (2) Methods: Data from a total of 156 patients were analyzed (32.1% NSTEMI, 32.7% TTS, and 35.3% controls). Serum concentrations of HSP70 were determined using ELISA and compared between patients and controls. ROC curve analysis, logistic regression analysis and propensity-score-weighted logistic regression were conducted. (3) Results: Concentrations of HSP70 were highest in patients with TTS (median 1727 pg/mL vs. ACS: median 1545 pg/mL vs. controls: median 583 pg/mL, p < 0.0001). HSP70 was predictive for TTS in binary logistic regression analysis (B(SE) = 0.634(0.22), p = 0.004), which even remained significant after correction for possible confounders in propensity-score-weighted analysis. ROC curve analysis also revealed a significant association of HSP70 with TTS (AUC: 0.633, p = 0.008). (4) Conclusions: Based on our findings, HSP70 constitutes a promising biomarker for discrimination between TTS and NSTEMI, especially in combination with established cardiovascular biomarkers like pBNP or high-sensitivity cardiac troponin.

Characterisation of glucose-induced protein fragments among the order Enterobacterales using matrix-assisted laser desorption ionization-time of flight mass spectrometry

To characterise the glucose-induced protein fragments by MALDI-TOF MS analysis, we compared data for samples from Escherichia coli cultured in media with or without glucose. Characteristic peaks were observed in the presence of glucose, and MS/MS revealed Asr-specific fragments. The amino acid sequences of the fragments suggested sequence-specific proteolysis. Blast-analysis revealed that numerous Enterobacterales harbored genes encoding Asr as well as E. coli. Here, we analysed 32 strains from 20 genera and 25 species of seven Enterobacterales families. We did not detect changes in the mass spectra of four strains of Morganellaceae lacking asr, whereas peaks of Asr-specific fragments were detected in the other 28 strains. We therefore concluded that the induction of Asr production in the presence of glucose is common among the Enterobacterales, except for certain Morganellaceae species. In members of family Budviciaceae, unfragmented Asr was detected. Molecular genetic information suggested that the amino acid sequences of Asr homologs are diverse, with fragments varying in number and size, indicating that Asr may serve as a discriminative biomarker for identifying Enterobacterales species.

Exhaled breath analysis for the discrimination of asthma and chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) and asthma are the most common chronic respiratory diseases. In middle-aged and elderly patients, it is difficult to distinguish between COPD and asthma based on clinical symptoms and pulmonary function examinations in clinical practice. Thus, an accurate and reliable inspection method is required. In this study, we aimed to identify breath biomarkers and evaluate the accuracy of breathomics-based methods for discriminating between COPD and asthma. In this multi-center cross-sectional study, exhaled breath samples were collected from 89 patients with COPD and 73 with asthma and detected on a high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform from 20 October 2022, to 20 May 2023, in four hospitals. Data analysis was performed from 15 June 2023 to 16 August 2023. The sensitivity, specificity, and accuracy were calculated to assess the overall performance of the volatile organic component (VOC)-based COPD and asthma discrimination models. Potential VOC markers related to COPD and asthma were also analyzed. The age of all participants ranged from to 18–86 years, and 54 (33.3%) were men. The age [median (minimum, maximum)] of COPD and asthma participants were 66.0 (46.0, 86.0), and 44.0 (17.0, 80.0). The male and female ratio of COPD and asthma participants were 14/75 and 40/33, respectively. Based on breathomics feature selection, ten VOCs were identified as COPD and asthma discrimination biomarkers via breath testing. The joint panel of these ten VOCs achieved an area under the curve of 0.843, sensitivity of 75.9%, specificity of 87.5%, and accuracy of 80.0% in COPD and asthma discrimination. Furthermore, the VOCs detected in the breath samples were closely related to the clinical characteristics of COPD and asthma. The VOC-based COPD and asthma discrimination model showed good accuracy, providing a new strategy for clinical diagnosis. Breathomics-based methods may play an important role in the diagnosis of COPD and asthma.

Ventricular volume asymmetry as a novel imaging biomarker for disease discrimination and outcome prediction.

Disruption of the predictable symmetry of the healthy heart may be an indicator of cardiovascular risk. This study defines the population distribution of ventricular asymmetry and its relationships across a range of prevalent and incident cardiorespiratory diseases. The analysis includes 44 796 UK Biobank participants (average age 64.1 ± 7.7 years; 51.9% women). Cardiovascular magnetic resonance (CMR) metrics were derived using previously validated automated pipelines. Ventricular asymmetry was expressed as the ratio of left and right ventricular (LV and RV) end-diastolic volumes. Clinical outcomes were defined through linked health records. Incident events were those occurring for the first time after imaging, longitudinally tracked over an average follow-up time of 4.75 ± 1.52 years. The normal range for ventricular symmetry was defined in a healthy subset. Participants with values outside the 5th-95th percentiles of the healthy distribution were classed as either LV dominant (LV/RV > 112%) or RV dominant (LV/RV < 80%) asymmetry. Associations of LV and RV dominant asymmetry with vascular risk factors, CMR features, and prevalent and incident cardiovascular diseases (CVDs) were examined using regression models, adjusting for vascular risk factors, prevalent diseases, and conventional CMR measures. Left ventricular dominance was linked to an array of pre-existing vascular risk factors and CVDs, and a two-fold increased risk of incident heart failure, non-ischaemic cardiomyopathies, and left-sided valvular disorders. Right ventricular dominance was associated with an elevated risk of all-cause mortality. Ventricular asymmetry has clinical utility for cardiovascular risk assessment, providing information that is incremental to traditional risk factors and conventional CMR metrics.

Heart proteomic profiling discovers MYH6 and COX5B as biomarkers for sudden unexplained death

Sudden unexplained death (SUD) is not uncommon in forensic pathology. Yet, diagnosis of SUD remains challenging due to lack of specific biomarkers. This study aimed to screen differentially expressed proteins (DEPs) and validate their usefulness as diagnostic biomarkers for SUD cases. We designed a three-phase investigation, where in the discovery phase, formalin-fixed paraffin-embedded (FFPE) heart specimens were screened through label-free proteomic analysis of cases dying from SUD, mechanical injury and carbon monoxide (CO) intoxication. A total of 26 proteins were identified to be DEPs for the SUD cases after rigorous criterion. Bioinformatics and Adaboost-recursive feature elimination (RFE) analysis further revealed that three of the 26 proteins (MYH6, COX5B and TNNT2) were potential discriminative biomarkers. In the training phase, MYH6 and COX5B were verified to be true DEPs in cardiac tissues from 29 independent SUD cases as compared with a serial of control cases (n = 42). Receiver operating characteristic (ROC) analysis illustrated that combination of MYH6 and COX5B achieved optimal diagnostic sensitivity (89.7 %) and specificity (84.4 %), with area under the curve (AUC) being 0.91. A diagnostic software based on the logistic regression formula derived from the training phase was then constructed. In the validation phase, the diagnostic software was applied to eight authentic SUD cases, seven (87.5 %) of which were accurately recognized. Our study provides a valid strategy towards practical diagnosis of SUD by integrating cardiac MYH6 and COX5B as dual diagnostic biomarkers.

Comparative Metabolic Profiling of Different Colored Rice Grains Reveals the Distribution of Major Active Compounds and Key Secondary Metabolites in Green Rice.

Pigmented rice grains are important resources for health and nutritional perspectives. Thus, a thorough dissection of the variation of nutrients and bioactive metabolites in different colored rice is of global interest. This study applied LC-MS-based widely targeted metabolite profiling and unraveled the variability of metabolites and nutraceuticals in long grain/non-glutinous black (BR), red (RR), green (GR), and white rice (WR) grains. We identified and classified 1292 metabolites, including five flavonoid compounds specific to BR. The metabolite profiles of the four rice grains showed significant variation, with 275-543 differentially accumulated metabolites identified. Flavonoid (flavone, flavonol, and anthocyanin) and cofactor biosynthesis were the most differentially regulated pathways among the four rice types. Most bioactive flavonoids, anthocyanidins (glycosylated cyanidins and peonidins), phenolic acids, and lignans had the highest relative content in BR, followed by RR. Most alkaloids, amino acids and derivatives, lipids, and vitamins (B6, B3, B1, nicotinamide, and isonicotinic acid) had higher relative contents in GR than others. Procyanidins (B1, B2, and B3) had the highest relative content in RR. In addition, we identified 25 potential discriminatory biomarkers, including fagomine, which could be used to authenticate GR. Our results show that BR and RR are important materials for medicinal use, while GR is an excellent source of nutrients (amino acids and vitamins) and bioactive alkaloids. Moreover, they provide data resources for the science-based use of different colored rice varieties in diverse industries.

Associations of Cardiometabolic Indices With Peptides Related to Hypertensive Disorders of Pregnancy in Adult Men.

Seven peptides with low molecular weights in blood have been identified as possible biomarkers of hypertensive disorders of pregnancy (HDP). A history of HDP is known to be associated with a high risk of cardiovascular disease in the later life of women with HDP. However, it remains to be determined whether HDP-related peptides are useful biomarkers of cardiovascular disease in the general population. The purpose of this study was to determine the relationships between these peptides and cardiometabolic risk in adult men. We investigated the relationships between HDP-related peptides and two recent indices of cardiometabolic risk, hematometabolic index (HMI)and lipid accumulation product (LAP), in male workers aged 35 to 69 years. Concentrations of the HDP-related seven peptides with mass/charge ratios (m/z) of 2081 (P-2081), 2091 (P-2091), 2127 (P-2127), 2209 (P-2209), 2378 (P-2378), 2858 (P-2858), and 3156 (P-3156) were measured simultaneously by using a mass spectrometer. Standardized partial regression coefficients (β) were obtained in multivariable linear regression analysis, and mean levels of the log-transformed HMI and LAP were compared in tertile groups of each peptide in the analysis of covariance with adjustment for age, habits of smoking and alcohol drinking, history of diabetes, and medication therapy for dyslipidemia. There was a significant positive correlation between the HMI and the serum level of P-2378 (β= 0.310), a fragment of complement component 4, while a significant inverse correlation (β= -0.389) was obtained between the LAP and the serum level of P-3156, a fragment of inter-α-trypsin inhibitor heavy chain H4. Other peptides (P-2081, P-2091, P-2127, P-2209, and P-2858) did not show significant correlations with the HMI or LAP. The log-transformed HMI tended to be higher with an increase in the tertile for P-2378. The mean level of log-transformed LAP in the first tertile group of P-3156 was significantly higher than those in the second and third tertile groups of P-3156. The HDP-related peptides with m/z of 2378 and m/z of 3156 were shown to be associated with the HMI and LAP, respectively, which are recent indices reflecting cardiometabolic risk. Therefore, the peptides with m/z of 2378 and m/z of 3156 were thought to be potential biomarkers for discrimination of cardiovascular risk in adult men. Further studies on the relationships between the peptides and cardiovascular risk factors in non-pregnant women are needed to confirm the findings of this study.

Peripheral blood immune cell parameters in patients with high-grade squamous intraepithelial lesion (HSIL) and cervical cancer and their clinical value: a retrospective study

Objective The objective of this study was to delineate the profile of peripheral blood lymphocytic indices in patients afflicted with high-grade squamous intraepithelial lesions (HSIL) and cervical neoplasms, and to elucidate the correlation of these hematologic markers with the clinicopathological spectra in individuals diagnosed with cervical carcinoma. Methods We adopted a retrospective case-control modality for this investigation. An aggregate of 39 HSIL patients and 42 cervical carcinoma patients, who were treated in our facility from July 2020 to September 2023, were meticulously selected. Each case of cervical malignancy was confirmed through rigorous histopathological scrutiny. Concomitantly, 31 healthy female individuals, who underwent prophylactic health evaluations during the corresponding timeframe, were enlisted as the baseline control group. We systematically gathered and analyzed clinical demographics, as well as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), from peripheral blood samples. Pearson’s correlation coefficient was deployed to dissect the interrelation between peripheral NLR and PLR concentrations and the clinicopathological features in the cervical cancer group. Results Inter-group comparative analysis unveiled statistically substantial variances in the PLR and NLR values among the tripartite clusters (F = 36.941, 14.998, P &lt; 0.001, respectively). Although discrepancy in NLR (P = 0.061) and PLR (P = 0.759) measures between the groups of cervical carcinoma and HSIL was not statistically appreciable, these indices were markedly elevated in the cervical carcinoma faction as juxtaposed with the normative control group (t = 5.094, 5.927; P &lt; 0.001 for both parameters). A discernible gradation in peripheral blood PLR and NLR concentrations was noted when stratified by clinical stage and the profundity of myometrial invasion in cervical cancer subjects (P &lt; 0.001). The correlation matrix demonstrated a positive liaison between peripheral blood PLR and the clinical gradation, as well as the invasiveness of the neoplastic cells into the muscularis propria (P &lt; 0.05); a similar trend was observed with the NLR values (P &lt; 0.05). Conclusion Augmented NLR and PLR levels in peripheral blood specimens are indicative of HSIL and cervical malignancy. These hematological parameters exhibit a pronounced interconnection with clinical staging and muscular wall penetration depth, serving as potential discriminative biomarkers for the diagnosis and prognosis of cervical cancer.

Deciphering dermatological distinctions: Cornulin as a discriminant biomarker between basal cell carcinoma and squamous cell carcinoma detected through e‐biopsy and machine learning

AbstractClinical misdiagnosis between cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC) poses treatment challenges and carries risks of recurrence, metastases and increased morbidity and mortality. We aimed to identify discriminant proteins markers for cSCC and BCC using a minimally invasive proteome sampling method called e‐biopsy, employing electroporation for non‐thermal cell permeabilization and machine learning. E‐biopsy facilitated ex vivo proteome extraction from 21 cSCC and 21 BCC pathologically validated human cancers. LC/MS/MS profiling of 126 proteomes was followed by machine learning analysis to identify proteins distinguishing cSCC from BCC. For identified panel validation, we used proteomes sampled by e‐biopsy from unrelated 20 cSCC and 46 BCC human cancers, and differential expression analysis of published transcriptomics. Cornulin, the most commonly chosen discriminative biomarker by machine learning models, was also validated using fluorescent immunohistochemistry. One hundred and ninety‐two proteomes sampled from one hundred eight patients were analysed. Machine learning‐based approaches resulted in a set of 11 potential biomarker proteins that can be used to construct a patient classification model with 95.2% average cross‐validation accuracy, BCC precision of 93.6 ± 14.5%, cSCC precision of 98.4 ± 7.2%, specificity of 97.7 ± 11.8% and sensitivity 92.7 ± 15.3%. Protein–protein interaction analysis revealed a novel interaction network connecting 10 of the 11 resulted proteins. Histological and transcriptomic validation confirmed cornulin as a discriminant marker significantly lower in cSCC than in BCC. E‐biopsy combined with machine learning provides a novel approach to molecular sampling from skin for biomarker detection and differential expression analysis between cSCC and BCC.

A prognostic model based on selected cell state and cellular community scores in patients with advanced melanoma treated with immune checkpoint inhibitors (Ecotype-ICI score) as a predictor of ICI immunotherapeutic benefits.

9568 Background: We conducted an enhanced immune cell state atlas analysis of the data of patients with melanoma treated with ICI to investigate predictors of therapeutic benefits and mechanisms of resistance. Methods: Leveraging RNA-seq from melanoma patients (n=141) treated with ICI within the ORIEN Avatar project (NCT03977402), we constructed a prognostic model based on selected cell state and cellular community scores, also known as ecotype-ICI score ( Li. AACR 2023). We evaluated the model’s predictive value using transcriptomic data from high-risk melanoma patients treated with adjuvant ipilimumab (n=471) or interferon-α (n=248) as part of the E1609 phase 3 trial (1). RNA-seq data were initially deconvoluted for cellular community signatures using EcoTyper. The six prognostic carcinoma ecotype (CE) signatures identified (CE1, CE2, C6, CE7, CD9, CD10) were utilized for calculating a risk score based on a multivariable Cox model, which was trained using Avatar data. For additional external validation, we analyzed data from patients with metastatic melanoma treated with anti-CTLA4 ( Va. n=40), anti-PD1 ( Liu. n=121), and anti-PD1 alone or combined with anti-CTLA4 ( Gide. n=66). A series of ad hoc survival analyses, including Kaplan-Meier analysis, log-rank tests, and Cox regression were further applied. Results: The ecotype-ICI score showed strong prognostic significance in predicting overall survival (OS) in the entire group of E1609 patients (N=719; Cox P &lt; 0.0001, log-rank P &lt; 0.0001), the ipilimumab cohort (Cox P &lt; 0.000143, log-rank P =0.00011), but less so with interferon (Cox P= 0.06). CE9, characterized by its proinflammatory nature and IFN-gamma signaling, and CE10, noted for its canonical T cell state signatures, both demonstrated a leading predictive value according to multivariable Cox regression analysis. Interestingly, CE2, noted for its lymphocyte deficiency signature, also demonstrated complementary predictive value for outcomes following ipilimumab. Within the validation cohorts of public data sets noted above, the ecotype-ICI scores showed similarly strong prognostic significance in predicting OS (Gide. P = 0.045; Liu. P = 0.029; Va. P = 0.011). Ecotype-ICI also demonstrated a distinct distribution in the responder group (complete or partial response) compared to the progressive disease group, underscoring its potential as a discriminative biomarker for ICI response and survival outcomes. Conclusions: Our analysis has successfully established the utility of the immune cell state atlas in predicting therapeutic benefits with ICIs across diverse ICI treatment regimens in melanoma. We will update our results with data related to gene ontology, KEGG pathways and the most complementary pathway signatures at the meeting. 1. Tarhini, 2020.

Molecular analysis of the HCRN GU16-260-Cohort A phase II study of first-line (1L) nivolumab (nivo) and salvage nivo + ipilimumab (ipi) in patients (pts) with advanced clear cell renal cell carcinoma (accRCC).

4546 Background: Seven molecular subsets of ccRCC have been proposed: 1: Angiogenic/Stromal; 2: Angiogenic; 3: Complement/Ω-oxidation; 4: T-effector/Proliferative; 5: Proliferative; 6: Stromal/Proliferative; and 7: snoRNA (Motzer et al. Can Cell 2020). In an analysis of IMmotion 151, which compared 1L atezolizumab/bevacizumab to sunitinib, combination immune checkpoint blockade (ICB) and anti-VEGF therapy was associated with improved progression-free survival (PFS) and overall survival (OS) in clusters (CLs) 4 and 5, increased PFS in CL 7, and decreased OS in CL 2 (Motzer Can Cell 2020, Motzer JAMA Onc 2022). CL 4 was also associated with higher PFS and objective response rate (ORR) in &gt;1L nivo compared to everolimus in an analysis of CheckMate-025 (Denize CCR 2022). To date, outcomes have not been compared among accRCC subtypes in pts who received 1L pure ICB. Methods: In HCRN GU16-260-Cohort A, pts with accRCC received 1L nivo monotherapy until progressive disease (PD), unacceptable toxicity, or completion of 96 weeks of therapy. Pre-treatment FFPE tumor samples were obtained. A machine learning model (random forest) was trained on annotated transcriptomic data from bulk RNA-seq and used to classify tumors that passed quality control into one of the 7 molecular subtypes. Results: 70 tumor samples contained adequate RNA quality and were classified as outlined in the table. IMDC rates and ORR (40%) for the RNAseq population closely matched those of the whole study (Atkins JCO 2022). No tumors were classified in CL 7 and only 3 in CL 5. Tumors in each IMDC group contained a mix of subtypes. The highest ORR occurred in CL 4 (6/10, 60.0%); CL 4 median PFS was 15.2 months and 1-year PFS was 60%. ORR was also high in CLs 1 and 3 at 52.4% and 40.0%, respectively and lowest in CL 2 (12.5%). PD as best response (5/10, 50.0%) and short PFS (5.1 months) were seen in CL 6. Conclusions: Consistent with prior analyses with combination ICB + anti-VEGF and &gt;1L ICB, CL 4 was associated with robust 1L ICB monotherapy antitumor efficacy. Surprisingly, ORR was also high in CLs 1 and 3. In this small dataset, transcriptomic signatures enriched for but did not fully predict ORR. Future prospective trials are needed to identify more discriminative biomarkers of efficacy for pure ICB therapy.[Table: see text]

Proteomic snapshot of saliva samples predicts new pathways implicated in SARS-CoV-2 pathogenesis.

Information on the microbiome's human pathways and active members that can affect SARS-CoV-2 susceptibility and pathogenesis in the salivary proteome is very scarce. Here, we studied a unique collection of samples harvested from April to June 2020 from unvaccinated patients. We compared 10 infected and hospitalized patients with severe (n = 5) and moderate (n = 5) coronavirus disease (COVID-19) with 10 uninfected individuals, including non-COVID-19 but susceptible individuals (n = 5) and non-COVID-19 and nonsusceptible healthcare workers with repeated high-risk exposures (n = 5). By performing high-throughput proteomic profiling in saliva samples, we detected 226 unique differentially expressed (DE) human proteins between groups (q-value ≤ 0.05) out of 3376 unambiguously identified proteins (false discovery rate ≤ 1%). Major differences were observed between the non-COVID-19 and nonsusceptible groups. Bioinformatics analysis of DE proteins revealed human proteomic signatures related to inflammatory responses, central cellular processes, and antiviral activity associated with the saliva of SARS-CoV-2-infected patients (p-value ≤ 0.0004). Discriminatory biomarker signatures from human saliva include cystatins, protective molecules present in the oral cavity, calprotectins, involved in cell cycle progression, and histones, related to nucleosome functions. The expression levels of two human proteins related to protein transport in the cytoplasm, DYNC1 (p-value, 0.0021) and MAPRE1 (p-value, 0.047), correlated with angiotensin-converting enzyme 2 (ACE2) plasma activity. Finally, the proteomes of microorganisms present in the saliva samples showed 4 main microbial functional features related to ribosome functioning that were overrepresented in the infected group. Our study explores potential candidates involved in pathways implicated in SARS-CoV-2 susceptibility, although further studies in larger cohorts will be necessary.