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PubMed2026

Bioinformatic Analysis of Bacillus pacificus B630: Molecular Understanding of Biofilm Production.

The aim of this study was to determine biofilm production and motility in Bacillus pacificus B630 and Bacillus cereus ATCC 14579, and to perform a comparative genome analysis using bioinformatic tools to understand the differences between the two strains. Biofilm production was performed in glass tubes stained with safranin; motility was determined on soft agar. Bioinformatic analysis was performed using genomic information from both strains, including the identification of orthologous genes, the similarity between genes of the eps1 and sipW-tasA-calY operons, and the SipW and TasA model prediction. B. pacificus B630 produces a greater amount of biofilm on glass than B. cereus ATCC 14579 (p < 0.01). Furthermore, B. pacificus B630 shows lower motility than B. cereus ATCC 14579 (p < 0.001). B. pacificus B630 contains 45 unshared genes, whereas B. cereus ATCC 14579 has 27 unshared genes. Differences in similarity were observed between the genes of the eps1 and sipW-tasA-calY operons. These differences between SipW and TasA may affect protein structural predictions. In SipW, the differences may affect the C-terminal region. In TasA, the number of B-sheets differed between the two proteins, and amino acid substitutions were found in regions of high protein aggregation. Genomic differences in genes associated with biofilm production may explain differences in biofilm production between the strains studied.

Biofilms↗

PubMed2026

Genetic and clinical insights into the coexistence of multiple myeloma and diffuse large B cell lymphoma from a case report and systematic review with bioinformatics analysis.

BACKGROUND: Multiple myeloma (MM) and diffuse large B-cell lymphoma (DLBCL) are B-cell malignancies that rarely coexist in a single patient, presenting significant diagnostic and therapeutic challenges. While MM primarily involves clonal plasma cells, DLBCL is an aggressive lymphoid neoplasm. Investigating shared genetic mutations and understanding their clinical relevance in both cancers could provide novel insights into their pathogenesis and underlying molecular mechanisms, thereby informing future translational research. MATERIALS AND METHODS: A case report was conducted on a 52-year-old male who presented with abdominal pain and anemia. Imaging revealed lymphadenopathy, and biopsy confirmed high-grade DLBCL with concurrent bone marrow involvement suggestive of MM. Laboratory tests identified monoclonal IgM gammopathy, and the patient was treated with R-CHOP (Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone) chemotherapy for DLBCL followed by autologous stem cell transplantation (ASCT) for MM relapse. A systematic review of the literature was performed using PubMed, Scopus, and Web of Science databases to identify cases of patients diagnosed with both MM and DLBCL. Data on patient demographics, clinical features, treatment regimens, and outcomes were extracted. Additionally, bioinformatics analysis was conducted using publicly available genomic data from cBioPortal and IntOGen to identify driver gene mutations in MM and DLBCL. Functional and pathway enrichment analysis was performed with KEGG and Gene Ontology (GO) databases. RESULTS: The case report highlighted a complex clinical course where the patient initially responded well to R-CHOP chemotherapy for DLBCL, achieving remission, but later relapsed with MM, treated with ASCT and lenalidomide. The systematic review revealed 14 eligible studies in which MM and DLBCL often occur in older patients, either simultaneously or sequentially, with variable treatment responses, including complete remission, partial remission, or relapse. The bioinformatics analysis identified several shared function and cancer-related pathways between two cancers including interleukin and cytokine-mediated signaling pathways, regulation of cell cycle, neurotrophin signaling pathway, FOXO signaling pathway, Epstein Barr virus infection, and viral carcinogenesis. CONCLUSION: This study provides valuable insights into the dual occurrence of MM and DLBCL, emphasizing the importance of tailored treatment approaches. The driver mutations identified highlight overlapping oncogenic pathways rather than implying a shared clonal origin, and may inform future studies exploring their biological and clinical implications. Further research into these shared molecular mechanisms could lead to more effective treatments for patients with coexisting MM and DLBCL.

Bioinformatics analysis↗

PubMed2024

The prognostic significance of ubiquitination-related genes in multiple myeloma by bioinformatics analysis.

BACKGROUND: Immunoregulatory drugs regulate the ubiquitin-proteasome system, which is the main treatment for multiple myeloma (MM) at present. In this study, bioinformatics analysis was used to construct the risk model and evaluate the prognostic value of ubiquitination-related genes in MM. METHODS AND RESULTS: The data on ubiquitination-related genes and MM samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The consistent cluster analysis and ESTIMATE algorithm were used to create distinct clusters. The MM prognostic risk model was constructed through single-factor and multiple-factor analysis. The ROC curve was plotted to compare the survival difference between high- and low-risk groups. The nomogram was used to validate the predictive capability of the risk model. A total of 87 ubiquitination-related genes were obtained, with 47 genes showing high expression in the MM group. According to the consistent cluster analysis, 4 clusters were determined. The immune infiltration, survival, and prognosis differed significantly among the 4 clusters. The tumor purity was higher in clusters 1 and 3 than in clusters 2 and 4, while the immune score and stromal score were lower in clusters 1 and 3. The proportion of B cells memory, plasma cells, and T cells CD4 naïve was the lowest in cluster 4. The model genes KLHL24, HERC6, USP3, TNIP1, and CISH were highly expressed in the high-risk group. AICAr and BMS.754,807 exhibited higher drug sensitivity in the low-risk group, whereas Bleomycin showed higher drug sensitivity in the high-risk group. The nomogram of the risk model demonstrated good efficacy in predicting the survival of MM patients using TCGA and GEO datasets. CONCLUSIONS: The risk model constructed by ubiquitination-related genes can be effectively used to predict the prognosis of MM patients. KLHL24, HERC6, USP3, TNIP1, and CISH genes in MM warrant further investigation as therapeutic targets and to combat drug resistance.

Humans↗

PubMed2026

Comprehensive bioinformatics analysis identifies candidate ciliogenesis-related genes preferentially associated with N0-stage lung squamous cell carcinoma.

PURPOSE: There is few research on which genes play an important role in tumors without lymph metastasis. This study aimed to identify candidate molecular alterations preferentially associated with N0-stage LUSC. METHODS: we conducted a comprehensive bioinformatics analysis using publicly available The Cancer Genome Atlas (TCGA) data. Differentially expressed genes (DEGs) were identified separately by comparing N0 tumors and N+ tumors with normal lung tissues. Genes dysregulated in both N0 and N+ tumors were excluded to identify candidate N0-associated genes PPI networks were constructed using STRING and Cytoscape, with module analysis performed via MCODE. Hub genes were identified using multiple Cytohubba algorithms. Functional enrichment analyses were conducted using GO, and KEGG pathways using DAVID. Gene interaction networks were further explored using GeneMANIA. Immune cell infiltration was evaluated with TIMER. Associations with pathological stage and patient survival were assessed using GEPIA and other relevant tools. RESULTS: A total of 1103 candidate N0-associated DEGs were identified, including 748 upregulated and 355 downregulated genes. The PPI network contained five major MCODE clusters. One cluster (MCODE 4) included TTC30A, TTC30B, BBS7, and KIF3B genes implicated in ciliogenesis. TTC30B showed significant differential expression across pathological stages in the overall LUSC cohort. Seven consensus hub genes (ERBB2, CHUK, CASP8, NOTCH1, HNF4A, CREBBP, and IRS1) were identified based on their consistent ranking across multiple CytoHubba algorithms. Upregulated candidate N0-associated genes were primarily enriched in immune-related processes, including B-cell-mediated immunity and humoral responses, whereas downregulated genes were enriched in lysosomal and trans-Golgi network-related pathways. Exploratory immune infiltration analyses identified associations between the four ciliogenesis-related genes and several immune cell populations. CONCLUSIONS: This study identified candidate molecular signatures preferentially associated with N0-stage LUSC, including ciliogenesis-related genes and consensus hub genes. These findings provide hypotheses regarding molecular features of N0-stage LUSC and warrant further validation in independent cohorts and experimental studies.

Humans↗

PubMed2026

Pan-cancer Bioinformatics Analysis Combined with Colon Cancer Experimental Validation: A Study on TMED3 as a Diagnostic and Prognostic Biomarker.

Transmembrane Emp24 Protein Transport Domain 3 (TMED3), a member of the p24 protein family, has been implicated in tumor proliferation, invasion, and migration. This study aimed to evaluate the expression patterns, prognostic significance, immune associations, and potential biological functions of TMED3 across multiple cancer types using pan-cancer bioinformatics analysis combined with immunohistochemical (IHC) validation in colon cancer. Multiomics datasets from The Cancer Genome Atlas, Genotype-Tissue Expression, UALCAN, Human Protein Atlas, and cBioPortal databases were analyzed to investigate TMED3 expression and genetic alterations in pan-cancer. Immunohistochemistry was performed to evaluate TMED3 protein expression in colon cancer tissues. Kaplan-Meier survival analysis and Cox regression analysis were used to assess the prognostic value of TMED3. Spearman correlation analysis was conducted to evaluate the associations of TMED3 with tumor mutational burden, microsatellite instability (MSI), immune cell infiltration, and immune checkpoints. Gene Set Enrichment Analysis was performed to investigate potential biological pathways associated with TMED3 in colon cancer. TMED3 expression was elevated in most tumor types and was associated with unfavorable overall survival and disease-specific survival in adrenocortical carcinoma, colon adenocarcinoma, and uveal melanoma. The greatest frequency of TMED3 genetic alterations was identified in mesothelioma, with amplification representing the predominant alteration type. In addition, TMED3 expression showed significant correlations with tumor mutational burden and microsatellite instability in kidney renal clear cell carcinoma, stomach adenocarcinoma, and uterine corpus endometrial carcinoma. TMED3 expression was also associated with immune infiltration and immune checkpoint expression in several tumors. IHC analysis demonstrated increased TMED3 expression in colon cancer tissues compared with normal colon tissues and showed an association with T stage. Functional enrichment analysis identified pathways related to ribosome, antigen processing and presentation, oxidative phosphorylation, and pentose phosphate. These findings indicate that TMED3 may represent a promising biomarker for the diagnosis and prognostic evaluation of colon cancer as well as other tumor types.

Humans↗

PubMed2026

Exploring the Role of HSD17B2 in Colorectal Cancer Through Bioinformatic Analysis: Preliminary Insights for Prognostic Evaluation.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer-related mortality worldwide. Although screening has reduced CRC in older adults, cases in younger individuals are rising, highlighting the need for early biomarkers. Emerging research highlights the role of estrogen metabolism in CRC progression, with enzymes such as hydroxysteroid (17-beta) dehydrogenase (HSD17B) being increasingly implicated. In this study, we performed a bioinformatics analysis using publicly available datasets, including The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) cohort and two independent Gene Expression Omnibus (GEO) cohorts (GSE40967 and GSE41258), to investigate the role of HSD17B enzymes in CRC. Our results suggest that HSD17B2 is frequently downregulated in precancerous lesions and early-stage CRC, which may contribute to elevated estradiol levels and a tumor-promoting microenvironment. In advanced stages, higher HSD17B2 expression levels are associated with poorer survival outcomes in retrospective cohorts. Other HSD17B enzymes also exhibit significant expression changes, further complicating the hormonal landscape of CRC. In addition, estrone, traditionally considered a weaker estrogen, emerges as a potential driver of CRC progression. Our in-silico analyses indicate that HSD17B2 and HSD17B11 warrant further investigation as candidate biomarkers for distinguishing CRC from benign and precancerous conditions, with the combination showing strong discriminatory power in Receiver Operating Characteristic (ROC) analyses. Overall, these findings highlight the potential role of estrogen metabolism in CRC and suggest that HSD17B enzymes may hold value as candidate prognostic and diagnostic indicators, though their clinical utility remains hypothetical at this stage. Experimental and clinical validation is strictly required to confirm these in silico observations and to clarify their mechanisms in CRC.

Humans↗

PubMed2025

Improved cohesin HiChIP protocol and bioinformatic analysis for robust detection of chromatin loops and stripes.

Chromosome Conformation Capture (3 C) methods, including Hi-C (a high-throughput variation of 3 C), detect pairwise interactions between DNA regions, enabling the reconstruction of chromatin architecture in the nucleus. HiChIP is a modification of the Hi-C experiment that includes a chromatin immunoprecipitation (ChIP) step, allowing genome-wide identification of chromatin contacts mediated by a protein of interest. In mammalian cells, cohesin protein complex is one of the major players in the establishment of chromatin loops. We present an improved cohesin HiChIP experimental protocol. Using comprehensive bioinformatic analysis, we show that a dual chromatin fixation method compared to the standard formaldehyde-only method, results in a substantially better signal-to-noise ratio, increased ChIP efficiency and improved detection of chromatin loops and architectural stripes. Additionally, we propose an automated pipeline called nf-HiChIP ( https://github.com/SFGLab/hichip-nf-pipeline ) for processing HiChIP samples starting from raw sequencing reads data and ending with a set of significant chromatin interactions (loops), which allows efficient and timely analysis of multiple samples in parallel, without requiring additional ChIP-seq experiments. Finally, using advanced approaches for biophysical modelling and stripe calling we generate accurate loop extrusion polymer models for a region of interest and provide a detailed picture of architectural stripes, respectively.

Chromatin↗

PubMed2026

Integrated bioinformatics analysis reveals cross-talking hub genes and therapeutic agents between sepsis and acute myocardial infarction.

BACKGROUND: Sepsis and acute myocardial infarction (AMI) are two significant diseases that may share overlapping etiological mechanisms. This study aims to systematically identify core genes common to both conditions and to explore their potential as therapeutic targets and drug candidates through an integrative analysis of clinical data and bioinformatics. METHODS: The AMI dataset was obtained from the GEO database, and RNA sequencing data were collected from blood samples of patients with sepsis at our hospital. Common genes were identified using differential expression gene analysis (DEG) and weighted gene co-expression network analysis (WGCNA). Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were performed. A protein-protein interaction (PPI) network was constructed, and hub genes were identified using the MCC/Degree algorithm. Diagnostic value was assessed via receiver operating characteristic curve analysis. Immune infiltration patterns, single-cell sequencing data, and molecular docking simulations were employed to evaluate immune relevance and identify potential therapeutic compounds. RESULTS: A total of 417 genes were identified between sepsis and AMI, with enrichment analysis revealing significant involvement in inflammatory responses. Three hub genes-JAK2, MYD88, and TIMP1-were selected for further investigation. ROC curves confirmed their strong diagnostic performance for both diseases. Immune infiltration analysis showed that these core genes were significantly correlated with the infiltration levels of various immune cell types. Molecular docking indicated that quercetin exhibited stable binding affinity with the proteins encoded by these genes. qPCR validation further confirmed the upregulation of these three genes, supporting the anti-inflammatory effects of quercetin as a potential targeted therapy. CONCLUSION: JAK2, MYD88, and TIMP1 were identified as shared core genes in sepsis and AMI. These genes not only serve as potential diagnostic biomarkers but also offer novel targets for developing common therapeutic strategies for both conditions. Furthermore, quercetin emerges as a promising candidate for targeted treatment.

Humans↗

PubMed2026

Interleukin-23 Receptor and Interleukin-17 Receptor A: Splice Variants, Isoforms and Their Relationship With Periodontitis-A Systematic Review and Bioinformatic Analysis.

This systematic review aimed to: (1) identify the splicing variants of IL23R and IL17RA reported in the literature; (2) perform a multiple alignment analysis to describe the isoforms of IL-23R and IL-17RA; and (3) compare the expression levels of IL-23R, IL-17RA, and their soluble isoforms (sIL-23R and sIL-17RA) in patients with periodontitis and periodontally healthy individuals. The study protocol followed PRISMA guidelines and was registered in PROSPERO (CRD420251267367). Six databases (PubMed, ScienceDirect, Scopus, Web of Science, EBSCO, and Google Scholar) were searched without restrictions on year or language. The descriptors used were: 'Interleukin-23 Receptor,' 'IL-23R,' 'Interleukin-17 Receptor A' 'IL-17RA,' 'Alternative Splicing,' 'Splice Variants,' 'Isoforms,' and 'Periodontitis.' The bioinformatics analysis was performed using CLUSTALW (V.1.83), InterPro and DeepTMHMM. Risk of bias was assessed with the QUIN and JBI tools for cross-sectional studies. Of 104 articles, four in vitro studies and eight cross-sectional studies were included. Qualitative analysis revealed that to date there are 32 splicing variants of the IL23R gene, while only one splicing variant has been reported for IL17RA. CLUSTALW, InterPro and DeepTMHMM analysis showed that these splicing variants result in 23 isoforms which can be soluble forms, complete intracellular peptides, truncated extracellular or intracellular peptides, or complete structures with truncated extracellular and/or intracellular domains. All studies had a low risk of bias. IL-23R and IL-17RA exhibit structural diversity resulting from alternative splicing, with IL-23R demonstrating significantly greater isoform complexity. However, the biological significance of these isoforms in periodontitis remains unclear and requires further investigation.

Humans↗

PubMed2026

Exploring shared biomarkers and their mechanisms in thyroid cancer and systemic lupus erythematosus via bioinformatics analysis.

BACKGROUND: Systemic lupus erythematosus (SLE), an autoimmune disorder, is linked to a heightened risk of multiple malignancies, including thyroid cancer. Thyroid cancer is the most prevalent malignancy of the endocrine system, and its autoimmune-related pathological features render it an optimal subject for investigating the mechanisms of their comorbidity. The molecular mechanisms underlying this comorbidity are still ambiguous. The accurate diagnosis and treatment of thyroid cancer urgently necessitate innovative molecular targets that extend beyond conventional pathological characteristics. This study seeks to employ integrated bioinformatics approaches to elucidate potential shared molecular mechanisms and immunological features between thyroid cancer and systemic lupus erythematosus (SLE), aiming to enhance understanding of their comorbidity and identify novel intervention targets. METHODS: This study initially acquired gene expression data for TC and SLE from the GEO database and subsequently screened and identified differentially expressed genes (DEGs) shared by both diseases. Subsequently, we conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome functional enrichment analyses on these 46 shared differentially expressed genes (DEGs) and further assessed the activation status of pertinent pathways using Gene Set Enrichment Analysis (GSEA). Subsequently, we employed CIBERSORTx to examine immune infiltration patterns and developed protein-protein interaction networks utilising the STRING database. We identified hub genes utilising the MCODE and cytoHubba plugins and visualised the findings with Cytoscape software. We additionally assessed the diagnostic efficacy of these core hub genes in an independent dataset utilising ROC curves and investigated their prognostic relevance in thyroid cancer through Kaplan-Meier survival analysis and multivariate Cox proportional hazards regression. Ultimately, we employed the Network Analyst platform to forecast transcription factor-gene and miRNA-gene regulatory networks and identified potential targeted therapeutic compounds utilising the DSigDB database. RESULTS: This study identified 46 differentially expressed genes (DEGs) commonly linked to thyroid cancer and systemic lupus erythematosus (SLE), which were significantly enriched in signalling pathways associated with immune-inflammatory activation, type I interferon responses, and complement pathway activation. Moreover, GSEA findings validated that immune-inflammatory and autoimmune-related pathways are markedly activated in both conditions. Twelve hub genes were discerned through protein-protein interaction networks. Analysis of immune infiltration indicated that thyroid cancer and systemic lupus erythematosus exhibit a shared characteristic of innate immune dysregulation, marked by the infiltration of myeloid cells (neutrophils, M0/M2 macrophages). Receiver operating characteristic (ROC) curve analysis identified six significant core hub genes with substantial diagnostic value: C1QB, LCN2, C1QC, LTF, VSIG4, and C3AR1. Univariate survival analysis indicated that elevated expression of C1QC and C3AR1 significantly enhances overall survival in thyroid cancer patients; however, multivariate COX regression analysis revealed that their independent prognostic significance necessitates further validation. This study predicted the interaction networks of transcription factors and miRNAs regulating key genes, with LCN2 demonstrating the highest connectivity to miRNAs, and identified candidate therapeutic compounds linked to it. CONCLUSION: This study employed bioinformatics analysis to identify critical shared hub genes and molecular pathways connecting thyroid cancer and systemic lupus erythematosus, offering novel insights into their shared pathogenesis and the advancement of targeted biomarkers and therapeutic strategies.

Bioinformatics analysis↗

PubMed2026

'PePApipe': A complete bioinformatics analysis pipeline for African Swine Fever Virus genome.

African Swine Fever Virus (ASFV) is of high concern in porcine livestock across the world due to both the high mortality rates and the trade restrictions imposed on affected regions. The viral genome is large and complex, and genomic analysis is essential for tracing its origin and evolution. Although several bioinformatics tools exist for genome assembly and analysis, no single platform integrates all necessary steps in an accessible and systematic way. In this study the authors developed 'PePApipe', a custom-built, user-friendly pipeline that enables rapid, complete, and efficient ASFV genome analysis. It is specifically designed for laboratory professionals with limited bioinformatics experience, requiring only basic command-line knowledge. Starting from raw sequencing data, PePApipe integrates thirteen software tools into one automated workflow, covering quality control and pre-processing of raw reads, de novo genome assembly and variant calling. Programmed in Python, it can be executed locally through bash scripts, or using a Slurm protocol for batch processing of multiple samples. The main outputs are the ASFV consensus genome sequence and a file listing its putative variants compared to the selected reference genome. PePApipe classifies generated files into structured folders and produces intermediate files that can be used as inputs for further or parallel analyses; users can also enable or disable specific steps in each particular case. This pipeline is adaptable and complementary to downstream steps such as viral genome annotation or genome visualization. By consolidating all stages of viral genome analysis into a single automated workflow, PePApipe reduces the likelihood of user error, and enhances reproducibility and efficiency. This user-friendly pipeline facilitates the transition from sequencing to assembly and downstream analysis of viral genomes, ensuring a fast and reliable response to molecular analysis demands. Finally, the pipeline can be easily adapted to the study of other viral species, expanding its application in infectious diseases surveillance.

African Swine Fever Virus↗

PubMed2025

Bioinformatics analysis of miR-2861 and miR-5011-5p that function as potential tumor suppressors in colorectal carcinogenesis.

BACKGROUND: The study aimed to was to investigate the relationship between miR-2861, miR-5011-5p, and colorectal carcinogenesis. METHOD: In the present study, it was isolated RNA from both the tumor and non-tumor tissue of a total of 80 CRC patients and after synthesizing the cDNA, it was performed qRT-PCR to determine the expression levels of miR‑2861 and miR‑5011-5p. In addition, it was predicted that dysregulated miRNAs targets, pathways and functional gene annotations that may be important in colorectal carcinogenesis using KEGG pathway and GO analysis. RESULTS: The resulting data revealed that both expression levels of miR-2861 and miR-5011-5p were significantly decreased in tumor tissues compared with non-tumor tissues of CRC patients. The GO and KEGG pathway analysis showed that miR-2861 and miR-5011-5p may participate in multiple the biological process, cellular components, and molecular function subcategories such as mitotic cell cycle, regulation of small GTPase mediated signal transduction, cell death, and acid binding transcription factor activity. It was also revealed that target genes of miRNAs can be found in signaling pathways such as TGF-beta, Rap1, Ras, cAMP, Wnt, mTOR and, PI3K-Akt signaling pathways. CONCLUSION: These findings imply that miR-2861 and miR-5011-5p might function as tumor suppressors in the development of CRC.

MicroRNAs↗

PubMed2026

Elucidating the Mechanism of Xiaoqinglong Decoction in Chronic Urticaria Treatment: An Integrated Approach of Network Pharmacology, Bioinformatics Analysis, Molecular Docking, and Molecular Dynamics Simulations.

INTRODUCTION: Xiaoqinglong Decoction (XQLD) is a traditional Chinese medicinal formula commonly used to treat chronic urticaria (CU). However, its underlying therapeutic mechanisms remain incompletely characterized. This study employed an integrated approach combining network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulations to identify the active components, potential targets, and related signaling pathways involved in XQLD's therapeutic action against CU, thereby providing a mechanistic foundation for its clinical application. METHODS: The active components of XQLD and their corresponding targets were identified using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CU-related targets were retrieved from the OMIM and GeneCards databases. Subsequently, core components and targets were determined via protein-protein interaction (PPI) network analysis and component-target-pathway network construction. Topological analyses were performed using Cytoscape software to prioritize core nodes within these networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via the DAVID database to identify enriched biological processes and signaling pathways. Molecular docking was performed to evaluate binding interactions between key components and core targets, while molecular dynamics (MD) simulations were employed to assess the stability of the component-target complexes with the lowest binding energy. Finally, CU-related targets of XQLD were validated using datasets from the Gene Expression Omnibus (GEO) database. RESULTS: A total of 135 active components and 249 potential targets of XQLD were identified, alongside 1,711 CU-related targets. Core components, such as quercetin, kaempferol, beta-sitosterol, naringenin, stigmasterol, and luteolin, exhibited high degree values in the constructed networks. The core targets identified included AKT1, TNF, IL6, TP53, PTGS2, CASP3, BCL2, ESR1, PPARG, and MAPK3. GO and KEGG pathway enrichment analyses revealed the PI3K-Akt signaling pathway as a central regulatory mechanism. Molecular docking studies demonstrated strong binding affinities between active components and core targets, with the stigmasterol-AKT1 complex exhibiting the lowest binding energy (-11.4 kcal/mol) and high stability in MD simulations. Validation using GEO datasets identified 12 core genes shared between CU-related targets and XQLD-associated targets, including PTGS2 and IL6, which were also prioritized as core targets in the network pharmacology analyses. DISCUSSION: This study comprehensively integrates multidisciplinary approaches to clarify the potential molecular mechanisms of XQLD in treating CU, highlighting its multitarget and multipathway synergistic effects. Molecular docking and dynamics simulations confirm the stable interaction between stigmasterol and the core target AKT1. Additionally, GEO dataset analysis verifies the pathogenic relevance of targets such as PTGS2 and IL6, significantly enhancing the credibility of our findings. These results provide a modern scientific basis for the traditional therapeutic effects of XQLD on CU and have important implications for developing multitarget treatments for this condition. However, this study mainly relies on database mining and computational simulations. Further in vitro and in vivo experimental validations are needed to confirm the predicted component-target-pathway interactions. CONCLUSION: This study identifies the active components, potential targets, and pathways through which XQLD exerts therapeutic effects on CU. These findings provide a theoretical foundation for further mechanistic studies and support their clinical application in the treatment of CU.

Molecular Docking Simulation↗

PubMed2025

Identification of potential biomarkers and mechanisms for keloid disorder based on comprehensive bioinformatics analysis and machine learning algorithms.

BACKGROUND: Keloid disorder (KD) encompasses a spectrum of fibroproliferative dermal conditions, the pathogenesis remains complex and incompletely understood. This study sought to identify biomarkers and potential therapeutic targets for KD through an integrative bioinformatics approach and machine learning analysis of RNA sequencing data. METHODS: RNA sequencing was performed on skin tissue samples from 13 patients with KD and 14 healthy controls. Using weighted gene co-expression network analysis and differential expression analysis revealed differentially expressed key module genes, and the CytoHubba plugin identified candidate genes. Subsequently analyzed using least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) methods to pinpoint feature genes associated with KD. Following this, biomarkers were determined through expression level validation, enrichment analysis, and immune infiltration analysis. RESULTS: A total of 420 differentially expressed key module genes were identified, and the top 10 genes with DMNC values were selected as candidate genes. Five feature genes were selected through LASSO and SVM-RFE, with NID2, MFAP2, COL8A1, and P4HA3 showing significant expression differences between KD and control samples, along with consistent expression patterns across datasets, identified as potential biomarkers. These four biomarkers were proved to possess high diagnostic potential, and they were found to exhibit significant positive correlations with one another. Functional enrichment analysis indicated that the primary KEGG pathways associated with these biomarkers included "steroid hormone biosynthesis" and "cytokine-cytokine receptor interaction." Moreover, immune infiltration analysis revealed that the four biomarkers were negatively correlated with type 17 T helper cells and positively correlated with 15 immune cell types, including activated B cells and central memory CD4 T cells. CONCLUSION: In conclusion, NID2, MFAP2, COL8A1, and P4HA3 were identified as key biomarkers for KD, offering new avenues for more targeted and effective diagnostic and therapeutic strategies for managing this condition.

Humans↗

PubMed2025

The cost and cost trajectory of genome sequencing and bioinformatics analysis for Indigenous children with suspected rare diseases.

PURPOSE: Indigenous peoples are underrepresented in reference genome libraries. Consequently, rare disease diagnosis may require bespoke bioinformatics analyses of genome sequences. Establishing diagnostic cost is crucial to support policy development for equitable diagnosis of rare diseases. We estimated the cost and cost trajectory of diagnostic genome sequencing and bioinformatics for Indigenous participants with suspected rare diseases. METHODS: We conducted a microcosting study of Indigenous children and their families receiving genome sequencing through Canada's Silent Genomes Project. Invoice data informed the costs of genome sequencing. We conducted a time-and-motion study for bioinformatics analyses, including labor, computing, and data storage costs. RESULTS: With standard bioinformatics, costs ranged from C$3645 (SD: 455) for singletons to C$7402 (SD: 566) for trios. With advanced, bespoke bioinformatics, costs ranged from C$5344 (SD: 634) for singletons to C$9760 (SD: 822) for trios. Genome sequencing was a primary cost driver; however, sequencing costs decreased by 61% over 4 years. Bioinformatics costs ranged from 21.3% to 58.3% of the total costs. The time required for bioinformatics ranged from 71 hours to 215 hours for standard and advanced analyses, respectively. CONCLUSION: Genome sequencing costs decreased over time. Bioinformatics is a significant cost driver, particularly for bespoke analyses arising from nonrepresentative reference libraries.

Humans↗

PubMed2026

Integrated Bioinformatics Analysis Revealing that the NSDHL Gene Might Be Associated with the Progression of Western HFD/SW-Induced Hepatocellular Carcinoma.

BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma (HCC) remains a significant global health concern. However, the etiology and pathogenesis of HCC have yet to be fully elucidated. Previous studies have indicated a close association between obesity and the occurrence and progression of HCC. The objective of this study was to employ bioinformatics strategies in order to explore key genes associated with the clinical diagnosis and prognosis of HCC induced by a Western high-fat diet and sugar water (HFD/SW). MATERIALS AND METHODS: We obtained the expression profile chip data GSE197884 from the Gene Expression Omnibus (GEO) database. Subsequently, “DESeq” and “Limma” R packages were employed to identify differentially expressed genes (DEGs) while constructing a co-expressed gene network using weighted gene co-expression analysis (WGCNA). Functional enrichment analyses were then carried out, followed by the construction of a protein-protein interaction (PPI) network to uncover core genes. The core genes were confirmed through data retrieved from The Cancer Genome Atlas (TCGA) database in order to determine their status as hub genes. Finally, survival and tumor immune infiltration analyses were performed to unveil the prognostic significance of these hub genes. RESULTS: In total, 126 intersection targets were retrieved through the Venn diagram. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that the DEGs were primarily related to the proliferation and apoptosis of HCC cells, the digestion and metabolism of liver cells, the HCC tumor microenvironment, and immune response. The PPI network analysis identified 11 core targets, among which seven hub genes, including NSDHL, MVK, SQLW, GCAT, ALAS2, GLDC, and AGXT, were obtained after TCGA database validation. Furthermore, it was found that NSDHL was closely associated with the clinical diagnosis and prognosis of HCC induced by HFD/SW and also affected the cellular immune infiltration in the HCC tumor microenvironment. CONCLUSION: The present study demonstrated a significantly elevated expression of NSDHL in HCC tissues, suggesting its potential as a specific biomarker for precise clinical diagnosis and prognosis assessment of HCC induced by HFD/SW.

Computational Biology↗

PubMed2026

Bioinformatic analysis reveals the potential association of ESRP1 with the splicing of cytoskeleton-associated genes in doxorubicin-resistant MCF7 breast cancer cells.

BACKGROUND: Breast cancer remains one of the most prevalent malignancies among women, with doxorubicin resistance posing a significant challenge that undermines treatment success and survival outcomes. Aberrant alternative splicing (AS), driven by dysregulation or mutations in splicing factors (SFs), is implicated in cancer initiation, progression, and drug resistance. This study aims to investigate the association of the epithelial cell-specific splicing factor ESRP1 with doxorubicin resistance in breast cancer, focusing on how ESRP1 deficiency correlates with AS changes that promote chemoresistance. METHODS: We analyzed RNA-sequencing (RNA-seq) data from doxorubicin-resistant (MCF7-DR) and parental (MCF7) breast cancer cell lines to identify enhanced alternative splicing events (ASEs) and changes in ESRP1 expression; we further leveraged The Cancer Genome Atlas (TCGA)-BRCA cohort to construct an SF-RASE correlation network for screening core SFs (including ESRP1). An integrative analysis combining crosslinking immunoprecipitation (CLIP-seq) data and The Cancer Genome Atlas (TCGA) database was performed to validate ESRP1 binding targets and assess the association between ESRP1-related splicing and cytoskeleton organization. RESULTS: We observed extensive AS changes and significantly downregulated ESRP1 expression in MCF7-DR cells. Integrative analysis identified 61 high-confidence ASEs that correlate with ESRP1 expression. Further bioinformatic integration suggests that ESRP1 expression is associated with the splicing patterns of SPTBN1, MAP2K7, FGFR3, and CYB561A3-four genes involved in cytoskeleton organization-though direct experimental verification to confirm a causal regulatory relationship between ESRP1 and the splicing of these genes is still pending. CONCLUSIONS: Our findings suggest that ESRP1 expression is closely associated with doxorubicin resistance in breast cancer cells, with concomitant alterations in key ASEs linked to cytoskeletal remodeling that correlate with ESRP1. Exploring the ESRP1-related splicing network may offer new strategies to overcome chemoresistance and improve patient outcomes. However, the small cell line sample size (n = 2 per group) constrains the robustness of ASE and SF-ASE correlation findings, and these results should be interpreted with caution and require further validation with larger sample cohorts.

Alternative splicing↗

PubMed2025

Identification of novel cytoskeleton protein involved in spermatogenic cells and sertoli cells of non-obstructive azoospermia based on microarray and bioinformatics analysis.

BACKGROUND: During mammalian spermatogenesis, the cytoskeleton system plays a significant role in morphological changes. Male infertility such as non-obstructive azoospermia (NOA) might be explained by studies of the cytoskeletal system during spermatogenesis. METHODS: The cytoskeleton, scaffold, and actin-binding genes were analyzed by microarray and bioinformatics (771 spermatogenic cellsgenes and 774 Sertoli cell genes). To validate these findings, we cross-referenced our results with data from a single-cell genomics database. RESULTS: In the microarray analyses of three human cases with different NOA spermatogenic cells, the expression of TBL3, MAGEA8, KRTAP3-2, KRT35, VCAN, MYO19, FBLN2, SH3RF1, ACTR3B, STRC, THBS4, and CTNND2 were upregulated, while expression of NTN1, ITGA1, GJB1, CAPZA1, SEPTIN8, and GOLGA6L6 were downregulated. There was an increase in KIRREL3, TTLL9, GJA1, ASB1, and RGPD5 expression in the Sertoli cells of three human cases with NOA, whereas expression of DES, EPB41L2, KCTD13, KLHL8, TRIOBP, ECM2, DVL3, ARMC10, KIF23, SNX4, KLHL12, PACSIN2, ANLN, WDR90, STMN1, CYTSA, and LTBP3 were downregulated. A combined analysis of Gene Ontology (GO) and STRING, were used to predict proteins' molecular interactions and then to recognize master pathways. Functional enrichment analysis showed that the biological process (BP) mitotic cytokinesis, cytoskeleton-dependent cytokinesis, and positive regulation of cell-substrate adhesion were significantly associated with differentially expressed genes (DEGs) in spermatogenic cells. Moleculare function (MF) of DEGs that were up/down regulated, it was found that tubulin bindings, gap junction channels, and tripeptide transmembrane transport were more significant in our analysis. An analysis of GO enrichment findings of Sertoli cells showed BP and MF to be common DEGs. Cell-cell junction assembly, cell-matrix adhesion, and regulation of SNARE complex assembly were significantly correlated with common DEGs for BP. In the study of MF, U3 snoRNA binding, and cadherin binding were significantly associated with common DEGs. CONCLUSION: Our analysis, leveraging single-cell data, substantiated our findings, demonstrating significant alterations in gene expression patterns.

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