Identify Candidate Biomarkers Research Articles

SFTPB in serum extracellular vesicles as a biomarker of progressive pulmonary fibrosis.

Progressive pulmonary fibrosis (PPF), defined as the worsening of various interstitial lung diseases (ILDs), currently lacks useful biomarkers. To identify novel biomarkers for early detection of patients at risk of PPF, we performed a proteomic analysis of serum extracellular vesicles (EVs). Notably, the identified candidate biomarkers were enriched for lung-derived proteins participating in fibrosis-related pathways. Among them, pulmonary surfactant-associated protein B (SFTPB) in serum EVs could predict ILD progression better than the known biomarkers, serum KL-6 and SP-D, and it was identified as an independent prognostic factor from ILD-gender-age-physiology index. Subsequently, the utility of SFTPB for predicting ILD progression was evaluated further in 2 cohorts using serum EVs and serum, respectively, suggesting that SFTPB in serum EVs but not in serum was helpful. Among SFTPB forms, pro-SFTPB levels were increased in both serum EVs and lungs of patients with PPF compared with those of the control. Consistently, in a mouse model, the levels of pro-SFTPB, primarily originating from alveolar epithelial type 2 cells, were increased similarly in serum EVs and lungs, reflecting pro-fibrotic changes in the lungs, as supported by single-cell RNA sequencing. SFTPB, especially its pro-form, in serum EVs could serve as a biomarker for predicting ILD progression.

Integrated biomarker profiling for predicting the response of type 2 diabetes to metformin.

To explore biomarkers that can predict the response of type 2 diabetes (T2D) patients to metformin at an early stage to provide better treatment for T2D. T2D patients with (responders) or without response (non-responders) to metformin were recruited, and their serum samples were used for metabolomic analysis to identify candidate biomarkers. Moreover, the efficacy of metformin was verified by insulin-resistant mice, and the candidate biomarkers were verified to determine the biomarkers. Five different machine learning methods were used to construct the integrated biomarker profiling (IBP) with the biomarkers to predict the response of T2D patients to metformin. A total of 73 responders and 63 non-responders were recruited, and 88 differential metabolites were identified in the serum samples. After being verified in mice, 19 of the 88 were considered as candidate biomarkers. Next, after metformin regulation, nine candidate biomarkers were confirmed as the biomarkers. After comparing five machine learning models, the nine biomarkers were constructed into the IBP for predicting the response of T2D patients to metformin based on the Naïve Bayes classifier, which was verified with an accuracy of 89.70%. The IBP composed of nine biomarkers can be used to predict the response of T2D patients to metformin, enabling clinicians to start a combined medication strategy as soon as possible if T2D patients do not respond to metformin.

Prognosis and diagnosis of prostate cancer based on hypergraph regularization sparse least partial squares regression algorithm.

Prostate cancer (PCa) is a malignant tumor of the male reproductive system, and its incidence has increased significantly in recent years. This study aimed to further identify candidate biomarkers with prognostic and diagnostic significance by integrating gene expression and DNA methylation data from PCa patients through association analysis. To this end, this paper proposes a sparse partial least squares regression algorithm based on hypergraph regularization (HR-SPLS) by integrating and clustering two kinds of data. Next, module 2, with the most significant weight, was selected for further analysis according to the weight of each module related to DNA methylation and mRNAs. Based on the DNA methylation sites in module 2, this paper uses multiple machine learning methods to construct a PCa diagnosis-related model of 10-DNA methylation sites. The results of Receiver Operating Characteristic (ROC) analysis showed that the DNA methylation-related diagnostic model we constructed could diagnose PCa patients with high accuracy. Subsequently, based on the mRNAs in module 2, we constructed a prognostic model for 7-mRNAs (MYH11, ACTG2, DDR2, CDC42EP3, MARCKSL1, LMOD1, and MYLK) using multivariate Cox regression analysis. The prognostic model could predict the disease free survival of PCa patients with moderate to high accuracy (area under the curve (AUC) =0.761). In addition, Gene Set EnrichmentAnalysis (GSEA) and immune analysis indicated that the prognosis of patients in the risk group might be related to immune cell infiltration. Our findings may provide new methods and insights for identifying disease-related biomarkers by integrating DNA methylation and gene expression data.

A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework

Neuroblastoma (NB) is one of the leading causes of cancer-associated death in children. MYCN amplification is a prominent genetic marker for NB, and its targeting to halt NB progression is difficult to achieve. Therefore, an in-depth understanding of the molecular interactome of NB is needed to improve treatment outcomes. Analysis of NB multi-omics unravels valuable insight into the interplay between MYCN transcriptional and miRNA post-transcriptional modulation. Moreover, it aids in the identification of various miRNAs that participate in NB development and progression. This study proposes an integrated computational framework with three levels of high-throughput NB data (mRNA-seq, miRNA-seq, and methylation array). Similarity Network Fusion (SNF) and ranked SNF methods were utilized to identify essential genes and miRNAs. The specified genes included both miRNA-target genes and transcription factors (TFs). The interactions between TFs and miRNAs and between miRNAs and their target genes were retrieved where a regulatory network was developed. Finally, an interaction network-based analysis was performed to identify candidate biomarkers. The candidate biomarkers were further analyzed for their potential use in prognosis and diagnosis. The candidate biomarkers included three TFs and seven miRNAs. Four biomarkers have been previously studied and tested in NB, while the remaining identified biomarkers have known roles in other types of cancer. Although the specific molecular role is yet to be addressed, most identified biomarkers possess evidence of involvement in NB tumorigenesis. Analyzing cellular interactome to identify potential biomarkers is a promising approach that can contribute to optimizing efficient therapeutic regimens to target NB vulnerabilities.

Development of an Automated Morphometric Approach to Assess Vascular Outcomes following Exposure to Environmental Chemicals in Zebrafish.

Disruptions in vascular formation attributable to chemical insults is a pivotal risk factor or potential etiology of developmental defects and various disease settings. Among the thousands of chemicals threatening human health, the highly concerning groups prevalent in the environment and detected in biological monitoring in the general population ought to be prioritized because of their high exposure risks. However, the impacts of a large number of environmental chemicals on vasculature are far from understood. The angioarchitecture complexity and technical limitations make it challenging to analyze the entire vasculature efficiently and identify subtle changes through a high-throughput in vivo assay. We aimed to develop an automated morphometric approach for the vascular profile and assess the vascular morphology of health-concerning environmental chemicals. High-resolution images of the entire vasculature in Tg(fli1a:eGFP) zebrafish were collected using a high-content imaging platform. We established a deep learning-based quantitative framework, ECA-ResXUnet, combined with MATLAB to segment the vascular networks and extract features. Vessel scores based on the rates of morphological changes were calculated to rank vascular toxicity. Potential biomarkers were identified by vessel-endothelium-gene-disease integrative analysis. Whole-trunk blood vessels and the cerebral vasculature in larvae exposed to 150 representative chemicals were automatically segmented as comparable to human-level accuracy, with sensitivity and specificity of 95.56% and 95.81%, respectively. Chemical treatments led to heterogeneous vascular patterns manifested by 31 architecture indexes, and the common cardinal vein (CCV) was the most affected vessel. The antipsychotic medicine haloperidol, flame retardant 2,2-bis(chloromethyl)trimethylenebis[bis(2-chloroethyl) phosphate], and tert-butylphenyl diphenyl phosphate ranked as the top three in vessel scores. Pesticides accounted for the largest group, with a vessel score of , characterized by a remarkable inhibition of subintestinal venous plexus and delayed development of CCV. Multiple-concentration evaluation of nine per- and polyfluoroalkyl substances (PFAS) indicated a low-concentration effect on vascular impairment and a positive association between carbon chain length and benchmark concentration. Target vessel-directed single-cell RNA sequencing of cells from larvae treated with , perfluorohexanesulfonic acid, or benzylbutyl phthalate, along with vessel-endothelium-gene-disease integrative analysis, uncovered potential associations with vascular disorders and identified biomarker candidates. This study provides a novel paradigm for phenotype-driven screenings of vascular-disrupting chemicals by converging morphological and transcriptomic profiles at a high-resolution level, serving as a powerful tool for large-scale toxicity tests. Our approach and the high-quality morphometric data facilitate the precise evaluation of vascular effects caused by environmental chemicals. https://doi.org/10.1289/EHP13214.

Hierarchical cluster analysis and nonlinear mixed-effects modelling for candidate biomarker detection in preclinical models of cancer.

BackgroundPreclinical models of cancer can be of translational benefit when assessing how different biomarkers are regulated in response to particular treatments. Detection of molecular biomarkers in preclinical models of cancer is difficult due inter-animal variability in responses, combined with limited accessibility of longitudinal data. MethodsNonlinear mixed-effects modelling (NLME) was used to analyse tumour growth data based on expected tumour growth rates observed 7 days after initial doses (DD7) of Radiotherapy (RT) and Combination of RT with DNA Damage Response Inhibitors (DDRi). Cox regression was performed to confirm an association between DD7 and survival. Hierarchical Cluster Analysis (HCA) was then used to identify candidate biomarkers impacting responses to RT and RT/DDRi and these were validated using NLME. ResultsCox regression confirmed significant associations between DD7 and survival. HCA of RT treated samples, combined with NLME confirmed significant associations between DD7 and Cluster specific CD8+ Ki67 MFI, as well as DD7 and cluster specific Natural Killer cell density in RT treated mice. ConclusionApplication of NLME, as well as HCA of candidate biomarkers may provide additional avenues to assess the effect of RT in MC38 syngeneic tumour models. Additional studies would need to be conducted to confirm association between DD7 and biomarkers in RT/DDRi treated mice.

Novel serum protein biomarker panel for early diagnosis of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Late presentation of disease at the time of diagnosis is one of the major reasons for dismal prognostic outcomes for PDAC patients. Currently, there is a lack of clinical biomarkers, which can be used to diagnose PDAC patients at an early resectable stage. This study performed proteomic mass spectrometry to identify novel blood-based biomarkers for early diagnosis of PDAC. Serum specimens from 88 PDAC patients and 88 healthy controls (60 discovery cohort and 28 validation cohort) were analyzed using data independent acquisition high resolution mass spectrometry to identify candidate biomarker proteins. A total of 249 proteins were identified and quantified by the mass spectrometric analysis. Six proteins were markedly (>1.5 fold) and significantly (p < .05; q < 0.1) increased in PDAC patients compared to healthy controls in discovery cohort. Notably, four of these six proteins were significantly upregulated in an independent validation cohort. The top three upregulated proteins (i.e., Polymeric Immunoglobulin Receptor [PIGR], von Willebrand Factor [vWF], and Fibrinogen) were validated using enzyme linked immunosorbent assay, which led to selection of PIGR and vWF as a diagnostic biomarker panel for PDAC. The panel showed high ability to diagnose early stage (stage I and II) PDAC patients (area under the curve [AUC]: 0.8926), which was further improved after the addition of clinically used prognostic biomarker (Ca 19-9) to the panel (AUC: 0.9798). In conclusion, a novel serum protein biomarker panel for early diagnosis of PDAC was identified.

Lipidomic profiling of cervical mucus reveals the potential role of pro-inflammatory derived metabolites on sperm transport across the ovine cervix

Internationally, cervical artificial insemination (AI) in sheep yields low pregnancy rates when frozen-thawed semen is used. An exception to this is in Norway where vaginal AI of frozen-thawed semen to a natural oestrus yields non-return rates in excess of 60%, which has been attributed to the ewe breed used in Norway. This study used both metabolomics and an RNA-sequencing approach to assess the lipid production and composition from cervical mucus and tissue of four European ewe breeds (n = 28–30 ewes per breed) with previously reported differences in pregnancy rates following cervical AI with frozen-thawed semen. These breeds included Suffolk (exhibiting low fertility), Belclare (medium fertility) as well as Norwegian White Sheep and Fur (both with high fertility and pregnancy rates > 60%) at both a synchronised and natural oestrous cycle. The aim was to explore the differences between ewe breeds in the lipidomic profile and to identify candidate biomarkers associated with an optimal environment for cervical sperm transport. The results revealed the identification of 255 lipids, of which 170, 102 and 83 were different between ewe breeds, types of cycle and affected by their interaction, respectively (P < 0.05). Reduced levels of lipids involved in the resolution of inflammation (i.e. 14-HDoHE,17-HDoHE, 15-HETE) were identified in the low-fertility Suffolk breed compared to high-fertility ewe breeds. However, there was an up-regulation of the COX pathway accompanied by increased levels of prostaglandins in the Suffolk breed. These findings indicated a sub-optimal and pro-inflammatory environment that could have a negative effect on cervical sperm transport.

Short-term test for the toxicogenomic assessment of ecotoxic modes of action in Myriophyllum spicatum

In environmental risk assessment of substances, the 14-day growth inhibition test following OECD test guideline 239 is employed to assess toxicity in the macrophyte Myriophyllum spicatum. Currently, this test evaluates physiological parameters and does not allow the identification of the mode of action (MoA) by which adverse effects are induced. However, for an improved ecotoxicity assessment of substances, knowledge about their ecotoxic MoA in non-target organisms is required. It has previously been suggested that the identification of gene expression changes can contribute to MoA identification. Therefore, we developed a shortened three-day assay for M. spicatum including the transcriptomic assessment of global gene expression changes and applied this assay to two model substances, the herbicide and photosynthesis inhibitor bentazone and the pharmaceutical and HMG-CoA reductase inhibitor atorvastatin. Due to the lack of a reference genome for M. spicatum we performed a de novo transcriptome assembly followed by a functional annotation to use the toxicogenomic results for MoA discrimination. The gene expression changes induced by low effect concentrations of these substances were used to identify differentially expressed genes (DEGs) and impaired biological functions for the respective MoA. We observed both concentration-dependent numbers and differentiated patterns of DEGs for both substances. While bentazone impaired genes involved in the response to reactive oxygen species as well as light response, and also genes involved in developmental processes, atorvastatin exposure led to a differential regulation of genes related to brassinosteroid response as well as potential metabolic shifts between the mevalonate and methyl erythritol 4-phosphate pathway. Based on these responses, we identified biomarker candidates for the assessment of MoA in M. spicatum. Utilizing the shortened assay developed in this study, the investigation of the identified biomarker candidates may contribute to the development of future MoA-specific screening approaches in the ecotoxicological hazard prediction using aquatic non-standard model organisms.

CHRNA6 RNA In Situ Hybridization Is a Useful Tool for the Diagnosis of Extraskeletal Myxoid Chondrosarcoma

Extraskeletal myxoid chondrosarcoma (EMC) is an uncommon mesenchymal neoplasm characteristically composed of uniform-appearing round to spindle-shaped cells with eosinophilic cytoplasm and abundant myxoid extracellular matrix. Although the majority of cases harbor a pathognomonic t(9;22) translocation that fuses EWSR1 with the orphan nuclear receptor NR4A3, there are less common variants that partner NR4A3 with TAF15, TCF12, or TFG. By immunohistochemistry, EMC has features of both cartilaginous and neuroendocrine differentiation, as evidenced by inconsistent expression of S100 protein and synaptophysin or INSM1, respectively, in a subset of cases. Given the limitations of available immunohistochemical stains for the diagnosis of EMC, we analyzed genome-wide gene expression microarray data to identify candidate biomarkers based on differential expression in EMC in comparison with other mesenchymal neoplasms. This analysis pointed to CHRNA6 as the gene with the highest relative expression in EMC (96-fold; P = 8.2 × 10−26) and the only gene with >50-fold increased expression in EMC compared with other tumors. Using RNA chromogenic in situ hybridization, we observed strong and diffuse expression of CHRNA6 in 25 cases of EMC, including both EWSR1-rearranged and TAF15-rearranged variants. All examined cases of histologic mimics were negative for CHRNA6 overexpression; however, limited CHRNA6 expression, not reaching a threshold of >5 puncta or 1 aggregate of chromogen in >25% of cells, was observed in 69 of 685 mimics (10.1%), spanning an array of mesenchymal tumors. Taken together, these findings suggest that, with careful interpretation and the use of appropriate thresholds, CHRNA6 RNA chromogenic in situ hybridization is a potentially useful ancillary histologic tool for the diagnosis of EMC.

Developmental outcome of electroencephalographic findings in SYNGAP1 encephalopathy.

SYNGAP1 haploinsufficiency results in a developmental and epileptic encephalopathy (DEE) causing generalized epilepsies accompanied by a spectrum of neurodevelopmental symptoms. Concerning interictal epileptiform discharges (IEDs) in electroencephalograms (EEG), potential biomarkers have been postulated, including changes in background activity, fixation-off sensitivity (FOS) or eye closure sensitivity (ECS). In this study we clinically evaluate a new cohort of 36 SYNGAP1-DEE individuals. Standardized questionnaires were employed to collect clinical, electroencephalographic and genetic data. We investigated electroencephalographic findings, focusing on the cortical distribution of interictal abnormalities and their changes with age. Among the 36 SYNGAP1-DEE cases 18 presented variants in the SYNGAP1 gene that had never been previously reported. The mean age of diagnosis was 8 years and 8 months, ranging from 2 to 17years, with 55.9% being male. All subjects had global neurodevelopmental/language delay and behavioral abnormalities; 83.3% had moderate to profound intellectual disability (ID), 91.7% displayed autistic traits, 73% experienced sleep disorders and 86.1% suffered from epileptic seizures, mainly eyelid myoclonia with absences (55.3%). A total of 63 VEEGs were revised, observing a worsening of certain EEG findings with increasing age. A disorganized background was observed in all age ranges, yet this was more common among older cases. The main IEDs were bilateral synchronous and asynchronous posterior discharges, accounting for ≥50% in all age ranges. Generalized alterations with maximum amplitude in the anterior region showed as the second most frequent IED (≥15% in all age ranges) and were also more common with increasing age. Finally, diffuse fast activity was much more prevalent in cases with 6years or older. To the best of our knowledge, this is the first study to analyze EEG features across different age groups, revealing an increase in interictal abnormalities over infancy and adolescence. Our findings suggest that SYNGAP1 haploinsufficiency has complex effects in human brain development, some of which might unravel at different developmental stages. Furthermore, they highlight the potential of baseline EEG to identify candidate biomarkers and the importance of natural history studies to develop specialized therapies and clinical trials.

Abstract IA003: Advancing novel therapeutics through reverse translation

Abstract Background: The established paradigm for cancer drug development was designed to advance cytotoxic agents targeting tumor cells. An increased appreciation of the role of the tumor microenvironment in treatment efficacy and disease outcomes has identified tumor-extrinsic therapeutic targets. The different mechanisms of action employed by anti-angiogenic drugs or immunotherapeutics highlight the need for updated study endpoints to identify the maximally effective dose for clinical applications. These advances, and the translation of novel therapeutics, can be accelerated through reverse translation, a term used to describe research using patient samples to inform mechanistic studies – going from bedside-to-bench. Objective: To present an example of reverse translation in the development of an immune therapy combination for ovarian cancer. Preclinical studies demonstrated therapeutic synergy of CTLA4 immune checkpoint blockade and poly(ADP ribose) polymerase (PARP) inhibition in high grade serous ovarian cancer models. These results were translated to two clinical trials for the treatment of recurrent ovarian cancer. Tumor samples from study subjects have been collected to support ongoing mechanistic investigations and exploratory biomarker studies. Both targeted and unbiased approaches identified candidate biomarkers of response to treatment for further development. Ongoing mechanistic studies are examining the significance of top prognostic markers. Conclusions: Strategies are needed to support the integration of translational studies with clinical research to inform next generation trials and to optimize clinical applications of novel therapeutics. Citation Format: Sarah F. Adams. Advancing novel therapeutics through reverse translation [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr IA003.

Serum Metabolomic Markers of Protein-Rich Foods and Incident CKD: Results From the Atherosclerosis Risk in Communities Study

Rationale & ObjectiveWhile urine excretion of nitrogen estimates total protein intake, biomarkers of specific dietary protein sources have been sparsely studied. Using untargeted metabolomics, this study aimed to identify serum metabolomic markers of six protein-rich foods and to examine whether dietary protein-related metabolites are associated with incident chronic kidney disease (CKD). Study DesignProspective cohort study. Setting & Participants3,726 participants from the Atherosclerosis Risk in Communities (ARIC) study without CKD at baseline. ExposureDietary intake of six protein-rich foods (fish, nuts, legumes, red and processed meat, eggs, poultry), serum metabolites. OutcomesIncident CKD [eGFR <60 mL/min/1.73 m2 with ≥25% eGFR decline relative to visit 1, hospitalization or death related to CKD, or end-stage kidney disease. Analytical ApproachMultivariable linear regression models estimated cross-sectional associations between protein-rich foods and serum metabolites. C-statistics assessed the metabolites’ ability to improve discrimination of highest versus lower three quartiles of intake of protein-rich foods beyond covariates (demographics, clinical factors, health behaviors, and intake of nonprotein food groups). Cox regression models identified prospective associations between protein-related metabolites and incident chronic kidney disease (CKD). ResultsThirty significant associations were identified between protein-rich foods and serum metabolites (fish, n=8; nuts, n=5; legumes, n=0; red and processed meat, n=5; eggs, n=3; poultry, n=9). Metabolites collectively significantly improved discrimination of high intake of protein-rich foods compared to covariates alone (difference in C-statistics=0.033, 0.051, 0.003, 0.024, and 0.025 for fish, nuts, red and processed meat, eggs, and poultry-related metabolites, respectively; p<1.00 x 10-16 for all). Dietary intake of fish was positively associated with 1-docosahexaenoylglycerophosphocholine (22:6n3), which was inversely associated with incident CKD (HR 0.82, 95% CI 0.75-0.89, p=7.81×10-6). LimitationsResidual confounding and sample storage duration. ConclusionsWe identified candidate biomarkers of fish, nuts, red and processed meat, eggs, and poultry. A fish-related metabolite, 1-docosahexaenoylglycerophosphocholine (22:6n3), was associated with lower risk of CKD.

Identification and Analysis of Gene Biomarkers for Ovarian Cancer.

Objective: To identify potential diagnostic markers for ovarian cancer (OC) and explore the contribution of immune cells infiltration to the pathogenesis of OC. Methods: As the study cohort, two gene expression datasets of human OC (GSE27651 and GSE26712, taken as the metadata) taken from the Gene Expression Omnibus (GEO) database were combined, comprising 228 OC and 16 control samples. Analysis was performed to identify the differentially expressed genes between the OC and control samples, while support vector machine analysis using the recursive feature elimination algorithm and least absolute shrinkage and selection operator regression were performed to identify candidate biomarkers that could discriminate OC. In addition, immunohistochemistry staining was performed to verify the diagnostic value and protein expression levels of the candidate biomarkers. The GSE146553 dataset (OC n = 40, control n = 3) was used to further validate the diagnostic values of those biomarkers. Further, the proportions of various immune cells infiltration in the OC and control samples were evaluated using the CIBERSORT algorithm. Results: CLEC4M, PFKP, and SCRIB were identified as potential diagnostic markers for OC in both the metadata (area under the receiver operating characteristic curve [AUC] = 0.996, AUC = 1.000, AUC = 1.000) and GSE146553 dataset (AUC = 0.983, AUC = 0.975, AUC = 0.892). Regarding immune cell infiltration, there was an increase in the infiltration of follicular helper dendritic cells, and a decrease in the infiltration of M2 macrophages and neutrophils, as well as activated natural killer (NK) cells and T cells in OC. CLEC4M showed a significantly positive correlation with neutrophils (r = 0.57, p < 0.001) and resting NK cells (r = 0.42, p = 0.0047), but a negative correlation with activated dendritic cells (r = -0.33, p = 0.032). PFKP displayed a significantly positive correlation with activated NK cells (r = 0.36, p = 0.016) and follicular helper T cells (r = 0.32, p = 0.035), but a negative correlation with the naive B cells (r = -0.3, p = 0.049) and resting NK cells (r = -0.41, p = 0.007). SCRIB demonstrated a significantly positive correlation with plasma cells (r = 0.39, p = 0.01), memory B cells (r = 0.34, p = 0.025), and follicular helper T cells (r = 0.31, p = 0.04), but a negative correlation with neutrophils (r = -0.46, p = 0.002) and naive B cells (r = -0.48, p = 0.0012). Conclusion: CLEC4M, PFKP, and SCRIB were identified and verified as potential diagnostic biomarkers for OC. This work and identification of the three biomarkers may provide guidance for future studies into the mechanism and treatment of OC.

Identification of VWA5A as a novel biomarker for inhibiting metastasis in breast cancer by machine-learning based protein prioritization

Distant metastasis is the leading cause of death in breast cancer (BC). The timing of distant metastasis differs according to subtypes of BCs and there is a need for identification of biomarkers for the prediction of early and late metastasis. To identify biomarker candidates whose abundance level can discriminate metastasis types, we performed a high-throughput proteomics assay using tissue samples from BCs with no metastasis, late metastasis, and early metastasis, processed data with machine learning-based feature selection, and found that low VWA5A could be responsible for shorter duration of metastasis-free interval. Low expression of VWA5A gene in METABRIC cohort was associated with poor survival in BCs, especially in hormone receptor (HR)-positive BCs. In-vitro experiments confirmed tumor suppressive effect of VWA5A on BCs in HR+ and triple-negative BC cell lines. We found that expression of VWA5A can be assessed by immunohistochemistry (IHC) on archival tissue samples. Decreasing nuclear expression of VWA5A was significantly associated with advanced T stage and lymphatic invasion in consecutive BCs of all subtypes. We discovered lower expression of VWA5A as the potential biomarker for metastasis-prone BCs, and our results support the clinical utility of VWA5A IHC, as an adjunctive tools for prognostication of BCs.

Development of a Novel Atherosclerotic Heart Disease Biomarker Program

Background: Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of heart attack, stroke, and sudden death worldwide. Clinical risk scores help estimate the likelihood of adverse cardiac events to guide preventative strategies. Current risk scores for atherosclerotic events like heart attack and stroke use known cardiovascular risk factors including smoking, diabetes, lipid levels, and blood pressure. However, these scores underestimate risk in certain patient populations and do not predict the atherosclerotic disease in individual patients that leads to subsequent events. Our aim is to design a novel biomarker-enabled model to predict the presence and burden of coronary artery atherosclerosis in patients with diabetes, to ultimately help personalize preventive therapies that target disease burden, not just risk factors, for more effective prevention of ASCVD events.&#x0D; Methods: Patients with diabetes have been prospectively enrolled in a health plans-based screening program with co-enrollment in the Indiana Biobank, which includes coronary CT angiography, an AI-based quantitation of atherosclerosis, and biobanking of DNA, plasma, and serum. We identified candidate biomarkers based on current literature and determined which could be impactful based on cost, efficacy, and feasibility of implementation guided by interviews with potential collaborating entities.&#x0D; Results: We have identified polygenic risk scores, non-coding RNAs and candidate blood biomarkers with predictive potential for atherosclerotic heart disease. Subsequent work will entail i) direct assays of banked biospecimens for the selected minimally invasive biomarkers and ii) computational analysis to assess incremental predictive value for disease burden over clinically available risk scores.&#x0D; Conclusions and Potential Impact: We are progressing towards the development of a novel mode to predict patient-specific burden of coronary atherosclerotic disease burden in patients with diabetes. We expect that a multidimensional approach including contemporary biomarkers will improve predictive value of atherosclerotic heart disease burden, affording more tailored treatment to prevent ASCVD events.

Identification of Molecular Markers Associated with Prostate Cancer Subtypes: An Integrative Bioinformatics Approach.

Prostate cancer (PCa) is characterised by androgen dependency. Unfortunately, under anti-androgen treatment pressure, castration-resistant prostate cancer (CRPC) emerges, characterised by heterogeneous cell populations that, over time, lead to the development of different androgen-dependent or -independent phenotypes. Despite important advances in therapeutic strategies, CRPC remains incurable. Context-specific essential genes represent valuable candidates for targeted anti-cancer therapies. Through the investigation of gene and protein annotations and the integration of published transcriptomic data, we identified two consensus lists to stratify PCa patients' risk and discriminate CRPC phenotypes based on androgen receptor activity. ROC and Kaplan-Meier survival analyses were used for gene set validation in independent datasets. We further evaluated these genes for their association with cancer dependency. The deregulated expression of the PCa-related genes was associated with overall and disease-specific survival, metastasis and/or high recurrence risk, while the CRPC-related genes clearly discriminated between adeno and neuroendocrine phenotypes. Some of the genes showed context-specific essentiality. We further identified candidate drugs through a computational repositioning approach for targeting these genes and treating lethal variants of PCa. This work provides a proof-of-concept for the use of an integrative approach to identify candidate biomarkers involved in PCa progression and CRPC pathogenesis within the goal of precision medicine.

Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension.

The burden of stroke in patients with hypertension is very high, and its prediction is critical. We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension. This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension. Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957). We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.

Targeted Metabolomics Analysis of Serum Amino Acids in T2DM Patients.

Amino acids are the important metabolites in the body and play a crucial role in biological processes. The purpose of this study is to provide a profile of amino acids change in the serum of T2DM patients and identify potential biomarkers. In this study, we quantitatively determined the serum amino acid profiles of 30 T2DM patients and 30 healthy volunteers. T test and multivariate statistical analysis were used to identify candidate biomarkers with GraphPad Prism 9.5 software and MetaboAnalyst 5.0 on-line platform. Thirty-four amino acids were quantified, and 19 amino acid levels differed significantly between T2DM and Healthy groups. Screened by the specific screening criteria (VIP>1.0; P<0.05; FC>1.5, or FC<0.67) in MetaboAnalyst 5.0 platform, 8 amino acids were identified as potential biomarkers. Pearson rank correlation test showed 14 differential amino acids were significantly correlated with T2DM-related physiological parameters. The results of this study provide theoretical basis for the subsequent development of dietary supplements for the prevention or treatment of T2DM and its complications.

Construction of immune-related molecular diagnostic and predictive models of hepatocellular carcinoma based on machine learning

BackgroundTo exploit hepatocellular carcinoma (HCC) diagnostic substances, we identify potential predictive markers based on machine learning and to explore the significance of immune cell infiltration in this pathology. MethodThree HCC gene expression datasets were used for weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest were applied to identify candidate biomarkers. The diagnostic value of HCC diagnostic gene biomarkers was further assessed by the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from HCC patients and to analyze their correlation with diagnostic markers. In addition, the prognostic value of the markers and the sensitivity of the drugs were analyzed. ResultWGCNA and differential expression analysis were used to screen 396 distinct gene signatures in HCC tissues. They were mostly engaged in cytoplasmic fusion and the cell division cycle, according to gene enrichment analyses. Five genes were shown to have a high diagnostic value for use as diagnostic biomarkers for HCC, including EFHD1 (AUC = 0.77), KIF4A (AUC = 0.97), UBE2C (AUC = 0.96), SMYD3 (AUC = 0.91), and MCM7 (AUC = 0.93). T cells, NK cells, macrophages, and dendritic cells were found to be related to diagnostic markers in HCC tissues by immune cell infiltration analysis, indicating that these cells are intimately linked to the onset and spread of HCC. Concurrently, these five genes and their constructed models have considerable prognostic value. ConclusionThese five genes (EFHD1, KIF4A, UBE2C, SMYD3, and MCM7) may serve as new candidate molecular markers for HCC, providing new insights for future diagnosis, prognosis, and molecular therapy of HCC.