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Introduction: Natural killer (NK) cells are associated with the pathogenesis of ulcerative colitis (UC); however, their precise contributions remain unclear. The present study aimed to investigate the diagnostic value of the activated NK-associated gene (ANAG) score in UC and evaluate its predictive value in response to biological therapy. Methods: Bulk RNA-seq and scRNA-seq datasets were obtained from the Gene Expression Omnibus (GEO) and Single Cell Portal (SCP) databases. In the bulk RNA-seq, differentially expressed genes (DEGs) were screened by the “Batch correction” and “Robust rank aggregation” (RRA) methods. The immune infiltration landscape was estimated using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT. DEGs that correlated with activated NK cells were identified as activated NK-associated genes (ANAGs). Protein-protein interaction (PPI) analysis and least absolute shrinkage and selection operator (LASSO) regression were used to screen key ANAGs and establish an ANAG score. The expression levels of the four key ANAGs were validated in human samples by real-time quantitative polymerase chain reaction (RT-qPCR) and immunofluorescence. The potential therapeutic drugs for UC were identified using the DSigDB database. Through scRNA-seq data analysis, the cell scores based on the ANAGs were calculated by “AddModuleScore” and “AUCell.” Results: Immune infiltration analysis revealed a higher abundance of activated NK cells in noninflamed UC tissues (ssGSEA, p < 0.001; CIBERSORT, p < 0.01). Fifty-four DEGs correlated with activated NK cells were identified as ANAGs. The ANAG score was established using four key ANAGs (SELP, TIMP1, MMP7, and ABCG2). The ANAG scores were significantly higher in inflamed tissues (p < 0.001) and in biological therapy nonresponders (NR) tissues before treatment (golimumab, p < 0.05; ustekinumab, p < 0.001). The ANAG score demonstrated an excellent diagnostic value (AUC = 0.979). Patients with higher ANAG scores before treatment were more likely to experience a lack of response to golimumab or ustekinumab (golimumab, p < 0.05; ustekinumab, p < 0.001). Conclusion: This study established a novel ANAG score with the ability to precisely diagnose UC and distinguish the efficacy of biological treatment. [ABSTRACT FROM AUTHOR]

Chenglin Ye,1,&ast; Sizhe Zhu,2,&ast; Jingping Yuan,1 Xiuxue Yuan3 1Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China; 2Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, Hubei, People’s Republic of China; 3Medical College of Wuhan University of Science and Technology, Wuhan, Hubei, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jingping Yuan; Xiuxue Yuan, Email yuanjingping@whu.edu.cn; exiuwust@gmail.comPurpose: Crohn’s disease (CD) represents a multifaceted inflammatory gastrointestinal condition, with a profound significance placed on unraveling its molecular pathways to enhance both diagnostic capabilities and therapeutic interventions. This study focused on identifying a robust macrophage-related signatures (MacroSig) for diagnosing CD, emphasizing the role of FPR1 in macrophage polarization and its implications in CD.Patients and Methods: Expression profiles from intestinal biopsies and macrophages of 1804 CD patients were retrieved from the Gene Expression Omnibus (GEO). Utilizing CIBERSORTx, differential expression analysis, and weighted correlation network analysis to to identify macrophage-related genes (MRGs). By unsupervised clustering, distinct clusters of CD were identified. Potential biomarkers were identified via using four machine learning algorithms, leading to the establishment of MacroSig which combines insights from 12 machine learning algorithms. Furthermore, the expression of FPR1 was verified in intestinal biopsies of CD patients and two murine experimental colitis models. Finally, we further explored the role of FPR1 in macrophage polarization through single-cell analysis as well as through the study of RAW264.7 cells and peritoneal macrophages.Results: Two distinct clusters with differential levels of macrophage infiltration and inflammation were identified. The MacroSig, which included FPR1 and LILRB2, exhibited high diagnostic accuracy and outperformed existing biomarkers and signatures. Clinical analysis demonstrated a strong correlation of FPR1 with disease activity, endoscopic inflammation status, and response to infliximab treatment. The expression levels of FPR1 were validated in our CD cohort by immunohistochemistry and confirmed in two colitis mouse models. Single-cell analysis indicated that FPR1 is predominantly expressed in macrophages and monocytes. In vitro studies demonstrated that FPR1 was upregulated in M1 macrophages, and its activation promoted M1 polarization.Conclusion: We developed a promising diagnostic signature for CD, and targeting FPR1 to modulate macrophage polarization may represent a novel therapeutic strategy.Keywords: Crohn’s disease, macrophage, diagnostic signature, FPR1, machine learning

Epigenetics, a potential underlying pathogenic mechanism of neurodegenerative diseases, has been in the scope of several studies performed so far. However, there is a gap in regard to analyzing different forms of early-onset dementia and the use of Lymphoblastoid cell lines (LCLs). We performed a genome-wide DNA methylation analysis on sixty-four samples (from the prefrontal cortex and LCLs) including those taken from patients with early-onset forms of Alzheimer's disease (AD) and frontotemporal dementia (FTD) and healthy controls. A beta regression model and adjusted p-values were used to obtain differentially methylated positions (DMPs) via pairwise comparisons. A correlation analysis of DMP levels with Clariom D array gene expression data from the same cohort was also performed. The results showed hypermethylation as the most frequent finding in both tissues studied in the patient groups. Biological significance analysis revealed common pathways altered in AD and FTD patients, affecting neuron development, metabolism, signal transduction, and immune system pathways. These alterations were also found in LCL samples, suggesting the epigenetic changes might not be limited to the central nervous system. In the brain, CpG methylation presented an inverse correlation with gene expression, while in LCLs, we observed mainly a positive correlation. This study enhances our understanding of the biological pathways that are associated with neurodegeneration, describes differential methylation patterns, and suggests LCLs are a potential cell model for studying neurodegenerative diseases in earlier clinical phases than brain tissue. [ABSTRACT FROM AUTHOR]

BackgroundTo identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections.MethodsChildren presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39).ResultsIn the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV.ConclusionsMIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C.

Abstract Background To investigate the diagnostic value of serum lncRNA growth arrest-specific transcript 5 (lncRNA GAS5) and microRNA-21 (miR-21) in patients with type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN), and elucidate their roles in the pathogenesis. Methods A microarray technology was used asses lncRNA GAS5 and miR-21 expression profiles in non-anticoagulant blood from 44 patients including T2DM without DN group (DM), T2DM with DN group (DN), and healthy controls group (N), followed by real-time PCR validation. Logistic regression and receiver operating characteristic (ROC) curves were applied to evaluate the clinical indicators among normal, T2DM, and DN patients. Results The serum lncRNA GAS5 expression in T2DM and DN patients was significantly down-regulated compared with the N group, while the expression of miR-21 was significantly up-regulated (all P

Abstract Background Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in the pathogenesis of AF. Existing investigations suggest that cuproptosis may also contribute to AF. This investigation aimed to identify a novel diagnostic gene signature associated with cuproptosis for AF using ensemble learning methods and discover the connection between AF and cuproptosis. Results Two genes connected to cuproptosis, including solute carrier family 31 member 1 (SLC31A1) and lipoic acid synthetase (LIAS), were selected by integration of random forests and eXtreme Gradient Boosting algorithms. Subsequently, a diagnostic model was constructed that includes the two genes for AF using the Light Gradient Boosting Machine (LightGBM) algorithm with good performance (the area under the curve value > 0.75). The microRNA-transcription factor-messenger RNA network revealed that homeobox A9 (HOXA9) and Tet methylcytosine dioxygenase 1 (TET1) could target SLC31A1 and LIAS in AF. Functional enrichment analysis indicated that cuproptosis might be connected to immunocyte activities. Immunocyte infiltration analysis using the CIBERSORT algorithm suggested a greater level of neutrophils in the AF group. According to the outcomes of Spearman’s rank correlation analysis, there was a negative relation between SLC31A1 and resting dendritic cells and eosinophils. The study found a positive relationship between LIAS and eosinophils along with resting memory CD4+ T cells. Conversely, a negative correlation was detected between LIAS and CD8+ T cells and regulatory T cells. Conclusions This study successfully constructed a cuproptosis-related diagnostic model for AF based on the LightGBM algorithm and validated its diagnostic efficacy. Cuproptosis may be regulated by HOXA9 and TET1 in AF. Cuproptosis might interact with infiltrating immunocytes in AF.

Epigenetics, a potential underlying pathogenic mechanism of neurodegenerative diseases, has been in the scope of several studies performed so far. However, there is a gap in regard to analyzing different forms of early-onset dementia and the use of Lymphoblastoid cell lines (LCLs). We performed a genome-wide DNA methylation analysis on sixty-four samples (from the prefrontal cortex and LCLs) including those taken from patients with early-onset forms of Alzheimer’s disease (AD) and frontotemporal dementia (FTD) and healthy controls. A beta regression model and adjusted p-values were used to obtain differentially methylated positions (DMPs) via pairwise comparisons. A correlation analysis of DMP levels with Clariom D array gene expression data from the same cohort was also performed. The results showed hypermethylation as the most frequent finding in both tissues studied in the patient groups. Biological significance analysis revealed common pathways altered in AD and FTD patients, affecting neuron development, metabolism, signal transduction, and immune system pathways. These alterations were also found in LCL samples, suggesting the epigenetic changes might not be limited to the central nervous system. In the brain, CpG methylation presented an inverse correlation with gene expression, while in LCLs, we observed mainly a positive correlation. This study enhances our understanding of the biological pathways that are associated with neurodegeneration, describes differential methylation patterns, and suggests LCLs are a potential cell model for studying neurodegenerative diseases in earlier clinical phases than brain tissue.

Abstract Background Duchenne muscular dystrophy (DMD) is an X-linked, incurable, degenerative neuromuscular disease that is exacerbated by secondary inflammation. N6-methyladenosine (m6A), the most common base modification of RNA, has pleiotropic immunomodulatory effects in many diseases. However, the role of m6A modification in the immune microenvironment of DMD remains elusive. Methods Our study retrospectively analyzed the expression data of 56 muscle tissues from DMD patients and 26 from non-muscular dystrophy individuals. Based on single sample gene set enrichment analysis, immune cells infiltration was identified and the result was validated by flow cytometry analysis and immunohistochemical staining. Then, we described the features of genetic variation in 26 m6A regulators and explored their relationship with the immune mircoenvironment of DMD patients through a series of bioinformatical analysis. At last, we determined subtypes of DMD patients by unsupervised clustering analysis and characterized the molecular and immune characteristics in different subgroups. Results DMD patients have a sophisticated immune microenvironment that is significantly different from non-DMD controls. Numerous m6A regulators were aberrantly expressed in the muscle tissues of DMD and inversely related to most muscle-infiltrating immune cell types and immune response-related signaling pathways. A diagnostic model involving seven m6A regulators was established using LASSO. Furthermore, we determined three m6A modification patterns (cluster A/B/C) with distinct immune microenvironmental characteristics. Conclusion In summary, our study demonstrated that m6A regulators are intimately linked to the immune microenvironment of muscle tissues in DMD. These findings may facilitate a better understanding of the immunomodulatory mechanisms in DMD and provide novel strategies for the treatment.

Background: To investigate the diagnostic value of serum lncRNA growth arrest-specific transcript 5 (lncRNA GAS5) and microRNA-21 (miR-21) in patients with type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN), and elucidate their roles in the pathogenesis. Methods: A microarray technology was used asses lncRNA GAS5 and miR-21 expression profiles in non-anticoagulant blood from 44 patients including T2DM without DN group (DM), T2DM with DN group (DN), and healthy controls group (N), followed by real-time PCR validation. Logistic regression and receiver operating characteristic (ROC) curves were applied to evaluate the clinical indicators among normal, T2DM, and DN patients. Results: The serum lncRNA GAS5 expression in T2DM and DN patients was significantly down-regulated compared with the N group, while the expression of miR-21 was significantly up-regulated (all P < 0.05). Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) were negatively correlated with serum lncRNA GAS5, and FBG was independently correlated with serum lncRNA GAS5. Urinary microalbumin, total cholesterol (TC), creatinine (Cr), urea, and systolic blood pressure (SBP) were significantly positively correlated with serum miR-21. Glomerular filtration rate (GFR) and albuminuria (ALB) were negatively correlated with serum miR-21, and ALB was independently correlated with serum miR-21. Serum lncRNA GAS5, miR-21 and lncRNA GAS5/miR-21 showed good diagnostic efficiency as the "diagnostic signature" of T2DM and DN. Conclusion: The lncRNA GAS5/miR-21 diagnostic signature may be a more effective non-invasive biomarker for detecting T2DM. In addition, miR-21 alone may be a more accurate serum biomarker for the early screening of DN patients. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in the pathogenesis of AF. Existing investigations suggest that cuproptosis may also contribute to AF. This investigation aimed to identify a novel diagnostic gene signature associated with cuproptosis for AF using ensemble learning methods and discover the connection between AF and cuproptosis. Results: Two genes connected to cuproptosis, including solute carrier family 31 member 1 (SLC31A1) and lipoic acid synthetase (LIAS), were selected by integration of random forests and eXtreme Gradient Boosting algorithms. Subsequently, a diagnostic model was constructed that includes the two genes for AF using the Light Gradient Boosting Machine (LightGBM) algorithm with good performance (the area under the curve value > 0.75). The microRNA-transcription factor-messenger RNA network revealed that homeobox A9 (HOXA9) and Tet methylcytosine dioxygenase 1 (TET1) could target SLC31A1 and LIAS in AF. Functional enrichment analysis indicated that cuproptosis might be connected to immunocyte activities. Immunocyte infiltration analysis using the CIBERSORT algorithm suggested a greater level of neutrophils in the AF group. According to the outcomes of Spearman's rank correlation analysis, there was a negative relation between SLC31A1 and resting dendritic cells and eosinophils. The study found a positive relationship between LIAS and eosinophils along with resting memory CD4+ T cells. Conversely, a negative correlation was detected between LIAS and CD8+ T cells and regulatory T cells. Conclusions: This study successfully constructed a cuproptosis-related diagnostic model for AF based on the LightGBM algorithm and validated its diagnostic efficacy. Cuproptosis may be regulated by HOXA9 and TET1 in AF. Cuproptosis might interact with infiltrating immunocytes in AF. [ABSTRACT FROM AUTHOR]

Background: The primary pathogenic cause of tooth loss in adults is periodontitis, although few reliable diagnostic methods are available in the early stages. One pathological factor that defines periodontitis pathology has previously been believed to be the equilibrium between inflammatory defense mechanisms and oxidative stress. Therefore, it is necessary to construct a model of oxidative stress-related periodontitis diagnostic markers through machine learning and bioinformatic analysis. Methods: We used LASSO, SVM-RFE, and Random Forest techniques to screen for periodontitis-related oxidative stress variables and construct a diagnostic model by logistic regression, followed by a biological approach to build a Protein-Protein interaction network (PPI) based on modelled genes while using modelled genes. Unsupervised clustering analysis was performed to screen for oxidative stress subtypes of periodontitis. we used WGCNA to explore the pathways correlated with oxidative stress in periodontitis patients. Networks. Finally, we used single-cell data to screen the cellular subpopulations with the highest correlation by scoring oxidative stress genes and performed a proposed temporal analysis of the subpopulations. Results: We discovered 3 periodontitis-associated genes (CASP3, IL-1β, and TXN). A characteristic line graph based on these genes can be helpful for patients. The primary hub gene screened by the PPI was constructed, where immune-related and cellular metabolism-related pathways were significantly enriched. Consistent clustering analysis found two oxidative stress categories, with the C2 subtype showing higher immune cell infiltration and immune function ratings. Therefore, we hypothesized that the high expression of oxidative stress genes was correlated with the formation of the immune environment in patients with periodontitis. Using the WGCNA approach, we examined the co-expressed gene modules related to the various subtypes of oxidative stress. Finally, we selected monocytes for mimetic time series analysis and analyzed the expression changes of oxidative stress genes with the mimetic time series axis, in which the expression of JUN, TXN, and IL-1β differed with the change of cell status. Conclusion: This study identifies a diagnostic model of 3-OSRGs from which patients can benefit and explores the importance of oxidative stress genes in building an immune environment in patients with periodontitis. [ABSTRACT FROM AUTHOR]

Background: Duchenne muscular dystrophy (DMD) is an X-linked, incurable, degenerative neuromuscular disease that is exacerbated by secondary inflammation. N6-methyladenosine (m6A), the most common base modification of RNA, has pleiotropic immunomodulatory effects in many diseases. However, the role of m6A modification in the immune microenvironment of DMD remains elusive. Methods: Our study retrospectively analyzed the expression data of 56 muscle tissues from DMD patients and 26 from non-muscular dystrophy individuals. Based on single sample gene set enrichment analysis, immune cells infiltration was identified and the result was validated by flow cytometry analysis and immunohistochemical staining. Then, we described the features of genetic variation in 26 m6A regulators and explored their relationship with the immune mircoenvironment of DMD patients through a series of bioinformatical analysis. At last, we determined subtypes of DMD patients by unsupervised clustering analysis and characterized the molecular and immune characteristics in different subgroups. Results: DMD patients have a sophisticated immune microenvironment that is significantly different from non-DMD controls. Numerous m6A regulators were aberrantly expressed in the muscle tissues of DMD and inversely related to most muscle-infiltrating immune cell types and immune response-related signaling pathways. A diagnostic model involving seven m6A regulators was established using LASSO. Furthermore, we determined three m6A modification patterns (cluster A/B/C) with distinct immune microenvironmental characteristics. Conclusion: In summary, our study demonstrated that m6A regulators are intimately linked to the immune microenvironment of muscle tissues in DMD. These findings may facilitate a better understanding of the immunomodulatory mechanisms in DMD and provide novel strategies for the treatment. [ABSTRACT FROM AUTHOR]

Background: Credible diagnostic stratification remains a challenge for coronary artery disease patients with clopidogrel resistance after percutaneous coronary intervention. Tongue diagnostic parameters-based diagnostic signatures might predict clopidogrel resistance.Methods: Clinical and tongue diagnostic parameters data were obtained from coronary artery disease patients with clopidogrel resistance after percutaneous coronary intervention patients and then analyzed. Tongue diagnostic parameters-based diagnostic signatures were developed through univariate and multivariate logistic regression analysis. The diagnostic prediction was assessed using a receiver operating characteristic curve.Results: A total of 101 patients were consecutively identified. Then, tongue diagnostic parameters were identified as significantly associated with clopidogrel resistance diagnosis and were combined with risk factors to develop a model. The receiver operating characteristic curve analysis showed that tongue diagnostic parameters-based diagnostic signatures performed well in diagnosing clopidogrel resistance with an area under the receiver operating characteristic curve value of 0.819.Conclusions: This study identified a novel tongue diagnostic parameters-based diagnostic signature to reliably distinguish clopidogrel resistance diagnosis in coronary artery disease patients undergoing percutaneous coronary intervention. Further larger, multicenter prospective studies are desired to validate this model. [ABSTRACT FROM AUTHOR]

The coagulation system is closely related to the physiological status and immune response of the body. Recent years, studies focusing on the association between coagulation system abnormalities and tumor progression have been widely reported. In clear cell renal cell carcinoma (ccRCC), poor prognosis often occurs in patients with venous tumor thrombosis and coagulation system abnormalities, and there is a lack of research in related fields. Significant differences in coagulation function were also demonstrated in our clinical sample of patients with high ccRCC stage or grade. Therefore, in this study, we analyzed the biological functions of coagulation-related genes (CRGs) in ccRCC patients using single-cell sequencing and TCGA data to establish the 5-CRGs based diagnostic signature and predictive signature for ccRCC. Univariate and multivariate Cox analyses suggested that prognostic signature could be an independent risk factor. Meanwhile, we applied CRGs for consistent clustering of ccRCC patients, and the two classes showed significant survival and genotype differences. The differences in individualized treatment between the two different subtypes were revealed by pathway enrichment analysis and immune cell infiltration analysis. In summary, we present the first systematic analysis of the significance of CRGs in the diagnosis, prognosis, and individualized treatment of ccRCC patients. [ABSTRACT FROM AUTHOR]

Background: Acute kidney injury (AKI) is a common and severe disease, which poses a global health burden with high morbidity and mortality. In recent years, ferroptosis has been recognized as being deeply related to Acute kidney injury. Our aim is to develop a diagnostic signature for Acute kidney injury based on ferroptosis-related genes (FRGs) through integrated bioinformatics analysis and machine learning.Methods: Our previously uploaded mouse Acute kidney injury dataset GSE192883 and another dataset, GSE153625, were downloaded to identify commonly expressed differentially expressed genes (coDEGs) through bioinformatic analysis. The FRGs were then overlapped with the coDEGs to identify differentially expressed FRGs (deFRGs). Immune cell infiltration was used to investigate immune cell dysregulation in Acute kidney injury. Functional enrichment analysis and protein-protein interaction network analysis were applied to identify candidate hub genes for Acute kidney injury. Then, receiver operator characteristic curve analysis and machine learning analysis (Lasso) were used to screen for diagnostic markers in two human datasets. Finally, these potential biomarkers were validated by quantitative real-time PCR in an Acute kidney injury model and across multiple datasets.Results: A total of 885 coDEGs and 33 deFRGs were commonly identified as differentially expressed in both GSE192883 and GSE153625 datasets. In cluster 1 of the coDEGs PPI network, we found a group of 20 genes clustered together with deFRGs, resulting in a total of 48 upregulated hub genes being identified. After ROC analysis, we discovered that 25 hub genes had an area under the curve (AUC) greater than 0.7; Lcn2, Plin2, and Atf3 all had AUCs over than this threshold in both human datasets GSE217427 and GSE139061. Through Lasso analysis, four hub genes (Lcn2, Atf3, Pir, and Mcm3) were screened for building a nomogram and evaluating diagnostic value. Finally, the expression of these four genes was validated in Acute kidney injury datasets and laboratory investigations, revealing that they may serve as ideal ferroptosis markers for Acute kidney injury.Conclusion: Four hub genes (Lcn2, Atf3, Pir, and Mcm3) were identified. After verification, the signature’s versatility was confirmed and a nomogram model based on these four genes effectively distinguished Acute kidney injury samples. Our findings provide critical insight into the progression of Acute kidney injury and can guide individualized diagnosis and treatment.

Background: Ferroptosis, an iron-dependent form of cell death, plays a crucial role in the progression of various cancers, including colon adenocarcinoma (COAD). However, the multi-omics signatures relevant to ferroptosis regulation in COAD diagnosis remain to be elucidated. Methods: The transcriptomic, miRNAomic, and methylomic profiles of COAD patients were acquired from the Cancer Genome Atlas (TCGA). Ferroptosis activity in these patients was determined, represented by a ferroptosis score (FS), using single-sample gene set enrichment analysis (ssGSEA) based on the expression of ferroptosis-related genes. Results: Results showed that the COAD patients with high-FS displayed favorable survival outcomes and heightened drug sensitivity. They also exhibited an up-regulation of genes involved in immune-related pathways (e.g., tumor necrosis factor signaling pathway), suggesting a correlation between immunity and ferroptosis in COAD progression. Furthermore, three survival prediction models were established based on 10 CpGs, 12 long non-coding RNAs (lncRNAs), and 14 microRNAs (miRNAs), respectively. These models demonstrated high accuracy in predicting COAD survival, achieving areas under the curve (AUC) >0.7. The variables used in the three models also showed strong correlations at different omics levels and were effective at discriminating between high-FS and low-FS COAD patients (AUC >0.7). Conclusions: This study identified different DNA methylation (DNAm), lncRNA, and miRNA characteristics between COAD patients with high and low ferroptosis activity. Furthermore, ferroptosis-related multi-omics signatures were established for COAD prognosis and classification. These insights present new opportunities for improving the efficacy of COAD therapy.

Pancreatic ductal adenocarcinoma cancer (PDAC) is a highly lethal malignancy requiring efficient detection when the primary tumor is still resectable. We previously developed the MxPancreasScore comprising 9 analytes and serum carbohydrate antigen 19-9 (CA19-9), achieving an accuracy of 90.6%. The necessity for 5 different analytical platforms and multiple analytical runs, however, hindered clinical applicability. We therefore aimed to develop a simpler single-analytical run, single-platform diagnostic signature. We evaluated 941 patients (PDAC, 356; chronic pancreatitis [CP], 304; nonpancreatic disease, 281) in 3 multicenter independent tests, and identification (ID) and validation cohort 1 (VD1) and 2 (VD2) were evaluated. Targeted quantitative plasma metabolite analysis was performed on a liquid chromatography–tandem mass spectrometry platform. A machine learning–aided algorithm identified an improved (i-Metabolic) and minimalistic metabolic (m-Metabolic) signatures, and compared them for performance. The i-Metabolic Signature, (12 analytes plus CA19-9) distinguished PDAC from CP with area under the curve (95% confidence interval) of 97.2% (97.1%-97.3%), 93.5% (93.4%-93.7%), and 92.2% (92.1%-92.3%) in the ID, VD1, and VD2 cohorts, respectively. In the VD2 cohort, the m-Metabolic signature (4 analytes plus CA19-9) discriminated PDAC from CP with a sensitivity of 77.3% and specificity of 89.6%, with an overall accuracy of 82.4%. For the subset of 45 patients with PDAC with resectable stages IA-IIB tumors, the sensitivity, specificity, and accuracy were 73.2%, 89.6%, and 82.7%, respectively; for those with detectable CA19-9 >2 U/mL, 81.6%, 88.7%, and 84.5%, respectively; and for those with CA19-9 <37 U/mL, 39.7%, 94.1%, and 76.3%, respectively. The single-platform, single-run, m-Metabolic signature of just 4 metabolites used in combination with serum CA19-9 levels is an innovative accurate diagnostic tool for PDAC at the time of clinical presentation, warranting further large-scale evaluation. [Display omitted] The m-Metabolic signature, based on just 4 plasma analytes combined with serum carbohydrate antigen 19-9, is highly accurate in diagnosing pancreatic cancer and could contribute to earlier treatment and improved prognosis of patients with pancreatic cancer. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to establish the transcriptome diagnostic signature of postmenopausal osteoporosis (PMOP) to identify diagnostic biomarkers and score patient risk to prevent and treat PMOP. Methods: Peripheral blood mononuclear cell (PBMC) expression data from PMOP patients were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using the "limma" package. The "WGCNA" package was used for a weighted gene co-expression network analysis to identify the gene modules associated with bone mineral density (BMD). Least absolute shrinkage and selection operator (LASSO) regression was used to construct a diagnostic signature, and its predictive ability was verified in the discovery cohort. The diagnostic values of potential biomarkers were evaluated by receiver operating characteristic curve (ROC) and coefficient analysis. Network pharmacology was used to predict the candidate therapeutic molecules. PBMCs from 14 postmenopausal women with normal BMD and 14 with low BMD were collected, and RNA was extracted for RT-qPCR validation. Results: We screened 2420 differentially expressed genes (DEGs) from the pilot cohort, and WGCNA showed that the blue module was most closely related to BMD. Based on the genes in the blue module, we constructed a diagnostic signature with 15 genes, and its ability to predict the risk of osteoporosis was verified in the discovery cohort. RT-qPCR verified the expression of potential biomarkers and showed a strong correlation with BMD. The functional annotation results of the DEGs showed that the diagnostic signature might affect the occurrence and development of PMOP through multiple biological pathways. In addition, 5 candidate molecules related to diagnostic signatures were screened out. Conclusion: Our diagnostic signature can effectively predict the risk of PMOP, with potential application for clinical decisions and drug candidate selection. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is the second most common progressive neurodegenerative disorder and the leading cause of disability in the daily activities. In the management of PD, accurate and specific biomarkers in blood for the early diagnosis of PD are urgently needed. DNA methylation is one of the main epigenetic mechanisms and associated with the gene expression and disease initiation of PD. We aimed to construct a methylation signature for the diagnosis of PD patients, and explore the potential value of DNA methylation in therapeutic options. Materials and methods: Whole blood DNA methylation and gene expression data of PD patients as well as healthy controls were extracted from Gene Expression Omnibus database. Next, differentially expressed genes (DEGs) and differentially methylated genes (DMGs) between PD patients and healthy controls were identified. Least absolute shrinkage and selection operator cox regression analysis was carried out to construct a diagnostic signature based on the overlapped genes. And, the receiver operating characteristic (ROC) curves were drawn and the area under the curve (AUC) was used to assess the diagnostic performance of the signature in both the training and testing datasets. Finally, gene ontology and gene set enrichment analysis were subsequently carried out to explore the underlying mechanisms. Results: We obtained a total of 9,596 DMGs, 1,058 DEGs, and 237 overlapped genes in the whole blood between PD patients and healthy controls. Eight methylation-driven genes (HIST1H4L, CDC42EP3, KIT, GNLY, SLC22A1, GCM1, INO80B, and ARHGAP26) were identified to construct the gene expression signature. The AUCs in predicting PD patients were 0.84 and 0.76 in training dataset and testing dataset, respectively. Additionally, eight methylation-altered CpGs were also identified to construct the CpGs signature which showed a similarly robust diagnostic capability, with AUCs of 0.8 and 0.73 in training dataset and testing dataset, respectively. Conclusion: We conducted an integrated analysis of the gene expression and DNA methylation data, and constructed a methylation-driven genes signature and a methylation-altered CpGs signature to distinguish the patients with PD from healthy controls. Both of them had a robust prediction power and provide a new insight into personalized diagnostic and therapeutic strategies for PD. [ABSTRACT FROM AUTHOR]

Background: Accumulating evidence has suggested that RNA binding protein (RBP) dysregulation plays an essential role during tumorigenesis. Here, we sought to explore the potential biological functions and clinical significance of RBP and develop diagnostic and prognostic signatures based on RBP in patients with head and neck squamous cell carcinoma (HNSCC)., Methods: The differently expressed RBPs between HNSCC samples and their normal counterparts were identified using the Limma package. The immunohistochemistry (IHC) images of several RBPs were collected from the Human Protein Atlas database. The diagnostic signature based on RBP was built by LASSO-logistic regression and random forest. The prognostic signature based on RBP was constructed by LASSO and stepwise Cox regression analysis in the training cohort and validated in the validation cohort., Results: Eighty-four aberrantly expressed RBPs were obtained, comprising 41 up-regulated and 43 down-regulated RBPs. Seven RBP genes (CPEB3, PDCD4, ENDOU, PARP12, DNMT3B, IGF2BP1, EXO1) were identified as diagnostic-related hub genes. They were used to establish a diagnostic RBP signature risk score (DRBPS) model by the coefficients in least absolute shrinkage and selection operator (LASSO)-logistic regression analysis and showed high specificity and sensitivity in the training (area under the receiver operating characteristic curve (AUC) = 0.998), and in all validation cohorts (AUC > 0.95 for all). Similarly, seven RBP genes (MKRN3, ZC3H12D, EIF5A2, AFF3, SIDT1, RBM24, and NR0B1) were identified as prognosis-associated hub genes by LASSO and stepwise multiple Cox regression analyses and were used to construct the prognostic model named as PRBPS. The AUC of the time-dependent receiver operator characteristic curve of the prognostic model was 0.664 at 3 years and 0.635 at 5 years in the training cohort and 0.720, 0.777 in the validation cohort, showing a favorable predictive efficacy for prognosis in HNSCC., Conclusions: Our results demonstrate the value of consideration of RBP in the diagnosis and prognosis for HNSCC and provide a novel insight into understanding the potential role of dysregulated RBP in HNSCC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Yan Liu,1 Zhenglu Wang,2 Lin Zhao1 1Department of Obstetrics, Tianjin First Central Hospital, Nankai University, Tianjin, 300192, People’s Republic of China; 2Biobank, Tianjin First Central Hospital, Nankai University, Tianjin, 300192, People’s Republic of ChinaCorrespondence: Lin ZhaoDepartment of Obstetrics, Tianjin First Central Hospital, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, People’s Republic of ChinaTel +86 22-85358245Email 5020200076@nankai.edu.cnBackground: Hypertensive disorders of pregnancy affect approximately 5– 10% of all pregnancies, and this study aims to identify potential diagnostic signatures.Methods: We downloaded the mRNA profiles of GSE75010 (placenta samples) and GSE48424 (blood samples) datasets with or without hypertension in pregnancy from the Gene Expression Omnibus database. Differential expression analysis was performed on the placenta samples using limma package of R language. GO terms and KEGG pathways enrichment analyses were performed on the placenta samples by the clusterProfiler package of R language. Infiltrating immune cell proportion of the placenta samples was evaluated using CIBERSORT software. The key genes involved in hypertension in pregnancy were screened from protein–protein interaction (PPI) network constructed based on the differentially expressed genes (DEGs). The logistic regression model was constructed by the glm package of R language, and receiver operating characteristic (ROC) curve was plotted to determine the accuracy of the model.Results: For the placenta samples, a total of 104 DEGs were identified, and 39 GO terms and 7 KEGG pathways were significantly enriched based on these 104 genes. Furthermore, the analysis of infiltrating immune cells indicated that the difference in the amount of immune cells might be the potential cause of hypertension in pregnancy. The logistic regression model was constructed based on three optimal genes (LEP, PRL and IGFBP1) screened from PPI network and could efficiently separate patients with hypertension in pregnancy from healthy subjects.Conclusion: A predictive model based on three potential genes LEP, PRL and IGFBP1 was obtained, suggesting that these genes might be potential diagnostic signatures for hypertension in pregnancy.Keywords: hypertension in pregnancy, diagnostic signature, GO terms and KEGG pathways analyses, PPI network, logistic regression model

Hydrological and climatic data at finer temporal resolutions are considered essential to model hydrological processes, especially for short duration flood events. Parameter transferability is an essential approach to obtain sub-daily hydrological simulations at many regions without sub-daily data. In this study, the objective is to investigate temporary dependency of parameter sensitivity for different flood types, which contributes to research into parameter transferability. This study is conducted in a medium-sized basin using a distributed hydrological model, DHSVM. Thirty-six flood events in the period of 04/12/2006–07/01/2013 in the Jinhua River basin, China, are classified into three flood types (FF: flash flood, SRF: short rainfall flood and LRF: long rainfall flood) by using the fuzzy decision tree method. The results show that SRF is the dominant flood type in the study area, followed by LRF and FF. Runoff simulations of FF and SRF are more sensitive to parameter perturbations than those of LRF. Sensitive parameters are highly dependent on temporal resolutions. The temporary dependency of LRF is the highest, followed by SRF and FF. More attention should be payed to sensitive and highly temporal dependent parameters in a subsequent parameter transfer process. Further study into this result is required to test the applicability. HIGHLIGHTS Runoff simulations of FF and SRF are more sensitive to parameter perturbations than those of LRF.; Sensitive parameters are highly dependent on temporal resolutions.; The temporary dependency of LRF is the highest, followed by SRF and FF.; This study contributes to further reduce uncertainty in parameter estimation by considering parameter temporary dependency.;

Background: Synovial macrophages play important roles in the formation and progression of osteoarthritis (OA). This study aimed to explore the biological and clinical significance of macrophage-associated genes (MAGs) in OA. Methods: The OA synovial gene expression profiles GSE89408 and GSE82107 were obtained from the GEO database. Single-sample gene set enrichment analysis (ssGSEA) and GSEA were employed to decipher differences in immune infiltration and macrophage-associated biological pathways, respectively. Protein-protein interaction (PPI) network analysis and machine learning were utilized to establish a macrophage-associated gene diagnostic signature (MAGDS). RT-qPCR was performed to test the expression of key MAGs in murine models. Results: OA synovium presented high levels of immune infiltration and activation of macrophage-associated biological pathways. A total of 55 differentially expressed MAGs were identified. Using PPI analysis and machine learning, a MAGDS consisting of IL1B, C5AR1, FCGR2B, IL10, IL6, and TYROBP was established for OA diagnosis (AUC = 0.910) and molecular pathological evaluation. Patients with high MAGDS scores may possess higher levels of immune infiltration and expression of matrix metalloproteinases (MMPs), implying poor biological alterations. The diagnostic value of MAGDS was also validated in an external cohort (AUC = 0.886). The expression of key MAGs was validated in a murine model using RT-qPCR. Additionally, a competitive endogenous RNA network was constructed to reveal the potential posttranscriptional regulatory mechanisms. Conclusions: We developed and validated a MAGDS model with the ability to accurately diagnose and characterize biological alterations in OA. The six key MAGs may also be latent targets for immunoregulatory therapy. [ABSTRACT FROM AUTHOR]

Background: Keloid disorder is a recurrent fibroproliferative cutaneous tumor. Due to the lack of early identification of keloid patients before the formation of keloids, it is impossible to carry out pre-traumatic intervention and prevention for these patients. This led us to identify and determine signatures with diagnostic significance for keloids.Methods: Public series of matrix files were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were calculated from expression profiling data, and the diagnostic signature was identified by constructing a protein-protein interaction (PPI) network. The diagnostic efficacy of the screened signature was assessed by employing receiver operating characteristic (ROC) curves. Furthermore, we calculated the proportion of different immune cells in the gene expression matrix microenvironment by the “ssGSEA” algorithm, and assessed the difference in immune cell abundance between keloids and control groups and the relationship between the signature and immune cell infiltration. Clinical keloid and normal skin tissues were collected, and the expression of the screened diagnostic signature was validated by RT-qPCR and immunohistochemical assay.Results: By screening the key genes in PPI, TGM2 was recognized and validated as a diagnostic signature and the infiltrating abundance of 10 immune cells was significantly correlated with TGM2 expression. Gene ontology enrichment analysis demonstrated that TGM2 and molecules interacting with it were mainly enriched in processes involving wound healing and collagen fiber organization. TGM2 correlated positively with HIF-1A (R = 0.82, p-value = 1.4e-05), IL6 (R = 0.62, p-value = 0.0053), and FN1 (R = 0.66, p-value = 0.0019). Besides, TGM2 was significantly upregulated in clinical keloid samples compared to normal skin tissues.Conclusion: TGM2 may serve as an auxiliary diagnostic indicator for keloids. However, the role of TGM2 in keloids has not been adequately reported in the current literature, which may provide a new direction for molecular studies of keloids.

Abstract Background Rheumatoid arthritis (RA) is an autoimmune rheumatic disease that carries a substantial burden for both patients and society. Early diagnosis of RA is essential to prevent disease progression and select an optimal therapeutic strategy. However, RA diagnosis is challenging, partly due to a lack of reliable biomarkers. Here, we aimed to explore the diagnostic signature and establish a predictive model of RA. Methods The mRNA expression profiling data of GSE17755, containing blood samples of 112 RA patients and 53 healthy control patients, were obtained from the Gene Expression Omnibus (GEO) database, followed by differential expression, GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis. A PPI network was constructed to select candidate hub genes, then logistic regression and random forest models were established based on the identified genes. Results Significantly, we identified 52 differentially expressed genes (DEGs), including 16 upregulated genes and 36 downregulated genes in RA samples compared with control samples. GO and KEGG analysis showed that several immune-related cellular processes were particularly enriched. We identified nine hub genes in the PPI network, including CFL1, COTL1, ACTG1, PFN1, LCP1, LCK, HLA-E, FYN, and HLA-DRA. The logistic regression and random forest models based on the nine identified genes reliably distinguished the RA samples from the healthy samples with substantially high AUC. Conclusion The diagnostic logistic regression and random forest models based on nine hub genes reliably predicted the occurrence of RA. Our findings could provide new insights into RA diagnostics.

Tong Liu,1 Sheng Yang,1 Yan-ping Cheng,1 Xiao-ling Kong,1 Dan-dan Du,1 Xian Wang,1 Yun-fei Bai,2 Li-hong Yin,1 Yue-pu Pu,1 Ge-yu Liang1 1Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210096, People’s Republic of China; 2State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, People’s Republic of ChinaCorrespondence: Ge-yu LiangSchool of Public Health, Southeast University, No. 87 Dingjiaqiao, Nanjing 210009, People’s Republic of ChinaTel +86-25-83272572Fax +86-25-832-72583Email lianggeyu@163.comPurpose: Gastric cancer (GC) is aggressive cancer with a high mortality rate worldwide. N6-methyladenosine (m6A) RNA methylation is related to tumorigenesis, which is dynamically regulated by m6A modulators (“writer,” “eraser,” and “reader”). We conducted a comprehensive analysis of the m6A genes of GC patients in TCGA datasets to identify the potential diagnostic biomarkers.Materials and Methods: We analyzed the expression profile of m6A genes in the TCGA cohort and constructed a diagnostic-m6A-score (DMS) by the LASSO-logistic model. In addition, by consensus cluster analysis, we identified two different subgroups of GC risk individuals by the expression profile of m6A modulators, revealing that YTHDF1’s expression variation profile in GC diagnosis. We also performed RT-qPCR and WB verification in 17 pairs of GC specimens and paired adjacent non-tumor tissues and GC cell lines, and verified the expression trend of YTHDF1 in five GEO GC datasets. YTHDF1 expression and clinical features of GC patients were assessed by the UALCAN.Results: The DMS with high specificity and sensitivity (AUC = 0.986) is proven to distinguish cancer from normal controls better. Moreover, we found that the expression profile variation of YTHDF1 was significantly associated with the high-risk subtype of GC patients. RT-qPCR and Western blot results are consistent with silicon analysis, revealing that YTHDF1’s potential oncogene role in GC tumor.Conclusion: In conclusion, we developed the m6A gene-based diagnostic signature for GC and found that YTHDF1 was significantly correlated with the high-risk subtype of GC patients, suggesting that YTHDF1 might be a potential target in GC early diagnosis.Keywords: m6A, RNA methylation, diagnostic signature, YTHDF1, gastric cancer

Abstract Background Circular RNAs (circRNAs), a novel type of noncoding RNAs, play critical roles in the initiation and progression of cancer. Emerging studies also shows that circRNAs may function as potential markers for cancer diagnosis and treatment. However, the diagnostic value of circRNAs in colorectal cancer (CRC) remains need to be unearthed. Methods CircRNA microarray was performed to detect the differentially expressed circRNAs in eight plasma samples, including four colorectal cancer (CRC) and four normal samples. Besides, the results of microarray were validated by quantitative real-time polymerase chain reaction (qRT-PCR). Moreover, ROC curve evaluation was performed to calculate the diagnostic value of significantly dysregulated circRNAs. In order to predict the potential mechanism of the significant circRNAs, circRNA–miRNA–mRNA network was constructed based on the TargetScan, miRTarBase and MIRDB database, as well as CircInteractome online software. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to further predict the function of meaningful circRNAs. Results Totally three differentially expressed circRNAs were identified in CRC plasma compared to normal plasma by circRNA microarray analysis, and the results was validated by qRT-PCR. Hsa_circ_0082182, hsa_circ_0000370 and hsa_circ_0035445 were identified and ROC curves analysis was used to calculate the single and joint diagnostic value. Furthermore, GO and KEGG analyses revealed that functions were mainly cancer-related, which indicated that the circRNAs were meaningfully associated with CRC cell proliferation and metastasis. Conclusion In conclusion, we have identified three circRNAs that are dysregulated in CRC plasma, including hsa_circ_0082182, hsa_circ_0000370 and hsa_circ_0035445. ROC curves showed that these circRNAs might have diagnostic value for colorectal cancer. Furthermore, bioinformatics analysis indicated that the above-mentioned circRNAs might be involved in the development of CRC.

Background: Acute rejection (AR) remains a major issue that negatively impacts long-term allograft survival in renal transplantation. The current study aims to apply machine learning methods to develop a non-invasive diagnostic test for AR based on gene signature in peripheral blood.Methods: We collected blood gene expression profiles of 251 renal transplant patients with biopsy-proven renal status from three independent cohorts in the Gene Expression Omnibus database. After differential expression analysis and machine learning algorithms, selected biomarkers were applied to the least absolute shrinkage and selection operator (LASSO) logistic regression to construct a diagnostic model in the training cohort. The diagnostic ability of the model was further tested in validation cohorts. Gene set enrichment analysis and immune cell assessment were also conducted for further investigation.Results: A novel diagnostic model based on three genes (TSEN15, CAPRIN1 and PRR34-AS1) was constructed in the training cohort (AUC = 0.968) and successfully verified in the validation cohort (AUC = 0.925) with high accuracy. Moreover, the diagnostic model also showed a promising value in discriminating T cell-mediated rejection (TCMR) (AUC = 0.786). Functional enrichment analysis and immune cell evaluation demonstrated that the AR model was significantly correlated with adaptive immunity, especially T cell subsets and dendritic cells.Conclusion: We identified and validated a novel three-gene diagnostic model with high accuracy for AR in renal transplant patients, and the model also performed well in distinguishing TCMR. The current study provided a promising tool to be used as a precise and cost-effective non-invasive test in clinical practice.

Background: Pancreatic ductal adenocarcinoma (PDAC) accounts for about 90% of pancreatic cancer, which is one of the most aggressive malignant neoplasms with a 9.3% five‐year survival rate. The pathological biopsy is the current golden standard for confirming suspicious lesions of PDAC, but it is not entirely reliable because of the insufficient sampling amount and inaccurate sampling location. Therefore, developing a robust signature to aid the accurate diagnosis of PDAC is critical. Methods: Based on the within‐sample relative expression orderings of gene pairs, we identified a qualitative signature to discriminate both PDAC and adjacent samples from both chronic pancreatitis and normal samples in the training datasets and validated it in other independent datasets produced by different laboratories with different measuring platforms. Results: A six‐gene‐pair signature was identified in the training data and validated in eight independent datasets. For surgical samples, 96.63% of 356 PDAC tissues, 100% of 11 pancreatitis tissues of non‐cancer patients, and 23 of 24 normal pancreatic tissues were correctly classified. Especially, 59 of 60 cancer‐adjacent normal tissues of PDAC patients were correctly identified as PDAC. For biopsy samples, all of 11 PDAC biopsy tissues were correctly classified as PDAC. Conclusion: The signature can distinguish both PDAC and PDAC‐adjacent normal tissues from both chronic pancreatitis and normal tissues of non‐cancer patients even when the sampling locations are inaccurate, which can aid the diagnosis of PDAC. [ABSTRACT FROM AUTHOR]

Abstract Background Early diagnosis is critical to reduce the mortality caused by nasopharyngeal carcinoma (NPC). MicroRNAs (miRNAs) are dysregulated and play important roles in carcinogenesis. Therefore, this study aimed to identify diagnostically relevant circulating miRNA signatures in patients with NPC. Methods Total RNA was extracted from whole blood samples obtained from 120 patients with NPC, 30 patients with head-neck tumors (HNT), and 30 healthy subjects (HSs), and examined by using a custom microarray. The expression levels of four miRNAs identified by using the microarray were validated with quantitative real-time reverse transcription polymerase chain reaction. The 120 patients with NPC and 30 HSs were randomly assigned to training group-1 and validation group-1, respectively. By using significance analysis of microarray (SAM), the specific miRNA expression profiles in whole blood from patients with NPC are obtained. By using lasso regression and adaptive boosting, a diagnostic signature was identified in training group-1, and its accuracy was verified in validation group-1. By using the same methods, another signature to distinguish patients with NPC from those with HNT and HSs was identified in training group-2 and confirmed in validation group-2. Results There were 117 differentially expressed miRNAs (upregulated and downregulated fold change ≥ 1.5) between the patients with NPC and HSs, among which an 8-miRNA signature was identified with 96.43% sensitivity and 100% specificity [area under the curve (AUC) = 0.995] to diagnose NPC in training group-1 and 86.11% sensitivity and 88.89% specificity (AUC = 0.941) in validation group-1. Compared with traditional Epstein–Barr virus (EBV) seromarkers, this signature was more specific for NPC. Furthermore, a 16-miRNA signature to differentiate NPC from HNT and HS (HNT-HS) was established from 164 differentially expressed miRNAs, which diagnosed NPC and HNT-HS with 100% accuracy (AUC = 1.000) in training group-2 and 87.04% (AUC = 0.924) in validation group-2. Conclusions The present study identified two miRNA signatures for the highly accurate diagnosis and differential diagnosis of patients with NPC from HSs and patients with HNT. The identified miRNAs might represent novel serological biomarkers and potential therapeutic targets for NPC.

Periodontitis progression is accompanied by irreversible alveolar bone absorption and leads to tooth loss. Early diagnosis is important for tooth stability and periodontal tissue preservation. However, there is no recognized miRNA diagnostic signature with convincing sensitivity and specificity for periodontitis. In this study, we obtained miRNA array expression profiles of periodontitis from the Gene Expression Omnibus (GEO) database. After screening for differentially expressed miRNAs, the least absolute shrinkage and selection operator (LASSO) method was performed to identify and construct a 17-miRNA-based diagnostic signature (hsa-miR-3917, hsa-mir-4271, hsa-miR-3156, hsa-miR-3141, hsa-miR-1246, hsa-miR-125a-5p, hsa-miR-671-5p, hcmv-mir-UL70, hsa-miR-650, hsa-miR-497-3p, hsa-miR-145-3p, hsa-miR-141-3p, hsa-miR-210-3p, hsa-miR-204-3p, hsa-miR-203a-5p, hsa-miR-99a-3p, and hsa-miR-30a-3p). Periodontal tissue samples with higher risk scores were more likely to show symptoms of periodontitis. Then, the receiver operating characteristic (ROC) curves were used to assess the diagnostic value of the miRNA signature, which indicated that the optimum cutoff value in periodontitis diagnosis was 0.5056 with an area under the ROC curve (AUC) of 0.996, a sensitivity of 97.3%, a specificity of 100.0% in the training cohort; in the testing cohort, the corresponding values were as follows: an AUC of 0.998, a sensitivity of 97.9%, and a specificity of 91.7%. We next evaluated the efficacy of the signature in differentiating disease subtype and affected range. Furthermore, we conducted functional enrichment analysis of the 17 miRNA-targeted mRNAs, including the regulation of mTOR activity and cell autophagy, Th1/Th2 cell balance and immunoregulation, cell apoptosis, and so on. In summary, our study identified and validated a 17-miRNA diagnostic signature with convincing AUC, sensitivity, and specificity for periodontitis.

Background: Rheumatoid arthritis (RA) is an autoimmune rheumatic disease that carries a substantial burden for both patients and society. Early diagnosis of RA is essential to prevent disease progression and select an optimal therapeutic strategy. However, RA diagnosis is challenging, partly due to a lack of reliable biomarkers. Here, we aimed to explore the diagnostic signature and establish a predictive model of RA. Methods: The mRNA expression profiling data of GSE17755, containing blood samples of 112 RA patients and 53 healthy control patients, were obtained from the Gene Expression Omnibus (GEO) database, followed by differential expression, GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis. A PPI network was constructed to select candidate hub genes, then logistic regression and random forest models were established based on the identified genes. Results: Significantly, we identified 52 differentially expressed genes (DEGs), including 16 upregulated genes and 36 downregulated genes in RA samples compared with control samples. GO and KEGG analysis showed that several immune-related cellular processes were particularly enriched. We identified nine hub genes in the PPI network, including CFL1, COTL1, ACTG1, PFN1, LCP1, LCK, HLA-E, FYN, and HLA-DRA. The logistic regression and random forest models based on the nine identified genes reliably distinguished the RA samples from the healthy samples with substantially high AUC. Conclusion: The diagnostic logistic regression and random forest models based on nine hub genes reliably predicted the occurrence of RA. Our findings could provide new insights into RA diagnostics. [ABSTRACT FROM AUTHOR]

Periodontitis progression is accompanied by irreversible alveolar bone absorption and leads to tooth loss. Early diagnosis is important for tooth stability and periodontal tissue preservation. However, there is no recognized miRNA diagnostic signature with convincing sensitivity and specificity for periodontitis. In this study, we obtained miRNA array expression profiles of periodontitis from the Gene Expression Omnibus (GEO) database. After screening for differentially expressed miRNAs, the least absolute shrinkage and selection operator (LASSO) method was performed to identify and construct a 17-miRNA-based diagnostic signature (hsa-miR-3917, hsa-mir-4271, hsa-miR-3156, hsa-miR-3141, hsa-miR-1246, hsa-miR-125a-5p, hsa-miR-671-5p, hcmv-mir-UL70, hsa-miR-650, hsa-miR-497-3p, hsa-miR-145-3p, hsa-miR-141-3p, hsa-miR-210-3p, hsa-miR-204-3p, hsa-miR-203a-5p, hsa-miR-99a-3p, and hsa-miR-30a-3p). Periodontal tissue samples with higher risk scores were more likely to show symptoms of periodontitis. Then, the receiver operating characteristic (ROC) curves were used to assess the diagnostic value of the miRNA signature, which indicated that the optimum cutoff value in periodontitis diagnosis was 0.5056 with an area under the ROC curve (AUC) of 0.996, a sensitivity of 97.3%, a specificity of 100.0% in the training cohort; in the testing cohort, the corresponding values were as follows: an AUC of 0.998, a sensitivity of 97.9%, and a specificity of 91.7%. We next evaluated the efficacy of the signature in differentiating disease subtype and affected range. Furthermore, we conducted functional enrichment analysis of the 17 miRNA-targeted mRNAs, including the regulation of mTOR activity and cell autophagy, Th1/Th2 cell balance and immunoregulation, cell apoptosis, and so on. In summary, our study identified and validated a 17-miRNA diagnostic signature with convincing AUC, sensitivity, and specificity for periodontitis. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is one of the most prevalent life-threatening human cancers and the leading cause of cancer-related mortality, with increased global incidence within the last decade. Identification of effective diagnostic and prognostic biomarkers would enable reliable risk stratification and efficient screening of high-risk patients, thereby facilitating clinical decision-making. Herein, we performed a comprehensive, robust DNA methylation analysis based on genome-wide DNA methylation profiling. We constructed a diagnostic signature with five DNA methylation markers, which precisely distinguished HCC patients from normal controls. Cox regression and LASSO analysis were applied to construct a prognostic signature with four DNA methylation markers. A one-to-one correlation analysis was carried out between genes of the whole genome and our prognostic signature. Exploration of the biological function and the role of the underlying significantly correlated genes was conducted. A mixed dataset of 463 HCC patients and 253 normal controls, derived from six independent datasets, was used to valid the diagnostic signature. Results showed a specificity of 96.84% and sensitivity of 96.77%. Class scores for the diagnostic signature were significantly different between normal controls, individuals with liver diseases, and HCC patients. The present signature has the potential to serve as a biomarker to monitor health in normal controls. Additionally, HCC patients were successfully separated into low-risk and high-risk groups by the prognostic signature, with a better prognosis for patients in the low-risk group. Kaplan-Meier and ROC analysis confirmed that the prognostic signature performed well. We found eight of the top ten genes to positively correlate with risk scores of the prognostic signature, and to be involved in cell cycle regulation. This eight-gene panel also served as a prognostic signature. The robust evidence presented in this study therefore demonstrates the effectiveness of the prognostic signature. In summary, we constructed diagnostic and prognostic signatures, which have potential for use in diagnosis, surveillance, and prognostic prediction for HCC patients. Eight genes that were significantly and positively correlated with the prognostic signature were strongly associated with cell cycle processes. Therefore, the prognostic signature can be used as a guide by which to measure responsiveness to cell-cycle-targeting agents.

Abstract Background Due to experimental batch effects, the application of a quantitative transcriptional signature for disease diagnoses commonly requires inter-sample data normalization, which would be hardly applicable under common clinical settings. Many cancers might have qualitative differences with the non-cancer states in the gene expression pattern. Therefore, it is reasonable to explore the power of qualitative diagnostic signatures which are robust against experimental batch effects and other random factors. Results Firstly, using data of technical replicate samples from the MicroArray Quality Control (MAQC) project, we demonstrated that the low-throughput PCR-based technologies also exist large measurement variations for gene expression even when the samples were measured in the same test site. Then, we demonstrated the critical limitation of low stability for classifiers based on quantitative transcriptional signatures in applications to individual samples through a case study using a support vector machine and a naïve Bayesian classifier to discriminate colorectal cancer tissues from normal tissues. To address this problem, we identified a signature consisting of three gene pairs for discriminating colorectal cancer tissues from non-cancer (normal and inflammatory bowel disease) tissues based on within-sample relative expression orderings (REOs) of these gene pairs. The signature was well verified using 22 independent datasets measured by different microarray and RNA_seq platforms, obviating the need of inter-sample data normalization. Conclusions Subtle quantitative information of gene expression measurements tends to be unstable under current technical conditions, which will introduce uncertainty to clinical applications of the quantitative transcriptional diagnostic signatures. For diagnosis of disease states with qualitative transcriptional characteristics, the qualitative REO-based signatures could be robustly applied to individual samples measured by different platforms.

Hepatocellular carcinoma (HCC) is one of the most prevalent life-threatening human cancers and the leading cause of cancer-related mortality, with increased global incidence within the last decade. Identification of effective diagnostic and prognostic biomarkers would enable reliable risk stratification and efficient screening of high-risk patients, thereby facilitating clinical decision-making. Herein, we performed a comprehensive, robust DNA methylation analysis based on genome-wide DNA methylation profiling. We constructed a diagnostic signature with five DNA methylation markers, which precisely distinguished HCC patients from normal controls. Cox regression and LASSO analysis were applied to construct a prognostic signature with four DNA methylation markers. A one-to-one correlation analysis was carried out between genes of the whole genome and our prognostic signature. Exploration of the biological function and the role of the underlying significantly correlated genes was conducted. A mixed dataset of 463 HCC patients and 253 normal controls, derived from six independent datasets, was used to valid the diagnostic signature. Results showed a specificity of 96.84% and sensitivity of 96.77%. Class scores for the diagnostic signature were significantly different between normal controls, individuals with liver diseases, and HCC patients. The present signature has the potential to serve as a biomarker to monitor health in normal controls. Additionally, HCC patients were successfully separated into low-risk and high-risk groups by the prognostic signature, with a better prognosis for patients in the low-risk group. Kaplan-Meier and ROC analysis confirmed that the prognostic signature performed well. We found eight of the top ten genes to positively correlate with risk scores of the prognostic signature, and to be involved in cell cycle regulation. This eight-gene panel also served as a prognostic signature. The robust evidence presented in this study therefore demonstrates the effectiveness of the prognostic signature. In summary, we constructed diagnostic and prognostic signatures, which have potential for use in diagnosis, surveillance, and prognostic prediction for HCC patients. Eight genes that were significantly and positively correlated with the prognostic signature were strongly associated with cell cycle processes. Therefore, the prognostic signature can be used as a guide by which to measure responsiveness to cell-cycle-targeting agents. [ABSTRACT FROM AUTHOR]

Background: Ferroptosis, an iron-dependent form of cell death, plays a crucial role in the progression of various cancers, including colon adenocarcinoma (COAD). However, the multi-omics signatures relevant to ferroptosis regulation in COAD diagnosis remain to be elucidated., Methods: The transcriptomic, miRNAomic, and methylomic profiles of COAD patients were acquired from the Cancer Genome Atlas (TCGA). Ferroptosis activity in these patients was determined, represented by a ferroptosis score (FS), using single-sample gene set enrichment analysis (ssGSEA) based on the expression of ferroptosis-related genes., Results: Results showed that the COAD patients with high-FS displayed favorable survival outcomes and heightened drug sensitivity. They also exhibited an up-regulation of genes involved in immune-related pathways (e.g., tumor necrosis factor signaling pathway), suggesting a correlation between immunity and ferroptosis in COAD progression. Furthermore, three survival prediction models were established based on 10 CpGs, 12 long non-coding RNAs (lncRNAs), and 14 microRNAs (miRNAs), respectively. These models demonstrated high accuracy in predicting COAD survival, achieving areas under the curve (AUC) >0.7. The variables used in the three models also showed strong correlations at different omics levels and were effective at discriminating between high-FS and low-FS COAD patients (AUC >0.7)., Conclusions: This study identified different DNA methylation (DNAm), lncRNA, and miRNA characteristics between COAD patients with high and low ferroptosis activity. Furthermore, ferroptosis-related multi-omics signatures were established for COAD prognosis and classification. These insights present new opportunities for improving the efficacy of COAD therapy., Competing Interests: The authors declare no conflict of interest., (© 2024 The Author(s). Published by IMR Press.)

Background: Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global health. Its hallmark pathological features include the deposition of β-amyloid plaques and the formation of neurofibrillary tangles. Given this context, it becomes imperative to develop an early and accurate biomarker model for AD diagnosis, employing machine learning and bioinformatics analysis., Methods: In this study, single-cell data analysis was employed to identify cellular subtypes that exhibited significant differences between the diseased and control groups. Following the identification of NK cells, hdWGCNA analysis and cellular communication analysis were conducted to pinpoint NK cell subset with the most robust communication effects. Subsequently, three machine learning algorithms-LASSO, Random Forest, and SVM-RFE-were employed to jointly screen for NK cell subset modular genes highly associated with AD. A logistic regression diagnostic model was then designed based on these characterized genes. Additionally, a protein-protein interaction (PPI) networks of model genes was established. Furthermore, unsupervised cluster analysis was conducted to classify AD subtypes based on the model genes, followed by the analysis of immune infiltration in the different subtypes. Finally, Spearman correlation coefficient analysis was utilized to explore the correlation between model genes and immune cells, as well as inflammatory factors., Results: We have successfully identified three genes (RPLP2, RPSA, and RPL18A) that exhibit a high association with AD. The nomogram based on these genes provides practical assistance in diagnosing and predicting patients' outcomes. The interconnected genes screened through PPI are intricately linked to ribosome metabolism and the COVID-19 pathway. Utilizing the expression of modular genes, unsupervised cluster analysis unveiled three distinct AD subtypes. Particularly noteworthy is subtype C3, characterized by high expression, which correlates with immune cell infiltration and elevated levels of inflammatory factors. Hence, it can be inferred that the establishment of an immune environment in AD patients is closely intertwined with the heightened expression of model genes., Conclusion: This study has not only established a valuable diagnostic model for AD patients but has also delved deeply into the pivotal role of model genes in shaping the immune environment of individuals with AD. These findings offer crucial insights into early AD diagnosis and patient management strategies., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Melanoma is an aggressive cancer with poor prognosis, requiring personalized management of advanced stages and establishment of molecular markers. Melanomas derive from melanocytes, which specifically express tyrosinase, the rate-limiting enzyme of melanin-synthesis. We demonstrate that melanomas with high levels of DNp73, a cancer-specific variant of the p53 family member p73 and driver of melanoma progression show, in contrast to their less-aggressive low-DNp73 counterparts, hypopigmentation in vivo. Mechanistically, reduced melanin-synthesis is mediated by a DNp73-activated IGF1R/PI3K/AKT axis leading to tyrosinase ER-arrest and proteasomal degradation. Tyrosinase loss triggers reactivation of the EMT signaling cascade, a mesenchymal-like cell phenotype and increased invasiveness. DNp73-induced depigmentation, Slug increase and changes in cell motility are recapitulated in neural crest-derived melanophores of Xenopus embryos, underscoring a previously unnoticed physiological role of tyrosinase as EMT inhibitor. This data provides a mechanism of hypopigmentation accompanying cancer progression, which can be exploited in precision diagnosis of patients with melanoma-associated hypopigmentation (MAH), currently seen as a favorable prognostic factor. The DNp73/IGF1R/Slug signature in colorless lesions might aid to clinically discriminate between patients with MAH-associated metastatic disease and those, where MAH is indeed a sign of regression. [ABSTRACT FROM AUTHOR]

In metabolomics, mass spectrometry is used to detect a large number of low-molecular substances in a single analysis. Such a capacity could have direct application in disease diagnostics. However, it is challenging because of the analysis complexity, and the search for a way to simplify it while maintaining the diagnostic capability is an urgent task. It has been proposed to use the metabolomic signature without complex data processing (mass peak detection, alignment, normalization, and identification of substances, as well as any complex statistical analysis) to make the analysis more simple and rapid. Methods: A label-free approach was implemented in the metabolomic signature, which makes the measurement of the actual or conditional concentrations unnecessary, uses only mass peak relations, and minimizes mass spectra processing. The approach was tested on the diagnosis of impaired glucose tolerance (IGT). Results: The label-free metabolic signature demonstrated a diagnostic accuracy for IGT equal to 88% (specificity 85%, sensitivity 90%, and area under receiver operating characteristic curve (AUC) of 0.91), which is considered to be a good quality for diagnostics. Conclusions: It is possible to compile label-free signatures for diseases that allow for diagnosing the disease in situ, i.e., right at the mass spectrometer without complex data processing. This achievement makes all mass spectrometers potentially versatile diagnostic devices and accelerates the introduction of metabolomics into medicine.

Small non-coding RNAs (sncRNAs) consisting of tRNA-derived small RNAs (tsRNAs) and miRNAs can be released by cancer cells and detected in blood, offering great potential for diagnosis of malignant tumors such as squamous cell carcinoma of the esophagus (ESCC). One of the major challenges for the clinical application of blood-based sncRNAs biomarkers is the difficulty of detection because of their small sncRNA size and low abundance. The deferentially expressed tsRNAs and miRNAs in plasma were studied with high-throughput sequencing and polymerase chain reaction in ESCC cohorts. A novel signature containing tRF-55:74-chrM.Phe-GAA, tRF-56:75-Ala-CGC-1-M4 and miR-4488 was identified with diagnostic potential. The signature was further confirmed by an attomolar-level ultrasensitive and rapid microfluidic biochip, which can achieve a multiplex, simple and low-cost detection. Our results indicated that a combination of tsRNAs and miRNAs has high diagnostic efficiency and tremendous potential to act as specific biomarkers through a reliable, highly sensitive, fast, and economic microfluidic biochip for ESCC diagnosis. [Display omitted] • Upregulated miRNAs and tsRNAs were screened in the plasma of 12 ESCC in comparison with 6 heathy individuals using high-throughput sequencing. • An attomolar-level ultrasensitive and rapid microfluidic biochip was applied to multiplex, simple and low-cost detection of the sncRNA diagnostic signature. • The expression levels of a sncRNA signature is of diagnostic potential for patients with ESCC after training and testing phases. [ABSTRACT FROM AUTHOR]

Background: Due to experimental batch effects, the application of a quantitative transcriptional signature for disease diagnoses commonly requires inter-sample data normalization, which would be hardly applicable under common clinical settings. Many cancers might have qualitative differences with the non-cancer states in the gene expression pattern. Therefore, it is reasonable to explore the power of qualitative diagnostic signatures which are robust against experimental batch effects and other random factors. Results: Firstly, using data of technical replicate samples from the MicroArray Quality Control (MAQC) project, we demonstrated that the low-throughput PCR-based technologies also exist large measurement variations for gene expression even when the samples were measured in the same test site. Then, we demonstrated the critical limitation of low stability for classifiers based on quantitative transcriptional signatures in applications to individual samples through a case study using a support vector machine and a naïve Bayesian classifier to discriminate colorectal cancer tissues from normal tissues. To address this problem, we identified a signature consisting of three gene pairs for discriminating colorectal cancer tissues from non-cancer (normal and inflammatory bowel disease) tissues based on within-sample relative expression orderings (REOs) of these gene pairs. The signature was well verified using 22 independent datasets measured by different microarray and RNA_seq platforms, obviating the need of inter-sample data normalization. Conclusions: Subtle quantitative information of gene expression measurements tends to be unstable under current technical conditions, which will introduce uncertainty to clinical applications of the quantitative transcriptional diagnostic signatures. For diagnosis of disease states with qualitative transcriptional characteristics, the qualitative REO-based signatures could be robustly applied to individual samples measured by different platforms. [ABSTRACT FROM AUTHOR]

Gangliosides (GAs) and sulfatides (STs) are acidic glycosphingolipids that are particularly abundant in the nervous system and are closely related to aging and neurodegenerative disorders. To explore their roles in brain diseases, in-depth molecular profiling, including structural variations of sphingoid backbone, fatty acyl group, and sugar chain of GAs and STs was performed. A total of 210 GAs and 38 STs were characterized in the inferior frontal gyrus (IFG) of human brain, with 90 GAs discovered in brain tissues for the first time. Influential MS parameters for detecting GAs and STs in multiple reaction monitoring (MRM) mode were systematically examined and optimized to minimize in-source fragmentation, resulting in remarkable signal intensity enhancement for GAs and STs, especially for polysialylated species. To eliminate analytical variations, isotopic interference-free internal standards were prepared by simple and fast reduction reaction. The final established method facilitated the simultaneous quantitation of 184 GAs and 30 STs from 25 subtypes, which represents the highest number of GAs quantitated among all quantitation methods recorded in literature so far. The method was further validated and applied to reveal the aberrant change of GAs and STs in the IFG of 12 Alzheimer's disease (AD) patients. Four GAs exhibited high classification capacity for AD (AUC ≥0.80) and were thereby considered the most promising signatures for AD. These findings suggested the close correlation between GAs and the pathogenesis of AD, highlighting the achievements of our robust method for investigating the roles of GAs and STs in various physiological states and diseases. [Display omitted] • A total of 210 GAs and 38 STs were rigorously characterized from human IFG. • Minimized in-source fragmentation were achieved upon optimization of MRM parameters. • The established method allows for quantitation of the most polysialylated GAs so far. • Internal standard free from isotopic interference were prepared for quantitation. • 62 GAs and 7 STs, including 10 GQ type were identified as potential signatures for AD. [ABSTRACT FROM AUTHOR]

Background: Acute kidney injury (AKI) is a common and severe disease, which poses a global health burden with high morbidity and mortality. In recent years, ferroptosis has been recognized as being deeply related to Acute kidney injury. Our aim is to develop a diagnostic signature for Acute kidney injury based on ferroptosis-related genes (FRGs) through integrated bioinformatics analysis and machine learning. Methods: Our previously uploaded mouse Acute kidney injury dataset GSE192883 and another dataset, GSE153625, were downloaded to identify commonly expressed differentially expressed genes (coDEGs) through bioinformatic analysis. The FRGs were then overlapped with the coDEGs to identify differentially expressed FRGs (deFRGs). Immune cell infiltration was used to investigate immune cell dysregulation in Acute kidney injury. Functional enrichment analysis and protein-protein interaction network analysis were applied to identify candidate hub genes for Acute kidney injury. Then, receiver operator characteristic curve analysis and machine learning analysis (Lasso) were used to screen for diagnostic markers in two human datasets. Finally, these potential biomarkers were validated by quantitative real-time PCR in an Acute kidney injury model and across multiple datasets. Results: A total of 885 coDEGs and 33 deFRGs were commonly identified as differentially expressed in both GSE192883 and GSE153625 datasets. In cluster 1 of the coDEGs PPI network, we found a group of 20 genes clustered together with deFRGs, resulting in a total of 48 upregulated hub genes being identified. After ROC analysis, we discovered that 25 hub genes had an area under the curve (AUC) greater than 0.7; Lcn2, Plin2, and Atf3 all had AUCs over than this threshold in both human datasets GSE217427 and GSE139061. Through Lasso analysis, four hub genes (Lcn2, Atf3, Pir, and Mcm3) were screened for building a nomogram and evaluating diagnostic value. Finally, the expression of these four genes was validated in Acute kidney injury datasets and laboratory investigations, revealing that they may serve as ideal ferroptosis markers for Acute kidney injury. Conclusion: Four hub genes (Lcn2, Atf3, Pir, and Mcm3) were identified. After verification, the signature's versatility was confirmed and a nomogram model based on these four genes effectively distinguished Acute kidney injury samples. Our findings provide critical insight into the progression of Acute kidney injury and can guide individualized diagnosis and treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Chen, Liu, Liu, Cai, Lu and Chen.)

Background: The primary pathogenic cause of tooth loss in adults is periodontitis, although few reliable diagnostic methods are available in the early stages. One pathological factor that defines periodontitis pathology has previously been believed to be the equilibrium between inflammatory defense mechanisms and oxidative stress. Therefore, it is necessary to construct a model of oxidative stress-related periodontitis diagnostic markers through machine learning and bioinformatic analysis., Methods: We used LASSO, SVM-RFE, and Random Forest techniques to screen for periodontitis-related oxidative stress variables and construct a diagnostic model by logistic regression, followed by a biological approach to build a Protein-Protein interaction network (PPI) based on modelled genes while using modelled genes. Unsupervised clustering analysis was performed to screen for oxidative stress subtypes of periodontitis. we used WGCNA to explore the pathways correlated with oxidative stress in periodontitis patients. Networks. Finally, we used single-cell data to screen the cellular subpopulations with the highest correlation by scoring oxidative stress genes and performed a proposed temporal analysis of the subpopulations., Results: We discovered 3 periodontitis-associated genes ( CASP3, IL-1β , and TXN ). A characteristic line graph based on these genes can be helpful for patients. The primary hub gene screened by the PPI was constructed, where immune-related and cellular metabolism-related pathways were significantly enriched. Consistent clustering analysis found two oxidative stress categories, with the C2 subtype showing higher immune cell infiltration and immune function ratings. Therefore, we hypothesized that the high expression of oxidative stress genes was correlated with the formation of the immune environment in patients with periodontitis. Using the WGCNA approach, we examined the co-expressed gene modules related to the various subtypes of oxidative stress. Finally, we selected monocytes for mimetic time series analysis and analyzed the expression changes of oxidative stress genes with the mimetic time series axis, in which the expression of JUN, TXN, and IL-1β differed with the change of cell status., Conclusion: This study identifies a diagnostic model of 3-OSRGs from which patients can benefit and explores the importance of oxidative stress genes in building an immune environment in patients with periodontitis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Song, Peng, Zhang, Song, Yang, Xie, Gou, Zhang, Yang, Chi and Tian.)

Background: Credible diagnostic stratification remains a challenge for coronary artery disease patients with clopidogrel resistance after percutaneous coronary intervention. Tongue diagnostic parameters-based diagnostic signatures might predict clopidogrel resistance., Methods: Clinical and tongue diagnostic parameters data were obtained from coronary artery disease patients with clopidogrel resistance after percutaneous coronary intervention patients and then analyzed. Tongue diagnostic parameters-based diagnostic signatures were developed through univariate and multivariate logistic regression analysis. The diagnostic prediction was assessed using a receiver operating characteristic curve., Results: A total of 101 patients were consecutively identified. Then, tongue diagnostic parameters were identified as significantly associated with clopidogrel resistance diagnosis and were combined with risk factors to develop a model. The receiver operating characteristic curve analysis showed that tongue diagnostic parameters-based diagnostic signatures performed well in diagnosing clopidogrel resistance with an area under the receiver operating characteristic curve value of 0.819., Conclusions: This study identified a novel tongue diagnostic parameters-based diagnostic signature to reliably distinguish clopidogrel resistance diagnosis in coronary artery disease patients undergoing percutaneous coronary intervention. Further larger, multicenter prospective studies are desired to validate this model., (Copyright © 2022 Elsevier Inc. All rights reserved.)