High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery

Usnic acid impacts energy production and iron metabolism in Mycobacterium tuberculosis H37Rv

Mycobacterium tuberculosis has developed a wide array of response mechanisms to various stress factors. Usnic acid has been demonstrated to be a potent antimycobacterial agent that induces stress responses and growth inhibition in many mycobacterial species. Previous studies have shown that it alters the expression of stress-responsive sigma factors, as well as the metabolites and lipid profile in M. tuberculosis H37Ra. This study was designed to examine potential differences in the strain-specific susceptibility of the virulent H37Rv strain to usnic acid. By combining lipidomic and transcriptomic analyses, we uncovered the impact of usnic acid on bacterial metabolism. The observed downregulation of key lipid classes suggested reduced metabolic activity. The simultaneous elevation of mycobactins-siderophores used by members of the genus Mycobacterium to transport free extracellular iron ions into the cytoplasm-indicated the involvement of iron in the stress response generated by usnic acid. The repressed tricarboxylic acid (TCA) cycle and oxidative phosphorylation were compensated by the upregulation of alternative energy production pathways, such as cytochrome P450 and the ferredoxin reductase system. This indicates that mycobacteria may switch to alternative electron transport mechanisms under usnic acid stress using iron-sulfur clusters to generate energy. From a therapeutic perspective, the study highlights iron metabolism as an essential drug target in mycobacteria. Simultaneously, the results confirm the strain-specific metabolic response of sister strains against the same stressing agent.

IMPORTANCE

A previous study on the influence of usnic acid on the avirulent H37Ra strain revealed that the early bacterial response was associated with redox homeostasis, lipid synthesis, and nucleic acid repair. The response of bacteria to antimicrobials is specific to each species and strain. Given the genetic and phenotypic differences between the avirulent H37Ra strain and the virulent H37Rv strain, we combined lipidomics and global transcriptomics to uncover the mechanism of action of usnic acid against H37Rv. The study identified strain-specific differences between the virulent H37Rv and avirulent H37Ra. The H37Ra strain exhibited increased metabolic activity, while the H37Rv strain showed a reduction in basic metabolic processes and activated alternative iron-dependent energy production. These differences highlight the varying susceptibility of sister strains within the same species to the same antibacterial agent.

Novel Approach to Screen Endocrine-Disrupting Chemicals via Endocrine-Enhanced Reduced Human Transcriptome

Endocrine-disrupting chemicals (EDCs) can interfere with multiple pathways and trigger different modes of action. Thus, the traditional EDC in vitro screening processes often require a battery of bioassays to cover multiple target pathways. Here we developed an endocrine-enhanced reduced human transcriptome (ERHT) focused on hormone receptor signaling induced by the EDCs regulating specific genes. ERHT was developed based on 1200 prioritized genes covering 110 endocrine-related biological pathways across eight potential adverse outcomes. The ability of this approach to identify EDCs was derived from machine learning of 1068 dose-dependent transcriptome profiles and enhanced by quantifying chemical-induced critical pathway responses, and thus, it demonstrated excellent classification performance (AUC = 0.84 ± 0.03) in internal cross-validation. We ultimately applied this approach to known EDCs and inactive substances to validate the reliability of this approach. Through external validation on 210 chemicals, the extrapolation accuracy exceeded 80%, demonstrating the outstanding practical performance of this approach. Meanwhile, the pathway responses induced by the same chemical were consistent with the experimental results reported by multiple sequencing platforms, highlighting the robustness of this approach. The above results demonstrate that this approach can provide novel insights for EDCs' high-throughput screening and comprehensive toxic mechanisms through biological pathways.

Quality and composition control of complex TCM preparations through a novel "Herbs-in vivo Compounds-Targets-Pathways" network methodology: The case of Lianhuaqingwen capsules

Lianhuaqingwen (LHQW) capsules, a Chinese patent medicine composed of 13 herbal ingredients, are widely used for respiratory diseases. However, the complex composition of LHQW poses challenges in assessing its quality and consistency. In this study, a comprehensive network of LHQW was constructed by integrating Digital RNA with pertUrbation of Genes (DRUG)-seq, RNA sequencing, and pharmacodynamic data. This approach enables rapid and systematic screening of compounds in LHQW that exhibit high-exposure in vivo and significant activity potential, serving as potential quality control markers. Specifically, DRUG-seq was employed to evaluate gene expression alterations in peripheral blood mononuclear cells derived from healthy volunteers. Ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS) identified 505 compounds in LHQW-treated rats. Additionally, absorption, distribution, metabolism, and excretion (ADME) profiles were plotted for 27 primary components of LHQW. Furthermore, an HPLC-MS/MS method quantified 46 compounds from LHQW, with 15 of them identified as potential quality markers with high exposure levels. These markers exhibited significant inhibitory effects on lipopolysaccharide (LPS)-induced pneumonia in mice, with mechanisms predicted by RNA-seq and verified by RT-qPCR. In summary, this study successfully constructed an "Herbs- in vivo Compounds-targets-pathways" network, offering novel insights into the mechanisms of LHQW and establishing a foundation for enhancing quality control measures.

Comprehensive analysis of differential mRNA and circRNA profiles in primary and metastatic pancreatic neuroendocrine tumors

The diagnosis of primary pancreatic neuroendocrine tumors (pNETs) presents significant challenges, and metastatic pancreatic neuroendocrine tumors are associated with high mortality. Understanding the characteristics of these tumors, particularly the key molecules involved in metastasis, is essential. To address this, we utilized mRNA expression data from human pNET and metastatic pancreatic tumor tissues available in the GEO database and integrated this data with bioinformatics analyses. And then we collected clinical primary tumor and liver metastasis samples from patients with pNETs, we conducted a comprehensive analysis of circular RNAs (circRNAs) to identify key circRNAs associated with the onset and metastasis of pNETs. We found that in pNET development and metastasis, 11 genes and 14 circRNAs were notably upregulated, while 25 genes and 35 circRNAs were significantly downregulated, compared to nearby non-cancerous tissue. Our analysis of differentially expressed RNA and circRNA genes revealed that tumor cell adhesion and integrin activation, regulated by genes like PIEZO1, IFT74, SKAP1, GPX1, F7, VTN, and OMG, are strongly linked to pNET metastasis. We found that SKAP1 levels are positively associated with tumor progression in pNET patients. Overall, our research indicates that the SKAP1-mediated pathway is crucial in pNET development and metastasis.

Identifying candidate RNA-seq biomarkers for severity discrimination in chemical injuries: A machine learning and molecular dynamics approach

INTRODUCTION

Biomarkers play a crucial role across various fields by providing insights into biological responses to interventions. High-throughput gene expression profiling technologies facilitate the discovery of data-driven biomarkers through extensive datasets. This study focuses on identifying biomarkers in gene expression data related to chemical injuries by mustard gas, covering a spectrum from healthy individuals to severe injuries.

MATERIALS AND METHODS

The study utilized RNA-Seq data comprising 52 expression data samples for 54,583 gene transcripts. These samples were categorized into four classes based on the GOLD classification for chemically injured individuals: Severe (n = 14), Moderate (n = 11), Mild (n = 16), and healthy controls (n = 11). Data preparation involved examining an Excel file created in the R programming environment using MLSeq and devtools packages. Feature selection was performed using Genetic Algorithm and Simulated Annealing, with Random Forest algorithm employed for classification. Ab initio methods ensured computational efficiency and result accuracy, while molecular dynamics simulation acted as a virtual experiment bridging the gap between experimental and theoretical experiences.

RESULTS

A total of 12 models were created, each introducing a list of differentially expressed genes as potential biomarkers. The performance of models varied across group comparisons, with the Genetic Algorithm generally outperforming Simulated Annealing in most cases. For the Severe vs. Moderate group, GA achieved the best performance with an accuracy of 94.38%, recall of 91.64%, and specificity of 97.10%. The results highlight the effectiveness of GA in most group comparisons, while SA performed better in specific cases involving Moderate and Mild groups. These biomarkers were evaluated against the gene expression data to assess their expression changes between different groups of chemically injured individuals. Four genes were selected based on level expression for further investigation: CXCR1, EIF2B2, RAD51, and RXFP2. The expression levels of these genes were analyzed to determine their differential expression between the groups.

CONCLUSION

This study was designed as a computational effort to identify diagnostic biomarkers in basic biological system research. Our findings proposed a list of discriminative biomarkers capable of distinguishing between different groups of chemically injured individuals. The identification of key genes highlights the potential for biomarkers to serve as indicators of chemical injury severity, warranting further investigation to validate their clinical relevance and utility in diagnosis and treatment.

Polymerase Chain Reaction Chips for Biomarker Discovery and Validation in Drug Development

Polymerase chain reaction (PCR) chips are advanced, microfluidic platforms that have revolutionized biomarker discovery and validation because of their high sensitivity, specificity, and throughput levels. These chips miniaturize traditional PCR processes for the speed and precision of nucleic acid biomarker detection relevant to advancing drug development. Biomarkers, which are useful in helping to explain disease mechanisms, patient stratification, and therapeutic monitoring, are hard to identify and validate due to the complexity of biological systems and the limitations of traditional techniques. The challenges to which PCR chips respond include high-throughput capabilities coupled with real-time quantitative analysis, enabling researchers to identify novel biomarkers with greater accuracy and reproducibility. More recent design improvements of PCR chips have further expanded their functionality to also include digital and multiplex PCR technologies. Digital PCR chips are ideal for quantifying rare biomarkers, which is essential in oncology and infectious disease research. In contrast, multiplex PCR chips enable simultaneous analysis of multiple targets, therefore simplifying biomarker validation. Furthermore, single-cell PCR chips have made it possible to detect biomarkers at unprecedented resolution, hence revealing heterogeneity within cell populations. PCR chips are transforming drug development, enabling target identification, patient stratification, and therapeutic efficacy assessment. They play a major role in the development of companion diagnostics and, therefore, pave the way for personalized medicine, ensuring that the right patient receives the right treatment. While this tremendously promising technology has exhibited many challenges regarding its scalability, integration with other omics technologies, and conformity with regulatory requirements, many still prevail. Future breakthroughs in chip manufacturing, the integration of artificial intelligence, and multi-omics applications will further expand PCR chip capabilities. PCR chips will not only be important for the acceleration of drug discovery and development but also in raising the bar in improving patient outcomes and, hence, global health care as these technologies continue to mature.

Targeted antimicrobial self-assembly peptide hydrogel with in situ bio-mimic remineralization for caries management

The single-function agents with wide-spectrum activity which tend to disturb the ecological balance of oral cavity cannot satisfy dental treatment need. A multi-functional agent with specifically targeted killing property and in situ remineralization is warranted for caries management. A novel multi-functional agent (8DSS-C8-P-113) consisting of three domains, i.e., a non-specific antimicrobial peptide (AMP) (P-113), a competence stimulating peptide (C8), and an enhancing remineralization domain (8DSS), is fabricated and evaluated in this study. The findings demonstrates that 2 μM mL-1 of 8DSS-C8-P-113 eliminates planktonic Streptococcus mutans (S. mutans) without disrupting the oral normal flora. At a concentration of 8 μM mL-1, it exhibits the ability to prevent S. mutans' adhesion. Furthermore, 8DSS-C8-P-113 self-assembles a hydrogel state at the higher concentration of 16 μM mL-1. This hydrogel self-adheres on the tooth surface, resisting acid attack, eradicating S. mutans' biofilm, and inducing mineralization in order to facilitate the repair of demineralized dental hard tissue. Its significant effectiveness in reducing the severity of dental caries is also demonstrated in vivo in a rat model. This study suggests that the multi-functional bioactive AMP 8DSS-C8-P-113 is a promising agent to specifically target pathogen, prevent tooth demineralization, and effectively induce in situ bio-mimic remineralization for the management of dental caries.

Discovery platforms for RNA therapeutics

RNA therapeutics are emerging as a unique opportunity to drug currently "undruggable" molecules and diseases. While their advantages over conventional, small molecule drugs, their therapeutic implications and the tools for their effective in vivo delivery have been extensively reviewed, little attention has been so far paid to the technological platforms exploited for the discovery of RNA therapeutics. Here, we provide an overview of the existing platforms and ex vivo assays for RNA discovery, their advantages and disadvantages, as well as their main fields of application, with specific focus on RNA therapies that have reached either phase 3 or market approval. LINKED ARTICLES: This article is part of a themed issue Non-coding RNA Therapeutics. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.2/issuetoc.

Disruption of BCAA degradation is a critical characteristic of diabetic cardiomyopathy revealed by integrated transcriptome and metabolome analysis

In this study, we integrated transcriptomic and metabolomic analyses to achieve a comprehensive understanding of the underlying mechanisms of diabetic cardiomyopathy (DCM) in a diabetic rat model. Functional and molecular characterizations revealed significant cardiac injury, dysfunction, and ventricular remodeling in DCM. A thorough analysis of global changes in genes and metabolites showed that amino acid metabolism, especially the breakdown of branched-chain amino acids (BCAAs) such as valine, leucine, and isoleucine, is highly dysregulated. Furthermore, the study identified the transcription factor Gata3 as a predicted negative regulator of the gene encoding the key enzyme for BCAA degradation. These findings suggest that the disruption of BCAA degradation is a critical characteristic of diabetic myocardial damage and indicate a potential role for Gata3 in the dysregulation of BCAA metabolism in the context of DCM.

Integration analysis of microRNAs as potential biomarkers in early-stage lung adenocarcinoma: the diagnostic and therapeutic significance of miR-183-3p

Introduction

Lung adenocarcinoma (LUAD) poses a significant therapeutic challenge, primarily due to delayed diagnosis and the limited efficacy of existing treatments.

Methods

To understand the pathogenesis and identify diagnostic biomarkers for LUAD in the early stage, we investigated differential miRNA expression in 33 stage I LUAD patients between tumor and matched paracancerous tissues by Illumina Sequencing. Target genes of differentially expressed miRNAs were predicted using TargetScan and miRDB databases and further analyzed by GO and KEGG pathway enrichment analysis. The miRNAs expression results were verified using qRT-PCR. Additionally, we evaluated the clinical significance of miRNAs by the TCGA database. miR-183-3p was chosen for subsequent biological functional studies by cell proliferation assays, cell migration and cell invasion assays, cell apoptosis and cell cycle assays in LUAD cells. The clinical relevance target genes of miR-183-3p were predicted by TargetScan databases and bioinformatics assays. Gene-specific experimental validation was performed using qRT-PCR, western blotting and luciferase reporter assays.

Results

We identified 36 differentially expressed miRNAs between LUAD tissues and matched paracancerous tissues. Target genes for these miRNAs revealed associations with processes and pathways such as RNA biosynthesis, intracellular signaling, protein transport, and the Ras, MAPK, and PI3K-AKT pathways. The qRT-PCR results were in alignment with the sequencing data for 19 out of these 21 miRNAs which not yet implicated in LUAD, 13 were up-regulated, 6 were down-regulated. The clinical relevance assays showed that 5 up-regulated miRNAs have diagnostic value for LUAD. miR-183-3p showed significant advantages in the result of sequencing, qRT-PCR, and clinical relevance assay. Biological functional assays showed that miR-183-3p emerged as a key regulator, promoting LUAD cell proliferation, decreasing apoptosis, and augmenting migration and invasion capabilities. The clinical relevance assays and experimental validation showed SESN1 as a clinical significance target of miR-183-3p.

Discussion

Our study lays the foundation for investigating miRNAs with diagnostic significance in early-stage LUAD, pointing out that inhibition of miR-183-3p may serve as a novel therapeutic in LUAD.

Outcome of IWGT workshop on transcriptomic biomarkers for genotoxicity: Key considerations for bioinformatics

As a part of the International Workshop on Genotoxicity Testing (IWGT) in 2022, a workgroup was formed to evaluate the level of validation and regulatory acceptance of transcriptomic biomarkers that identify genotoxic substances. Several such biomarkers have been developed using various molecular techniques and computational approaches. Within the IWGT workgroup on transcriptomic biomarkers, bioinformatics was a central topic of discussion, focusing on the current approaches used to process the underlying molecular data to distill a reliable predictive signal; that is, a gene set that is indicative of genotoxicity and can then be used in later studies to predict potential DNA damaging properties for uncharacterized chemicals. While early studies used microarray data, a technological shift occurred in the past decade to incorporate modern transcriptome measuring techniques such as high-throughput transcriptomics, which in turn is based on high-throughput sequencing. Herein, we present the workgroup's review of the current bioinformatic approaches to identify genes comprising transcriptomic biomarkers. Within the context of regulatory toxicology, the reproducibility of a given analysis is critical. Therefore, the workgroup provides consensus recommendations on how to facilitate sufficient reporting of experimental parameters for the analytical procedures used in a transcriptomic biomarker study, including the recommendation to develop a biomarker-specific reporting module within the OECD Omics Reporting Framework.

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Novel bioinformatic approaches show the role of driver genes in the progression of cervical cancer: An in-silico study

Background

The goal of this bioinformatics research is to get a comprehensive understanding of the driver genes and their function in the development, progression, and treatment of cervical cancer. This study constitutes a pioneering attempt, adding to our knowledge of genetic diversity and its ramifications.

Material and methods

In this project, we use bioinformatics and systems biology methods to identify candidate transcription factors and the genes they regulate in order to identify microRNAs and LncRNAs that regulate these transcription factors and lead to the discovery of new medicines for the treatment of cervical cancer. From the differentially expressed genes available via GEO's GSE63514 accession, we use driver genes to choose these candidates. We then used the WGCNA tool in R to rebuild the co-expression network and its modules. The hub genes of each module were determined using CytoHubba, a Cystoscope plugin. The biomarker potential of hub genes was analyzed using the UCSC Xena browser and the GraphPad prism program. The TRRUST database is used to locate the TFs that regulate the expression of these genes. In order to learn how drugs, MicroRNAs, and LncRNAs interact with transcription factors, we consulted the Drug Target Information Database (DGIDB), the miRWalk database, and the LncHub database. Finally, the online database Enrichr is utilized to analyze the enrichment of Gene Ontology and KEGG pathways.

Results

By combining the mRNA expression levels of 2041 driver genes from 14 early-stage Cervical cancer and 24 control samples, a co-expression network was built. The cluster analysis shows that the collection of shared genes may be broken down into seven distinct groups, or "modules." According to the average linkage hierarchical clustering and Summary smaller than 2, we found five modules (represented by the colors blue, brown, red, green, and grey) in our research. Then, we identify 5 high-degree genes from these modules that may serve as diagnostic biomarkers (ZBBX, PLCH1, TTC7B, DNAH7, and ZMYND10). In addition, we identify four transcription factors (SRF, RELA, NFKB1, and SP1) that regulate the expression of genes in the co-expression module. Drugs, microRNAs, and long noncoding RNAs are then shown to cooperate with transcription factors. At last, the KEGG database's pathways were mined for information on how the co-expression module fits within them. More clinical trials are required for more trustworthy outcomes, and we collected this data using bioinformatics methods.

Conclusion

The major goal of this research was to identify diagnostic and therapeutic targets for cervical cancer by learning more about the involvement of driver genes in cancer's earliest stages.

Mapping the Potential Genes and Associated Pathways Involved in Long COVID-Associated Brain Fog Using Integrative Bioinformatics and Systems Biology Strategy

One of the recent emerging global health issues is long COVID. Among long COVID patients, long COVID-associated brain fog is an important area. We noted an immense gap in understanding the genes and associated pathways involved in long COVID-associated brain fog. Therefore, the study has been selected to understand the genes and pathways involved in patients with long COVID-associated brain fog. A GEO dataset, which was developed through the RNA-seq, was used for the analysis. The dataset encompasses 22 human samples of PBMC. The dataset (human samples of PBMC) was grouped into four cohorts for this study: healthy cohort, COVID convalescent, long COVID, and long COVID brain fog. Therefore, the selection criteria for the 22 PBMC samples were based on the individual infection type (COVID convalescent, long COVID, and long COVID brain fog) and the healthy cohort. Using DEG profile evaluation, we revealed 250 top-ranked DEGs with P values, Padj, baseMean, etc. From the top-ranked DEGs, we listed 24 significant DEGs and some significant DEGs are SMAD3 (P value = 6.34e-07), PF4 (P value = 1.88e-05), TNFAIP3 (P value = 3.70e-06), CXCL5 (P value = 1.22e-08), etc. Among the top-ranked DEGs, we found some genes linked with different biological functions, such as inflammatory cytokine secretion, inflammation, microclot formation, and BBB disruption. From our investigation, we found some genes that are associated with this condition, namely PF4, SMAD3, CXCL5, TNFAIP3, etc. From the literature survey and functional pathway enrichment analysis, we noted the function of the genes such as PF4, SMAD3, and CXCL5. We found that PF4 assists in clot formation, and SMAD3 is associated with neuroinflammation. Similarly, CXCL5 is an inflammatory marker associated with neuroinflammation and BBB damage. At the same time, the study with functional pathway enrichment analysis reflects that DEGs of long COVID-related brain fog might be associated with several biological pathways and processes, cell signatures, and gene-disease associations. It reflects that the disease is a highly complex one. Our study will provide an understanding of the genes and associated pathways in long COVID-related brain fog, which will assist in the next-generation biomarker discovery and therapeutics for these patients.

Combining Network Pharmacology and Transcriptomic Strategies to Explore the Pharmacological Mechanism of Total Ginsenoside Ginseng Root and Its Impact on Antidepressant Effects

Depression is one of the most common neurological diseases, which imposes a substantial social and economic burden on modern society. The purpose of this study was to explore the mechanism of total ginsenoside ginseng root (TGGR) in the treatment of depression through a comprehensive strategy combining network pharmacology, transcriptomics, and in vivo experimental validation. The Traditional Chinese Medicine Systematic Pharmacology (TCMSP) database and literature were used to collect the main components and targets of TGGR. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied to explore the underlying mechanisms. In addition, the chronic unpredictable mild stress (CUMS)-induced C57BL/6 mouse model was used to evaluate the antidepressant activity of TGGR. The results showed that TGGR improved depression-like behavior in mice and increased the decrease in serum 5-hydroxytryptamine (5-HT) and brain-derived neurotrophic factor (BDNF) levels caused by CUMS. Combined network pharmacology and transcriptomic analysis showed that the AMP-activated kinase (AMPK) signaling pathway mainly enriched the core target. Immunohistochemistry, Western blotting, and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were used to confirm whether TGGR exerts antidepressant effects by regulating this pathway. The results showed that TGGR has a regulatory impact on related proteins in the AMPK pathway, and the regulatory effect of TGGR on proteins was inhibited after the administration of related pathway inhibitors. In summary, total ginsenosides may regulate the AMPK signaling pathway and activate the sirtuin 1 (SIRT1) peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PGC-1α) pathway to have therapeutic effects on depression.

The Effects of Dexamethasone on Human Lens Epithelial Cells and the Analysis of Related Pathways with Transcriptome Sequencing

BACKGROUND

The goal of this study was to investigate the effects of dexamethasone on human lens epithelial cells (HLECs) and the potential mechanisms.

METHODS

HLECs (HLE-B3) were cultured in vitro to assess the effects of dexamethasone on cell size at different concentrations. Immunofluorescence staining was used to detect specific protein expression in HLE-B3 cells. The cell size was observed using phase-contrast microscopy, and the length and area were quantitatively measured with ImageJ software for statistical analysis. Flow cytometry was used to verify these outcomes. The means of three groups were statistically analyzed using one-way analysis of variance, whereas the means of two groups were statistically analyzed with the parametric Student's t-test. Additionally, high-throughput transcriptome sequencing was performed to compare messenger RNA (mRNA) expression levels between different concentrations of dexamethasone treatment groups and the control group, to identify potential signaling pathways. Subsequently, we performed quantitative Polymerase Chain Reaction (qPCR), immunofluorescence staining, and molecular docking experiments on the key differentially expressed genes.

RESULTS

Dexamethasone affected the size of HLE-B3 cells. Both 0.25 and 0.5 μmol/L dexamethasone increased cell length and area, exhibiting no significant difference between the two treatment groups. Flow cytometry showed that dexamethasone increased cell size and granularity, with 0.25 μmol/L dexamethasone leading to larger cell areas and higher intracellular granularity. High-throughput transcriptome sequencing revealed significant upregulation of lysophosphatidic acid receptor 1 (LPAR1) and the pathways related to the glucocorticoid (GC) receptor.

CONCLUSIONS

Certain concentrations of dexamethasone impact the morphology and biological functions of HLECs. As a subtype of G protein-coupled receptors, LPAR1 on the cell membrane may interact with dexamethasone, affecting cell size and inhibiting autophagy via the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway. These discoveries offer crucial biological insights into how dexamethasone influences the morphology and function of HLECs and the pathogenesis of GC-induced cataracts, offering potential molecular targets for future therapeutic strategies.

Proteomic and transcriptomic analyses identify apo-transcobalamin-II as a biomarker of overall survival in osteosarcoma

Background

The large-scale proteomic platform known as the SomaScan® assay is capable of simultaneously measuring thousands of proteins in patient specimens through next-generation aptamer-based multiplexed technology. While previous studies have utilized patient peripheral blood to suggest serum biomarkers of prognostic or diagnostic value in osteosarcoma (OSA), the most common primary pediatric bone cancer, they have ultimately been limited in the robustness of their analyses. We propose utilizing this aptamer-based technology to describe the systemic proteomic milieu in patients diagnosed with this disease.

Methods

To determine novel biomarkers associated with overall survival in OSA, we deployed the SomaLogic SomaScan® 7k assay to investigate the plasma proteomic profile of naive primary, recurrent, and metastatic OSA patients. Following identification of differentially expressed proteins (DEPs) between 2-year deceased and survivor cohorts, publicly available databases including Survival Genie, TIGER, and KM Plotter Immunotherapy, among others, were utilized to investigate the significance of our proteomic findings.

Results

Apo-transcobalamin-II (APO-TCN2) was identified as the most DEP between 2-year deceased and survivor cohorts (Log2 fold change = 6.8, P-value = 0.0017). Survival analysis using the Survival Genie web-based platform indicated that increased intratumoral TCN2 expression was associated with better overall survival in both OSA (TARGET-OS) and sarcoma (TCGA-SARC) datasets. Cell-cell communication analysis using the TIGER database suggested that TCN2+ Myeloid cells likely interact with marginal zone and immunoglobin-producing B lymphocytes expressing the TCN2 receptor (CD320) to promote their proliferation and survival in both non-small cell lung cancer and melanoma tumors. Analysis of publicly available OSA scRNA-sequencing datasets identified similar populations in naive primary tumors. Furthermore, circulating APO-TCN2 levels in OSA were then associated with a plasma proteomic profile likely necessary for robust B lymphocyte proliferation, infiltration, and formation of intratumoral tertiary lymphoid structures for improved anti-tumor immunity.

Conclusions

Overall, APO-TCN2, a circulatory protein previously described in various lymphoproliferative disorders, was associated with 2-year survival status in patients diagnosed with OSA. The relevance of this protein and apparent immunological function (anti-tumor B lymphocyte responses) was suggested using publicly available solid tumor RNA-sequencing datasets. Further studies characterizing the biological function of APO-TCN2 and its relevance in these diseases is warranted.

New potential diagnostic markers for verrucous hyperplasia and verrucous carcinoma based on RNA-sequencing data

Verrucous carcinoma (VC) is a rare subtype of squamous cell carcinoma (SCC) characterized by its histological presentation as a low-grade tumor with no potential for metastasis, setting it apart from invasive SCC. However, distinguishing VC from its benign counterpart, verrucous hyperplasia (VH), is challenging due to their clinical and morphological similarities. Despite the importance of accurate diagnosis for determining treatment strategies, diagnosis of VH and VC relied only on lesion recurrence after resection. To address this challenge, we generated RNA profiling data from tissue samples of VH and VC patients to identify novel diagnostic markers. We analyzed differentially expressed (DE) mRNA and long non-coding RNA (lncRNA) in tissue samples from VH and VC patients. Additionally, ChIP-X Enrichment Analysis 3 (ChEA3) was conducted to identify the top five transcription factors potentially regulating the expression of DE mRNAs in VH and VC. Our analysis of mRNA and lncRNA expression profiles in VH and VC provides insights into the underlying molecular characteristics of these diseases and offers potential new diagnostic markers. The identification of specific DE genes and lncRNAs may enable clinicians to more accurately differentiate between VH and VC, leading to better treatment choices.

Selection and application of small non-coding RNAs for normalizing RT-qPCR data of bovine preimplantation embryo conditioned medium

Small non-coding RNAs (sncRNAs) present in the conditioned medium (CM) of bovine preimplantation embryos are potential noninvasive biomarkers for assessing embryo quality. Accurate quantification of sncRNA levels in the spent CM is of utmost importance in this regard. RT-qPCR is considered as the gold standard for quantifying RNA. In order to standardize RT-qPCR data in the sample type under investigation, the use of suitable stable sncRNAs is essential. Here, we selected 10 sncRNAs from small RNA sequencing of CM samples derived from both bovine blastocysts and degenerate embryos, and evaluated their expression stability together with that of cel-miR-39 as a spike and the often-used U6 small nuclear RNA at different embryo developmental stages. In CM of 2-cell embryos, rsRNA-1044 showed the most stable expression, while tDR-1:32-Gly-CCC-1 was the most stable expressed sncRNA in CM of the stages beyond the 2-cell stage. Next, tDR-1:32-Gly-CCC-1 was used for normalizing the RT-qPCR data from the CM of blastocysts and degenerate embryos. Bta-miR-155 and tDR-39:75-Arg-CCG-2 were found to be significantly up-regulated in the CM of blastocysts compared to that of the degenerated embryos (P = 0.028 and P = 0.017, respectively), suggesting their expression levels are related to embryo development stage. In conclusion, tDR-1:32-Gly-CCC-1 can serve as a suitable reference sncRNA for normalization of RT-qPCR data of the CM from bovine blastocysts.

From Bench to Babies - Drug Development for Male Subfertility

Infertility is estimated to affect more than 50 million couples around the world, with male factor accounting for half of these cases, yet there is a notable absence of effective treatment options for men, other than in-vitro fertilisation (IVF) or intracytoplasmic sperm injection (ICSI). This review considers unlicensed and empirical treatments used for male subfertility, including hormonal therapy, phosphodiesterase inhibitors, and antioxidants. Compounds generally demonstrate variable improvements in sperm function but benefits for fertility are less clear. There is a pressing need for effective treatment options for subfertile men, however, our knowledge of sperm function is limited, restricting the identification of precise treatment targets. The traditional drug discovery pathway is also notorious for its extensive resource and time requirements, often extending over decades and demanding significant financial investment. Unfortunately, a substantial number of potential therapies fail before reaching the marketplace. Furthermore, reliance on mammalian models is not possible in the drug development process for male subfertility, due to significant variability between animals and man. We review recent breakthroughs and highlight novel methods aimed at improving the effectiveness and efficiency of drug discovery for male subfertility. High-throughput screening, combinatorial chemistry, and the repurposing of established medications have great potential. These strategies offer the promise of accelerating the pace of drug development, curbing the extensive demand for resources, and, in the case of drug repurposing, diminish the demand for comprehensive pharmacokinetic and pharmacodynamic studies. As these innovative approaches are adopted, the feasibility of addressing male subfertility through scientific advancements appears to be increasingly attainable.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Network Pharmacology and Transcriptomics to Explore the Pharmacological Mechanisms of 20(S)-Protopanaxatriol in the Treatment of Depression

Depression is one of the most common psychological disorders nowadays. Studies have shown that 20(S)-protopanaxatriol (PPT) can effectively improve depressive symptoms in mice. However, its mechanism needs to be further explored. In this study, we used an integrated approach combining network pharmacology and transcriptomics to explore the potential mechanisms of PPT for depression. First, the potential targets and pathways of PPT treatment of depression were screened through network pharmacology. Secondly, the BMKCloud platform was used to obtain brain tissue transcription data of chronic unpredictable mild stress (CUMS) model mice and screen PPT-altered differential expression genes (DEGs). Gene ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed using network pharmacology and transcriptomics. Finally, the above results were verified by molecular docking, Western blotting, and quantitative real-time polymerase chain reaction (qRT-PCR). In this study, we demonstrated that PPT improved depression-like behavior and brain histopathological changes in CUMS mice, downregulated nitric oxide (NO) and interleukin-6 (IL-6) levels, and elevated serum levels of 5-hydroxytryptamine (5-HT) and brain-derived neurotrophic factor (BDNF) after PPT treatment compared to the CUMS group. Eighty-seven potential targets and 350 DEGs were identified by network pharmacology and transcriptomics. Comprehensive analysis showed that transthyretin (TTR), klotho (KL), FOS, and the phosphatidylinositol 3-kinase-protein kinase B (PI3K-AKT) signaling pathway were closely associated with the therapeutic effects of PPT. Molecular docking results showed that PPT had a high affinity for PI3K, AKT, TTR, KL, and FOS targets. Gene and protein level results showed that PPT could increase the expression of PI3K, phosphorylation of PI3K (p-PI3K), AKT, phosphorylation of AKT (p-AKT), TTR, and KL and inhibit the expression level of FOS in the brain tissue of depressed mice. Our data suggest that PPT may achieve the treatment of depression by inhibiting the expression of FOS, enhancing the expression of TTR and KL, and modulating the PI3K-AKT signaling pathway.

Buyang huanwu decoction promotes remyelination via miR-760-3p/GPR17 axis after intracerebral hemorrhage

ETHNOPHARMACOLOGICAL RELEVANCE

The repairment of myelin sheaths is crucial for mitigating neurological impairments of intracerebral hemorrhage (ICH). However, the current research on remyelination processes in ICH remains limited. A representative traditional Chinese medicine, Buyang Huanwu decoction (BYHWD), shows a promising therapeutic strategy for ICH treatment.

AIM OF THE STUDY

To investigate the pro-remyelination effects of BYHWD on ICH and explore the underlying mechanisms.

MATERIALS AND METHODS

The collagenase-induced mice ICH model was created for investigation. BYHWD's protective effects were assessed by behavioral tests and histological staining. Transmission electron microscopy was used for displaying the structure of myelin sheaths. The remyelination and oligodendrocyte differentiation were evaluated by the expressions of myelin proteolipid protein (PLP), myelin basic protein (MBP), MBP/TAU, Olig2/CC1, and PDGFRα/proliferating cell nuclear antigen (PCNA) through RT-qPCR and immunofluorescence. Transcriptomics integrated with disease database analysis and experiments in vivo and in vitro revealed the microRNA-related underlying mechanisms.

RESULTS

Here, we reported that BYHWD promoted the neurological function of ICH mice and improved remyelination by increasing PLP, MBP, and TAU, as well as restoring myelin structure. Besides, we showed that BYHWD promoted remyelination by boosting the differentiation of PDGFRα+ oligodendrocyte precursor cells into olig2+/CC1+ oligodendrocytes. Additionally, we demonstrated that the remyelination effects of BYHWD worked by inhibiting G protein-coupled receptor 17 (GPR17). miRNA sequencing integrated with miRNA database prediction screened potential miRNAs targeting GPR17. By applying immunofluorescence, RNA in situ hybridization and dual luciferase reporter gene assay, we confirmed that BYHWD suppressed GPR17 and improved remyelination by increasing miR-760-3p.

CONCLUSIONS

BYHWD improves remyelination and neurological function in ICH mice by targeting miR-760-3p to inhibit GPR17. This study may shed light on the orchestration of remyelination mechanisms after ICH, thus providing novel insights for developing innovative prescriptions with brain-protective properties.

RNA Sequencing and Bioinformatics Analysis Reveals the Downregulation of DNA Replication Genes by Morindone in Colorectal Cancer Cells

Morindone, a natural anthraquinone compound, has been reported to have significant pharmacological properties in different cancers. However, its anticancer effects in colorectal cancer (CRC) and the underlying molecular mechanisms remain obscure. In this study, RNA sequencing was used to assess the differentially expressed genes (DEGs) following morindone treatment in two CRC cell lines, HCT116 and HT29 cells. Functional enrichment analysis of overlapping DEGs revealed that negative regulation of cell development from biological processes and the MAPK signalling pathway were the most significant Gene Ontology terms and Kyoto Encyclopaedia of Genes and Genome pathway, respectively. Seven hub genes were identified among the overlapping genes, including MCM5, MCM6, MCM10, GINS2, POLE2, PRIM1, and WDHD1. All hub genes were found downregulated and involved in DNA replication fork. Among these, GINS2 was identified as the most cancer-dependent gene in both cells with better survival outcomes. Validation was performed on seven hub genes with rt-qPCR, and the results were consistent with the RNA sequencing findings. Collectively, this study provides corroboration of the potential therapeutic benefits and suitable pharmacological targets of morindone in the treatment of CRC.

Novel Isolating Approaches to Circulating Tumor Cell Enrichment Based on Microfluidics: A Review

Circulating tumor cells (CTCs), derived from the primary tumor and carrying genetic information, contribute significantly to the process of tumor metastasis. The analysis and detection of CTCs can be used to assess the prognosis and treatment response in patients with tumors, as well as to help study the metastatic mechanisms of tumors and the development of new drugs. Since CTCs are very rare in the blood, it is a challenging problem to enrich CTCs efficiently. In this paper, we provide a comprehensive overview of microfluidics-based enrichment devices for CTCs in recent years. We explore in detail the methods of enrichment based on the physical or biological properties of CTCs; among them, physical properties cover factors such as size, density, and dielectric properties, while biological properties are mainly related to tumor-specific markers on the surface of CTCs. In addition, we provide an in-depth description of the methods for enrichment of single CTCs and illustrate the importance of single CTCs for performing tumor analyses. Future research will focus on aspects such as improving the separation efficiency, reducing costs, and increasing the detection sensitivity and accuracy.

De Novo Generation of Chemical Structures of Inhibitor and Activator Candidates for Therapeutic Target Proteins by a Transformer-Based Variational Autoencoder and Bayesian Optimization

Deep generative models for molecular generation have been gaining much attention as structure generators to accelerate drug discovery. However, most previously developed methods are chemistry-centric approaches, and comprehensive biological responses in the cell have not been taken into account. In this study, we propose a novel computational method, TRIOMPHE-BOA (transcriptome-based inference and generation of molecules with desired phenotypes using the Bayesian optimization algorithm), to generate new chemical structures of inhibitor or activator candidates for therapeutic target proteins by integrating chemically and genetically perturbed transcriptome profiles. In the algorithm, the substructures of multiple molecules that were selected based on the transcriptome analysis are fused in the design of new chemical structures by exploring the latent space of a Transformer-based variational autoencoder using Bayesian optimization. Our results demonstrate the usefulness of the proposed method in terms of having high reproducibility of existing ligands for 10 therapeutic target proteins when compared with previous methods. Moreover, this method can be applied to proteins without detailed 3D structures or known ligands and is expected to become a powerful tool for more efficient hit identification.

Drug repositioning for immunotherapy in breast cancer using single-cell analysis

Immunomodulatory peptides, while exhibiting potential antimicrobial, antifungal, and/or antiviral properties, can play a role in stimulating or suppressing the immune system, especially in pathological conditions like breast cancer (BC). Thus, deregulation of these peptides may serve as an immunotherapeutic strategy to enhance the immune response. In this meta-analysis, we utilized single-cell RNA sequencing data and known therapeutic peptides to investigate the deregulation of these peptides in malignant versus normal human breast epithelial cells. We corroborated our findings at the chromatin level using ATAC-seq. Additionally, we assessed the protein levels in various BC cell lines. Moreover, our in-house drug repositioning approach was employed to identify potential drugs that could positively impact the relapse-free survival of BC patients. Considering significantly deregulated therapeutic peptides and their role in BC pathology, our approach aims to downregulate B2M and SLPI, while upregulating PIGR, DEFB1, LTF, CLU, S100A7, and SCGB2A1 in BC epithelial cells through our drug repositioning pipeline. Leveraging the LINCS L1000 database, we propose BRD-A06641369 for B2M downregulation and ST-4070043 and BRD-K97926541 for SLPI downregulation without negatively affecting the MHC complex as a significantly correlated pathway with these two genes. Furthermore, we have compiled a comprehensive list of drugs for the upregulation of other selected immunomodulatory peptides. Employing an immunotherapeutic approach by integrating our drug repositioning pipeline with single-cell analysis, we proposed potential drugs and drug targets to fortify the immune system against BC.

Improving drug response prediction via integrating gene relationships with deep learning

Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.

Whole Blood Transcriptome Analysis in Patients with Trigeminal Neuralgia: a Prospective Clinical Study

Trigeminal neuralgia (TN) brings a huge burden to patients, without long-term effective treatment. This study aimed to explore the differentially expressed genes (DEGs) and related enrichment pathways in patients with TN. This was a study of transcriptome sequencing and bioinformatics analysis of human samples. Whole blood samples were collected from the TN patients and pain-free controls. RNA was extracted to conduct the RNA-sequencing and the subsequent bioinformatics analysis. DEGs between the two groups were derived. Kyoto encyclopedia of genes and genomes (KEGG) and Gene ontology (GO) was used to find the enrichment pathways of DEGs. Protein protein interaction (PPI) network was used to depict the interaction between DEGs and find the most important gene, hub gene. Compared with the control group, there were 117 up-regulated DEGs and 103 down-regulated DEGs in the whole blood of patients in the TN group. Pathway enrichment analysis showed that DEGs were mainly enriched in the neuroimmune and metabolic pathways. The PPI network demonstrated that colony stimulating factor 2 (CSF2) was the most important hub gene in the whole blood of TN patients. This study shows the expression of the transcriptome in the whole blood samples of TN patients. The neuroimmune responses and key hub gene CSF2 in the whole blood cells play a vital role in the occurrence of TN. Our research provides a theoretical basis for the diagnosis and treatments of TN. This study was registered at clinicaltrials.gov in June 2021 (No. NCT04923399).

A novel strategy of integrating network pharmacology and transcriptome reveals antiapoptotic mechanisms of Buyang Huanwu Decoction in treating intracerebral hemorrhage

ETHNOPHARMACOLOGICAL RELEVANCE

Buyang Huanwu Decoction (BYHWD), as a traditional Chinese medical prescription, has been used to treat intracerebral hemorrhage (ICH) for hundreds of years, but the antiapoptotic properties have not yet been studied.

AIM OF THE STUDY

This study aims to elucidate the antiapoptotic mechanism of BYHWD in ICH.

MATERIALS AND METHODS

The therapeutic effect of BYHWD on ICH was assessed by modified neurological severity scores (mNSS), foot fault, and histopathological staining. Then, we used a modified comprehensive strategy by integrating transcriptome and network pharmacology to reveal the underlying mechanism. TUNEL assay, qRT-PCR, and western blot were further applied to evaluate the antiapoptotic effect of BYHWD on ICH. Dual-luciferase reporter assay and plasmid transfections were implemented to validate the potential competing endogenous RNAs (ceRNA) mechanism of Sh2b3.

RESULTS

Network pharmacology analysis indicated that the regulation of the apoptotic process was the highest enriched GO term, and that MAP kinase activity, ERK1, and ERK2 cascade were strongly correlated. Transcriptome analysis screened 180 differentially expressed mRNAs, which were highly enriched in the immune system process and negative regulation of programmed cell death. By checking the literature, we found that Sh2b3 was of great importance to apoptosis by modulating MAPK cascades. TUNEL assay validated the anti-apoptotic effect of BYHWD. Moreover, BYHWD was proven to regulate the Sh2b3-mediated ERK1/2 signaling pathway in ICH mice by qRT-PCR and western blot. We further explored the lncRNA-miRNA-mRNA network underlying the therapeutic effect, among which 4933404O12Rik/miR-185-5p is the upstream regulatory mechanism of Sh2b3.

CONCLUSIONS

We explored the antiapoptotic mechanism of BYHWD in treating ICH by a novel integrated strategy, which involved the 4933404O12Rik/miR-185-5p/Sh2b3 ceRNAs axis.

CMAUP database update 2024: extended functional and association information of useful plants for biomedical research

Knowledge of the collective activities of individual plants together with the derived clinical effects and targeted disease associations is useful for plant-based biomedical research. To provide the information in complement to the established databases, we introduced a major update of CMAUP database, previously featured in NAR. This update includes (i) human transcriptomic changes overlapping with 1152 targets of 5765 individual plants, covering 74 diseases from 20 027 patient samples; (ii) clinical information for 185 individual plants in 691 clinical trials; (iii) drug development information for 4694 drug-producing plants with metabolites developed into approved or clinical trial drugs; (iv) plant and human disease associations (428 737 associations by target, 220 935 reversion of transcriptomic changes, 764 and 154121 associations by clinical trials of individual plants and plant ingredients); (v) the location of individual plants in the phylogenetic tree for navigating taxonomic neighbors, (vi) DNA barcodes of 3949 plants, (vii) predicted human oral bioavailability of plant ingredients by the established SwissADME and HobPre algorithm, (viii) 21-107% increase of CMAUP data over the previous version to cover 60 222 chemical ingredients, 7865 plants, 758 targets, 1399 diseases, 238 KEGG human pathways, 3013 gene ontologies and 1203 disease ontologies. CMAUP update version is freely accessible at https://bidd.group/CMAUP/index.html.

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

Identification of key genes and regulatory mechanisms in adult degenerative scoliosis

BACKGROUND

Adult degenerative scoliosis (ADS) is a spinal disorder, but its pathogenesis remain unclear. Therefore, in this study, we utilized data from the GEO database and explored the key genes and regulatory mechanisms involved in ADS.

METHODS

We performed bioinformatics analysis on the GSE209825 dataset of GEO database. Weighted gene co-expression network analysis (WGCNA) was used to identify ADS-related gene modules, and we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. We constructed a protein-protein interaction (PPI) network using the STRING database. We validated the specificity of hub genes in ADS using the GSE34095 dataset and plotted ROC curves for the identification of different degenerative spinal diseases based on the hub genes expression RESULTS: We identified 113 differentially expressed lncRNAs. WGCNA identified the MEblack module had the strongest correlation to ADS. GO and KEGG analyses of target genes in lncRNAs revealed their involvement in immune responses, inflammation, cellular processes, and metabolic pathways. Through PPI and ROC analysis, 10 hub genes linked to ADS diseases with certain specificity were found: ELANE, LTF, DEFA1B, SLC2A4, DEFA1, FAXDC2, LCN2, CTSB, FDFT1, and AURKA.

CONCLUSIONS

We identified 10 potential hub genes associated with ADS and constructed a transcription factors (TFs)-lncRNAs-hub genes regulatory network. These findings provide a new direction and research basis for the targeted treatment and mechanism research of ADS.

Dual Targeting of DNA Damage Response Proteins Implicated in Cancer Radioresistance

Ionizing radiation can induce different types of DNA lesions, leading to genomic instability and ultimately cell death. Radiation therapy or radiotherapy, a major modality in cancer treatment, harnesses the genotoxic potential of radiation to target and destroy cancer cells. Nevertheless, cancer cells have the capacity to develop resistance to radiation treatment (radioresistance), which poses a major obstacle in the effective management of cancer. It has been shown that administration of platinum-based drugs to cancer patients can increase tumor radiosensitivity, but despite this, it is associated with severe adverse effects. Several lines of evidence support that activation of the DNA damage response and repair machinery in the irradiated cancer cells enhances radioresistance and cellular survival through the efficient repair of DNA lesions. Therefore, targeting of key DNA damage repair factors would render cancer cells vulnerable to the irradiation effects, increase cancer cell killing, and reduce the risk of side effects on healthy tissue. Herein, we have employed a computer-aided drug design approach for generating ab initio a chemical compound with drug-like properties potentially targeting two proteins implicated in multiple DNA repair pathways. The findings of this study could be taken into consideration in clinical decision-making in terms of co-administering radiation with DNA damage repair factor-based drugs.

Gastric protective effect of Alpinia officinarum flavonoids: mediating TLR4/NF-κB and TRPV1 signalling pathways and gastric mucosal healing

CONTEXT

Our previous studies have found that total flavonoid of Alpinia officinarum Hance (Zingiberaceae) (F.AOH) had protective effects on gastric ulcer (GU).

OBJECTIVE

To investigate the protective mechanism of F.AOH on acetic acid-induced chronic GUs in rats and ethanol-induced GES-1 cells damage.

MATERIALS AND METHODS

In vivo: Gastric damage was induced in SD rats by administering acetic acid after oral treatment with F-AOH at 54, 27 and 13.5 mg/kg (2 weeks of continuous gavage). After a comprehensive evaluation of rats' serum and gastric tissue-related indicators, gene transcriptome sequencing, qPCR and Western blotting were used to investigate the mechanism further. In vivo: GES-1 cells were incubated with F-AOH (8, 4 and 2 μg/mL) for 16 h and treated with 7% ethanol for 4 h. Transwell and flow cytometry were employed to detect migration and apoptosis of cells.

RESULTS

F.AOH effectively reduced the area of GUs in rats (from 11.2 ± 1.89 to 2.19 ± 0.95), reversing ethanol-induced cells apoptosis (from 23 ± 1.3 to 8.11 ± 0.93%). It also inhibited the expression of endothelin-1 (ET-1) and iNOS proteins, decreasing the levels of TNF-α IL-6 in serum, improving oxidative stress levels and increasing the expression of Bcl-2/Bax dimer genes. In addition, 4005 differentially expressed genes between the acetic acid model and the drug groups. Through experimental verification, F.AOH can inhibit the activation of TLR4/NF-κB signalling pathway and TRPV1 receptor.

CONCLUSIONS

F.AOH, as an effective gastric protective plant component, had potential therapeutic value in anti-inflammatory pain and antioxidative stress gastrointestinal diseases.

A 15-Gene-Based Risk Signature for Predicting Overall Survival in SCLC Patients Who Have Undergone Surgical Resection

Small cell lung cancer (SCLC) is a malignancy with a poor prognosis whose treatment has not progressed for decades. The survival benefit of surgery and the selection of surgical candidates are still controversial in SCLC. This study is the first report to identify transcriptomic alterations associated with prognosis and propose a gene expression-based risk signature that can be used to predict overall survival (OS) in SCLC patients who have undergone potentially curative surgery. An integrative transcriptome analysis of three gene expression datasets (GSE30219, GSE43346, and GSE149507) revealed 1734 up-regulated and 2907 down-regulated genes. Cox-Mantel test, Cox regression, and Lasso regression analyses were used to identify genes to be included in the risk signature. EGAD00001001244 and GSE60052-cohorts were used for internal and external validation, respectively. Overall survival was significantly poorer in patients with high-risk scores compared to the low-risk group. The discriminatory performance of the risk signature was superior to other parameters. Multivariate analysis showed that the risk signature has the potential to be an independent predictor of prognosis. The prognostic genes were enriched in pathways including regulation of transcription, cell cycle, cell metabolism, and angiogenesis. Determining the roles of the identified prognostic genes in the pathogenesis of SCLC may contribute to the development of new treatment strategies. The risk signature needs to be validated in a larger cohort of patients to test its usefulness in clinical decision-making.

Pharmacogenomic Analysis of Combined Therapies against Glioblastoma Based on Cell Markers from Single-Cell Sequencing

Glioblastoma is the most common and aggressive form of primary brain cancer and the lack of viable treatment options has created an urgency to develop novel treatments. Personalized or predictive medicine is still in its infancy stage at present. This research aimed to discover biomarkers to inform disease progression and to develop personalized prophylactic and therapeutic strategies by combining state-of-the-art technologies such as single-cell RNA sequencing, systems pharmacology, and a polypharmacological approach. As predicted in the pyroptosis-related gene (PRG) transcription factor (TF) microRNA (miRNA) regulatory network, TP53 was the hub gene in the pyroptosis process in glioblastoma (GBM). A LASSO Cox regression model of pyroptosis-related genes was built to accurately and conveniently predict the one-, two-, and three-year overall survival rates of GBM patients. The top-scoring five natural compounds were parthenolide, rutin, baeomycesic acid, luteolin, and kaempferol, which have NFKB inhibition, antioxidant, lipoxygenase inhibition, glucosidase inhibition, and estrogen receptor agonism properties, respectively. In contrast, the analysis of the cell-type-specific differential expression-related targets of natural compounds showed that the top five subtype cells targeted by natural compounds were endothelial cells, microglia/macrophages, oligodendrocytes, dendritic cells, and neutrophil cells. The current approach-using the pharmacogenomic analysis of combined therapies-serves as a model for novel personalized therapeutic strategies for GBM treatment.

Comparative Metabolomic and Transcriptomic Analyses Reveal Distinct Ascorbic Acid (AsA) Accumulation Patterns between PCA and PCNA Persimmon Developing Fruit

Persimmon fruit has a high nutritional value and significantly varies between pollination-constant astringent (PCA) and pollination-constant non-astringent (PCNA) persimmons. The astringency type affects sugar, flavonoids, and tannin accumulation and is well known in persimmon fruit. However, the impact of the fruit astringency type on ascorbic acid (AsA) accumulation is limited. In this study, typical PCA varieties ('Huojing' and 'Zhongshi5') and PCNA varieties ('Yohou' and 'Jiro') of persimmon fruit were sampled at four developing stages (S1-S4) to provide valuable information on AsA content variation in PCA and PCNA persimmon. Persimmon fruit is rich in ascorbic acid; the AsA content of the four varieties 'Zhongshi5', 'Huojing', 'Jiro', and 'Youhou' mature fruit reached 104.49, 48.69, 69.69, and 47.48 mg/100 g. Fruit of the same astringency type persimmon showed a similar AsA accumulation pattern. AsA content was significantly higher in PCA than PCNA fruit at S1-S3. The initial KEGG analysis of metabolites showed that galactose metabolism is the major biosynthetic pathway of AsA in persimmon fruit. There were significant differences in galactose pathway-related metabolite content in developing PCA and PCNA fruit, such as Lactose, D-Tagatose, and D-Sorbitol content in PCA being higher than that of PCNA. Combined gene expression and WGCNA analyses showed that the expression of the GME (evm.TU.contig4144.37) gene was higher in PCA-type than in PCNA-type fruit in S1-S3 and exhibited the highest correlation with AsA content (r = 690 **, p < 0.01). Four hub genes, including the DNA methylation gene, methyltransferase gene, F-box, and Actin-like Protein, were identified as potential regulators of the GME gene. These results provide basic information on how astringency types affect AsA accumulation and will provide valuable information for further investigation on AsA content variation in persimmon fruit.

Molecular biomarkers for assessing the heat-adapted phenotype: a narrative scoping review

Heat acclimation/acclimatisation (HA) mitigates heat-related decrements in physical capacity and heat-illness risk and is a widely advocated countermeasure for individuals operating in hot environments. The efficacy of HA is typically quantified by assessing the thermo-physiological responses to a standard heat acclimation state test (i.e. physiological biomarkers), but this can be logistically challenging, time consuming, and expensive. A valid molecular biomarker of HA would enable evaluation of the heat-adapted state through the sampling and assessment of a biological medium. This narrative review examines candidate molecular biomarkers of HA, highlighting the poor sensitivity and specificity of these candidates and identifying the current lack of a single 'standout' biomarker. It concludes by considering the potential of multivariable approaches that provide information about a range of physiological systems, identifying a number of challenges that must be overcome to develop a valid molecular biomarker of the heat-adapted state, and highlighting future research opportunities.

Learning Individual Survival Models from PanCancer Whole Transcriptome Data

PURPOSE

Personalized medicine attempts to predict survival time for each patient, based on their individual tumor molecular profile. We investigate whether our survival learner in combination with a dimension reduction method can produce useful survival estimates for a variety of patients with cancer.

EXPERIMENTAL DESIGN

This article provides a method that learns a model for predicting the survival time for individual patients with cancer from the PanCancer Atlas: given the (16,335 dimensional) gene expression profiles from 10,173 patients, each having one of 33 cancers, this method uses unsupervised nonnegative matrix factorization (NMF) to reexpress the gene expression data for each patient in terms of 100 learned NMF factors. It then feeds these 100 factors into the Multi-Task Logistic Regression (MTLR) learner to produce cancer-specific models for each of 20 cancers (with >50 uncensored instances); this produces "individual survival distributions" (ISD), which provide survival probabilities at each future time for each individual patient, which provides a patient's risk score and estimated survival time.

RESULTS

Our NMF-MTLR concordance indices outperformed the VAECox benchmark by 14.9% overall. We achieved optimal survival prediction using pan-cancer NMF in combination with cancer-specific MTLR models. We provide biological interpretation of the NMF model and clinical implications of ISDs for prognosis and therapeutic response prediction.

CONCLUSIONS

NMF-MTLR provides many benefits over other models: superior model discrimination, superior calibration, meaningful survival time estimates, and accurate probabilistic estimates of survival over time for each individual patient. We advocate for the adoption of these cancer survival models in clinical and research settings.

Integrated gene expression profiling and functional enrichment analyses to discover biomarkers and pathways associated with Guillain-Barré syndrome and autism spectrum disorder to identify new therapeutic targets

Guillain-Barré syndrome (GBS) is one of the most prominent and acute immune-mediated peripheral neuropathy, while autism spectrum disorders (ASD) are a group of heterogeneous neurodevelopmental disorders. The complete mechanism regarding the neuropathophysiology of these disorders is still ambiguous. Even after recent breakthroughs in molecular biology, the link between GBS and ASD remains a mystery. Therefore, we have implemented well-established bioinformatic techniques to identify potential biomarkers and drug candidates for GBS and ASD. 17 common differentially expressed genes (DEGs) were identified for these two disorders, which later guided the rest of the research. Common genes identified the protein-protein interaction (PPI) network and pathways associated with both disorders. Based on the PPI network, the constructed hub gene and module analysis network determined two common DEGs, namely CXCL9 and CXCL10, which are vital in predicting the top drug candidates. Furthermore, coregulatory networks of TF-gene and TF-miRNA were built to detect the regulatory biomolecules. Among drug candidates, imatinib had the highest docking and MM-GBSA score with the well-known chemokine receptor CXCR3 and remained stable during the 100 ns molecular dynamics simulation validated by the principal component analysis and the dynamic cross-correlation map. This study predicted the gene-based disease network for GBS and ASD and suggested prospective drug candidates. However, more in-depth research is required for clinical validation.Communicated by Ramaswamy H. Sarma.

Early Pharmacodynamic Changes Measured Using RNA Sequencing of Peripheral Blood from Patients in a Phase I Study with Mitazalimab, a Potent CD40 Agonistic Monoclonal Antibody

CD40-targeting therapies can enhance the dendritic cell priming of tumor-specific T cells and repolarize intratumoral macrophages to alleviate the tumoral immunosuppressive environment and remodel the extracellular matrix. Mitazalimab is a potent agonistic CD40 monoclonal IgG1 antibody currently under clinical development. This study used RNA sequencing of blood samples from a subset of patients from a Phase I trial with mitazalimab (NCT02829099) to assess peripheral pharmacodynamic activity. We found that mitazalimab induced transient peripheral transcriptomic alterations (at 600 µg/kg and 900 µg/kg dose administered intravenously), which mainly were attributed to immune activation. In particular, the transcriptomic alterations showed a reduction in effector cells (e.g., CD8+ T cells and natural killer cells) and B cells peripherally with the remaining cells (e.g., dendritic cells, monocytes, B cells, and natural killer cells) showing transcription profiles consistent with activation. Lastly, distinct patient subgroups based on the pattern of transcriptomic alterations could be identified. In summary, the data presented herein reinforce the anticipated mode of action of mitazalimab and support its ongoing clinical development.

Next-generation sequencing reveals altered gene expression and enriched pathways in triple-negative breast cancer cells treated with oleuropein and oleocanthal

Triple-negative breast cancer (TNBC) is a subtype of breast cancer characterized by poor prognosis and limited treatment options. Oleuropein and oleocanthal are bioactive chemicals found in extra-virgin olive oil; they have been shown to have anti-cancer potential. In this study, we examined the inhibitory effects of these two natural compounds, on MDA-MB-231 and MDA-MB-468 TNBC cell lines. The human TNBC MDA-MB-231 and MDA-MB-468 cell lines were treated with oleuropein or oleocanthal at ranging concentrations for 48 h. After determining the optimum concentration to reach IC50, using the sulforhodamine B assay, total RNA was extracted after 12, 24, and 48 h from treated and untreated cells. Poly(A)-RNA selection was conducted, followed by library construction and RNA sequencing. Differential gene expression (DEG) analysis was performed to identify DEGs between treated and untreated cells. Pathway analysis was carried out using the KEGG and GO databases. Oleuropein and oleocanthal considerably reduced the proliferation of TNBC cells, with oleocanthal having a slightly stronger effect than oleuropein. Furthermore, multi-time series RNA sequencing showed that the expression profile of TNBC cells was significantly altered after treatment with these compounds, with temporal dynamics and groups of genes consistently affected at all time points. Pathway analysis revealed several significant pathways associated with TNBC, including cell death, apoptotic process, programmed cell death, response to stress, mitotic cell cycle process, cell division, and cancer progression. Our findings suggest that oleuropein and oleocanthal have potential therapeutic benefits for TNBC and can be further investigated as alternative treatment options.

Use of high-plex data provides novel insights into the temporal artery processes of giant cell arteritis

Objective

To identify the key coding genes underlying the biomarkers and pathways associated with giant cell arteritis (GCA), we performed an in situ spatial profiling of molecules involved in the temporal arteries of GCA patients and controls. Furthermore, we performed pharmacogenomic network analysis to identify potential treatment targets.

Methods

Using human formalin-fixed paraffin-embedded temporal artery biopsy samples (GCA, n = 9; controls, n = 7), we performed a whole transcriptome analysis using the NanoString GeoMx Digital Spatial Profiler. In total, 59 regions of interest were selected in the intima, media, adventitia, and perivascular adipose tissue (PVAT). Differentially expressed genes (DEGs) (fold-change > 2 or < -2, p-adjusted < 0.01) were compared across each layer to build a spatial and pharmacogenomic network and to explore the pathophysiological mechanisms of GCA.

Results

Most of the transcriptome (12,076 genes) was upregulated in GCA arteries, compared to control arteries. Among the screened genes, 282, 227, 40, and 5 DEGs were identified in the intima, media, adventitia, and PVAT, respectively. Genes involved in the immune process and vascular remodeling were upregulated within GCA temporal arteries but differed across the arterial layers. The immune-related functions and vascular remodeling were limited to the intima and media.

Conclusion

This study is the first to perform an in situ spatial profiling characterization of the molecules involved in GCA. The pharmacogenomic network analysis identified potential target genes for approved and novel immunotherapies.

Advances in non-hormonal male contraception targeting sperm motility

BACKGROUND

The high rates of unintended pregnancy and the ever-growing world population impose health, economic, social, and environmental threats to countries. Expanding contraceptive options, including male methods, are urgently needed to tackle these global challenges. Male contraception is limited to condoms and vasectomy, which are unsuitable for many couples. Thus, novel male contraceptive methods may reduce unintended pregnancies, meet the contraceptive needs of couples, and foster gender equality in carrying the contraceptive burden. In this regard, the spermatozoon emerges as a source of druggable targets for on-demand, non-hormonal male contraception based on disrupting sperm motility or fertilization.

OBJECTIVE AND RATIONALE

A better understanding of the molecules governing sperm motility can lead to innovative approaches toward safe and effective male contraceptives. This review discusses cutting-edge knowledge on sperm-specific targets for male contraception, focusing on those with crucial roles in sperm motility. We also highlight challenges and opportunities in male contraceptive drug development targeting spermatozoa.

SEARCH METHODS

We conducted a literature search in the PubMed database using the following keywords: 'spermatozoa', 'sperm motility', 'male contraception', and 'drug targets' in combination with other related terms to the field. Publications until January 2023 written in English were considered.

OUTCOMES

Efforts for developing non-hormonal strategies for male contraception resulted in the identification of candidates specifically expressed or enriched in spermatozoa, including enzymes (PP1γ2, GAPDHS, and sAC), ion channels (CatSper and KSper), transmembrane transporters (sNHE, SLC26A8, and ATP1A4), and surface proteins (EPPIN). These targets are usually located in the sperm flagellum. Their indispensable roles in sperm motility and male fertility were confirmed by genetic or immunological approaches using animal models and gene mutations associated with male infertility due to sperm defects in humans. Their druggability was demonstrated by the identification of drug-like small organic ligands displaying spermiostatic activity in preclinical trials.

WIDER IMPLICATIONS

A wide range of sperm-associated proteins has arisen as key regulators of sperm motility, providing compelling druggable candidates for male contraception. Nevertheless, no pharmacological agent has reached clinical developmental stages. One reason is the slow progress in translating the preclinical and drug discovery findings into a drug-like candidate adequate for clinical development. Thus, intense collaboration among academia, private sectors, governments, and regulatory agencies will be crucial to combine expertise for the development of male contraceptives targeting sperm function by (i) improving target structural characterization and the design of highly selective ligands, (ii) conducting long-term preclinical safety, efficacy, and reversibility evaluation, and (iii) establishing rigorous guidelines and endpoints for clinical trials and regulatory evaluation, thus allowing their testing in humans.

Integrated analysis of inflammatory mRNAs, miRNAs, and lncRNAs elucidates the molecular interactome behind bovine mastitis

Mastitis is known as intramammary inflammation, which has a multifactorial complex phenotype. However, the underlying molecular pathogenesis of mastitis remains poorly understood. In this study, we utilized a combination of RNA-seq and miRNA-seq techniques, along with computational systems biology approaches, to gain a deeper understanding of the molecular interactome involved in mastitis. We retrieved and processed one hundred transcriptomic libraries, consisting of 50 RNA-seq and 50 matched miRNA-seq data, obtained from milk-isolated monocytes of Holstein-Friesian cows, both infected with Streptococcus uberis and non-infected controls. Using the weighted gene co-expression network analysis (WGCNA) approach, we constructed co-expressed RNA-seq-based and miRNA-seq-based modules separately. Module-trait relationship analysis was then performed on the RNA-seq-based modules to identify highly-correlated modules associated with clinical traits of mastitis. Functional enrichment analysis was conducted to understand the functional behavior of these modules. Additionally, we assigned the RNA-seq-based modules to the miRNA-seq-based modules and constructed an integrated regulatory network based on the modules of interest. To enhance the reliability of our findings, we conducted further analyses, including hub RNA detection, protein-protein interaction (PPI) network construction, screening of hub-hub RNAs, and target prediction analysis on the detected modules. We identified a total of 17 RNA-seq-based modules and 3 miRNA-seq-based modules. Among the significant highly-correlated RNA-seq-based modules, six modules showed strong associations with clinical characteristics of mastitis. Functional enrichment analysis revealed that the turquoise module was directly related to inflammation persistence and mastitis development. Furthermore, module assignment analysis demonstrated that the blue miRNA-seq-based module post-transcriptionally regulates the turquoise RNA-seq-based module. We also identified a set of different RNAs, including hub-hub genes, hub-hub TFs (transcription factors), hub-hub lncRNAs (long non-coding RNAs), and hub miRNAs within the modules of interest, indicating their central role in the molecular interactome underlying the pathogenic mechanisms of S. uberis infection. This study provides a comprehensive insight into the molecular crosstalk between immunoregulatory mRNAs, miRNAs, and lncRNAs during S. uberis infection. These findings offer valuable directions for the development of molecular diagnosis and biological therapies for mastitis.

De novo drug design based on patient gene expression profiles via deep learning

Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candidate molecular structures from patient gene expression profiles via deep learning, which we call DRAGONET. Our model can generate new molecules that are likely to counteract disease-specific gene expression patterns in patients, which is made possible by exploring the latent space constructed by a transformer-based variational autoencoder and integrating the substructures of disease-correlated molecules. We applied DRAGONET to generate drug candidate molecules for gastric cancer, atopic dermatitis, and Alzheimer's disease, and demonstrated that the newly generated molecules were chemically similar to registered drugs for each disease. This approach is applicable to diseases with unknown therapeutic target proteins and will make a significant contribution to the field of precision medicine.

Hypergraph factorization for multi-tissue gene expression imputation

Integrating gene expression across tissues and cell types is crucial for understanding the coordinated biological mechanisms that drive disease and characterise homeostasis. However, traditional multitissue integration methods cannot handle uncollected tissues or rely on genotype information, which is often unavailable and subject to privacy concerns. Here we present HYFA (Hypergraph Factorisation), a parameter-efficient graph representation learning approach for joint imputation of multi-tissue and cell-type gene expression. HYFA is genotype-agnostic, supports a variable number of collected tissues per individual, and imposes strong inductive biases to leverage the shared regulatory architecture of tissues and genes. In performance comparison on Genotype-Tissue Expression project data, HYFA achieves superior performance over existing methods, especially when multiple reference tissues are available. The HYFA-imputed dataset can be used to identify replicable regulatory genetic variations (eQTLs), with substantial gains over the original incomplete dataset. HYFA can accelerate the effective and scalable integration of tissue and cell-type transcriptome biorepositories.

Therapeutic effects of guanidine hydrochloride on breast cancer through targeting KCNG1 gene

BACKGROUND

Triple-negative breast cancer (TNBC) is one of the subtypes of breast cancer (BC) that is associated with poor survival rates and failure to respond to hormonal and targeted therapies.

OBJECTIVE

The aim of this study was to identify a specific gene at the expression level for TNBC and targeting of this type of breast cancer based on it. Using TCGA database, genes that are particularly high expression in TNBC subtypes compared to other BC subtypes (in terms of receptor status) and normal samples were identified and their sensitivity and specificity were evaluated. Using PharmacoGX and Drug Bank data, drug sensitivity and drug-appropriate genes were identified, respectively. The effects of the identified drug on triple-negative cell lines (MDA-MB-468) were evaluated in comparison with the cell line of other subtypes (MCF7) by apoptosis and MTS tests.

RESULTS

Data analyzes showed that the expression level of KCNG1 gene in the TNBC subgroup was significantly higher compared to other BC subtypes from the KCN gene family and ROC results showed that this gene had highest sensitivity and specificity in TNBC subtype. The results of drug resistance and sensitivity showed that an increase in the expression level of KCNG1 was associated with sensitivity to Cisplatin and Oxaliplatin. Moreover, Drug Bank results showed that Guanidine hydrochloride (GuHCl) was a suitable inhibitor for KCNG1. In vitro results showed that the expression level of KCNG1 was higher in MDA-MB-468 compared to MCF7. In addition, the rate of apoptosis in response to GuHCl treatment in MDA-MB-468 cell line as TNBC cell model was higher than MCF7 in the same concentration.

CONCLUSION

This study revealed that GuHCl could be a suitable treatment for TNBC subtype by targeting of KCNG1.