Identification Of Potential Biomarkers Research Articles

Identification of potential biomarkers associated with cuproptosis and immune microenvironment analysis in acute myocardial infarction: A diagnostic accuracy study.

Acute myocardial infarction (AMI), a critical cardiovascular condition, is often associated with serious health risks. Recent studies suggest a link between copper-induced apoptosis and immune cell infiltration. Specifically, abnormal accumulation of copper ions can lead to intracellular oxidative stress and apoptosis, while also affecting immune cell function and infiltration. Nevertheless, studies exploring this relationship in the context of AMI are notably scarce, underscoring the necessity of identifying biomarkers associated with cuproptosis in AMI. Consensus clustering analysis was employed to classify distinct subtypes of AMI in the GSE66360 dataset. Concurrently, differential expression analysis was performed to identify differentially expressed genes (DEGs) across subtypes and between AMI and control samples. We employed Venn diagrams to validate the selection of cuproptosis-related DEGs in patients with AMI. A protein-protein interaction network was constructed to pinpoint potential candidate genes. Receiver operating characteristic curves were generated to identify promising biomarkers. The immune infiltration milieu was analyzed using CIBERSORT algorithms. Finally, the expression levels of identified cuproptosis-related biomarkers were validated at the transcriptional level. We classified AMI into 2 distinct cuproptosis-related subtypes, leading to the identification of 157 cuproptosis-related DEGs. Further analysis refined this list to 10 potential candidate genes. Among these, 5 emerged as significant biomarkers for AMI: granzyme A (GZMA), GTPase immunity-associated proteins (GIMPAs) GIMAP7, GIMAP5, GIMAP6, and TRAF3 interacting protein 3 (TRAF3IP3). A comprehensive examination of immune infiltration in AMI samples revealed significant differences in the levels of 11 types of immune cells, with GZMA displaying the highest correlation with activated mast cells and CD8 + T cells. We observed markedly lower expression levels of GZMA, GIMAP6, and TRAF3IP3 in the AMI group compared to controls. This study identified 5 cuproptosis-related biomarkers (GZMA, GIMAP7, GIMAP5, GIMAP6, and TRAF3IP3) associated with AMI, laying a theoretical foundation for the treatment of AMI.

Biochemical markers in hand osteoarthritis: a path to precision medicine.

Hand osteoarthritis (HOA) is a heterogeneous joint disease with high radiographic and symptomatic prevalence. The diagnosis of HOA is based on clinical and radiographic features. The identification of potential biomarkers for diagnosis, prognosis, disease severity assessment, and therapeutic efficacy evaluation of НОА remains an active area of research. To summarize the eligible biomarker data, a comprehensive narrative review was performed using the PubMed and Scopus databases covering publications from inception to December 2024. Our search uncovered five distinct groups of biomarkers associated with HOA, categorized based on their origin and involvement in distinct biological processes: (1) cartilage synthesis and catabolism, (2) bone remodeling, (3) inflammation, (4) adipokines, and (5) others classified separately. Each biomarker was evaluated in accordance with the Burden of disease, Investigative, Prognostic, Efficacy of intervention, and Diagnostic (BIPED) criteria. In conclusion, no biomarker has yet demonstrated sufficient sensitivity, specificity, or reproducibility to meet the BIPED criteria for classification. The early diagnosis and treatment of HOA require the development of more sensitive assays, advanced platforms, and rigorous bio-clinical trials to stratify previously studied biomarkers and identify novel ones. Precision medicine in HOA demands reliable biomarkers, cost-effective assays, and standardized, reproducible methodologies for global applicability.

Studying Alzheimer’s disease through an integrative serum metabolomic and lipoproteomic approach

BackgroundAlzheimer’s disease (AD) is the most frequent neurodegenerative disorder worldwide. The great variability in disease evolution and the incomplete understanding of the molecular mechanisms underlying AD make it difficult to predict when a patient will convert from prodromal stage to dementia. We hypothesize that metabolic alterations present at the level of the brain could be reflected at a systemic level in blood serum of patients, and that these alterations could be used as prognostic biomarkers.MethodsThis pilot study proposes a serum investigation via nuclear magnetic resonance (NMR) spectroscopy in a consecutive series of AD patients including 57 patients affected by Alzheimer’s disease at dementia stage (AD-dem) and 45 patients with mild cognitive impairment (MCI) due to AD (MCI-AD). As control group, we considered 31 subjects with mild cognitive impairment in whom AD and other neurodegenerative disorders were excluded (MCI). A panel of 26 metabolites and 112 lipoprotein-related parameters was quantified and the logistic LASSO regression algorithm was employed to identify the optimal combination of metabolites-lipoproteins and their ratios to discriminate the groups of interest.ResultsIn the training set, our model classified AD-dem and MCI with an accuracy of 81.7%. These results were reproduced in the validation set (accuracy 75.0%). Evolution of MCI-AD patients was evaluated over time. Patients who displayed a decrease in MMSE < 1.5 point per year were considered at lower progression rate: we obtained a division in 18 MCI-AD at lower progression rate (MCI-AD LR) and 27 at higher progression rate (MCI-AD HR). The model calculated using 4 metabolic features identified MCI-AD LR and MCI-AD HR with an accuracy of 73.3%.ConclusionsThe identification of potential novel peripheral biomarkers of Alzheimer’s disease, as proposed in this study, opens a new prospect for an innovative and minimally invasive method to identify AD in its very early stages. We proposed a novel approach able to sub-stratify MCI-AD patients identifying those associated with a faster rate of clinical progression.

Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach

Monkeypox virus (MPXV), a zoonotic pathogen, re-emerged in 2022 with the Clade IIb variant, raising global health concerns due to its unprecedented spread in non-endemic regions. Recent studies have shown that Clade IIb (2022 MPXV) is marked by unique genomic mutations and epidemiological behaviors, suggesting variations in host-virus interactions. This study aimed to identify the differentially expressed genes (DEGs) induced by the 2022 MPXV infection through comprehensive bioinformatics analyses of microarray and RNA-Seq datasets from post-infected cell types with different MPXV clades. Subsequently, gene expression network analyses pinpoint the key DEGs, followed by their candidate drug assessment using the Drug SIGnatures DataBase (DSigDB) and validation by multiple machine learning algorithms. Comparative differential gene expression (DGE) analysis revealed 798 DEGs exclusive to the 2022 MPXV invasion in the skin cell types (keratinocytes). Intriguingly, 13 key DEGs were identified across hubs and clusters, highlighting their aberrant expressions in cell cycle regulation, immune responses, and cancer pathways. Biomarker screening via Random Forest (RF) model (selected with PyCaret from multiple models) and validation through t-distributed stochastic neighbor embedding (t-SNE) algorithm, principal component analysis (PCA), and ROC curve analysis employing Logistic Regression and Random Forest, identified 6 key DEGs (TXNRD1, CCNB1, BUB1, CDC20, BUB1B, and CCNA2) as promising biomarkers (AUC > 0.7) for clade IIb infection. This study anticipates that further investigation and clinical trials will catalyze novel detection and therapeutic options to combat 2022 MPXV infection in humans.

Research Trends and Development Dynamics of qPCR-based Biomarkers: A Comprehensive Bibliometric Analysis.

Quantitative polymerase chain reaction (qPCR) is a vital molecular technique for biomarker detection; however, its clinical application is impeded by the scarcity of robust biomarkers and the inherent limitations of the technology. This study conducted a bibliometric analysis of 4063 qPCR-based biomarker studies sourced from the Web of Science (WOS) database, employing VOSviewer and CiteSpace to generate multi-dimensional structural insights into this field. The results reveal a growing trend in research within this domain, with gene expression analysis playing a central role in the identification of potential biomarkers. Among these, cancer-related biomarkers are the most prominent, while research on biomarkers for other diseases remains limited. Liquid biopsy biomarkers, including microRNA (miRNA), circulating free DNA (cfDNA), and circulating tumor DNA (ctDNA), are increasingly being explored. The integration of bioinformatics, omics analysis, and high-throughput technologies with qPCR is accelerating biomarker discovery. Furthermore, large-scale parallel sequencing is emerging as a potential alternative to relative quantification and microarray techniques. Nevertheless, qPCR remains essential for validating specific biomarkers, and further standardization of its protocols is necessary to enhance reliability. This study provides a systematic analysis of qPCR-based biomarker research and underscores the need for future technological integration and standardization to facilitate broader clinical applications.

Analysis and identification of potential biomarkers for dysfunctional uterine bleeding.

Clinical evidence increasingly suggests that traditional treatments for dysfunctional uterine bleeding (DUB) have limited success. In this study, blood samples from 10 DUB patients and 10 healthy controls were collected for transcriptome sequencing. Then, the differentially expressed genes (DEGs) were screened and crossed with the DUB-related module genes to obtain the target genes. These target genes were analyzed for functional enrichment. Further, the biomarkers were screened by protein-protein interaction (PPI) analysis and analyzed by the gene set enrichment analysis (GSEA) and ingenuity pathway analysis (IPA). To explore the pathogenesis of DUB, immune microenvironment analyses were also performed. Potential drugs targeting these biomarkers were predicted for clinical treatment. The expression of these biomarkers was validated using quantitative real-time polymerase chain reaction (qRT-PCR). The results showed that, a total of 754 target genes were found to be related to cell proliferation and senescence. Five biomarkers-CENPE, KIF11, PIK3R1, SMC3, and SMC4-were identified, all of which were down-regulated in the DUB group, and most of these findings were confirmed by qRT-PCR. Notably, CENPE expression showed a negative association with activated NK cells and a positive association with resting NK cells. In addition, 44 potential drugs were predicted for DUB treatment. In conclusion, five DUB biomarkers were identified, enhancing understanding of gene regulation in DUB and providing a theoretical foundation for clinical diagnosis and treatment.

Dynamics in zebrafish development define transcriptomic specificity after angiogenesis inhibitor exposure.

Testing for developmental toxicity is an integral partof chemical regulations. The applied tests are laborious and costly and require a large number of vertebrate test animals. To reduce animal numbers and associated costs, the zebrafish embryo was proposed as an alternative model. In this study, we investigated the potential of transcriptome analysis in the zebrafish embryo model to support the identification of potential biomarkers for key events in developmental toxicity, using the inhibition of angiogenesis as a proof of principle. Therefore, the effects on the zebrafish transcriptome after exposure to the tyrosine kinase inhibitors, sorafenib (1.3µM and 2.4µM) and SU4312 (1µM, 2µM, and 5µM), and the putative vascular disruptor compound rotenone (25nM and 50nM) were analyzed. An early (2hpf-hours post fertilization) and a late (24hpf) exposure start with a time resolved transcriptome analysis was performed to compare the specificity and sensitivity of the responses with respect to anti-angiogenesis. We also showed that toxicodynamic responses were related to the course of the internal concentrations. To identify differentially expressed genes (DEGs) the time series data were compared by applying generalized additive models (GAMs). We observed mainly unspecific developmental toxicity in the early exposure scenario, while a specific repression of vascular related genes was only partially observed. In contrast, differential expression of vascular-related genes could be identified clearly in the late exposure scenario. Rotenone did not show angiogenesis-specific response on a transcriptomic level, indicating that the observed mild phenotype of angiogenesis inhibition may represent a secondary effect.

Identification and Validation of Potential Biomarkers and Therapeutic Targets of COVID-19-related Depression.

The prevalence of depression in COVID-19 patients is notably high, disrupting daily life routines and compounding the burden of other chronic health conditions. In addition, to elucidate the connection between COVID-19 and depression, we conducted an analysis of commonly differentially expressed genes [co-DEGs], uncovering potential biomarkers and therapeutic avenues specific to COVID-19-related depression. We obtained gene expression profiles from the Gene Expression Omnibus [GEO] database with strategic keyword searches ["COVID-19", "depression," and "SARS"]. We used functional enrichment analysis of the co-DEGs to decipher their likely biological roles. Then, we utilized protein-protein interaction [PPI] network analysis to identify hub genes among the co- DEGs. These findings were validated via an independent third-party dataset. Our analysis of blood samples from COVID-19 patients revealed 10,716 upregulated genes and 10,319 downregulated genes. In addition, by applying the same approach to depression samples, we identified 571 upregulated and 847 downregulated genes. Furthermore, by intersecting these datasets, we extracted 121 upregulated and 175 downregulated co-DEGs. Through PPI network construction and hub gene selection, we identified MPO, ARG1, CD163, FCGR1A, ELANE, LCN2, and CR1 as co-upregulated hub genes and MRPL13, RPS23, and MRPL1 as co-downregulated hub genes. The incorporation of third-party datasets revealed that these hub genes are specific targets of SARS-CoV-2, not generic viral response mechanisms. The identification of potential biomarkers represents a groundbreaking strategy for assessing and treating depression in the context of COVID-19, with the potential to reduce its prevalence among these patients. However, to fully harness this potential, additional clinical research is paramount.

Comparative glucocorticoid receptor agonism: In silico, in vitro, and in vivo and identification of potential biomarkers for synthetic glucocorticoid exposure

Abstract The glucocorticoid receptor (GR) is present in almost every vertebrate cell and is utilized in many biological processes. Despite an abundance of mammalian data, the structural conservation of the receptor and cross-species susceptibility, particularly for aquatic species, has not been well defined. Efforts to reduce, refine, and/or replace animal testing have increased, driving the impetus to advance development of new approach methodologies (NAMs). Here we used in silico, in vitro, and in vivo methods to elucidate a greater understanding of receptor-mediated effects of synthetic glucocorticoid exposure in teleost fish. Evolutionary conservation of amino acid residues critical for transcriptional activation was confirmed in silico using sequence alignment to predict across species susceptibility. Subsequent in vitro assays using zebrafish and human GR provided evidence of physiological congruence of GR agonism. Finally, adult fathead minnows (Pimephales promelas) were exposed in vivo to the synthetic glucocorticoids, dexamethasone (0.04, 400, 4,000 µg/L) and beclomethasone dipropionate (130 µg/L), and GR agonism confirmed via digital polymerase chain reaction; in addition, EcoToxChip analyses identified potential mRNA biomarkers following glucocorticoid exposure. These findings support the use of NAMs to potentially reduce multispecies in vivo experimentation while providing empirical evidence that expands the taxonomic domain of applicability for the GR agonism molecular initiating event within the broader GR agonism adverse outcome pathway network.

Potential Biomarkers and Treatment of Neuroinflammation in Parkinson's Disease.

Parkinson's disease (PD) is a degenerative disease of the central nervous system primarily affecting middle-aged and elderly individuals, significantly compromising their quality of life. Neuroinflammation is now recognized as a key feature in the pathogenesis of PD. This study reviews recent advances in the identification of potential biomarkers associated with neuroinflammation in PD and their significance for therapeutic strategies. These findings suggest that inflammatory factors play a pivotal role in PD treatment, and interventions involving anti-inflammatory drugs, physical exercise, and dietary modifications have shown promising results in mitigating disease progression.

Identification of Potential Biomarkers of Esophageal Squamous Cell Carcinoma using Community Detection Algorithms

Identification of Potential Biomarkers of Esophageal Squamous Cell Carcinoma using Community Detection Algorithms

Identification of potential biomarkers of esophageal squamous cell carcinoma using community detection algorithms

Identification of potential biomarkers of esophageal squamous cell carcinoma using community detection algorithms

Identification of potential biomarkers and pathways involved in high-altitude pulmonary edema using GC-MS and LC-MS metabolomic methods

High-altitude pulmonary edema (HAPE) is a life-threatening altitude sickness afflicting certain individuals after rapid ascent to high altitude above 2500 m. In the setting of HAPE, an early diagnosis is critical and currently based on clinical evaluation. The aim of this study was to utilize the metabolomics to identify the altered metabolic patterns and potential biomarkers for HAPE. Serum samples from HAPE patients (n = 24) and healthy controls (n = 21) were analyzed by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to profile differential metabolites and explore dysregulated metabolic pathways. The correlation analysis and receiver operating characteristic (ROC) curve analysis were further performed to screen biomarkers for HAPE. A total of 119 differential metabolites between the control and HAPE groups were identified. Top dysregulated pathways included pyrimidine metabolism, citrate cycle, sulfur metabolism, phenylalanine metabolism and purine metabolism. After correlation analysis with clinical indices, 39 differential metabolites were obtained as potential biomarkers related to HAPE. Finally, 7 biomarkers, specifically S-nitroso-N-acetylcysteine, aminocaproic acid, emodin, threo-hydroxyaspartic acid, 6-hydroxynicotinic acid, 3-acetylphenol sulfate and cis-aconitic acid, were screened out through ROC analysis, which displayed high diagnostic accuracy for HAPE. Taken together, the altered serum metabolic profile is associated with the occurrence of HAPE. Diagnostic tests based on the biomarkers from metabolomics may hold promise as a strategy for early detection of HAPE.

Identification of Anoikis-related potential biomarkers and therapeutic drugs in chronic thromboembolic pulmonary hypertension via bioinformatics analysis and in vitro experiment

There is growing evidence that programmed cell death plays a significant role in the pathogenesis of chronic thromboembolic pulmonary hypertension (CTEPH). Anoikis is a newly discovered type of programmed death and has garnered great attention. However, the precise involvement of Anoikis in the progression of CTEPH remains poorly understood. The goal of this study was to identify Anoikis-related genes (ARGs) and explore potential therapeutic drugs for CTEPH. Differentially expressed genes were identified by limma and weighted gene co-expression network analysis (WGCNA) packages, and functional analyses were conducted based on the differentially expressed genes. Subsequently, a combination of protein–protein interaction (PPI), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine Recursive Feature Elimination (SVM-RFE) methodologies was employed to screen hub genes associated with CTEPH, which were further verified by dataset GSE188938, quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot. CIBERSORT was utilized to evaluate the infiltration of immune cells and the relationship between infiltration-related immune cells and ARGs. Finally, targeted drug analysis and molecular docking were used to predict drugs targeting Anoikis process to treat CTEPH. Thirty-two differentially expressed genes related to Anoikis and CTEPH were screened through WGCNA analysis. Then, the key ARGs FASN, PLAUR, BCL2L1, HMOX1 and RHOB were screened by PPI, Lasso and SVM-RFE machine learning. Validation through dataset GSE188938, qRT-PCR, and Western blot analyses confirmed HMOX1 and PLAUR as powerful and promising biomarkers in CTEPH. In addition, CIBERSORT immunoinfiltration revealed that Mast_cells_activated and Neutrophils were involved in the pathological regulation of CTEPH. Correlation analysis indicated that HMOX1 was positively correlated with Neutrophils, while PLAUR was negatively correlated with Mast_cells_activated. Finally we used targeted drug analysis and molecular docking to identify that STANNSOPORFIN as a potential drug targeting HMOX1 for the treatment of CTEPH. HMOX1 and PLAUR emerge as potential biomarkers for CTEPH and may influence the development of CTEPH by regulating Anoikis. Mast_cells_activated and Neutrophils may be involved in Anoikis resistance in CTEPH patients, presenting novel insights into CTEPH therapeutic targets. STANNSOPORFIN is a potential agents targeting Anoikis process therapy for CTEPH.

The Application of RNA Sequencing in Understanding the Molecular Mechanisms and Therapeutic Potential in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterized by the progressive loss of motor neurons, leading to muscle weakness and paralysis. While significant advances have been made in understanding the pathophysiology of ALS, there is still no curative treatment, and the underlying molecular mechanisms remain incompletely understood. RNA sequencing (RNA-seq) has emerged as a powerful tool to analyze the transcriptome, providing critical insights into gene expression changes, alternative splicing events, and RNA metabolism. This review highlights the application of RNA-seq in ALS research, including its use in identifying ALS-related gene mutations and differential gene expressions. Key findings include the role of C9orf72, SOD1, FUS, TARDBP, and UBQLN2 mutations in disease progression, as well as the identification of potential biomarkers in cerebrospinal fluid and peripheral blood samples. Additionally, novel RNA-seq technologies, such as single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics, are explored for their ability to capture the heterogeneity of cellular populations and unravel the complex molecular mechanisms underlying ALS. Despite the promise of RNA-seq, several technical, analytical, and ethical challenges must be addressed to fully realize its potential in ALS research. Future directions include integrating RNA-seq data with other multi-omics approaches, advancing personalized medicine, and applying emerging sequencing technologies. Overall, RNA-seq continues to play a pivotal role in uncovering the molecular mechanisms of ALS and holds promise for the development of targeted therapies.

Identification of potential biomarkers for hepatocellular carcinoma based on machine learning and bioinformatics analysis

Metastasis is the major cause of hepatocellular carcinoma (HCC) mortality. But the effective biomarkers for HCC metastasis remain underexplored. Here we integrated GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) datasets to screen candidate genes for hepatocellular carcinoma metastasis, a consensus metastasis-derived prognostic signature (MDPS) was constructed by machine learning. Based on the risk scores, HCC patients were stratified into high-risk and low-risk groups. Comprehensive analyses were conducted to investigate various aspects including survival outcomes, clinical characteristics, immune cell infiltration, as well as in vitro experiments. Together, we develop a comprehensive machine learning-based program for constructing a consensus MDPS including four genes (SPP1, TYMS, HMMR and MYCN). Our findings revealed that four genes could serve as efficient prognostic biomarkers and therapeutic targets in HCC. In addition, in vitro experiments showed that HMMR overregulation exacerbated tumor progression, including proliferation, migration and invasion.

Metronomic chemotherapy in pediatric neuroblastoma

AbstractMetronomic chemotherapy (MC) is an innovative therapeutic approach that involves the chronic administration of low doses of chemotherapy agents. This strategy aims to sustain prolonged and active plasma levels of drugs, which can result in favorable tolerability. MC has shown promise in the treatment of hematologic and solid tumors, including high‐risk neuroblastoma and relapsed/refractory (R/R) neuroblastoma. In the contemporary management of neuroblastoma, MC stands as a viable maintenance therapy for newly diagnosed patients lacking access to autologous stem cell transplantation or immunotherapy, particularly in resource‐constrained regions. Furthermore, it serves as a pragmatic alternative for individuals intolerant to intensified regimens or undergoing palliative care for R/R neuroblastoma. Nevertheless, the quest for the optimal MC regimen persists, necessitating comprehensive investigations to delineate standardized protocols. Moreover, the identification of potential biomarkers or prognostic indicators assumes paramount significance in refining MC strategies for future breakthroughs in this domain. This review embarks on a comprehensive exploration of MC in neuroblastoma, offering insights into its historical underpinnings, diverse applications, adverse effect and future prospects, endeavoring to enrich our understanding of MC role in neuroblastoma management.

Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.

Acute coronary syndrome (ACS) is a leading cause of death worldwide. Prompt and accurate diagnosis of acute myocardial infarction (AMI) or ACS is crucial for improved management and prognosis of patients. The rapid growth of machine learning (ML) research has significantly enhanced our understanding of ACS. Most studies have focused on applying ML to detect ACS, predict prognosis, manage treatment, identify risk factors, and discover potential biomarkers, particularly using data from electrocardiograms (ECGs), electronic medical records (EMRs), imaging, and omics as the main data modality. Additionally, integrating ML with smart devices such as wearables, smartphones, and sensor technology enables real-time dynamic assessments, enhancing clinical care for patients with ACS. This review provided an overview of the workflow and key concepts of ML as they relate to ACS. It then provides an overview of current ML algorithms used for ACS diagnosis, prognosis, identification of potential risk biomarkers, and management. Furthermore, we discuss the current challenges faced by ML algorithms in this field and how they might be addressed in the future, especially in the context of medicine.

Identification of Potential Biomarkers and Pathways in Acute Myeloid Leukemia: Correlation Between the Calcineurin Signaling Pathway and Vascular Brittleness in Acute Myeloid Leukemia.

In this study, clinical bioinformatics analysis was used to identify potential biomarkers of acute myeloid leukemia (AML) occurrence and development, drug resistance, and poor prognosis to provide a theoretical basis for the treatment of AML. On the basis of the TCGA, GEO, and GTEx databases, an AML secondary database was established, and differential expression analysis and WGCNA were carried out to identify genes related to the prognosis of AML patients. Survival analysis was carried out for internal verification of key genes, and GEO data were used for external verification to obtain core genes related to prognosis. For differentially expressed genes, the EpiMed platform independently developed by the team was used for drug prediction. A total of 36 overlapping genes were obtained via difference analysis and WGCNA. Enrichment analysis revealed that the overlapping genes were associated with neutrophil activation, transcription dysregulation, AML, apoptosis, and other biological indicators. A protein interaction network was constructed for NCOA4, ACSL4, DPP4, ATL1, MT1G, ALOX15, and SLC7A11, which are key genes. Survival analysis revealed that NCOA4, ACSL4, DPP4, and ATL1 significantly affected the survival of patients with AML. The GSE142698 dataset verified that MPO, BCL2A1, and STMN1 had a statistically significant impact on the survival of AML patients. NCOA4, ACSL4, DPP4, and ATL1 may be potential biomarkers related to the survival and prognosis of patients with AML, and the calcineurin signaling pathway is associated with the risk of vascular fragility in AML patients, which can provide a reference for further research and optimization of treatment regimens.

Identification of potential biomarkers from amino acid transporter in the activation of hepatic stellate cells via bioinformatics.

The etiopathogenesis of hepatic stellate cells (HSC) activation has yet to be completely comprehended, and there has been broad concern about the interplay between amino acid transporter and cell proliferation. This study proposed exploring the molecular mechanism from amino acid transport-related genes in HSC activation by bioinformatic methods, seeking to identify the potentially crucial biomarkers. GSE68000, the mRNA expression profile dataset of activated HSC, was applied as the training dataset, and GSE67664 as the validation dataset. Differently expressed amino acid transport-related genes (DEAATGs), GO, DO, and KEGG analyses were utilized. We applied the protein-protein interaction analysis and machine learning of LASSO and random forests to identify the target genes. Moreover, single-gene GESA was executed to investigate the potential functions of target genes via the KEGG pathway terms. Then, a ceRNA network and a drug-gene interaction network were constructed. Ultimately, correlation analysis was explored between target genes and collagen alpha I (COL1A), alpha-smooth muscle actin (α-SMA), and immune checkpoints. We identified 15 DEAATGs, whose enrichment analyses indicated that they were primarily enriched in the transport and metabolic process of amino acids. Moreover, two target genes (SLC7A5 and SLC1A5) were recognized from the PPI network and machine learning, confirmed through the validation dataset. Then single-gene GESA analysis revealed that SLC7A5 and SLC1A5 had a significant positive correlation to ECM-receptor interaction, cell cycle, and TGF-β signaling pathway and negative association with retinol metabolism conversely. Furthermore, the mRNA expression of target genes was closely correlated with the COL1A and α-SMA, as well as immune checkpoints. Additionally, 12 potential therapeutic drugs were in the drug-gene interaction network, and the ceRNA network was constructed and visualized. SLC7A5 and SLC1A5, with their relevant molecules, could be potentially vital biomarkers for the activation of HSC.