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At least 451 records · Page 25Linked to original sources
PubMed2024

Identification of autophagy-related genes as potential biomarkers correlated with immune infiltration in bipolar disorder: a bioinformatics analysis.

BACKGROUND: Bipolar disorder (BPD) is a kind of manic and depressive phase alternate episodes of serious mental illness, and it is correlated with well-documented cortical brain abnormalities. Emerging evidence supports that autophagy dysfunction in neuronal system contributes to pathophysiological changes in neurological disease. However, the role of autophagy in bipolar disorder has rarely been elucidated. This study aimed to identify the autophagy-related gene as a potential biomarker Correlated to immune infiltration in BPD. METHODS: The microarray dataset GSE23848 and autophagy-related genes (ARGs) were downloaded. Differentially expressed genes (DEGs) between normal and BPD samples were screened using the R software. Machine learning algorithms were performed to screen the significant candidate biomarker from autophagy-related differentially expressed genes (ARDEGs). The correlation between the screened ARDEGs and infiltrating immune cells was explored through correlation analysis. RESULTS: In this study, the autophagy pathway was abundantly enriched and activated in BPD, as indicated by Pathway enrichment analysis. We identified 16 ARDEGs in BPD compared to the normal group. A signature of 4 ARDEGs (ERN1, ATG3, CTSB, and EIF2AK3) was screened. ROC analysis showed that the above genes have good diagnostic performance. In addition, immune correlation analysis considered that the above four genes significantly correlated with immune cells in BPD. CONCLUSIONS: Autophagy - immune cell axis mediates pathophysiological changes in BPD. Four important ARDEGs are prospective to be potential biomarkers associated with immune infiltration in BPD and helpful for the prediction or diagnosis of BPD.

Bipolar Disorder↗
PubMed2025

Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease.

INTRODUCTION: Gastrointestinal disorders (GIDs) affect nearly 40% of the global population, with gut microbiome-metabolome interactions playing a crucial role in gastric cancer (GC), colorectal cancer (CRC), and inflammatory bowel disease (IBD). This study aims to investigate how microbial and metabolic alterations contribute to disease development and assess whether biomarkers identified in one disease could potentially be used to predict another, highlighting cross-disease applicability. METHODS: Microbiome and metabolome datasets from Erawijantari et al. (GC: n = 42, Healthy: n = 54), Franzosa et al. (IBD: n = 164, Healthy: n = 56), and Yachida et al. (CRC: n = 150, Healthy: n = 127) were subjected to three machine learning algorithms, eXtreme gradient boosting (XGBoost), Random Forest, and Least Absolute Shrinkage and Selection Operator (LASSO). Feature selection identified microbial and metabolite biomarkers unique to each disease and shared across conditions. A microbial community (MICOM) model simulated gut microbial growth and metabolite fluxes, revealing metabolic differences between healthy and diseased states. Finally, network analysis uncovered metabolite clusters associated with disease traits. RESULTS: Combined machine learning models demonstrated strong predictive performance, with Random Forest achieving the highest Area Under the Curve(AUC) scores for GC(0.94[0.83-1.00]), CRC (0.75[0.62-0.86]), and IBD (0.93[0.86-0.98]). These models were then employed for cross-disease analysis, revealing that models trained on GC data successfully predicted IBD biomarkers, while CRC models predicted GC biomarkers with optimal performance scores. CONCLUSION: These findings emphasize the potential of microbial and metabolic profiling in cross-disease characterization particularly for GIDs, advancing biomarker discovery for improved diagnostics and targeted therapies.

Humans↗
PubMed2005

Lack of CpG island methylator phenotype defines a clinical subtype of T-cell acute lymphoblastic leukemia associated with good prognosis.

PURPOSE: To examine cancer genes undergoing epigenetic inactivation in a set of T-cell acute lymphoblastic leukemias (T-ALLs) to obtain the CpG island methylator phenotype (CIMP) in the disease and its possible correlation with clinical features and outcome of the patients. PATIENTS AND METHODS: Methylation-specific polymerase chain reaction was used to analyze methylation of the ADAMTS-1, ADAMTS-5, APAF-1, ASPP-1, CDH1, CDH13, DAPK, DIABLO, DKK-3, LATS-1, LATS-2, NES-1, p14, p15, p16, p57, p73, PARK-2, PTEN, sFRP1/2/4/5, SHP-1, SYK, TMS-1, and WIF-1 genes in samples from 50 consecutive T-ALL patients (19 children and 31 adults). Results were compared with results obtained in 286 B-cell acute lymphoblastic leukemias (B-ALLs). RESULTS: A total of 88% of the T-ALL samples had at least one gene methylated. According to the number of methylated genes observed in each individual sample, 12 patients (24%) were included in the CIMP- group (zero to two methylated genes), and 38 patients (76%) were included in the CIMP+ group (> two methylated genes). Clinical features and remission rate did not differ significantly among both groups of patients. Estimated disease-free survival (DFS) rate at 12 years and overall survival (OS) rate at 13 years were 100% and 91% for the CIMP- group and 20% and 17% for the CIMP+ group, respectively (P = .0006 and P = .003, respectively). Multivariate analysis demonstrated that methylation profile was an independent prognostic factor in predicting DFS (P = .05) and OS (P = .02). A group of five genes (SYK-1, ASPP-1, sFRP-2, sFRP-5, and WIF-1) showed specificity for T-ALL compared with B-ALL. CONCLUSION: Our results suggest that the methylation profile may be a potential new biomarker of risk prediction in T-ALL.

Adolescent↗
PubMed2006

Biomarkers for amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is the most common form of motor neuron disease. ALS is a fatal neurodegenerative disease and clinical diagnosis typically takes many months to complete. Early disease diagnosis through the use of biomarkers may aid in correct clinical management of patients and possibly delay time to ventilator and morbidity. This review explores the progress of biomarker discovery efforts for ALS and the many challenges that remain. Included are different technologies utilized in biomarker discovery efforts (proteomic, genomic and metabolomic) and putative biomarkers uncovered using these techniques. These studies have discovered genetic mutations leading to familial forms of ALS, and specific protein alterations that occur in biological fluids (cerebrospinal fluid and blood) and/or tissues of ALS subjects. More recent high-throughput technologies have revealed panels of proteomic or metabolic biomarkers that can discriminate between ALS and control groups. The identification of disease-specific biomarkers will provide opportunities to develop early diagnostic measures as well as surrogate markers to monitor disease progression and test drug efficacy in clinical trials.

Algorithms↗
PubMed2006

A robust statistical procedure to discover expression biomarkers using microarray genomic expression data.

Microarray has become increasingly popular biotechnology in biological and medical researches, and has been widely applied in classification of treatment subtypes using expression patterns of biomarkers. We developed a statistical procedure to identify expression biomarkers for treatment subtype classification by constructing an F-statistic based on Henderson method III. Monte Carlo simulations were conducted to examine the robustness and efficiency of the proposed method. Simulation results showed that our method could provide satisfying power of identifying differentially expressed genes (DEGs) with false discovery rate (FDR) lower than the given type I error rate. In addition, we analyzed a leukemia dataset collected from 38 leukemia patients with 27 samples diagnosed as acute lymphoblastic leukemia (ALL) and 11 samples as acute myeloid leukemia (AML). We compared our results with those from the methods of significance analysis of microarray (SAM) and microarray analysis of variance (MAANOVA). Among these three methods, only expression biomarkers identified by our method can precisely identify the three human acute leukemia subtypes.

Biomarkers↗
PubMed2001

Aberrant promoter methylation profile of bladder cancer and its relationship to clinicopathological features.

We investigated the aberrant promoter methylation profile of bladder cancers and correlated the data with clinicopathological findings. The methylation status of 10 genes was determined in 98 surgically resected bladder cancers, and we calculated the median methylation index (MI), a reflection of the methylated fraction of the genes tested. Methylation frequencies of the genes tested in bladder cancers were 36% for CDH1, 35% for RASSF1A and APC, 29% for CDH13, 16% for FHIT, 15% for RAR beta, 11% for GSTP1, 7% for p16(INK4A), 4% for DAPK, and 2% for MGMT. Methylation of four of the individual genes (CDH1, RASSF1A, APC, and CDH13) and the MI were significantly correlated with several parameters of poor prognosis (tumor grade, growth pattern, muscle invasion, tumor stage, and ploidy pattern). Methylation of CDH1, FHIT, and a high MI were associated with shortened survival. CDH1 methylation positive status was independently associated with poor survival in multivariate analyses. Our results suggest that the methylation profile may be a potential new biomarker of risk prediction in bladder cancer.

Adult↗
PubMed2005

Metabolomic identification of novel biomarkers of myocardial ischemia.

BACKGROUND: Recognition of myocardial ischemia is critical both for the diagnosis of coronary artery disease and the selection and evaluation of therapy. Recent advances in proteomic and metabolic profiling technologies may offer the possibility of identifying novel biomarkers and pathways activated in myocardial ischemia. METHODS AND RESULTS: Blood samples were obtained before and after exercise stress testing from 36 patients, 18 of whom demonstrated inducible ischemia (cases) and 18 of whom did not (controls). Plasma was fractionated by liquid chromatography, and profiling of analytes was performed with a high-sensitivity electrospray triple-quadrupole mass spectrometer under selected reaction monitoring conditions. Lactic acid and metabolites involved in skeletal muscle AMP catabolism increased after exercise in both cases and controls. In contrast, there was significant discordant regulation of multiple metabolites that either increased or decreased in cases but remained unchanged in controls. Functional pathway trend analysis with the use of novel software revealed that 6 members of the citric acid pathway were among the 23 most changed metabolites in cases (adjusted P=0.04). Furthermore, changes in 6 metabolites, including citric acid, differentiated cases from controls with a high degree of accuracy (P<0.0001; cross-validated c-statistic=0.83). CONCLUSIONS: We report the novel application of metabolomics to acute myocardial ischemia, in which we identified novel biomarkers of ischemia, and from pathway trend analysis, coordinate changes in groups of functionally related metabolites.

Adenosine Monophosphate↗
PubMed2002

Combined use of oligonucleotide and tissue microarrays identifies cancer/testis antigens as biomarkers in lung carcinoma.

High density oligonucleotide microarrays (OMAs) have been used recently to profile gene expression in lung carcinoma tissue homogenates. The length of the lists of potentially interesting genes generated by these studies is daunting, and biological and clinical relevance of these lists remains to be validated. Moreover, specific identification of individual biomarkers that might be used for early detection and surveillance has not been the objective of these early studies. We have developed a schema for combining the data derived from the OMA analysis of a few lung cancer cell lines with immunohistochemical testing of tissue microarrays to rapidly identify biomarkers of potential clinical relevance. Initially, we profiled gene expression in lung tumor cell lines using the Affymetrix HG-U95Av2 OMA. RNA from 2 non-small cell lung cancer (NSCLC) cell lines (A549 and H647) and 2 small cell lung cancer (SCLC) cell lines (SHP-77 and UMC-19) were tested. Cells from 1 histologically and cytogenetically normal bronchial epithelial primary culture from a volunteer who had never smoked and 10 samples of histologically unremarkable lung tissue from resection specimens served as normalization controls. Array results were analyzed with Gene Spring software. Results were confirmed by reverse transcription-PCR in an expanded number of cell lines. We then validated the cell line data by immunohistochemical testing for protein using a tissue microarray containing 187 NSCLC clinical samples. Of the 20 most highly expressed genes in the tumor lines, 6 were members of the cancer/testis antigen (CTAG) gene group including 5 MAGE-A subfamily members and NY-ESO-1. SCLC lines strongly expressed all of the MAGE-A genes as well as NY-ESO-1, whereas NSCLC lines expressed a subset of MAGE-A genes at a lower level of intensity and failed to express NY-ESO-1. Reverse transcription-PCR of an extended series of 25 lung cancer cell lines including 13 SCLC, 9 NSCLC, and 3 mesothelioma lines indicated that MAGE-A10 and NY-ESO-1 were expressed only by SCLC, and that MAGE-A1, 3, 6, 12, and 4b were expressed by both SCLC and NSCLC. By immunohistochemistry using the monoclonal antibody 6C1 that recognizes several MAGE-A gene subfamily members, 44% of NSCLC clearly expressed MAGE-A proteins in cytoplasm and/or nucleus. Expression of MAGE-A genes did not correlate with survival but did correlate with histological classification with squamous carcinomas more frequently MAGE-A positive than other NSCLC types (P < 0.00002). We conclude that expression of CTAG gene products, whereas apparently not of prognostic importance, may be useful for early detection and surveillance because of a high level of specificity for central airway squamous and small cell carcinomas.

Antigens, Neoplasm↗
PubMed2005

Use of 'Omic' technologies to study humans exposed to benzene.

'Omic' technologies include genomics, transcriptomics (gene expression profiling), proteomics and metabolomics. We are utilizing these new technologies in an effort to develop novel biomarkers of exposure, susceptibility and response to benzene. Advances in genomics allow one to study hundreds to thousands of single nucleotide polymorphisms simultaneously on small quantities of DNA using array-based technologies. We are currently utilizing these technologies to examine genetic variation in pathways relating to biotransformation, DNA repair, folate metabolism and immune response with the goal of finding biomarkers of susceptibility to benzene hematotoxicity. Transcriptomics is used to measure the full complement of activated genes, mRNAs or transcripts in a particular tissue at a particular time typically using microarray technology. We have applied microarrays to the study of global gene expression in the peripheral blood cells of benzene-exposed workers. More than 100 genes were identified as being potentially differentially expressed, with genes related to apoptosis and immune function being the most significantly affected. Initial studies employing proteomics have also shown that several proteins are altered in the serum of exposed compared to control subjects and these proteins are potential biomarkers of benzene exposure. Omic technologies therefore have significant potential in generating novel biomarkers of exposure, susceptibility and response to benzene.

Air Pollutants, Occupational↗
PubMed2025

Global lncRNA expression profiles in medulloblastoma reveal crucial lncRNA-oncogene interactions in Sonic hedgehog and Group 4.

BACKGROUND: Advances in multi-omic studies have improved medulloblastoma (MB) characterization, yet novel molecular biomarkers are needed to refine tumor biology and therapeutic strategies. Current profiling mainly targets the protein-coding genome, while the potential of noncoding regions remains unexplored. This study aims to identify long noncoding RNAs (lncRNAs), emerging as crucial regulators in MB, as potential key biomarkers specific to molecular group, enhancing understanding of MB's genomic landscape. METHODS: RNA-seq data from 54 Spanish MB patients (C1) and 207 public samples (C2) were analyzed to profile lncRNAs. Expression and Weighted Gene Coexpression Network (WGCNA) analyses were performed to identify lncRNA-oncogene interactions. Group-specific interactions were examined to infer their role in MB pathogenesis and highlight potential lncRNA involvement in disease mechanisms. RESULTS: LncRNA expression profiles identified 4 clusters corresponding to the MB molecular groups, confirming their potential as biomarkers. Expression and WGCNA analyses revealed group-specific lncRNAs for Sonic hedgehog (SHH), Group 3 (Gr3), and Group 4 (Gr4) MB. Lnc-SMARCA2 was exclusively upregulated in SHH MB, and associated with ATOH1 and PDLIM3, key cilium regulators of this group's cell of origin. In Gr4 MB, MGC32805 and LOC107986446 were upregulated and linked to SNCAIP, potentially influencing PRDM6 activation via enhancer hijacking. Additionally, a 5-lncRNA signature linked to phototransduction was exclusive to Gr3, offering insights into its lineage switch and molecular regulation. CONCLUSIONS: Lnc-SMARCA2 and, MGC32805 and LOC107986446, are exclusively deregulated in SHH and Gr4 MB, respectively, and directly associated with group-specific MB oncogenes, representing promising novel biomarkers and therapeutic targets in MB.

cancer biomarkers↗
PubMed2026

Harnessing metabolomics and proteomics in a clinical trial for pulmonary arterial hypertension: insights from post-hoc analysis of the REHAB-PH trial.

BACKGROUND: The significant clinical and molecular heterogeneity of pulmonary arterial hypertension (PAH) poses challenges in identifying effective therapies. Advanced multidimensional profiling offers an opportunity to capture molecular responses and assess biomarker stability, yet its application in randomised trials remains limited. METHODS: We evaluated the multi-omic profiles of participants with PAH in a randomised, placebo-controlled trial of famotidine. Plasma metabolomic and proteomic profiling was performed at enrolment and 24 weeks. Baseline profiles were compared between treatment arms to assess randomisation balance. Intraclass correlation coefficients quantified within-subject stability over time. Linear regression models adjusting for age, sex, body mass index and PAH aetiology evaluated famotidine's molecular effects. False discovery rate was controlled for multiple comparisons. FINDINGS: For the 79 participants, baseline multi-omic profiles were similar between groups. At 24 weeks, 34 and 37 participants remained in the famotidine and placebo groups respectively. The placebo group showed high molecular stability, while greater variability was observed in the famotidine group. Famotidine treatment was associated with significant changes across 191 proteomic pathways (q-value <0.05), but no metabolomic changes remained significant after multiple-testing correction. INTERPRETATION: Integrating multi-omics into a prospective clinical trial is feasible and yields stable longitudinal profiles in the absence of intervention. While famotidine did not yield clinical benefit, associated proteomic changes illustrate how molecular profiling can reveal treatment-related biology and inform future trial design. These findings highlight the broader utility of multi-omics for evaluating drug responses and identifying molecular endotypes in PAH and beyond. FUNDING: US National Institutes of Health.

Humans↗
PubMed2006

Evaluation of hydrological processes in a mountainous small basin using a quinone biomarker.

An applicability of quinone biomarker to the analysis of hillslope runoff was investigated. At first, quinone profiles of three streams as well as a hillslope runoff in a forested headwater catchment were compared. The quinone composition of hillslope runoff differed from others. Moreover, there were remarkable differences in quinone profile of hillslope runoff under different rainfall conditions. Then, the behavior of quinone biomarker during the increase and decrease of hillslope runoff after a rainfall event was examined. The fractional changes in Q-9 (H2), Q-10 (H2), Q-11, MK-6 and MK-10 suggested the effect of interflow.

Biomarkers↗
PubMed2004

Pre-B-cell colony-enhancing factor as a potential novel biomarker in acute lung injury.

Although the pathogenic and genetic basis of acute lung injury (ALI) remains incompletely understood, the identification of novel ALI biomarkers holds promise for unique insights. Expression profiling in animal models of ALI (canine and murine) and human ALI detected significant expression of pre-B-cell colony-enhancing factor (PBEF), a gene not previously associated with lung pathophysiology. These results were validated by real-time polymerase chain reaction and immunohistochemistry studies, with PBEF protein levels significantly increased in both bronchoalveolar lavage fluid and serum of ALI models and in cytokine- or cyclic stretch-activated lung microvascular endothelium. We genotyped two PBEF single-nucleotide polymorphisms (SNPs) in a well characterized sample of white patients with sepsis-associated ALI, patients with severe sepsis, and healthy subjects and observed that carriers of the haplotype GC from SNPs T-1001G and C-1543T had a 7.7-fold higher risk of ALI (95% confidence interval 3.01-19.75, p < 0.001). The T variant from the SNP C-1543T resulted in a significant decrease in the transcription rate (1.8-fold; p < 0.01) by the reporter gene assay. Together, these results strongly indicate that PBEF is a potential novel biomarker in ALI and demonstrate the successful application of robust genomic technologies in the identification of candidate genes in complex lung disease.

Adult↗
PubMedDate not supplied

Review: porphyrins as biomarkers for hazard assessment of bird populations: destructive and non-destructive use.

In this review the biochemical, metabolic and toxicological significance of porphyrins in birds is examined, and their use as biomarkers of exposure to xenobiotics and heavy metals is explored. Laboratory studies pinpointing the main classes of compounds that alter porphyrin profiles are described, as well as those which defined the resulting porphyrin profiles and target organs. Field studies in which the biomarker was validated on natural populations of several species of birds are then reviewed. We finally illustrate their potential as a nondestructive biomarker suitable for situations in which destructive sampling of natural populations must be avoided, suggesting the implementation of this approach.

Animals↗
PubMed2003

Tissue microarray study for classification of breast tumors.

Clinical and pathological heterogeneity of breast cancer hinders selection of appropriate treatment for individual cases. Molecular profiling at gene or protein levels may elucidate the biological variance of tumors and provide a new classification system that correlates better with biological, clinical and prognostic parameters. We studied the immunohistochemical profile of a panel of seven important biomarkers using tumor tissue arrays. The tumor samples were then classified with a monothetic (binary variables) clustering algorithm. Two distinct groups of tumors are characterized by the estrogen receptor (ER) status and tumor grade (p = 0.0026). Four biomarkers, c-erbB2, Cox-2, p53 and VEGF, were significantly overexpressed in tumors with the ER-negative (ER-) phenotype. Eight subsets of tumors were further identified according to the expression status of VEGF, c-erbB2 and p53. The malignant potential of the ER-/VEGF+ subgroup was associated with the strong correlations of Cox-2 and c-erbB2 with VEGF. Our results indicate that this molecular classification system, based on the statistical analysis of immunohistochemical profiling, is a useful approach for tumor grouping. Some of these subgroups have a relative genetic homogeneity that may allow further study of specific genetically-controlled metabolic pathways. This approach may hold great promise in rationalizing the application of different therapeutic strategies for different subgroups of breast tumors.

Algorithms↗
PubMed2005

Gene expression analysis of renal carcinoma: adipose differentiation-related protein as a potential diagnostic and prognostic biomarker for clear-cell renal carcinoma.

The gene expression profiles of 33 renal cell carcinomas (RCCs) and nine normal kidney samples were examined using high-density oligonucleotide microarrays in an attempt to identify biomolecular markers for the diagnosis of tumour subtypes and also for prediction of prognosis. Hierarchical clustering demonstrated that clear-cell RCC, chromophobe RCC, and normal kidney tissue showed distinctive gene expression profiles. The mean expression levels of 149 of 12 500 genes were more than three times higher in clear-cell RCC than in chromophobe RCC and normal kidney tissue. Among the genes whose expression was upregulated in clear-cell RCC, adipose differentiation-related protein (ADFP) and nicotinamide N-methyltransferase (NNMT) were selected for further analysis. Consistent with the results of the microarray, increased levels of ADFP and NNMT mRNA were found more frequently in clear-cell RCCs than in other non-clear-cell tumour subtypes using real-time quantitative PCR. Immunohistochemistry for ADFP showed strong and unique tumour cell staining patterns in the majority of clear-cell RCCs. More importantly, patients bearing tumours with higher AFDP mRNA levels showed significantly better survival in both univariate and multivariate analyses. ADFP is a lipid storage droplet-associated protein and its transcription is considered to be regulated by the von Hippel-Lindau/hypoxia-inducible factor pathway. It is known that clear-cell RCC contains abundant lipids and cholesterols. Thus it is likely that sustained upregulation of ADFP following VHL inactivation is involved in the morphological appearance of clear-cell RCC. Moreover ADFP expression status may provide useful prognostic information as a biomolecular marker in patients with clear-cell RCC.

Adenocarcinoma, Clear Cell↗