Panel Of Biomarkers Research Articles

Dual analysis of IL-8 in non-small cell lung cancer: A clinical perspective on protein and RNA evaluation via imaging mass cytometry.

e15068 Background: The cytokine Interleukin-8 (IL-8) plays a pivotal role in the tumorigenesis and progression of non-small cell lung cancer (NSCLC), influencing both the tumor microenvironment and immune escape mechanisms. Given its significance, accurately assessing IL-8 levels at the protein and RNA levels could provide valuable insights into its prognostic value and inform therapeutic targeting strategies. This study utilizes imaging mass cytometry (IMC) to conduct a comprehensive evaluation of IL-8 expression in NSCLC, integrating both protein presence and gene expression analysis within the spatial context of the tumor microenvironment. Methods: Our approach leverages the capabilities of IMC to simultaneously detect protein and RNA levels of IL-8 in NSCLC tissue samples. For protein analysis, antibodies tagged with heavy metal isotopes specifically target IL-8, allowing for precise quantification and spatial localization within the tumor. Concurrently, RNA detection is achieved through hybridization probes designed for IL-8 mRNA, enabling the direct observation of gene expression patterns. This method provides a unique opportunity to correlate IL-8's protein abundance with its mRNA expression, offering insights into the regulatory mechanisms at play and their impact on the tumor's behavior and patient outcomes. Results: Preliminary data demonstrate a marked heterogeneity in IL-8 distribution at both the protein and RNA levels, with distinct patterns emerging that relate to tumor aggressiveness and immune infiltration. High IL-8 expression, identified through our integrated IMC approach, correlates with poor prognosis and reduced response to standard therapies, underscoring its potential as a biomarker for NSCLC management. Furthermore, the spatial analysis afforded by IMC highlights the microenvironmental niches where IL-8-mediated interactions could drive tumor progression and immune evasion. Conclusions: By employing IMC for the dual analysis of IL-8 protein and RNA in NSCLC, our study provides a comprehensive view of this cytokine's role in lung cancer pathology. The ability to visualize and quantify IL-8 expression within the spatial context of the tumor microenvironment opens new avenues for understanding its biological impact and for developing targeted therapies. Our findings advocate for the inclusion of IL-8 in the biomarker panel for NSCLC, with potential implications for patient stratification and personalized treatment planning. Future research will focus on expanding these observations to a larger cohort and exploring the therapeutic modulation of IL-8 as a strategy for improving NSCLC outcomes.

Discovering urinary protein biomarkers for hepatocellular carcinoma in the population of patients with chronic hepatitis B: A single-center case-control study.

e16230 Background: Chronic hepatitis B (CHB) can result in hepatocellular carcinoma (HCC) imposing a substantial health and economic burden worldwide. Early detection of hepatitis B virus-related HCC (HBV-HCC) in CHB with potential biomarkers has emerged as an urgent and challenging task. Recent progression of urinary proteomics provides a promising method for HBV-HCC biomarker discovery. Methods: This single-center case-control study, conducted at Peking Union Medical College Hospital, enrolled 33 patients with HBV-HCC, 26 patients with CHB, 30 healthy controls, and 33 controls with other types of cancer. Urine proteomes were analyzed using liquid chromatography with tandem mass spectrometry by data-dependent acquisition or parallel reaction monitoring, respectively. Differential analysis was carried out with limma package in R software, and the origin of selected proteins was confirmed with immunohistochemistry of surgical specimens. The diagnostic performance of the biomarker panel was evaluated with area under receiver operator curves (AUROC). Results: 21 urinary proteins were significantly upregulated in HBV-HCC compared with CHB samples, playing key roles in vesicular transportation, cytoskeleton-extracellular matrix interaction, and tumor development according to enrichment analysis. Immunohistochemistry validated the differential expression of 4 urinary proteins (4UP: HNRNPM, ADGRG1, APOC1, and RALA) across cancerous and adjacent normal liver tissues. These proteins were specifically found in HBV-HCC but not in CHB, healthy control, or other cancer controls. The AUROCs of 4UP individually (0.642 for APOC1, 0.780 for HNRNPM, 0.755 for ADGRG1, 0.729 for RALA) were no worse than that of serum α-fetoprotein (AUROC = 0.751), and the 4UP panel unified via logistic regression showed an AUROC of 0.828. Conclusions: Urinary proteomics revealed HNRNPM, ADGRG1, APOC1, and RALA as biomarkers to distinguish HBV-HCC from CHB. The 4UP could be traced back to HBV-HCC tumor cells, shedding light on pathogenesis of HBV-HCC. [Table: see text]

Relationship of adaptive subtyping and tumour heterogeneity of treatment response to neoadjuvant therapy in hormone receptor–positive HER2-negative early breast cancer: PENELOPE-B.

566 Background: The concept of intrinsic subtyping has been an important step for understanding of breast cancer (BC) as a heterogenous disease. However, these subtypes are not adapted to therapy-induced molecular plasticity. We have evaluated a high-risk luminal BC clinical trial cohort to identify new additional adaptive BC subtypes based on molecular alterations induced during neoadjuvant chemotherapy (NACT). Methods: A total of 1250 ER+/HER2- BC patients (pts) with residual disease after NACT and increased risk (CPS-EG score of ≥3 or 2 with ypN+) were randomized into the PENELOPE-B (NCT01864746) trial to receive palbociclib or placebo. Biomarker analysis was performed in 1411 pre- or postNACT tumor samples including 540 paired samples using the HTG EdgeSeq Oncology Biomarker Panel targeting 2549 genes (HTG Molecular Diagnostics Inc.), with assessment of the absolute assignment of breast cancer intrinsic molecular subtype (AIMS). For a subcohort of 29 pts, we evaluated triplicate samples before and after NACT as well as at the time of metastatic disease. Results: In paired biopsies, a total of 335 genes were significantly different between the pre- and the postNACT cohort. With hierarchical unsupervised clustering of these 335 genes, we characterized five different tumor subtypes with highly significant iDFS survival differences (p<0.0001, n=539). We identified two large groups of tumors with excellent prognosis (adaptive cluster AC1 and AC2, together n=284, 6 events), as well as two groups with a poor prognosis; AC-3 (n=163, 82 events) and AC-4 (n=78, 29 events). The worst prognosis was observed in a small group of tumors with a pre-NACT Basal/HER2E AIMS subtype (n=14, 11 events). AC1 and AC3 are enriched for tumors with an AIMS subtype change from LumB to LumA during NACT, with improved iDFS for AC1. AC2 and AC4 are enriched for tumors with a constant LumA AIMS subtype before and after NACT, with improved iDFS for AC2. The low-risk adaptive subtypes AC1 and AC2 cover a total of 52.7% of pts with a combined event rate of 2.1%. In contrast, the best pretherapeutic conventional AIMS-subtype, preNACT LumA, covers 51.7% of pts, but still has an event rate of 15.4%. This suggests that adaptive subtypes are superior to classical AIMS subtypes for prediction of survival after NACT in luminal BC. Conclusions: Classical approach of intrinsic subtyping relies on constant gene expression, limiting its scope. Adaptive subtyping can complement the classical approach, focusing on those genes that are changed during therapy. Comparison of paired samples before and after therapy is superior to classical subtyping of baseline samples. This approach identified large subgroups of pts with excellent prognosis after NACT despite clinical high risk. This can be an important step to focus on high-risk pts for new neoadjuvant and post-neoadjuvant therapy concepts.

Discovery of long non-coding RNA biomarkers for prognosis in pediatric acute lymphoblastic leukemia.

3055 Background: Acute lymphoblastic leukemia (ALL) ranks among the most prevalent pediatric cancers. Molecular testing, including cytogenetics, has been widely used for prognosticating pediatric ALL. However, cytogenetic testing in pediatric ALL is incapable of detecting submicroscopic genetic changes and suffers from variable sensitivity, which may lead to heterogeneous outcomes within cytogenetic subgroups. In addition to conventional prognostic biomarkers, long non-coding RNAs (lncRNAs) are expressed in both a disease- and tissue-specific manner, emerging as promising biomarkers. However, well-established lncRNA prognostic biomarkers for pediatric ALL have yet to be identified. Therefore, in this study, we discovered three singular lncRNA prognostic biomarkers and a prognostic panel composed of multi-lncRNAs for pediatric ALL using our newly developed BAMBI method. Methods: This study utilized RNA-seq data from 561 distinct individuals, comprising 647 blood or bone marrow biospecimens obtained from pediatric patients with ALL from the dbGaP TARGET dataset. Among these individuals with RNA-seq and survival data, 25.8% deceased during follow-up. The average age at enrollment was 8.1 years. Of this cohort, 36.0% were female, and 61.5% were Caucasian white. Initially, our lncRNA identification tool, Flnc (Li Z., et al., Noncoding RNA. 2022), was employed to identify and quantify both novel and known lncRNAs expressed in pediatric ALL. Subsequently, our machine-learning based method, BAMBI, was utilized to discover lncRNAs as new prognostic biomarkers in pediatric ALL. Results: Three lncRNAs (ZBTB47 and NKTR antisense RNA, AC097359.2’s new isoform, and a novel lncRNA near STXBP5) were identified as individual putative biomarkers for prognosis. Each of these three lncRNA biomarkers exhibited predictive power for survival, with accuracy ranging from 68.2% to 72.8% and AUROC scores ranging from 67.7% to 75%. Furthermore, we established three lncRNA biomarker panels composed of these three lncRNAs along with a few additional lncRNAs, which showed improved prediction power with accuracy ranging from 78.3% to 79.6%, and AUROC scores ranging from 82.1% to 85.2%. Conclusions: We discovered both singular lncRNA and panels of multi-lncRNAs as prognostic biomarkers of pediatric ALL patients. These putative lncRNA biomarkers could be further validated through independent cohorts or prospective clinical study designs.

Blood-based inflammatory protein biomarker panel for the prediction of relapse and severity in patients with neuromyelitis optica spectrum disorder: A prospective cohort study.

To date, most existing models for predicting neuromyelitis optica spectrum disorder (NMOSD) are based primarily on clinical characteristics. Blood-based NMOSD severity and prognostic predictive immune- and inflammation-related biomarkers are needed. We aimed to investigate the associations between plasma inflammatory biomarkers and relapse and attack severity in NMOSD. This two-step, single-center prospective cohort study included discovery and validation cohorts. We quantified 92 plasma inflammatory proteins by using Olink's proximity extension assay and identified differentially expressed proteins in the relapse group (relapse within 1 year of follow-up) and severe attack group. To define a new molecular prognostic model, we calculated the risk score of each patient based on the key protein signatures and validated the results in the validation cohort. The relapse prediction model, including FGF-23, DNER, GDNF, and SLAMF1, predicted the 1-year relapse risk. The severe attack prediction model, including PD-L1 and MCP-2, predicted the severe clinical attack risk. Both the relapse and severe attack prediction models demonstrated good discriminative ability and high accuracy in the validation cohort. Our discovered biomarker signature and prediction models may complement current clinical risk stratification approaches. These inflammatory biomarkers could contribute to the discovery of therapeutic interventions and prevent NMOSD progression.

Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning.

Ovarian cancer (OC) is the most lethal gynecological cancer in the United States. Among the different types of OC, serous ovarian cancer (SOC) stands out as the most prevalent. Transcriptomics techniques generate extensive gene expression data, yet only a few of these genes are relevant to clinical diagnosis. Methods for feature selection (FS) address the challenges of high dimensionality in extensive datasets. This study proposes a computational framework that applies FS techniques to identify genes highly associated with platinum-based chemotherapy response on SOC patients. Using SOC datasets from the Gene Expression Omnibus (GEO) database, LASSO and varSelRF FS methods were employed. Machine learning classification algorithms such as random forest (RF) and support vector machine (SVM) were also used to evaluate the performance of the models. The proposed framework has identified biomarkers panels with 9 and 10 genes that are highly correlated with platinum-paclitaxel and platinum-only response in SOC patients, respectively. The predictive models have been trained using the identified gene signatures and accuracy of above 90% was achieved. In this study, we propose that applying multiple feature selection methods not only effectively reduces the number of identified biomarkers, enhancing their biological relevance, but also corroborates the efficacy of drug response prediction models in cancer treatment.

Molecular characterization of growing teratoma syndrome in patients with testicular germ cell tumor.

e17017 Background: Teratomas are unique neoplastic growths that encompass a diverse array of tissue types derived from multiple germ layers, often exhibiting varying degrees of differentiation. Among the phenomena associated with teratomas, the growing teratoma syndrome (GTS) stands out as a rare yet clinically significant occurrence. GTS, characterized by the persistence or regrowth of teratomatous elements despite initial treatment, poses challenges for both diagnosis and management. In this abstract, we present the results of our expression array analysis of teratomas, aiming to uncover novel insights into their molecular composition, heterogeneity, and potential implications for clinical management. Methods: This study included 53 patients with retroperitoneal teratoma (7 chemotherapy naïve and 46 with postchemotherapy teratoma) and 20 patients with viable postchemotherapy germ cell tumor from retroperitoneum that had surgery at National Cancer Institute in Slovakia and for whom formalin-fixed paraffin-embedded (FFPE) tumor tissue was available. Study also includes samples from 5 patients with normal testis as control group. Ten patients (18.9%) in this cohort had growing teratoma syndrome. Analysis was performed by HTG EdgeSeq Oncology Biomarker panel (HTG Molecular Diagnostics, Inc., Tucson, USA). Identified candidate genes associated with GTS were further validated by immunohistochemistry on FFPE. Results: Molecular profiling indicates no differences in expression profile in retroperitoneal teratomas regardless of previous chemotherapy. Expression analysis indicates 1586 genes being differently expressed with high significance ( p < 0.05) in teratomas compared to normal testis and 1571 genes differentially expressed compared postchemotherapy viable tumors. We identified 14 genes being differently expressed in growing teratoma syndrome compared to teratoma without growth (CYP4A22, IGFBP1, CXCL6, LTF, CYP2C8, CFTR, CCL20, PFKFB3, SLC2A3, LIPA, HMOX1, GPNMB, CCL18, DLK1. Pathway analysis revealed MAPK and PI3K−Akt signaling pathways are mostly dysregulated in GTS. Conclusions: This molecular profiling identify genes differentially expressed in teratomas that are potential biomarkers for prediction of teratoma element in postchemotherapy residues in testicular germ cell tumor patients. Moreover, inhibition of identified signaling pathways associated with GTS could represent potentially novel therapeutic targets in case of unresectable disease.

Exploration of microRNA biomarker panel as a predictor of evolution of pancreatitis to pancreatic ductal adenocarcinoma.

Exploration of microRNA biomarker panel as a predictor of evolution of pancreatitis to pancreatic ductal adenocarcinoma.

Non-invasive early detection on esophageal squamous cell carcinoma and precancerous lesions by microbial biomarkers combining epidemiological factors in China.

Microbiota may be associated with esophageal squamous cell carcinoma (ESCC) development. However, it is not known the predictive value of microbial biomarkers combining epidemiological factors for the early detection of ESCC and precancerous lesions. A total of 449 specimens (esophageal swabs and saliva) were collected from 349 participants with different esophageal statuses in China to explore and validate ESCC-associated microbial biomarkers from genes level to species level by 16S rRNA sequencing, metagenomic sequencing and real-time quantitative polymerase chain reaction. A bacterial biomarker panel including Actinomyces graevenitzii (A.g_1, A.g_2, A.g_3, A.g_4), Fusobacteria nucleatum (F.n_1, F.n_2, F.n_3), Haemophilus haemolyticus (H.h_1), Porphyromonas gingivalis (P.g_1, P.g_2, P.g_3) and Streptococcus australis (S.a_1) was explored by metagenomic sequencing to early detect the participants in Need group (low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia and ESCC) vs participants without these lesions as the Noneed group. Significant quantitative differences existed for each microbial target in which the detection efficiency rate was higher in saliva than esophageal swab. In saliva, the area under the curve (AUC) based on the microbial biomarkers (A.g_4 ∩ P.g_3 ∩ H.h_1 ∩ S.a_1 ∩ F.n_2) was 0.722 (95% CI 0.621-0.823) in the exploration cohort. Combining epidemiological factors (age, smoking, drinking, intake of high-temperature food and toothache), the AUC improved to 0.869 (95% CI 0.802-0.937) in the exploration cohort, which was validated with AUC of 0.757 (95% CI 0.663-0.852) in the validation cohort. It is feasible to combine microbial biomarkers in saliva and epidemiological factors to early detect ESCC and precancerous lesions in China.

Validation of a Blood-Based Protein Biomarker Panel for a Risk Assessment of Lethal Lung Cancer in the Physicians' Health Study.

This study aimed to assess a four-marker protein panel (4MP)'s performance, including the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19, for predicting lung cancer in a cohort enriched with never- and ever-smokers. Blinded pre-diagnostic plasma samples collected within 2 years prior to a lung cancer diagnosis from 25 cases and 100 sex-, age-, and smoking-matched controls were obtained from the Physicians' Health Study (PHS). The 4MP yielded AUC performance estimates of 0.76 (95% CI: 0.61-0.92) and 0.69 (95% CI: 0.56-0.82) for predicting lung cancer within one year and within two years of diagnosis, respectively. When stratifying into ever-smokers and never-smokers, the 4MP had respective AUCs of 0.77 (95% CI: 0.63-0.92) and 0.72 (95% CI: 0.17-1.00) for a 1-year risk of lung cancer. The AUCs of the 4MP for predicting metastatic lung cancer within one year and two years of the blood draw were 0.95 (95% CI: 0.87-1.00) and 0.78 (95% CI: 0.62-0.94), respectively. Our findings indicate that a blood-based biomarker panel may be useful in identifying ever- and never-smokers at high risk of a diagnosis of lung cancer within one-to-two years.

The prowess of metabolomics in cancer research: current trends, challenges and future perspectives.

Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.

Development of a novel non-invasive biomarker panel for hepatic fibrosis in MASLD

Accurate non-invasive biomarkers to diagnose metabolic dysfunction-associated steatotic liver disease (MASLD)-related fibrosis are urgently needed. This study applies a translational approach to develop a blood-based biomarker panel for fibrosis detection in MASLD. A molecular gene expression signature identified from a diet-induced MASLD mouse model (LDLr−/−.Leiden) is translated into human blood-based biomarkers based on liver biopsy transcriptomic profiles and protein levels in MASLD patient serum samples. The resulting biomarker panel consists of IGFBP7, SSc5D and Sema4D. LightGBM modeling using this panel demonstrates high accuracy in predicting MASLD fibrosis stage (F0/F1: AUC = 0.82; F2: AUC = 0.89; F3/F4: AUC = 0.87), which is replicated in an independent validation cohort. The overall accuracy of the model outperforms predictions by the existing markers Fib-4, APRI and FibroScan. In conclusion, here we show a disease mechanism-related blood-based biomarker panel with three biomarkers which is able to identify MASLD patients with mild or advanced hepatic fibrosis with high accuracy.

Diagnosis of epilepsy by machine learning of high-performance plasma metabolic fingerprinting

Epilepsy is a chronic neurological disorder that causes a major threat to public health and the burden of disease worldwide. High-performance diagnostic tools for epilepsy need to be developed to improve diagnostic accuracy and efficiency while still missing. Herein, we utilized nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI MS) to acquire plasma metabolic fingerprints (PMFs) from epileptic and healthy individuals for timely and accurate screening of epilepsy. The NELDI MS enabled high detection speed (∼30 s per sample), high throughput (up to 384 samples per run), and favorable reproducibility (coefficients of variation <15 %), acquiring high-performed PMFs. We next constructed an epilepsy diagnostic model by machine learning of PMFs, achieving desirable diagnostic capability with the area under the curve (AUC) value of 0.941 for the validation set. Furthermore, four metabolites were identified as a diagnostic biomarker panel for epilepsy, with an AUC value of 0.812–0.860. Our approach provides a high-performed and high-throughput platform for epileptic diagnostics, promoting the development of metabolic diagnostic tools in precision medicine.

Investigating protease-mediated peptides of inflammation and tissue remodeling as biomarkers associated with flares in psoriatic arthritis.

Psoriatic arthritis (PsA) is an inflammatory arthritis associated with psoriasis. PsA disease involves flares, which are associated with increased joint inflammation and tissue remodeling. There is a need for identifying biomarkers related to PsA disease activity and flares to improve the management of PsA patients and decrease flares. The tissue turnover imbalance that occurs during the inflammatory and fibro-proliferative processes during flares leads to an increased degradation and/or reorganization of the extracellular matrix (ECM), where increased proteolysis plays a key role. Hence, protease-mediated fragments of inflammatory and tissue-remodeling components could be used as markers reflecting flares in PsA patients. A broad panel of protease-mediated biomarkers reflecting inflammation and tissue remodeling was measured in serum and synovial fluid (SF) obtained from PsA patients experiencing flares (acutely swollen joint[s], PsA-flare). In serum, biomarker levels assessed in PsA-flare patients were compared to controls and in early-diagnosed PsA patients not experiencing flares (referred to as PsA without flare). Furthermore, the biomarker levels assessed in SF from PsA-flare patients were compared to the levels in SF of osteoarthritis (OA) patients. In serum, levels of the PRO-C3 and C3M, reflecting formation and degradation of the interstitial matrix, were found significantly elevated in PsA-flare compared to controls and PsA without flare. The remodeling marker of the basement membrane, PRO-C4, was significantly elevated in PsA-flare compared to PsA without flare. The inflammation and immune cell activity related markers, CRPM, VICM, and CPa9-HNE were significantly elevated in PsA-flare patients compared to controls and PsA without flare. In addition, VICM (AUC = 0.71), CPa9-HNE (AUC = 0.89), CRPM (AUC = 0.76), and PRO-C3 (AUC = 0.86) showed good discriminatory performance for separating PsA-flare from PsA without flare. In SF, the macrophage activity marker, VICM, was significantly elevated whereas the type II collagen formation marker, PRO-C2, was significantly reduced in the PsA-flare compared to OA. The combination of five serum markers reflecting type III and IV collagen degradation (C3M and C4M, respectively), type III and VI collagen formation (PRO-C3 and PRO-C6, respectively), and neutrophil activity (CPa9-HNE) showed an excellent discriminatory performance (AUC = 0.98) for separating PsA-flare from PsA without flares. The serum biomarker panel of C3M, C4M, PRO-C3, PRO-C6, and CPa9-HNE reflecting synovitis, enthesitis, and neutrophil activity may serve as novel tool for quantitatively monitoring flares in PsA patients.

Biomarkers to predict improvement of left ventricular ejection fraction after atrial fibrillation ablation

Abstract Background Atrial fibrillation (AF) and heart failure (HF) frequently coexist. Catheter ablation (CA) of AF is increasingly used in patients with AF and HF. However, prediction of improvement of HF after CA is challenging. Aim We aimed to evaluate the value of biomarkers, alone and in conjunction with a clinical tool (Antwerp score) to predict left ventricular ejection fraction (LVEF) recovery after CA in patients with AF and HF. Methods Patients undergoing CA for AF with depressed LVEF (&amp;lt;50%) were included. Plasma levels of 13 biomarkers were measured immediately prior to CA. Patients were categorized into "responders" (LVEF normalisation after CA in case of HFmrEF and at least 10% LVEF improvement and LVEF &amp;gt; 40% at follow-up in case of HFrEF) and "non-responders" (no LVEF recovery after CA). Models were constructed by comparing the plasma levels of the various biomarkers in responders and non-responders and by adjusting for age, sex, and Antwerp score and subsequent biomarkers were then combined according based on likelihood-ratios. The predictive power of the biomarkers alone and combined in outcome prediction, as well as improvement of the Antwerp score was then evaluated. Results Out of 1665 patients undergoing CA of AF, we included 208 patients with depressed LVEF (median age 63 years, 19% female, median LAVI 42 ml/m2, median LVEF 43%). The median follow-up time was 30 months [IQR 20-34] and the median time to LVEF recovery was 8 months [IQR 3-22]. Of these, 161 (77%) were responders and 47 (23%) were non-responders. Out of 13 biomarkers, four biomarkers (ANG2, GDF15, FGF23 and MyBPC3) were significantly different between responders and non-responders (p ≤0.001). A biomarker panel using these four biomarkers could predict LVEF recovery after CA for AF with an AUC of 0.72 (95%CI 0.64-0.81) overall, 0.69 (95%CI 0.59-0.78) in HFmrEF and 0.88 (95%CI 0.77-0.98) in HFrEF (Figure, A). Only ANG2 and GDF15 remained significantly associated with LVEF recovery after adjustment for age, sex, and the Antwerp score. The AUC of the Antwerp score for prediction of LVEF recovery improved from 0.75 (95% CI 0.67-0.83, Figure, B) to 0.78 (95% CI 0.70-0.86, Figure, C) in the overall population, from 0.71 (95% CI 0.61-0.81) to 0.74 (95% CI 0.64-0.84) in case of HFmrEF and from 0.87 (95% CI 0.78-0.97) to 0.92 (95% CI 0.85-0.99) in case of HFrEF when ANG2 and GDF15 were included in the model. Conclusion A biomarker panel using ANG2, GDF15, FGF23 and MyBPC3 performs similarly for prediction of LVEF recovery after CA in AF compared to the Antwerp score. The biomarkers ANG2 and GDF15 slightly improve the predictive value of the Antwerp score.Figure

#3004 Serum miRNAs as novel biomarkers and modulators of rapidly progressing chronic kidney disease in patients with diabetes

Abstract Background and Aims Chronic kidney disease (CKD) is very common in diabetes, occurring in around 40% of patients. Without treatment, CKD can lead to end stage renal disease (ESRD), which requires costly dialysis and transplantation. Current treatments aim to prevent CKD or slow down its progression to ESRD. The speed of CKD progression is patient-specific, and at present there are no biomarkers which can predict which patient with diabetes will develop CKD; level of albuminuria is an important but variably reliable biomarker of speed of CKD progression. The current methods for rapid identification of patients prone to accelerated CKD progression are limited, as they are based on measuring estimated glomerular filtration rate (eGFR) over prolonged periods of time, as well as albuminuria. Therefore, there is a clinical need for novel biomarkers that can predict early potential rapid CKD progression in patients. The aim of this project is to determine whether serum microRNAs (miRs) can be used as biomarkers to predict the speed of CKD progression in diabetes patients, and to identify potential treatment targets associated with such dysregulated miRs. Method miRs were isolated from sera of 34 patients with type 2 diabetes classed with either slow or fast progressing CKD by their eGFR (n = 17; obtained from the Salford Kidney Study sample collection). Several miRs were found to be significantly dysregulated in fast progressing patients by using Next Generation Sequencing (NGS) of miRs in individual serum samples. An additional 49 diabetic CKD patient serum samples (slow (n = 29) or fast progressors (n = 20)) were used to validate the potential miR biomarker panel by qPCR. Results The NGS miR analysis of the initial 34 CKD patient sera produced a panel of 16 miRs that were either significantly upregulated or downregulated in the fast-progressing patients compared to slow progressors. The qPCR validation of this initial panel in 49 additional CKD patient serum samples demonstrated that 6 miRs were significantly upregulated in the fast-progressing patients. The levels of these miRs correlated with demographic and clinical parameters. ROC curve analysis revealed that the individual predictive power of each of those 6 miRs to define either a slow or fast progressing CKD patient was between 63-75%. Determination of a miR combination the strongest predictive power is ongoing. Conclusion A potential biomarker panel of 6 miRs has been identified and validated by qPCR, with each miR having a predictive power of between 63-75% for fast progression of CKD. A panel of miRs that could accurately predict progression speed of CKD patients could lead to faster and earlier diagnosis of patients to enable better treatment. Work is ongoing to explore the predictive power of this miR biomarker panel, and potential novel therapeutic targets to prevent CKD or delay its progression to ESRD.

#1831 Circulating MiRNA 195-5p may be associated with early renal impairment in patients with prediabetes

Abstract Background and Aims Type 2 diabetes mellitus (DM) is one of the leading causes of chronic kidney disease worldwide. However, there are few data concerning early kidney impairment in prediabetes patients. The aim of the study was to demonstrate early kidney damage in patients with prediabetes by assessing proximal tubule (PT) dysfunction, podocytes injury, and miRNA expression. Method A cohort of 192 individuals were analysed and divided such as follows: 52-normoalbuminuria-, 48-microalbuminuria-, and 50-macroalbuminuria type 2 DM patients, respectively; 12 patients-prediabetes and 30-healthy controls. The miRNA profile was assessed by real-time PCR, while PT dysfunction biomarker, N-acetyl-β-D-glucosaminidase (NAG), and podocyte injury biomarker, podocalyxin, were assessed by ELISA technique. The study was conducted in two distinct stages. In the first part of the study was detected a particular miRNAs profile in serum in patients with prediabetes; subsequently, the expression of the most relevant miRNA in patients with prediabetes was identified. Results i) in prediabetes patients, serum miRNA195-5p was 2.69 fold change as compared to the control group and correlated with NAG (p&amp;lt;0.05) and podocalyxin (p&amp;lt;0.03); ii) in type 2 DM patients, the expression of serum miRNA195-5p was higher and correlated with NAG (p&amp;lt;0.05), podocalyxin (p&amp;lt;0.04), urinary albumin: creatinine ratio (p&amp;lt;0.001) and with eGFR (p&amp;lt;0.001); iii) urinary NAG and urinary podocalyxin were higher in prediabetes patients and type 2 DM patients, even in the normoalbuminuria stage, as compared to controls (p&amp;lt;0.04; p&amp;lt;0.001). Conclusion A panel of molecular biomarkers, such as miRNAs, remain useful tools for the early diagnosis of renal involvement in the course of type 2 DM. MiRNA 195-5p may be associated with PT dysfunction and podocyte damage, and could predict renal dysfunction in both prediabetes and early diabetic kidney disease in type 2 DM patients.

#1550 Incident hypertension: immune biomarkers and vascular phenotypes

Abstract Background and Aims Blood pressure (BP) regulation is mediated by cardiac, vascular, endocrine, renal, and immune system interactions. Hence, hypertension may exist as different phenotypic variants, with different clinical features and susceptibility to target organ damage. We aimed to analyse the circulating protein biomarker ‘signature’ of: 1) untreated hypertension; 2) hypertensive phenotypes identified by arterial and blood pressure parameters. Method 61 patients with hypertension, and 61 controls were assessed using 24hr ambulatory BP monitoring; endothelial function, arterial stiffness, carotid intima-media thickness (CIMT), and cardiovascular variability [1]. Circulating protein biomarkers analysed using Olink® Inflammation plasma biomarker panel, reported as NPX (Normalized Protein eXpression), in Log2 scale. Results 34 biomarkers dominated by cytokines and chemokines differed between normotensive and hypertensive subjects (Fig. 1), though failed to meet Bonferroni-adjusted threshold. Inflammatory biomarkers correlated with BP and arterial stiffness, BP variability, and CIMT, but not endothelial function. Associations were concordant across systolic and diastolic BP; TPP1, CCL7, CCL11, and CCL21 positively correlating; IL18R1, and KYNU negatively. These relationships were more pronounced in hypertensive subgroup, 85 biomarkers correlating including CD molecules (CD200R1, CD22, CD58, CD6, CD70) and cytokines (IL-5, IL-11, IL-15, IL-18, IL-32, IL-1RL2, IL-22RA1, IL-5RA). HGF, AGE, and CCL21 showed greatest between group differences and correlated with multiple BP or vascular parameters. Systolic nocturnal dipping demonstrated negative correlation with biomarkers relating to immune cell interactions and cellular adhesion (CTRC, EPHA1, LGALS4, SIT1, SMOC, IL-18 and TNFSF11). Machine learning techniques identified three phenotypes of hypertension, ‘arterially stiffened’, ‘vaso-protected’, and ‘non-dipper’ [1]. Sixteen of the 85 correlating biomarkers also differed between these phenotypic groups: Conclusion Hypertension is linked to alterations in circulating immune biomarkers. Multiple biomarkers correlated with both arterial stiffness and BP parameters. Some biomarkers are only correlated in the hypertensive group. Many also relate to nocturnal dipping, and differed across the hypertension endotypes. HGF is a promising biomarker for BP and arterial function; warranting further validation.

Extracellular vesicle biomarkers in circulation for colorectal cancer detection: a systematic review and meta-analysis

We provided an overview which evaluated the diagnostic performance of circulation EV biomarkers for CRC from PubMed, Medline, and Web of Science until 21 August 2022.Weidentified 48 studies that involved 7727 participants and evaluated 162 plasma/serum individual EV biomarkers including 117 RNAs and 45 proteins, as well as 45 EV biomarker panels for CRC detection. 12 studies evaluated the diagnostic performance of EV biomarkers for early CRC. The summarized sensitivity, specificity, and AUC value of individual EV RNAs and EV RNA panels were 76%, 75%, 0.87 and 82%, 79% and 0.90, respectively. Meanwhile, those of individual EV proteins and EV protein panels were 85%, 84%, 0.92 and 87%, 83%, 0.92, respectively. These results indicated that EV biomarker panels revealed superior diagnostic performance than the corresponding individual biomarkers. In early CRC, EV biomarkers showed available diagnostic value with the sensitivity, specificity, and AUC value of 80%, 75%, and 0.89.In subgroup analyses, EV miRNAs and LncRNAs held similar diagnostic value with the sensitivity, specificity and AUC value of 75%, 78%, 0.90 and 79%, 72%, 0.83, which was highly consistent with the whole EV RNAs. Significantly, the diagnostic values of EV miRNAs in plasma were marginally higher than those based on serum. In detail, the sensitivity, specificity, and AUC values were 79%, 81%, and 0.92 in plasma, as well as 74%, 77%, and 0.88 in serum, respectively. Therefore, circulation EV biomarkers could be considered as a promising biomarker for the early detection of CRC.

A glutamatergic biomarker panel enables differentiating Grade 4 gliomas/astrocytomas from brain metastases.

The differentiation of high-grade glioma and brain tumors of an extracranial origin is eminent for the decision on subsequent treatment regimens. While in high-grade glioma, a surgical resection of the tumor mass is a fundamental part of current standard regimens, in brain metastasis, the burden of the primary tumor must be considered. However, without a cancer history, the differentiation remains challenging in the imaging. Hence, biopsies are common that may help to identify the tumor origin. An additional tool to support the differentiation may be of great help. For this purpose, we aimed to identify a biomarker panel based on the expression analysis of a small sample of tissue to support the pathological analysis of surgery resection specimens. Given that an aberrant glutamate signaling was identified to drive glioblastoma progression, we focused on glutamate receptors and key players of glutamate homeostasis. Based on surgically resected samples from 55 brain tumors, the expression of ionotropic and metabotropic glutamate receptors and key players of glutamate homeostasis were analyzed by RT-PCR. Subsequently, a receiver operating characteristic (ROC) analysis was performed to identify genes whose expression levels may be associated with either glioblastoma or brain metastasis. Out of a total of 29 glutamatergic genes analyzed, nine genes presented a significantly different expression level between high-grade gliomas and brain metastases. Of those, seven were identified as potential biomarker candidates including genes encoding for AMPA receptors GRIA1, GRIA2, kainate receptors GRIK1 and GRIK4, metabotropic receptor GRM3, transaminase BCAT1 and the glutamine synthetase (encoded by GLUL). Overall, the biomarker panel achieved an accuracy of 88% (95% CI: 87.1, 90.8) in predicting the tumor entity. Gene expression data, however, could not discriminate between patients with seizures from those without. We have identified a panel of seven genes whose expression may serve as a biomarker panel to discriminate glioblastomas and brain metastases at the molecular level. After further validation, our biomarker signatures could be of great use in the decision making on subsequent treatment regimens after diagnosis.