Biomarkers最新文献

Background: Coronary microvascular disease (CMD) is defined by impaired myocardial stress flow reactivity and is associated with worse cardiovascular outcomes. Studying CMD is complicated by the overlap of its risk factors and patient-important cardiovascular sequelae with those of epicardial atherosclerotic disease. Published studies have not yet used longitudinal data to investigate the time dependencies of dynamic processes like obesity in their effects on microvascular health.

Methods and results: In a mixed-sex cohort of 85 patients for whom epicardial obstruction was angiographically excluded, a multivariate model was developed to measure strengths of association between repeated-measurement metabolic data and microvascular stress flow reactivity as assessed by position emission tomography myocardial perfusion imaging (PET-MPI). Body mass index (BMI) and the diagnosis of insulin-dependent diabetes mellitus (DM) were associated with CMD on clinically meaningful scales when analysing all metabolic data collected in the year prior to stress PET-MPI (β [95% CI]: -0.019 [-0.033, -0.0051], p = 0.0072; -0.33 [-0.65, -0.0026], p = 0.048). Parallel modelling using single-time-point metabolomics data generated comparable results, suggesting that simplified assessments may be used as valid surrogates for repeated-measurement data in this setting.

Background: Clear cell renal cell carcinoma (ccRCC) accounts for over 75% of all renal cancer and contributes significantly to cancer-associated mortality. This study aims to identify a new ccRCC biomarker.

Methods: Univariate and multivariate Cox regression models were performed for prognostic analysis. Differentially expressed genes (DEGs) were identified based on high and low expression AFAP1L1 groups. Functional enrichment analysis was conducted on the DEGs. Immunoassays were conducted on immune checkpoints, immune cells, and other components. Single-cell expression of AFAP1L1, along with associated pseudo-temporal trajectory analyses, was evaluated. The Connectivity Map was employed to identify potentially small-molecules. AFAP1L1 mRNA expression was measured via quantitative real-time PCR (qRT-PCR) experiment.

Results: AFAP1L1 was significantly upregulated in training cohort and qRT-PCR, and lower expression level was correlated with shorter overall survival, which was also confirmed in E-MTAB-1980 and CPTAC validation cohorts. The upregulated DEGs were primarily involved in signaling pathways, nephron development, and transporter activity. AFAP1L1 was positively related with neutrophils and macrophages. AFAP1L1 exhibited a relatively higher expression level in endothelial cells at the single-cell level. Five potential therapeutic agents targeting ccRCC were identified.

Conclusions: AFAP1L1 may impact the ccRCC development and progression, acting as an immune-related prognostic marker for ccRCC.

Background: Early and accurate detection of lung cancer remains a major clinical challenge. Conventional diagnostics, including X-ray, tissue biopsy etc., have limited sensitivity for identifying tumors early. Recent advances in molecular biology and computational technologies have significantly transformed lung cancer diagnostics.

Methods: This review examines recent developments in biomarker-driven and technology-assisted diagnostic strategies for lung cancer. It highlights clinical relevance of molecular biomarkers, including EGFR, ALK etc., and evaluates emerging approaches like next-generation sequencing (NGS), ctDNA analysis, AI-based analytical tools.

Results: Integration of molecular biomarkers into routine diagnostics has improved tumor subtyping and enabled more targeted therapeutic selection. Non-invasive approaches like liquid biopsy facilitate real-time tumor characterization and disease monitoring. In parallel, NGS and multi-omics technologies like genomics, transcriptomics provide comprehensive insights into tumor biology and tumor microenvironment. Advances in radiomics and AI-driven image analysis, particularly machine learning and deep learning applied to low-dose CT imaging, enhancing early detection and risk stratification. AI-powered detection systems and predictive models further support clinical decision-making.

Conclusions: The convergence of biomarker research, multi-omics technologies, and AI-driven analytics is reshaping lung cancer diagnostics toward more precise and personalized approaches. However, challenges related to data standardization, interpretability, clinical validation, and ethical considerations must be addressed to enable widespread clinical implementation.

Background: The leukocyte-specific protein 1 (LSP1) has been implicated in cancer progression, and this paper aims to reveal the prognostic value and pathogenic role of LSP1 in acute myeloid leukemia (AML).

Methods: The TCGA and GTEx datasets were performed to assess the expression and prognostic significance of LSP1 in AML. RT-qPCR was utilized to detect LSP1 expression in AML patients. The impact of LSP1 knockdown on AML was assessed using CCK-8, 7-AAD/Annexin-V assays, and xenograft mouse models. Gene Set Enrichment Analysis (GSEA), RT-qPCR, and functional experiments were employed to explore and verify the potential mechanism of LSP1 in AML.

Results: Our findings revealed that a high expression level of LSP1 indicated poor prognosis for AML. Meanwhile, the knockdown of LSP1 could inhibit AML in vitro and in vivo. Next, we observed that the NF-κB signaling pathway, associated with anti-apoptotic effects, was significantly upregulated in the high LSP1 expression group, and knocking down LSP1 could inhibit it. In addition, we also found that the NF-κB pathway-related anti-AML effect of bortezomib partially relied on LSP1.

Conclusions: This study revealed that LSP1 plays a crucial role in the progression of AML, indicating its potential as a prognostic biomarker and therapeutic target.

Background: This study evaluates dielectric conductivity as a diagnostic tool for assessing lead (Pb) levels in albino rats. It also investigated the ameliorative potentials of nano-ZnO-capped sodium carboxymethyl cellulose (nZnO/CMC).

Methods: The synthesis of nZnO/CMC was carried out using the co-precipitation technique. The average crystallite size obtained from X-ray diffraction measurements is 33 nm. The effect of Pb induced toxicity was evaluated using four groups of six albino rats each, administered with Pb for 61 days. Haematological results show decreasing trends in packed cell volume, red blood and white blood cell counts, and haemoglobin concentration with Pb administration, indicating anaemia.

Results: Upon nZnO/CMC administration, both haematological and erythrocyte surface sialic acids parameters were restored, suggesting that nZnO/CMC has Pb chelating capabilities. Toxicity studies revealed nZnO/CMC to be non-toxic to tissues below 2000 mg/kg body weight per os. Dielectric conductivity of normal and exposed blood samples measured between 200 Hz and 4 MHz shows dominance of ionic conductivity; highest for Pb-exposed samples and lowest for the normal/control sample. It also showed a decreasing trend for samples treated with nZnO/CMC at 100 and 200 mg/kg.

Conclusions: This work strongly correlates changes in dielectric ionic conductivity with haematological/Pb exposure concentration, suggesting that dielectric ionic conductivity of blood is a promising biomarker for Pb-exposed animals.

Background: Apigenin, a flavonoid predominantly found in citrus fruits, particularly grapefruit, has attracted significant attention in ethnopharmacology for its diverse therapeutic properties.

Method: This systematic review focuses on the potential therapeutic impact of Apigenin on sepsis complications. Google Scholar, Scopus, Web of Science, PubMed, and Embase databases were searched for relevant keywords up to August 2025.

Results: A total of 421 articles were screened in the databases. Finally, only 30 studies remain. Evidence from in vivo and in vitro studies indicates that Apigenin modulates key signaling pathways, such as Rho-associated coiled-coil containing protein kinase (ROCK)/Ras homolog family member A (RhoA)/Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB), Peroxisome Proliferator-activated receptor gamma (PPARγ)/microRNA-21 (miR-21), and Kelch-like eCH-associated protein 1 (KEAP1)/Nuclear factor erythroid 2-related factor 2 (Nrf2), to alleviate sepsis-induced damage in organs such as the lungs, intestines, heart, and kidneys. Additionally, Apigenin promotes macrophage polarization toward the M2 macrophage phenotype (M2) phenotype and reduces proinflammatory cytokines (Interleukin-6 (IL-6), Tumor necrosis factor alpha (TNF-α), Interleukin-1 beta (IL-1β).

Conclusion: Despite its promising effects, further clinical trials are needed to confirm its efficacy and safety in septic patients.

A precise diagnosis and customized treatment become more difficult by the genomic heterogeneity of breast cancer (BRCA). In order to examine gene expression data from two separate Gene Expression Omnibus (GEO) microarray datasets, we used a integrative approach in this study that combined bioinformatics and machine learning. We were able to distinguish between universal and subtype-specific transcriptome patterns by identifying both common and subtype-specific differentially expressed genes (DEGs) using dual-level differential expression analysis. Functional enrichment analysis and the creation of protein-protein interaction networks identified important hub genes, including TPM3, MYLK, and COL17A1, which showed substantial dysregulation and were linked to high mutation rates and a bad prognosis. Survival analyses, which identified COL17A1 as a predictive predictor for the general population and MYLK for the Luminal B subtype, highlighted the clinical significance of these hub genes. We used both Random Forest and K-Nearest Neighbors classifiers to ensure robust biomarker identification. In the analysis, we prioritized 35 model-agnostic biomarkers that performed well in subtype categorization, such as PNMT and KRTAP10-8. This dual-model approach improved the reliability of biomarker identification while reducing model-specific biases. These results set the stage for early identification, more accurate subtype classification, and possible therapeutic targeting in breast cancer.

Background: This study aimed to explore the effect of cerebral ischemia reperfusion and losartan on oxidative stress, inflammation, apoptosis and its molecular mediators in the rat heart.

Methods: The groups were as follows (n = 6): 1) sham 2) ischemia-reperfusion (IR, 20 min ischemia, 24 hours reperfusion), 3) ischemia-reperfusion +losartan (IR+LOS, 3 mg/kg, ip, 1 hour before ischemia).

Results: In IR animals' heart Malondialdehyde (MDA) and Total oxidative status (TOS) increased while Total antioxidant capacity (TAC) and Superoxide dismutase (SOD) concentration decreased compared to the sham rats. Cerebral IR increased apoptotic index evidenced by increased cytochrome c and c-caspase3/p-caspase3 ratio, as well as elevated Toll-like receptor4 (TLR4), Nuclear factor kappa B (NF-κB) and Heat Shock Protein70 (HSP70) in the heart tissue. These alterations could be mitigated by losartan. TLR4 is positively correlated with NF-κB, r = 0.61; MDA, r = 0.64; TOS, r = 0.59 (p < 0.01), cytochrome c, r = 0.83; c-caspase3/p-caspase3, r = 0.72 (p < 0.001) and negatively correlated with SOD, r = -0.73 and TAC, r = -0.83 (p < 0.001).

Conclusion: In all, cerebral IR increased cardiac oxidative stress and apoptosis which positively correlates with TLR4/NF-κB pathway. Additionally, losartan can be considered as a potential target for preventing cardiac injury following stroke.

Background: Reliable molecular biomarkers are urgently needed for early diagnosis of colorectal cancer (CRC). This study aimed to identify and validate a robust diagnostic gene signature using integrated transcriptomic analysis.

Methods: Eleven public gene expression datasets published between 2014 and 2024 were combined, including 329 CRC samples and 48 normal controls. Batch effects were corrected using the COMBAT algorithm. Differentially expressed genes (DEGs) were identified and subjected to Gene Ontology, KEGG, and REACTOME enrichment analyses. A protein-protein interaction network was constructed to screen hub genes. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis and a disease risk score (DRS), followed by external validation in independent cohorts.

Results: A total of 1,101 DEGs were identified, including 544 upregulated and 557 downregulated genes. Functional enrichment analysis indicated that upregulated genes were mainly involved in extracellular matrix organization and inflammatory signaling, whereas downregulated genes were associated with transport and metal ion homeostasis. A ten-gene signature was established. The DRS demonstrated strong diagnostic performance in the pooled dataset (AUC = 0.96) and in external validation cohorts (AUC = 0.985). Stage-stratified analysis confirmed robust discrimination across all CRC stages, including early disease.

Conclusions: This integrative analysis identified a stable ten-gene diagnostic signature with potential clinical utility for CRC detection.