Search Results (549)

Background/Objectives: Alterations in DNA damage repair mechanisms can impair the therapeutic effectiveness of cisplatin. MicroRNAs (miRNAs), key regulators of DNA damage repair processes, have been proposed as promising biomarkers for predicting the response to platinum-based chemotherapy (CT) in non-small cell lung cancer (NSCLC). In this study, by using a bioinformatics approach, we identified six miRNAs, which were differentially expressed (DE) between NSCLC patients characterized as responders and non-responders to platinum-based CT. We further validated the differential expression of the selected miRNAs on tumor and matched normal tissues from patients with resected NSCLC. Methods: Two miRNA microarray expression datasets were retrieved from the Gene Expression Omnibus (GEO) repository, comprising a total of 69 NSCLC patients (N = 69) treated with CT and annotated data from their response to treatment. Differential expression analysis was performed using the Linear Models for Microarray Analysis (Limma) package in R to identify DE miRNAs between responders (N = 33) and non-responders (N = 36). Quantitative real-time PCR (qRT-PCR) was used to assess miRNA expression levels in clinical tissue samples (N = 20). Results: Analysis with the Limma package revealed 112 DE miRNAs between responders and non-responders. A random-effects meta-analysis further identified 24 miRNAs that were consistently up- or downregulated in at least two studies. Survival analysis using the Kaplan–Meier plotter (KM plotter) indicated that 22 of these miRNAs showed significant associations with prognosis in NSCLC. Functional and pathway enrichment analysis revealed that several of the identified miRNAs were linked to key pathways implicated in DNA damage repair, including the p53, Hippo, PI3K and TGF-β signaling pathways. We finally distinguished a six-miRNA signature consisting of miR-26a, miR-29c, miR-34a, miR-30e-5p, miR-30e-3p and miR-497, which were downregulated in non-responders and are involved in at least three DNA damage repair pathways. Comparative expression analysis on tumor and matched normal tissues from surgically treated NSCLC patients confirmed their differential expression in clinical samples. Conclusions: In summary, we identified a signature of six miRNAs that are suppressed in NSCLC and may serve as a predictor of cisplatin response in NSCLC. Full article

Huntington’s disease (HD) is a severe and progressive neurodegenerative disease for which therapeutic options have so far been confined to symptomatic treatment. Currently, the diagnosis relies on the signs and symptoms shown by patients; however, by that stage, the psychomotor issues have progressed to a point where reversal of the condition is unattainable. Although numerous clinical trials have been actively investigating therapeutic agents aimed at preventing the onset of disease or slowing down the disease progression, there has been a constant need for reliable biomarkers to assess neurodegeneration, monitor disease progression, and assess the efficacy of treatments accurately. Therefore, to discover the key biomarkers associated with the progression of HD, we employed bioinformatics and machine learning (ML) to create a robust pipeline that integrated differentially expressed gene (DEG) analysis with ML to select potential biomarkers. We performed a meta-analysis to identify DEGs using three Gene Expression Omnibus (GEO) microarray datasets from different platforms related to HD-affected brain tissue, applying both relaxed and strict criteria to identify differentially expressed genes. Subsequently, focusing only on genes identified through the inclusive threshold, we employed 19 diverse ML techniques to explore the common genes that contributed to the top three selected ML algorithms and the shared genes that had an impact on the ML algorithms and were observed in the meta-analysis using the stringent condition were selected. Additionally, a receiver operating characteristic (ROC) analysis was conducted on external datasets to validate the discriminatory power of the identified genes. Based on the results of an inverse variance weighted meta-analysis of the AUCs across both human and mouse cohorts, GABRD and PHACTR1 were identified as the most robust candidates and were selected as key biomarkers for HD. Our comprehensive methodology, which integrates DEG meta-analysis with ML techniques, enabled a systematic prioritization of these biomarkers, providing valuable insights into their biological significance and potential for further validation in clinical research. Full article

Background: Glioblastoma (GBM) is one of the most challenging malignancies in all of neoplasms. These malignancies are associated with unfavorable clinical outcomes and significantly compromised patient wellbeing. The immunological landscape within the tumor microenvironment (TME) plays a critical role in determining GBM prognosis. By mining data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and correlating them with immune responses in the TME, genes associated with the immune microenvironment with potential prognostic value were obtained. Method: We selected GSE16011 as the training set. Gene expression profiles were substrates scored by both ESTIMATE and xCell, and immune cell subpopulations in GBM were analyzed by CIBERSORT. Gene expression profiles associated with low immune scores were performed by lasso regression, Cox analysis and random forest (RF) to identify a prognostic model for the multiple genes associated with immune infiltration in GBM. Then we constructed a nomogram to optimize the prognostic model using GSE7696 and TCGA-GBM as validation sets and evaluated these data for gene mutation and gene enrichment analysis. Result: The prognostic correlation between the six genes (MEOX2, PHYHIP, RBBP8, ST18, TCF12, and THRB) and GBM was finally found by lasso regression, Cox regression, and RF, and the online database obtained that all six genes were differentially expressed in GBM. Therefore, a prognostic correlation model was constructed based on the six genes. Kaplan–Meier (KM) survival analysis showed that this prognostic model had excellent prognostic ability. Conclusions: Prognostic models based on tumor microenvironment and immune score stratification and the construction of related genes have potential applications for prognostic analysis of GBM patients. Full article

Background: Aerobic exercise induces a range of complex molecular adaptations in skeletal muscle. However, a complete understanding of the specific transcriptional changes following exercise warrants further research. Methods: This study aimed to identify gene expression patterns following acute aerobic exercise by analyzing Gene Expression Omnibus (GEO) datasets. We performed a comparative analysis of transcriptional profiles of related genes in two independent studies, focusing on both established and novel genes involved in muscle physiology. Results: Our analysis revealed ten consistently upregulated and eight downregulated genes across both datasets. The upregulated genes were predominantly associated with mitochondrial function and cellular respiration, including MDH1, ATP5MC1, ATP5IB, and ATP5F1A. Conversely, downregulated genes such as YTHDC1, CDK5RAP2, and PALS2 were implicated in vascular structure and cellular organization. Importantly, our findings also revealed novel exercise-responsive genes not previously characterized in this context. Among these, MRPL41 and VEGF were significantly upregulated and are associated with p53-mediated apoptotic signaling and fatty acid metabolism, respectively. Novel downregulated genes included LIMCH1, CMYA5, and FOXJ3, which are putatively involved in cytoskeletal dynamics and muscle fiber type specification. Conclusions: These findings enhance our understanding of the transcriptional landscape of skeletal muscle following acute aerobic exercise and identify novel molecular targets for further investigation in the fields of exercise physiology and metabolic health. Full article

Oral squamous cell carcinoma (OSCC), an aggressive and poorly prognosed subtype of head and neck squamous cell carcinoma (HNSCC), has prompted urgent calls for innovative therapeutic approaches. Evodiamine (EVO), a natural alkaloid extracted from the Chinese herb Evodia rutaecarpa, has demonstrated significant potential in curbing tumor cell proliferation and slowing tumor expansion. However, its specific effects on cell senescence within the context of OSCC have remained shrouded in uncertainty. This study delves into the mechanisms of EVO’s impact on OSCC by harnessing databases such as the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and CellAge to pinpoint potential targets and carry out in-depth bioinformatics analysis. The findings reveal that EVO can markedly enhance the expression of the androgen receptor (AR) in OSCC cells, inducing cellular senescence and thereby inhibiting tumor progression. Furthermore, the research indicates that AR expression is considerably lower in OSCC tissues than in normal tissues. This low expression of AR in tumor tissues is closely associated with advanced clinical stages and unfavorable prognoses in HNSCC patients. These discoveries open up new avenues for therapeutic strategies, and suggest that AR holds promise as a potential therapeutic target for OSCC, and EVO may amplify its antitumor effects by enhancing AR-mediated cellular senescence in the treatment of OSCC. Full article

Background/Objectives: Gastric cancer (GC) remains a significant global health burden due to its high mortality rate and frequent diagnosis at advanced stages. This study aimed to identify reliable diagnostic biomarkers and elucidate molecular mechanisms underlying GC by integrating transcriptomic data from independent platforms and applying machine learning techniques. Methods: Two transcriptomic datasets from the Gene Expression Omnibus were analyzed: GSE26899 (microarray, n = 108) as the discovery dataset and GSE248612 (RNA-seq, n = 12) for validation. Differential expression analysis was conducted using limma and DESeq2, selecting genes with |log2FC| > 1 and adjusted p < 0.05. The top 200 differentially expressed genes (DEGs) were used to develop machine learning models (random forest, logistic regression, neural networks). Functional enrichment analyses (GO, KEGG, Hallmark) were applied to explore relevant biological pathways. Results: In GSE26899, 627 DEGs were identified (201 upregulated, 426 downregulated), with key genes including CST1, KIAA1199, TIMP1, MSLN, and ATP4A. The random forest model demonstrated excellent classification performance (AUC = 0.952). GSE248612 validation yielded 738 DEGs. Cross-platform comparison confirmed 55.6% concordance among core genes, highlighting CST1, TIMP1, KRT17, ATP4A, CHIA, KRT16, and CRABP2. Enrichment analyses revealed involvement in ECM–receptor interaction, PI3K-Akt signaling, EMT, and cell cycle. Conclusions: This integrative transcriptomic and machine learning framework effectively identified high-confidence biomarkers for GC. Notably, CST1, TIMP1, KRT16, and ATP4A emerged as consistent, biologically relevant candidates with strong diagnostic performance and potential clinical utility. These findings may aid early detection strategies and guide future therapeutic developments in gastric cancer. Full article

Background/Objectives: Neuropilin-1 (NRP1) is a key co-receptor for SARS-CoV-2, complementing the ACE2 receptor. Several investigations have documented highly conserved sequences in this receptor, supporting the implication of NRP1 as a key mediator in SARS-CoV-2 cellular entry mechanisms. Methods: To investigate this hypothesis, we examined 104,737 SARS-CoV-2 genome fastas from GISAID genomic data, corresponding to isolates collected between 2020 and 2025 in Mexico. Specifically, we focused on the RRAR motif, a known furin-binding site for NRP-1 and the binding site for ACE2 with the spike protein. Our analysis revealed high conservation (>98%) of the RRAR domain compared to a rapidly diminishing ACE2-binding domain. A complementary analysis, using Data from Gene Expression Omnibus (GEO, GSE150316), showed that NRP1 expression in lung tissue remains relatively stable, whereas ACE2 displayed high inter-individual variability and lower abundance compared to NRP1. Based on this evidence, we designed two humans–rats NRP1 siRNAs that were tested in vivo using a melittin-induced lung injury model. Results: The RT-PCR assays confirmed an effective NRP1 knockdown, and the siRNA-treated group showed a significant reduction in the lesions severity. These findings highlight NRP1 as a stable and relevant therapeutic target and suggest the protective potential of siRNA-mediated gene silencing. Conclusions: The evidence presented here supports the rational design of NRP1-directed therapies for multiple circulating SARS-CoV-2 variants in Mexico. Full article

Meniscal degradation is considered a driver of osteoarthritis (OA) progression, but the underlying mechanisms leading to age-related meniscus degeneration remain unknown. This study aimed to identify key genes and pathways involved in meniscal degradation through a computational analysis. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Differential expression gene (DEG) analysis was performed using DESeq2 accompanied by functional enrichment analysis, protein–protein interaction (PPI) and clustering analysis. Additionally, gene set enrichment analysis (GSEA) was performed. A total of 85 mRNAs (DEMs) and 8 long non-coding RNAs (DE LncRNAs) were found to be differentially expressed in OA meniscus tissues. Among 85 DEMs, 12 genes were found to be known OA-related genes, whereas 15 genes acted as transcription regulators, including RUNX2 and TBX4, which were identified as effector genes for OA. Enrichment analysis revealed the implication of DEMs in cartilage-degradation-related processes, including inflammatory pathways, lipid metabolism, extracellular matrix organization and superoxide/nitric oxide metabolic processes. Target genes of DE lncRNAs were found to be involved in chondrocyte differentiation and pathways related to cartilage degradation. A comparative analysis of meniscus, synovium and cartilage datasets identified three genes (GJB2, PAQR5 and CLEC12A) as being differentially expressed across all three OA-affected tissues, which were implicated in inflammatory and cholesterol metabolism processes. Our results support that shared mechanisms lead to meniscal and cartilage degradation during OA progression, providing further insights into the processes underlying OA pathogenesis and potential therapeutic targets for knee OA. Full article

Verticillium longisporum, a soil-borne fungus responsible for Verticillium wilt, primarily colonizes members of the Brassicaceae family. Using Arabidopsis thaliana roots as an experimental host, we systematically identify V. longisporum-responsive genes and pathways through comprehensive transcriptomic analysis, alongside screening of potential hub genes and evaluation of infection-associated regulatory mechanisms. The GSE62537 dataset was retrieved from the Gene Expression Omnibus database. After performing GEO2R analysis and filtering out low-quality data, 222 differentially expressed genes (DEGs) were identified, of which 184 were upregulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on these DEGs. A protein–protein interaction network was constructed using the STRING database. CytoHubba and CytoNCA plugins in Cytoscape v3.10.3 were used to analyze and evaluate this network; six hub genes and four functional gene modules were identified. The GeneMANIA database was used to construct a co-expression network for hub genes. Systematic screening of transcription factors within the 14 DEGs revealed the inclusion of the hub gene NAC042. Integrative bioinformatics analysis centered on NAC042 enabled prediction of a pathogen-responsive regulatory network architecture. We report V. longisporum-responsive components in Arabidopsis, providing insights for disease resistance studies in Brassicaceae crops. Full article

Background/Objectives: Laryngeal squamous cell carcinoma (LSCC) is a common malignancy with unsatisfactory survival rates, highlighting the need for reliable biomarkers to improve its diagnosis and prognosis. Growth differentiation factor 15 (GDF15), a protein implicated in various cancers, has not been thoroughly investigated in LSCC. This study aimed to evaluate the significance of GDF15 expression in LSCC by integrating immunohistochemical analysis of archival tissue samples with RNA sequencing data from public databases (TCGA and GEO) to provide comprehensive clinical insights. Methods: We analyzed archival tissue samples from 65 patients with LSCC using immunohistochemistry and evaluated GDF15 expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Statistical analyses included Kaplan–Meier survival analysis and Cox proportional hazards regression to assess the correlation between GDF15 expression, clinicopathological variables, and survival outcomes. Results: GDF15 expression did not significantly differ between tumor and adjacent normal tissues. However, in the tissue macroarray (TMA) cohort, high GDF15 expression was significantly associated with a lower TNM stage and less advanced pT status. Kaplan–Meier analysis revealed that high GDF15 expression correlated with reduced overall survival in the TMA cohort, suggesting its utility in risk stratification. Multivariate analysis identified GDF15 as an independent prognostic factor for disease-free survival in LSCC. Conclusions: Our findings suggest that GDF15 may serve as a prognostic biomarker for LSCC, particularly in early-stage disease. Elevated GDF15 levels, which are associated with poorer overall survival, could be integrated into diagnostic panels to enhance risk stratification and inform treatment decisions. Furthermore, GDF15 may be a promising target for therapeutic intervention. Further research is warranted to validate these results and explore their potential in clinical practice. Full article

Recent findings have identified high-density lipoprotein (HDL) as a carrier of microRNAs, small non-coding RNAs that regulate gene expression, suggesting a potential novel functional and biochemical role for HDL-microRNA cargo. Here, we conduct an in-depth bioinformatics analysis of unique HDL-microRNA cargo to uncover their molecular mechanisms and potential applications as clinical biomarkers. First, using the Gene Expression Omnibus (GEO), we performed computational analysis on public human microRNA array datasets (GSE 25425; platform GPL11162) obtained from highly purified fractions of HDL in human plasma in order to identify their unique miRNA cargo. This led to the identification of eleven miRNAs present only in HDL, herein listed: hsa-miR-210, hsa-miR-26a-1, hsa-miR-628-3p, hsa-miR-31, hsa-miR-501-5p, hsa-miR-100-3p, hsa-miR-571, hsa-miR-100-5p, hsa-miR-23a, hsa-miR-550, and hsa-miR-432. Then, these unique miRNAs present in HDL were analyzed using a bioinformatics approach to recognize their validated target genes. The ClusterProfiler R package applied gene ontology and KEGG enrichment analysis. The key genes mainly enriched in the biological process of cellular regulation were identified and linked to neurodegeneration. Finally, the protein–protein interaction and co-expression network were analyzed using the STRING and GeneMANIA Cytoscape plugins. Full article

Background: Obesity is characterized by a chronic state of low-grade inflammation. Investigating immune-critical genes and their biological functions in the adipose tissue of postmenopausal obese women is crucial for elucidating the underlying mechanisms of immune dysregulation associated with obesity. Methods: In this study, microarray (GSE151839) and single-cell RNA-seq (GSE176171) datasets were obtained from the Gene Expression Omnibus (GEO). For microarray data analysis, weighted gene co-expression network analysis (WGCNA), protein–protein interaction network (PPI) analysis, and immune infiltration analysis (ssGSEA) were employed to identify obesity-related immune-critical genes. Subsequently, the candidate genes were validated using scRNA-seq data to explore their expression patterns at the single-cell level. Finally, the expression levels of these immune-critical genes were experimentally verified in adipose tissue from obese and control zebrafish models using RT-qPCR. Results: Analysis of microarray data through WGCNA, PPI and ssGSEA identified 16 obesity-related immune-critical genes, including IL7R, CD3E, CD2, CCR5, CD3D, MS4A1, TRAT1, SLAMF8, CCL3L1, SPP1, CCL5, IL2RG, CD3G, TLR8, ITK, and CCL3. Differential expression of SPP1, ITK and CCL5 was confirmed in scRNA-seq data, with ITK and CCL5 showing distinct expression patterns in natural killer (NK) cells. Furthermore, RT-qPCR analysis revealed upregulation of SPP1 and ITK in adipose tissue of obese zebrafish compared to lean controls. Conclusions: This study identifies SPP1, ITK and CCL5 as key immune hub genes in the adipose tissue of postmenopausal obese women, with NK cells playing a significant role in adipose tissue inflammation through the expression of these genes. These findings provide novel insights into potential therapeutic targets for the prevention and treatment of obesity in postmenopausal women. Full article

Diabetic nephropathy (DN) represents a severe microvascular complication of diabetes mellitus. As a Traditional Chinese Medicine (TCM) with extensive clinical applications, Ligustri Lucidi Fructus (LLF) exhibits significant anti-DN activity. However, the underlying pharmacological mechanisms, crucial components, and targets for LLF in DN treatment remain unclear. By integrating network pharmacology, molecular docking, and molecular dynamics simulations, the bioactive compounds, potential therapeutic targets, and underlying mechanisms of LLF in the treatment of DN were elucidated, followed by biological validation in a palmitic acid (PA)-induced MPC5 podocyte injury model. Among the 383 DN-related LLF targets identified, TNF emerged as a pivotal one, demonstrating potential binding interaction with the active components salidroside (Sal), apigenin (Api), and tormentic acid (TA). Moreover, Gene Expression Omnibus (GEO) database and KEGG enrichment analysis collectively highlighted the cytosolic DNA-sensing pathway. Notably, the cGAS-STING pathway is central to this pathway. Experimental studies further demonstrated that LLF-containing serum exerted a protective effect on MPC5 podocytes through cGAS-STING pathway suppression. Overall, these findings elucidate the pleiotropic mechanisms underlying LLF’s protective effects against DN, integrating compound–target–pathway interactions and thus offering a rationale for further investigation. Full article

Oral squamous cell carcinoma (OSCC) is a malignancy that affects the oral mucosa and is characterized by indurated oral lesions. The RNAseq of formalin-fixed, paraffin-embedded (FFPE) samples is readily available in clinical settings. Such samples have long-term preservation and can provide highly accurate transcriptomic information regarding gene fusions, isoforms, and allele-specific expression. We determined differentially expressed genes using the transcriptomic profiles of oral potentially malignant disorder (OPMD) FFPE oral lesion samples of patients who developed OSCC over years. A technical comparison was completed comparing breast cancer (BC) FFPE publicly available data in this proof-of-concept pilot study. OSCC FFPE samples were collected from patients (N = 3) who developed OSCC 3 to 5 years following OPMD diagnosis (n = 3) and were analyzed using RNAseq. RNAseq sequences from the FFPE OSCC samples and publicly available FFPE samples of BC patients (n = 6) (Gene Expression Omnibus Database, GSE58135) aligned to human reference (GRCh38.p13). Genes were counted using the Spliced Transcripts Alignment to a Reference (STARv2.7.9a) software. Differential expression was determined in R using DESeq2v1.40.2 comparing OSCC to BC samples. Principal component analysis (PCA) plots were completed. Differential Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined via the Pathviewv.1.40.0 program. STRING v12.0 was used to determine protein–protein interactions between genes represented in more than one KEGG pathway. STARv2.7.9a identified 27,237 and 30,343 genes among the OSCC and BC groups, respectively. DESeq2v1.40.2 determined 9194 differentially expressed genes (DEPs), 4466 being upregulated (OSCC > BC) and 4728 being downregulated (BC > OSCC) (padj < 0.05). Most significant genes included KRT6B, SERPINB5, and DSC3 (5- to 10-fold change range; padj < 10 × 10−100). PCA showed that BC and OSCC samples clustered as separate groups. Pathviewv.1.40.0 identified 17 downregulated KEGG pathways in OSCC compared to the BC group. No upregulated KEGG pathways were identified. STRINGv12.0 determined Gene Ontology Biological Process enrichments for leukocytes and apoptosis in upregulated KEGG genes including multiple PIK3 genes and NIK/NF-kappaB signaling and metabolic responses from lipopolysaccharides in downregulated KEGG genes including CHUK and NFKB1. Using FFPE samples, we determined DEPs characteristic of OSCC and distinct from BC. KRT-family genes and lipopolysaccharide producing periodontal pathogens may be further investigated for their involvement in the OPMD to OSCC transition. Full article

Background/Objectives: Inflammatory breast carcinoma (IBC) is an aggressive and rare neoplasm, accounting for 1–5% of all breast cancers. Toll-like receptor type 4 (TLR4) and Advanced Glycation End Products Receptor (AGER/RAGE) have been implicated in breast cancer, and have been shown to promote tumor growth, metastasis, and resistance to therapy by modulating the tumor microenvironment and inflammatory pathways. However, the role of TLR4 and AGER in IBC has not been elucidated. Methods: TLR4 and AGER immunofluorescence expression were evaluated in 27 IBC and 24 non-IBC samples. The expression data and clinicopathological parameters, including the prognostic values of these biomarkers, were compared. TLR4 and AGER gene expression were investigated using the microarray transcriptomic dataset of IBC and non-IBC samples (Gene Expression Omnibus repository—GEO). Results: IBC samples showed higher TLR4 and AGER immunoexpression than the non-IBC group and were associated with obesity and Ki-67 expression (p < 0.05). AGER expression in IBC versus non-IBC was also statistically associated with triple-negative molecular subtypes. Non-IBC subjects with AGER immunoexpression above the cutoff (106.1%, sensitivity of 92.3%, and specificity of 56.2%) showed reduced metastasis-free survival (p = 0.032). In the multivariate analysis, high TLR4 immunostaining increased the risk of metastasis-free survival by 1.029-fold. Analyzing three external GEO datasets confirmed that TLR4 and AGER expression increased in IBC compared to non-IBC samples. Conclusions: Overall, IBC samples showed higher TLR4 and AGER expressions than other breast cancer types, shedding light on the significance of these markers on IBC biology. Full article