Integrated Bioinformatics Analysis Research Articles

Integrative bioinformatic and experimental analysis of benzoylbenzodioxol derivatives: hypoglycemic potential in diabetic mice.

We investigated the hypoglycemic activity and pharmacokinetic study of two synthesized benzoyl benzodioxol derivatives, compound I (methyl 2-(6-(2-bromobenzoyl)benzo[d][1,3]dioxol-5-yl)acetate), and compound II, 2-(6-benzoylbenzo[d][1,3]dioxol-5-yl)acetic acid, which showed very strong α-amylase inhibiting activity in our previous study. Then, diabetes was induced by the injection of streptozotocin to mice. The molecular docking simulations and analyses of density functional theory analyses were conducted to study the binding interactions with human pancreatic alpha-amylase, and their pharmacokinetic properties were further evaluated by ADMET profiling. Compound I showed the most important hypoglycemic effect, decreasing the blood glucose by 32.4%, higher than that of compound II by 14.8% and even the positive control acarbose by 22.9%. Histopathological examination revealed that diabetic livers showed portal inflammation with some apoptotic hepatocytes due to streptozotocin treatment, whereas controls without any treatment maintained normal liver architecture. Molecular docking studies gave results for the best binding affinity of the compound I, through its strong water bridges and π-π interactions, and also through analysis with density functional theory, was more stable and reactive when compared to compound II. Further ADMET analysis showed that both compounds shared a promising pharmacokinetic profile, and compound I had the potential for CNS penetration. Thus, compound I was selected as the best candidate for developing new hypoglycemic agents with potent efficacy, good binding interactions, and excellent pharmacokinetic properties.

Integrated bioinformatics analysis identifies PCSK9 as a prognosticator correlated with lipid metabolism in pancreatic adenocarcinoma

BackgroundPancreatic adenocarcinoma (PAAD) is the most frequent kind of pancreatic cancer (PC). Recent studies suggest that lipid metabolism facilitates tumorigenesis, disease progression, and resistance to therapy by promoting lipid synthesis, accumulation, and breakdown. Thus, exploring the lipid metabolism network could unveil novel therapeutic avenues for early detection, precision medicine, and prognostication in PAAD. This project intends to develop new lipid metabolism-related biomarkers for PAAD diagnosis and investigate the link between important genes and immune cell infiltration (ICI).MethodsTissue samples from 20 PAAD patients and 20 healthy controls were obtained. Analysis were focused on the datasets GSE71729 and GSE16515, which include samples of PAAD (n = 161) and those from healthy human tissue (n = 61), derived from the GEO database. Knockdown of PCSK9 on PC cells were conducted by si-RNA and sh-RNA. Migration and cell functional experiments were performed to assess the role of PCSK9 in cell multiplication. Furthermore, a xenograft mouse model was employed to confirm PCSK9's function in vivo.ResultsThe expression level of Proprotein convertase subtilisin/kexin type 9 (PCSK9) is significantly elevated in tissues affected by PAAD when compared to normal tissues. Survival analyses indicated that increased PCSK9 levels are inversely related to overall and disease-free survival (DFS). PCSK9’s functional annotation associated it with the cell cycle and metabolism, especially energy metabolism. Examination of ICI data determined that PCSK9 expression demonstrated an unambiguous association with the M0 macrophages, T follicular helper cells (Tfh), gamma delta T cells and activated DC, and an inverse relationship with Monocytes, CD8+ T cells, memory B cells, resting CD4+ memory T cells, activated NK cells and resting DC abundance. PCSK9 expression knockdown has the ability to impede PC cells’ migration and proliferation.ConclusionOur study identified PCSK9 as a critical gene in PAAD. Expression levels of PCSK9 varied between PAAD and normal samples. ROC analysis verified PCSK9's strong capacity to differentiate PC from normal samples. Importantly, PCSK9 expression was considerably elevated in PC cell lines and tissues. Furthermore, PCSK9 stimulates the migration and proliferation of tumor cells in vivo and vitro.

A network pharmacology approach-based decoding of Resveratrol's anti-fibrotic mechanisms

BackgroundInhalation of crystalline silica (CS) frequently leads to chronic lung inflammation and pulmonary fibrosis (PF), a condition with limited effective treatments. Resveratrol (Res) has demonstrated potential in PF treatment; however, its underlying mechanisms remain incompletely elucidated. PurposeThis study represents the first comprehensive attempt to uncover the novel mechanisms underlying Res's anti-fibrotic effects against PF through an innovative, integrated approach combining network pharmacology and experimental validation. MethodsWe employed network pharmacology to investigate the holistic pharmacological mechanism of Res, then validated the predicted pharmacological effects using in vivo and in vitro studies. ResultsIn total, 216 genes were identified to be simultaneously associated with PF and Res. An integrated bioinformatics analysis implicated a crucial role of the autophagy signaling pathway in dominating PF, with AMPK and mTOR showing high docking scores. Animal studies revealed that Res significantly alleviated silica-induced lung damage in silicotic mice, with decreased collagen I (Col-I) levels and reduced expression of vimentin and α-SMA. In-depth investigation demonstrated that Res modulated CS-dysregulated autophagy by targeting the AMPK/mTOR pathway. in vitro, Res treatment significantly reduced lactate dehydrogenase (LDH), TNF-α, and TGF-β levels and improved cell viability of Raw264.7 cells post-CS exposure. Notably, Res was demonstrated to suppress fibroblast-to-myofibroblast transition via mediating macrophage autophagy through the AMPK/mTOR pathway. ConclusionRes can alleviate CS-induced PF by targeting AMPK in the autophagy signaling pathway, which sheds light on Res' therapeutic potential in treating PF.

ENO2 in progression and treatment of colon adenocarcinoma: integrative bioinformatics analysis on non-apoptotic cell death

Colon adenocarcinoma (COAD) is one of the most common types of cancer. The interconnection between non-apoptotic cell death and COAD has not been adequately addressed. In our study, an integrative bioinformatics analysis was performed to explore non-apoptotic cell death-related biomarkers in COAD. ENO2 was determined as a potent biomarker for prognosis, drug response, immunity, and immunotherapy prediction. We used EdU and RT-qPCR assays to test our hypothesis and investigate how the ENO2 gene may influence or regulate cancer-related processes. ENO2 was expected to be a potential target in COAD.

Identification of exosome-related gene features in psoriasis and construction of a diagnostic model via integrated bioinformatics analysis

Background Psoriasis, a chronic inflammatory dermatosis, profoundly affects patients’ well-being. Although exosomes are key in disease etiology, diagnostic potentials of associated genes are unclear. Our research targeted bioinformatics-based characterization of exosome-related genes and the development of a diagnostic model for psoriasis. Methods Within GSE30999 dataset, an exosome-centric diagnostic model was formulated. Its diagnostic capability was appraised in GSE30999 and GSE14905 cohorts. Human keratinocytes (HaCaT) were used to construct psoriasis cell model. qRT-PCR was used to detect expression of diagnostic genes in the model. Construction of a protein-protein interaction network was undertaken, complemented by enrichment analyses. Comparative evaluation of immunological microenvironments between healthy controls and disease cohort was executed. Prospective miRNAs and transcription factors (TFs) were prognosticated using online prediction tools. Results A distinctive diagnostic model with superior diagnostic performance, evidenced by an AUC value greater than 0.88, was unveiled. The model featured seven exosome-related biomarker genes (CCNA2, NDC80, CCNB1, CDCA8, KIF11, CENPF, and ASPM) interwoven in a complex network and chiefly linked in the regulation of Cell Cycle and Cellular Senescence. These genes were significantly overexpressed in psoriasis cell models. Immune infiltration analysis distinguished profound discrepancies (p < 0.05) in immunological microenvironment between disease and control groups with enrichment of T cells CD4 memory activated, Macrophages M1, and Neutrophils in the disease group. 11 miRNAs and 27 TFs were identified. Conclusion The study introduces a new and potent diagnostic model for psoriasis, with selection of credible exosome-associated biomarker genes. These discoveries aid in clinical diagnostics and research on exosome involvement in psoriasis.

Integrated bioinformatics analysis for identifying fibroblast-associated biomarkers and molecular subtypes in human membranous nephropathy

BackgroundMembranous nephropathy (MN) is characterized by immune complex deposition in the glomerular basement membrane, leading to proteinuria and potentially progressive renal dysfunction. Fibroblasts have been implicated in the pathogenesis of MN through their involvement in tissue remodeling and immune modulation. MethodsWe employed integrated bioinformatics analyses to identify fibroblast-associated biomarkers and molecular subtypes in MN. The xCell algorithm was used to assess fibroblast infiltration, and weighted gene co-expression network analysis (WGCNA) identified fibroblast-related gene modules. Differentially expressed fibroblast-associated genes (DEFAGs) were screened between MN and healthy controls (HC) using differential expression analysis and protein-protein interaction (PPI) network construction. Consensus clustering categorized MN patients into distinct subtypes based on DEFAG expression profiles. ResultsFibroblast scores were a significant elevation in MN compared to HC, indicating increased fibroblast involvement in MN pathogenesis. WGCNA identified 13 fibroblast-related gene modules, with the brown and turquoise modules showing strong correlation with fibroblast scores (correlation coefficient = 0.79 and 0.75, respectively, p < 0.01). DEFAG analysis revealed 308 genes overlapping between WGCNA and differentially expressed genes (DEGs) in MN. Consensus clustering identified two molecular subtypes (C1 and C2) based on DEFAG expression patterns, with differential gene expression enriching pathways related to immune response and extracellular matrix remodeling. Core biomarker analysis highlighted COL3A1 and TGFB1 as central genes associated with MN, with elevated expression validated across multiple datasets. ConclusionIntegrated bioinformatics analysis identified fibroblast-associated molecular subtypes in MN, revealing distinct immune profiles and biomarkers. COL3A1 emerged as a potential diagnostic and therapeutic target, implicating its role in immune regulation and disease progression in MN.

Similarity-based metric analysis approach for predicting osteogenic differentiation correlation coefficients and discovering the novel osteogenic-related gene FOXA1 in BMSCs.

As a powerful tool, bioinformatics analysis is playing an increasingly important role in many fields. Osteogenic differentiation is a complex biological process involving the fine regulation of numerous genes and signaling pathways. Osteogenic differentiation-related genes are collected from the online databases. Then, we proposed two indexes Jaccard similarity and Sorensen-Dice similarity to measure the topological relevance of genes in the human PPI network. Furthermore, we selected three pathways involving osteoblast-related transcription factors, osteoblast differentiation, and RUNX2 regulation of osteoblast differentiation for investigation. Subsequently, we performed functional a enrichment analysis of these top-ranked genes to check whether these candidate genes identified by similarity-based metrics are enriched in some specific biological functions and states. we performed a permutation test to investigate the similarity score with four well-known osteogenic differentiation-related pathways including hedgehog signaling pathway, BMP signaling, ERK pathway, and Wnt signaling pathway to check whether these osteogenic differentiation-related pathways can be regulated by FOXA1. Lentiviral transfection was used to knockdown and overexpress gene FOXA1 in human bone mesenchymal stem cells (hBMSCs). Alkaline phosphatase (ALP) staining and Alizarin Red staining (ARS) were employed to investigate osteogenic differentiation of hBMSCs. After data collection, human PPI network involving 19,344 genes is included in our analysis. After simplifying, we used Jaccard and Sorensen-Dice similarity to identify osteogenic differentiation-related genes and integrated into a final similarity matrix. Furthermore, we calculated the sum of similarity scores with these osteogenic differentiation-related genes for each gene and found 337 osteogenic differentiation-related genes are involved in our analysis. We selected three pathways involving osteoblast-related transcription factors, osteoblast differentiation, and RUNX2 regulation of osteoblast differentiation for investigation and performed functional enrichment analysis of these top-ranked 50 genes. The results collectively demonstrate that these candidate genes can indeed capture osteogenic differentiation-related features of hBSMCs. According to the novel analyzing method, we found that these four pathways have significantly higher similarity with FOXA1 than random noise. Moreover, knockdown FOXA1 significantly increased the ALP activity and mineral deposits. Furthermore, overexpression of FOXA1 dramatically decreased the ALP activity and mineral deposits. In summary, this study showed that FOXA1 is a novel significant osteogenic differentiation-related transcription factor. Moreover, our study has tightly integrated bioinformatics analysis with biological knowledge, and developed a novel method for analyzing the osteogenic differentiation regulatory network.

Transcriptomic Analysis and Finding of Potential Key mRNA Expression Profile in Human Cumulus Cells During in Vitro Culture and Different Passages Based on Integrated Bioinformatics Analysis.

This study leveraged microarray datasets to investigate differentially expressed genes (DEGs) in cumulus cells and their relevance in predicting the successful implantation of embryos in human in-vitro fertilization procedures. The microarray data were obtained from the GEO database, encompassing samples of cumulus cells during in vitro culture and different passages. To ensure data consistency, inter-batch normalization was performed, and Principal Component Analysis (PCA) was applied to assess the impact of normalization on sample group clustering. The integrated dataset included samples from cumulus cells during in vitro culture, comprising 17,662 genes. Utilizing the "limma" software package, 1906 DEGs were identified, with 437 genes downregulated and 589 genes upregulated in the cumulus cells of infertility cases, while 748 genes were upregulated, and 1317 genes were downregulated in cumulus cells of successful implantation cases. Functional enrichment analysis utilized Gene Ontology, Metascape, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment tools. Biological processes and molecular functions were enriched, including protein targeting, mRNA processing, and molecular binding among the identified DEGs. Furthermore, target prediction and functional enrichment analysis of microRNAs (miRNAs) revealed 25 key genes and 13 relevant miRNAs were identified. Notably, hsa-miR-149, hsa-miR23b, hsa-miR-877, hsa-miR593, hsa-miR-18a, hsa-miR25, hsa-miR185, mmu-miR-207, hsa-miR425, hsa-miR214, hsa-miR-129, hsa-miR-629, and hsa-miR-194 emerged as the most prominent miRNAs with potential regulatory roles in successful embryo implantation. This comprehensive analysis provides valuable insights into the molecular mechanisms underlying embryo implantation, offering potential targets for further research and therapeutic interventions in assisted reproductive technologies.

The transcriptome of playfulness is sex-biased in the juvenile rat medial amygdala: a role for inhibitory neurons.

Social play is a dynamic behavior known to be sexually differentiated; in most species, males play more than females, a sex difference driven in large part by the medial amygdala (MeA). Despite the well-conserved nature of this sex difference and the importance of social play for appropriate maturation of brain and behavior, the full mechanism establishing the sex bias in play is unknown. Here, we explore "the transcriptome of playfulness" in the juvenile rat MeA, assessing differences in gene expression between high- and low-playing animals of both sexes via bulk RNA-sequencing. Using weighted gene co-expression network analysis (WGCNA) to identify gene modules combined with analysis of differentially expressed genes (DEGs), we demonstrate that the transcriptomic profile in the juvenile rat MeA associated with playfulness is largely distinct in males compared to females. Of the 13 play-associated WGCNA networks identified, only two were associated with play in both sexes, and very few DEGs associated with playfulness were shared between males and females. Data from our parallel single-cell RNA-sequencing experiments using amygdala samples from newborn male and female rats suggests that inhibitory neurons drive this sex difference, as the majority of sex-biased DEGs in the neonatal amygdala are enriched within this population. Supporting this notion, we demonstrate that inhibitory neurons comprise the majority of play-active cells in the juvenile MeA, with males having a greater number of play-active cells than females, of which a larger proportion are GABAergic. Through integrative bioinformatic analyses, we further explore the expression, function, and cell-type specificity of key play-associated modules and the regulator "hub genes" predicted to drive them, providing valuable insight into the sex-biased mechanisms underlying this fundamental social behavior.

An integrated analysis of multiple datasets reveals novel gene signatures in human granulosa cells

Granulosa cells (GCs) play crucial roles in oocyte maturation. Through gap junctions and extracellular vesicles, they mediate the exchange of molecules such as microRNAs and messenger RNAs. Different ovarian cell types exhibit unique gene expression profiles, reflecting their specialized functions and stages. By combining RNA-seq data from various cell types forming the follicle, we aimed at capturing a wide range of expression patterns, offering insights into the functional diversity and complexity of the transcriptome regulation across GCs. Herein, we performed an integrated bioinformatics analysis of RNA sequencing datasets present in public databases, with a unique and standardized workflow., By combining the data from different studies, we successfully increased the robustness and reliability of our findings and discovered novel genes, miRNAs, and signaling pathways associated with GCs function and oocyte maturation. Moreover, our results provide a valuable resource for further wet-lab research on GCs biology and their impact on oocyte development and competence.

Antibiotic resistance challenge: evaluating anthraquinones as rifampicin monooxygenase inhibitors through integrated bioinformatics analysis

ObjectiveAntibiotic resistance poses a pressing and crucial global public health challenge, leading to significant clinical and health-related consequences. Substantial evidence highlights the pivotal involvement of rifampicin monooxygenase (RIFMO) in the context of antibiotic resistance. Hence, inhibiting RIFMO could offer potential in the treatment of various infections. Anthraquinones, a group of organic compounds, have shown promise in addressing tuberculosis. This study employed integrated bioinformatics approaches to evaluate the potential inhibitory effects of a selection of anthraquinones on RIFMO. The findings were subsequently compared with those of rifampicin (RIF), serving as a positive control inhibitor.MethodsThe AutoDock 4.0 tool assessed the binding free energy between 21 anthraquinones and the RIFMO catalytic cleft. The ligands were ranked based on the most favorable scores derived from ΔGbinding. The docking analyses for the highest-ranked anthraquinone and RIF underwent a cross-validation process. This validation procedure utilized the SwissDock server and the Schrödinger Maestro docking software. Molecular dynamics simulations were conducted to scrutinize the stability of the backbone atoms in free RIFMO, RIFMO-RIF, and RIFMO complexed with the top-ranked anthraquinone throughout a 100-ns computer simulation. The Discovery Studio Visualizer tool visualized interactions between RIFMO residues and ligands. An evaluation of the pharmacokinetics and toxicity profiles of the tested compounds was also conducted.ResultsFive anthraquinones were indicated with ΔGbinding scores less than − 10 kcal/mol. Hypericin emerged as the most potent RIFMO inhibitor, boasting a ΔGbinding score and inhibition constant value of − 12.11 kcal/mol and 798.99 pM, respectively. The agreement across AutoDock 4.0, SwissDock, and Schrödinger Maestro results highlighted hypericin’s notable binding affinity to the RIFMO catalytic cleft. The RIFMO-hypericin complex achieved stability after a 70-ns computer simulation, exhibiting a root-mean-square deviation of 0.55 nm. Oral bioavailability analysis revealed that all anthraquinones except hypericin, sennidin A, and sennidin B may be suitable for oral administration. Furthermore, the carcinogenicity prediction analysis indicated a favorable safety profile for all examined anthraquinones.ConclusionInhibiting RIFMO, particularly with anthraquinones such as hypericin, holds promise as a potential therapeutic strategy for infectious diseases.

Construction of Prognostic ceRNA Network Landscape in Breast Cancer to Explore Impacting Genes on Drug Response by Integrative Bioinformatics Analysis

Background: Breast cancer accounts for 30% of all new female cancers yearly. Bioinformatics serves us to find new biomarkers and facilitate future experimental research. Exploring a distinct network of competing endogenous RNA (ceRNA) that includes potential prognostic, diagnostic, and therapeutic biomarkers is captivating. Methods: Differentially expressed lncRNAs, mRNAs, and miRNAs were collected using Gene Expression Omnibus datasets. DEGs were validated based on TCGA. Functional analysis and pathway activity were also done. Drug sensitivity analyses were done, and IC50 vs. gene expression plots were depicted. Results: A total of 696 mRNAs, 48 lncRNAs, and, 43 miRNAs were identified to have significant differential expression in cancerous breast tissue than normal breast tissue samples. Functional analysis showed significant pathway enrichments in cancer. We found that 13 individual genes, lncRNAs, and miRNAs, CDC6, ERBB2, EZR, HELLS, MAPK13, MCM2, MMP1, SLC7A5, TINCR, TRIP13, hsa-miR-376a, hsa-miR-21, hsa-miR-454 were significantly predictive of poor overall survival and AKAP12, CXCL12, FGF2, IRS2, LINC00342, LINC01140, MEG3, MIR250HG, NAV3, NDRG2, NEAT1, TGFBR3 and, hsa-miR-29c were associated with favorable overall survival. We reached a set of five genes (EGR1, NFIB, TGFBR3, SMARCA4, and MCM2) that exhibit altered expression patterns in breast cancer, resulting in increased susceptibility of cancer cells to certain drug treatments. Conclusion: We successfully made a unique ce-network, providing new clues to understand the regulatory functions of non-coding RNAs (miRNAs and lncRNAs) in the pathogenesis and prognosis of breast cancer and will facilitate further experimental studies to develop new biomarkers in the diagnosis, prognosis and, therapy of breast cancer.

Integrated bioinformatics analysis and validation identify KIR2DL4 as a novel biomarker for predicting chemotherapy resistance and prognosis in colorectal cancer

BackgroundChemotherapy and immunotherapy have improved the cure rate and survival period for colorectal cancer (CRC), but genetic heterogeneity among patients leads to chemotherapy resistance and disease progression. Identifying new molecular markers is crucial for improving prognosis for CRC patients. KIR2DL4, a transmembrane glycoprotein expressed by immune cells, has shown potential therapeutic and prognostic value in other cancers, but in CRC remains unclear. MethodsThis study validated the expression levels of KIR2DL4 in CRC by integrating multiple public databases and assessed through immunohistochemistry (IHC). We further evaluated the diagnostic and prognostic value of KIR2DL4 and explored correlation with immune cell infiltration and chemotherapy sensitivity. The role of KIR2DL4 was further validated through functional enrichment analysis. Cellular assays were conducted using CCK8, colony-formation assay and scratch wound assay. ResultsThe study found that KIR2DL4 is significantly downregulated in CRC and closely associated with poor prognosis. The low expression of KIR2DL4 is associated with decreased immune cell infiltration and reduced chemotherapy sensitivity. Functional enrichment analysis suggests that KIR2DL4 may inhibit development of CRC by affecting immune cell infiltration and modulating chemotherapy sensitivity. Cellular assays have confirmed that inhibiting KIR2DL4 significantly promotes the proliferation and migration of CRC. Inhibition of KIR2DL4 expression significantly decreased the chemosensitivity of CRC cells to oxaliplatin and 5-FU. ConclusionThe significant downregulation of KIR2DL4 in CRC, associated with CRC metastasis and poor prognosis, highlights its importance as a potential new biomarker for treatment and prognosis assessment of CRC. Future research should delve into the molecular mechanisms of KIR2DL4 and potential applications in regulating immunotherapy and chemotherapy sensitivity.

Cytokine profile of cerebrospinal fluid in pediatric patients with metastatic medulloblastoma

BackgroundMedulloblastoma (MB) is a malignant pediatric central nervous system tumor that is prone to leptomeningeal metastasis. Currently, apart from magnetic resonance imaging and cerebrospinal fluid (CSF) cytology, there are no reliable biomarkers for MB progression. Cytokines are key proteins in signaling pathways in the tumor microenvironment and are closely related to tumor recurrence and progression. This study aimed to investigate the CSF cytokine profile in pediatric patients with MB to identify biomarkers of tumor progression and metastasis. MethodsIn total, 10 patients were recruited for this study. Five patients had nonmetastatic MB and five had metastatic MB. A cytokine antibody array was used to detect the expression of 120 cytokines in the CSF, and differentially expressed cytokines were screened by integrated bioinformatics analysis. ResultsTwenty-seven cytokines were upregulated in patients with MB compared to control individuals. Of these, eight were upregulated by >1.5-fold (CCL2, BMP-4, beta-NGF, FGF-7, IL-12p40, eotaxin-2, M-CSF, and NT-4). Twelve cytokines were differentially expressed between patients with metastatic MB and nonmetastatic (nine cytokines were upregulated and three were downregulated). Among them, NAP-2, MIP-1α, MIP-1β, IGFBP-1, IGFBP-2 and IGFBP-3 were upregulated by more than two-fold. Gene Ontology analysis revealed that the upregulated cytokines were enriched mainly in “epithelial cell proliferation” and “chemotaxis,” and the Kyoto Encyclopedia of Genes and Genomes analysis indicated the enrichment of the “MAPK,” “PI3K-Akt,” and “Ras” signaling pathways. ConclusionsThe present study investigated cytokine profiles in the CSF of pediatric patients with MB. Our results suggest that these differentially expressed cytokines may serve as novel markers for detecting MB, especially for assessing the risk of progression and metastasis.

An Integrated Immune-Related Bioinformatics Analysis in Lung Squamous Cell Carcinoma Patients

Through combined bioinformatics analysis, the goal of untrrec research was to develop and confirm the immune-related prognostic signature in LUSC (lung squamous cell carcinoma). We constructed an optimized prognostic risk model consisting of five PIR-lncRNAs (AC107884.1, LCMT1-AS1, AL163051.1, AC005730.3 and LINC02635). To evaluate and verify the prognostic value of the model, we subsequently conducted independent prognostic and mortality analysis on the prognostic risk model. Additionally, we conducted a distinct study of immune cell infiltration in the model among high- and low individuals. By using co-expression network analysis, we were able to identify 654 immune-related lncRNAs (IR-lncRNAs) and 18 prognostic IR-lncRNAs (PIR-lncRNAs) and derive 546 differently expressed genes and 21 immune-related genes. We proved that the impact of immunotherapy in individuals in the high-risk category may be lessened through the study of immune escape and immunotherapy. Our findings elucidate the intrinsic molecular biological link between the pathogenic genes of LUSC and immune cells, which has important exploration and reference significance for the precise and potential immunotherapy of LUSC patients, especially for high-risk patients.

Identification of cuproptosis-related genes related to the progression of ankylosing spondylitis by integrated bioinformatics analysis.

Ankylosing spondylitis (AS) is an autoimmune disease, and the relationship between copper death and AS is not clear. The aim of this study was to analyze and identify potential cuprosis-related genes associated with the onset of AS by bioinformatics methods. We obtained the AS gene expression profile GSE25101 from the Gene Expression Omnibus (GEO) database, which consists of blood samples from 16 active AS patients and 16 sex-and age-matched controls. After analyzing the data, we utilized the WGCNA method to identify genes that exhibited significant differential expression. In order to assess the prognostic and predictive power of these genes, we constructed receiver operating characteristic (ROC) curves. To further validate our predictions, we employed nomograms, calibration curves, decision curve analysis, and external datasets. Lastly, we conducted an analysis on immune infiltration and explored the correlation between key genes and immune response. Three genes, namely INPP5E, CYB5R1, and HGD, have been identified through analysis to be associated with AS. The diagnosis of patients using these genes has been found to possess a high level of accuracy. The area under the ROC curve is reported to be 0.816 for INPP5E, 0.879 for CYB5R1, and also 0.879 for HGD. Furthermore, the nomogram demonstrates an excellent predictive power, and it has been calibrated using a Calibration curve. Its clinical usefulness and net benefit have been thoroughly analyzed and estimated through the use of a DCA curve. Moreover, INPP5E, CYB5R1, and HGD are found to be associated with various types of immune cells. In conclusion, the systematic analysis of cuprosis-related genes may aid in the identification of mechanisms related to copper-induced cell death in AS and offer valuable biomarkers for the diagnosis and treatment of AS.

Prognostic significance of a 3-gene ferroptosis-related signature in lung cancer via LASSO analysis and cellular functions of UBE2Z

Ferroptosis is a newly identified form of non-apoptotic programmed cell death resulting from iron-dependent lipid peroxidation. It is controlled by integrated oxidation and antioxidant systems. Ferroptosis exerts a crucial effect on the carcinogenesis of several cancers, including pulmonary cancer. Herein, a ferroptosis-associated gene signature for lung cancer prognosis and diagnosis was identified using integrative bioinformatics analyses. From the FerrDB database, 256 ferroptotic regulators and markers were identified. Of these, 25 exhibited differential expression between lung cancer and non-cancerous samples, as evidenced by the GSE19804 and GSE7670 datasets from the GEO database. Utilizing LASSO Cox regression analysis on TCGA-LUAD data, a potent 3-gene risk signature comprising CAV1, RRM2, and EGFR was established. This signature adeptly differentiates various survival outcomes in lung cancer patients, including overall survival and disease-specific intervals. Based on the 3-gene risk signature, lung cancer patients were categorized into high-risk and low-risk groups. Comparative analysis revealed 69 differentially expressed genes between these groups, with UBE2Z significantly associated with overall survival in TCGA-LUAD. UBE2Z was found to be upregulated in LUAD tissues and cells compared to normal controls. Functionally, the knockdown of UBE2Z curtailed aggressive behaviors in LUAD cells, including viability, migration, and invasion. Moreover, this knockdown led to a decrease in the mesenchymal marker vimentin while elevating the epithelial marker E-cadherin within LUAD cell lines. In conclusion, the ferroptosis-associated 3-gene risk signature effectively differentiates prognosis and clinical features in patients with lung cancer. UBE2Z was identified through this model, and it is upregulated in LUAD samples. Its knockdown inhibits aggressive cellular behaviors, suggesting UBE2Z's potential as a therapeutic target for lung cancer treatment.

Integrative bioinformatics analysis of transcriptomic data from CD8+ T cells in Systemic Lupus Erythematosus

IntroductionSystemic Lupus Erythematosus (SLE) is a complex, multisystem autoimmune disorder characterized by extensive inflammation that affects nearly all organ systems in the body. It is primarily mediated by auto-antibodies and immune complexes, and it predominantly affects women more than men. This study employs an in-silico approach to identify key genes potentially involved in the pathogenesis of SLE. ObjectivesTo identify key genes potentially involved in SLE pathogenesis using in-silico approach. MethodsHigh-throughput sequencing dataset GSE97264, from the Gene Expression Omnibus (GEO) database, which contains RNA transcriptome data from CD8+ T-cells of 18 SLE patients and 14 healthy controls was utilized for the analysis. Differentially expressed genes (DEGs) were identified using the Bioconductor DESeq2 package in R platform. Gene Ontology (GO) and pathway enrichment analyses were performed using the ToppGene suite. Motif analysis of the genes’ promoter regions was conducted using HOMER software. Protein-protein interaction (PPI) and Reactome functional interaction (FI) networks were created using Cytoscape plugins StringApp and ReactomeFIViz, and analysed to identify hub genes. ResultsOur analysis identified 931 DEGs, with 577 upregulated and 354 downregulated. GO and pathway enrichment analyses indicated that upregulated genes were associated with immune responses, including cytokine production and receptor activation. Motif analysis identified key regulatory motifs linked to immune regulation in upregulated genes and T-cell activation in downregulated genes. PPI and FI networks analyses revealed 29 cell cycle-associated hub genes, with 10 genes—CDK1, TPX2, BIRC5, CCNA2, BUB1, BUB1B, AURKA, KIF2C, PLK1, and CDCA8—common to both biological networks, suggesting their crucial role in SLE pathogenesis. ConclusionThis study suggests that dysregulation of the identified 10 genes may impact immune responses and contribute to the autoimmune-like conditions observed in SLE. Several of these genes are also implicated in other autoimmune diseases, highlighting their potential as SLE biomarkers. Despite their known roles in other immune-related diseases involving CD8+ T cells, their direct association with SLE had not been previously established. This novel finding underscores the potential of these genes as therapeutic targets and may contribute to the development of diagnostic tools.

Integrative single-cell and spatial transcriptomic analyses identify a pathogenic cholangiocyte niche and TNFRSF12A as therapeutic target for biliary atresia.

Biliary Atresia (BA) is a devastating fibro-inflammatory biliary disease that is the leading indication for pediatric liver transplants worldwide. Although cholangiocytes are the primary target cells, the pathogenic mechanisms involving cholangiocytes remain elusive. Here, we aimed to characterize the pathogenic role of cholangiocytes in BA. Integration of single-cell RNA sequencing of 12 liver tissues (from 9 BA and 3 controls) and the spatial transcriptome of another four liver sections (from 2 BA and 2 controls) provided a comprehensive spatial liver cell atlas of BA. In particular, we identified a cholangiocyte-enriched spatial niche with infiltration of activated hepatic stellate cells, activated portal fibroblasts, macrovascular endothelial cells and TREM2+ macrophages that were elevated in the portal triad of BA. This niche was positively correlated with bile duct profiles, liver fibrosis and poor survival in two independent cohorts of patients with BA. Using integrative bioinformatics analyses to mine the cell-cell communication and regulatory network in BA cholangiocytes, we uncovered the fibro-inflammatory phenotype of cholangiocytes with TNFSF12-TNFRSF12A as a significant signal. Genetic ablation or blockade of TNFRSF12A suppresses liver injury, inflammation, and bile duct profiles in a mouse model of disease. Using human biliary organoids, we revealed that BA organoids expressed higher levels of CCL2 in response to TNFSF12 stimulation and promoted monocyte chemotaxis via the CCL2-CCR2 axis. Pathogenic cholangiocytes enriched niche identifies TNFRSF12A as a potential therapeutic target for BA.

Network medicine based approach for identifying the type 2 diabetes, osteoarthritis and triple negative breast cancer interactome: Finding the hub of hub genes

The increasing prevalence of multi-morbidities, particularly the incidence of breast cancer in diabetic/osteoarthritic patients emphasize on the need for exploring the underlying molecular mechanisms resulting in carcinogenesis. To address this, present study employed a systems biology approach to identify switch genes pivotal to the crosstalk between diseased states resulting in multi-morbid conditions. Hub genes previously reported for type 2 diabetes mellitus (T2DM), osteoarthritis (OA), and triple negative breast cancer (TNBC), were extracted from published literature and fed into an integrated bioinformatics analyses pipeline. Thirty-one hub genes common to all three diseases were identified. Functional enrichment analyses showed these were mainly enriched for immune and metabolism associated terms including advanced glycation end products (AGE) pathways, cancer pathways, particularly breast neoplasm, immune system signalling and adipose tissue. The T2DM-OA-TNBC interactome was subjected to protein-protein interaction network analyses to identify meta hub/clustered genes. These were prioritized and wired into a three disease signalling map presenting the enriched molecular crosstalk on T2DM-OA-TNBC axes to gain insight into the molecular mechanisms underlying disease-disease interactions. Deciphering the molecular bases for the intertwined metabolic and immune states may potentiate the discovery of biomarkers critical for identifying and targeting the immuno-metabolic origin of disease.