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PubMedDate not supplied

Clinical proteomics: from biomarker discovery and cell signaling profiles to individualized personal therapy.

The discovery of new highly sensitive and specific biomarkers for early disease detection and risk stratification coupled with the development of personalized "designer" therapies holds the key to future treatment of complex diseases such as cancer. Mounting evidence confirms that the low molecular weight (LMW) range of the circulatory proteome contains a rich source of information that may be able to detect early stage disease and stratify risk. Current mass spectrometry (MS) platforms can generate a rapid and high resolution portrait of the LMW proteome. Emerging novel nanotechnology strategies to amplify and harvest these LMW biomarkers in vivo or ex vivo will greatly enhance our ability to discover and characterize molecules for early disease detection, subclassification and prognostic capability of current proteomics modalities. Ultimately genetic mutations giving rise to disease are played out and manifested on a protein level, involving derangements in protein function and information flow within diseased cells and the interconnected tissue microenvironment. Newly developed highly sensitive, specific and linearly dynamic reverse phase protein microarray systems are now able to generate circuit maps of information flow through phosphoprotein networks of pure populations of microdissected tumor cells obtained from patient biopsies. We postulate that this type of enabling technology will provide the foundation for the development of individualized combinatorial therapies of molecular inhibitors to target tumor-specific deranged pathways regulating key biologic processes including proliferation, differentiation, apoptosis, immunity and metastasis. Hence future therapies will be tailored to the specific deranged molecular circuitry of an individual patient's disease. The successful transition of these groundbreaking proteomic technologies from research tools to integrated clinical diagnostic platforms will require ongoing continued development, and optimization with rigorous standardization development and quality control procedures.

Biomarkers↗
PubMed2005

Cholinesterase inhibition and impacts on behavior of the Asian clam, Corbicula fluminea, after exposure to an organophosphate insecticide.

This study assessed the effects of exposure to an insecticide formulation containing the organophosphate, chlorpyrifos on cholinesterase activity, siphoning and burrowing ability in the Asian clam, Corbicula fluminea. Clams were exposed to concentrations of the pesticide ranging from 0.05 to 50 mg/L (as chlorpyrifos) in a series of 96-h static bioassays. Those organisms exposed to pesticide concentrations at or above 3.13 mg/L avoided exposure through valve closure during the 96-h test period, with no resultant effects on cholinesterase activity or behavior. Similarly, no effect was observed at low-test concentrations ranging from 0.05 to 0.1 mg/L, even though clams actively siphoned in these treatments. Asian clams exposed to the mid-range of concentrations, 0.5-1.0 mg/L, experienced a significant reduction in cholinesterase activity and a reduced capacity to burrow into the substrate. While these data indicate that cholinesterase activity in C. fluminea could be used as a biomarker of organophosphorous pesticide exposure, valve closure can clearly influence exposure profiles and biomarker response.

Analysis of Variance↗
PubMedDate not supplied

Cancer proteomics: from biomarker discovery to signal pathway profiling.

In the post-genomic era of science, the field of proteomics promises the discovery of new molecular targets for therapy, biomarkers for early detection, and new endpoints for therapeutic efficacy and toxicity. Patient-specific targeted therapeutics with reduced toxicity and increased efficacy, the ultimate goal for rational drug development, can only be achieved if direct analyses of the tissue cells in the actual human malignancy are analyzed. To that end, technologies such as Laser Capture Microdissection (LCM), is providing unparalleled access to the purified diseased human cells directly from tissue specimens. However, limited availability of patient material is a challenge towards the development of new highly sensitive proteomic methodologies. Two-dimensional gel electrophoresis (2D-PAGE), still the mainstay of most proteomic analysis of disease, is being complemented, and in some instances replaced by new exciting approaches to multiparametric protein characterization. The use of rapid, high throughput mass spectrometric-based fingerprints of peptides and proteins may prove to be valuable for new molecular classification of human tumors and disease stages. Coupled with LCM, high-density protein arrays, antibody arrays, and small molecular arrays, could have a substantial impact on proteomic profiling of human malignancies. Finally, detailed real-time knowledge about the states of intracellular signaling circuitry and pathways in the normal and malignant cells before, during and after therapy will yield invaluable information about mechanism of action and efficacy of existing and novel therapeutics for the treatment of human cancer.

Biomarkers↗
PubMed2004

Molecular profiling approaches for identifying novel biomarkers.

An unprecedented interest in biomarker development has arisen from the increasing use of genomic information and high-throughput technologies in the field of drug development. Monitoring global cellular responses to perturbation due to disease, drug treatment or toxicity is achieved using molecular profiling methods such as DNA microarrays, proteomics and metabonomics. Unique fingerprints composed of molecular changes are captured and subjected to interpretation with the goal of class discovery, comparison or prediction. Each fingerprint reflects a cumulative response of complex molecular interactions, and if these interactions can be significantly correlated to an end point, the molecular fingerprint may be qualified as a predictive biomarker. Furthermore, in cases where the predictive power of any single response or set of responses is statistically significant, a molecular fingerprint can provide novel information related to the underlying disease biology or mechanism of toxicity. There is an acute need for effective biomarkers in every phase of drug development, from discovery, to preclinical studies, through to clinical trials. The context in which these molecular biomarkers are used will depend upon the nature of the biological problem being addressed. This review will summarise experimental and computational efforts in the field of molecular profiling and discuss the significant challenges in interpreting molecular profiling data and qualifying novel transcriptional biomarkers.

Biomarkers↗
PubMed2003

A novel approach toward development of a rapid blood test for breast cancer.

Mammography remains the diagnostic test of choice for breast cancer, but 20% of cancers still go undetected. Many serum biomarkers have been reported for breast cancer but none have proven to represent effective diagnostic strategies. ProteinChip mass spectrometry is an innovative technology that searches the proteome for differentially expressed proteins, allowing for the creation of a panel or profile of biomarkers. The objective of this study was to construct unique cancer-associated serum profiles that, combined with a classification algorithm, would enhance the detection of breast cancer Pretreatment serum samples from 134 female patients (45 with cancer, 42 with benign disease, 47 normal) were procured prospectively following institutional review board-approved protocols. Proteins were denatured, applied onto ProteinChip affinity surfaces, and subjected to surface enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry. The SELDI output was analyzed using Biomarker Pattern Software to develop a classification tree based on group-specific protein profiles. The cross-validation analysis of cancer versus normal revealed sensitivity and specificity rates of 80% and 79%, and for cancer versus benign disease, 78% and 83%, respectively. When 2 different chip surfaces were combined the sensitivity and specificity increased to 90% and 93%, respectively. The sensitivity and specificity of this technique are comparable to those of mammography and, if confirmed in a larger study, this technique could provide the means toward development of a simple blood test to aid in the early detection of breast cancer. The combination of SELDI ProteinChip mass spectrometry and a classification- and regression-tree algorithm has the potential to use serum protein expression profiles for detection and diagnosis of breast cancer.

Adult↗
PubMed2006

Meta-analysis and meta-review of thyroid cancer gene expression profiling studies identifies important diagnostic biomarkers.

PURPOSE: An estimated 4% to 7% of the population will develop a clinically significant thyroid nodule during their lifetime. In many cases, preoperative diagnoses by needle biopsy are inconclusive. Thus, there is a clear need for improved diagnostic tests to distinguish malignant from benign thyroid tumors. The recent development of high-throughput molecular analytic techniques should allow the rapid evaluation of new diagnostic markers. However, researchers are faced with an overwhelming number of potential markers from numerous thyroid cancer expression profiling studies. MATERIALS AND METHODS: To address this challenge, we have carried out a comprehensive meta-review of thyroid cancer biomarkers from 21 published studies. A gene ranking system that considers the number of comparisons in agreement, total number of samples, average fold-change and direction of change was devised. RESULTS: We have observed that genes are consistently reported by multiple studies at a highly significant rate (P < .05). Comparison with a meta-analysis of studies reprocessed from raw data showed strong concordance with our method. CONCLUSION: Our approach represents a useful method for identifying consistent gene expression markers when raw data are unavailable. A review of the top 12 candidates revealed well known thyroid cancer markers such as MET, TFF3, SERPINA1, TIMP1, FN1, and TPO as well as relatively novel or uncharacterized genes such as TGFA, QPCT, CRABP1, FCGBP, EPS8 and PROS1. These candidates should help to develop a panel of markers with sufficient sensitivity and specificity for the diagnosis of thyroid tumors in a clinical setting.

Biomarkers, Tumor↗
PubMed2003

Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro.

Assays of drug action typically evaluate biochemical activity. However, accurately matching therapeutic efficacy with biochemical activity is a challenge. High-content cellular assays seek to bridge this gap by capturing broad information about the cellular physiology of drug action. Here, we present a method of predicting the general therapeutic classes into which various psychoactive drugs fall, based on high-content statistical categorization of gene expression profiles induced by these drugs. When we used the classification tree and random forest supervised classification algorithms to analyze microarray data, we derived general "efficacy profiles" of biomarker gene expression that correlate with anti-depressant, antipsychotic and opioid drug action on primary human neurons in vitro. These profiles were used as predictive models to classify naïve in vitro drug treatments with 83.3% (random forest) and 88.9% (classification tree) accuracy. Thus, the detailed information contained in genomic expression data is sufficient to match the physiological effect of a novel drug at the cellular level with its clinical relevance. This capacity to identify therapeutic efficacy on the basis of gene expression signatures in vitro has potential utility in drug discovery and drug target validation.

Algorithms↗
PubMed2005

Integrating time-course microarray gene expression profiles with cytotoxicity for identification of biomarkers in primary rat hepatocytes exposed to cadmium.

MOTIVATION: DNA microarrays can provide information about the expression levels of thousands of genes simultaneously at the transcriptomic level, while conventional cell viability and cytotoxicity measurement methods provide information about the biological functions at the cellular level. Integrating these data at different levels provides a promising approach for evaluating or predicting how cells respond to chemical exposure. It is important to investigate the multi-scale biological system in a systematic way to better understand the gene regulation networks and signal transduction pathways involved in the cellular responses to environmental factors. RESULTS: Primary rat hepatocytes were exposed to cadmium acetate at 0, 1.25 and 2 microM. mRNA expression profiles at 0, 3, 6, 12 and 24 h were measured using the Affymetrix RatTox U34 GeneChip arrays. Simultaneously, cytotoxicity was assessed by lactase dehydrogenase leakage assay. Gene expression profiles at different time points were used to evaluate cytotoxicity at subsequent time points using partial least squares, and it was found that gene expression profiles at 0 h had the best prediction accuracy for the cytotoxicity observed at 12 h. Some biomarkers whose expression profiles showed strong relationship with cytotoxicity were identified and the underlying pathways were reconstructed to illustrate how hepatocytes respond to cadmium exposure. Permutation studies were also applied to assess the reliability of the predictive models. AVAILABILITY: Matlab source code is available upon request and DNA microarray data are available at GEO (http://www.ncbi.nlm.nih.gov/geo).

Algorithms↗
PubMed2006

Urinary proteomic profiles distinguish between active and inactive lupus nephritis.

OBJECTIVES: Key aims of the treatment of lupus nephritis (LN) are to induce and maintain remission with minimal side effects. However, assessing ongoing renal inflammatory activity is poorly served by current diagnostic tests apart from renal biopsy, but frequent biopsies cannot be justified. Our long-term aim is to identify novel biomarkers from urinary protein profiles to improve diagnosis and monitoring of activity and response to therapy in LN. METHODS: We used surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) to identify biomarkers able to discriminate between urine samples from patients with inactive (n= 49) and active (n= 26) LN. Discriminant function analysis was used to define the minimum number of proteins whose levels best distinguished between the two patient groups. Serial urines of six biopsied patients were studied prospectively, and multiple regression (MR) scores calculated. RESULTS: Proteins with masses of 3340 and 3980 distinguished active from inactive LN with 92% sensitivity and specificity of 92% each. The prospective study of the biopsied patients demonstrated that MR scores could predict both relapse and remission earlier than traditional clinical markers. CONCLUSIONS: SELDI-TOF MS identified potential biomarker profiles strongly associated with activity in LN. Identification of these proteins will allow us to devise specific assays to routinely monitor disease progression, and alter immunosuppressive drug regimens accordingly. These proteins may also play a critical role in the pathogenesis of glomerulonephritis, and could therefore provide targets for therapeutic intervention.

Adult↗
PubMed2006

XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification.

Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculating a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.

Algorithms↗
PubMed2006

Pilot study using SELDI-TOF-MS based proteomic profile for the identification of diagnostic biomarkers of thyroid proliferative diseases.

Biomarkers for thyroid cancer (TCa) lack specificity. To develop TCa specific biomarkers, SELDI-TOF-MS was used to examine the proteomic profile of biopsies obtained from papillary TCa along with adjacent normal tissue. Sixty-three potential biomarkers were categorized by univariate analysis into single biomarker candidates and segregated by multivariate analysis into normal and cancerous groups. Our studies demonstrate the sensitivity and reproducibility of this approach to detect biomarkers for TCa.

Biomarkers, Tumor↗
PubMed2005

Gene expression in caged fish as a first-tier indicator of contaminant exposure in streams.

The development of sensitive, biologically based indicators of contaminant exposure (i.e., biomarkers) is an ongoing topic of research. These indicators have been proposed as a first-tier method of identifying contaminant exposure. The primary objective of this research was to implement a biomarker-based method of exposure assessment using caged fish and real-time reverse-transcriptase polymerase chain reaction (rtRT-PCR) measurements of gene expression. Primers were developed for the CYPIA, metallothionein, and vitellogenin genes in rainbow trout (Oncorhynchyus mykiss), cutbow trout (Oncorhynchyus clarkii x mykiss), and Atlantic salmon (Salmo salar). Each of these genes has been shown to respond specifically to planar aromatic compounds, heavy metals, and environmental estrogens, respectively. Juvenile fish were placed in cages and exposed in situ at reference and contaminated sites on the Cache la Poudre River (CO, USA), the Arkansas River (CO, USA), the St. John River (NB, Canada), and two urban creeks near Dayton (OH, USA). Quantitative gene expression was determined using rtRT-PCR. Biomarker expression profiles were obtained that demonstrated differences in CYPIA, metallothionein, and vitellogenin mRNA production unique to each site, indicating that specific types of compounds were bioavailable and present in sufficient concentrations to elicit transcriptional responses in the organism. These findings support the use of a biomarker-based approach to exposure identification and assessment.

Animals↗
PubMed2006

A database application for pre-processing, storage and comparison of mass spectra derived from patients and controls.

BACKGROUND: Statistical comparison of peptide profiles in biomarker discovery requires fast, user-friendly software for high throughput data analysis. Important features are flexibility in changing input variables and statistical analysis of peptides that are differentially expressed between patient and control groups. In addition, integration the mass spectrometry data with the results of other experiments, such as microarray analysis, and information from other databases requires a central storage of the profile matrix, where protein id's can be added to peptide masses of interest. RESULTS: A new database application is presented, to detect and identify significantly differentially expressed peptides in peptide profiles obtained from body fluids of patient and control groups. The presented modular software is capable of central storage of mass spectra and results in fast analysis. The software architecture consists of 4 pillars, 1) a Graphical User Interface written in Java, 2) a MySQL database, which contains all metadata, such as experiment numbers and sample codes, 3) a FTP (File Transport Protocol) server to store all raw mass spectrometry files and processed data, and 4) the software package R, which is used for modular statistical calculations, such as the Wilcoxon-Mann-Whitney rank sum test. Statistic analysis by the Wilcoxon-Mann-Whitney test in R demonstrates that peptide-profiles of two patient groups 1) breast cancer patients with leptomeningeal metastases and 2) prostate cancer patients in end stage disease can be distinguished from those of control groups. CONCLUSION: The database application is capable to distinguish patient Matrix Assisted Laser Desorption Ionization (MALDI-TOF) peptide profiles from control groups using large size datasets. The modular architecture of the application makes it possible to adapt the application to handle also large sized data from MS/MS- and Fourier Transform Ion Cyclotron Resonance (FT-ICR) mass spectrometry experiments. It is expected that the higher resolution and mass accuracy of the FT-ICR mass spectrometry prevents the clustering of peaks of different peptides and allows the identification of differentially expressed proteins from the peptide profiles.

Algorithms↗
PubMed2006

Disease state differentiation and identification of tuberculosis biomarkers via native antigen array profiling.

A critical element of tuberculosis control is early and sensitive diagnosis of infection and disease. Our laboratories recently showed that different stages of disease were distinguishable via two-dimensional Western blot analyses of Mycobacterium tuberculosis culture filtrate proteins. However, this methodology is not suitable for high throughput testing. Advances in protein microarray technology provide a realistic mechanism to screen a large number of serum samples against thousands of proteins to identify biomarkers of disease states. Techniques were established for separation of native M. tuberculosis cytosol and culture filtrate proteins, resulting in 960 unique protein fractions that were used to generate protein microarrays. Evaluation of serological reactivity from 42 patients in three tuberculosis disease states and healthy purified protein derivative-positive individuals demonstrated that human immunodeficiency virus (HIV)-negative cavitary and noncavitary tuberculosis (TB) patients' sera recognized 126 and 59 fractions, respectively. Sera from HIV patients coinfected with TB recognized 20 fractions of which five overlapped with those recognized by non-HIV TB patients' sera and 15 were unique to the HIV+TB+ disease state. Identification of antigens within the reactive fractions yielded 11 products recognized by both cavitary and noncavitary TB patients' sera and four proteins (HspX, MPT64, PstS1, and TrxC) specific to cavitary TB patients. Moreover four novel B cell antigens (BfrB, LppZ, SodC, and TrxC) of human tuberculosis were identified.

Amino Acid Sequence↗
PubMed2026

S100P as a Shared Biomarker in Inflammatory Bowel Disease, Colorectal Cancer, and Pancreatic Adenocarcinoma: An Integrated Transcriptomic Analysis.

Inflammatory bowel disease (IBD) is associated with an increased risk of colorectal cancer (CRC) and pancreatic adenocarcinoma (PAAD), yet the molecular features shared among these diseases remain incompletely understood. This study aimed to identify common genes and biological pathways associated with IBD, CRC, and PAAD through integrated transcriptomic analysis and experimental validation. Gene expression datasets for IBD, CRC, and PAAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Weighted gene co-expression network analysis and differential expression analysis were performed to identify disease-associated and shared genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (analyses were used to explore enriched biological functions and pathways. Immune cell infiltration was evaluated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts. Receiver operating characteristic analysis was performed to assess the diagnostic performance of common genes. Single-cell RNA sequencing analysis was conducted to examine the cellular distribution of S100P. In addition, the effects of S100P downregulation were evaluated in lipopolysaccharide (LPS)-stimulated colonic epithelial cells. A total of 162 disease-associated genes and four common genes were identified. Functional enrichment analyses indicated significant enrichment of immune- and inflammation-related pathways, including the interleukin-17 signaling pathway. Immune infiltration analysis revealed similar trends in several immune cell populations across IBD, CRC, and PAAD. Single-cell analysis showed elevated S100P expression in epithelial cells from all three diseases. Downregulation of S100P restored the proliferative capacity of LPS-stimulated colonic epithelial cells and reduced inflammatory cytokine expression. Integrated transcriptomic analysis identified S100P as a biomarker associated with IBD, CRC, and PAAD and highlighted shared immune-related features across these diseases.

Humans↗
PubMed2005

Development and validation of a whole-cell inhibition assay for bacterial methionine aminopeptidase by surface-enhanced laser desorption ionization-time of flight mass spectrometry.

Bacterial methionine aminopeptidase (MAP) is a protease that removes methionine from the N termini of newly synthesized bacterial proteins after the peptide deformylase enzyme cleaves the formyl group from the initiator formylmethionine. MAP is an essential bacterial gene product and thus represents a potential target for therapeutic intervention. A fundamental challenge in the antibacterial drug discovery field is demonstrating conclusively that compounds with in vitro enzyme inhibition activity produce the desired antibacterial effect by interfering with the same target in whole bacterial cells. One way to address the activity of inhibitor compounds is by profiling cellular biomarkers in whole bacterial cells using compounds that are known inhibitors of a particular target. However, in the case of MAP, no specific inhibitors were available for such studies. Instead, a genetically attenuated MAP strain was generated in which MAP expression was placed under the control of an inducible arabinose promoter. Thus, MAP inhibition in whole cells could be mimicked by growth in the absence of arabinose. This genetically attenuated strain was used as a benchmark for MAP inhibition by profiling whole-cell lysates for unprocessed proteins using surface-enhanced laser desorption ionization-time of flight mass spectrometry (MS). Eight proteins between 4 and 14 kDa were confirmed as being unprocessed and containing the initiator methionine by adding back purified MAP to the preparations prior to MS analysis. Upon establishing these unprocessed proteins as biomarkers for MAP inhibition, the assay was used to screen small-molecule chemical inhibitors of purified MAP for whole-cell activity. Fifteen compound classes yielded three classes of compound with whole-cell activity for further optimization by chemical expansion. This report presents the development, validation, and implementation of a whole-cell inhibition assay for MAP.

Aminopeptidases↗
PubMed2024

Systematic Proteome Profiling of Maternal Plasma for Development of Preeclampsia Biomarkers.

Preeclampsia (PE) is a hypertensive disorder of pregnancy with various clinical symptoms. However, traditional markers for the disease including high blood pressure and proteinuria are poor indicators of the related adverse outcomes. Here, we performed systematic proteome profiling of plasma samples obtained from pregnant women with PE to identify clinically effective diagnostic biomarkers. Proteome profiling was performed using TMT-based liquid chromatography-mass spectrometry (LC-MS/MS) followed by subsequent verification by multiple reaction monitoring (MRM) analysis on normal and PE maternal plasma samples. Functional annotations of differentially expressed proteins (DEPs) in PE were predicted using bioinformatic tools. The diagnostic accuracies of the biomarkers for PE were estimated according to the area under the receiver-operating characteristics curve (AUC). A total of 1307 proteins were identified, and 870 proteins of them were quantified from plasma samples. Significant differences were evident in 138 DEPs, including 71 upregulated DEPs and 67 downregulated DEPs in the PE group, compared with those in the control group. Upregulated proteins were significantly associated with biological processes including platelet degranulation, proteolysis, lipoprotein metabolism, and cholesterol efflux. Biological processes including blood coagulation and acute-phase response were enriched for down-regulated proteins. Of these, 40 proteins were subsequently validated in an independent cohort of 26 PE patients and 29 healthy controls. APOM, LCN2, and QSOX1 showed high diagnostic accuracies for PE detection (AUC >0.9 and p&#xa0;<&#xa0;0.001, for all) as validated by MRM and ELISA. Our data demonstrate that three plasma biomarkers, identified by systematic proteomic profiling, present a possibility for the assessment of PE, independent of the clinical characteristics of pregnant women.

Humans↗
PubMed2026

Transcriptomic analysis identifies novel ferroptosis-related biomarkers and therapeutic targets in pulmonary arterial hypertension.

BACKGROUND: Ferroptosis plays a significant role in pulmonary arterial hypertension (PAH), although its underlying mechanisms and key pathogenic genes remain unclear. METHODS: Transcriptomic data from human PAH and control lung tissue were obtained from the Gene Expression Omnibus (GEO) database, whereas ferroptosis-related genes (FRGs) were sourced from the MsigDb and FerrDb databases. Differentially expressed FRGs (DE-FRGs) were identified through the intersection of FRGs with differentially expressed genes (DEGs). Functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Key hub genes were identified through Least Absolute Shrinkage and Selection Operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and weighted correlation network analysis (WGCNA). Gene set enrichment analysis (GSEA) was conducted to explore the functional roles and associated pathways of hub genes. The relationship between hub genes and immune infiltration was investigated. Expression levels of potential biomarkers were validated via Quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) in two PAH animal models (monocrotaline-induced and Sugen5416 plus hypoxia-induced PAH). Finally, molecular docking was employed to screen potential therapeutic compounds. RESULTS: A total of 133 DE-FRGs were identified, with KEGG and GO analyses highlighting their involvement in intracellular iron homeostasis and ferroptosis. Hub genes, notably FZD7 and NFE2, were identified using LASSO, SVM-RFE, and WGCNA. Immune infiltration analysis suggested that monocytes and neutrophils play key roles in PAH pathogenesis. Validation in PAH animal models showed significant upregulation of Fzd7 and downregulation of Nfe2 in lung tissues of both MCT- and SuHx-induced PAH models. Molecular docking identified tetrachlorodibenzodioxin (TCDD) has good binding affinity. CONCLUSION: In summary, we investigated two ferroptosis-related biomarkers, FZD7 and NFE2, in PAH using transcriptomics, offering new insights into molecular mechanisms and potential targeted therapies for the disease.

Ferroptosis↗