The discovery of novel drugs from the ocean is a relatively recent development. However, for most studies of marine drugs, the priority is the discovery of compounds with new structures. Normally, only small amounts (< 1 mg) of new compounds could be extracted from marine microbes, and it is difficult to evaluate the therapeutic potentials of these newly-identified marine-derived natural compounds by traditional cell- or animal-based phenotypic screenings. Genes play a crucial role in determining the phenotype of diseases and the action of drugs. By comparing genomic expression associated with disease conditions and compound treatments, it is possible to predict the potential of a certain compound to counteract a specific type of disease. In this study, marine-derived natural compounds-induced genomic changes in cells were collected either from public databases or by using RNA-seq analysis. The therapeutic potentials of representative marine-derived natural compounds, namely phycocyanobilin, cycloheximide and NBU-1, a newly-identified natural compound extracted from a marine sponge-associated Streptomyces, on bipolar disorder (BD), Parkinson's disease (PD) and Alzheimer's disease (AD), were predicted by gene set enrichment analysis (GSEA), respectively. The anti-neurological disorder activity of these marine-derived natural compounds were further validated in methamphetamine-induced rats mimicking manic phase of BD, 6-OHDA-treated PC12 cells mimicking PD neurotoxicity and β-amyloid oligomer-incubated SH-SY5Y cells mimicking AD neuronal loss. Our study provides not only new insights for pharmacological applications of the marine-derived natural compounds here studied, but also a method for predicting and evaluating therapeutic potentials of newly-identified marine-derived natural compounds with small quantities.

Previous studies have shown significant sex differences in AD with regarding its epidemiology, pathophysiology, clinical presentation, and treatment response. However, the transcriptome variances associated with sex in AD remain unclear.

RNA sequencing (RNA-seq) and transcriptomic analyses were performed on peripheral blood samples from total of 54 patients, including male AD patients (n = 15), female AD patients (n = 10), male MCI patients (n = 7), female MCI patients (n = 11), male healthy controls (n = 6), female healthy controls (n = 5). The snRNA-seq dataset (GSE167494, GSE157827) of prefrontal cortex tissues was obtained from the Gene Expression Omnibus (GEO). We conducted an investigation into differentially expressed genes and pathways in the peripheral blood cells as well as prefrontal cortex tissues of both male and female AD patients with consideration to sex-related factors. Additionally, we analyzed the distribution and characteristics of cells in the cerebral cortex as well as the interaction and communication between cells of male and female AD patients. Connectivity Map (CMap) was utilized for predicting and screening potential sex-specific drugs for AD.

The transcriptome profile and associated biological processes in the peripheral blood of male and female AD and MCI patients exhibit discernible differences, including upregulation of BASP1 in AD male patients and arousing TNS1 in AD female patients. The distribution of various cell types in the prefrontal cortex tissues differs between male and female AD patients, like neuron and oligodendrocyte decreased and endothelial cell and astrocyte increased in female compared with male, while a multitude of genes exhibit significant differential expression. The results of cell communication analysis, such as collagen signaling pathway, suggest that sex disparities impact intercellular interactions within prefrontal cortex tissues among individuals with AD. By drug repositioning, several drugs, including torin-2 and YM-298198, might have the potential to therapeutic value of MCI or AD, while drugs like homoharringtonine and teniposide have potential opposite effects in different sexes.

The characteristics of the transcriptome in peripheral blood and single-cell transcriptome in the prefrontal cortex exhibit significant differences between male and female patients with AD, which providing a basis for future sex stratified treatment of AD.

TAM (TYRO3, AXL, MERTK) receptor tyrosine kinases (RTKs) have intrinsic roles in tumor cell proliferation, migration, chemoresistance, and suppression of antitumor immunity. The overexpression of TAM RTKs is associated with poor prognosis in various types of cancer. Single-target agents of TAM RTKs have limited efficacy because of an adaptive feedback mechanism resulting from the cooperation of TAM family members. This suggests that multiple targeting of members has the potential for a more potent anticancer effect. The present study used a deep-learning based drug-target interaction (DTI) prediction model called molecule transformer-DTI (MT-DTI) to identify commercially available drugs that may inhibit the three members of TAM RTKs. The results showed that fostamatinib, a spleen tyrosine kinase (Syk) inhibitor, could inhibit the three receptor kinases of the TAM family with an IC50 <1 µM. Notably, no other Syk inhibitors were predicted by the MT-DTI model. To verify this result, this study performed in vitro studies with various types of cancer cell lines. Consistent with the DTI results, this study observed that fostamatinib suppressed cell proliferation by inhibiting TAM RTKs, while other Syk inhibitors showed no inhibitory activity. These results suggest that fostamatinib could exhibit anticancer activity as a pan-TAM inhibitor. Taken together, these findings demonstrated that this artificial intelligence model could be effectively used for drug repurposing and repositioning. Furthermore, by identifying its novel mechanism of action, this study confirmed the potential for fostamatinib to expand its indications as a TAM inhibitor.

The combined analysis of dual diseases can provide new insights into pathogenic mechanisms, identify novel biomarkers, and develop targeted therapeutic strategies. Polycythemia vera (PV) is a chronic myeloproliferative neoplasm associated with a risk of acute myeloid leukemia (AML) transformation. However, the chronic nature of disease transformation complicates longitudinal high-throughput sequencing studies of patients with PV before and after AML transformation. This study aimed to develop a diagnostic model for malignant transformation of chronic proliferative diseases, addressing the challenges of early detection and intervention. Integrated public datasets of PV and AML were analyzed to identify differentially expressed genes (DEGs) and construct a weighted correlation network. Machine-learning algorithms screen genes for potential biomarkers, leading to the development of diagnostic models. Clinical specimens were collected to validate gene expression. cMAP and molecular docking predicted potential drugs. In vitro experiments were performed to assess drug efficacy in PV and AML cells. CIBERSORT and single-cell RNA-sequencing (scRNA-seq) analyses were used to explore the impact of hub genes on the tumor microenvironment. We identified 24 genes shared between PV and AML, which were enriched in immune-related pathways. Lactoferrin (LTF) and G protein-coupled receptor 65 (GPR65) were integrated into a nomogram with a robust predictive power. The predicted drug vemurafenib inhibited proliferation and increased apoptosis in PV and AML cells. TME analysis has linked these biomarkers to macrophages. Clinical samples were used to confirm LTF and GPR65 expression levels. We identified shared genes between PV and AML and developed a diagnostic nomogram that offers a novel avenue for the diagnosis and clinical management of AML-related PV.

Right ventricular (RV) maladaptive remodeling has been demonstrated to be more severe in males than in females under similar afterload, with androgen potentially involved. However, the mechanism remains unknown.

We performed RV proteomics and metabolomics in male and castrated rats with pulmonary artery banding (PAB) or sham surgery. The core pathway was tested in other sets of male, castrated male, and testosterone-replaced rats with and without pathway inhibitors administration and in RV remodeling patients. Metabolite verification was carried out by matching secondary spectra.

With the same extent of increases in RV afterload, male PAB rats exhibited more pronounced RV hypertrophy and fibrosis than castrated PAB rats (p < 0.05). The omics analysis indicated that pathways and functions related to oxidative stress were exhibited in the male group, with the platelet-derived growth factor (PDGF) pathway being among them. More proteins and metabolites associated with fatty acid metabolism were downregulated in males. Correlation analysis showed that PDGF receptor beta (PDGFRB) and signal transducer and activator of transcription 3 (STAT3) were negatively correlated with carnitine and reactive oxygen species scavenging metabolites only in male rats. The activation of the PDGF pathway was verified in testosterone-replaced PAB rats and male patients with RV remodeling. Treatments with PDGFRB inhibitor and STAT3 inhibitor could reverse RV maladaptive remodeling in male and testosterone-replaced PAB rats but not in castrated ones.

Androgen might exacerbate RV maladaptive remodeling via intensified oxidative stress and insufficient energy supply, with activating the PDGFRB-STAT3 signaling being one of the possible pathways.

Innovation in psychiatric therapeutics has stagnated on known mechanisms. Psychiatric genome-wide association studies (GWAS) have identified hundreds of genome-wide significant (GWS) loci that have rapidly advanced our understanding of disease etiology. However, whether these results can be leveraged to improve clinical treatment for specific psychiatric disorders remains poorly understood.

In this proof-of-principal evaluation of GWAS clinical utility, we test whether the targets of drugs used to treat Attention Deficit Hyperactivity Disorder (ADHD), Bipolar Disorder (BiP), Generalized Anxiety Disorder (GAD), Major Depressive Disorder (MDD), Post-Traumatic Stress Disorder (PTSD), Schizophrenia (SCZ), Substance Use Disorders (SUDs), and insomnia (INS), are enriched for GWAS meta-analysis findings.

The genes coding for treatment targets of medications used to SCZ, BiP, MDD, and SUDs (but not ADHD, PTSD, GAD, or INSOM) are enriched for GWS loci identified in their respective GWAS (ORs: 2.78-27.63; all ps <1.15e-3). Enrichment is largely driven by the presence of a GWS locus or loci within a gene coding for a drug target (i.e., proximity matching). Broadly, additional annotation (i.e., functional: Combined Annotation Dependent Depletion [CADD] scores, regulomeDB scores, eQTL, chromatin loop, and gene region; statistical: effect size of genome-wide significant SNPs; Z-score of SNPs; number of drug targets implicated by GWAS), with the exception of weighting by the largest SNP effect size, does not further improve enrichment across disorders. Evaluation of prior smaller GWAS reveal that more recent larger GWAS improve enrichment.

GWAS results may assist in the prioritization of medications for future psychopharmaceutical research.

The incidence of nonalcoholic steatohepatitis (NASH) is increasing worldwide, and effective treatment is urgently needed. To understand the molecular mechanisms behind the effectiveness of bariatric surgery in treating NASH, we integrated single-cell and bulk RNA sequencing data to identify the role of liver macrophage polarization in alleviating NASH and screen possible drugs for treatment. Analysis revealed that bariatric surgery alleviates NASH by inhibiting liver M1 macrophage polarization with 12 differentially expressed M1 macrophage-related genes. Additionally, 56 potentially effective drugs were predicted for NASH treatment. These findings shed light on the effectiveness of bariatric surgery in treating NASH and offer potential drug candidates for further exploration.

Solute carrier (SLC) transporters govern most of the chemical exchange across cellular membranes and are integral to metabolic regulation, which in turn is linked to cellular function and identity. Despite their key role, individual functions of the SLC superfamily members were not evaluated systematically. We determined the metabolic and transcriptional profiles upon SLC overexpression in knock-out or wild-type isogenic cell backgrounds for 378 SLCs and 441 SLCs, respectively. Targeted metabolomics provided a fingerprint of 189 intracellular metabolites, while transcriptomics offered insights into cellular programs modulated by SLC expression. Beyond the metabolic profiles of 102 SLCs directly related to their known substrates, we identified putative substrates or metabolic pathway connections for 71 SLCs without previously annotated bona fide substrates, including SLC45A4 as a new polyamine transporter. By comparing the molecular profiles, we identified functionally related SLC groups, including some with distinct impacts on osmolyte balancing and glycosylation. The assessment of functionally related human genes presented here may serve as a blueprint for other systematic studies and supports future investigations into the functional roles of SLCs.

Klebsiella pneumoniae (K. pneumoniae) has become a serious global health concern due to its rising virulence and antibiotic resistance. As one of the leading members of ESKAPE pathogens, it plays a major role in a wide range of infections that cause pneumonia, urinary tract infections, and bacteremia, especially in immunocompromised and hospitalized patients. The recent increase in multidrug-resistant (MDR) and hypervirulent (hvKP) strains due to the production of extended-spectrum beta-lactamases (ESBLs) and carbapenemases, has greatly limited therapeutic options that highlights the need for novel approaches to combat the pathogen. This review outlines the virulence mechanisms, profiles of antibiotic resistance, and immune evasion strategies in K. pneumoniae. Also, it points out the role of capsular polysaccharides, lipopolysaccharides, and fimbriae in host colonization and immune evasion. Additionally, the review discusses the emerging therapeutic strategies of vaccine development, computational drug discovery, and the use of artificial intelligence (AI). The progress achieved in reverse vaccinology and structural biology enables the identification of new drug and vaccine targets, whereas AI and machine learning (ML) stand out as powerful candidates for high-throughput screening and drug design. However, challenges with antigenic variability, safety, and the need to collaborate globally still exist. This review focuses on the need for interdisciplinary approaches involving molecular biology and immunology with computational sciences to address K. pneumoniae infections and provide appropriate therapies in the era of antibiotic resistance.

Long non-coding RNAs (lncRNAs) play a pivotal role in tumor prognostic models. This study targets cuproptosis-related lncRNAs in laryngeal squamous cell carcinoma (LSCC) using RNA-seq data from The Cancer Genome Atlas (TCGA)-LSCC. Differentially expressed genes were identified, and Pearson correlation analysis pinpointed cuproptosis-related lncRNAs. Combining clinical data, a prognostic risk model was constructed using LASSO Cox regression and Cox proportional hazards analyses. Nine lncRNAs (LINCO1473, SNHG12, AC007938.3, AC040970.1, AC023669.1, AL158166.2, GIHCG, AC007240.3, and AC011370.1) were identified, with GIHCG showing significant correlation with LSCC prognosis. GIHCG's competing endogenous RNAs (ceRNA) and co-expression networks (CEN) were established, revealing sensitivity to drugs like BMS-509744, YM155, and KU-55933. Silencing of GIHCG inhibited migration, invasion, EMT, and other biological processes in LSCC cells, suggesting GIHCG as a potential therapeutic target. Moreover, METTL16-mediated m6A methylation regulates GIHCG expression. In conclusion, this study successfully established a prognostic model comprising nine cuproptosis-related lncRNAs, accurately predicting LSCC prognosis, and highlighted the crucial role of GIHCG as a novel nucleic acid biomarker in regulating LSCC progression.

As part of the Library of Integrated Network-Based Cellular Signatures (LINCS) NIH initiative, 248 human cell lines were profiled with the L1000 assay to measure the effect of 33 621 small molecules and 7508 single-gene CRISPR knockouts. From this massive dataset, we computed 1.678 million sets of up- and down-regulated genes. These gene sets are served for search by the LINCS L1000 Signature Search (L2S2) web server application. With L2S2, users can identify small molecules and single gene CRISPR KOs that produce gene expression profiles similar or opposite to their submitted single or up/down gene sets. L2S2 also includes a consensus search feature that ranks perturbations across all cellular contexts, time points, and concentrations. To demonstrate the utility of L2S2, we crossed the L2S2 gene sets with gene sets collected for the RummaGEO resource. The analysis identified clusters of differentially expressed genes that match drug classes, tissues, and diseases, pointing to many opportunities for drug repurposing and drug discovery. Overall, the L2S2 web server application can be used to further the development of personalized therapeutics while expanding our understanding of complex human diseases. The L2S2 web server application is available at https://l2s2.maayanlab.cloud.

BackgroundDespite advances in therapeutics, hepatocellular carcinoma (HCC) remains one of the most malignant types of digestive tract cancers with a poor prognosis. Pyroptosis is a form of programmed cell death induced by inflammatory caspases. Recent studies have identified pyroptosis, a form of programmed cell death induced by inflammatory caspases, as playing a role in tumorigenesis and cancer progression. However, the functions and mechanisms of pyroptosis in HCC are barely explored.MethodsGene expression and clinical data were derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prognostic signature and nomogram were constructed by on differentially expressed genes and clinical data. Pathway enrichment and immune cell infiltration were further analyzed. Potential drugs to modulate the pathways were explored.ResultsIn this study, a pyroptosis-related gene signature was developed and identified to be significantly correlated with the survival of HCC patients. Additionally, a nomogram on the basis of pyroptosis-related genes was constructed with distinct prognostic values. Furthermore, the pyroptosis-related gene signature might correlate with immune-related pathways and the regulation of the immune microenvironment, and several compounds (KIN001-220, TPCA-1, LY-303511, physostigmine, vemurafenib, etc.) could potentially reverse the pathogenic gene-expression patterns.Conclusions: Our study provides evidence that pyroptosis is involved in HCC development, progression and immune microenvironment, which is promising in predicting the prognosis and developing targeted therapy.

Although kinesin family member 2C (KIF2C) is implicated in various cancers, its role in osteosarcoma (OS) and the associated inflammatory microenvironment remains unclear.

Publicly available datasets were analyzed to determine KIF2C expression, diagnostic value, and prognostic relevance in OS. In vitro (proliferation, colony formation, apoptosis, migration, invasion) and in vivo assays assessed its biological functions. KEGG enrichment and GSVA explored underlying pathways. ssGSEA, ESTIMATE algorithms, and single-cell sequencing evaluated the immune context, and molecular docking and molecular dynamics identified potential inhibitory compounds.

KIF2C was significantly overexpressed in OS, effectively distinguishing OS from normal tissues. Elevated KIF2C levels correlated with poor survival outcomes. Silencing KIF2C suppressed OS cell proliferation, migration, invasion, and in vivo tumor growth, while promoting apoptosis; conversely, overexpression of KIF2C had the opposite effect. Mechanistically, co-immunoprecipitation results indicated that KIF2C can bind to β-catenin to regulate the Wnt/β-catenin pathway. Furthermore, high KIF2C expression was associated with an immunosuppressive tumor microenvironment characterized by immune exhaustion. Molecular docking and molecular dynamics suggested butein as a candidate small-molecule inhibitor targeting KIF2C-related oncogenic mechanisms.

KIF2C drives OS progression by enhancing Wnt/β-catenin signaling and fostering an immunosuppressive microenvironment. Targeting KIF2C may offer new therapeutic approaches in managing OS.

Oral mucositis (OM) is a prevalent adverse effect of radiotherapy and chemotherapy, significantly impacting cancer patients' well-being and potentially increasing mortality rates. Understanding OM's pathogenesis and identifying effective preventative and therapeutic agents are clinically crucial.

This study analyzed RNA-Seq data from the GEO database, focusing on OM samples post-radiotherapy and chemotherapy. Differential gene expression analysis between OM and non-OM groups, followed by gene ontology (GO) enrichment analysis of differentially expressed genes (DEGs), was conducted. LASSO regression identified five potential biomarkers, and CIBERSORT assessed immune infiltration in OM samples. Correlations between biomarkers and immune infiltration were explored, and the connectivity map (CMAP) screened potential therapeutic drugs. The top 10 drugs were validated through molecular docking.

A total of 47 DEGs were identified, primarily involved in mitotic sister chromatid separation according to GO enrichment analysis. CIBERSORT analysis revealed significant changes in B cell naive and dendriform cells (DCs) resting content in the OM group. PRKAA2, encoding the AMP-activated protein kinase (AMPK) catalytic subunit, showed a negative correlation with DC resting content. Molecular docking from CMAP identified aloisine and teniposide as potential agents for OM induced by radiotherapy and chemotherapy.

AMPK emerges as a crucial regulator in OM post radiotherapy and chemotherapy, implicating sister chromatid separation, where DCs may play a pivotal role. Aloisine and Teniposide appear promising for OM prevention or treatment associated with these treatments.

Serrated colorectal cancer (SCC) is a rare and aggressive subtype of colorectal cancer. Identifying SCC is crucial due to its high mortality rate and limited therapeutic options. Traditional methods to identify BRAF hotspot mutations and MLH1 methylation are insufficient in clinical practice. This study aims to explore the WNT pathway alterations in the CRC and to develop a WNT-derived subtyping model to identify SCC patients by using multi-OMICs data.

We included multi-omics data of 1751 colorectal cancer patients from the TCGA and GEO databases, and single-cell transcriptome data of 33 normal and cancer tissues from the SMC study cohort. The comprehensive study process incorporated unsupervised clustering, enrichment analysis, and statistical analysis.

In this study, we investigated WNT pathway alterations in SCC by integrating both bulk and single-cell data into the multi-OMICs framework. The SCC subtype demonstrated significant WNT pathway heterogeneity and a more stable genomic structure. These findings support the development of a WNT-derived subtyping model that accurately identifies SCC patients across different CRC cohorts. In addition, the SCC subtype also presented a distinct immune microenvironment characterized by CD8+ T cell exhaustion. Finally, we utilized drug perturbation data to explore the potential drug targets for this severe cancer subtype.

We developed a WNT-derived subtyping method to identify SCC from canonical CRC, which enhances the molecular understanding of this severe cancer subtype and provides potential therapeutic strategies. Our findings suggest that SCC patients may benefit from the HSP90 inhibitor NVP-AUY922, highlighting its potential as a targeted therapy.

Accurately predicting cellular responses to perturbations is essential for understanding cell behaviour in both healthy and diseased states. While perturbation data is ideal for building such predictive models, its availability is considerably lower than baseline (non-perturbed) cellular data. To address this limitation, several foundation cell models have been developed using large-scale single-cell gene expression data. These models are fine-tuned after pre-training for specific tasks, such as predicting post-perturbation gene expression profiles, and are considered state-of-the-art for these problems. However, proper benchmarking of these models remains an unsolved challenge. In this study, we benchmarked two recently published foundation models, scGPT and scFoundation, against baseline models. Surprisingly, we found that even the simplest baseline model-taking the mean of training examples-outperformed scGPT and scFoundation. Furthermore, basic machine learning models that incorporate biologically meaningful features outperformed scGPT by a large margin. Additionally, we identified that the current Perturb-Seq benchmark datasets exhibit low perturbation-specific variance, making them suboptimal for evaluating such models. Our results highlight important limitations in current benchmarking approaches and provide insights into more effectively evaluating post-perturbation gene expression prediction models.

The anti-hepatocellular carcinoma (HCC) effect of the active ingredients of natural Chinese herbal medicine has become a hot topic at home and abroad. A list of studies evidence the therapeutic efficacy of Dahuang (DH) or Dangshen (DS) against HCC, but the combination effect of DH and DS in HCC treatment is rarely reported and the molecular mechanism of the drug pairs is not yet clear. Therefore, in this study, the combined effects and potential mechanisms of DH and DS drug pairs were investigated through network pharmacology, bioinformatics, molecular docking, and a series of pharmacological experiments, including the MTT assay, clone formation, wound healing, JC-1 staining, and western blotting. In total, 140 intersection targets between the DH-DS drug pairs and HCC were identified. In the PPI network, the top ten hub targets with the highest node connection values were VEGFA, AKT, CTNNB1, EGFR, TNF, CASP3, HRAS, SRC, JUN, and ESR1. GO functional and KEGG pathway enrichment analysis involved 289 biological processes, 33 cellular components, 57 molecular functions, and 143 signaling pathways. Bioinformatic analysis indicated that EGFR and AKT were promising candidate genes that can serve as diagnostic and prognostic biomarkers for HCC. β-sitosterol from the DH drug and luteolin from the DS drug were found as promising small molecules for HCC. The experimental results showed that the combination of β-sitosterol and luteolin was more potent in suppressing cell proliferation, migration and inducing cell apoptosis when compared to β-sitosterol or luteolin alone. The western blot and molecular docking studies demonstrated that the potential mechanism may be related to the EGFR/AKT signaling pathway. In summary, the combination of DH and DS may resist cell proliferation, migration and promote apoptosis activity through the EGFR/AKT signaling pathway, which provides new insights for further exploring plant extract treatment for HCC.

Glucocorticoids (GCs), commonly used for anti-inflammatory and cancer treatments, have been linked to the promotion of cancer metastasis. Yet, the molecular mechanisms behind this potential remain poorly understood. Clarifying these mechanisms is crucial for a nuanced understanding and potential refinement of GC therapies in the context of cancer treatment. In HEK293T cells, co-immunoprecipitation (Co-IP) and chromatin immunoprecipitation sequencing (ChIP-seq) were used with antibodies of glucocorticoid receptor (GR) and ten-eleven translocation enzymes (TET) family proteins (TET1, TET2, TET3). Drug repositioning was performed through the Connectivity Map database, using common target genes of GR and TET2 in HEK293 and HCT116 cell lines and differentially expressed genes (DEGs) of colorectal cancer (CRC). Cell migration and invasion were tested in CRC cell lines with varying GR expression, that is, HCT116 and HT29 cell lines. Dexamethasone (Dex) treatment resulted in a significant difference in cell migration rates in two CRC cell lines with disparate GR expression levels. Co-IP and ChIP-seq analyses substantiated the interaction between GR and TET family proteins in HEK293T cells. Belinostat, the selected compound, was successfully validated for its potential to counteract the effects of GC-induced invasion in CRC cells in vitro. Transcriptomic analyses of Belinostat-treated HCT116 cells revealed down-regulation of target genes associated with cancer metastasis. This study provides valuable insights into the molecular mechanisms underlying GC-induced metastasis, introducing newly repositioned compounds that could serve as potential adjuvant therapy to GC treatment. Furthermore, it opens avenues for exploring novel drug candidates for CRC treatment.

Cancer progression and therapeutic resistance are closely linked to a stemness phenotype. Here, we introduce a protein-expression-based stemness index (PROTsi) to evaluate oncogenic dedifferentiation in relation to histopathology, molecular features, and clinical outcomes. Utilizing datasets from the Clinical Proteomic Tumor Analysis Consortium across 11 tumor types, we validate PROTsi's effectiveness in accurately quantifying stem-like features. Through integration of PROTsi with multi-omics, including protein post-translational modifications, we identify molecular features associated with stemness and proteins that act as active nodes within transcriptional networks, driving tumor aggressiveness. Proteins highly correlated with stemness were identified as potential drug targets, both shared and tumor specific. These stemness-associated proteins demonstrate predictive value for clinical outcomes, as confirmed by immunohistochemistry in multiple samples. The findings emphasize PROTsi's efficacy as a valuable tool for selecting predictive protein targets, a crucial step in customizing anti-cancer therapy and advancing the clinical development of cures for cancer patients.

Artificial intelligence (AI) is increasingly being utilized in cancer research as a computational strategy for analyzing multiomics datasets. Advances in single-cell and spatial profiling technologies have contributed significantly to our understanding of tumor biology, and AI methodologies are now being applied to accelerate translational efforts, including target discovery, biomarker identification, patient stratification, and therapeutic response prediction. Despite these advancements, the integration of AI into clinical workflows remains limited, presenting both challenges and opportunities. This review discusses AI applications in multiomics analysis and translational oncology, emphasizing their role in advancing biological discoveries and informing clinical decision-making. Key areas of focus include cellular heterogeneity, tumor microenvironment interactions, and AI-aided diagnostics. Challenges such as reproducibility, interpretability of AI models, and clinical integration are explored, with attention to strategies for addressing these hurdles. Together, these developments underscore the potential of AI and multiomics to enhance precision oncology and contribute to advancements in cancer care.

Lithium is prescribed as a mood stabilizer for bipolar disorder and severe depression. However, the mechanism of action of lithium is unknown and there are major side effects associated with prolonged medication. This motivates a search for safer alternative drug repurposing candidates. Given that the drug mechanism may be encoded in transcriptional changes, we generated the gene expression profile for acute lithium treatment of cortical neuronal cultures. We found that the lithium-associated transcription response harbors a significant component that is the reverse of that seen in human brain samples from patients with major depression, bipolar disorder, and a mouse model of depression. Interrogating publicly available drug-driven expression data, we found that cardiotonic steroids drive gene expression in a correlated manner to our acute lithium profile. An analysis of the psychiatric medication cohort of the Norwegian Prescription Database showed that cardiotonic prescription is associated with a lower incidence of lithium prescription. Our transcriptional and epidemiological observations point towards cardiotonic steroids as possible repurposing candidates for lithium. These observations motivate a controlled trial to establish a causal connection and genuine therapeutic benefit in the context of depression.

Neurons are susceptible to oxidative stress due to the elevated reactive oxygen species (ROS) production and the limited antioxidant defense mechanisms. Therefore, it is possible to treat oxidative stress-related neurological disorders via the inhibition of oxidative stress. Chryxanthone A is an extracted substance derived from the endophytic fungal Aspergillus versicolor, with an atypical dihydropyran ring. However, it is unknown whether and how chryxanthone A could produce anti-oxidant protection.

The activity and mechanisms underlying the anti-oxidant protection of chryxanthone A were explored in the study.

HT22 neuronal cells were used to evaluate the anti-oxidant protection of chryxanthone A. Comprehensive bioinformatic methods, including RNA-seq analysis, transcription factor prediction, CMap prediction and molecular docking analysis, were utilized to explore the molecular mechanisms how chryxanthone A prevented oxidative stress, which was confirmed by Western blotting analysis.

Chryxanthone A concentration-dependently prevented H2O2-induced cell death and increase in intracellular ROS in HT22 cells. Results from RNA-seq and bioinformatic analysis indicated that chryxanthone A might act on mTOR/CREB axis, possibly via binding to the Val2227 site within ATP binding pocket of mTOR. The action of chryxanthone A on H2O2-induced alteration of mTOR/CREB axis were further confirmed in HT22 cells.

These results suggested that chryxanthone A produced anti-oxidant protection via the action on mTOR/CREB axis, providing a support that chryxanthone A might be developed as a novel drug candidate for the treatment of oxidative stress-related disorders.

Lysyl oxidases (LOX/LOXL1-4) are crucial for cancer progression, yet their transcriptional regulation, potential therapeutic targeting, prognostic value and involvement in immune regulation remain poorly understood. This study comprehensively evaluates LOX/LOXL expression in cancer and highlights cancer types where targeting these enzymes and developing LOX/LOXL-based prognostic models could have significant clinical relevance.

We assessed the association of LOX/LOXL expression with survival and drug sensitivity via analyzing public datasets (including bulk and single-cell RNA sequencing data of six datasets from Gene Expression Omnibus (GEO), Chinese Glioma Genome Atlas (CGGA) and Cancer Genome Atlas Program (TCGA)). We performed comprehensive machine learning-based bioinformatics analyses, including unsupervised consensus clustering, a total of 10 machine-learning algorithms for prognostic prediction and the Connectivity map tool for drug sensitivity prediction.

The clinical significance of the LOX/LOXL family was evaluated across 33 cancer types. Overexpression of LOX/LOXL showed a strong correlation with tumor progression and poor survival, particularly in glioma. Therefore, we developed a novel prognostic model for glioma by integrating LOX/LOXL expression and its co-expressed genes. This model was highly predictive for overall survival in glioma patients, indicating significant clinical utility in prognostic assessment. Furthermore, our analysis uncovered a distinct LOXL2-overexpressing malignant cell population in recurrent glioma, characterized by activation of collagen, laminin, and semaphorin-3 pathways, along with enhanced epithelial-mesenchymal transition. Apart from glioma, our data revealed the role of LOXL3 overexpression in macrophages and in predicting the response to immune checkpoint blockade in bladder and renal cancers. Given the pro-tumor role of LOX/LOXL genes in most analyzed cancers, we identified potential therapeutic compounds, such as the VEGFR inhibitor cediranib, to target pan-LOX/LOXL overexpression in cancer.

Our study provides novel insights into the potential value of LOX/LOXL in cancer pathogenesis and treatment, and particularly its prognostic significance in glioma.

Disuse osteoporosis (DOP) poses a significant health risk during extended space missions. Although the importance of long non-coding RNA (lncRNA) in bone marrow mesenchymal stem cells (BMSCs) and orthopedic diseases is recognized, the precise mechanism by which lncRNAs contribute to DOP remains elusive. This research aims to elucidate the potential regulatory role of lncRNAs in DOP.

Sequencing data were obtained from Gene Expression Omnibus (GEO) datasets, including coding and non-coding RNAs. Positive co-expression pairs of lncRNA-mRNA were identified using weighted gene co-expression network analysis, while miRNA-mRNA expression pairs were derived from the prediction database. A mRNA-miRNA-lncRNA network was established according to the shared mRNA. Functional enrichment analysis was conducted for the shared mRNAs using genome ontology and KEGG pathways. Hub genes were identified through protein-protein interaction analysis, and connectivity map analysis was employed to identify potential therapeutic agents for DOP.

Integration of 74 lncRNAs, 19 miRNAs, and 200 mRNAs yielded a comprehensive mRNA-miRNA-lncRNA network. Enrichment analysis highlighted endoplasmic reticulum stress and extracellular matrix (ECM) pathways as significant in the ceRNA network. PPI analysis revealed three hub genes (COL4A1, LAMC1, and LAMA4) and identified five lncRNA-miRNA-hub gene regulatory axes. Furthermore, three potential therapeutic compounds (SB-216763, oxymetholone, and flubendazole) for DOP were identified.

This study sheds light on the involvement of lncRNAs in the pathogenesis and treatment of DOP through the construction of a ceRNA network, linking protein-coding mRNA functions with non-coding RNAs.

C-type lectin domain family 12 member A (CLEC12A) is a type II transmembrane glycoprotein widely expressed in innate immune cells, where it plays a crucial role in immune modulation and has been implicated in cancer progression. However, its precise function in oncogenesis and immune infiltration remains incompletely understood. To investigate this, we utilized multiple databases to assess the mRNA and protein expression levels of CLEC12A across normal tissues and a broad spectrum of cancers. We also evaluated its prognostic and diagnostic significance in pan-cancer contexts. Furthermore, the relationship between CLEC12A expression and immune cell infiltration, immune checkpoints, and immune predictors was explored. In addition, Weighted Gene Co-Expression Network Analysis (WGCNA) and differential expression analysis were performed to examine the biological relevance of CLEC12A in lung adenocarcinoma (LUAD). We also leveraged various databases to predict CLEC12A's response to immunotherapy and drug sensitivity. Finally, in vitro experiments validated the functional role of CLEC12A in LUAD. Our comprehensive pan-cancer analysis revealed that CLEC12A exhibited distinct expression patterns across different cancer types, suggesting its potential as both a diagnostic and prognostic biomarker. Notably, CLEC12A expression was strongly correlated with immune cell infiltration, immune checkpoints, and immune predictors. Functional enrichment analysis highlighted that increased CLEC12A expression in LUAD was associated with a variety of immune-related biological processes and pathways. Moreover, CLEC12A showed significant predictive value for immunotherapy outcomes, and several drugs targeting CLEC12A were identified. In vitro experiments further demonstrated that CLEC12A overexpression inhibited the proliferation, migration, and invasion of LUAD cells. Taken together, our findings position CLEC12A as a promising candidate for cancer detection, prognosis, and as a therapeutic target, particularly in LUAD, where it may serve as a potential target for both immunotherapy and targeted therapy.

Protein Tyrosine Phosphatase Non-Receptor Type 6 (PTPN6) plays a crucial regulatory role in cellular processes and has been implicated in oncogenesis. This pan-cancer analysis aimed to elucidate PTPN6's involvement across various cancer types, with a particular emphasis on its association with tumor immunity. We analyzed PTPN6 expression data from open access databases using various statistical techniques, including survival analysis, genetic heterogeneity analysis, immune profiling, single-cell analysis, drug sensitivity analysis, and protein interaction analysis. We also conducted in vitro experiments utilizing colorectal cancer cell lines to validate PTPN6's functional role. PTPN6 exhibited distinct expression patterns across cancers, and its prognostic significance was apparent in several cancer types, particularly in glioblastoma, sarcoma, and melanoma. We observed correlations between PTPN6 and immune genes/cell infiltration in these cancers, suggesting a potential role in modulating the tumor immune microenvironment. Single-cell analysis revealed that PTPN6 is predominantly localized in macrophages, B cells, and dendritic cells within the tumor microenvironment, implying its involvement in regulating immune cell function. Enrichment analysis highlighted PTPN6's role in immune-related pathways. Drug sensitivity analysis identified specific drugs, including PAC-1, SNX-2112, BELINOSTAT, VORINOSTAT, TPCA-1, and PHA-893,888, whose efficacy may be influenced by PTPN6 expression. Knocking down PTPN6 expression inhibited the proliferation and migration of colorectal cancer cells in vitro, confirming its oncogenic role in this cancer type. This pan-cancer analysis establishes PTPN6's multifaceted influence on tumor immunity and its potential as a biomarker and therapeutic target.

Keloid is a benign skin tumor that result from abnormal wound healing and excessive collagen deposition. The pathogenesis is believed to be linked to genetic predisposition and immune imbalance, although the precise mechanisms remain poorly understood. Current therapeutic approaches may not consistently yield satisfactory outcomes and are often accompanied by potential side effects and risks. The high recurrence rate and refractory nature of keloid nodules present significant challenges and uncertainties in their management. Given the lack of effective treatment strategies, it is essential to identify key molecular pathways and potential therapeutic targets for keloid.

This study aimed to identify the potential pathogenic mechanisms, hub genes, and immune cell involvement in keloid formation, with the goal of providing novel insights for targeted therapies.

We utilized a combination of bulk RNA sequencing to analyze gene expression profiles in keloid tissues. Differentially expressed genes (DEGs) were identified and subjected to pathway enrichment analysis to reveal key biological processes involved in keloid pathogenesis. Mendelian randomization was performed to investigate the causal relationship between genetic factors and keloid formation, identifying potential hub genes. Immune infiltration analysis was conducted to determine the role of specific immune cells in keloid development. Subsequently, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed to investigate the functional pathways associated with the hub genes. Network analysis was employed to identify transcription factors, miRNAs, and potential drugs in the Connectivity Map associated with the hub genes. Single-cell RNA sequencing was also used to identify cell-specific expression patterns of these genes.

Pathway enrichment analysis highlighted the association of keloid pathogenesis with cell proliferation and division, providing insights into the molecular processes involved. Mendelian randomization revealed that DUSP1 acts as an inhibitor of keloid formation, while HOXA5 promotes keloid pathogenesis. Immune infiltration analysis suggested that mast cells and macrophages play critical roles in the disease's progression. Based on hub gene analysis, the IL17 signaling pathway emerged as a key pathway implicated in keloid development. Further drug prediction models identified 9-methyl-5H-6-thia-4, 5-diaza-chrysene-6, 6-dioxide, zebularine, temozolomide and valproic acid targeting these hub genes.

DUSP1 and HOXA5 are hub genes in keloid pathogenesis, with DUSP1 acting as an inhibitor and HOXA5 as a promoter of disease progression. Targeting the regulatory networks associated with these genes could provide novel therapeutic strategies. Mast cells and macrophages are identified as critical immune cell types involved in the disease process. Additionally, the IL17 signaling pathway plays a crucial role in keloid development, highlighting its potential as a therapeutic target. These findings suggest that a multi-target approach focusing on these pathways could offer effective treatment options for keloid patients.

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality, while the hepatocyte mechanisms driving oncogenesis remains poorly understood. In this study, single-nucleus RNA sequencing of samples from 22 HCC patients revealed 10 distinct hepatocyte subtypes, including beneficial Hep0, predominantly malignant Hep2, and immunosuppressive Hep9. These subtypes were strongly associated with patient prognosis, confirmed in TCGA-LIHC and Fudan HCC cohorts through hepatocyte composition deconvolution. A quantile-based scoring method is developed to integrate data from 29 public HCC datasets, creating a Quantile Distribution Model (QDM) with excellent diagnostic accuracy (Area Under the Curve, AUC = 0.968-0.982). QDM was employed to screen potential biomarkers, revealing that PDE7B functions as a key gene whose suppression promotes HCC progression. Guided by the genes specific to Hep0/2/9 subtypes, HCC is categorized into metabolic, inflammatory, and matrix classes, which are distinguishable in gene mutation frequencies, survival times, enriched pathways, and immune infiltration. Meanwhile, the sensitive drugs of the three HCC classes are identified, namely ouabain, teniposide, and TG-101348. This study presents the largest single-cell hepatocyte dataset to date, offering transformative insights into hepatocarcinogenesis and a comprehensive framework for advancing HCC diagnostics, prognostics, and personalized treatment strategies.

Gene mutations are responsible for a sizeable proportion of cases of heart failure. However, the number of patients with any specific mutation is small. Repositioning of existing US Food and Drug Administration-approved compounds to target specific mutations is a promising approach to efficient identification of new therapies for these patients.

The National Institutes of Health Library of Integrated Network-Based Cellular Signatures database was interrogated to identify US Food and Drug Administration-approved compounds that demonstrated the ability to reverse the transcriptional effects of LMNA knockdown. Top hits from this screening were validated in vitro with patient-specific induced pluripotent stem cell-derived cardiomyocytes combined with force measurement, gene expression profiling, electrophysiology, and protein expression analysis.

Several angiotensin receptor blockers were identified from our in silico screen. Of these, olmesartan significantly elevated the expression of sarcomeric genes and rate and force of contraction and ameliorated arrhythmogenic potential. In addition, olmesartan exhibited the ability to reduce phosphorylation of extracellular signal-regulated kinase 1 in LMNA-mutant induced pluripotent stem cell-derived cardiomyocytes.

In silico screening followed by in vitro validation with induced pluripotent stem cell-derived models can be an efficient approach to identifying repositionable therapies for monogenic cardiomyopathies.

Anaplastic lymphoma kinase (ALK; also known as ALK tyrosine kinase receptor) inhibitors (ALKi) are effective in treating lung cancer patients with chromosomal rearrangement of ALK. However, continuous treatment with ALKis invariably leads to acquired resistance in cancer cells. In this study, we propose an efficient strategy to suppress ALKi resistance through a meta-analysis of transcriptome data from various cell models of acquired resistance to ALKis. We systematically identified gene signatures that consistently showed altered expression during the development of resistance and conducted computational drug screening using these signatures. We identified emetine as a promising candidate compound to inhibit the growth of ALKi-resistant cells. We demonstrated that emetine exhibited effectiveness in inhibiting the growth of ALKi-resistant cells, and further interpreted its impact on the resistant signatures through drug-induced RNA-sequencing data. Our transcriptome-guided systematic approach paves the way for efficient drug discovery to overcome acquired resistance to cancer therapy.

We sought to identify new candidate drugs for repurposing to myelodysplastic syndromes (MDS). Connectivity map analysis was performed on gene expression signatures generated from bone marrow CD34+ cells of splicing factor mutant MDS patients. Celastrol and Withaferin A (WA), two top-ranking compounds identified, markedly inhibited proliferation, arrested the cell cycle and induced apoptosis in leukaemia cells. These compounds also inhibited the viability of primary bone marrow MDS cells. We showed that Celastrol and WA inhibit interleukin-1 receptor-associated kinase 4-mediated nuclear factor kappa-light-chain-enhancer of activated B cells signalling activation in splicing factor mutant MDS and leukaemia cells. Celastrol and WA may represent novel candidate drugs for the treatment of MDS.

Identifying treatments that can alter the natural history of amyotrophic lateral sclerosis (ALS) is challenging. For years, drug discovery in ALS has relied upon traditional approaches with limited success. Drug repurposing, where clinically approved drugs are reevaluated for other indications, offers an alternative strategy that overcomes some of the challenges associated with de novo drug discovery.

In this review, the authors discuss the challenge of drug discovery in ALS and examine the potential of drug repurposing for the identification of new effective treatments. The authors consider a range of approaches, from screening in experimental models to computational approaches, and outline some general principles for preclinical and clinical research to help bridge the translational gap. Literature was reviewed from original publications, press releases and clinical trials.

Despite remaining challenges, drug repurposing offers the opportunity to improve therapeutic options for ALS patients. Nevertheless, stringent preclinical research will be necessary to identify the most promising compounds together with innovative experimental medicine studies to bridge the translational gap. The authors further highlight the importance of combining expertise across academia, industry and wider stakeholders, which will be key in the successful delivery of repurposed therapies to the clinic.

Atopic dermatitis (ad) is a chronic inflammatory skin disease, with recent studies indicating that immune cells, such as monocytes and inflammatory cytokines, play a crucial role. By retrieving datasets from public databases and analysing immune cell infiltration in lesional skin using CIBERSORT, we found that monocytes and M2 macrophages were significantly upregulated in atopic dermatitis. Differentially expressed gene (DEG) functional enrichment analysis revealed that cytokine-cytokine receptor interaction was the most significantly enriched pathway. Further analysis of cytokines and their receptors, along with their correlation with infiltrating immune cells, identified IL36G-expressing monocytes as a key target in atopic dermatitis. We compared immune cell infiltration and cytokine-related targets in similar inflammatory skin diseases, such as psoriasis and urticaria, to evaluate similarities and differences among these three skin conditions. The analysis revealed that IL36G-expressing monocytes were also highly expressed in psoriasis but did not play a pivotal role in urticaria. Finally, we used molecular docking to predict and validate drugs targeting IL36G. Our study highlights IL36G-expressing monocytes as a common key target in atopic dermatitis and psoriasis, offering novel insights and therapeutic strategies for these related diseases.