Biomarker For Progression Research Articles

Evaluation of aortic inflammation in marfan syndrome patients with 18-fluorodeoxyglucose positron emission and computed tomography

Abstract Background The role of inflammation in Marfan Syndrome (MS) has been well studied in current research. These studies have investigated the role of serum Interleukin 6 (IL-6), Transforming Growth Factor-b (TGF-b) and Macrophage Colony Stimulating Factor (MCSF) as biomarkers of disease severity and progression. Surgical repair is the recommended treatment of aortic aneurysms when aortic diameter reaches the indication threshold. It has been observed however that dissection may occur even before this threshold is reached. 18-fluorodeoxyglucose (18 F-FDG) positron emission tomography / computed tomography (PET/CT) is the gold standard imaging modality to noninvasively assess vascular inflammation. Purpose Our aim was to investigate whether MS patients have increased aortic wall inflammation as assessed by 18F-FDG PET/CT, and the relationship between aortic wall inflammation with serum levels of IL-6, TGF-b and MCSF. Methods The study population consisted of fifteen MS patients and seventeen sex and age matched controls. In all subjects, serum levels of circulating IL-6, TGF-b and MCSF were measured. In PET/CT, the arterial target-to-background ratio (TBR) was calculated for aortic root (AR), ascending aorta(As), aortic arch (AA) and descending aorta (DA). Also, all patients were assessed with transthoracic echocardiography where the diameters of AR, As, AA, and DA were estimated at baseline and were reexamined at the end of the follow up period. Results TBR for MS vs. controls was 2.17±0.75 vs 2.01±0.36, p=0.75 at AR; 2.15±0.67 vs 1.84±0.35, p=0.040 at As; 2.16±0.64 vs 1.70±0.25, p=0.03 at AA and 1.97±0.52 vs 1.83±0.42, p=0.23 at DA respectively. Additionally, Marfan patients had increased levels of serum biomarkers. However, this difference was statistically significant (SS) only for IL-6 serum levels; 2.17±0.46 vs 1.08±0.12 p=0.011 and IL-6 was SS associated with AR TBR (rho=0.572, p=0.033), As TBR (rho=0.604, p=0.022) and DA TBR (rho=0.714, p=0.004). Two of our patients were operated due to severe dilatation of the aortic root and the surgical specimens showed aortic wall inflammation of moderate severity along with fibrosis. Conclusion MS patients have increased of aortic wall inflammation as assessed by 18F-FDG PET/CT, a finding that highlights the role of inflammation in the pathway of disturbed TGF signaling. Further research is needed to determine its prognostic role and value in identifying patients with an indication for early intervention.

Role of baseline soluble tumor necrosis factor receptor 2 as a biomarker in primary podocytopathy: Implications for renal impairment and disease progression

BackgroundPodocytopathies, including minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), and collapsing glomerulopathy (CG), are kidney diseases that damage glomerular podocytes, leading to heavy proteinuria and nephrotic syndrome (NS). Inflammation plays a critical role in the progression of chronic kidney disease (CKD), with recent studies linking inflammatory biomarkers to declining kidney function. Tumor necrosis factor-alpha (TNF-α), an essential inflammatory cytokine, interacts with its circulating receptors, TNFR1 and TNFR2. The TNF-α pathway has been implicated in the pathogenesis of FSGS and MCD. Increased circulating TNFR2 levels have been associated with worsening renal function in podocytopathies, suggesting that the TNF-α inflammatory pathway significantly contributes to disease progression.MethodsWe conducted a study involving 53 patients with biopsy-proven MCD or FSGS and 53 healthy, age- and gender-matched controls. All patients were followed for 18 months. We analyzed serum and urine TNFR2 levels and gene expression at baseline and after three months. To assess the ability of TNFR2 to predict persistent decline in estimated glomerular filtration rate (eGFR < 30 mL/min/1.73m2), remission, and relapse, we employed Cox regression analysis. Additionally, we evaluated its prognostic utility for predicting progression to stage 4 CKD using ROC curve analysis.ResultsSerum and urine TNFR2 levels were significantly elevated in patients compared to controls. Serum TNFR2 was a significant predictor in univariate Cox regression analysis for persistent eGFR decline (HR 1.017, 95% CI: 1.003 to 1.032, p = 0.018), remission (HR 0.995, 95% CI: 0.992 to 0.999, p = 0.006), and relapse (HR 1.005, 95% CI: 1.001 to 1.010, p = 0.029). The ROC curve analysis demonstrated that serum TNFR2 levels had a strong prognostic ability for predicting progression to stage 4 CKD, with an AUC of 0.848 (95% CI: 0.737—0.960), sensitivity of 81%, and specificity of 71%.ConclusionThis study underscores the critical role of circulating TNFR2 in kidney injury among patients with primary podocytopathy. Elevated TNFR2 levels are significant predictors of persistent eGFR decline and disease relapse, highlighting their potential as biomarkers for disease progression and prognosis.

Prospective Longitudinal Analysis of Physiologic MRI-based Tumor Habitat Predicts Short-term Patient Outcomes in IDH-wildtype Glioblastoma.

This study validates MRI-based tumor habitats in predicting time-to-progression (TTP), overall survival (OS), and progression site in isocitrate dehydrogenase (IDH)-wildtype glioblastoma patients. Seventy-nine patients were prospectively enrolled between January 2020 and June 2022. MRI, including diffusion-weighted and dynamic susceptibility contrast imaging, were obtained immediately post-operation and at three serial timepoints. Voxels from cerebral blood volume and apparent diffusion coefficient maps were grouped into three habitats (hypervascular cellular, hypovascular cellular, and nonviable tissue) using k-means clustering. Pre-defined cutoffs for increases in hypervascular and hypovascular cellular habitat were applied to calculate the habitat risk score. Associations between spatiotemporal habitats, habitat risk score, TTP, and OS were investigated using Cox proportional hazards modeling. Habitat risk score was compared to tumor volume using time-dependent receiver operating characteristics analysis. Progression sites were matched with spatial habitats. Increases in hypervascular and hypovascular cellular habitats and habitat risk scores were associated with shorter TTP and OS (all p < .05). Hypovascular cellular habitat and habitat risk scores 1 and 2 independently predicted TTP (hazard ratio [HR], 4.14; p=.03, HR, 4.51; p=.001 and HR, 10.02; p<.001, respectively). Hypovascular cellular habitat and habitat risk score 2 independently predicted OS (HR, 4.01, p=.003; and HR, 3.27, p<.001, respectively). Habitat risk score outperformed tumor volume in predicting TTP (12-month AUC, 0.762 vs. 0.646, p=.048). Hypovascular cellular habitat predicted progression sites (mean Dice index: 0.31). Multiparametric physiologic MRI-based spatiotemporal tumor habitats and habitat risk scores are useful biomarkers for early tumor progression and outcomes in IDH-wildtype glioblastoma patients.

Identification and Characterization of Candidate Platelet Proteins as Potential Biomarkers for Alcoholism Patients

ABSTRACT Chronic alcoholism is a significant health issue associated with severe liver complications, including alcohol-related liver disease (ARLD), progresses from fatty liver to cirrhosis and hepatocellular carcinoma. ARLD affects liver function and hemostasis, leading to increased mortality. This study intended to investigate the platelet proteome and detect platelet cell apoptosis via Annexin V in ARLD patients in order to identify potential biomarkers for disease progression. Twenty-three patients with ARLD were recruited, and their platelet proteins were analyzed via mass spectrometry. Approximately 441 proteins were identified, with 73 candidate biomarkers selected on the basis of clinical importance. Key proteins involved in coagulation, inflammation, and cellular interactions are highlighted. Gene ontology and STRING analyses revealed functional clusters and pathways associated with these proteins. This study identified critical platelet proteins, such as von Willebrand factor, vitronectin, and serpin family members, which play essential roles in hemostasis and liver disease pathogenesis. These findings suggest that changes in platelet proteomes could serve as noninvasive biomarkers for diagnosing, monitoring, and treating ARLD.

Enzyme Activity-Based Optical Probe: Imaging Aminopeptidases N in Vulnerable Plaque and Serum.

Cardiovascular disease, a chronic and progressive arterial wall disease, is increasingly recognized for its clinical significance. Aminopeptidases N (APN), crucial in the pathophysiological processes of vulnerable plaque, have been linked to endothelial dysfunction, oxidative stress, and plaque formation, thus highlighting their potential as biomarkers for disease progression. However, current detection methods for APN in body fluids and in vivo have limitations, including insufficient sensitivity and specificity, time delays, and the inability to directly reflect enzyme activity in plaques. To address these challenges, we developed an optical probe, HD-APN, for in vivo imaging of aminopeptidases, providing a potential implementation in cardiovascular disease. Our work demonstrated the applicability of HD-APN for specific monitoring of aminopeptidase levels in plaques and serum, shedding light on its potential for further research in cardiovascular disease.

Oxidative Stress-mediated Lipid Peroxidation-derived Lipid Aldehydes in the Pathophysiology of Neurodegenerative Diseases.

Neurodegenerative diseases such as Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis cause damage and gradual loss of neurons affecting the central nervous system. Neurodegenerative diseases are most commonly seen in the ageing process. Ageing causes increased reactive oxygen species and decreased mitochondrial ATP generation, resulting in redox imbalance and oxidative stress. Oxidative stress-generated free radicals cause damage to membrane lipids containing polyunsaturated fatty acids, leading to the formation of toxic lipid aldehyde products such as 4- hydroxynonenal and malondialdehyde. Several studies have shown that lipid peroxidation-derived aldehyde products form adducts with cellular proteins, altering their structure and function. Thus, these lipid aldehydes could act as secondary signaling intermediates, modifying important metabolic pathways, and contributing to the pathophysiology of several human diseases, including neurodegenerative disorders. Additionally, they could serve as biomarkers for disease progression. This narrative review article discusses the biological and clinical significance of oxidative stress-mediated lipid peroxidation-derived lipid aldehydes in the pathophysiology of various neurodegenerative diseases.

A data mining approach to identify key radioresponsive genes in mouse model of radiation induced intestinal injury

Radiation-mediated GI injury (RIGI) in humans either due to accidental or intentional exposures, can only be managed with supporting care with no approved countermeasures available till now. Early detection and monitoring of RIGI is important for effective medical management and improve survival chances in exposed individual. The present study aims to identify new signatures of RIGI using data mining approach followed by validation of selected hub genes in mouse model. Using microarray datasets from Gene Expression Omnibus database, differentially expressed genes were identified. Pathway analysis suggested lipid metabolism as one of the predominant pathways altered in irradiated GI tissue. A protein-protein interaction network revealed top 08 hub genes related to lipid metabolism, namely Fabp1, Fabp2, Fabp6, Npc1l1, Ppar-α, Abcg8, Hnf-4α, and Insig1. qRT-PCR analysis revealed significant up-regulation of Fabp6 and Hnf-4α and down-regulation of Fabp1, Fabp2 and Insig1 transcripts in irradiated intestine. Radiation dose and time kinetics study revealed that the selected 05 genes were altered differentially in the irradiated intestine. Extensive alteration in lipid profiles and modification was observed in irradiated intestine. Finding suggests that lipid metabolism is one of the key targets of radiation and its mediators may act as biomarkers in detection and progression of RIGI.

Exploring the Gene Expression and Plasma Protein Levels of HSP90, HSP60, and GDNF in Multiple Sclerosis Patients and Healthy Controls.

Multiple sclerosis (MS) is a chronic neurodegenerative disease characterized by immune-mediated inflammation and neurodegeneration in the central nervous system (CNS). In this study; we aimed to investigate the gene expression and plasma protein levels of three neuroprotective genes-heat shock proteins (HSP90 and HSP60) and glial cell line-derived neurotrophic factor (GDNF)-in MS patients compared to healthy controls. Forty patients with relapsing-remitting MS and 40 healthy volunteers participated in this study. Gene expression was measured using reverse transcription quantitative real-time PCR, and protein levels were assessed via ELISA. The results showed a significant increase in HSP90 (1.7-fold) and HSP60 (2-fold) gene expression in MS patients compared to controls, along with corresponding increases in protein levels (1.5-fold for both HSP90 and HSP60). In contrast, GDNF gene expression and protein levels were significantly reduced in MS patients, with a 7-fold decrease in gene expression and a 1.6-fold reduction in protein levels. Notably, a non-linear relationship between GDNF gene expression and protein concentration was observed in MS patients, suggesting complex regulatory mechanisms influencing GDNF in the disease. The upregulation of HSP90 and HSP60 in MS highlights their roles in immune regulation and stress responses, while the reduction in GDNF indicates impaired neuroprotection. These findings suggest that HSP90, HSP60, and GDNF could serve as biomarkers for disease progression and as potential therapeutic targets in MS, offering promising avenues for future research and treatment development.

Serum metabolite and metal ions profiles for breast cancer screening

Enhancing early-stage breast cancer detection requires integrating additional screening methods with current diagnostic imaging. Omics screening, using easily collectible serum samples, could serve as an initial step. Alongside biomarker identification capabilities, omics analysis allows for a comprehensive analysis of prevalent histological types—DCIS and IDC. Employing metabolomics, metallomics, and machine learning, could yield accurate screening models with valuable insights into organism responses. Serum samples of confirmed breast cancer patients were utilized to analyze metabolite and metal ion profiles, using two distinct analysis methods, proton NMR for metabolomics and ICP-OES for metallomics. The resulting responses were then subjected to discriminant analysis, progression biomarker exploration, examination of correlations between patients’ metabolites and metal ions, and the impact of age and menopause status. Measured NMR spectra and metabolite relative integrals were used to achieve statistically significant discrimination through MVA between breast cancer and control groups. The analysis identified 24 metabolites and 4 metal ions crucial for discrimination. Furthermore, four metabolites were associated with disease progression. Additionally, there were important correlations and relationships between metabolite relative integrals, metal ion concentrations, and age/menopausal status subgroups. Quantified relative integrals allowed for discrimination between studied subgroups, validated with a holdout set. Feature importance and statistical analysis for metabolomics and metallomics extracted a set of common entities which in combination provides valuable insights into ongoing molecular disturbances and disease progression.

In vivo CRISPRi screen identified lncRNA portfolio crucial for cutaneous squamous cell carcinoma tumor growth.

Cutaneous squamous cell carcinoma (cSCC) accounts for 20% of all skin cancer deaths globally, making it the second-highest subtype of skin cancer. The prevalence of cSCC in humans, as well as the poor capacity for an efficient prognosis, highlights the need to uncover alternative actors and mechanisms at the foundation of skin cancer development. Significant advances have been made to better understand some key factors in cSCC progression. However, little is known about the role of noncoding RNAs, particularly of a specific category called long noncoding RNA (lncRNA). By performing pseudobulk analysis of single-cell sequencing data from normal and cSCC human skin tissues, we determined a global portfolio of lncRNAs specifically expressed in keratinocyte subpopulations. Integration of CRISPR interference screens in vitro and the xenograft model identified several lncRNAs impacting the growth of cSCC cancer lines both in vitro and in vivo. Among these, we further validated LINC00704 and LINC01116 as proliferation-regulating lncRNAs in cSCC lines and potential biomarkers of cSCC progression. Taken together, our study provides a comprehensive signature of lncRNAs with roles in regulating cSCC progression.

In Silico Drug Designing for the Treatment of Cervical Cancer

Cervical cancer is a significant global health issue, especially in developing countries where limited resources and access to health services pose challenges. Human papillomavirus (HPV) is the primary cause of cervical cancer and plays a crucial role in its development. Despite the availability of prophylactic vaccines, effective treatment options for patients with pre-existing HPV infections or HPV-induced carcinomas remain elusive. High-risk (HR) HPV E6 and E7 oncoproteins serve as biomarkers in cervical cancer progression. Research has focused on these proteins as potential targets for novel therapeutics and to find a novel compound that can overcome the problem of drug ineffectiveness in cancerous cells as they become multi-drug resistant to the existing drugs and treatments. Computational-aided methods, including in silico approaches, have been valuable in discovering and developing small molecules that can block or inhibit the action of the E6 oncoproteins. This review discusses existing small molecules explored as HPV E6 protein blockers and outlines future perspectives in cervical cancer therapy. In molecular docking studies, it is identified that four compounds exhibit the properties of a lead molecule, possess more negative binding energy, and form a highly stable ligand-receptor complex. These four compounds will help in future research and development. Advancing these compounds through further studies will help develop potential and effective drugs for treating cervical cancer.

A Machine Learning Approach to Gene Expression in Hypertrophic Cardiomyopathy

Background/Objectives: Hypertrophic cardiomyopathy (HCM) is a common heart disorder characterized by the thickening of the heart muscle, particularly in the left ventricle, which increases the risk of cardiac complications. This study aims to analyze the expression of apoptosis-regulating genes (CASP8, CASP9, CASP3, BAX, and BCL2) in blood samples from HCM patients, to better understand their potential as biomarkers for disease progression. Methods: Quantitative real-time PCR (qPCR) was used to evaluate gene expression in blood samples from 93 HCM patients. The correlation between apoptosis-regulating genes was conducted and clinical parameters were integrated for feature importance and clustering analysis. Results: Most patients exhibited significant downregulation of CASP8, CASP9, and CASP3. In contrast, BAX expression was elevated in 71 out of 93 patients, while BCL2 was increased in 55 out of 93 patients. Correlation analysis revealed weak negative correlations between the BAX/BCL2 ratio and CASP gene expression. Conclusions: These findings suggest that reduced expression of apoptotic genes may indicate a protective cellular mechanism, which could serve as a biomarker for disease progression. Further studies are needed to investigate the potential for therapeutic modulation of these pathways to improve patient outcomes.

Metabolic remodeling in glioblastoma: a longitudinal multi-omics study

Monitoring tumor evolution and predicting survival using non-invasive liquid biopsy is an unmet need for glioblastoma patients. The era of proteomics and metabolomics blood analyzes, may help in this context. A case–control study was conducted. Patients were included in the GLIOPLAK trial (ClinicalTrials.gov Identifier: NCT02617745), a prospective bicentric study conducted between November 2015 and December 2022. Patients underwent biopsy alone and received radiotherapy and temozolomide. Blood samples were collected at three different time points: before and after concomitant radiochemotherapy, and at the time of tumor progression. Plasma samples from patients and controls were analyzed using metabolomics and proteomics, generating 371 omics features. Descriptive, differential, and predictive analyses were performed to assess the relationship between plasma omics feature levels and patient outcome. Diagnostic performance and longitudinal variations were also analyzed. The study included 67 subjects (34 patients and 33 controls). A significant differential expression of metabolites and proteins between patients and controls was observed. Predictive models using omics features showed high accuracy in distinguishing patients from controls. Longitudinal analysis revealed temporal variations in a few omics features including CD22, CXCL13, EGF, IL6, GZMH, KLK4, and TNFRSP6B. Survival analysis identified 77 omics features significantly associated with OS, with ERBB2 and ITGAV consistently linked to OS at all timepoints. Pathway analysis revealed dynamic oncogenic pathways involved in glioblastoma progression. This study provides insights into the potential of plasma omics features as biomarkers for glioblastoma diagnosis, progression and overall survival. Clinical implication should now be explored in dedicated prospective trials.

Acetylation of Histone H3 in Cancer Progression and Prognosis

Cancer is a multifactorial disease resulting from both genetic factors and epigenetic changes. Histone acetylation, a post-translational modification, which alters chromatin architecture and regulates gene expression is associated with cancer initiation, development and progression. Aberrations in global histone acetylation levels are observed in various cancer cells and are also associated with patients’ tumor aggressiveness. Therefore, histone acetylation may have prognostic utility and serve as a potential biomarker of cancer progression and patients’ prognosis. The reversible modification of histones by an acetyl group is versatile. One particular histone can be acetylated on different lysine residues, subsequently resulting in different biological outcomes. Here, we discuss recent findings on the acetylation of the highly conserved histone protein H3 in the context of cancer biology. Specifically, we review the acetylation of particular H3 residues in various cancer types. We further highlight the significance of H3 acetylation levels as a potential cancer biomarker with prognostic implications.

Determination of digital biomarkers of disease progression for digital phenotyping of patients with arterial hypertension.

Background: To compare the effectiveness of digital phenotyping of patients diagnosed with arterial hypertension with traditional monitoring methods over a three-year period. Patients and methods: The study was conducted from January 2021 to January 2024 among 800 patients diagnosed with arterial hypertension at 6 clinics in Moscow, Russia, evenly divided into experimental (identification of digital biomarkers of disease progression for digital phenotyping) and control (standard monitoring methods) groups. The intervention included lifestyle changes focused on increasing physical activity, improving sleep quality, reducing stress, and modifying diet. Significant improvements were observed in the experimental group compared to the control group. Systolic blood pressure decreased by 10 mmHg (p<0.001), pulse by 5 beats per minute (p<0.001), and stress level by 2 points (p<0.001) in the experimental group. Additionally, physical activity increased by 15 minutes per day (p<0.001), and sleep quality improved by 2 points on a scale from 1 to 10 (p<0.001). Results: Multiple regression analysis showed a decrease in the significance of digital biomarkers over the study period, indicating a positive response to the intervention. Conclusions: The obtained results emphasize the importance of comprehensive interventions in managing arterial hypertension and its related conditions. Implementing comprehensive lifestyle changes can lead to significant health improvements and serve as an effective preventive strategy. Further research is needed to explore optimal intervention strategies for promoting societal health.

TRANSFORMING HEALTHCARE DELIVERY THROUGH BIG DATA IN HOSPITAL MANAGEMENT SYSTEMS: A REVIEW OF RECENT LITERATURE TRENDS

This systematic review investigates the transformative role of big data technologies in biomedical research, analyzing 40 peer-reviewed articles published between 2010 and 2023. The review specifically explores advancements in next-generation sequencing (NGS), multi-omics approaches, machine learning, and artificial intelligence (AI), all of which have significantly enhanced the understanding of complex biological systems and diseases. NGS has emerged as a key tool in personalized medicine, enabling rapid and cost-effective genome sequencing that has facilitated the identification of genetic mutations and biomarkers associated with various diseases, particularly in oncology. Of the 40 studies reviewed, 12 focused on the integration of multi-omics data—genomics, transcriptomics, proteomics, and metabolomics—to provide a comprehensive view of biological processes. These multi-omics approaches have been instrumental in identifying biomarkers for disease progression and response to treatments, offering new avenues for drug development and precision medicine. Additionally, 15 studies highlighted the growing application of machine learning and AI algorithms in managing and analyzing vast biomedical datasets. These tools are now critical in uncovering hidden patterns within large datasets, predicting disease outcomes, and improving the accuracy of clinical decision-making. However, 10 studies emphasized ongoing challenges related to data storage, privacy concerns, and the lack of standardized data formats, which hinder effective data sharing across institutions. Despite these challenges, the integration of AI, IoT devices, and big data analytics is paving the way for more personalized, real-time healthcare monitoring and treatment solutions. This review concludes that while significant advancements have been made, further efforts are required to address the ethical and technical barriers that limit the full potential of big data technologies in biomedical research.

Circulating Factors as Potential Biomarkers of Cardiovascular Damage Progression Associated with Type 2 Diabetes.

Background: Diabetes, particularly type 2 diabetes (T2D), is linked with an increased risk of developing coronary heart disease (CHD). The present study aimed to evaluate potential circulating biomarkers of CHD by adopting a targeted proteomic approach based on proximity extension assays (PEA). Methods: The study was based on 30 patients with both T2D and CHD (group DC), 30 patients with T2D without CHD (group DN) and 29 patients without diabetes but with a diagnosis of CHD (group NC). Plasma samples were analyzed using PEA, with an Olink Target 96 cardiometabolic panel expressed as normalized protein expression (NPX) units. Results: Lysosomal Pro-X carboxypeptidase (PRCP), Liver carboxylesterase 1 (CES1), Complement C2 (C2), and Intercellular adhesion molecule 3 (ICAM3) were lower in the DC and NC groups compared with the DN groups. Lithostathine-1-alpha (REG1A) and Immunoglobulin lambda constant 2 (IGLC2) were found higher in the DC group compared to DN and NC groups. ROC analysis suggested a significant ability of the six proteins to distinguish among the three groups (whole model test p < 0.0001, AUC 0.83-0.88), with a satisfactory discriminating performance in terms of sensitivity (77-90%) and specificity (70-90%). A possible role of IGLC2, PRCP, and REG1A in indicating kidney impairment was found, with a sensitivity of 92% and specificity of 83%. Conclusions: The identified panel of six plasma proteins, using a targeted proteomic approach, provided evidence that these parameters could be considered in the chronic evolution of T2D and its complications.

The contribution of the sphingosine 1-phosphate signaling pathway to chronic kidney diseases: recent findings and new perspectives.

Chronic kidney disease (CKD) is a multifactorial condition with diverse etiologies, such as diabetes mellitus, hypertension, and genetic disorders, often culminating in end-stage renal disease (ESRD). A hallmark of CKD progression is kidney fibrosis, characterized by the excessive accumulation of extracellular matrix components, for which there is currently no effective anti-fibrotic therapy. Recent literature highlights the critical role of sphingosine 1-phosphate (S1P) signaling in CKD pathogenesis and renal fibrosis. This review provides an in-depth analysis of the latest findings on S1P metabolism and signaling in renal fibrosis and in specific CKDs, including diabetic nephropathy (DN), lupus nephritis (LN), focal segmental glomerulosclerosis (FSGS), Fabry disease (FD), and IgA nephropathy (IgAN). Emerging studies underscore the therapeutic potential of modulating S1P signaling with receptor modulators and inhibitors, such as fingolimod (FTY720) and more selective agents like ozanimod and cenerimod. Additionally, the current knowledge about the effects of established kidney protective therapies such as glucocorticoids and SGLT2 and ACE inhibitors on S1P signaling will be summarized. Furthermore, the review highlights the potential role of S1P as a biomarker for disease progression in CKD models, particularly in Fabry disease and diabetic nephropathy. Advanced technologies, including spatial transcriptomics, are further refining our understanding of S1P's role within specific kidney compartments. Collectively, these insights emphasize the need for continued research into S1P signaling pathways as promising targets for CKD treatment strategies.

A proinflammatory stem cell niche drives myelofibrosis through a targetable galectin-1 axis.

Myeloproliferative neoplasms are stem cell-driven cancers associated with a large burden of morbidity and mortality. Most patients present with early-stage disease, but a substantial proportion progress to myelofibrosis or secondary leukemia, advanced cancers with a poor prognosis and high symptom burden. Currently, it remains difficult to predict progression, and therapies that reliably prevent or reverse fibrosis are lacking. A major bottleneck to the discovery of disease-modifying therapies has been an incomplete understanding of the interplay between perturbed cellular and molecular states. Several cell types have individually been implicated, but a comprehensive analysis of myelofibrotic bone marrow is lacking. We therefore mapped the cross-talk between bone marrow cell types in myelofibrotic bone marrow. We found that inflammation and fibrosis are orchestrated by a "quartet" of immune and stromal cell lineages, with basophils and mast cells creating a TNF signaling hub, communicating with megakaryocytes, mesenchymal stromal cells, and proinflammatory fibroblasts. We identified the β-galactoside-binding protein galectin-1 as a biomarker of progression to myelofibrosis and poor survival in multiple patient cohorts and as a promising therapeutic target, with reduced myeloproliferation and fibrosis in vitro and in vivo and improved survival after galectin-1 inhibition. In human bone marrow organoids, TNF increased galectin-1 expression, suggesting a feedback loop wherein the proinflammatory myeloproliferative neoplasm clone creates a self-reinforcing niche, fueling progression to advanced disease. This study provides a resource for studying hematopoietic cell-niche interactions, with relevance for cancer-associated inflammation and disorders of tissue fibrosis.

Multi-omics analysis identifies BCAT2 as a potential pan-cancer biomarker for tumor progression and immune microenvironment modulation.

Branched-chain amino acid transaminase 2 (BCAT2) encodes a crucial protein involved in the initial catalysis of branched-chain amino acid (BCAA) catabolism, with emerging evidence suggesting its association with tumor progression. This study explores BCAT2 in a pan-cancer multi-omics context and evaluates its prognostic significance. We utilized a multi-database approach, analyzing cBioPortal for genetic alterations, RNA-Seq data from TCGA and GTEx for expression patterns, and RSEM for transcript analysis. Protein expression and interaction networks were assessed using the Human Protein Atlas, UniProt, and STRING. Prognostic value was determined through Cox regression analysis of TCGA clinical survival data, while immune cell infiltration across various cancers was examined using TCGA data and the TIMER2 platform. Our results revealed that BCAT2 alterations are primarily amplifications and is upregulated in various tumors, correlating with poor survival rates in several tumor types, including GBMLGG, LGG, and UVM. Elevated BCAT2 protein levels were common in pan-cancer, interacting with a range of metabolic enzymes. Additionally, BCAT2 expression significantly influenced CD4+ T cells, CD8+ T cells, and Treg cells infiltration, with varied correlations across cancer types. These findings indicate BCAT2 as a potential biomarker for cancer diagnosis and therapy, potentially regulating key metabolic and immune factors to mediate tumor progression and the microenvironment.