Identification of TFRC as a biomarker for pulmonary arterial hypertension based on bioinformatics and experimental verification

Respiratory Research, Journal Year: 2024, Volume and Issue: 25(1)

Published: Aug. 3, 2024

Pulmonary arterial hypertension (PAH) is a life-threatening chronic cardiopulmonary disease. However, there paucity of studies that reflect the available biomarkers from separate gene expression profiles in PAH. The GSE131793 and GSE113439 datasets were combined for subsequent analyses, batch effects removed. Bioinformatic analysis was then performed to identify differentially expressed genes (DEGs). Weighted co-expression network (WGCNA) protein-protein interaction (PPI) used further filter hub genes. Functional enrichment intersection using Gene Ontology (GO), Disease (DO), Kyoto encyclopedia genomes (KEGG) set (GSEA). level diagnostic value pulmonary patients also analyzed validation GSE53408 GSE22356. In addition, target validated lungs monocrotaline (MCT)-induced (PH) rat model serum PAH patients. A total 914 (DEGs) identified, with 722 upregulated 192 downregulated key module relevant selected WGCNA. By combining DEGs WGCNA, 807 selected. Furthermore, protein–protein identified HSP90AA1, CD8A, HIF1A, CXCL8, EPRS1, POLR2B, TFRC, PTGS2 as GSE22356 evaluate which showed robust value. According GSEA analysis, PAH-relevant biological functions pathways enriched high TFRC levels. found be lung tissues our experimental PH compared those controls, same conclusion reached bioinformatics observed increase tissue human patients, indicated by transcriptomic data, consistent alterations rodent models. These data suggest may serve potential biomarker

Language: Английский

---

Murine Model Insights: Identifying Dusp15 as a Novel Biomarker for Diabetic Cardiomyopathy Uncovered Through Integrated Omics Analysis and Experimental Validation

Diabetes Metabolic Syndrome and Obesity, Journal Year: 2025, Volume and Issue: Volume 18, P. 515 - 527

Published: Feb. 1, 2025

Diabetic Cardiomyopathy (DCM) is a heart condition that arises specifically from diabetes mellitus, characterized by cardiac dysfunction in the absence of coronary artery disease or hypertension. The prevalence DCM rising tandem with global increase diabetes, necessitating development early diagnostic markers and therapeutic targets. This study integrates bioinformatics analysis experimental validation to identify potential biomarkers for DCM. We performed gene expression data mining Gene Expression Omnibus (GEO) database. employed Weighted Co-expression Network Analysis (WGCNA) coupled machine learning techniques sift through hub differentially expressed genes (DEGs). Functional enrichment protein-protein interaction (PPI) network were also conducted pinpoint key functions. Subsequent vitro vivo experiments validate findings. Our revealed six core significantly associated Dusp15 was notably downregulated validated both high-glucose cultured cardiomyocytes animal models, suggesting its role pathogenesis. integration approaches has identified as promising candidate biomarker, offering valuable insights diagnosis development.

Language: Английский

---

Identification of potential biomarkers for lung cancer using integrated bioinformatics and machine learning approaches

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0317296 - e0317296

Published: Feb. 27, 2025

Lung cancer is one of the most common and leading cause cancer-related death worldwide. Early detection lung can help reduce rate; therefore, identification potential biomarkers crucial. Thus, this study aimed to identify for by integrating bioinformatics analysis machine learning (ML)-based approaches. Data were normalized using robust multiarray average method batch effect corrected ComBat method. Differentially expressed genes identified LIMMA approach carcinoma-associated selected Enrichr, based on DisGeNET database. Protein-protein interaction (PPI) network was performed STRING, PPI visualized Cytoscape. The core hub overlapping obtained from degree, betweenness, closeness, MNC. Moreover, MCODE plugin Cytoscape used perform module analysis, optimal modules scores along with their associated genes. Subsequently, Boruta-based ML utilized important Consequently, networks, ML-based approach. prognostic discriminative power assessed through survival ROC analysis. We extracted five datasets USA cohort three Taiwan same experimental protocols determine biomarkers. Four ( LPL, CLDN18, EDNRB, MME ) cohort, while DNRB, MME, ROBO4 cohort. Finally, two EDNRB intersecting genes, cohorts. proposed significantly improve patient outcomes enabling earlier detection, precise diagnosis, tailored treatment, ultimately contributing better rates quality life patients.

Language: Английский

---

Identification of noval diagnostic biomarker for HFpEF based on proteomics and machine learning

Proteome Science, Journal Year: 2025, Volume and Issue: 23(1)

Published: April 8, 2025

Language: Английский

---

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next generation sequencing data analysis

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 8, 2024

Abstract Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain intercourse and infertility. However, early diagnosis endometriosis still restricted. The purpose this investigation to identify validate key biomarkers endometriosis. Next generation sequencing (NGS) dataset GSE243039 was obtained from Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) between normal control samples were identified. After screening DEGs, gene ontology (GO) REACTOME pathway enrichment analyses performed. Furthermore, protein-protein interaction (PPI) network constructed modules analysed using Human Integrated Protein-Protein Interaction rEference (HIPIE) database Cytoscape software, hub Subsequantely, miRNAs genes, TFss miRNet NetworkAnalyst tool, possible TFs predicted. Finally, receiver operating characteristic curve (ROC) analysis used genes. A total 958 including 479 up regulated down screened samples. GO DEGs showed that they mainly involved in multicellular organismal process, developmental signaling by GPCR muscle contraction. Further PPI identified 10 VCAM1, SNCA, PRKCB, ADRB2, FOXQ1, MDFI, ACTBL2, PRKD1, DAPK1 ACTC1. Possible target miRNAs, hsa-mir-3143 hsa-mir-2110, TFs, TCF3 CLOCK, predicted constructing miRNA-hub regulatory TF-hub network. This bioinformatics techniques explore potential novel biomarkers. These might provide new ideas methods for diagnosis, treatment, monitoring

Language: Английский

---

Exploring potential ADHD biomarkers through advanced machine learning: An examination of audiovisual integration networks

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 183, P. 109240 - 109240

Published: Oct. 23, 2024

Language: Английский

---

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

Egyptian Journal of Medical Human Genetics, Journal Year: 2024, Volume and Issue: 25(1)

Published: Oct. 12, 2024

Abstract Background Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain intercourse and infertility. However, early diagnosis endometriosis still restricted. The purpose this investigation to identify validate key biomarkers endometriosis. Methods Next-generation sequencing dataset GSE243039 was obtained from Gene Expression Omnibus database, differentially expressed genes (DEGs) between normal control samples were identified. After screening DEGs, gene ontology (GO) REACTOME pathway enrichment analyses performed. Furthermore, protein–protein interaction (PPI) network constructed modules analyzed using Human Integrated Protein–Protein Interaction rEference database Cytoscape software, hub Subsequently, miRNAs genes, TFs miRNet NetworkAnalyst tool, possible predicted. Finally, receiver operating characteristic curve analysis used genes. Results A total 958 including 479 upregulated downregulated screened samples. GO DEGs showed that they mainly involved in multicellular organismal process, developmental signaling by GPCR muscle contraction. Further PPI identified 10 vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 actc1. Possible target miRNAs, hsa-mir-3143 hsa-mir-2110, TFs, tcf3 (transcription factor 3) clock (clock circadian regulator), predicted constructing miRNA-hub regulatory TF-hub network. Conclusions This bioinformatics techniques explore potential novel biomarkers. These might provide new ideas methods for diagnosis, treatment monitoring

Language: Английский

---

MACC1 revisited – an in-depth review of a master of metastasis

Biomarker Research, Journal Year: 2024, Volume and Issue: 12(1)

Published: Nov. 23, 2024

Abstract Cancer metastasis remains the most lethal characteristic of tumors mediating majority cancer-related deaths. Identifying key molecules responsible for metastasis, understanding their biological functions and therapeutically targeting these is therefore tremendous value. Metastasis Associated in Colon 1 (MACC1), a gene first described 2009, such driver metastatic processes, initiating cellular proliferation, migration, invasion, vitro vivo. Since its discovery, value MACC1 as prognostic biomarker has been confirmed over 20 cancer entities. Additionally, several therapeutic strategies pro-metastatic have developed. In this review, we will provide comprehensive overview on MACC1, from clinical relevance, towards structure role signaling cascades well molecular networks. We highlight specific consequences expression, an increase stem cell properties, immune-modulatory effects induced therapy resistance. Lastly, explore various interfering with expression and/or functions. Conclusively, review underlines importance individual metastasis.

Language: Английский

---

Identification of an immune-related gene panel for the diagnosis of pulmonary arterial hypertension using bioinformatics and machine learning

International Immunopharmacology, Journal Year: 2024, Volume and Issue: 144, P. 113694 - 113694

Published: Nov. 30, 2024

Language: Английский

---