Expression Profile Of Biomarkers Research Articles

Identification of mRNA biomarkers in extremely early hypertensive intracerebral hemorrhage (HICH)

IntroductionHypertensive intracerebral hemorrhage (HICH) stands out as a critical complication of primary hypertension. Consequently, investigating messenger RNA (mRNA) biomarkers becomes imperative, offering potential targets. This study is conducted for elucidating the expression profile of blood mRNA biomarkers in HICH.MethodsTwenty-five HICH patients were constituted the HICH group.Twenty-two healthy volunteers recruited and comprised the control group. Peripheral blood cells were extracted to identify candidate mRNA. The identified differential expressions of genes between the two groups were validated, and the potential associations between these differentially expressed genes and adverse events were analyzed. GO and KEGG enrichment of DEGs, Weighted Gene Co-expression Network and Protein Interaction Network were established. target mRNA was screened.ResultsThe study identified 3163 differentially expressed genes in HICH. 8 candidate mRNA (SPI1, HK3, HCK, SYK, CD14, FCER1G, CYBB, FGR) were pinpointed. Associations with pathways affecting HICH development included HIF-1 signaling, NF-kappa B signaling, and C-type lectin receptor signaling. In the HICH group, higher expressions of HK3, HCK, SYK, CD14, FCER1G, CYBB, and FGR, and lower SPI1 expression compared to the control group. HICH patients experienced high rates of complications: pulmonary infection (84%), epilepsy (16%), enlarged hematoma (20%), gastrointestinal bleeding (48%), malnutrition (84%), and lower limb deep vein thrombosis (DVT) (12%). Factors contributing to pulmonary infection included age and elevated expression of HCK, SYK, CD14, and FGR. SPI1 was associated with epilepsy, while its lower expression correlated with hematoma enlargement. Gastrointestinal bleeding was linked to increased cerebral hemorrhage. Malnutrition was associated with higher age, and expressions of HK3, HCK, SYK, CD14, FCER1G, CYBB, and FGR. Patients with lower limb DVT had elevated expressions of the identified genes.ConclusionIn hypertensive intracerebral hemorrhage, there are elevated expressions of HK3, HCK, SYK, CD14, FCER1G, CYBB, and FGR, along with reduced expression of SPI1. Furthermore, age, along with elevated expressions of HCK, SYK, CD14, and FGR, serves as influencing factors contributing to pulmonary infection in patients.

NeuroVar: an open-source tool for the visualization of gene expression and variation data for biomarkers of neurological diseases.

The expanding availability of large-scale genomic data and the growing interest in uncovering gene-disease associations call for efficient tools to visualize and evaluate gene expression and genetic variation data. Here, we developed a comprehensive pipeline that was implemented as an interactive Shiny application and a standalone desktop application. NeuroVar is a tool for visualizing genetic variation (single nucleotide polymorphisms and insertions/deletions) and gene expression profiles of biomarkers of neurological diseases. Data collection involved filtering biomarkers related to multiple neurological diseases from the ClinGen database. NeuroVar provides a user-friendly graphical user interface to visualize genomic data and is freely accessible on the project's GitHub repository (https://github.com/omicscodeathon/neurovar).

Spatial transcriptomics analysis identifies therapeutic targets in diffuse high-grade gliomas.

Diffuse high-grade gliomas are the most common malignant adult neuroepithelial tumors in humans and a leading cause of cancer-related death worldwide. The advancement of high throughput transcriptome sequencing technology enables rapid and comprehensive acquisition of transcriptome data from target cells or tissues. This technology aids researchers in understanding and identifying critical therapeutic targets for the prognosis and treatment of diffuse high-grade glioma. Spatial transcriptomics was conducted on two cases of isocitrate dehydrogenase (IDH) wild-type diffuse high-grade glioma (Glio-IDH-wt) and two cases of IDH-mutant diffuse high-grade glioma (Glio-IDH-mut). Gene set enrichment analysis and clustering analysis were employed to pinpoint differentially expressed genes (DEGs) involved in the progression of diffuse high-grade gliomas. The spatial distribution of DEGs in the spatially defined regions of human glioma tissues was overlaid in the t-distributed stochastic neighbor embedding (t-SNE) plots. We identified a total of 10,693 DEGs, with 5,677 upregulated and 5,016 downregulated, in spatially defined regions of diffuse high-grade gliomas. Specifically, SPP1, IGFBP2, CALD1, and TMSB4X exhibited high expression in carcinoma regions of both Glio-IDH-wt and Glio-IDH-mut, and 3 upregulated DEGs (SMOC1, APOE, and HIPK2) and 4 upregulated DEGs (PPP1CB, UBA52, S100A6, and CTSB) were only identified in tumor regions of Glio-IDH-wt and Glio-IDH-mut, respectively. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses revealed that upregulated DEGs were closely related to PI3K/Akt signaling pathway, virus infection, and cytokine-cytokine receptor interaction. Importantly, the expression of these DEGs was validated using GEPIA databases. Furthermore, the study identified spatial expression patterns of key regulatory genes, including those involved in protein post-translational modification and RNA binding protein-encoding genes, with spatially defined regions of diffuse high-grade glioma. Spatial transcriptome analysis is one of the breakthroughs in the field of medical biotechnology as this can map the analytes such as RNA information in their physical location in tissue sections. Our findings illuminate previously unexplored spatial expression profiles of key biomarkers in diffuse high-grade glioma, offering novel insight for the development of therapeutic strategies in glioma.

Multicellular 3D bioprinted human gallbladder carcinoma for in vitro mimicry of tumor microenvironment and intratumoral heterogeneity

Gallbladder carcinoma (GBC) is a malignant hepatobiliary cancer characterized by an intricate tumor microenvironments (TME) and heterogeneity. The traditional GBC 2D culture models cannot faithfully recapitulate the characteristics of the TME. Three-dimensional (3D) bioprinting enables the establishment of high-throughput and high-fidelity multicellular GBC models. In this study, we designed a concentric cylindrical tetra-culture model to reconstitute the spatial distribution of cells in tumor tissue, with the inner portion containing GBC cells, and the outer ring containing a mixture of endothelial cells, fibroblasts, and macrophages. We confirmed the survival, proliferation, biomarker expression and gene expression profiles of GBC 3D tetra-culture models. Hematoxylin-eosin (HE) and immunofluorescence staining verified the morphology and robust expression of GBC/endothelial/fibroblast/macrophage biomarkers in GBC 3D tetra-culture models. Single-cell RNA sequencing revealed two distinct subtypes of GBC cells within the model, glandular epithelial and squamous epithelial cells, suggesting the mimicry of intratumoral heterogeneity. Comparative transcriptome profile analysis among variousin vitromodels revealed that cellular interactions and the TME in 3D tetra-culture models reshaped the biological processes of tumor cells to a more aggressive phenotype. GBC 3D tetra-culture models restored the characteristics of the TME as well as intratumoral heterogeneity. Therefore, this model is expected to have future applications in tumor biology research and antitumor drug development.

Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine.

Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO), and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD). The Kidney Tissue Atlas Ontology (KTAO) imports and integrates kidney-related terms from existing ontologies (e.g., HPO, CL, and Uberon) and represents 259 kidney-related biomarkers. We have also developed a precision medicine metadata ontology (PMMO) to integrate 50 variables from KPMP and CZ CellxGene data resources and applied PMMO for integrative kidney data analysis. The gene expression profiles of kidney gene biomarkers were specifically analyzed under healthy control or AKI/CKD disease states. This work demonstrates how ontology-based approaches support multi-domain data and knowledge integration in precision medicine.

Clinicohistopathological Correlation and Prognostic Significance of Molecular Biomarkers in Urinary Bladder Neoplasms: A Comprehensive Analysis.

Urinary bladder neoplasms constitute a heterogeneous group of tumors with diverse clinical behaviors and outcomes. Understanding the correlation between clinicopathological characteristics and the prognostic significance of molecular biomarkers in bladder cancer is vital for personalized treatment strategies and improved patient outcomes. This prospective observational studyaimed to comprehensively investigate the clinicopathological correlations and prognostic significance of molecular biomarkers in urinary bladder neoplasms. A cohort of 174 patients diagnosed with urinary bladder neoplasm participated in this study. Clinicopathological data, including demographic information, medical history, imaging findings, and histopathological reports, were collected from the patient records. Tissue samples obtained from transurethral resection or biopsy were subjected to molecular biomarker analysis using immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and molecular profiling techniques. Longitudinal follow-up assessments were conducted to monitor disease progression, recurrence, and overall survival. Out of 174 patients diagnosed with bladder neoplasms, the mean age of the patients was 62.4 years (±8.7), indicating that the study cohort primarily comprised elderly individuals. The majority of patients were male (126, 72.4%), reflecting the higher prevalence of bladder cancer among men compared to women. Preliminary analysis revealed significant associations between clinicopathological parameters, molecular biomarker expression profiles, and clinical outcomes in patients with urinary bladder neoplasms. Elevated expression levels of specific biomarkers such as tumor protein p53 (p53), Ki-67, and estimated glomerular filtration rate (EGFR) were observed in advanced tumor stages (p < 0.001) and higher histological grades (p < 0.05), indicating their potential prognostic significance. Furthermore, genetic alterations detected using molecular profiling techniques, including chromosomal gains and losses, were significantly correlated with aggressive disease phenotypes and increased recurrence risk (p < 0.01). Longitudinal follow-up data demonstrated that patients with elevated biomarker expression levels or genetic alterations had poorer treatment responses and shorter overall survival durations than those with lower biomarker expression levels. This study highlights the importance of integrating clinicopathological parameters and molecular biomarker data for the risk stratification, treatment selection, and prognostic assessment of urinary bladder neoplasms.

Biomarker Multiplexing with Rational Design of Nucleic Acid Probe Complex

AbstractThe expression profiles of intracellular biomarkers hold significance for understanding cellular biological functions and tracking pathological activities. Due to its programmability and biocompatibility, extensive efforts have been devoted to design various kinds of nucleic acid probes for biomarker detection. However, pinpointing a single biomarker could end up in a false positive signal, delaying diagnosis. In this review, we present an overview of current advances in biomarker detection and signal amplification techniques. We highlight strategies for biomarker multiplexing and signal amplification with combination of isothermal approaches. High specificity and sensitivity are the two criteria for a desired probe, as are the challenges encountered by a probe that operates efficiently in biological systems. With higher biomarker identification accuracy, we may be able to move one step closer to precision medicine.

Unveiling Spatial Immune Cell Profile in Kidney Allograft Rejections Using 36-plex Immunofluorescence Imaging.

Kidney allograft rejections are orchestrated by a variety of immune cells. Because of the complex histopathologic features, accurate pathological diagnosis poses challenges even for expert pathologists. The objective of this study was to unveil novel spatial indices associated with transplant rejection by using a spatial bioinformatic approach using 36-plex immunofluorescence image data. The image obtained from 11 T cell-mediated rejection (TCMR) and 12 antibody-mediated rejection (AMR) samples were segmented into 753 737 single cells using DeepCell's Mesmer algorithm. These cells were categorized into 13 distinct cell types through unsupervised clustering based on their biomarker expression profiles. Cell neighborhood analysis allowed us to stratify kidney tissue into 8 distinct neighborhood components consisting of unique cell type enrichment profiles. In contrast to TCMR samples, AMR samples exhibited a higher frequency of neighborhood components that were characterized by an enrichment of CD31+ endothelial cells. Although the overall frequency of CD68+ macrophages in AMR samples was not significantly high, CD68+ macrophages within endothelial cell-rich lesions exhibited a significantly higher frequency in AMR samples than TCMR samples. Furthermore, the frequency of interactions between CD31+ cells and CD68+ cells was significantly increased in AMR samples, implying the pivotal role of macrophages in AMR pathogenesis. Importantly, patients demonstrating a high frequency of CD31:CD68 interactions experienced significantly poorer outcomes in terms of chronic AMR progression. Collectively, these data indicate the potential of spatial bioinformatic as a valuable tool for aiding in pathological diagnosis and for uncovering new insights into the mechanisms underlying transplant rejection.

Using sample reinterrogation to guide high-parameter, spatially resolved tissue biomarker discovery.

e15182 Background: High-parameter, spatially resolved phenotyping can accelerate the discovery of tissue biomarkers as well as reveal emergent disease indicators like cellular neighborhood architecture. These spatial biology techniques hold high potential as diagnostic and prognostic indicators yet can be challenging to deploy due to high cost and lack of standardization. We have developed a powerful spatial biology workflow based on multiplex cyclic immunofluorescent (IF) staining using validated markers and assay kits that allows the researcher to adjust the markers included in the assay with feedback from marker expression in previous rounds. We term this sample re-interrogation. Here we present an illustration of this new workflow. Methods: Three FFPE lung biopsy specimens were repeatedly assayed for biomarker expression using CellScape™ Precise Spatial Multiplexing paired with VistaPlex™ Assay Kits. In this workflow, tissues are mounted in enclosed fluidic chambers. IF Staining and imaging were conducted on the CellScape instrument as iterative rounds. Between each imaging round, we conducted analyses on data from previously collected rounds. Conclusions drawn between staining rounds then guided decisions of whether and how to continue the experiment. Results: Each sample provided evidence for the utility of staged rounds of tissue staining, analysis, and rational assay planning. Two FFPE lung samples from the biopsy were not considered for further iterative staining after initial interrogation for consensus markers resulted in poor expression ( e.g.: CD4, CD20, CD45, pan-cytokeratin). One of these two samples was stained with markers indicative of cell death pathways and fibrosis to provide a positive explanation for initial poor staining. A third lung tissue showed punitive indication of tertiary lymphoid structure (TLS) inclusion. Further rounds of staining were conducted to iteratively explore the nature of these punitive TLS structures. Each round of additional stains was chosen based on data derived from the previous staining series following analysis and deliberation among the biomarker team. Conclusions: The data illustrate that even among tissue sections taken from the same biopsy, a specimen’s unique biomarker expression profile may require adjustments to assay parameters. Two of the FFPE sections proved unacceptable for further investigation after only a handful of stains. This finding highlights the benefit of rational reinterrogation, as the saved resources in not staining these tissues could measure in the thousands of dollars in reagents alone. The third lung tissue specimen offers an example of a tailored spatial biology assay through iterative reinterrogation. All included biomarkers were judged best suited to elucidate the next round of staining, representing an optimized and flexible spatial biology workflow.

Correlating transcription and protein expression profiles of immune biomarkers following lipopolysaccharide exposure in lung epithelial cells.

Universal and early recognition of pathogens occurs through recognition of evolutionarily conserved pathogen associated molecular patterns (PAMPs) by innate immune receptors and the consequent secretion of cytokines and chemokines. The intrinsic complexity of innate immune signaling and associated signal transduction challenges our ability to obtain physiologically relevant, reproducible and accurate data from experimental systems. One of the reasons for the discrepancy in observed data is the choice of measurement strategy. Immune signaling is regulated by the interplay between pathogen-derived molecules with host cells resulting in cellular expression changes. However, these cellular processes are often studied by the independent assessment of either the transcriptome or the proteome. Correlation between transcription and protein analysis is lacking in a variety of studies. In order to methodically evaluate the correlation between transcription and protein expression profiles associated with innate immune signaling, we measured cytokine and chemokine levels following exposure of human cells to the PAMP lipopolysaccharide (LPS) from the Gram-negative pathogen Pseudomonas aeruginosa. Expression of 84 messenger RNA (mRNA) transcripts and 69 proteins, including 35 overlapping targets, were measured in human lung epithelial cells. We evaluated 50 biological replicates to determine reproducibility of outcomes. Following pairwise normalization, 16 mRNA transcripts and 6 proteins were significantly upregulated following LPS exposure, while only five (CCL2, CSF3, CXCL5, CXCL8/IL8, and IL6) were upregulated in both transcriptomic and proteomic analysis. This lack of correlation between transcription and protein expression data may contribute to the discrepancy in the immune profiles reported in various studies. The use of multiomic assessments to achieve a systems-level understanding of immune signaling processes can result in the identification of host biomarker profiles for a variety of infectious diseases and facilitate countermeasure design and development.

Proteomics on human cerebral cavernous malformations reveals novel biomarkers in neurovascular dysfunction for the disease pathology

BackgroundCerebral cavernous malformation (CCM) is a disease associated with an elevated risk of focal neurological deficits, seizures, and hemorrhagic stroke. The disease has an inflammatory profile and improved knowledge of CCM pathology mechanisms and exploration of candidate biomarkers will enable new non-invasive treatments. MethodsWe analyzed protein signatures in human CCM tissue samples by using a highly specific and sensitive multiplexing technique, proximity extension assay. FindingsData analysis revealed CCM specific proteins involved in endothelial dysfunction/inflammation/activation, leukocyte infiltration/chemotaxis, hemostasis, extracellular matrix dysfunction, astrocyte and microglial cell activation. Biomarker expression profiles matched bleeding status, especially with higher levels of inflammatory markers and activated astrocytes in ruptured than non-ruptured samples, some of these biomarkers are secreted into blood or urine. Furthermore, analysis was also done in a spatially resolving manner by separating the lesion area from the surrounding brain tissue. Our spatial studies revealed that although appearing histologically normal, the CCM border areas were pathological when compared to control brain tissues. Moreover, the functional relevance of CD93, ICAM-1 and MMP9, markers related to endothelial cell activation and extracellular matrix was validated by a murine pre-clinical CCM model. InterpretationHere we present a novel strategy for proteomics analysis on human CCMs, offering a possibility for high-throughput protein screening acquiring data on the local environment in the brain. Our data presented here describe CCM relevant brain proteins and specifically those which are secreted can serve the need of circulating CCM biomarkers to predict cavernoma's risk of bleeding.

Abstract 5497: Towards a drag and drop spatial biology workflow: Introducing sample reinterrogation as a conduit for the progressive development and on-line analysis of high parameter spatial biology data

Abstract The potential for spatial biology in cancer research is understood and edified. With it we can understand the tumor microenvironment, unravel responsiveness to drug treatments and cell therapies, and ultimately stratify patients for targeted treatment and clinical trial enrollment. Yet, researchers exploring spatial biology may be discouraged by what can be a resource intensive exercise, and most importantly, a commitment of high value patient samples. Furthermore, the design of reagent panels used to study samples is not trivial, and a specimen’s unique biomarker expression profile may require adjustments. Here we present a novel solution that permits sample re-interrogation and in-flight adjustments of reagent panels in our spatial biology workflow. The CellScape™ platform for Precise Spatial Multiplexing paired with VistaPlex™ Assay Kits provide a ready-to-use cyclic multiplex immunofluorescence (IF) solution to interrogate fresh frozen and FFPE tissue samples. Tissues are mounted in enclosed fluidic chambers, while staining and imaging are conducted on the CellScape instrument. The experiment requires no additional autostainer equipment and is divided into separate staining and imaging rounds. The end user may therefore pause between rounds and conduct inter-round analyses, which can then guide decisions of whether and how to continue the experiment. To illustrate the benefit of this approach, we present a use case in which we applied VistaPlex antibody panels to reveal the spatial distribution of major immune cell populations. Inter-round analysis and conclusions were then drawn from this preliminary phenotype map before successive rounds of rationally planned staining were conducted to further deepen the depth and breadth of spatial phenotyping. The CellScape sample reinterrogation feature is a pragmatic spatial biology solution that optimizes the use of precious resources, whilst allowing researchers to generate informed decisions about antibody panel designs almost in real time. This enhanced utility may reduce the resistance to spatial biology among researchers working with samples that are limited or irreplaceable. Citation Format: Matthew H. Ingalls, Xenia Meshik, Charles E. Jackson, Kayla E. Cashion, Arne Christians, Anke Brix, Marcus Boettcher, J Spencer Schwarz, Oliver Braubach. Towards a drag and drop spatial biology workflow: Introducing sample reinterrogation as a conduit for the progressive development and on-line analysis of high parameter spatial biology data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5497.

Abstract 3763: Comparative spatial analyses of the tumor immune landscape in different mouse models of glioblastoma

Abstract Background: Despite advances in treatment, glioblastoma (GBM) remains one of the most difficult types of cancer to treat, and the prognosis is poor. The current median survival time for patients with GBM is about 12-15 months, and the five-year survival rate is less than 10%. There is an unmet need for better GBM treatment options, leveraged from relevant experimental models. To develop new therapies, preclinical animal models are important for analyzing the biology of GBM and evaluating the efficacy of novel therapeutic strategies. While a variety of experimental models are used to study GBM, most preclinical investigations involve mice. In this study we utilize a spatial phenotyping application that permits comprehensive characterization and comparison of key proteins in the brain tumor immune microenvironment (TiME), and detailed comparison the TiME between different immune-competent GBM mouse models. Methods: The Phenocycler-Fusion is a fast spatial biology solution that affords ultrahigh-plex single-cell spatial readouts. We used this solution for the whole slide imaging of mouse FFPE tissues and deep immune phenotyping of &amp;gt;40 proteins, comprising immune cell lineages, activation states and checkpoints, as well as biomarkers for tumor, vascular and neural landscapes of various GBM mouse models. Results: Via single cell spatial phenotyping, we isolated spatial signatures within the mouse GBM tissue immune microenvironment. We focused on multiple immune biomarkers, including microglia/myeloid cells that were identified via the combinatorial expression of the key markers CD68, Iba-1, F4/80, Tmem119 and CD11b. The macrophages and their biomarker expression profiles exhibited significant inter- and intra-tumoral heterogeneity within different mouse GBM models, indicative of the heterogeneous and complex biology of GBM. Conclusions: Our work encompasses the development of a custom antibody panel, an imaging workflow, as well as a novel bioinformatic analysis method. Deployment of this workflow on different mouse GBM models allowed us to study cell populations, according to biomarker profiles and spatial distribution. This study provides a deeper characterization of the diverse mouse GBM models to determine the optimal model that most accurately recapitulates the complex TiME of human GBM, including key features such as invasive tumor margins, high vascularity, blood-brain barrier, etc. We anticipate that this approach has enormous potential for a broad range of applications for which biomolecules’ spatial information is important and will deepen our understanding of the GBM TiME. Citation Format: Dmytro Klymyshyn, Niyati Jhaveri, Aditya Pratapa, Nadezhda Nikulina, Tsz H. Tam, Nadine Nelson, Riccardo Dolcetti, Davide Moi, Roberta Mazzieri, Theo Mantamadiotis. Comparative spatial analyses of the tumor immune landscape in different mouse models of glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3763.

Are Renal Tumor Diagnostics Becoming Too Advanced for Many Pathology Laboratories?

The recent influx of novel renal neoplasms, particularly molecularly-defined renal carcinomas, has introduced new challenges in the daily practice of most pathology laboratories. These tumors are uncommon, they do not always have well-established morphologic features, and the expression profile of most common biomarkers is not well understood. Moreover, the diagnosis of molecularly-defined renal carcinomas requires the documentation of the disease-defining molecular alteration, with molecular studies or surrogate immunohistochemical markers. Unfortunately, most pathology laboratories lack molecular laboratories, or it is not cost-effective to maintain assays of the specific biomarkers in these unusual tumors. Pathologists should have updated knowledge about the recent changes in renal neoplasms and be aware of these limitations.

Discordance of Biomarker Expression Profile between Primary Breast Cancer and Synchronous Axillary Lymph Node Metastasis in Preoperative Core Needle Biopsy

Background: Breast cancer (BC) is a heterogeneous disease made up of clones with different metastatic potential. Intratumoral heterogeneity may cause metastases to show divergent biomarker expression, potentially affecting chemotherapy response. Methods: We investigated the immunohistochemical (IHC) and FISH profile of estrogen receptors (ER), progesterone (PR) receptors, Ki67, and HER2 in a series of BC-matched primary tumors (PTs) and axillary lymph node (ALN) metastases in pre-operative core needle biopsies (CNBs). Phenotypical findings were correlated to morphological features and their clinical implications. Results: Divergent expression between PTs and ALNs was found in 10% of the tumors, often involving multiple biomarkers (12/31, 39%). Most (52%) displayed significant differences in ER and PR staining. HER2 divergences were observed in almost three-quarters of the cases (23/31, 74%), with five (16%) switching from negativity to overexpression/amplification in ALNs. Roughly 90% of disparities reflected significant morphological differences between PTs and ALN metastases. Less than half of the discrepancies (12/31, 39%) modified pre/post-operative treatment options. Conclusions: We observed relevant discrepancies in biomarker expression between PTs and metastatic ALNs in a noteworthy proportion (10%) of preoperative BC CNBs, which were often able to influence therapies. Hence, our data suggest routine preoperative assessment of biomarkers in both PTs and ALNs in cases showing significant morphological differences.

MiR482f and miR482c-5p from edible plant-derived foods inhibit the expression of pro-inflammatory genes in human THP-1 macrophages

BackgroundEdible plants can exert anti-inflammatory activities in humans, being potentially useful in the treatment of inflammatory diseases. Plant-derived microRNAs have emerged as cross-kingdom gene expression regulators and could act as bioactive molecules involved in the beneficial effects of some edible plants. We investigated the role of edible plant-derived microRNAs in the modulation of pro-inflammatory human genes.MethodsMicroRNAs from plant-derived foods were identified by next-generation sequencing. MicroRNAs with inflammatory putative targets were selected, after performing in silico analyses. The expression of candidate plant-derived miRNAs was analyzed by qPCR in edible plant-derived foods and their effects were evaluated in THP-1 monocytes differentiated to macrophages. The bioavailability of candidate plant miRNAs in humans was evaluated in feces and serum samples by qPCR.ResultsmiR482f and miR482c-5p are present in several edible plant-derived foods, such as fruits, vegetables, and cooked legumes and cereals, and fats and oils. Transfections with miR482f and miR482c-5p mimics decreased the gene expression of CLEC7A and NFAM1, and TRL6, respectively, in human THP-1 monocytes differentiated to macrophages, which had an impact on gene expression profile of inflammatory biomarkers. Both microRNAs (miR482f and miR482c-5p) resisted degradation during digestion and were detected in human feces, although not in serum.ConclusionOur findings suggest that miR482f and miR482c-5p can promote an anti-inflammatory gene expression profile in human macrophages in vitro and their bioavailability in humans can be achieved through diet, but eventually restricted at the gut level.

Machine learning-based metabolism-related genes signature, single-cell RNA sequencing, and experimental validation in hypersensitivity pneumonitis.

Metabolism is involved in the pathogenesis of hypersensitivity pneumonitis. To identify diagnostic feature biomarkers based on metabolism-related genes (MRGs) and determine the correlation between MRGs and M2 macrophages in patients with hypersensitivity pneumonitis (HP). We retrieved the gene expression matrix from the Gene Expression Omnibus database. The differentially expressed MRGs (DE-MRGs) between healthy control (HC) and patients with HP were identified using the "DESeq2" R package. The "clusterProfiler" R package was used to perform "Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses" on DE-MRGs. We used machine learning algorithms for screening diagnostic feature biomarkers for HP. The "receiver operating characteristic curve" was used to evaluate diagnostic feature biomarkers' discriminating ability. Next, we used the "Cell-type Identification by Estimating Relative Subsets of RNA Transcripts" algorithm to determine the infiltration status of 22 types of immune cells in the HC and HP groups. Single-cell sequencing and qRT-PCR were used to validate the diagnostic feature biomarkers. Furthermore, the status of macrophage polarization in the peripheral blood of patients with HP was determined using flow cytometry. Finally, the correlation between the proportion of M2 macrophages in peripheral blood and the diagnostic biomarker expression profile in HP patients was determined using Spearman analysis. We identified a total of 311 DE-MRGs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that DE-MRGs were primarily enriched in processes like steroid hormone biosynthesis, drug metabolism, retinol metabolism, etc. Finally, we identified NPR3, GPX3, and SULF1 as diagnostic feature biomarkers for HP using machine learning algorithms. The bioinformatic results were validated using the experimental results. The CIERSORT algorithm and flow cytometry showed a significant difference in the proportion of M2 macrophages in the HC and HP groups. The expression of SULF1 was positively correlated with the proportion of M2-type macrophages. In addition, a positive correlation was observed between SULF1 expression and M2 macrophage proportion. Finally, we identified NPR3, GPX3, and SULF1 as diagnostic feature biomarkers for HP. Further, a correlation between SULF1 and M2 macrophages was observed, providing a novel perspective for treating patients with HP and future studies.

Chemoresistance Mechanisms in Non-Small Cell Lung Cancer-Opportunities for Drug Repurposing.

Globally, lung cancer contributes significantly to the public health burden-associated mortality. As this form of cancer is insidious in nature, there is an inevitable diagnostic delay leading to chronic tumor development. Non-small cell lung cancer (NSCLC) constitutes 80-85% of all lung cancer cases, making this neoplasia form a prevalent subset of lung carcinoma. One of the most vital aspects for proper diagnosis, prognosis, and adequate therapy is the precise classification of non-small cell lung cancer based on biomarker expression profiling. This form of biomarker profiling has provided opportunities for improvements in patient stratification, mechanistic insights, and probable druggable targets. However, numerous patients have exhibited numerous toxic side effects, tumor relapse, and development of therapy-based chemoresistance. As a result of these exacting situations, there is a dire need for efficient and effective new cancer therapeutics. De novo drug development approach is a costly and tedious endeavor, with an increased attrition rate, attributed, in part, to toxicity-related issues. Drug repurposing, on the other hand, when combined with computer-assisted systems biology approach, provides alternatives to the discovery of new, efficacious, and safe drugs. Therefore, in this review, we focus on a comparison of the conventional therapy-based chemoresistance mechanisms with the repurposed anti-cancer drugs from three different classes-anti-parasitic, anti-depressants, and anti-psychotics for cancer treatment with a primary focus on NSCLC therapeutics. Certainly, amalgamating these novel therapeutic approaches with that of the conventional drug regimen in NSCLC-affected patients will possibly complement/synergize the existing therapeutic modalities. This approach has tremendous translational significance, since it can combat drug resistance and cytotoxicity-based side effects and provides a relatively new strategy for possible application in therapy of individuals with NSCLC.

Characterization of the circulating transcriptome expression profile and identification of novel miRNA biomarkers in hypertrophic cardiomyopathy

BackgroundHypertrophic cardiomyopathy (HCM), one of the most common genetic cardiovascular diseases, but cannot be explained by single genetic factors. Circulating microRNAs (miRNAs) are stable and highly conserved. Inflammation and immune response participate in HCM pathophysiology, but whether the miRNA profile changes correspondingly in human peripheral blood mononuclear cells (PBMCs) with HCM is unclear. Herein, we aimed to investigate the circulating non-coding RNA (ncRNA) expression profile in PBMCs and identify potential miRNAs for HCM biomarkers.MethodsA Custom CeRNA Human Gene Expression Microarray was used to identify differentially expressed (DE) mRNAs, miRNAs, and ncRNAs (including circRNA and lncRNA) in HCM PBMCs. Weighted correlation network analysis (WGCNA) was used to identify HCM-related miRNA and mRNA modules. The mRNAs and miRNAs from the key modules were used to construct a co-expression network. Three separate machine learning algorithms (random forest, support vector machine, and logistic regression) were applied to identify potential biomarkers based on miRNAs from the HCM co-expression network. Gene Expression Omnibus (GEO) database (GSE188324) and experimental samples were used for further verification. Gene set enrichment analysis (GSEA) and competing endogenous RNA (ceRNA) network was used to determine the potential functions of the selected miRNAs in HCM.ResultsWe identified 1194 DE-mRNAs, 232 DE-miRNAs and 7696 DE-ncRNAs in HCM samples compared with normal controls from the microarray data sets. WGCNA identified key miRNA modules and mRNA modules evidently associated with HCM. We constructed a miRNA‒mRNA co-expression network based on these modules. A total of three hub miRNAs (miR-924, miR-98 and miR-1) were identified by random forest, and the areas under the receiver operator characteristic curves of miR-924, miR-98 and miR-1 were 0.829, 0.866, and 0.866, respectively.ConclusionsWe elucidated the transcriptome expression profile in PBMCs and identified three hub miRNAs (miR-924, miR-98 and miR-1) as potential biomarkers for HCM detection.

New Biomarker Combination Related to Oxidative Stress and Inflammation in Primary Open-Angle Glaucoma.

Glaucoma is a multifactorial neurodegenerative disease and the second leading cause of blindness. Detection of clinically relevant biomarkers would aid better diagnoses and monitoring during treatment. In glaucoma, the protein composition of aqueous humor (AH) is relevant for the discovery of biomarkers. This study analyzes AH protein concentrations of putative biomarkers in patients with primary open-angle glaucoma (POAG) compared to a control group. Biomarkers were selected from known oxidative-stress and inflammatory pathways. Osteopontin (OPN), matrix metalloproteinase 9 (MMP-9), tumor necrosis factor-alpha (TNF-alpha), transforming growth factor-beta (TGF-beta), and interleukin-10 (IL-10) were measured using the ELISA technique. Thirty-two patients were recruited to the study, including sixteen control and sixteen glaucoma patients. The glaucoma group consisted of patients diagnosed with glaucoma. In both groups, the aqueous humor sample was obtained during cataract surgery. A significant increase in OPN, MMP-9, TNF-alpha, and IL-10 was observed in the POAG aqueous humor, compared to the control group (p < 0.05). Of note, the AH of POAG patients contained 5.6 ± 1.2-fold more OPN compared to that of control patients. Different expression profiles of oxidative stress-related and inflammatory biomarkers were observed between patients with POAG and controls. This confirms the reported involvement of inflammatory and oxidative stress pathways in POAG pathophysiology. In the future, several, targeted AH proteins may be used to generate a potential biomarker expression profile of this disease, aiding diagnoses and disease progression monitoring. This approach highlights the importance of biomarkers in the future. Biomarkers provide a way to measure disease progression and response to treatment. In the future, biomarkers will play a more critical role in the toolkit of ophthalmology healthcare professionals as the field moves towards personalized medicine and precision healthcare.