Screening of common genomic biomarkers to explore common drugs for the treatment of pancreatic and kidney cancers with type-2 diabetes through bioinformatics analysis

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 2, 2025

Type 2 diabetes (T2D) is a crucial risk factor for both pancreatic cancer (PC) and kidney (KC). However, effective common drugs treating PC and/or KC patients who are also suffering from T2D currently lacking, despite the probability of their co-occurrence. Taking disease-specific multiple during co-existence diseases may lead to adverse side effects or toxicity due drug-drug interactions. This study aimed identify T2D-, KC-causing genomic biomarkers (cGBs) highlighting pathogenetic mechanisms explore as treatment. We analyzed transcriptomic profile datasets, applying weighted gene co-expression network analysis (WGCNA) protein-protein interaction (PPI) approaches PC-, cGBs. then disclosed through ontology (GO) terms, KEGG pathways, regulatory networks, DNA methylation these Initially, we identified 78 differentially expressed genes (cDEGs) that could distinguish T2D, PC, samples controls based on profiles. From these, six top-ranked cDEGs (TOP2A, BIRC5, RRM2, ALB, MUC1, E2F7) were selected cGBs considered targets exploring drug molecules each three diseases. Functional enrichment analyses, including GO analyses involving transcription factors (TFs) microRNAs, along with immune infiltration studies, revealed critical molecular linked KC, T2D. Finally, (NVP.BHG712, Irinotecan, Olaparib, Imatinib, RG-4733, Linsitinib) potential treatments co-existence, supported by literature reviews. Thus, this bioinformatics provides valuable insights resources developing genome-guided treatment strategy

Язык: Английский

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The Identification of Novel Therapeutic Biomarkers in Rheumatoid Arthritis: A Combined Bioinformatics and Integrated Multi-Omics Approach

International Journal of Molecular Sciences, Год журнала: 2025, Номер 26(6), С. 2757 - 2757

Опубликована: Март 19, 2025

Rheumatoid arthritis (RA) is a multifaceted autoimmune disease that marked by complex molecular profile influenced an array of factors, including genetic, epigenetic, and environmental elements. Despite significant advancements in research, the precise etiology RA remains elusive, presenting challenges developing innovative therapeutic markers. This study takes integrated multi-omics approach to uncover novel markers for RA. By analyzing both transcriptomics epigenomics datasets, we identified common gene candidates span these two omics levels patients diagnosed with Remarkably, discovered eighteen multi-evidence genes (MEGs) are prevalent across epigenomics, twelve which have not been previously linked directly The bioinformatics analyses MEGs revealed they part tightly interconnected protein–protein interaction networks related RA-associated KEGG pathways ontology terms. Furthermore, exhibited direct interactions miRNAs RA, underscoring their critical role disease’s pathogenicity. Overall, this comprehensive opens avenues identifying new candidate empowering researchers validate efficiently through experimental studies. advancing our understanding can pave way more effective therapies improved patient outcomes.

Язык: Английский

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Exploring common genomic biomarkers to disclose common drugs for the treatment of colorectal cancer and hepatocellular carcinoma with type-2 diabetes through transcriptomics analysis

PLoS ONE, Год журнала: 2025, Номер 20(3), С. e0319028 - e0319028

Опубликована: Март 24, 2025

Type 2 diabetes (T2D) is a crucial risk factor for both colorectal cancer (CRC) and hepatocellular carcinoma (HCC). However, so far, there was no study that has investigated common drugs against HCC CRC during their co-occurrence with T2D patients. Consequently, patients often require multiple disease-specific drugs, which can lead toxicities adverse effects to the due drug-drug interactions. This aimed identify genomic biomarkers (cGBs) associated pathogenetic mechanisms underlying CRC, HCC, uncover potential therapeutic compounds these three diseases. Firstly, we identified 86 differentially expressed genes (cDEGs) capable of separating each from control groups based on transcriptomic profiling. Of cDEGs, 37 were upregulated 49 downregulated. Genetic association studies average Log2 fold-change (aLog2FC) cDEGs suggested genetic among T2D. Subsequently, six top-ranked (MYC, MMP9, THBS1, IL6, CXCL1, SPP1) as through protein-protein interaction (PPI) network analysis. Further analysis cGBs GO-terms KEGG pathways revealed shared diseases, including specific biological processes, molecular functions, cellular components signaling pathways. The gene co-regulatory two transcription factors (FOXC1 GATA2) miRNAs (hsa-mir-195-5p, hsa-mir-124a-3p, hsa-mir-34a-5p) transcriptional post-transcriptional regulators cGBs. Finally, cGBs-guided seven candidate (Digitoxin, Camptosar, AMG-900, Imatinib, Irinotecan, Midostaurin, Linsitinib) treatment T2D, docking, cross-validation, ADME/T (Absorption–Distribution–Metabolism–Excretion–Toxicity) Most findings received support by literature review diseases individual studies. Thus, this offers valuable insights researchers clinicians improve diagnosis and/or

Язык: Английский

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A hybrid hierarchical health monitoring solution for autonomous detection, localization and quantification of damage in composite wind turbine blades for tinyML applications

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 11, 2025

Abstract Composites are widely used in wind turbine blades due to their excellent strength-to-weight ratio and operational flexibilities. However, turbines often operate harsh environmental conditions that can lead various types of damage, including abrasion, corrosion, fractures, cracks, delamination. Early detection through structural health monitoring (SHM) is essential for maintaining the efficient reliable operation turbines, minimizing downtime maintenance costs, optimizing energy output. Further, Damage localization challenging curved composites anisotropic nature, edge reflections, generation higher harmonics. Previous work has focused on damage using deep-learning approaches. these models computationally expensive, multiple need be trained independently tasks such as classification, localization, sizing identification. Also, data generated AE waveforms at a minimum sampling rate 1MSPS huge, requiring tinyML enabled hardware real time ML which reduce size cloud storage required. TinyML run efficiently with low power consumption. This paper presents Hybrid Hierarchical Machine-Learning Model (HHMLM) leverages acoustic emission (AE) identify, classify, locate different single unified model. The collected sensor, simulated by artificial sources (Pencil break) low-velocity impacts. Additionally, abrasion blade’s leading resembles wear. HHMLM model achieved 96.4% overall accuracy less computation than 83.8% separate conventional Convolutional Neural Network (CNN) models. developed SHM solution provides more effective practical in-service blades, particularly farm settings, potential future wireless sensors tiny applications.

Язык: Английский

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Exploring bacterial key genes and therapeutic agents for breast cancer among the Ghanaian female population: Insights from In Silico analyses

PLoS ONE, Год журнала: 2024, Номер 19(11), С. e0312493 - e0312493

Опубликована: Ноя. 25, 2024

Breast cancer (BC) is yet a significant global health challenge across various populations including Ghana, though several studies on host-genome associated with BC have been investigated molecular mechanisms of development and progression, candidate therapeutic agents. However, little attention has given microbial genome in this regard, although alterations microbiota epigenetic modifications are recognized as substantial risk factors for BC. This study focused identifying bacterial key genes (bKGs) infections the Ghanaian population exploring potential drug molecules by targeting these bKGs through silico analyses. At first, 16S rRNA sequence data were downloaded from NCBI database comprising 520 samples patients 442 healthy controls. Analysis rRNA-Seq showed differences abundance between groups identified 26 differential genera threshold values at |log2FC|>2.0 p-value≤0.05. It was observed that two Prevotella Anaerovibria significantly upregulated others downregulated. Functional analysis based all 19 MetaCyc signaling pathways, twelve which enriched containing 165 Top-ranked 10 mdh, pykF, gapA, zwf, pgi, tpiA, pgk, pfkA, ppsA, pykA BC-causing protein-protein interaction network analysis. Subsequently, bKG-guided top ranked Digitoxin, Digoxin, Ledipasvir, Suramin, Ergotamine, Venetoclax, Nilotinib, Conivaptan, Dihydroergotamine, Elbasvir using docking The stability top-ranked three drug-target complexes (Digitoxin-pykA, Digoxin-mdh, Ledipasvir-pgi) confirmed dynamics simulation studies. Therefore, findings might be useful resources to wet-lab researchers further experimental validation therapies against

Язык: Английский

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