The Connectivity Map: a new tool for biomedical research

Exploring the molecular mechanisms of asthma across multiple datasets

BACKGROUND

Asthma, a prevalent chronic respiratory disorder, remains enigmatic, notwithstanding considerable advancements in our comprehension. Continuous efforts are crucial for discovering novel molecular targets and gaining a comprehensive understanding of its pathogenesis.

MATERIALS AND METHODS

In this study, we analyzed gene expression data from 212 individuals, including asthma patients and healthy controls, to identify 267 differentially expressed genes, among which C1orf64 and C7orf26 emerged as potential key genes in asthma pathogenesis. Various bioinformatics tools, including differential gene expression analysis, pathway enrichment, drug target prediction, and single-cell analysis, were employed to explore the potential roles of the genes.

RESULTS

Quantitative PCR demonstrated differential expression of C1orf64 and C7orf26 in the asthmatic airway epithelial tissue, implying their potential involvement in asthma pathogenesis. GSEA enrichment analysis revealed significant enrichment of these genes in signaling pathways associated with asthma progression, such as ABC transporters, cell cycle, CAMs, DNA replication, and the Notch signaling pathway. Drug target prediction, based on upregulated and downregulated differential expression, highlighted potential asthma treatments, including Tyrphostin-AG-126, Cephalin, Verrucarin-a, and Emetine. The selection of these drugs was based on their significance in the analysis and their established anti-inflammatory and antiviral invasion properties. Utilizing Seurat and Celldex packages for single-cell sequencing analysis unveiled disease-specific gene expression patterns and cell types. Expression of C1orf64 and C7orf26 in T cells, NK cells, and B cells, instrumental in promoting hallmark features of asthma, was observed, suggesting their potential influence on asthma development and progression.

CONCLUSION

This study uncovers novel genetic aspects of asthma, highlighting potential therapeutic pathways. It exemplifies the power of integrative bioinformatics in decoding complex disease patterns. However, these findings require further validation, and the precise roles of C1orf64 and C7orf26 in asthma warrant additional investigation to validate their therapeutic potential.

Pharmacogenomic screening identifies and repurposes leucovorin and dyclonine as pro-oligodendrogenic compounds in brain repair

Oligodendrocytes are critical for CNS myelin formation and are involved in preterm-birth brain injury (PBI) and multiple sclerosis (MS), both of which lack effective treatments. We present a pharmacogenomic approach that identifies compounds with potent pro-oligodendrogenic activity, selected through a scoring strategy (OligoScore) based on their modulation of oligodendrogenic and (re)myelination-related transcriptional programs. Through in vitro neural and oligodendrocyte progenitor cell (OPC) cultures, ex vivo cerebellar explants, and in vivo mouse models of PBI and MS, we identify FDA-approved leucovorin and dyclonine as promising candidates. In a neonatal chronic hypoxia mouse model mimicking PBI, both compounds promote neural progenitor cell proliferation and oligodendroglial fate acquisition, with leucovorin further enhancing differentiation. In an adult MS model of focal de/remyelination, they improve lesion repair by promoting OPC differentiation while preserving the OPC pool. Additionally, they shift microglia from a pro-inflammatory to a pro-regenerative profile and enhance myelin debris clearance. These findings support the repurposing of leucovorin and dyclonine for clinical trials targeting myelin disorders, offering potential therapeutic avenues for PBI and MS.

SCUBE2 regulates adherens junction dynamics and vascular barrier function during inflammation

AIMS

SCUBE2 (signal peptide-CUB-epidermal growth factor-like domain-containing protein 2) is a secreted or membrane-bound protein originally identified from endothelial cells (ECs). Our previous work showed that SCUBE2 forms a complex with E-cadherin and stabilizes epithelial adherens junctions (AJs) to promote epithelial phenotypes. However, it remains unclear whether SCUBE2 also interacts with vascular endothelial (VE)-cadherin and modulates EC barrier function. In this study, we investigated whether and how SCUBE2 in ECs regulates vascular barrier maintenance.

METHODS AND RESULTS

We showed that SCUBE2 colocalized and interacted with VE-cadherin and VE-protein tyrosine phosphatase (VE-PTP) within EC AJs. Furthermore, SCUBE2 knockdown disrupted EC AJs and increased EC permeability. Expression of EC SCUBE2 was suppressed at both mRNA and protein levels via the nuclear factor-κB signalling pathway in response to pro-inflammatory cytokines or permeability-inducing agents. In line with these findings, EC-specific deletion of Scube2 (EC-KO) in mice impaired baseline barrier function and worsened vascular leakiness of peripheral capillaries after local injection of histamine or vascular endothelial growth factor. EC-KO mice were also sensitive to pulmonary vascular hyperpermeability and leucocyte infiltration in response to acute endotoxin- or influenza virus-induced systemic inflammation. Meanwhile, EC-specific SCUBE2-overexpressing mice were protected from these effects. Molecular studies suggested that SCUBE2 acts as a scaffold molecule enabling VE-PTP to dephosphorylate VE-cadherin, which prevents VE-cadherin internalization and stabilizes EC AJs. As such, loss of SCUBE2 resulted in hyperphosphorylation of VE-cadherin at tyrosine 685, which led to its endocytosis, thus destabilizing EC AJs and reducing barrier function. All of these effects were exacerbated by inflammatory insults.

CONCLUSION

We found that SCUBE2 contributes to vascular integrity by recruiting VE-PTP to dephosphorylate VE-cadherin and stabilize AJs, thereby promoting EC barrier function. Moreover, our data suggest that genetic overexpression or pharmacological up-regulation of SCUBE2 may help to prevent vascular leakage and oedema in inflammatory diseases.

Unlocking the potential of signature-based drug repurposing for anticancer drug discovery

Cancer is the leading cause of death worldwide and is often associated with tumor relapse even after chemotherapeutics. This reveals malignancy is a complex process, and high-throughput omics strategies in recent years have contributed significantly in decoding the molecular mechanisms of these complex events in cancer. Further, the omics studies yield a large volume of cancer-specific molecular signatures that promote the discovery of cancer therapy drugs by a method termed signature-based drug repurposing. The drug repurposing method identifies new uses for approved drugs beyond their intended initial therapeutic use, and there are several approaches to it. In this review, we discuss signature-based drug repurposing in cancer, how cancer omics have revolutionized this method of drug discovery, and how one can use the cancer signature data for repurposed drug identification by providing a step-by-step procedural handout. This modern approach maximizes the use of existing therapeutic agents for cancer therapy or combination therapy to overcome chemotherapeutics resistance, making it a pragmatic and efficient alternative to traditional resource-intensive and time-consuming methods.

Identifying the HIV-Resistance-Related Factors and Regulatory Network via Multi-Omics Analyses

For research on HIV/AIDS, it is important to elucidate the complex viral-host interaction, host dependency factors (HDFs), and restriction factors. However, the regulatory network of HIV-resistance-related factors remains not well understood. Therefore, we integrated four publicly available HIV-related transcriptome datasets, along with three datasets on HIV-infection-related DNA methylation, miRNA, and ChIP-seq, to predict the factors influencing HIV resistance and infection. Our approach involved differential analysis, functional annotation, and protein-protein interaction network analysis. Through comprehensive analyses, we identified 25 potential HIV-resistance-related genes (including shared EGF) and 24 HIV-infection-related hub genes (including shared JUN). Additionally, we pinpointed five key differentially methylated genes, five crucial differentially expressed microRNAs, and five significant pathways associated with HIV resistance. We mapped the potential regulatory pathways involving these HIV-resistance-related factors. Among the predicted factors, RHOA, RAD51, GATA1, IRF4, and CXCL8 have been validated as HDFs or restriction factors. The identified factors, such as JUN, EGF, and PLEK, are potential HDFs or restriction factors. This study uncovers the gene signatures and regulatory networks associated with HIV-1 resistance, suggesting potential targets for the development of new therapies against HIV/AIDS.

ZSTK474 targeting PIK3R3 inhibits the Wilms' tumor through G0 / G1 phase arrest

PURPOSE

Wilms' tumor (WT), also known as nephroblastoma, is the predominant form of primary malignant renal cancer. The unfavorable prognoses linked to anaplastic nephroblastoma and recurrent nephroblastoma emphasize the crucial requirement for the exploration of innovative treatment modalities for WT.

METHODS

Our study conducted one-way Cox regression and Kaplan-Meier analyses using TARGET-WT nephroblastoma data to identify differentially expressed genes in nephroblastoma and evaluate their prognostic relevance. Utilizing the Connectivity Map database, ZSTK474 emerged as a viable therapeutic option for WT. The effect of ZSTK474 on WT and related underlying mechanisms were further investigated through in vitro and in vivo investigations.

RESULTS

The in vivo experiment results indicated that ZSTK474 effectively inhibited subcutaneous tumor growth in WT mice. CCK-8 assays revealed two nephroblastoma cell lines exhibited half-inhibitory concentrations of 2μM and 2.51μM for ZSTK474, respectively. ZSTK474 was shown to inhibit the migration and invasion capabilities of WT cells in both Transwell and wound healing assays. Flow cytometry apoptosis and TUNEL assays demonstrated that ZSTK474 induced apoptosis in WT cells. Cell cycle analysis revealed that ZSTK474 led to the induction of G0/G1 phase arrest. Sequencing of ZSTK474-treated WiT49 cells suggested that the impact of ZSTK474 on WT might be mediated by the PI3K/Akt pathway, specifically by inhibiting PIK3R3. Knock-down of PIK3R3 confirmed that ZSTK474 downregulated PIK3R3, reducing Akt phosphorylation, cyclin D and CDK4 levels and elevating P21 expression in nephroblastoma cells. However, current research has limitations, including a lack of understanding of the long-term effects and potential resistance mechanisms of new therapies.

CONCLUSION

This research provides insight into the potential of ZSTK474 and other PI3K inhibitors for treating nephroblastoma.

LncRNA-mRNA co-expression analysis reveals aquaporin-9-promoted neutrophil extracellular trap formation and inflammatory activation in sepsis

Sepsis is a life-threatening condition caused by an excessive inflammatory response to an infection. However, the precise regulatory mechanism of sepsis remains unclear. Using a strand-specific RNA-sequencing, we identified 115 hub differentially expressed long noncoding RNAs (lncRNAs) and 443 mRNAs in septic patients, primarily participated in crucial pathways including neutrophil extracellular trap (NET) formation and toll-like receptor signaling. Notably, NETs related gene aquaporin-9 (AQP9) and its associated lncRNAs exhibited significant upregulation in septic neutrophils. Functional experiments revealed AQP9 interacts with its lncRNAs to augment the formation of neutrophil NETs. In murine sepsis models, AQP9 inhibition with phloretin reduced proinflammatory cytokine production and lung damage. These findings provide crucial insights into the regulatory role of AQP9 in sepsis, unraveling its interaction with associated lncRNAs in transmitting downstream signals, holding promise in informing the development of novel therapeutic strategies aimed at ameliorating the debilitating effects of sepsis.

Molecular Indicator for Distinguishing Multi-drug-Resistant Tuberculosis from Drug Sensitivity Tuberculosis and Potential Medications for Treatment

The issue of multi-drug-resistant tuberculosis (MDR-TB) presents a substantial challenge to global public health. Regrettably, the diagnosis of drug-resistant tuberculosis (DR-TB) frequently necessitates an extended period or more extensive laboratory resources. The swift identification of MDR-TB poses a particularly challenging endeavor. To identify the biomarkers indicative of multi-drug resistance, we conducted a screening of the GSE147689 dataset for differentially expressed genes (DEGs) and subsequently conducted a gene enrichment analysis. Our analysis identified a total of 117 DEGs, concentrated in pathways related to the immune response. Three machine learning methods, namely random forest, decision tree, and support vector machine recursive feature elimination (SVM-RFE), were implemented to identify the top 10 genes according to their feature importance scores. A4GALT and S1PR1, which were identified as common genes among the three methods, were selected as potential molecular markers for distinguishing between MDR-TB and drug-susceptible tuberculosis (DS-TB). These markers were subsequently validated using the GSE147690 dataset. The findings suggested that A4GALT exhibited area under the curve (AUC) values of 0.8571 and 0.7121 in the training and test datasets, respectively, for distinguishing between MDR-TB and DS-TB. S1PR1 demonstrated AUC values of 0.8163 and 0.5404 in the training and test datasets, respectively. When A4GALT and S1PR1 were combined, the AUC values in the training and test datasets were 0.881 and 0.7551, respectively. The relationship between hub genes and 28 immune cells infiltrating MDR-TB was investigated using single sample gene enrichment analysis (ssGSEA). The findings indicated that MDR-TB samples exhibited a higher proportion of type 1 T helper cells and a lower proportion of activated dendritic cells in contrast to DS-TB samples. A negative correlation was observed between A4GALT and type 1 T helper cells, whereas a positive correlation was found with activated dendritic cells. S1PR1 exhibited a positive correlation with type 1 T helper cells and a negative correlation with activated dendritic cells. Furthermore, our study utilized connectivity map analysis to identify nine potential medications, including verapamil, for treating MDR-TB. In conclusion, our research identified two molecular indicators for the differentiation between MDR-TB and DS-TB and identified a total of nine potential medications for MDR-TB.

Comprehensive analysis of MAPK genes in the prognosis, immune characteristics, and drug treatment of renal clear cell carcinoma using bioinformatic analysis and Mendelian randomization

Mitogen-activated protein kinase (MAPK) signalling is vitally important in tumour development and progression. This study is the first to comprehensively analyse the role of MAPK-family genes in the progression, prognosis, immune-cell infiltration, methylation, and potential therapeutic value drug candidates in ccRCC. We identified a novel prognostic panel of six MAPK-signature genes (MAP3K12, MAP3K1, MAP3K5, MAPK1, MAPK8, MAPK9), and introduced a robust MAPK-signature risk model for predicting ccRCC prognosis. Model construction, evaluation, and external validation using datasets from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database demonstrated its stability, as well as high sensitivity and specificity. Enrichment analysis suggested the participation of immune-mediated mechanism in MAPK dysregulation in ccRCC. Immune-infiltration analysis confirmed the relationship and revealed that the MAPK-signature risk model might stratify immunotherapy response in ccRCC, which was verified in drug sensitivity analysis and validated in external ccRCC immunotherapy dataset (GSE67501). Potential therapeutic drug predictions for key MAPKs using DSigDB, Network Analyst, CTD, and DGIdb were subsequently verified by molecular docking with AutoDock Vina and PyMol. Mendelian randomization further demonstrated the possibilities of the MAPK-signature genes as targets for therapeutic drugs in ccRCC. Methylation analysis using UALCAN and MethSurv revealed the participation of epigenetic modifications in dysregulation and survival difference of MAPK pathway in ccRCC. Among the key MAPKs, MAP3K12 exhibited the highest significance, indicating its independent prognostic value as single gene in ccRCC. Knockout and overexpression validation experiments in vitro and in vivo found that MAP3K12 acted as a promoter of tumour progression in RCC, suggesting a pivotal role for MAP3K12 in the proliferation, migration, and invasion of RCC cells. Our findings proposed the potential of MAPK-signature genes as biomarkers for prognosis and therapy response, as well as targets for therapeutic drugs in ccRCC.

Structural analysis of genomic and proteomic signatures reveal dynamic expression of intrinsically disordered regions in breast cancer

Structural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here, we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We analyzed gene expression data from 23 breast cancer patients and genetic mutation data from the Catalog of Somatic Mutations In Cancer database as well as 17 breast tumor protein expression profiles. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.

Pan‑cancer analysis on the role of KMT2C expression in tumor progression and immunotherapy

Histone lysine N-methyltransferase 2C (KMT2C) is involved in transcriptional regulation and DNA damage repair. Mutations in KMT2C have been implicated in the progression, metastasis, and drug resistance of multiple cancer types. However, the roles of KMT2C in the regulation of tumor prognosis, immune cell infiltration and the immune microenvironment in these multiple cancer types remain unclear. Therefore, in the present study, data from The Cancer Genome Atlas and Genotype-Tissue Expression databases were used for KMT2C expression analyses. Kaplan-Meier and univariate Cox regression analyses were also performed to investigate the prognostic role of KMT2C. In addition, Gene Set Enrichment Analysis (GSEA) was conducted to study the KMT2C-related signaling pathways. Tumor immune estimation resource 2 and single-sample GSEA were conducted to investigate the correlation between KMT2C expression and immune cell infiltrations, and Spearman's analysis was conducted to study the correlations among KMT2C, tumor mutational burden, microsatellite instability, immune regulators, chemokines and immune receptors. Immunohistochemistry of patient kidney tumor samples was performed to verify the correlation between KMT2C and programmed death-ligand 1 (PD-L1) expression. Finally, RNA interference, wound healing and colony formation assays were conducted to evaluate the effects of KMT2C expression on cell proliferation and metastasis. The results of the present study demonstrated that KMT2C was highly expressed in multiple cancer types, was a protective factor in kidney renal clear cell carcinoma and ovarian serous cystadenocarcinoma, and a risk factor for lung squamous cell carcinoma and uveal melanoma. In addition, KMT2C levels were negatively correlated with immune-activated pathways and the infiltration of immune cells, and positively correlated with inhibitory immune factors and tumor angiogenesis. Patients with low KMT2C expression had higher objective response rates to immunotherapy, and drug sensitivity analysis indicated that topoisomerase, histone deacetylase, DOT1-like histone H3K79 methyltransferase and G9A nuclear histone lysine methyltransferase inhibitors could potentially be used to treat tumors with high KMT2C expression levels. Finally, the KMT2C and PD-L1 expression levels were shown to be positively correlated, and KMT2C knockdown markedly promoted the proliferation and invasion capacities of A549 cells. In conclusion, the present study revealed that low KMT2C expression may be a promising biomarker for predicting the response of patients with cancer to immunotherapy. Conversely, high KMT2C expression was shown to promote tumor angiogenesis, which may contribute to the formation of the immunosuppressive tumor microenvironment.

DDSBC: A Stacking Ensemble Classifier-Based Approach for Breast Cancer Drug-Pair Cell Synergy Prediction

Breast cancer (BC) ranks as a leading cause of mortality among women worldwide, with incidence rates continuing to rise. The quest for effective treatments has led to the adoption of drug combination therapy, aiming to enhance drug efficacy. However, identifying synergistic drug combinations remains a daunting challenge due to the myriad of potential drug pairs. Current research leverages machine learning (ML) and deep learning (DL) models for drug-pair synergy prediction and classification. Nevertheless, these models often underperform on specific cancer types, including BC, as they are trained on data spanning various cancers without any specialization. Here, we introduce a stacking ensemble classifier, the drug-drug synergy for breast cancer (DDSBC), tailored explicitly for BC drug-pair cell synergy classification. Unlike existing models that generalize across cancer types, DDSBC is exclusively developed for BC, offering a more focused approach. Our comparative analysis against classical ML methods as well as DL models developed for drug synergy prediction highlights DDSBC's superior performance across test and independent datasets on BC data. Despite certain metrics where other methods narrowly surpass DDSBC by 1-2%, DDSBC consistently emerges as the top-ranked model, showcasing significant differences in scoring metrics and robust performance in ablation studies. DDSBC's performance and practicality position it as a preferred choice or an adjunctive validation tool for identifying synergistic or antagonistic drug pairs in BC, providing valuable insights for treatment strategies.

Targeting treatment resistance: unveiling the potential of RNA methylation regulators and TG-101,209 in pan-cancer neoadjuvant therapy

BACKGROUND

Tumor recurrence and mortality rates remain challenging in cancer patients despite comprehensive treatment. Neoadjuvant chemotherapy and immunotherapy aim to eliminate residual tumor cells, reducing the risk of recurrence. However, drug resistance during neoadjuvant therapy is a significant hurdle. Recent studies suggest a correlation between RNA methylation regulators (RMRs) and response to neoadjuvant therapy.

METHODS

Using a multi-center approach, we integrated advanced techniques such as single-cell transcriptomics, whole-genome sequencing, RNA sequencing, proteomics, machine learning, and in vivo/in vitro experiments. Analyzing pan-cancer cohorts, the association between neoadjuvant chemotherapy/immunotherapy effectiveness and RNA methylation using single-cell sequencing was investigated. Multi-omics analysis and machine learning algorithms identified genomic variations, transcriptional dysregulation, and prognostic relevance of RMRs, revealing distinct molecular subtypes guiding pan-cancer neoadjuvant therapy stratification.

RESULTS

Our analysis unveiled a strong link between neoadjuvant therapy efficacy and RNA methylation dynamics, supported by pan-cancer single-cell sequencing data. Integration of omics data and machine learning algorithms identified RMR genomic variations, transcriptional dysregulation, and prognostic implications in pan-cancer. High-RMR-expressing tumors displayed increased genomic alterations, an immunosuppressive microenvironment, poorer prognosis, and resistance to neoadjuvant therapy. Molecular investigations and in vivo/in vitro experiments have substantiated that the JAK inhibitor TG-101,209 exerts notable effects on the immune microenvironment of tumors, rendering high-RMR-expressing pan-cancer tumors, particularly in pancreatic cancer, more susceptible to chemotherapy and immunotherapy.

CONCLUSIONS

This study emphasizes the pivotal role of RMRs in pan-cancer neoadjuvant therapy, serving as predictive biomarkers for monitoring the tumor microenvironment, patient prognosis, and therapeutic response. Distinct molecular subtypes of RMRs aid individualized stratification in neoadjuvant therapy. Combining TG-101,209 adjuvant therapy presents a promising strategy to enhance the sensitivity of high-RMR-expressing tumors to chemotherapy and immunotherapy. However, further validation studies are necessary to fully understand the clinical utility of RNA methylation regulators and their impact on patient outcomes.

A novel CAF-cancer cell crosstalk-related gene prognostic index based on machine learning: prognostic significance and prediction of therapeutic response in head and neck squamous cell carcinoma

BACKGROUND

Cancer-associated fibroblast (CAF)-cancer cell crosstalk (CCCT) plays an important role in tumor microenvironment shaping and immunotherapy response. Current prognostic indexes are insufficient to accurately assess immunotherapy response in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to develop a CCCT-related gene prognostic index (CCRGPI) for assessing the prognosis and response to immune checkpoint inhibitor (ICI) therapy of HNSCC patients.

METHODS

Two cellular models, the fibroblast-cancer cell indirect coculture (FCICC) model, and the fibroblast-cancer cell organoid (FC-organoid) model, were constructed to visualize the crosstalk between fibroblasts and cancer cells. Based on a HNSCC scRNA-seq dataset, the R package CellChat was used to perform cell communication analysis to identify gene pairs involved in CCCT. Least absolute shrinkage and selection operator (LASSO) regression was then applied to further refine the selection of these gene pairs. The selected gene pairs were subsequently subjected to stepwise regression to develop CCRGPI. We further performed a comprehensive analysis to determine the molecular and immune characteristics, and prognosis associated with ICI therapy in different CCRGPI subgroups. Finally, the connectivity map (CMap) analysis and molecular docking were used to screen potential therapeutic drugs.

RESULTS

FCICC and FC-organoid models showed that cancer cells promoted the activation of fibroblasts into CAFs, that CAFs enhanced the invasion of cancer cells, and that CCCT was somewhat heterogeneous. The CCRGPI was developed based on 4 gene pairs: IGF1-IGF1R, LGALS9-CD44, SEMA5A-PLXNA1, and TNXB-SDC1. Furthermore, a high CCRGPI score was identified as an adverse prognostic factor for overall survival (OS). Additionally, a high CCRGPI was positively correlated with the activation of the P53 pathway, a high TP53 mutation rate, and decreased benefit from ICI therapy but was inversely associated with the abundance of various immune cells, such as CD4+ T cells, CD8+ T cells, and B cells. Moreover, Ganetespib was identified as a potential drug for HNSCC combination therapy.

CONCLUSIONS

The CCRGPI is reliable for predicting the prognosis and immunotherapy response of HSNCC patients and may be useful for guiding the individualized treatment of HNSCC patients.

Discovering Potential in Non-Cancer Medications: A Promising Breakthrough for Multiple Myeloma Patients

MM is a common type of cancer that unfortunately leads to a significant number of deaths each year. The majority of the reported MM cases are detected in the advanced stages, posing significant challenges for treatment. Additionally, all MM patients eventually develop resistance or experience relapse; therefore, advances in treatment are needed. However, developing new anti-cancer drugs, especially for MM, requires significant financial investment and a lengthy development process. The study of drug repurposing involves exploring the potential of existing drugs for new therapeutic uses. This can significantly reduce both time and costs, which are typically a major concern for MM patients. The utilization of pre-existing non-cancer drugs for various myeloma treatments presents a highly efficient and cost-effective strategy, considering their prior preclinical and clinical development. The drugs have shown promising potential in targeting key pathways associated with MM progression and resistance. Thalidomide exemplifies the success that can be achieved through this strategy. This review delves into the current trends, the challenges faced by conventional therapies for MM, and the importance of repurposing drugs for MM. This review highlights a noncomprehensive list of conventional therapies that have potentially significant anti-myeloma properties and anti-neoplastic effects. Additionally, we offer valuable insights into the resources that can help streamline and accelerate drug repurposing efforts in the field of MM.

Comprehensive quantifications of tumour microenvironment to predict the responsiveness to immunotherapy and prognosis for paediatric neuroblastomas

Treatment strategies for paediatric neuroblastoma as well as many other cancers are limited by the unfavourable tumour microenvironment (TME). In this study, the TMEs of neuroblastoma were grouped by their genetic signatures into four distinct subtypes: immune enriched, immune desert, non-proliferative and fibrotic. An Immune Score and a Proliferation Score were constructed based on the molecular features of the subtypes to quantify the immune microenvironment or malignancy degree of cancer cells in neuroblastoma, respectively. The Immune Score correlated with a patient's response to immunotherapy; the Proliferation Score was an independent prognostic biomarker for neuroblastoma and proved to be more accurate than the existing clinical predictors. This double scoring system was further validated and the conserved molecular pattern associated with immune landscape and malignancy degree was confirmed. Axitinib and BI-2536 were confirmed as candidate drugs for neuroblastoma by the double scoring system. Both in vivo and in vitro experiments demonstrated that axitinib-induced pyroptosis of neuroblastoma cells activated anti-tumour immunity and inhibited tumour growth; BI-2536 induced cell cycle arrest at the S phase in neuroblastoma cells. The comprehensive double scoring system of neuroblastoma may predict prognosis and screen for therapeutic strategies which could provide personalized treatments.

Colorectal cancer subtyping and immune landscape analysis based on natural killer cell-related genes

BACKGROUND AND STUDY AIMS

The prognosis of colorectal cancer (CRC) is related to natural killer (NK) cells, but the molecular subtype features of CRC based on NK cells are still unknown. This study aimed to identify NK cell-related molecular subtypes of CRC and analyze the survival status and immune landscape of patients with different subtypes.

PATIENTS/MATERIAL AND METHODS

mRNA expression data, single nucleotide variant (SNV) data, and clinical information of CRC patients were obtained from The Cancer Genome Atlas. Differentially expressed genes (DEGs) were obtained through differential analysis, and the intersection was taken with NK cell-associated genes to obtain 103 NK cell-associated CRC DEGs (NCDEGs). Based on NCDEGs, CRC samples were divided into three clusters through unsupervised clustering analysis. Survival analysis, immune analysis, Gene Set Enrichment Analysis (GSEA), and tumor mutation burden (TMB) analysis were performed. Finally, NCDEG-related small-molecule drugs were screened using the CMap database.

RESULTS

Survival analysis revealed that cluster2 had a lower survival rate than cluster1 and cluster3 (p < 0.05). Immune infiltration analysis found that the immune infiltration levels and immune checkpoint expression levels of cluster1_3 were substantially higher than those of cluster2, and the tumor purity was the opposite (p < 0.05). GSEA presented that cluster1_3 was significantly enriched in the chemokine signaling pathway, ECM receptor interaction, and antigen processing and presentation pathways (p < 0.05). The TMB of cluster1_3 was significantly higher than that of cluster2 (p < 0.05). Genes with the highest mutation rate in CRC were APC, TP53, TTN, and KRAS. Drug prediction results showed that small-molecule drugs that reverse the upregulation of NCDEGs, deoxycholic acid, dipivefrine, phenformin, and other drugs may improve the prognosis of CRC.

CONCLUSION

NK cell-associated CRC subtypes can be used to evaluate the tumor characteristics of CRC patients and provide an important reference for CRC patients.

Identifying therapeutic candidates for endometriosis through a transcriptomics-based drug repositioning approach

Existing medical treatments for endometriosis-related pain are often ineffective, underscoring the need for new therapeutic strategies. In this study, we applied a computational drug repurposing pipeline to stratified and unstratified disease signatures based on endometrial gene expression data to identify potential therapeutics from existing drugs, based on expression reversal. Of 3,131 unique genes differentially expressed by at least one of six endometriosis signatures, only 308 (9.8%) were in common; however, 221 out of 299 drugs identified, (73.9%) were shared. We selected fenoprofen, an uncommonly prescribed NSAID that was the top therapeutic candidate for further investigation. When testing fenoprofen in an established rat model of endometriosis, fenoprofen successfully alleviated endometriosis-associated vaginal hyperalgesia, a surrogate marker for endometriosis-related pain. These findings validate fenoprofen as a therapeutic that could be utilized more frequently for endometriosis and suggest the utility of the aforementioned computational drug repurposing approach for endometriosis.

Predisposing deleterious variants in the cancer-associated human kinases in the global populations

Human kinases play essential and diverse roles in the cellular activities of maintaining homeostasis and growth. Genetic mutations in the genes encoding the kinases (or phosphotransferases) have been linked with various types of cancers. In this study, we cataloged mutations in 500 kinases genes in >65,000 individuals of global populations from the Human Genetic Diversity Project (HGDP) and ExAC databases, and assessed their potentially deleterious impact by using the in silico tools SIFT, Polyphen2, and CADD. The analysis highlighted 35 deleterious non-synonymous SNVs in the ExAC and 5 SNVs in the HGDP project. Notably, a higher number of deleterious mutations was observed in the Non-Finnish Europeans (26 SNVs), followed by the Africans (14 SNVs), East Asians (13 SNVs), and South Asians (12 SNVs). The gene set enrichment analysis highlighted NTRK1 and FGFR3 being most significantly enriched among the kinases. The gene expression analysis revealed over-expression of NTRK1 in liver cancer, whereas, FGFR3 was found over-expressed in lung, breast, and liver cancers compared to their expression in the respective normal tissues. Also, 13 potential drugs were identified that target the NTRK1 protein, whereas 6 potential drugs for the FGFR3 target were identified. Taken together, the study provides a framework for exploring the predisposing germline mutations in kinases to suggest the underlying pathogenic mechanisms in cancers. The potential drugs are also suggested for personalized cancer management.

Incorporating machine learning and PPI networks to identify mitochondrial fission-related immune markers in abdominal aortic aneurysms

Purpose

The aim of this study is to investigate abdominal aortic aneurysm (AAA), a disease characterised by inflammation and progressive vasodilatation, for novel gene-targeted therapeutic loci.

Methods

To do this, we used weighted co-expression network analysis (WGCNA) and differential gene analysis on samples from the GEO database. Additionally, we carried out enrichment analysis and determined that the blue module was of interest. Additionally, we performed an investigation of immune infiltration and discovered genes linked to immune evasion and mitochondrial fission. In order to screen for feature genes, we used two PPI network gene selection methods and five machine learning methods. This allowed us to identify the most featrue genes (MFGs). The expression of the MFGs in various cell subgroups was then evaluated by analysis of single cell samples from AAA. Additionally, we looked at the expression levels of the MFGs as well as the levels of inflammatory immune-related markers in cellular and animal models of AAA. Finally, we predicted potential drugs that could be targeted for the treatment of AAA.

Results

Our research identified 1249 up-regulated differential genes and 3653 down-regulated differential genes. Through WGCNA, we also discovered 44 genes in the blue module. By taking the point where several strategies for gene selection overlap, the MFG (ITGAL and SELL) was produced. We discovered through single cell research that the MFG were specifically expressed in T regulatory cells, NK cells, B lineage, and lymphocytes. In both animal and cellular models of AAA, the MFGs' mRNA levels rose.

Conclusion

We searched for the AAA novel targeted gene (ITGAL and SELL), which most likely function through lymphocytes of the B lineage, NK cells, T regulatory cells, and B lineage. This analysis gave AAA a brand-new goal to treat or prevent the disease.

Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study

BACKGROUND

Male-pattern baldness (MPB) is the most common cause of hair loss in men. It can be categorized into three types: type 2 (T2), type 3 (T3), and type 4 (T4), with type 1 (T1) being considered normal. Although various MPB-associated genetic variants have been suggested, a comprehensive study for linking these variants to gene expression regulation has not been performed to the best of our knowledge.

RESULTS

In this study, we prioritized MPB-related tissue panels using tissue-specific enrichment analysis and utilized single-tissue panels from genotype-tissue expression version 8, as well as cross-tissue panels from context-specific genetics. Through a transcriptome-wide association study and colocalization analysis, we identified 52, 75, and 144 MPB associations for T2, T3, and T4, respectively. To assess the causality of MPB genes, we performed a conditional and joint analysis, which revealed 10, 11, and 54 putative causality genes for T2, T3, and T4, respectively. Finally, we conducted drug repositioning and identified potential drug candidates that are connected to MPB-associated genes.

CONCLUSIONS

Overall, through an integrative analysis of gene expression and genotype data, we have identified robust MPB susceptibility genes that may help uncover the underlying molecular mechanisms and the novel drug candidates that may alleviate MPB.

Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop

Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases.

Medium-Dose Formoterol Attenuated Abdominal Aortic Aneurysm Induced by EPO via β2AR/cAMP/SIRT1 Pathway

Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease but effective drugs for treatment of AAA are still lacking. Recently, erythropoietin (EPO) is reported to induce AAA formation in apolipoprotein-E knock out (ApoE-/-) mice but an effective antagonist is unknown. In this study, formoterol, a β2 adrenergic receptor (β2AR) agonist, is found to be a promising agent for inhibiting AAA. To test this hypothesis, ApoE-/- mice are treated with vehicle, EPO, and EPO plus low-, medium-, and high-dose formoterol, respectively. The incidence of AAA is 0, 55%, 35%,10%, and 55% in these 5 groups, respectively. Mechanistically, senescence of vascular smooth muscle cell (VSMC) is increased by EPO while decreased by medium-dose formoterol both in vivo and in vitro, manifested by the altered expression of senescence biomarkers including phosphorylation of H2AXserine139, senescence-associated β-galactosidase activity, and P21 protein level. In addition, expression of sirtuin 1 (SIRT1) in aorta is decreased in EPO-induced AAA but remarkably elevated by medium-dose formoterol. Knockdown of β2AR and blockage of cyclic adenosine monophosphate (cAMP) attenuate the inhibitory role of formoterol in EPO-induced VSMC senescence. In summary, medium-dose formoterol attenuates EPO-induced AAA via β2AR/cAMP/SIRT1 pathways, which provides a promising medication for the treatment of AAA.

Oncogenic STAT Transcription Factors as Targets for Cancer Therapy: Innovative Strategies and Clinical Translation

Despite advances in our understanding of molecular aspects of oncogenesis, cancer remains a leading cause of death. The malignant behavior of a cancer cell is driven by the inappropriate activation of transcription factors. In particular, signal transducers and activators of transcription (STATs), which regulate many critical cellular processes such as proliferation, apoptosis, and differentiation, are frequently activated inappropriately in a wide spectrum of human cancers. Multiple signaling pathways converge on the STATs, highlighting their importance in the development and progression of oncogenic diseases. STAT3 and STAT5 are two members of the STAT protein family that are the most frequently activated in cancers and can drive cancer pathogenesis directly. The development of inhibitors targeting STAT3 and STAT5 has been the subject of intense investigations in the last decade, although effective treatment options remain limited. In this review, we investigate the specific roles of STAT3 and STAT5 in normal physiology and cancer biology, discuss the opportunities and challenges in pharmacologically targeting STAT proteins and their upstream activators, and offer insights into novel therapeutic strategies to identify STAT inhibitors as cancer therapeutics.

Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets

Genetic mechanisms of blood pressure (BP) regulation remain poorly defined. Using kidney-specific epigenomic annotations and 3D genome information we generated and validated gene expression prediction models for the purpose of transcriptome-wide association studies in 700 human kidneys. We identified 889 kidney genes associated with BP of which 399 were prioritised as contributors to BP regulation. Imputation of kidney proteome and microRNAome uncovered 97 renal proteins and 11 miRNAs associated with BP. Integration with plasma proteomics and metabolomics illuminated circulating levels of myo-inositol, 4-guanidinobutanoate and angiotensinogen as downstream effectors of several kidney BP genes (SLC5A11, AGMAT, AGT, respectively). We showed that genetically determined reduction in renal expression may mimic the effects of rare loss-of-function variants on kidney mRNA/protein and lead to an increase in BP (e.g., ENPEP). We demonstrated a strong correlation (r = 0.81) in expression of protein-coding genes between cells harvested from urine and the kidney highlighting a diagnostic potential of urinary cell transcriptomics. We uncovered adenylyl cyclase activators as a repurposing opportunity for hypertension and illustrated examples of BP-elevating effects of anticancer drugs (e.g. tubulin polymerisation inhibitors). Collectively, our studies provide new biological insights into genetic regulation of BP with potential to drive clinical translation in hypertension.

GINS2 regulates temozolomide chemosensitivity via the EGR1/ECT2 axis in gliomas

Temozolomide (TMZ), a DNA alkylating agent, has become the primary treatment for glioma, the most common malignancy of the central nervous system. Although TMZ-containing regimens produce significant clinical response rates, some patients inevitably suffer from inferior treatment outcomes or disease relapse, likely because of poor chemosensitivity of glioma cells due to a robust DNA damage response (DDR). GINS2, a subunit of DNA helicase, contributes to maintaining genomic stability and is highly expressed in various cancers, promoting their development. Here, we report that GINS2 was upregulated in TMZ-treated glioma cells and co-localized with γH2AX, indicating its participation in TMZ-induced DDR. Furthermore, GINS2 regulated the malignant phenotype and TMZ sensitivity of glioma cells, mostly by promoting DNA damage repair by affecting the mRNA stability of early growth response factor 1 (EGR1), which in turn regulates the transcription of epithelial cell-transforming sequence 2 (ECT2). We constructed a GINS2-EGR1-ECT2 prognostic model, which accurately predicted patient survival. Further, we screened Palbociclib/BIX-02189 which dampens GINS2 expression and synergistically inhibits glioma cell proliferation with TMZ. These findings delineate a novel mechanism by which GINS2 regulates the TMZ sensitivity of glioma cells and propose a promising combination therapy to treat glioma.

A robust primary liver cancer subtype related to prognosis and drug response based on a multiple combined classifying strategy

The recurrence or resistance to treatment of primary liver cancer (PLL) is significantly related to the heterogeneity present within the tumor. In this study, we integrated prognosis risk score, mRNAsi index, and immune characteristics clustering to classify patients. The four subtypes obtained from the combined classification are associated with PLC's prognosis and drug response. In these subtypes, we observed mRNAsiH_ICCA subtype, the intersection between high mRNAsi and immune characteristics clustering A, had the worst prognosis. Specifically, immune characteristics clustering B (ICC_B) had high drug sensitivity in most drugs regardless of the value of mRNAsi. On the other hand, patients with low mRNAsi responded better to ten drugs including KU-55933 and NU7441, while patients with high mRNAsi might benefit from drugs like Leflunomide. By matching the specific characteristics of each combined subtype with the drug-induced cell line expression profile, we identified a group of potential therapeutic drugs that might regulate the expression of disease signature genes. We developed a feasible multiple combined typing strategy, hoping to guide therapeutic selection and promote the development of precision medicine.

Bronchial epithelial transcriptomics and experimental validation reveal asthma severity-related neutrophilc signatures and potential treatments

Airway epithelial transcriptome analysis of asthma patients with different severity was used to disentangle the immune infiltration mechanisms affecting asthma exacerbation, which may be advantageous to asthma treatment. Here we introduce various bioinformatics methods and develop two models: an OVA/CFA-induced neutrophil asthma mouse model and an LPS-induced human bronchial epithelial cell damage model. Our objective is to investigate the molecular mechanisms, potential targets, and therapeutic strategies associated with asthma severity. Multiple bioinformatics methods identify meaningful differences in the degree of neutrophil infiltration in asthma patients with different severity. Then, PTPRC, TLR2, MMP9, FCGR3B, TYROBP, CXCR1, S100A12, FPR1, CCR1 and CXCR2 are identified as the hub genes. Furthermore, the mRNA expression of 10 hub genes is determined in vivo and in vitro models. Reperixin is identified as a pivotal drug targeting CXCR1, CXCR2 and MMP9. We further test the potential efficiency of Reperixin in 16HBE cells, and conclude that Reperixin can attenuate LPS-induced cellular damage and inhibit the expression of them. In this study, we successfully identify and validate several neutrophilic signatures and targets associated with asthma severity. Notably, Reperixin displays the ability to target CXCR1, CXCR2, and MMP9, suggesting its potential therapeutic value for managing deteriorating asthma.

In-Silico Identification of Novel Pharmacological Synergisms: The Trabectedin Case

The in-silico strategy of identifying novel uses for already existing drugs, known as drug repositioning, has enhanced drug discovery. Previous studies have shown a positive correlation between expression changes induced by the anticancer agent trabectedin and those caused by irinotecan, a topoisomerase I inhibitor. Leveraging the availability of transcriptional datasets, we developed a general in-silico drug-repositioning approach that we applied to investigate novel trabectedin synergisms. We set a workflow allowing the identification of genes selectively modulated by a drug and possible novel drug interactions. To show its effectiveness, we selected trabectedin as a case-study drug. We retrieved eight transcriptional cancer datasets including controls and samples treated with trabectedin or its analog lurbinectedin. We compared gene signature associated with each dataset to the 476,251 signatures from the Connectivity Map database. The most significant connections referred to mitomycin-c, topoisomerase II inhibitors, a PKC inhibitor, a Chk1 inhibitor, an antifungal agent, and an antagonist of the glutamate receptor. Genes coherently modulated by the drugs were involved in cell cycle, PPARalpha, and Rho GTPases pathways. Our in-silico approach for drug synergism identification showed that trabectedin modulates specific pathways that are shared with other drugs, suggesting possible synergisms.

DendroX: multi-level multi-cluster selection in dendrograms

BACKGROUND

Cluster heatmaps are widely used in biology and other fields to uncover clustering patterns in data matrices. Most cluster heatmap packages provide utility functions to divide the dendrograms at a certain level to obtain clusters, but it is often difficult to locate the appropriate cut in the dendrogram to obtain the clusters seen in the heatmap or computed by a statistical method. Multiple cuts are required if the clusters locate at different levels in the dendrogram.

RESULTS

We developed DendroX, a web app that provides interactive visualization of a dendrogram where users can divide the dendrogram at any level and in any number of clusters and pass the labels of the identified clusters for functional analysis. Helper functions are provided to extract linkage matrices from cluster heatmap objects in R or Python to serve as input to the app. A graphic user interface was also developed to help prepare input files for DendroX from data matrices stored in delimited text files. The app is scalable and has been tested on dendrograms with tens of thousands of leaf nodes. As a case study, we clustered the gene expression signatures of 297 bioactive chemical compounds in the LINCS L1000 dataset and visualized them in DendroX. Seventeen biologically meaningful clusters were identified based on the structure of the dendrogram and the expression patterns in the heatmap. We found that one of the clusters consisting of mostly naturally occurring compounds is not previously reported and has its members sharing broad anticancer, anti-inflammatory and antioxidant activities.

CONCLUSIONS

DendroX solves the problem of matching visually and computationally determined clusters in a cluster heatmap and helps users navigate among different parts of a dendrogram. The identification of a cluster of naturally occurring compounds with shared bioactivities implicates a convergence of biological effects through divergent mechanisms.

Prediction of prognostic and immune therapy response in lung adenocarcinoma based on MHC-I-related genes

OBJECTIVES

The study investigated the prognostic and immune predictive potential of major histocompatibility complex class I (MHC-I) in lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

With The Cancer Genome Atlas (TCGA)-LUAD and Gene Expression Omnibus datasets (GSE26939, GSE72094) as the training and validation sets, respectively, we used Cox regression analysis to construct a prognostic model, and verified independence of riskscore. The predictive capacity of the model was assessed in both sets using the receiver operating characteristic curve and Kaplan-Meier survival curves. Immune analysis was performed by using ssGSEA. Additionally, immune checkpoint blockade therapy was assessed by using immunophenoscore, Tumor Immune Dysfunction and Exclusion score. Based on the cMAP database, effective small molecule compounds were predicted.

RESULTS

A prognostic model was established based on 8 MHC-I-related genes, and the predictive capacity of the model was accurate. Immune analysis results revealed that patients classified as high-risk had lower levels of immune cell infiltration and impaired immune function. The low-risk group possessed a better response to immune checkpoint blockade therapy. Theobromine and pravastatin were identified as having great potential in improving the prognosis of LUAD.

CONCLUSION

Overall, the study revealed MHC-I-related molecular prognostic biomarkers as robust indicators for LUAD prognosis and immune therapy response.

Repositioning VU-0365114 as a novel microtubule-destabilizing agent for treating cancer and overcoming drug resistance

Microtubule-targeting agents represent one of the most successful classes of anticancer agents. However, the development of drug resistance and the appearance of adverse effects hamper their clinical implementation. Novel microtubule-targeting agents without such limitations are urgently needed. By employing a gene expression-based drug repositioning strategy, this study identifies VU-0365114, originally synthesized as a positive allosteric modulator of human muscarinic acetylcholine receptor M5 (M5 mAChR), as a novel type of tubulin inhibitor by destabilizing microtubules. VU-0365114 exhibits a broad-spectrum in vitro anticancer activity, especially in colorectal cancer cells. A tumor xenograft study in nude mice shows that VU-0365114 slowed the in vivo colorectal tumor growth. The anticancer activity of VU-0365114 is not related to its original target, M5 mAChR. In addition, VU-0365114 does not serve as a substrate of multidrug resistance (MDR) proteins, and thus, it can overcome MDR. Furthermore, a kinome analysis shows that VU-0365114 did not exhibit other significant off-target effects. Taken together, our study suggests that VU-0365114 primarily targets microtubules, offering potential for repurposing in cancer treatment, although more studies are needed before further drug development.

Targeting FBXO22 enhances radiosensitivity in non-small cell lung cancer by inhibiting the FOXM1/Rad51 axis

Radioresistance is a major constraint on the efficacy of lung cancer radiotherapy, but its mechanism has not been fully elucidated. Here, we found that FBXO22 was aberrantly highly expressed in lung cancer and that FBXO22 knockdown increased the radiosensitivity of lung cancer cells. Mechanistically, FBXO22 promoted Rad51 gene transcription by increasing the level of FOXM1 at the Rad51 promoter, thereby inducing the formation of lung cancer radioresistance. Furthermore, we found that deguelin, a potential inhibitor of FBXO22, enhanced radiosensitivity in an FBXO22/Rad51-dependent manner and was safely tolerated in vivo. Collectively, our results illustrate that FBXO22 induces lung cancer radioresistance by activating the FOXM1/Rad51 axis and provide preclinical evidence for the clinical translation of this critical target.

Identifying the prognosis implication, immunotherapy response prediction value, and potential targeted compound inhibitors of integrin subunit α3 (ITGA3) in human cancers

The integrin subunit α3 (ITGA3) is a member of the integrin alpha chain protein family, which could promote progression, metastasis, and invasion in some cancers. Still, its function in the tumor microenvironment (TME), cancer prognosis, and immunotherapy remains unclear. A multifaceted analysis of ITGA3 in pan-cancer utilizing various databases and online web tools revealed ITGA3 was aberrantly expressed in tumor tissues and upregulated in most cancers, which may be related to ITGA3 genomic alterations and methylation modification. In addition, ITGA3 was significantly correlated with the poor or better prognosis of cancer patients, immune-related pathways in hallmark, immune infiltration, and immune checkpoints, revealing a biological function of ITGA3 in the tumor progression, tumor microenvironment, and tumor immunity. We also found that ITGA3 could predict the response to tumor immunotherapy based on cytokine-treated samples and immunotherapy cohorts. ITGA3 may participate in shaping and regulating the tumor microenvironment to affect the tumor immune response, which was a promising immunotherapy response predictive biomarker and potential therapeutic target to work synergistically with cancer immunotherapy to boost the response and efficacy. Finally, potential targeted compound inhibitors and sensitive drugs were screened using databases ConnectivityMap (CMap) and CellMiner, and AutoDock Tools was used for molecular docking.

Integrative analysis reveals chemokines CCL2 and CXCL5 mediated shear stress-induced aortic dissection formation

Background

Aortic dissection (AD) is a critical emergency in cardiovascular disease. AD occurs only in specific sites of the aorta, and the variation of shear stress in different aortic segments is a possible cause not reported. This study investigated the key molecules involved in shear stress-induced AD through quantitative bioinformatic analysis of a public RNA sequencing database and clinical tissue sample validation.

Methods

Gene expression data from the GSE153434, GSE147026, and GSE52093 datasets were downloaded from the Gene Expression Omnibus. Next, differently expressed genes (DEGs) in each dataset were identified and integrated to identify common AD DEGs. STRING, Cytoscape, and MCODE were used to identify hub genes and crucial clustering modules, and Connectivity Map (CMap) was used to identify positive and negative agents. The same procedure was performed for the GSE160611 dataset to obtain shear stress-induced human aortic endothelial cell (HAEC) DEGs. After the integration of these two DEGs sets to identify shear stress-associated hub DEGs in AD, Gene Ontology Enrichment Analysis was performed. The common chemokine receptors and ligands in AD were identified by analyzing AD's three RNA sequencing datasets. Their origin was verified by analyzing AD single-cell sequencing data and validated by immunoblotting and immunofluorescence.

Results

We identified 100 down-regulated and 50 up-regulated AD common DEGs. Enrichment results showed that common DEGs were closely related to blood vessel morphogenesis, muscle structure development, muscle tissue development, and chemotaxis. Among those DEGs, MYC, CCL2, and SPP1 are the three molecules with the highest degree. A crucial cluster of 15 genes was identified using MCODE, which contained inflammation-related genes with elevated expression and muscle cell-related genes with decreased expression, and CCL2 is central to immune-related genes. CMap confirmed MEK inhibitors and ALK inhibitors as possible therapeutic agents for AD. Moreover, 366 shear stress-associated DEGs in HAEC were identified in the GSE160611 dataset. After taking the intersection, we identified five shear stress-associated hub DEGs in AD (ANGPTL4, SNAI2, CCL2, GADD45B, and PROM1), and the enrichment analysis indicated they were related to the endothelial cell apoptotic process. Chemokine CCL2 was the molecule with a high degree in both DEG sets. Besides CCL2, CXCL5 was the only chemokine ligand differentially expressed in the three datasets. Additionally, immunoblotting confirmed the increased expression of CCL2 and CXCL5 in clinical tissue samples. Further research at the single-cell level revealed that CCL2 has multiple origins, and CXCL5 is macrophage-derived.

Conclusion

Through integrative analysis, we identified core common AD DEGs and possible therapeutic agents based on these DEGs. We elucidated that the chemokine CCL2 and CXCL5-mediated "Endothelial-Monocyte-Neutrophil" axis may contribute to the development of shear stress-induced AD. These findings provide possible therapeutic targets for the prevention and treatment of AD.

A gene-based score for the risk stratification of stage IA lung adenocarcinoma

OBJECTIVE

We aim to molecularly stratify stage IA lung adenocarcinoma (LUAD) for precision medicine.

METHODS

Twelve multi-institution datasets (837 cases of IA) were used to classify the high- and low-risk types (based on survival status within 5 years), and the biological differences were compared. Then, a gene-based classifying score (IA score) was trained, tested and validated by several machine learning methods. Furthermore, we estimated the significance of the IA score in the prognostic assessment, chemotherapy prediction and risk stratification of stage IA LUAD. We also developed an R package for the clinical application. The SEER database (15708 IA samples) and TCGA Pan-Cancer (1881 stage I samples) database were used to verify clinical significance.

RESULTS

Compared with the low-risk group, the high-risk group of stage IA LUAD has obvious enrichment of the malignant pathway and more driver mutations and copy number variations. The effect of the IA score on the classification of high- and low-risk stage IA LUAD was much better than that of classical clinicopathological factors (training set: AUC = 0.9, validation set: AUC = 0.7). The IA score can significantly predict the prognosis of stage IA LUAD and has a prognostic effect for stage I pancancer. The IA score can effectively predict chemotherapy sensitivity and occult metastasis or invasion in stage IA LUAD. The R package IAExpSuv has a good risk probability prediction effect for both groups and single stages of IA LUAD.

CONCLUSIONS

The IA score can effectively stratify the risk of stage IA LUAD, offering good assistance in precision medicine.

A multiomic investigation of lung adenocarcinoma molecular subtypes

BACKGROUND

Lung adenocarcinoma-an aggressive and life-threatening malignancy-is a type of non-small-cell lung cancer. Despite medical advancements, the prognosis of lung adenocarcinoma remains unfavorable, likely because of its heterogeneous nature. Furthermore, few subtype-specific treatments are available for lung adenocarcinoma. This study was conducted to explore the molecular subtypes of lung adenocarcinoma.

METHODS

We performed a joint analysis of transcriptome and proteome data from East Asian patients with lung adenocarcinoma (nonsmokers, 86.5%).

RESULTS

Four novel subtypes were identified based on distinct molecular characteristics: subtypes I, II, III, and IV. In patients with subtype I lung adenocarcinoma, eukaryotic translation initiation factor 4 gamma 1 activates cell proliferation; inhibiting this factor suppresses tumor growth, and reducing its level induces autophagy. Subtype II is characterized by Kristen rat sarcoma viral oncogene homolog-activating oncogenesis; the onset age of this subtype is the lowest among all subtypes. Subtype III manifests as an advanced disease at diagnosis; it is characterized by a core serum response-related oncogenic signature, which indicates poor overall survival in Western patients with lung cancer. Subtype IV is more common in men than in women; it has astroglial characteristics. A Connectivity Map analysis revealed that the oncogenic expression patterns corresponding to subtypes I, II, III, and IV can be reversed by the inhibitors of Inhibitor of κB (IκB) kinase (eg, withaferin A), mammalian target of rapamycin (eg, everolimus), Src proto-oncogene (Src) (eg, saracatinib), and Transforming Growth Factor (TGF)-β/Smad (eg, LY-364947), respectively.

CONCLUSION

This study introduced an innovative multiomics data analysis pipeline. Using this approach, we successfully identified four molecular subtypes of lung adenocarcinoma and their candidate therapeutic agents. The newly identified subtypes can be combined with the current biomarkers to generate a comprehensive roadmap for treatment decision-making.

G-protein signaling of oxytocin receptor as a potential target for cabazitaxel-resistant prostate cancer

Although the treatment armamentarium for patients with metastatic prostate cancer has improved recently, treatment options after progression on cabazitaxel (CBZ) are limited. To identify the mechanisms underlying CBZ resistance and therapeutic targets, we performed single-cell RNA sequencing of circulating tumor cells (CTCs) from patients with CBZ-resistant prostate cancer. Cells were clustered based on gene expression profiles. In silico screening was used to nominate candidate drugs for overcoming CBZ resistance in castration-resistant prostate cancer. CTCs were divided into three to four clusters, reflecting intrapatient tumor heterogeneity in refractory prostate cancer. Pathway analysis revealed that clusters in two cases showed up-regulation of the oxytocin (OXT) receptor-signaling pathway. Spatial gene expression analysis of CBZ-resistant prostate cancer tissues confirmed the heterogeneous expression of OXT-signaling molecules. Cloperastine (CLO) had significant antitumor activity against CBZ-resistant prostate cancer cells. Mass spectrometric phosphoproteome analysis revealed the suppression of OXT signaling specific to CBZ-resistant models. These results support the potential of CLO as a candidate drug for overcoming CBZ-resistant prostate cancer via the inhibition of OXT signaling.

Drug Repurposing Using FDA Adverse Event Reporting System (FAERS) Database

Drug repurposing is an emerging approach to reassigning existing pre-approved therapies for new indications. The FDA Adverse Event Reporting System (FAERS) is a large database of over 28 million adverse event reports submitted by medical providers, patients, and drug manufacturers and provides extensive drug safety signal data. In this review, four common drug repurposing strategies using FAERS are described, including inverse signal detection for a single disease, drug-drug interactions that mitigate a target ADE, identifying drug-ADE pairs with opposing gene perturbation signatures and identifying drug-drug pairs with congruent gene perturbation signatures. The purpose of this review is to provide an overview of these different approaches using existing successful applications in the literature. With the fast expansion of adverse drug event reports, FAERS-based drug repurposing represents a promising strategy for discovering new uses for existing therapies.

Reimagining the Past: A Future for Antibiotic Drug Discovery in Ophthalmology

ABSTRACT

Antibiotic resistance has emerged as a critical threat for the treatment of bacterial ocular infections. To address the critical need for novel therapeutics, antibiotic drug repurposing holds significant promise. As such, examples of existing FDA-approved drugs currently under development for new applications, novel combinations, and improved delivery systems are discussed.

Identifying multi-target drugs for prostate cancer using machine learning-assisted transcriptomic analysis

Prostate cancer is a leading cause of cancer death in men, and the development of effective treatments is of great importance. This study explored to identify the candidate drugs for prostate cancer by transcriptomic data and CMap database analysis. After integrating the results of omics analysis, bisoprolol is confirmed as a promising drug. Moreover, cell experiment reveals its potential inhibitory effect on the proliferation of prostate cancer cells. Importantly, machine learning methods are employed to predict the targets of bisoprolol, and the dual-target ADRB3 and hERG are explored by dynamic simulation. The findings of this study demonstrate the potential of bisoprolol as a multi-target drug for prostate cancer treatment and the feasibility of using beta-adrenergic receptor inhibitors in prostate cancer treatment. In addition, the proposed research approach is promising for discovering potential drugs for cancer treatment by leveraging the concept of drug side effects leading to anticancer effects. Further research is necessary to investigate the pharmacological action, potential toxicity, and underlying mechanisms of bisoprolol in treating prostate cancer with ADRB3.Communicated by Ramaswamy H. Sarma.

A novel approach for predicting upstream regulators (PURE) that affect gene expression

External factors such as exposure to a chemical, drug, or toxicant (CDT), or conversely, the lack of certain chemicals can cause many diseases. The ability to identify such causal CDTs based on changes in the gene expression profile is extremely important in many studies. Furthermore, the ability to correctly infer CDTs that can revert the gene expression changes induced by a given disease phenotype is a crucial step in drug repurposing. We present an approach for Predicting Upstream REgulators (PURE) designed to tackle this challenge. PURE can correctly infer a CDT from the measured expression changes in a given phenotype, as well as correctly identify drugs that could revert disease-induced gene expression changes. We compared the proposed approach with four classical approaches as well as with the causal analysis used in Ingenuity Pathway Analysis (IPA) on 16 data sets (1 rat, 5 mouse, and 10 human data sets), involving 8 chemicals or drugs. We assessed the results based on the ability to correctly identify the CDT as indicated by its rank. We also considered the number of false positives, i.e. CDTs other than the correct CDT that were reported to be significant by each method. The proposed approach performed best in 11 out of the 16 experiments, reporting the correct CDT at the very top 7 times. IPA was the second best, reporting the correct CDT at the top 5 times, but was unable to identify the correct CDT at all in 5 out of the 16 experiments. The validation results showed that our approach, PURE, outperformed some of the most popular methods in the field. PURE could effectively infer the true CDTs responsible for the observed gene expression changes and could also be useful in drug repurposing applications.

Prognostic value of amino acid metabolism-related gene expression in invasive breast carcinoma

BACKGROUND

An increasing number of studies indicated that metabolic reprogramming of amino acid metabolism may either promote or inhibit tumor progression. The purpose of this study was to investigate the ability of a gene risk signature associated with amino acid metabolism to predict the prognosis and immune characteristics of invasive breast carcinoma.

METHODS

LASSO Cox regression analysis was performed to construct and validate the prognostic risk signature based on the expression of 9 amino acid metabolism-related genes. The predictive value of the signature, immune characteristics, and chemotherapeutic drugs was also predicted. Finally, 9 significant genes were examined in MDA-MB-231 and MCF-7 cells, and the predicted chemotherapeutic drugs were also verified.

RESULTS

The prognosis of the low-risk group was better than that of the high-risk group. The areas under the curve (AUCs) at 1, 2, and 3 years were 0.852, 0.790, and 0.736, respectively. In addition, the GSEA results for KEGG and GO revealed that samples with a high-risk score exhibited a variety of highly malignant manifestations. The high-risk group was characterized by an increased number of M2 macrophages, a high level of tumor purity, low levels of APC co-stimulation, cytolytic activity, HLA, para-inflammation, and type I IFN response. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) confirmed that MDA-MB-231 and MCF-7 cells express 9 amino acid metabolism-related genes differently. In addition, cell experiments were conducted to examine the effect of cephaeline-induced on cell viability, migration ability, and protein expression of the PI3K/AKT signaling pathway and HIF-1α.

CONCLUSION

We established a risk signature based on 9 amino acid metabolism-related genes for invasive breast carcinoma. Further analyses revealed that this risk signature is superior to other clinical indexes in survival prediction and that the subgroups identified by the risk signature exhibit distinct immune characteristics. Cephaeline was determined to be a superior option for patients in high-risk groups.

Integrating single-nucleus sequence profiling to reveal the transcriptional dynamics of Alzheimer's disease, Parkinson's disease, and multiple sclerosis

BACKGROUND

Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS) are three nervous system diseases that partially overlap clinically and genetically. However, bulk RNA-sequencing did not accurately detect the core pathogenic molecules in them. The availability of high-quality single cell RNA-sequencing data of post-mortem brain collections permits the generation of a large-scale gene expression in different cells in human brain, focusing on the molecular features and relationships between diseases and genes. We integrated single-nucleus RNA-sequencing (snRNA-seq) datasets of human brains with AD, PD, and MS to identify transcriptomic commonalities and distinctions among them.

METHODS

The snRNA-seq datasets were downloaded from Gene Expression Omnibus (GEO) database. The Seurat package was used for snRNA-seq data processing. The uniform manifold approximation and projection (UMAP) were utilized for cluster identification. The FindMarker function in Seurat was used to identify the differently expressed genes. Functional enrichment analysis was carried out using the Gene Set Enrichment Analysis (GSEA) and Gene ontology (GO). The protein-protein interaction (PPI) analysis of differentially expressed genes (DEGs) was analyzed using STRING database ( http://string-db.org ). SCENIC analysis was performed using utilizing pySCENIC (v0.10.0) based on the hg19-tss-centered-10 kb-10species databases. The analysis of potential therapeutic drugs was analyzed on Connectivity Map ( https://clue.io ).

RESULTS

The gene regulatory network analysis identified several hub genes regulated in AD, PD, and MS, in which HSPB1 and HSPA1A were key molecules. These upregulated HSP family genes interact with ribosome genes in AD and MS, and with immunomodulatory genes in PD. We further identified several transcriptional regulators (SPI1, CEBPA, TFE3, GRHPR, and TP53) of the hub genes, which has important implications for uncovering the molecular crosstalk among AD, PD, and MS. Arctigenin was identified as a potential therapeutic drug for AD, PD, and MS.

CONCLUSIONS

Together, the integrated snRNA-seq data and findings have significant implications for unraveling the shared and unique molecular crosstalk among AD, PD, and MS. HSPB1 and HSPA1A as promising targets involved in the pathological mechanisms of neurodegenerative diseases. Additionally, the identification of arctigenin as a potential therapeutic drug for AD, PD, and MS further highlights its potential in treating these neurological disorders. These discoveries lay the groundwork for future research and interventions to enhance our understanding and treatment of AD, PD, and MS.

A drug repurposing method based on inhibition effect on gene regulatory network

Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel Drug Repurposing method based on the Inhibition Effect on gene regulatory network (DRIE) to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.

Identifying a causal link between prolactin signaling pathways and COVID-19 vaccine-induced menstrual changes

COVID-19 vaccines have been instrumental tools in the fight against SARS-CoV-2 helping to reduce disease severity and mortality. At the same time, just like any other therapeutic, COVID-19 vaccines were associated with adverse events. Women have reported menstrual cycle irregularity after receiving COVID-19 vaccines, and this led to renewed fears concerning COVID-19 vaccines and their effects on fertility. Herein we devised an informatics workflow to explore the causal drivers of menstrual cycle irregularity in response to vaccination with mRNA COVID-19 vaccine BNT162b2. Our methods relied on gene expression analysis in response to vaccination, followed by network biology analysis to derive testable hypotheses regarding the causal links between BNT162b2 and menstrual cycle irregularity. Five high-confidence transcription factors were identified as causal drivers of BNT162b2-induced menstrual irregularity, namely: IRF1, STAT1, RelA (p65 NF-kB subunit), STAT2 and IRF3. Furthermore, some biomarkers of menstrual irregularity, including TNF, IL6R, IL6ST, LIF, BIRC3, FGF2, ARHGDIB, RPS3, RHOU, MIF, were identified as topological genes and predicted as causal drivers of menstrual irregularity. Our network-based mechanism reconstruction results indicated that BNT162b2 exerted biological effects similar to those resulting from prolactin signaling. However, these effects were short-lived and didn't raise concerns about long-term infertility issues. This approach can be applied to interrogate the functional links between drugs/vaccines and other side effects.

Integrated analysis of diagnostic, prognostic value and potential drug treatment of GSDME in head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSC) poses a global health challenge. Effective biomarkers for early detection are necessary to improve the survival rate of HNSC patient. The purpose of this study was using integrated bioinformatic analysis to investigate the potential biological roles of GSDME in HNSC.

METHODS

The Gene Expression Omnibus (GEO) and Cancer Gnome Atlas (TCGA) databases were used to analyze the expression of GSDME in different cancer types. The correlation between GSDME expression and immune cell infiltration or immune checkpoint genes was examined by Spearman correlation analysis. DNA methylation analysis of the GSDME gene was conducted using the MethSurv database. Kaplan-Meier (K-M) survival curves, diagnostic receiver operating characteristic (ROC) curves, nomogram model, and Cox regression analysis were chosen to evaluate the diagnostic and prognostic predictive value of GSDME. Connectivity Map (Cmap) online platform, Protein Data Bank (PDB) database and Chem3D, AutoDock Tool and PyMol software were used to predict and visualize potential molecular drugs aimed for GSDME.

RESULTS

GSDME expression level in HNSC was significantly higher than in the controls (p < 0.001). Differentially expressed genes (DEGs) correlation with GSDME were enriched in the GO pathways, such as protein activation cascade, complement activation and classical pathway (p < 0.05). According to GSEA, GSDME-associated differentially expressed genes were significantly enriched in KRAS signaling pathway and cytokine signaling molecule (p < 0.05). There is a significant relation between GSDME expression and immune cell infiltration in HNSC tissues, as well as immune checkpoint genes expression (p < 0.001). DNA methylation status of cg17790129 CpG islands of GSDME gene is correlated with HNSC prognosis (p < 0.05). Based on Cox regression analysis of HNSC patients, GSDME as a potential risk gene has high correlation with overall survival (OS) and disease specific survival (DSS) (p < 0.05). In a ROC curve analysis, HNSC tissues were differentiated from adjacent peritumoral tissues based on GSDME expression levels (AUC = 0.928). Totally six potential drugs targeted for GSDME were screened and the molecular docking tests between GSDME protein and candidate drugs were conducted.

CONCLUSIONS

GSDME is a promising therapeutic target as well as a potential clinical biomarker in HNSC patients.

Applying genomics in regulatory toxicology: a report of the ECETOC workshop on omics threshold on non-adversity

In a joint effort involving scientists from academia, industry and regulatory agencies, ECETOC's activities in Omics have led to conceptual proposals for: (1) A framework that assures data quality for reporting and inclusion of Omics data in regulatory assessments; and (2) an approach to robustly quantify these data, prior to interpretation for regulatory use. In continuation of these activities this workshop explored and identified areas of need to facilitate robust interpretation of such data in the context of deriving points of departure (POD) for risk assessment and determining an adverse change from normal variation. ECETOC was amongst the first to systematically explore the application of Omics methods, now incorporated into the group of methods known as New Approach Methodologies (NAMs), to regulatory toxicology. This support has been in the form of both projects (primarily with CEFIC/LRI) and workshops. Outputs have led to projects included in the workplan of the Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) group of the Organisation for Economic Co-operation and Development (OECD) and to the drafting of OECD Guidance Documents for Omics data reporting, with potentially more to follow on data transformation and interpretation. The current workshop was the last in a series of technical methods development workshops, with a sub-focus on the derivation of a POD from Omics data. Workshop presentations demonstrated that Omics data developed within robust frameworks for both scientific data generation and analysis can be used to derive a POD. The issue of noise in the data was discussed as an important consideration for identifying robust Omics changes and deriving a POD. Such variability or "noise" can comprise technical or biological variation within a dataset and should clearly be distinguished from homeostatic responses. Adverse outcome pathways (AOPs) were considered a useful framework on which to assemble Omics methods, and a number of case examples were presented in illustration of this point. What is apparent is that high dimension data will always be subject to varying processing pipelines and hence interpretation, depending on the context they are used in. Yet, they can provide valuable input for regulatory toxicology, with the pre-condition being robust methods for the collection and processing of data together with a comprehensive description how the data were interpreted, and conclusions reached.

Leveraging genetic diversity to identify small molecules that reverse mouse skeletal muscle insulin resistance

Systems genetics has begun to tackle the complexity of insulin resistance by capitalising on computational advances to study high-diversity populations. 'Diversity Outbred in Australia (DOz)' is a population of genetically unique mice with profound metabolic heterogeneity. We leveraged this variance to explore skeletal muscle's contribution to whole-body insulin action through metabolic phenotyping and skeletal muscle proteomics of 215 DOz mice. Linear modelling identified 553 proteins that associated with whole-body insulin sensitivity (Matsuda Index) including regulators of endocytosis and muscle proteostasis. To enrich for causality, we refined this network by focusing on negatively associated, genetically regulated proteins, resulting in a 76-protein fingerprint of insulin resistance. We sought to perturb this network and restore insulin action with small molecules by integrating the Broad Institute Connectivity Map platform and in vitro assays of insulin action using the Prestwick chemical library. These complementary approaches identified the antibiotic thiostrepton as an insulin resistance reversal agent. Subsequent validation in ex vivo insulin-resistant mouse muscle and palmitate-induced insulin-resistant myotubes demonstrated potent insulin action restoration, potentially via upregulation of glycolysis. This work demonstrates the value of a drug-centric framework to validate systems-level analysis by identifying potential therapeutics for insulin resistance.

The 3Rs in Experimental Liver Disease

Patients with cirrhosis present multiple physiological and immunological alterations that play a very important role in the development of clinically relevant secondary complications to the disease. Experimentation in animal models is essential to understand the pathogenesis of human diseases and, considering the high prevalence of liver disease worldwide, to understand the pathophysiology of disease progression and the molecular pathways involved, due to the complexity of the liver as an organ and its relationship with the rest of the organism. However, today there is a growing awareness about the sensitivity and suffering of animals, causing opposition to animal research among a minority in society and some scientists, but also about the attention to the welfare of laboratory animals since this has been built into regulations in most nations that conduct animal research. In 1959, Russell and Burch published the book "The Principles of Humane Experimental Technique", proposing that in those experiments where animals were necessary, everything possible should be done to try to replace them with non-sentient alternatives, to reduce to a minimum their number, and to refine experiments that are essential so that they caused the least amount of pain and distress. In this review, a comprehensive summary of the most widely used techniques to replace, reduce, and refine in experimental liver research is offered, to assess the advantages and weaknesses of available experimental liver disease models for researchers who are planning to perform animal studies in the near future.