The biological basis of the development of cancer of unknown primary (CUP) remains largely unknown, with no evidence of whether a common biological basis exists at present. Our previous multicenter clinical study predicted the primary site of CUP for site‑specific therapy. Concomitantly with the study, a microarray analysis of tumor mRNA samples obtained from 60 participants of the study with CUP was performed, and a gene expression profile specific to CUP was constructed. Several of the genes identified as being upregulated/downregulated in CUP could potentially be clinically useful common biomarkers of CUP. In the present study, to identify genes that may be more closely related to the development of CUP (characterized by its metastatic potential) among the upregulated genes, cell‑based small interfering RNA screening was performed in vitro, and two genes, protein kinase DNA‑activated catalytic subunit (PRKDC) and proteasome subunit β type‑4 (PSMB4), were identified to be possibly involved in the metastatic ability of CUP, since knockdown of these genes resulted in reduced migration of A549 cells. These genes were further knocked down in A549 cells using short hairpin RNAs (shRNAs) and the cells were implanted into the footpad of mice. Marked suppression of the metastatic ability of implanted cells from the footpad to the popliteal lymph node (LN) was observed in cells transfected with the shRNAs for PRKDC and PSMB4. In addition, bortezomib, a proteasome inhibitor, markedly reduced the ability of cells implanted into the footpad to metastasize to the LNs, as well as cell growth at the metastatic site, compared with vehicle or NU7447 (inhibitor of PRKDC). These findings indicated that proteasomal function activation augmented the metastatic ability of malignant CUP cells.

The interaction between keratinocytes (KCs) and immune cells is essential in the pathogenesis of psoriasis. Understanding this crosstalk is crucial for developing effective treatment strategies. Recent studies indicate that Rh family C-type glycoprotein (RHCG) enhances cell proliferation and alters cell differentiation; however, its exact pathogenic mechanisms in psoriasis remain unclear. We employed bioinformatics approaches, including spatial transcriptomics analysis, single-cell transcriptomics analysis, and bulk data analysis, to elucidate the biological functions of RHCG. These predictions were validated through ex vivo experiments and analysis of clinical specimens. In psoriatic skin, RHCG protein levels were significantly upregulated, with an expanded expression area. Notably, RHCG expression was induced under hypoxic conditions. Furthermore, the upregulation of RHCG enhanced the expression of KC markers S100 Calcium Binding Protein A family (S100A) and Keratin 17 (KRT17), while decreasing Keratin 1 (KRT1) expression. Additionally, RHCG overexpression increased the secretion of C-X-C motif chemokine ligand 14 (CXCL14) from KCs, which subsequently activated dendritic cells. Importantly, treatment with secukinumab effectively ameliorated psoriasis by downregulating RHCG expression and inhibiting associated signaling pathways, whereas glucocorticoid and methotrexate treatments resulted in elevated RHCG expression. These findings indicate that RHCG plays a significant role in hypoxia-induced cellular crosstalk and suggest that RHCG-associated signaling may contribute to the superior efficacy of biological agents compared to conventional hormonal and immunosuppressive therapies.

Leprosy and tuberculosis caused by Mycobacterium leprae and Mycobacterium tuberculosis, respectively, are chronic infections with significant public health implications. While leprosy affects the skin and peripheral nerves and tuberculosis primarily targets the lungs, both diseases involve systemic immune responses. This study integrates transcriptomic analysis cheminformatics and molecular dynamics simulations to identify molecular mechanisms and potential therapeutic targets.

Transcriptomic datasets were analyzed to identify dysregulated genes and pathways. Pathway enrichment tissue-specific and bulk RNA-seq expression analyses provided biological context. System biology networks revealed regulatory hub genes and molecular docking studies evaluated CHEMBL compounds as potential therapeutics. Molecular dynamics (MD) simulations assessed the stability of top ligand-protein complexes through RMSD RMSF and MM-GBSA free energy calculations.

Gene expression analysis identified 13 core dysregulated genes, including HSP90AA1 MAPK8IP3 and ZMPSTE24. Tissue-specific expression localized pivotal genes to lung tissues and immune cells with HSP90AA1 highly expressed in alveolar macrophages and epithelial cells. HSP90AA1 gene emerged as a central hub gene with 96 interactions involved in stress response pathways. Docking studies identified CHEMBL3653862 and CHEMBL3653884 with strong binding affinities (-10.16 to -12.69 kcal/mol) interacting with Asp93 and Tyr139. MD simulations confirmed binding stability with RMSD fluctuations within 2.1-3.5 Å and MM-GBSA energy values supporting ligand-protein stability.

This study identifies HSP90AA1 as a potential drug target in leprosy and tuberculosis. Findings support host-directed therapy approaches and highlight the importance of computational modeling in accelerating drug discovery. The study provides a foundation for future experimental validation, including in vitro and in vivo testing to advance drug repurposing strategies for these chronic infections.

Bladder cancer (BLCA) is an aggressive malignancy characterized by limited therapeutic options and a poor prognosis. Research has indicated that abnormally expressed miRNAs play a significant role in the pathogenesis of BLCA, although the specific mechanisms remain unclear. MiR-125b plays a tumor suppressor role in a variety of cancers and affects the biological processes of cancer cells such as proliferation, invasion, migration and apoptosis by regulating different signaling pathways. Elucidation of the molecular mechanisms underlying miR-125b may provide clinical therapeutic strategies for bladder cancer. Here, miR-125b was downregulated whereas its targets IL-6R and STAT3 were upregulated in BLCA, as evidenced by bioinformatics analysis. Kaplan-Meier analysis confirmed that miR-125b serves as an independent prognostic factor linked to overall survival (OS) in patients with bladder cancer. Furthermore, overexpression of miR-125b significantly inhibited BLCA cell proliferation, migration, and invasion, while promoting apoptosis, as evidenced by an increased Bax/Bcl-2 ratio and activated cleaved caspase-3. Further investigations demonstrated that miR-125b directly targets and downregulates both IL-6R and STAT3. In a xenograft model, miR-125b overexpression effectively inhibited tumor growth in bladder cancer by blocking IL-6/IL-6R and STAT3 signaling pathways. Collectively, these findings broaden our understanding of the mechanism by which miR-125b acting as a BLCA suppressor in apoptotic regulation by targeting the IL-6/IL-6R/STAT3 signaling pathway, providing novel insights regarding the design of novel miRNA based therapeutic strategies against BLCA.

To explore the influence of non-SMC condensin I complex subunit D2 (NCAPD2) on the prognosis of oral squamous cell carcinoma (OSCC) and the correlation between NCAPD2 and OSCC.

In this study, NCAPD2 gene expression profiles of OSCC and normal tissues were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The real-time quantitative polymerase chain reaction (RT-qPCR) was employed to preliminarily validate OSCC cell strains and normal epithelial cell strains. Besides EdU, cell scratch, and transwell assays were performed to assess the proliferation, migration, and invasion of OSCC cell strains with the silence of NCAPD2. Moreover, immunohistochemistry (IHC) staining was utilised to measure the expression of NCAPD2 and tumour-related markers in 74 OSCC specimens. Finally, the Kaplan-Meier analysis was performed to evaluate the influence of NCAPD2 in the prognosis of OSCC.

The expression of NCAPD2 in OSCC tissues was higher than that in normal tissues. Inhibiting NCAPD2 can reduce the proliferation and migration of OSCC cell lines and inhibit the invasion of these cells. The IHC staining results indicated that the high expression of NCAPD2 in OSCC tissues was positively correlated with T stages, Ki67 expression, and affected sites. The Kaplan-Meier analysis results validated that the up-regulated expression of NCAPD2 was significantly correlated with the poor overall survival (OS) of OSCC patients.

NCAPD2 is a potential molecular marker for the poor prognosis of OSCC, and it is expected to become a target for the treatment of this carcinoma.

Bulk transcriptomic analyses of high-grade serous ovarian cancer (HGSOC) so far have not uncovered potential drug targets, possibly because subtle, disease-relevant transcriptional patterns are overshadowed by dominant, non-relevant ones. Our aim was to uncover disease-outcome-related patterns in HGSOC transcriptomes that may reveal novel drug targets. Using consensus-independent component analysis, we dissected 678 HGSOC transcriptomes of systemic therapy naïve patients-sourced from public repositories-into statistically independent transcriptional components (TCs). To enhance c-ICA's robustness, we added 447 transcriptomes from non-serous histotypes, low-grade serous, and non-cancerous ovarian tissues. Cox regression and survival tree analysis were performed to determine the association between TC activity and overall survival (OS). Finally, we determined the activity of the OS-associated TCs in 11 publicly available spatially resolved ovarian cancer transcriptomes. We identified 374 TCs, capturing prominent and subtle transcriptional patterns linked to specific biological processes. Six TCs, age, and tumor stage stratified patients with HGSOC receiving platinum-based chemotherapy into ten distinct OS groups. Three TCs were linked to copy-number alterations affecting expression levels of genes involved in replication, apoptosis, proliferation, immune activity, and replication stress. Notably, the TC identifying patients with the shortest OS captured a novel transcriptional pattern linked to synaptic signaling, which was active in tumor regions within all spatially resolved transcriptomes. The association between a synaptic signaling-related TC and OS supports the emerging role of neurons and their axons as cancer hallmark-inducing constituents of the tumor microenvironment. These constituents might offer a novel drug target for patients with HGSOC.

Oral squamous cell carcinoma (OSCC) is characterized by poor prognosis and high mortality. Understanding programmed cell death-related genes could provide valuable insights into disease progression and treatment strategies.

RNA-sequencing data from 341 OSCC tumor tissues and 31 healthy samples were analyzed from TCGA database, with validation using 76 samples from GSE41613. Single-cell RNA sequencing data was obtained from GSE172577 (6 OSCC samples). Differentially expressed genes (DEGs) were identified and intersected with 1,254 programmed cell death-related genes. A protein-protein interaction network was constructed, and key modules were identified. Univariate Cox, LASSO, and multivariate Cox regression analyses were performed to build a prognostic model. Model performance was evaluated using Kaplan-Meier analysis, ROC curves, and nomogram validation.

The study identified 200 candidate genes from the intersection of DEGs and programmed cell death-related genes, which were further refined to 57 hub genes through PPI network analysis. A prognostic signature consisting of five genes (MET, GSDMB, KIT, PRKAG3, and CDKN2A) was established and validated. The model demonstrated good predictive performance in both training and validation cohorts (AUC > 0.6 for 1-, 2-, and 3-year survival). Single-cell analysis revealed that prognostic genes were predominantly expressed in stromal and epithelial cells. Cell communication analysis indicated strong interactions between stromal and epithelial cells.

This study developed and validated a novel five-gene prognostic signature for OSCC based on programmed cell death-related genes. The model shows promising clinical application potential for risk stratification and personalized treatment of OSCC patients.

Lupus nephritis (LN) leads to widespread kidney damage and nephron loss, establishing it as a major contributor to acute and chronic kidney injury, which can progress to end-stage renal disease. Phlorizin (PHZ), a major pharmacologically active constituent derived from Lithocarpus polystachyus Rehd., has been shown to exhibit significant immunomodulatory and anti-inflammatory properties. Growing evidence indicates that PHZ may exert a protective influence on kidney function. However, the therapeutic effect and mechanism of PHZ in treating LN need to be elucidated.

The PHZ-associated targets were identified through tools such as PharmMapper, SwissTargetPrediction, SuperPred and Targetnet. Simultaneously, LN-associated target spots were retrieved fromOMIM, DisGeNET, GeneCards, and GEO databases. Additionally, Venny 2.1.0 was employed to analyze the overlap between drug targets and disease targets. Following this, the DAVID software was employed to perform enrichment analyses for GO terms and KEGG pathways on the shared drug-disease target sites. Following this, the construction of protein-protein interaction (PPI) networks for these intersecting targets was carried out using the STRING database and Cytoscape software, aiming to pinpoint critical targets. Ultimately, molecular docking alongside dynamic simulations was used to evaluate the binding affinity between PHZ and the critical genes. Based on these findings, PHZ or Dexamethasone (DXSM) was administered to female MRL/lpr mice, which are predisposed to lupus. The therapeutic effects of PHZ on LN were evaluated by assessing renal function and the degree of kidney inflammation. Concurrently, flow cytometry was employed to measure the percentage of CD4+ T cell subsets. Additionally, relevant signaling pathways were examined through western blot analysis. Furthermore, CD4+CD25+Foxp3+ regulatory T (Treg) cells were induced in vitro. Flow cytometry and immunoblotting were performed to confirm the role and mechanism of PHZ in Treg cell differentiation.

The PHZ compound specifically targeted 161 genes associated with LN. PPI analysis revealed that among all the target genes, Akt1, ALB, MMP9, HSP90AA1, and NF-κB1 exhibited the highest centrality. KEGG pathway analysis suggested that the phosphatidylinositol 3 kinase/protein kinase B (PI3K/AKT) signaling pathway could play a crucial role in the treatment of LN. Molecular docking revealed that PHZ exhibits a strong affinity for binding with AKT1. Experimental studies, both in vitro and in vivo, showed that PHZ might alleviate LN by promoting Treg differentiation via activation of the PI3K/AKT signaling pathway.

Integrating network pharmacology, bioinformatics, and experimental validation, our study systematically deciphers the therapeutic efficacy and molecular mechanisms of PHZ against LN. Network pharmacology analysis and bioinformatics suggested PI3K/AKT signaling as the pivotal pathway to treat LN, while subsequent in vivo and in vitro experiments confirmed that PHZ exerts its therapeutic effects through activating the PI3K/AKT signaling pathway, ultimately driving FOXP3-dependent regulatory T cell differentiation.

Stress response in bacterial pathogens promotes adaptation, virulence and antibiotic resistance. In this study, a network approach is applied to identify the common central mediators of stress response in five emerging opportunistic pathogens; Enterococcus faecium Aus0004, Staphylococcus aureus subsp. aureus USA300, Klebsiella pneumoniae MGH 78,578, Pseudomonas aeruginosa PAO1, and Mycobacterium tuberculosis H37Rv. A Protein-protein interaction network (PPIN) was constructed for each stressor using Cytoscape3.7.1 from the differentially expressed genes obtained from Gene expression omnibus datasets. A merged PPIN was constructed for each bacterium. Hub-bottlenecks in each network were the central stress response proteins and common pathways enriched in stress response were identified using KOBAS3.0. 31 hub-bottlenecks were common to each individual stress response, merged networks in all five pathogens and an independent cross stress (CS) response dataset of Escherichia coli. The 31 central nodes are in the RpoS mediated general stress regulon and also regulated by other stress response systems. Analysis of the 20 common metabolic pathways modulating stress response in all five bacteria showed that carbon metabolism pathway had the highest crosstalk with other pathways like amino acid biosynthesis and purine metabolism pathways. The central proteins identified can serve as targets for novel wide-spectrum antibiotics to overcome multidrug resistance.

There are no validated biomarkers for assessing hepatocellular carcinoma (HCC) treatment response (TR). Extracellular vesicles (EVs) are promising circulating biomarkers that may detect minimal residual disease in patients with treated HCC.

We developed the HCC EV TR Score using HCC EV Digital Scoring Assay involving click chemistry-mediated enrichment of HCC EVs, followed by absolute quantification of HCC EV-specific genes by RT-digital PCR. Six HCC EV-specific genes were selected and validated through i) a comprehensive data analysis pipeline with an unprecedentedly large collection of liver transcriptome datasets (n = 9,160), ii) RNAscope validation on HCC tissues (n = 6), and iii) a pilot study on early- or intermediate-stage HCC and liver cirrhosis patients (n = 70). The performance of HCC EV TR Score was assessed in a phase-2 retrospective case-control study (n = 100).

HCC EV TR Scores, calculated from pre- and post-treatment plasma samples in the phase-2 case-control study, accurately differentiated post-treatment viable from nonviable HCC in the training (area under the ROC curve [AUROC] of 0.90, n = 49) and validation set (AUROC of 0.88, n = 51). At an optimal cutoff of 0.76 identified in the training set, HCC EV TR Score had high accuracy in detecting viable tumors (sensitivity: 76.5%, specificity: 88.2%) and found residual disease not initially observed on MRI in six patients with a median lead time of 63 days.

This EV-based digital scoring approach shows great promise to augment cross-sectional imaging for the assessment of HCC treatment response.

A quarter of human population is infected with Mycobacterium tuberculosis, but less than 10% of those infected develop pulmonary TB. We developed a genetically defined sst1-susceptible mouse model that uniquely reproduces a defining feature of human TB: the development of necrotic lung granulomas and determined that the sst1-susceptible phenotype was driven by the aberrant macrophage activation. This study demonstrates that the aberrant response of the sst1-susceptible macrophages to prolonged stimulation with TNF is primarily driven by conflicting Myc and antioxidant response pathways leading to a coordinated failure 1) to properly sequester intracellular iron and 2) to activate ferroptosis inhibitor enzymes. Consequently, iron-mediated lipid peroxidation fueled Ifn-beta superinduction and sustained the Type I Interferon (IFN-I) pathway hyperactivity that locked the sst1-susceptible macrophages in a state of unresolving stress and compromised their resistance to Mtb. The accumulation of the aberrantly activated, stressed, macrophages within granuloma microenvironment led to the local failure of anti-tuberculosis immunity and tissue necrosis. The upregulation of Myc pathway in peripheral blood cells of human TB patients was significantly associated with poor outcomes of TB treatment. Thus, Myc dysregulation in activated macrophages results in an aberrant macrophage activation and represents a novel target for host-directed TB therapies.

The exact role of cell adhesion molecule (CADM) family members in neuroblastoma is still being explored. Here we uncovered the survival association and the possible mechanisms of CADMs in neuroblastoma through comprehensive bioinformatic analyses. Then the results of CADM1 were verified in neuroblastoma cell lines.

CADMs expression was examined by cBioPortal and TARGET databases and verified in several GEO datasets. Kaplan-Meier plot, log-rank test, the ROC curve, and Cox regression analysis were utilized to assess the prognostic value of CADMs in neuroblastoma. Through functional enrichment analysis and interaction network construction, hub genes were screened to explore the molecular mechanism of CADMs in neuroblastoma. We tested the abilities of cell growth and migration in neuroblastoma cells when CADM1 was silenced and overexpressed respectively. We then used western blot to verify the phosphorylation levels of AKT/GSK-3β pathways.

The expression of CADM1-4 was significantly down-regulated in neuroblastoma patients with unfavorable prognostic factors. Moreover, CADM1 and CADM3 increased the accuracy of classical clinical indicators for predicting survival rate. The top 10 KEGG pathways for CADMs and their co-expression genes were mainly enriched in the mitotic cell cycle and the process of chromosomal duplication. Furthermore, our study showed that CADM1 inhibited neuroblastoma cells proliferation, migration and the phosphorylation of GSK-3β.

Decreased expression of CADM1 and CADM3 was significantly associated with poor outcomes in neuroblastoma. CADM1 may suppress neuroblastoma cell proliferation and migration through regulating the phosphorylation of GSK-3β.

Single-cell multi-omics is a transformative technology that measures both gene expression and chromatin accessibility in individual cells. However, most studies concentrate on a single tissue and are unable to determine whether a gene is regulated by a cis-regulatory element (CRE) in just one tissue or across multiple tissues. We developed Compass for comparative analysis of gene regulation across a large number of human and mouse tissues. Compass consists of a database, CompassDB, and an open-source R software package, CompassR. CompassDB contains processed single-cell multi-omics data of more than 2.8 million cells from hundreds of cell types. Building upon CompassDB, CompassR enables visualization and comparison of gene regulation across multiple tissues. We demonstrated that CompassR can identify CRE-gene linkages specific to a tissue type and their associated transcription factors in real examples.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that has multiple factors that make its molecular pathogenesis difficult to understand and its diagnosis and treatment during the early stages difficult to determine. Discovering novel biomarkers in ALS for diagnostic and therapeutic potential has become important. Consequently, bioinformatics and machine learning algorithms are useful for identifying differentially expressed genes (DEGs) and potential biomarkers, as well as understanding the molecular mechanisms and intricacies of diseases such as ALS. To achieve the aim of the present study, six datasets obtained from the Gene Expression Omnibus (GEO) were utilized and analyzed using an integrative bioinformatics and machine learning approach. Log transformation was done during data preprocessing, RMA normalization was performed, and the batch effect was corrected. Differential expression analysis identified 206 DEGs that were significantly associated with different biological processes, including muscle function, energy metabolism, and mitochondrial membrane activity. Functional enrichment analysis highlighted pathways, including those related to prion disease, Parkinson's disease, and ATP synthesis via chemiosmotic coupling. We employed a multi-step machine learning framework incorporating random forest, LASSO regression, and SVM-RFE to identify robust biomarkers. This approach identified three key genes, CHRNA1, DLG5, and PLA2G4C, which could be explored as promising biomarkers for ALS after further validation. The internal validation, including principal component analysis (PCA) and ROC-AUC analysis, demonstrated strong diagnostic potential of these hub genes, achieving an AUC of 0.96. This work highlights the utility of bioinformatics and machine learning in identifying key genes as biomarkers for diagnostic and therapeutic potential in ALS.

To identify and verify potential biomarkers for primary Sjögren's disease (pSjD) using bioinformatics analysis and explore the molecular immune mechanisms of biomarkers.male-to-female ratio of 1:9 METHODS: The pSjD datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis, weighted gene co-expression network analysis (WGCNA) and functional analysis were conducted. PPI network analysis was performed and the hub genes were screened by Cytoscape software. The diagnostic value was assessed by receiver operating characteristic (ROC) analysis. To explore biomarker-immune cell relations, we used CIBERSORT for cell-type identification, combined with scRNA-seq data. Lastly, we validated the expression of the biomarker in human samples.

A total of 96 overlapping genes, including 1 downregulated and 95 upregulated genes, were obtained. Based on the enrichment analysis, these overlapping genes were mapped to terms related to the functions and regulation of the immune system. CCR5 was identified as a critical biomarker and demonstrated high diagnostic accuracy for pSjD. From CIBERSORT analysis, CCR5 was significantly associated with diverse immune cells. Further scRNA-seq analysis indicated that CCR5 was specifically upregulated in T cells of pSjD salivary gland tissues, which was confirmed in pSjD patients.

Our findings show the role of CCR5 in pSjD, mediated by immune mechanisms. CCR5 is localized in T cells of pSjD salivary glands. Elevated CCR5 expression may be a key biomarker, and increased CCR5 + T cells could aid future diagnosis, prognosis, and treatment of pSjD.

Hepatocellular carcinoma (HCC) poses a significant global burden as a highly prevalent and life-threatening malignant tumor that endangers human life and wellbeing. The purpose of this study was to examine how DNA methylation-driven genes impact the prognosis of HCC patients.

Differentially expressed genes from The Cancer Genome Atlas, GSE76427, GSE25097 and GSE14520 datasets were collected to perform differential expression analysis between HCC patients and controls. Weighted gene coexpression network analysis (WGCNA) was subsequently performed to create coexpression modules for the DEGs. Then, ssGSEA was employed to investigate the infiltration of immune cells in HCC. Enrichment analysis and methylation were carried out for the module genes. We utilized Kaplan-Meier survival analysis to assess patient prognosis.

Eight coexpression modules were identified via WGCNA for 1927 upregulated and 1,231 downregulated DEGs, after which the hub genes of the modules were identified. Module 5 had high immune infiltration, and the hub gene SCAMP3 was positively associated with Tcm. Module 3 exhibited a low level of immune infiltration, and the expression of the hub gene HCLS1 was negatively correlated with T cells and dendritic cells. Furthermore, we obtained five hub genes (BOP1, BUB1B, NOTCH3, SCAMP3, and SNRPD2) as methylation-driven genes. BOP1 and BUB1B were found to be correlated with unfavorable overall survival in patients with HCC.

HCLS1 and SCAMP3 are associated with immunity, whereas BOP1 and BUB1B are modified by methylation and may serve as prognostic markers for HCC.

Neuroblastoma (NB) is the most common extracranial malignant tumor in children, presenting significant challenges in prognosis and treatment stratification. This study aims to identify novel biomarkers for NB and develop a prognostic model using comprehensive analytical methods, including Mendelian randomization (MR) analysis.

Utilizing bioinformatics and Mendelian randomization methods, we explored biomarkers associated with neuroblastoma at the mRNA level. We used chip expression data from the GEO database to screen for differentially expressed genes (DEGs) and conducted two-sample MR analysis using expression quantitative trait loci (eQTL) and neuroblastoma data from the IEU database to identify co-expressed genes through colocalization. A relevant prognostic model was constructed using lasso regression based on the co-expressed genes. Furthermore, we confirmed the correlation between high-risk and low-risk groups with the tumor microenvironment and immune cell infiltration. Subsequently, we evaluated the relationship between risk scores and sensitivity to immunotherapy and anti-tumor drugs.

Differential analysis identified 485 downregulated and 349 upregulated genes that play important roles in NB. MR analysis identified 4 significant co-expressed genes associated with NB: CAV2, CTSK, LXN, and NDRG2. GO and KEGG enrichment analyses revealed that these genes are involved in crucial biological processes and pathways. A prognostic model based on these four genes was constructed, and its independence as a prognostic factor was confirmed. NB patients were divided into two different risk score groups, with survival analysis indicating that the high-risk group had poorer overall survival, lower immune infiltration, and poorer immune therapy response. In contrast, the low-risk group showed potential efficacy in immunotherapy and higher sensitivity to anti-tumor drugs.

Our findings provide new insights into the molecular basis of NB, identifying four novel biomarkers and developing a risk scoring model based on four co-expressed genes. This model has the potential to become an effective tool for predicting prognosis and guiding treatment in NB patients.

Clear cell renal cell carcinoma (ccRCC) accounts for 70% of renal cell carcinoma (RCC) cases. Although surgery remains the mainstay treatment, renal injury and high metastasis rates after nephrectomy dramatically reduce patient quality of life. Drugs that stimulate the immune system by targeting checkpoint pathways improve overall survival in patients with RCC. Here, we investigated the applicability of lysophosphatidylcholine acyltransferase 3 (LPCAT3) as a target for immunotherapy.

In the present study, high LPCAT3 expression in ccRCC was identified using The Cancer Genome Atlas (TCGA) data and validated in two external cohorts from the Gene Expression Omnibus (GEO) database. qRT-PCR was performed to identify the mRNA level of LPCAT3 in tumors and adjacent normal tissues. And immunohistochemistry was used to evaluate the protein level of LPCAT3 between two groups of samples. Furthermore, gene set enrichment analysis was performed to explore the biological processes and pathways related to LPCAT3 expression. Key gene expression and correlation analyses were performed to determine the crosstalk among LPCAT3 expression, ferroptosis, and endoplasmic reticulum stress (ERS). Subsequently, CIBERSORT was used to analyze the immune infiltration status of patients with high and low LPCAT3 expression.

TCGA and GEO data revealed that LPCAT3 expression in ccRCC tumor tissues was higher than that in adjacent normal tissues; moreover, patients with high LPCAT3 expression had better survival outcomes. qRT-PCR and immunohistochemistry verified the high LPCAT3 expression in tumor tissue. Pathways related to ferroptosis and ERS were upregulated in patients with high LPCAT3 expression. Univariate and multivariate regression analyses revealed that low LPCAT3 levels represent an independent risk factor for ccRCC. LPCAT3 expression was positively correlated with M2 macrophage infiltration levels but negatively correlated with the memory B cell, CD8+ T cell, follicular helper T cell, regulatory T cell, activated natural killer cell, and activated memory CD4+ T cell infiltration levels.

LPCAT3was identified as a ccRCC biomarker and may regulate immune infiltration and prognosis in ccRCC by mediating ferroptosis and ERS. Thus, it has potential for exploitation as a prognostic and immune therapeutic target for patients with ccRCC.

Trichophyton mentagrophytes (T. mentagrophytes) is a prevalent pathogen that causes human and animal dermatophytosis. The clinical treatment of the infections is challenging due to the prolonged treatment duration, limited efficacy, antifungal resistance and side effects of existing drugs. Modern research has reported that the classic Traditional Chinese medicine (TCM) prescription Huangqin decoction (HQD) along with its principal ingredients could exhibit antifungal properties. Given the valued advantages of TCM such as broad-spectrum antifungal activity, low incidence of drug resistance and low toxicity, this study investigated the antifungal activity of HQD against T. mentagrophytes and explored the potential inhibitory mechanism, aimed to provide new clues for the treatment of dermatophytosis. By detecting minimal inhibitory concentration (MIC) using the broth microdilution method, the results showed that HQD could significantly inhibit the growth of T. mentagrophytes, with a minimal inhibitory concentration (MIC) of 3.13 mg/mL. The transcriptome sequencing and quantitative real-time PCR (qRT-PCR) technology were combined to shed light on the complicated adaptive responses of T. mentagrophytes upon HQD. The results demonstrated that at MIC, compared with the control group, a total of 730 differentially expressed genes (DEGs) were detected in T. mentagrophytes after HQD exposure (FDR adjusted p-value < 0.05), of which 547 were up-regulated and 183 were down-regulated. These DEGs were abundant in "single-organism metabolic process", "catalytic activity" and "oxidoreductase activity", and were significantly enriched in seven signaling pathways including glutathione metabolism, DNA replication, glyoxylate and dicarboxylate metabolism, taurine and hypotaurine metabolism, carotenoid biosynthesis, ubiquitin-mediated proteolysis, and cyanoamino acid metabolism. The results of transcriptome profiling were verified using qRT-PCR for a subset of 10 DEGs. The overall evidence indicated that HQD had a significant anti-dermatophyte activity and the adaptive responses of T. mentagrophytes upon HQD might be related to targeting glutathione S-transferase (GST) gene that could conjugate with toxic xenobiotics to defense oxidative stress, the inhibition of DNA replication pathway by downgrading the DNA replication licensing factors MCM3, MCM5 and ribonuclease H1 (RNaseH1) genes, and the repressed expression of phosphatidylserine decarboxylase (PSD) gene related to phospholipid synthesis which was indispensable for hyphal morphology, hyphal differentiation and cell wall integrity. Our study showed a new theoretical basis for the effective control of T. mentagrophytes infection and the effect of HQD on fungi, which are expected to offer aids for discovering new antifungal agents upon dermatophytosis.

Gastric cancer (GC) is one of the leading causes of cancer-related mortality worldwide, with limited therapeutic options and a poor prognosis, particularly in advanced stages. Glycometabolism, a hallmark of cancer, plays a critical role in tumor progression, immune evasion, and response to therapy. However, the specific roles of glycometabolism-related genes and their prognostic and therapeutic implications in GC remain inadequately understood.

Transcriptomic and clinical data from GC patients were retrieved from TCGA and GEO databases. Glycometabolism-related genes were identified and analyzed using machine learning algorithms to construct a prognostic model. Functional assays, immune profiling, and pathway enrichment analyses were performed to explore the roles of these genes in tumor progression, immune-modulatory effects, and drug resistance. PLOD2, the gene with the highest prognostic significance, was further investigated to uncover its underlying regulatory mechanisms, roles in immune modulation, and contribution to therapeutic resistance.

A glycometabolism-related prognostic model consisting of four genes (PLOD2, CHSY3, SLC2A3 and SLC5A1) was developed and validated, effectively stratifying GC patients into high- and low-risk subgroups with distinct survival outcomes. Among these, PLOD2 emerged as the most significant gene, exhibiting strong associations with tumor progression and poor survival. Functional analyses revealed that PLOD2 promotes glycolysis and tumor progression through activation of the PI3K/AKT/mTOR pathway. Immune profiling revealed that PLOD2 overexpression is associated with an immunosuppressive tumor microenvironment, characterized by increased M2 macrophage infiltration and reduced immune activity. Moreover, treatment with rapamycin, an mTOR inhibitor, significantly suppressed PLOD2-mediated proliferation and anchorage-independent growth in GC cells, highlighting the central role of the PI3K/AKT/mTOR pathway in PLOD2-driven oncogenic behaviors.

This study identifies PLOD2 as a key prognostic biomarker and therapeutic target in gastric cancer. As a central component in a glycometabolism-related model, PLOD2 promotes glycolysis, tumor progression, and immune evasion via the PI3K/AKT/mTOR pathway. The model effectively stratifies patient risk, offering both prognostic utility and therapeutic insight. Targeting PLOD2-mediated pathways may represent a promising strategy for precision therapy and improved clinical outcomes in gastric cancer.

Brain imaging genomics has manifested considerable potential in illuminating the genetic determinants of human brain structure and function. This has propelled us to develop the GIANT (Genetically Informed brAiN aTlas) that accounts for genetic and neuroanatomical variations simultaneously. Integrating voxel-wise heritability and spatial proximity, GIANT clusters brain voxels into genetically informed regions, while retaining fundamental anatomical knowledge. Compared to conventional (non-genetics) brain atlases, GIANT exhibits smaller intra-region variations and larger inter-region variations in terms of voxel-wise heritability. As a result, GIANT yields increased regional SNP heritability, enhanced polygenicity, and its polygenic risk score explains more brain volumetric variation than traditional neuroanatomical brain atlases. We provide extensive validation to GIANT and demonstrate its neuroanatomical validity, confirming its generalizability across populations with diverse genetic ancestries and various brain conditions. Furthermore, we present a comprehensive genetic architecture of the GIANT regions, covering their functional annotation at the molecular levels, their associations with other complex traits/diseases, and the genetic and phenotypic correlations among GIANT-defined imaging endophenotypes. In summary, GIANT constitutes a brain atlas that captures the complexity of genetic and neuroanatomical heterogeneity, thereby enhancing the discovery power and applicability of imaging genomics investigations in biomedical science.

Alzheimer's disease (AD) is a complex neurodegenerative disorder with an unclear pathogenesis; the traditional ″single gene-single target-single drug″ strategy is insufficient for effective treatment. This study explores a novel strategy for the multitarget therapy of AD by integrating multiomics data and employing network analysis. Different from conventional single-target methods, TCnet adopts a mechanism-driven strategy, utilizing multiomics data to decompose disease mechanisms, construct potential target combinations, and prioritize the optimal combinations using a scoring function. TCnet not only advances our understanding of disease mechanisms but also facilitates large-scale drug screening. This approach was further employed to screen active compounds from Huang-Lian-Jie-Du-Tang (HLJDT), identifying quercetin as a candidate targeting GSK3β and ADAM17. Subsequent in vitro experiments confirmed the neuroprotective and anti-inflammatory effects of quercetin. Overall, TCnet offers a promising approach for predicting target combinations and provides new insights and directions for drug discovery in AD.

The rapid evolution of high-throughput sequencing technologies has revolutionized biomedical research, producing vast amounts of gene expression data that hold immense potential for biological discovery and clinical applications. Effectively mining these large-scale, high-dimensional data is crucial for facilitating disease detection, subtype differentiation, and understanding the molecular mechanisms underlying disease progression. However, the conventional paradigm of single-gene profiling, measuring absolute expression levels of individual genes, faces critical limitations in clinical implementation. These include vulnerability to batch effects and platform-dependent normalization requirements. In contrast, emerging approaches analyzing relative expression relationships between gene pairs demonstrate unique advantages. By focusing on binary comparisons of two genes' expression magnitudes, these methods inherently normalize experimental variations while capturing biologically stable interaction patterns. In this review, we systematically evaluate gene pair-based analytical frameworks. We classify eleven computational approaches into two fundamental categories: expression value-based methods quantifying differential expression patterns, and rank-based methods exploiting transcriptional ordering relationships. To bridge methodological development with practical implementation, we establish a reproducible analytical pipeline incorporating feature selection, classifier construction, and model evaluation modules using real-world benchmark datasets from pulmonary tuberculosis studies. These findings position gene pair analysis as a transformative paradigm for mining high-dimensional omics data, with direct implications for precision biomarker discovery and mechanistic studies of disease progression.

Deoxyribonucleic acid (DNA) serves as fundamental genetic blueprint that governs development, functioning, growth, and reproduction of all living organisms. DNA can be altered through germline and somatic mutations. Germline mutations underlie hereditary conditions, while somatic mutations can be induced by various factors including environmental influences, chemicals, lifestyle choices, and errors in DNA replication and repair mechanisms which can lead to cancer. DNA sequence analysis plays a pivotal role in uncovering the intricate information embedded within an organism's genetic blueprint and understanding the factors that can modify it. This analysis helps in early detection of genetic diseases and the design of targeted therapies. Traditional wet-lab experimental DNA sequence analysis through traditional wet-lab experimental methods is costly, time-consuming, and prone to errors. To accelerate large-scale DNA sequence analysis, researchers are developing AI applications that complement wet-lab experimental methods. These AI approaches can help generate hypotheses, prioritize experiments, and interpret results by identifying patterns in large genomic datasets. Effective integration of AI methods with experimental validation requires scientists to understand both fields. Considering the need of a comprehensive literature that bridges the gap between both fields, contributions of this paper are manifold: It presents diverse range of DNA sequence analysis tasks and AI methodologies. It equips AI researchers with essential biological knowledge of 44 distinct DNA sequence analysis tasks and aligns these tasks with 3 distinct AI-paradigms, namely, classification, regression, and clustering. It streamlines the integration of AI into DNA sequence analysis tasks by consolidating information of 36 diverse biological databases that can be used to develop benchmark datasets for 44 different DNA sequence analysis tasks. To ensure performance comparisons between new and existing AI predictors, it provides insights into 140 benchmark datasets related to 44 distinct DNA sequence analysis tasks. It presents word embeddings and language models applications across 44 distinct DNA sequence analysis tasks. It streamlines the development of new predictors by providing a comprehensive survey of 39 word embeddings and 67 language models based predictive pipeline performance values as well as top performing traditional sequence encoding-based predictors and their performances across 44 DNA sequence analysis tasks.

Precision medicine is a transformative healthcare model that utilizes an understanding of a person's genome, environment, lifestyle, and interplay to deliver customized healthcare. Precision medicine has the potential to improve the health and productivity of the population, enhance patient trust and satisfaction in healthcare, and accrue health cost-benefits both at an individual and population level. Through faster and cost-effective genomics data, next-generation sequencing has provided us the impetus to understand the nuances of complex interactions between genes, diet, and lifestyle that are heterogeneous across the population. The emergence of multiomics technologies, including transcriptomics, proteomics, epigenomics, metabolomics, and microbiomics, has enhanced the knowledge necessary for maximizing the applicability of genomics data for better health outcomes. Integrative multiomics, the combination of multiple 'omics' data layered over each other, including the interconnections and interactions between them, helps us understand human health and disease better than any of them separately. Integration of these multiomics data is possible today with the phenomenal advancements in bioinformatics, data sciences, and artificial intelligence. Our review presents a broad perspective on the utility and feasibility of a genomics-first approach layered with other omics data, offering a practical model for adopting an integrated multiomics approach in pediatric health care and research. IMPACT: Precision medicine provides a paradigm shift from a conventional, reactive disease control approach to proactive disease prevention and health preservation. Phenomenal advancements in bioinformatics, data sciences, and artificial intelligence have made integrative multiomics feasible and help us understand human health and disease better than any of them separately. The genotype-first approach or reverse phenotyping has the potential to overcome the limitations of the phenotype-first approach by identifying new genotype-phenotype associations, enhancing the subclassification of diseases by widening the phenotypic spectrum of genetic variants, and understanding functional mechanisms of genetic variations.

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in the digestive tract, characterized by a high recurrence rate and inadequate immunotherapy options. We analyzed mutation data of ESCC from public databases and employed 10 machine learning algorithms to generate 101 algorithm combinations. Based on the optimal range determined by the concordance index, we randomly selected one combination from the best-performing algorithms to construct a prognostic model consisting of five genes (DLX5, MAGEA4, PMEPA1, RCN1, and TIMP1). By validating the correlation between the prognostic model and antigen-presenting cells (APCs), we revealed the antigen-presentation efficacy of the model. Through the analysis of immune infiltration in ESCC, we uncovered the mechanisms of immune evasion associated with the disease. In addition, we examined the potential impact of the five prognostic genes on ESCC progression. Based on these insights, we identified anti-tumor small-molecule compounds targeting these prognostic genes. This study primarily simulates the tumor microenvironment (TME) and antigen presentation processes in ESCC patients, predicting the role of the neoantigen-based prognostic model in ESCC patients and their potential responses to immunotherapy. These results suggest a potential approach for identifying therapeutic targets in ESCC, which may contribute to the development of more effective treatment strategies.

CRISPR-associated endoribonucleases (Cas RNases) cleave single-stranded RNA in a highly sequence-specific manner by recognizing and binding to short RNA sequences known as direct repeats (DRs). Here, we investigate the potential of exploiting Cas RNases for the regulation of target genes with one or more DRs introduced into the 3' untranslated region, an approach we refer to as DREDGE (direct repeat-enabled downregulation of gene expression). The DNase-dead version of Cas12a (dCas12a) was identified as the most efficient among five different Cas RNases tested and was subsequently evaluated in doxycycline-regulatable systems targeting either stably expressed fluorescent proteins or an endogenous gene. DREDGE performed superbly in stable cell lines, resulting in up to 90% downregulation with rapid onset, notably in a fully reversible and highly selective manner. Successful control of an endogenous gene with DREDGE was demonstrated in two formats, including one wherein both the DR and the transgene driving expression of dCas12a were introduced in one step by CRISPR-Cas. Our results establish DREDGE as an effective method for regulating gene expression in a targeted, highly selective, and fully reversible manner, with several advantages over existing technologies.

Drug-induced liver injury (DILI) poses critical challenges in preclinical drug development and is a primary reason for candidate drug attrition. The incidence of DILI has risen in recent years. While immune-related genes (IRGs) are crucial in immune infiltration, their expression and regulatory mechanisms in tolvaptan-induced DILI remain largely uncharacterized. RNA sequencing data related to DILI and associated clinical data were sourced from the Gene Expression Omnibus (GEO), and IRGs were obtained from the ImmPort database. Differentially expressed genes (DEGs) from DILI and IRGs were intersected to identify differentially expressed immune-related genes (DEIRGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to elucidate the biological functions of DEIRGs. In addition, a protein-protein interaction (PPI) network of DEIRGs was constructed. Immunocytes and immune regulation analyses were conducted using the CIBERSORT tool. Receiver operating characteristic (ROC) curves were constructed to assess the diagnostic accuracy of individual DEIRGs. Networks of transcription factor and microRNA co-regulation were constructed using the NetworkAnalyst database. The expression of DEIRGs in DILI samples was quantified with RT-qPCR. From GSE99878, 204 DEGs were identified, with 23 matching IRGs exhibiting significant expression differences in 17 DEIRGs. The ROC curve analysis suggested satisfactory diagnostic values for six DEIRGs. The potential gene regulatory network comprised 214 microRNAs, 257 transcription factors, and 23 DEIRGs. Finally, RT-qPCR confirmed the expression levels of nine DEIRGs, aligning with public database results. The study revealed numerous immune-related biomarkers, verifying expression in five pivotal genes (ICAM1, CXCL10, IGF1, CX3CL1, and EGFR) and highlighting four genes with notable diagnostic potential (TNFAIP3, BDNF, NR1D2, and PPARA). Additionally, it explored the roles of key biomarkers in inflammatory responses, relevant signaling pathways, and interaction networks, offering new insights into DILI diagnosis, mechanistic understanding, and treatment strategies.

Schizophrenia (SCZ) is a severe mental disorder with complex etiology. Research shows propionate metabolism is crucial for neurological function and health. This suggests abnormalities in propionate metabolism may link to SCZ. Therefore, identifying biomarkers associated with propionate metabolism might be beneficial for the diagnosis and treatment of SCZ patients.

SCZ datasets and propionate metabolism-related genes (PMRGs) from public databases were obtained. DE-PMRGs were identified through differential and correlation analysis of PMRGs. Machine learning was used to screen for key genes and validate expression levels, aiming to identify potential biomarkers. Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis were performed on the biomarkers. An upstream regulatory network was constructed, and potential drugs targeting these biomarkers were explored. Finally, real-time fluorescence quantitative PCR (qPCR) was used to verify biomarker expression levels.

A total of 11 DE-PMRGs were identified, and machine learning technology was employed to further screen for 5 key genes. Among these, LY96 and TMEM123 emerged as potential biomarkers through expression verification. A diagnostic model was developed, achieving an area under the curve (AUC) greater than 0.7, which indicates strong diagnostic performance. Additionally, nomograms based on these biomarkers demonstrated promising predictive capabilities in assessing the risk of SCZ. To explore gene functions and regulatory mechanisms at a deeper level, a competitive endogenous RNA (ceRNA) regulatory network was constructed, including 2 biomarkers, 72 microRNAs, and 202 long non-coding RNAs. In addition, a regulatory network containing 2 biomarkers and 104 transcription factors (TFs) was also established to investigate the transcription factors interacting with the biomarkers. Potential biomarker-targeted drugs were identified by exploring the DrugBank database; notably, LY96 exhibited higher binding affinities for four drugs, with docking scores consistently below-5 kcal/mol. The qPCR results indicated that the expression levels of LY96 and TMEM123 in the whole blood of SCZ patients were significantly higher than those in the healthy control group, which was consistent with the results in the GSE38484 and GSE27383 datasets.

This study identified disease diagnostic biomarkers associated with propionate metabolism in SCZ, specifically LY96 and TMEM123. These findings offer novel perspectives for the diagnosis and management of SCZ.

Curcumol, a bioactive sesquiterpenoid extracted from traditional Chinese medicine (TCM), has demonstrated potential in overcoming tumor drug resistance. However, its mechanisms in reversing drug resistance, particularly in hepatocellular carcinoma (HCC) resistant to sorafenib, are not yet fully elucidated. This study aims to explore the molecular mechanisms by which curcumol reverses sorafenib resistance in HCC using a combination of network pharmacology, molecular docking, and in vivo and in vitro experiments.

We identified curcumol targets and genes associated with sorafenib-resistant HCC, resulting in a set of overlapping targets. These intersection targets underwent enrichment analysis using DAVID, and a protein-protein interaction (PPI) network was constructed via the STRING database and Cytoscape. Molecular docking confirmed the binding of curcumol to core targets. In vitro assays, including CCK-8, colony formation assay, apoptosis detection, wound healing, and Transwell assays, evaluated curcumol's effects on sorafenib-resistant HCC cells. Western blotting assessed the impact on PI3K/AKT and JAK/STAT3 signaling pathways. Additionally, a sorafenib-resistant HCC xenograft mouse model was established to observe the in vivo efficacy of curcumol combined with sorafenib.

We identified 117 potential targets for curcumol in reversing sorafenib resistance in HCC. Among them, five core targets-ALB, STAT3, HSP90AA1, HSP90AB1, and SRC-showed strong binding affinity with curcumol. KEGG pathway analysis of the intersecting genes highlighted significant involvement of the PI3K/AKT, JAK/STAT3, Ras, Rap1, HIF-1, FoxO, and mTOR signaling pathways. In vitro experiments revealed that curcumol significantly enhanced the sensitivity of sorafenib-resistant HCC cells to sorafenib, inhibiting cell proliferation, invasion, and migration while promoting apoptosis. In vivo studies further confirmed that curcumol combined with sorafenib effectively inhibited tumor growth in sorafenib-resistant HCC models.

This study provides compelling evidence that curcumol can reverse sorafenib resistance in HCC by modulating multiple signaling pathways, including PI3K/AKT and JAK/STAT3 pathways. Our findings suggest that curcumol holds promise as a novel therapeutic agent for overcoming drug resistance in HCC, offering a new avenue for clinical intervention.

Early detection and intervention of precancerous lesions are crucial in reducing cancer morbidity and mortality. Comprehensive analysis of genomic, transcriptomic, proteomic and epigenomic alterations can provide insights into the early stages of carcinogenesis. However, the lacke of an integrated, well-curated data resource of molecular signatures limits our understanding of precancerous processes. Here, we introduce a comprehensive PreCancerous Molecular Resource (PCMR), which compiles 25,828 molecular profiles of precancerous samples paired with normal or malignant counterparts. These profiles cover precancerous lesions of 35 cancer types across 20 organs and tissues, derived from tissue samples, liquid biopsies, cell lines and organoids, with data from transcriptomics, proteomics and epigenomics. PCMR includes 62,566 precancer-gene associations derived from differential analysis and text-mining using the ChatGPT large language model. We examined PCMR dataset reliability and significance by the authoritative precancerous molecular signature, along with its biological and clinical relevance. Overall, PCMR will serve as a valuable resource for advancing precancer research and ultimately improving patient outcomes.

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

DEK is a chromatin protein that interacts with DNA to influence chromatin formation, thereby affecting plant growth, development, and stress response. This study investigates the molecular evolution of the DEK family in plants, with a particular focus on the Brassica species. A total of 127 DEK genes were identified in 34 plants and classified into seven groups based on the phylogenetic analysis. The distribution of motifs and gene structure is similar within each group, indicating a high degree of conservation. The results of the collinearity analysis indicated that the DEK protein has undergone a certain degree of evolutionary conservation. The expansion of the DEK family is primarily attributable to whole-genome duplication (WGD) or segmental duplication events. The DEK protein has undergone purification during its evolutionary history, and several positively selected sites have been identified. Moreover, the examination of cis-acting elements and expression patterns revealed that the BnDEKs play a significant role in plant growth and stress response. The protein-protein interaction network identified several noteworthy proteins that interact with DEK. These analyses enhance our comprehension of the DEK gene family and establish the foundation for additional validation of its function. Further research demonstrated that the overexpression of one DEK family member, BnaA02g08940D, enhanced the transgenic Arabidopsis tolerance to drought and osmosis. This indicates that the DEK family may respond when plants are subjected to drought stress, thereby strengthening the plant's resilience.

Huntington's disease (HD) is characterized by progressive motor dysfunction and cognitive decline. Early diagnosis and new therapeutic targets are essential for effective interventions. We performed integrative analyses of mRNA profiles from three microarrays and one RNA-seq dataset from the Gene Expression Omnibus database. The datasets included were GSE8762, GSE24250, GSE45516, and GSE64810. Data pre-processing included background correction, normalization, log2 transformation, probe-to-gene symbol mapping, and differential expression analysis. We identified 80 differentially expressed genes (DEGs) based on a significance threshold (p < 0.05) and absolute log fold change (logFC) >0.65. Additionally, we conducted Gene Ontology (GO) and pathway analyses of the identified genes. Protein-protein interactions among DEGs revealed a network from which seven hub genes (VIM, COL1A1, COL3A1, COL1A2, DCN, CXCR2, and S100A9) were identified using the cytoHubba plugin in Cytoscape software. Two top DEGs, IGHG1 (up-regulated) and PITX1 (up-regulated), also hold potential as therapeutic targets. Insofar as biological contextualization of the findings is concerned, the top enriched GO terms were skeletal system development, blood vessel development, and vasculature development. Molecular function terms highlighted signaling receptor binding, extracellular matrix structural constituent, and platelet-derived growth factor binding. Notably, the significant KEGG pathways included amoebiasis, the AGE-RAGE signaling pathway in diabetic complications, and the relaxin signaling pathway. In conclusion, the present computational biology integrative analyses of multiple datasets discovered new DEGs and seven hub genes, shedding light on molecular mechanisms of HD. These findings call for translational clinical omics research and may potentially lead to future precision medicine interventions and novel diagnostic biomarkers and therapeutic targets.

The Playbook Workflow Builder (PWB) is a web-based platform to dynamically construct and execute bioinformatics workflows by utilizing a growing network of input datasets, semantically annotated API endpoints, and data visualization tools contributed by an ecosystem of collaborators. Via a user-friendly user interface, workflows can be constructed from contributed building-blocks without technical expertise. The output of each step of the workflow is added into reports containing textual descriptions, figures, tables, and references. To construct workflows, users can click on cards that represent each step in a workflow, or construct workflows via a chat interface that is assisted by a large language model (LLM). Completed workflows are compatible with Common Workflow Language (CWL) and can be published as research publications, slideshows, and posters. To demonstrate how the PWB generates meaningful hypotheses that draw knowledge from across multiple resources, we present several use cases. For example, one of these use cases prioritizes drug targets for individual cancer patients using data from the NIH Common Fund programs GTEx, LINCS, Metabolomics, GlyGen, and ExRNA. The workflows created with PWB can be repurposed to tackle similar use cases using different inputs. The PWB platform is available from: https://playbook-workflow-builder.cloud/.

Fanconi anemia (FA) is a genetic disorder that occurs when certain genes responsible for repairing DNA replication and promoting homologous recombination fail to function properly. This leads to severe clinical symptoms and a wide range of cancer-related characteristics. Recent treatment approaches for FA involve hematopoietic stem cell transplantation (HSCT), which helps restore the population of stem cells. A survival study using p-values indicated that specific hub genes play a significant role in diagnosing and predicting the disease. To find potential medications that interact with the identified hub genes, researchers inferred drugs. Among hub genes, TP53 was found to be particularly promising through computational analysis. Further investigation focused on two drugs, Topiramate and Tocofersolan predicted based on drug bank database analysis. Molecular docking strategies were employed to assess the best binding pose of these drugs with TP53. Topiramate showed a binding affinity of -6.5 kcal/mol, while Tocofersolan showed -8.5 kcal/mol against the active residues within the binding pocket. Molecular dynamics (MD) simulations were conducted to observe the stability of each drug's interaction with the TP53 protein over time. Both drugs exhibited stable confirmation with only slight changes in the loop region of the TP53 protein during the simulation intervals. Results also shows that there was a high fluctuation observed during apo-sate simulation time intervals as compared to complex system. Hence, it is suggested that the exploration of structure-based drug design holds promising results to specific target. This could potentially lead to a breakthrough in future experimental approaches for FA treatment.

Large-scale gene-expression data are being leveraged to pretrain models that implicitly learn gene and cellular functions. However, such models require extensive data curation and training. Here we explore a much simpler alternative: leveraging ChatGPT embeddings of genes based on the literature. We used GPT-3.5 to generate gene embeddings from text descriptions of individual genes and to then generate single-cell embeddings by averaging the gene embeddings weighted by each gene's expression level. We also created a sentence embedding for each cell by using only the gene names ordered by their expression level. On many downstream tasks used to evaluate pretrained single-cell embedding models-particularly, tasks of gene-property and cell-type classifications-our model, which we named GenePT, achieved comparable or better performance than models pretrained from gene-expression profiles of millions of cells. GenePT shows that large-language-model embeddings of the literature provide a simple and effective path to encoding single-cell biological knowledge.

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive and functional decline. Its incidence increases significantly with age and is more prevalent in women than men. We investigated the folate receptor alpha (FOLR1) gene expression levels in the central nervous system (CNS) of AD and non-demented healthy control (NDHC) subjects. Our cohort included 3,946 samples: 2,391 NDHC and 1,555 AD patients, stratified by brain region, age, and sex. Interestingly, a significant increase in FOLR1 expression was observed only in females with AD compared to NDHC females. Furthermore, we found that FOLR1 expression was differentially increased in the prefrontal cortex (PFC) and diencephalon (DIE) only in AD females. Moreover, in females, genes involved in the folic acid (FA) cycle that drives DNA synthesis were significantly modulated. In contrast, in males, downregulation of TYMS effectively blocks the completion of the cycle, thereby preventing downstream DNA synthesis. Tissue Transcriptome Deconvolution (TTD) analysis revealed astrocytes and endothelial cells associated with FOLR1 expression in both AD males and females. Gene Ontology analysis supported these findings, showing enrichment in processes aligned with these cell types. Positive correlations between brain FOLR1 expression and markers for astrocytes (glial fibrillary acidic protein) and endothelial cells (CD31) provided further validation. Our findings suggest a potential role for sex-dependent FOLR1 expression and its association with specific brain regions and cellular processes in AD.