Identification Of Novel Biomarkers Research Articles

Extracellular vesicles as nanotheranostic platforms for targeted neurological disorder interventions.

Central Nervous System (CNS) disorders represent a profound public health challenge that affects millions of people around the world. Diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and traumatic brain injury (TBI) exemplify the complexities and diversities that complicate their early detection and the development of effective treatments. Amid these challenges, the emergence of nanotechnology and extracellular vesicles (EVs) signals a new dawn for treating and diagnosing CNS ailments. EVs are cellularly derived lipid bilayer nanosized particles that are pivotal in intercellular communication within the CNS and have the potential to revolutionize targeted therapeutic delivery and the identification of novel biomarkers. Integrating EVs with nanotechnology amplifies their diagnostic and therapeutic capabilities, opening new avenues for managing CNS diseases. This review focuses on examining the fascinating interplay between EVs and nanotechnology in CNS theranostics. Through highlighting the remarkable advancements and unique methodologies, we aim to offer valuable perspectives on how these approaches can bring about a revolutionary change in disease management. The objective is to harness the distinctive attributes of EVs and nanotechnology to forge personalized, efficient interventions for CNS disorders, thereby providing a beacon of hope for affected individuals. In short, the confluence of EVs and nanotechnology heralds a promising frontier for targeted and impactful treatments against CNS diseases, which continue to pose significant public health challenges. By focusing on personalized and powerful diagnostic and therapeutic methods, we might improve the quality of patients.

Gut Microbiota Are a Novel Source of Biomarkers for Immunotherapy in Non-Small-Cell Lung Cancer (NSCLC)

Despite the recent availability of immune checkpoint inhibitors, not all patients affected by Non-Small-Cell Lung Cancer (NSCLC) benefit from immunotherapy. The reason for this variability relies on a variety of factors which may allow for the identification of novel biomarkers. Presently, a variety of biomarkers are under investigation, including the PD1/PDL1 axis, the tumor mutational burden, and the microbiota. The latter is made by all the bacteria and other microorganisms hosted in our body. The gut microbiota is the most represented and has been involved in different physiological and pathological events, including cancer. In this light, it appears that all conditions modifying the gut microbiota can influence cancer, its treatment, and its treatment-related toxicities. The aim of this review is to analyze all the conditions influencing the gut microbiota and, therefore, affecting the response to immunotherapy, iRAEs, and their management in NSCLC patients. The investigation of the landscape of these biological events can allow for novel insights into the optimal management of NSCLC immunotherapy.

New genetic insights into immunotherapy outcomes in gastric cancer via single-cell RNA sequencing and random forest model

ObjectiveThe high mortality rate of gastric cancer, traditionally managed through surgery, underscores the urgent need for advanced therapeutic strategies. Despite advancements in treatment modalities, outcomes remain suboptimal, necessitating the identification of novel biomarkers to predict sensitivity to immunotherapy. This study focuses on utilizing single-cell sequencing for gene identification and developing a random forest model to predict immunotherapy sensitivity in gastric cancer patients.MethodsDifferentially expressed genes were identified using single-cell RNA sequencing (scRNA-seq) and gene set enrichment analysis (GESA). A random forest model was constructed based on these genes, and its effectiveness was validated through prognostic analysis. Further, analyses of immune cell infiltration, immune checkpoints, and the random forest model provided deeper insights.ResultsHigh METTL1 expression was found to correlate with improved survival rates in gastric cancer patients (P = 0.042), and the random forest model, based on METTL1 and associated prognostic genes, achieved a significant predictive performance (AUC = 0.863). It showed associations with various immune cell types and negative correlations with CTLA4 and PDCD1 immune checkpoints. Experiments in vitro and in vivo demonstrated that METTL1 enhances gastric cancer cell activity by suppressing T cell proliferation and upregulating CTLA4 and PDCD1.ConclusionThe random forest model, based on scRNA-seq, shows high predictive value for survival and immunotherapy sensitivity in gastric cancer patients. This study underscores the potential of METTL1 as a biomarker in enhancing the efficacy of gastric cancer immunotherapy.

AProceedings of the 5th Meeting of Translational Hepatology, organized by the Spanish Association for the Study of the Liver (AEEH)

This is the summary report of the 5th Translational Hepatology Meeting, endorsed by the Spanish Association for the Study of the Liver (AEEH) and held in Seville, Spain, in October 2023. The meeting aimed to provide an update on the latest advances in the field of basic and translational hepatology, covering different molecular, cellular, and pathophysiological aspects of the most relevant clinical challenges in liver pathologies. This includes the identification of novel biomarkers and diagnostic tools, the understanding of the relevance of immune response and inflammation in liver diseases, the characterization of current medical approaches to reverse liver diseases, the incorporation of novel molecular insights through omics techniques, or the characterization of the impact of toxic and metabolic insults, as well as other organ crosstalk, in liver pathophysiology.

Gut-Gonad Perturbations in Type-1 Diabetes Mellitus: Role of Dysbiosis, Oxidative Stress, Inflammation and Energy-Dysbalance.

Type 1 diabetes mellitus is a major metabolic disorder that affects people of all age groups throughout the world. It is responsible for the alterations in male gonadal physiology in experimental models as well as in clinical cases. On the other side, diabetes mellitus has also been associated with perturbations in the gut physiology and microbiota dysbiosis. The accumulating evidence suggests a link between the gut and gonad as evident from the i) experimental data providing insights into type 1 diabetes mellitus induced gut perturbations, ii) link of gut physiology with alterations of testicular health, iii) role of gut microbiota in androgen metabolism in the intestine, and iv) epidemiological evidence linking type 1 diabetes mellitus with inflammatory bowel disease and male infertility. Considering all the pieces of evidence, it is summarized that gut dysbiosis, oxidative stress, inflammation and energy dys-balance are the prime factors involved in the gonadal damage under type 1 diabetes mellitus, in which the gut contributes significantly. Identification of novel biomarkers and intervention of suitable agents targeting these prime factors may be a step forward to restore the gonadal damage in diabetic conditions.

4D label-free proteomics analysis of oxygen-induced retinopathy with or without anti-VEGF treatment

Oxygen-induced retinopathy (OIR) animal model is widely used for retinopathy of prematurity (ROP) researches. The purpose of this study was to identify proteins and related pathways of OIR with or without anti-vascular endothelial growth factor (VEGF) treatment, for use as biomarkers in diagnosing and treating ROP. Nine samples were subjected to proteomic analysis. Retina specimens were collected from 3 OIR mice, 3 OIR mice with anti-VEGF treatment and 3 normal mice (control group). Liquid chromatography-tandem mass spectrometry analysis was performed using the 4D label-free technique. Statistically significant differentially expressed proteins, gene ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway representations, InterPro (IPR) and protein interactions were analyzed. In total, 4585 unique proteins were identified as differentially expressed proteins (DEPs). Enrichment analysis of the GO and KEGG indicated functional clusters related to peptide biosynthetic and metabolic process, cellular macromolecule biosynthetic process and nucleic acid binding in OIR group. For anti-VEGF treatment group, DEPs were clustered in DNA replication, PI3K/Akt signaling pathway and Jak/STAT signaling pathway. Proteomic profiling is useful for the exploration of molecular mechanisms of OIR and mechanisms of anti-VEGF treatment. These findings may be useful for identification of novel biomarkers for ROP pathogenesis and treatment.

Molecular pathogenesis of ameloblastoma.

Ameloblastoma (AM) is a benign, although aggressive, epithelial odontogenic tumour originating from tooth-forming tissues or remnants. Its aetiopathogenesis remains unclear; however, molecular analysis techniques have allowed researchers to progress in understanding its genetic basis. The high frequency of BRAF p.V600E as a main driver mutation in AM is well established; nevertheless, it is insufficient to explain its tumourigenesis. In this review, we aimed to integrate the current knowledge about the biology of AM and to describe the main genetic alterations reported, focusing on the findings of large-scale sequencing and gene expression profiling techniques. Current evidence shows that besides BRAF mutation and activation of the MAPK pathway, alterations in Hedgehog and Wnt/β-catenin pathway-related genes are also involved in AM pathogenesis. Recently, a tumour suppressor gene, KMT2D, has been reported as mutated by different research groups. The biological impact of these mutations in the pathogenesis of AM has yet to be elucidated. Further studies are needed to clarify the impact of these findings in the identification of novel biomarkers that could be useful for diagnosing, classifying, and molecular targeting this neoplasm.

Construction of ceRNA network mediated by circRNAs screening from microarray and identification of novel biomarkers for myasthenia gravis

BackgroundRecent studies have revealed that circRNA can serve as ceRNA to participate in multiple autoimmune diseases. Our study aims to explore the key circRNA as ceRNA and biomarker for MG. Methods: We used circRNA microarray to explore differentially expressed circRNAs (DECs) from MG and compare with control. Then, we predicted the target miRNA associated with DECs and screened miRNAs by the algorithm of random walk with restart (RWR). Next, we constructed the circRNA-miRNA-mRNA ceRNA regulated network (CMMC) to identify the hub objects. Following, we detected the expression of hub-circRNAs by RT-PCR. We verify has_circ_0004183 (circFRMD4) sponging miR-145-5p regulate cells proliferation using luciferase assay and CCK-8. Results: We found that the expression level of circFRMD4 and has_circ_0035381 (circPIGB) were upregulated and has_circ_0089153(circ NUP214) had the lowest expression level in MG. Finally, we proved circFRMD4 sponging miR-145-5p regulate Jurkat cells proliferation. CircFRMD4 take part in the genesis and development of MG via circFRMD4/miR145-5p axis. Conclusions: We found that circFRMD4, circPIGB and circNUP214 can be considered as valuable potential novel biomarkers for AchR + MG. CircFRMD4 participate in the development of AchR + MG via targeting binding with miR-145-5p.

Metabolome Profiling of Malignant Ascites Identifies Candidate Metabolic Biomarkers of Hepatocellular Carcinoma.

Malignant ascites is one of the severe complications of hepatocellular carcinoma, which can be regarded as a unique tumor microenvironment of hepatocellular carcinoma. The identification of novel biomarkers in malignant ascites could be crucial to differentiate patients with hepatocellular carcinoma and cirrhotic ascites. The study aimed to distinguish the metabolomics of malignant ascites in patients with hepatocellular carcinoma from that of non-malignant ascites (cirrhotic ascites). Liquid chromatography-mass spectrometry was performed to analyze the differentially distributed biomarkers in patients with malignant ascites and hepatocellular carcinoma (n = 39), as well as in patients with cirrhotic ascites, which were taken as controls (n = 36). A total of 20 differential metabolites associated with malignant ascites were identified, of which 8 metabolites were upregulated and 12 metabolites were downregulated (ratio < 0.5 or > 1.5, respectively). Moreover, pathway and enrichment analyses revealed nitrogen metabolism, urea cycle, phenylalanine, and tyrosine metabolism to be implicated in the formation of malignant ascites in patients with hepatocellular carcinoma. Our results suggest that the key factors associated with pathways, such as arachidonic acid, phenylalanine, and glutamic acid pathways, are potential ascitic fluidbased biomarkers for differentiating hepatocellular carcinoma with cirrhosis ascites; the results also provide a clinical pathophysiological interpretation of biomarkers and metabolic pathways relevant to disease status.

An integrated metabo-lipidomics profile of induced sputum for the identification of novel biomarkers in the differential diagnosis of asthma and COPD

BackgroundDue to their complexity and to the presence of common clinical features, differentiation between asthma and chronic obstructive pulmonary disease (COPD) can be a challenging task, complicated in such cases also by asthma–COPD overlap syndrome. The distinct immune/inflammatory and structural substrates of COPD and asthma are responsible for significant differences in the responses to standard pharmacologic treatments. Therefore, an accurate diagnosis is of central relevance to assure the appropriate therapeutic intervention in order to achieve safe and effective patient care. Induced sputum (IS) accurately mirrors inflammation in the airways, providing a more direct picture of lung cell metabolism in comparison to those specimen that reflect analytes in the systemic circulation.MethodsAn integrated untargeted metabolomics and lipidomics analysis was performed in IS of asthmatic (n = 15) and COPD (n = 22) patients based on Ultra-High-Pressure Liquid Chromatography-Mass Spectrometry (UHPLC-MS) and UHPLC–tandem MS (UHPLC-MS/MS). Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to resulting dataset. The analysis of main enriched metabolic pathways and the association of the preliminary metabolites/lipids pattern identified to clinical parameters of asthma/COPD differentiation were explored. Multivariate ROC analysis was performed in order to determine the discriminatory power and the reliability of the putative biomarkers for diagnosis between COPD and asthma.ResultsPLS-DA indicated a clear separation between COPD and asthmatic patients. Among the 15 selected candidate biomarkers based on Variable Importance in Projection scores, putrescine showed the highest score. A differential IS bio-signature of 22 metabolites and lipids was found, which showed statistically significant variations between asthma and COPD. Of these 22 compounds, 18 were decreased and 4 increased in COPD compared to asthmatic patients. The IS levels of Phosphatidylethanolamine (PE) (34:1), Phosphatidylglycerol (PG) (18:1;18:2) and spermine were significantly higher in asthmatic subjects compared to COPD.ConclusionsThis is the first pilot study to analyse the IS metabolomics/lipidomics signatures relevant in discriminating asthma vs COPD. The role of polyamines, of 6-Hydroxykynurenic acid and of d-rhamnose as well as of other important players related to the alteration of glycerophospholipid, aminoacid/biotin and energy metabolism provided the construction of a diagnostic model that, if validated on a larger prospective cohort, might be used to rapidly and accurately discriminate asthma from COPD.Graphical

Abstract 3867: Identification of novel biomarkers of immune toxicity from CAR T-cell therapy using ultrasensitive NULISA™ proteome technology

Abstract Introduction: The efficacy of anti-CD19 chimeric antigen receptor (CAR) T-cell therapy in patients with relapsed/refractory large B-cell lymphoma (LBCL) is limited by acute toxic events, most notably cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS). Identification of biomarkers of toxicities can allow for selection of high-risk patients and elucidate targetable mechanisms for mitigation. Previous biomarker studies have been constrained by relatively narrow panels of potential mediators and limited time points. Here, we employed NULISA, a novel ultrasensitive assay capable of simultaneously quantifying 204 inflammatory proteins from a single sample, to identify temporal proteome associations with acute toxicities in anti-CD19 CAR T-cell treated patients. Methods: Baseline and post-treatment peripheral blood samples from 80 patients with LBCL who underwent anti-CD19 CAR-T cell therapy were collected within specific time intervals. Plasma samples (n=480) were analyzed with NULISA. CRS and ICANS were graded according to ASTCT consensus criteria. Patients were grouped according to their maximum CRS and ICANS scores. Differential protein abundance across the toxicity groups was assessed using linear mixed effects models fit for each protein, including a time by toxicity group interaction. Proteins showing significant associations were subjected to pathway enrichment and network analysis. Results: Severe CRS and ICANS patient groups showed proteome upregulation beginning on day 1-2 and peaking at day 6-9, followed by extensive downregulation on day 11-16. The strongest upregulated pathway associations of severe CRS and ICANS were inflammatory response, IL-17 signaling, non-genomic action of vitamin D3, regulation of leukocyte proliferation, and cellular extravasation. Downregulated pathways included anti-microbial humoral response and TNFs binding to receptors. In addition to the previously identified inflammatory proteins IL-6, IFNγ, sIL-2Rα, CXCL8, CCL2, our analysis revealed significant association of several novel immune regulators and mediators. These included upregulated Th2 cytokines, IL-17A, IL-22, GZMB, CTLA4, IFNA1;IFNA13, and downregulated S100A12, IL-12B, CCL22, BDNF and TNFSFs, revealing exquisite temporal orchestration of lymphoid and myeloid activities during CRS and ICANS development. Conclusions: This study represents the most comprehensive characterization of immune response to CAR T-cell therapy to date. Using the novel NULISA technology, we identified new proteins and functional pathways associated with CAR T-cell-induced toxicity, implicating them as potential biomarkers. These previously unidentified factors also provide a platform to further investigate the causative immune mechanisms of acute toxicities in CAR-T cell therapy. Citation Format: Riley Kirkpatrick, Joanne Beer, Manishkumar S. Patel, Akansha Jalota, Agrima Mian, Ishara S. Ariyapala, Qinyu Hao, Wei Feng, Xiao-Jun Ma, Yuling Luo, Brian T. Hill, Neetu Gupta. Identification of novel biomarkers of immune toxicity from CAR T-cell therapy using ultrasensitive NULISA™ proteome technology [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 3867.

Abstract 6244: MultiTME: A Bayesian method for integration and analysis of multimodal, multi-patient spatial profiling data

Abstract The role of the tumor microenvironment (TME) in cancer evolution and progression is complex and multi-faceted. Multiple cellular phenotypes and patterns of cell-cell communication have been implicated as drivers of drug resistance and metastasis. The spatial architecture of the TME is emerging as particularly important in characterizing the trajectory of tumor growth and response to therapy. Accordingly, there has been a rapid rise in technologies to spatially profile the TME. Each technology focuses on a different modality (e.g. transcriptomics, proteomics, metabolomics) and operates at a different granularity, from coarse-grained tissue spots aggregating dozens of cells to fine-grained subcellular resolution. There is currently no computational method capable of drawing unified inferences across modalities. We introduce MultiTME, a single, holistic generative model for jointly modeling multimodal data across multiple samples in a cohort. MultiTME addresses these challenges through a two-dimensional integration of both samples and multimodal data. MultiTME incorporates a cohort of tissue samples, enabling the joint modeling of different samples within a single analytical framework. This integration allows for a comprehensive analysis of the spatial distribution of cell types across multiple samples, enhancing the understanding of TME variability and the identification of robust biomarkers. MultiTME also integrates multimodal data with varying resolutions, such as Spatial Transcriptomics (ST), single-cell RNA sequencing (scRNA-seq), and Imaging Mass Cytometry (IMC), and provides a multi-layered understanding of the TME. This integration enables the modeling of TME at a finer scale and the discovery of cell types and subtypes that are not distinguishable using single-modal approaches. It also provides concurrent expression profiles across different modalities, leading to the identification of novel biomarkers. We demonstrate MultiTME's efficiency and scalability with a dataset of coupled ST, scRNA-seq, and IMC from a cohort of 8 lung cancer patients. MultiTME's capability to handle both intra-cohort and inter-modality variability offers a panoramic view of TME and helps identify biomarkers and therapeutic targets for cancer screening and treatment. MultiTME is open source. Citation Format: Haoran Zhang, Jeff Quinn, Liron Yoffe, Vivek Mittal, Wesley Tansey. MultiTME: A Bayesian method for integration and analysis of multimodal, multi-patient spatial profiling 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 6244.

Abstract 2012: Extrachromosomal DNA promotes drug resistance in pancreatic ductal adenocarcinoma cells

Abstract Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related deaths in Western Countries and has been predicted to become the second leading cause of cancer-related deaths by 2030. Difficulties in early detection as well as strong chemoresistance of the disease prevent the successful treatment of PDAC patients. Although we have previously identified CYP3A5 as a mediator of paclitaxel resistance, further targets remain elusive. Thus, we set out to identify these CYP3A5-independent mechanisms which could lead to the identification of novel biomarkers and drug targets to improve PDAC treatment. For this project, pancreatic cancer cell lines were derived from treatment-naive PDAC samples and exposed to increasing paclitaxel concentrations to induce resistance. The cells were characterized on a genomic and transcriptomic level using short-read sequencing as well as optical mapping to characterize potential candidate genes in vitro. In our results, multi-drug resistance protein 1 (ABCB1) was highly upregulated in our differential gene expression analysis and confirmed as a CYP3A5-independent mediator of acquired paclitaxel resistance. Its overexpression was mediated by extrachromosomal DNA (ecDNA) amplifications upon drug treatment. These circular DNAs were characterized by short-read sequencing as well as long-read optical mapping, which revealed the structures of the most abundant molecules. Furthermore, we investigated the biogenesis of the ecDNA. Current models propose different events such as chromothripsis or breakage-fusion-bridge cycles to initiate ecDNA formation, however, knowledge about the underlying mechanisms remains limited. To study the ecDNA dynamics, we generated ecDNA-negative cell clones from a paclitaxel resistant PDAC cell line. Interestingly, our clonal cell population appears to show the capacity for ecDNA de novo generation and quickly adapts to changing selective pressure such as therapy. This finding might unveil new insights into the triggers of ecDNA formation and its dynamic regulation. In conclusion, our results suggest the clinical relevance of ABCB1 as a mediator of paclitaxel resistance in PDAC and we currently validate our findings in patient samples. However, ABCB1 inhibition is associated with severe side effects. Instead, the regulatory pathways associated with enhanced ABCB1 expression and the formation of ecDNAs might provide therapeutic potential. Additionally, the characterization of ecDNA de novo generation might unveil new insights into how cancer cells adapt to changing selective pressures. Citation Format: Tim Vorberg, Manuel Reitberger, Bernardo Rodriguez-Martin, Maja Starostecka, Ornella Kossi, Vanessa Vogel, Jan Korbel, Andreas Trumpp, Martin Sprick. Extrachromosomal DNA promotes drug resistance in pancreatic ductal adenocarcinoma cells [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 2012.

Abstract 5496: Multi-omic spatial analysis of the tumor microenvironment in gliomas

Abstract Introduction: The diverse tumor environment of high-grade glioma, which remains refractory to treatment, demands new innovative, multi-omic approaches to characterize tumor heterogeneity and expression profiles associated with progression and therapeutic response. Approach: We have applied an integrated, multi-omic approach using paired spatial in situ sequencing (Xenium) and protein profiling (PhenoCycler® Fusion) of glioma tissue to identify distinct glioma cellular phenotypes, activation states and metabolic pathways at true single cell resolution. Our multi-omic approach used hundreds of phenotypic RNA target probes and &amp;gt;50 antibodies in an unbiased single-cell analysis of primary, recurrent, and IDH1 mutant/wild type tumors with a range of heterogeneity. Summary: We were able to distinguish differentially regulated genes and pathways between aggregated primary and recurrent GBM, including cell cycle pathways being upregulated in primary vs recurrent GBM glial cell clusters and a down regulation of ERBB4 signaling in vascular cell clusters in primary GBMs. Our data also suggests that SOX11, known to be involved in tumorigenesis, is &amp;gt;2-fold upregulated in annotated tumor cells in primary compared to recurrent tumors. We were also able to quantitatively localize the expression of Epidermal Growth Factor Receptor, variant III (EGFRvIII) to specific tumor-cell sub-types, which is important given its association with therapeutic resistance. Further, we have been able to establish the cellular neighborhoods and cell-cell and receptor-ligand relationships within the tumors. Our spatial protein analysis identified distinct tumor and immune phenotypes with varying abundance of myeloid and lymphoid populations in IDH1 wt and mt tumors. Moreover, spatial proximity analyses and cellular neighborhood analyses revealed differences in higher order organizational landscapes that may contribute to the differential outcomes across wt and mt tumor subtypes. Conclusion: Multiomic spatial analysis enables deeper characterization of the glioma cellular and functional landscape to broaden our understanding of the key TME features that contribute to disease pathogenesis and prognoses. Our study provides an analytical framework to combine RNA and protein-based spatial data for a holistic investigation into a variety of glioma subtypes and aid in the identification of novel biomarkers, spatial neighborhoods, and functional states that drive glioma progression. Citation Format: Dmytro Klymyshyn, Vaibhav Jain, Lauren Whaley, Emily Hocke, Bassem Ben Cheikh, Alan Smith, Karen Abramson, Nadine Nelson, Diane Satterfield, Elizabeth Thomas, Giselle Lopez, Seetha Hariharan, Michael Brown, Niyati Jhaveri, Roger McLendon, David Ashley, Matthew Waitkus, Simon Gregory. Multi-omic spatial analysis of the tumor microenvironment in gliomas [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 5496.

Abstract 6843: Comprehensive multi-omics approaches for identification of genetic alterations in head and neck squamous cell carcinoma

Abstract Background: Head and neck squamous cell carcinoma (HNSCC) is one of the most difficult and challenging malignancies, with a high level of heterogeneity and a wide range of treatment responses, regardless of the clinical stage. Oral squamous cell carcinoma (OSCC) is the most common head and neck cancer and accounts for over 90% of cancers that develop in the mucosal epithelium of the oral cavity. Additional carcinogenic risk factors, including tobacco, alcohol, and HPV infection are also associated with the pathogenesis of oral carcinogenesis. Unfortunately, today the failure of standard treatment modalities such as surgery, radiotherapy, and chemotherapy underscore the need for comprehensive genomic profiling to provide a better picture of the genetic alterations and gain an improved understanding of the complex signaling networks involved in the development of potential treatment resistance as well as the identification of novel biomarkers that discriminate between smokers and non-smokers. Methods: A retrospective analysis was performed on biopsied Formalin-Fixed Paraffin-Embedded (FFPE) samples from OSCC patients with and without a history of smoking and treated +/- chemotherapy. Patient samples representing matched normal tissue to serve as controls were compared to tumor squamous cell carcinoma samples and profiled using a single assay panel that includes 517 genes by both DNA and RNA NGS, microsatellite instability (MSI), and tumor mutational burden (TMB). Further analysis was also performed using the NanoString nCounter® PanCancer Immune Profiling Panel (PCIP), 770-comprehensive gene expression panel Results: The Neo ComprehensiveTM Solid Tumor pan cancer assay enabled simultaneously analysis of both DNA and RNA in one integrated workflow with an accompanying DragenTM bioinformatics analysis platform and provided comprehensive genomic profiling of sequence and structural variants, as well as genomic signatures such as TMB and MSI in OSCC patients. Utility of the NanoString PCIP highlighted immune-relevant gene expression changes within the different sample cohorts. The detailed molecular information highlighted by our study emphasizes the need for improved patient classification based on smoking status in the management of OSCC and also in establishing potential therapeutic options based on the many altered cell signaling pathways that we identified. By combining a multi-omics approach with expression data for immune-related genes and associated overall survival data, we provide an effective strategy and workflow for the identification of prognostic biomarkers in OSCC. Citation Format: Kirsteen Maclean, Qinqin Zha, Lakshmi Chandramohan, Anna Juncker-Jensen. Comprehensive multi-omics approaches for identification of genetic alterations in head and neck squamous cell carcinoma [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 6843.

MKRN1 regulates the expression profiles and transcription factor activity in HeLa cells inhibition suppresses cervical cancer cell progression

Cervical cancer is one of the most common gynecologic malignancies worldwide, necessitating the identification of novel biomarkers and therapeutic targets. This study aimed to investigate the significance of MKRN1 in cervical cancer and explore its potential as a diagnostic marker and therapeutic target. The results indicated that MKRN1 expression was up-regulated in cervical cancer tissues and correlated with advanced tumor stage, higher grade, and poor patient survival. Functional studies demonstrated that targeting MKRN1 effectively inhibited cell proliferation, migration, and invasion, highlighting its critical role in tumor progression and metastasis. Moreover, the knockdown of MKRN1 resulted in altered expression patterns of six transcription factor-encoding genes, revealing its involvement in gene regulation. Co-expression network analysis unveiled complex regulatory mechanisms underlying the effects of MKRN1 knockdown on gene expression. Furthermore, the results suggested that MKRN1 might serve as a diagnostic marker for personalized treatment strategies and a therapeutic target to inhibit tumor growth, metastasis, and overcome drug resistance. The development of MKRN1-targeted interventions might hold promise for advancing personalized medicine approaches in cervical cancer treatment. Further research is warranted to validate these findings, elucidate underlying mechanisms, and translate these insights into improved management and outcomes for cervical cancer patients.

Serum klotho as a novel biomarker for metabolic syndrome: findings from a large national cohort.

Metabolic syndrome is a cluster of metabolic abnormalities that significantly increase the risk of cardiovascular disease and mortality. The identification of novel biomarkers associated with mortality in patients with metabolic syndrome could facilitate early risk stratification and targeted interventions. We conducted a large prospective cohort study using data from five cycles (2009-2016) of the National Health and Nutrition Examination Survey (NHANES) database, including a total of 40,439 participants. Logistic regression analysis was used to assess the association between serum klotho protein levels and metabolic syndrome, while Cox regression analysis was employed to examine the correlation between serum klotho levels and all-cause mortality. Mortality data were updated until December 31, 2019. After adjusting for demographic and socioeconomic confounders, the logistic regression model demonstrated that higher serum klotho levels were significantly associated with a decreased prevalence of metabolic syndrome (OR [95% CI] Highest vs. lowest quartile: 0.84 [0.70-0.99], P=0.038). In the Cox regression model, elevated klotho levels were found to significantly reduce the risk of all-cause mortality among individuals with metabolic syndrome (HR [95% CI] Highest vs. lowest quartile: 0.68 [0.51-0.90], P=0.006). Serum klotho levels were found to be inversely associated with the prevalence of metabolic syndrome, independent of potential confounding factors such as demographics, socioeconomic status, and lifestyle factors. Furthermore, higher klotho levels strongly indicated a lower risk of all-cause mortality in individuals with metabolic syndrome.

Advances and Challenges in Sepsis Management: Modern Tools and Future Directions.

Sepsis, a critical condition marked by systemic inflammation, profoundly impacts both innate and adaptive immunity, often resulting in lymphopenia. This immune alteration can spare regulatory T cells (Tregs) but significantly affects other lymphocyte subsets, leading to diminished effector functions, altered cytokine profiles, and metabolic changes. The complexity of sepsis stems not only from its pathophysiology but also from the heterogeneity of patient responses, posing significant challenges in developing universally effective therapies. This review emphasizes the importance of phenotyping in sepsis to enhance patient-specific diagnostic and therapeutic strategies. Phenotyping immune cells, which categorizes patients based on clinical and immunological characteristics, is pivotal for tailoring treatment approaches. Flow cytometry emerges as a crucial tool in this endeavor, offering rapid, low cost and detailed analysis of immune cell populations and their functional states. Indeed, this technology facilitates the understanding of immune dysfunctions in sepsis and contributes to the identification of novel biomarkers. Our review underscores the potential of integrating flow cytometry with omics data, machine learning and clinical observations to refine sepsis management, highlighting the shift towards personalized medicine in critical care. This approach could lead to more precise interventions, improving outcomes in this heterogeneously affected patient population.

Emerging Role of Non-collagenous Bone Proteins as Osteokines in Extraosseous Tissues.

Bone is a unique tissue, composed of various types of cells embedded in a calcified extracellular matrix (ECM), whose dynamic structure consists of organic and inorganic compounds produced by bone cells. The main inorganic component is represented by hydroxyapatite, whilst the organic ECM is primarily made up of type I collagen and non-collagenous proteins. These proteins play an important role in bone homeostasis, calcium regulation, and maintenance of the hematopoietic niche. Recent advances in bone biology have highlighted the importance of specific bone proteins, named "osteokines", possessing endocrine functions and exerting effects on nonosseous tissues. Accordingly, osteokines have been found to act as growth factors, cell receptors, and adhesion molecules, thus modifying the view of bone from a static tissue fulfilling mobility to an endocrine organ itself. Since bone is involved in a paracrine and endocrine cross-talk with other tissues, a better understanding of bone secretome and the systemic roles of osteokines is expected to provide benefits in multiple topics: such as identification of novel biomarkers and the development of new therapeutic strategies. The present review discusses in detail the known osseous and extraosseous effects of these proteins and the possible respective clinical and therapeutic significance.

Protein kinase C delta enhances the diagnostic performance of hepatocellular carcinoma

Background The conventional markers for hepatocellular carcinoma (HCC), α-fetoprotein (AFP) and des-γ-carboxy prothrombin (DCP), have several limitations; both have low sensitivity in patients with early-stage HCC; low sensitivity for AFP with HCC after eliminating hepatitis C virus (HCV); low specificity for DCP in patients with non-viral HCC, which is increasing worldwide; low specificity for AFP in patients with liver injury; and low specificity for DCP in patients treated with warfarin. To overcome these issues, the identification of novel biomarkers is an unmet need. Objective This study aimed to assess the usefulness of serum protein kinase C delta (PKCδ) for detecting these HCCs. Methods PKCδ levels were measured using a sandwich enzyme-linked immunosorbent assay in 363 chronic liver disease (CLD) patients with and without HCC. Results In both viral and non-viral CLD, PKCδ can detect HCCs with high sensitivity and specificity, particularly in the very early stages. Notably, the value and sensitivity of PKCδ were not modified by HCV elimination status. Liver injury and warfarin administration, which are known to cause false-positive results for conventional markers, did not modify PKCδ levels. Conclusions PKCδ is an enhanced biomarker for the diagnosis of HCC that compensates for the drawbacks of conventional markers.