Weighted gene co-expression network analysis and connectivity map identifies lovastatin as a treatment option of gastric cancer by inhibiting HDAC2

FDX1 as a novel biomarker and treatment target for stomach adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) is one of the main reasons for cancer-related deaths worldwide. This investigation aimed to define the connection between STAD and Cuproptosis-related genes (CRGs). Cuproptosis is a newly identified form of mitochondrial cell death triggered by copper.

AIM

To explore the identification of potential biomarkers for STAD disease based on cuproptosis.

METHODS

A predictive model using Gene Ontology (GO), Least Absolute Shrinkage and Selection Operator (LASSO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Variation Analysis (GSVA), and Gene Set Enrichment Analysis analyzed gene interconnections, focusing on 3 copper-related genes and their expression in The Cancer Genome Atlas-STAD. Networks for mRNA-miRNA and mRNA-transcription factor interactions were constructed. The prognostic significance of CRG scores was evaluated using time-receiver operating characteristic, Kaplan-Meier curves, and COX regression analysis. Validation was conducted with datasets GSE26942, GSE54129, and GSE66229. Expression of copper-related differentially expressed genes was also analyzed in various human tissues and gastric cancer subpopulations using the human protein atlas.

RESULTS

Three significant genes (FDX1, LIAS, MTF1) were identified and selected via LASSO analysis to predict and classify individuals with STAD into high and low CRG score subgroups. These genes were down-regulated in both risk categories. GO and KEGG analyses highlighted their involvement mainly in the electron transport chain. After validating their differential expression, FDX1 emerged as the most accurate diagnostic marker for gastric cancer. Additionally, the RCircos package localized FDX1 on chromosome 11.

CONCLUSION

Our study revealed that FDX1 could be a potential biomarker and treatment target for gastric malignancy, providing new ideas for further scientific research.

In silico transcriptome screens identify epidermal growth factor receptor inhibitors as therapeutics for noise-induced hearing loss

Noise-induced hearing loss (NIHL) is a common sensorineural hearing impairment that lacks U.S. Food and Drug Administration-approved drugs. To fill the gap in effective screening models, we used an in silico transcriptome-based drug screening approach, identifying 22 biological pathways and 64 potential small molecule treatments for NIHL. Two of these, afatinib and zorifertinib [epidermal growth factor receptor (EGFR) inhibitors], showed efficacy in zebrafish and mouse models. Further tests with EGFR knockout mice and EGF-morpholino zebrafish confirmed their protective role against NIHL. Molecular studies in mice highlighted EGFR's crucial involvement in NIHL and the protective effect of zorifertinib. When given orally, zorifertinib was found in the perilymph with favorable pharmacokinetics. In addition, zorifertinib combined with AZD5438 (a cyclin-dependent kinase 2 inhibitor) synergistically prevented NIHL in zebrafish. Our results underscore the potential for in silico transcriptome-based drug screening in diseases lacking efficient models and suggest EGFR inhibitors as potential treatments for NIHL, meriting clinical trials.

Drug repurposing for cancer therapy

Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.

An AI Approach to Identifying Novel Therapeutics for Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that has a significant impact on quality of life and work capacity. Treatment of RA aims to control inflammation and alleviate pain; however, achieving remission with minimal toxicity is frequently not possible with the current suite of drugs. This review aims to summarise current treatment practices and highlight the urgent need for alternative pharmacogenomic approaches for novel drug discovery. These approaches can elucidate new relationships between drugs, genes, and diseases to identify additional effective and safe therapeutic options. This review discusses how computational approaches such as connectivity mapping offer the ability to repurpose FDA-approved drugs beyond their original treatment indication. This review also explores the concept of drug sensitisation to predict co-prescribed drugs with synergistic effects that produce enhanced anti-disease efficacy by involving multiple disease pathways. Challenges of this computational approach are discussed, including the availability of suitable high-quality datasets for comprehensive analysis and other data curation issues. The potential benefits include accelerated identification of novel drug combinations and the ability to trial and implement established treatments in a new index disease. This review underlines the huge opportunity to incorporate disease-related data and drug-related data to develop methods and algorithms that have strong potential to determine novel and effective treatment regimens.

A Cross-Talk about Radioresistance in Lung Cancer-How to Improve Radiosensitivity According to Chinese Medicine and Medicaments That Commonly Occur in Pharmacies

Lung cancer is one of the most common cancers in the population and is characterized by non-specific symptoms that delay the diagnosis and reduce the effectiveness of oncological treatment. Due to the difficult placement of the tumor, one of the main methods of lung cancer treatment is radiotherapy, which damages the DNA of cancer cells, inducing their apoptosis. However, resistance to ionizing radiation may develop during radiotherapy cycles, leading to an increase in the number of DNA points of control that protect cells from apoptosis. Cancer stem cells are essential for radioresistance, and due to their ability to undergo epithelial-mesenchymal transition, they modify the phenotype, bypassing the genotoxic effect of radiotherapy. It is therefore necessary to search for new methods that could improve the cytotoxic effect of cells through new mechanisms of action. Chinese medicine, with several thousand years of tradition, offers a wide range of possibilities in the search for compounds that could be used in conventional medicine. This review introduces the potential candidates that may present a radiosensitizing effect on lung cancer cells, breaking their radioresistance. Additionally, it includes candidates taken from conventional medicine-drugs commonly available in pharmacies, which may also be significant candidates.

In Silico Transcriptome-based Screens Identify Epidermal Growth Factor Receptor Inhibitors as Therapeutics for Noise-induced Hearing Loss

Noise-Induced Hearing Loss (NIHL) represents a widespread disease for which no therapeutics have been approved by the Food and Drug Administration (FDA). Addressing the conspicuous void of efficacious in vitro or animal models for high throughput pharmacological screening, we utilized an in silico transcriptome-oriented drug screening strategy, unveiling 22 biological pathways and 64 promising small molecule candidates for NIHL protection. Afatinib and zorifertinib, both inhibitors of the Epidermal Growth Factor Receptor (EGFR), were validated for their protective efficacy against NIHL in experimental zebrafish and murine models. This protective effect was further confirmed with EGFR conditional knockout mice and EGF knockdown zebrafish, both demonstrating protection against NIHL. Molecular analysis using Western blot and kinome signaling arrays on adult mouse cochlear lysates unveiled the intricate involvement of several signaling pathways, with particular emphasis on EGFR and its downstream pathways being modulated by noise exposure and Zorifertinib treatment. Administered orally, Zorifertinib was successfully detected in the perilymph fluid of the inner ear in mice with favorable pharmacokinetic attributes. Zorifertinib, in conjunction with AZD5438 - a potent inhibitor of cyclin dependent kinase 2 - produced synergistic protection against NIHL in the zebrafish model. Collectively, our findings underscore the potential application of in silico transcriptome-based drug screening for diseases bereft of efficient screening models and posit EGFR inhibitors as promising therapeutic agents warranting clinical exploration for combatting NIHL.

Highlights

In silico transcriptome-based drug screens identify pathways and drugs against NIHL.EGFR signaling is activated by noise but reduced by zorifertinib in mouse cochleae.Afatinib, zorifertinib and EGFR knockout protect against NIHL in mice and zebrafish.Orally delivered zorifertinib has inner ear PK and synergizes with a CDK2 inhibitor.

Monacolin K Induces Apoptosis of Human Glioma U251 Cells by Triggering ROS-Mediated Oxidative Damage and Regulating MAPKs and NF-κB Pathways

Monacolin K (MK), a polyketo secondary metabolic compound of the mold genus Monascus, can promote the apoptosis of malignant cancer cells, possessing potential antitumor properties. However, its mechanism of action on gliomas remains unclear. Here, we explored and investigated the potential of the monacolin K's antitumor effect on human glioma U251 cells and its possible molecular mechanism. Results showed that the application of 10 μM monacolin K inhibited the proliferation of U251 cells, with an inhibitory rate of up to 53.4%. Additionally, monacolin K induced the generation of reactive oxygen species and activated mitochondria-mediated pathways, including decreased MMP, activation of caspase3/caspase9, decreased Na⁺/K⁺-ATPase and Ca²⁺-ATPase activities, and disruption of the antioxidant system, resulting in the disruption of intracellular reduction-oxidation homeostasis. Monacolin K also activated MAPK and NF-κB pathways, upregulating P38 activity and downregulating JNK/ERK/P65/IκBα expression, ultimately leading to apoptosis of U251 cells. Importantly, monacolin K was not cytotoxic to normal human cells, hUC-MSCs. We concluded that monacolin K can induce apoptosis in U251 cells by triggering ROS-mediated oxidative damage and regulating MAPKs and NF-κB pathways.

Data mining combines bioinformatics discover immunoinfiltration-related gene SERPINE1 as a biomarker for diagnosis and prognosis of stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a type of cancer which often at itsadvanced stage apon diagnosis and mortality in clinical practice. Several factors influencethe prognosis of STAD, including the expression and regulation of immune cells in the tumor microenvironment. We here investigated the biomarkers related to the diagnosis and prognosis of gastric cancer, hoping to provide insights for the diagnosis and treatment of gastric cancer in the future. STAD and normal patient RNA sequencing data sets were accessed from the cancer genome atlas (TCGA database). Differential genes were determined and obtained by using the R package DESeq2. The stromal, immune, and ESTIMATE scores are calculated by the ESTIMATE algorithm, followed by the modular genes screening using the R package WGCNA. Subsequently, the intersection between the modular gene and the differential gene was taken and the STRING database was used for PPI network module analysis. The R packages clusterProfiler, enrichplot, and ggplot2 were used for GO and KEGG enrichment analysis. Cox regression analysis was used to screen survival-related genes, and finally, the R package Venn Diagram was used to take the intersection and obtain 7 hub genes. The time-dependent ROC curve and Kaplan-Meier survival curve were used to find the SERPINE1 gene, which plays a critical role in prognosis. Finally, the expression pattern, clinical characteristics, and regulatory mechanism of SERPINE1 were analyzed in STAD. We revealed that the expression of SERPINE1 was significantly increased in the samples from STAD compared with normal samples. Cox regression, time-dependent ROC, and Kaplan-Meier survival analyses demonstrated that SERPINE1 was significantly related to the adverse prognosis of STAD patients. The expression of SERPINE1 increased with the progression of T, N, and M classification of the tumor. In addition, the results of immune infiltration analysis indicated that the immune cells' expression were higher in high SERPINE1 expression group than that in low SERPINE1 expression group, including CD4⁺ T cells, B cells, CD8⁺ T cells, macrophages, neutrophils and other immune cells. SERPINE1 was closely related to immune cells in the STAD immune microenvironment and had a synergistic effect with the immune checkpoints PD1 and PD-L1. In conclusion, we proved that SERPINE1 is a promising prognostic and diagnostic biomarker for STAD and a potential target for immunotherapy.

Identifying biomarkers of differential chemotherapy response in TNBC patient-derived xenografts with a CTD/WGCNA approach

Although systemic chemotherapy remains the standard of care for TNBC, even combination chemotherapy is often ineffective. The identification of biomarkers for differential chemotherapy response would allow for the selection of responsive patients, thus maximizing efficacy and minimizing toxicities. Here, we leverage TNBC PDXs to identify biomarkers of response. To demonstrate their ability to function as a preclinical cohort, PDXs were characterized using DNA sequencing, transcriptomics, and proteomics to show consistency with clinical samples. We then developed a network-based approach (CTD/WGCNA) to identify biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (c, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). CTD/WGCNA multigene biomarkers are predictive in PDX datasets (RNAseq and Affymetrix) for both taxane- (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating cross-expression platform and cross-drug class robustness. These biomarkers were also predictive in clinical datasets, thus demonstrating translational potential.

Repurposed Drugs in Gastric Cancer

Gastric cancer (GC) is one of the major causes of death worldwide, ranking as the fifth most incident cancer in 2020 and the fourth leading cause of cancer mortality. The majority of GC patients are in an advanced stage at the time of diagnosis, presenting a poor prognosis and outcome. Current GC treatment approaches involve endoscopic detection, gastrectomy and chemotherapy or chemoradiotherapy in an adjuvant or neoadjuvant setting. Drug development approaches demand extreme effort to identify molecular mechanisms of action of new drug candidates. Drug repurposing is based on the research of new therapeutic indications of drugs approved for other pathologies. In this review, we explore GC and the different drugs repurposed for this disease.

Histone Deacetylase Functions in Gastric Cancer: Therapeutic Target?

Gastric cancer (GC) is one of the most aggressive cancers. Therapeutic treatments are based on surgery combined with chemotherapy using a combination of platinum-based agents. However, at metastatic stages of the disease, survival is extremely low due to late diagnosis and resistance mechanisms to chemotherapies. The development of new classifications has not yet identified new prognostic markers for clinical use. The studies of epigenetic processes highlighted the implication of histone acetylation status, regulated by histone acetyltransferases (HATs) and by histone deacetylases (HDACs), in cancer development. In this way, inhibitors of HDACs (HDACis) have been developed and some of them have already been clinically approved to treat T-cell lymphoma and multiple myeloma. In this review, we summarize the regulations and functions of eighteen HDACs in GC, describing their known targets, involved cellular processes, associated clinicopathological features, and impact on survival of patients. Additionally, we resume the in vitro, pre-clinical, and clinical trials of four HDACis approved by Food and Drug Administration (FDA) in cancers in the context of GC.

Identification and Verification of Potential Biomarkers in Gastric Cancer By Integrated Bioinformatic Analysis

Background: Gastric cancer (GC) is a common cancer with high mortality. This study aimed to identify its differentially expressed genes (DEGs) using bioinformatics methods.

Methods: DEGs were screened from four GEO (Gene Expression Omnibus) gene expression profiles. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. A protein–protein interaction (PPI) network was constructed. Expression and prognosis were assessed. Meta-analysis was conducted to further validate prognosis. The receiver operating characteristic curve (ROC) was analyzed to identify diagnostic markers, and a nomogram was developed. Exploration of drugs and immune cell infiltration analysis were conducted.

Results: Nine up-regulated and three down-regulated hub genes were identified, with close relations to gastric functions, extracellular activities, and structures. Overexpressed Collagen Type VIII Alpha 1 Chain (COL8A1), Collagen Type X Alpha 1 Chain (COL10A1), Collagen Triple Helix Repeat Containing 1 (CTHRC1), and Fibroblast Activation Protein (FAP) correlated with poor prognosis. The area under the curve (AUC) of ADAM Metallopeptidase With Thrombospondin Type 1 Motif 2 (ADAMTS2), COL10A1, Collagen Type XI Alpha 1 Chain (COL11A1), and CTHRC1 was >0.9. A nomogram model based on CTHRC1 was developed. Infiltration of macrophages, neutrophils, and dendritic cells positively correlated with COL8A1, COL10A1, CTHRC1, and FAP. Meta-analysis confirmed poor prognosis of overexpressed CTHRC1.

Conclusion: ADAMTS2, COL10A1, COL11A1, and CTHRC1 have diagnostic values in GC. COL8A1, COL10A1, CTHRC1, and FAP correlated with worse prognosis, showing prognostic and therapeutic values. The immune cell infiltration needs further investigations.

Identification of Novel Drugs Targeting Cell Cycle Regulators for the Treatment of High-Grade Serous Ovarian Cancer via Integrated Bioinformatics Analysis

High-grade serous ovarian carcinoma (HGSC), the most common and aggressive histological type of ovarian cancer, remains the leading cause of cancer-related deaths among females. It is important to develop novel drugs to improve the therapeutic outcomes of HGSC patients, thereby reducing their mortality. Symmetry is one of the most important properties of the biological network, which determines the stability of a biological system. As aberrant gene expression is a critical symmetry-breaking event that perturbs the stability of biological networks and triggers tumor progression, we aim in this study to discover new candidate drugs and predict their targets for HGSC therapy based on differentially expressed genes involved in HGSC pathogenesis. Firstly, 98 up-regulated genes and 108 down-regulated genes were identified from three independent transcriptome datasets. Then, the small-molecule compounds PHA-793887, pidorubicine and lestaurtinib, which target cell-cycle-related processes, were identified as novel candidate drugs for HGSC treatment by adopting the connectivity map (CMap)-based drug repositioning approach. Furthermore, through a topological analysis of the protein–protein interaction network, cell cycle regulators CDK1, TOP2A and AURKA were identified as bottleneck nodes, and their expression patterns were validated at the mRNA and protein expression levels. Moreover, the results of molecular docking analysis showed that PHA-793887, pidorubicine and lestaurtinib had a strong binding affinity for CDK1, TOP2A and AURKA, respectively. Therefore, our study repositioned PHA-793887, pidorubicine and lestaurtinib, which can inhibit cell cycle regulators, as novel agents for HGSC treatment, thereby helping to optimize the therapeutic strategy for HGSC.

Identification of the high-yield monacolin K strain from Monascus spp. and its submerged fermentation using different medicinal plants

BACKGROUND

Medical plants confer various benefits to human health and their bioconversion through microbial fermentation can increase efficacy, reduce toxicity, conserve resources and produce new chemical components. In this study, the cholesterol-lowering monacolin K genes and content produced by Monascus species were identified. The high-yield monacolin K strain further fermented with various medicinal plants. The antioxidant and anti-inflammatory activities, red pigment and monacolin K content, total phenolic content, and metabolites in the fermented products were analyzed.

RESULTS

Monacolin K was detected in Monascus pilosus (BCRC 38072), and Monascus ruber (BCRC 31533, 31523, 31534, 31535, and 33323). It responded to the highly homologous mokA and mokE genes encoding polyketide synthase and dehydrogenase. The high-yield monacolin K strain, M. ruber BCRC 31535, was used for fermentation with various medicinal plants. A positive relationship between the antioxidant capacity and total phenol content of the fermented products was observed after 60 days of fermentation, and both declined after 120 days of fermentation. By contrast, red pigment and monacolin K accumulated over time during fermentation, and the highest monacolin K content was observed in the fermentation of Glycyrrhiza uralensis, as confirmed by RT-qPCR. Moreover, Monascus-fermented medicinal plants including Paeonia lactiflora, Alpinia oxyphylla, G. uralensis, and rice were not cytotoxic. Only the product of Monascus-fermented G. uralensis significantly exhibited the anti-inflammatory capacity in a dose-dependent manner in lipopolysaccharide-induced Raw264.7 cells. The metabolites of G. uralensis with and without fermentation (60 days) were compared by LC/MS. 2,3-Dihydroxybenzoic acid, 3,4-dihydroxyphenylglycol, and 3-amino-4-hydroxybenzoate were considered to enhance the antioxidant and anti-inflammatory ability.

CONCLUSIONS

Given that highly homologous monacolin K and citrinin genes can be observed in Monascus spp., monacolin K produced by Monascus species without citrinin genes can be detected through the complementary methods of PCR and HPLC. In addition, the optimal fermentation time was important to the acquisition of antioxidants, red pigment and monacolin K. These bioactive substances were significantly affected by medicinal plants over fermentation time. Consequently, Monascus-fermented G. uralensis had a broad spectrum of biological activities.

Identification of anthelmintic parbendazole as a therapeutic molecule for HNSCC through connectivity map-based drug repositioning

Head and neck squamous cell carcinoma (HNSCC) is one of the most common human cancers; however, its outcome of pharmacotherapy is always very limited. Herein, we performed a batch query in the connectivity map (cMap) based on bioinformatics, queried out 35 compounds with therapeutic potential, and screened out parbendazole as a most promising compound, which had an excellent inhibitory effect on the proliferation of HNSCC cell lines. In addition, tubulin was identified as a primary target of parbendazole, and the direct binding between them was further verified. Parbendazole was further proved as an effective tubulin polymerization inhibitor, which can block the cell cycle, cause apoptosis and prevent cell migration, and it exhibited reasonable therapeutic effect and low toxicity in the in vivo and in vitro anti-tumor evaluation. Our study repositioned an anthelmintic parbendazole to treat HNSCC, which revealed a therapeutic utility and provided a new treatment option for human cancers.

Screening of Medications for Idiopathic Membranous Nephropathy Using Glomerular Whole-Genome Sequencing

Objective

To explore medications that have a therapeutic effect on idiopathic membranous nephropathy (IMN) using the Gene Expression Omnibus (GEO), the Connectivity Map (CMap) database, and bioinformatics approaches.

Methods

IMN patients' glomerular whole-genome sequencing data were retrieved and screened in the GEO database, differentially expressed genes were identified using GEO2R analysis, a PPI network was built in the STRING database, node degree values were calculated, and topological analysis was performed using the degree value to identify core genes. The WebGestalt database was used to perform GO enrichment and KEGG pathway analyses on the core genes. Candidate medications for the therapy of IMN were collected from the CMap database, and the candidate medications were then searched and analyzed.

Results

113 core genes were identified by topological analysis from the 1157 genes that were shown to be differentially expressed. The enrichment analysis identified several important gene functions and signaling pathways related to IMN. Some possible medications for the treatment of IMN have been found using the CMap database. Naringin, with the lowest CMap score, meaningful P value, and specificity score, was predicted as the most likely medication.

Conclusion

The GEO and CMap databases can be used to understand the molecular changes of IMN and to provide new ideas for medication research. However, medication candidates must undergo clinical and experimental testing.

A New Strategy to Identify ceRNA-Based CCDC144NL-AS1/SERPINE1 Regulatory Axis as a Novel Prognostic Biomarker for Stomach Adenocarcinoma via High Throughput Transcriptome Data Mining and Computational Verification

Stomach adenocarcinoma (STAD) is one of the most malignant cancers that endanger human health. There is growing evidence that competitive endogenous RNA (ceRNA) regulatory networks play an important role in various human tumors. However, the complexity and behavioral characteristics of the ceRNA network in STAD are still unclear. In this study, we constructed a ceRNA regulatory network to identify the potential prognostic biomarkers associated with STAD. The expression profile of lncRNA, miRNA, and mRNA was downloaded from The Cancer Genome Atlas (TCGA). After performing bioinformatics analysis, the CCDC144NL-AS1/hsa-miR-145-5p/SERPINE1 ceRNA network associated to STAD prognosis of STAD was obtained. The CCDC144NL-AS1/SERPINE1 axis in the ceRNA network was identified by correlation analysis and considered as a clinical prognosis model by Cox regression analysis. In addition, methylation analysis indicated that the abnormal upregulation of CCDC144NL-AS1/SERPINE1 axis might be related to the aberrant methylation of some sites, and immune infiltration analysis suggested that CCDC144NL-AS1/SERPINE1 axis probably influences the alteration of tumor immune microenvironment and the occurrence and development of STAD. In particular, the CCDC144NL-AS1/SERPINE1 axis based on the ceRNA network constructed in the present study might be an important novel factor correlating with the diagnosis and prognosis of STAD.

Epigenetic Modulators as Treatment Alternative to Diverse Types of Cancer

DNA is packaged in rolls in an octamer of histones forming a complex of DNA and proteins called chromatin. Chromatin as a structural matrix of a chromosome and its modifications are nowadays considered relevant aspects for regulating gene expression, which has become of high interest in understanding genetic mechanisms regulating various diseases, including cancer. In various types of cancer, the main modifications are found to be DNA methylation in the CpG dinucleotide as a silencing mechanism in transcription, post-translational histone modifications such as acetylation, methylation and others that affect the chromatin structure, the ATP-dependent chromatin remodeling and miRNA-mediated gene silencing. In this review we analyze the main alterations in gene expression, the epigenetic modification patterns that cancer cells present, as well as the main modulators and inhibitors of each epigenetic mechanism and the molecular evolution of the most representative inhibitors, which have opened a promising future in the study of HAT, HDAC, non-glycoside DNMT inhibitors and domain inhibitors.

Agent Clustering Strategy Based on Metabolic Flux Distribution and Transcriptome Expression for Novel Drug Development

The network module-based method has been used for drug repositioning. The traditional drug repositioning method only uses the gene characteristics of the drug but ignores the drug-triggered metabolic changes. The metabolic network systematically characterizes the connection between genes, proteins, and metabolic reactions. The differential metabolic flux distribution, as drug metabolism characteristics, was employed to cluster the agents with similar MoAs (mechanism of action). In this study, agents with the same pharmacology were clustered into one group, and a total of 1309 agents from the CMap database were clustered into 98 groups based on differential metabolic flux distribution. Transcription factor (TF) enrichment analysis revealed the agents in the same group (such as group 7 and group 26) were confirmed to have similar MoAs. Through this agent clustering strategy, the candidate drugs which can inhibit (Japanese encephalitis virus) JEV infection were identified. This study provides new insights into drug repositioning and their MoAs.

GeneCoNet: A web application server for constructing cancer patient-specific gene correlation networks with prognostic gene pairs

BACKGROUND AND OBJECTIVE

Most prognostic gene signatures that have been known for cancer are either individual genes or combination of genes. Both individual genes and combination of genes do not provide information on gene-gene relations, and often have less prognostic significance than random genes associated with cell proliferation. Several methods for generating sample-specific gene networks have been proposed, but programs implementing the methods are not publicly available.

METHODS

We have developed a method that builds gene correlation networks specific to individual cancer patients and derives prognostic gene correlations from the networks. A gene correlation network specific to a patient is constructed by identifying gene-gene relations that are significantly different from normal samples. Prognostic gene pairs are obtained by carrying out the Cox proportional hazards regression and the log-rank test for every gene pair.

RESULTS

We built a web application server called GeneCoNet with thousands of tumor samples in TCGA. Given a tumor sample ID of TCGA, GeneCoNet dynamically constructs a gene correlation network specific to the sample as output. As an additional output, it provides information on prognostic gene correlations in the network. GeneCoNet found several prognostic gene correlations for six types of cancer, but there were no prognostic gene pairs common to multiple cancer types.

CONCLUSION

Extensive analysis of patient-specific gene correlation networks suggests that patients with a larger subnetwork of prognostic gene pairs have shorter survival time than the others and that patients with a subnetwork that contains more genes participating in prognostic gene pairs have shorter survival time than the others. GeneCoNet can be used as a valuable resource for generating gene correlation networks specific to individual patients and for identifying prognostic gene correlations. It is freely accessible at http://geneconet.inha.ac.kr.

An Advanced Systems Pharmacology Strategy Reveals AKR1B1, MMP2, PTGER3 as Key Genes in the Competing Endogenous RNA Network of Compound Kushen Injection Treating Gastric Carcinoma by Integrated Bioinformatics and Experimental Verification

Gastric carcinoma (GC) is a severe tumor of the digestive tract with high morbidity and mortality and poor prognosis, for which novel treatment options are urgently needed. Compound Kushen injection (CKI), a classical injection of Chinese medicine, has been widely used to treat various tumors in clinical practice for decades. In recent years, a growing number of studies have confirmed that CKI has a beneficial therapeutic effect on GC, However, there are few reports on the potential molecular mechanism of action. Here, using systems pharmacology combined with proteomics analysis as a core concept, we identified the ceRNA network, key targets and signaling pathways regulated by CKI in the treatment of GC. To further explore the role of these key targets in the development of GC, we performed a meta-analysis to compare the expression differences between GC and normal gastric mucosa tissues. Functional enrichment analysis was further used to understand the biological pathways significantly regulated by the key genes. In addition, we determined the significance of the key genes in the prognosis of GC by survival analysis and immune infiltration analysis. Finally, molecular docking simulation was performed to verify the combination of CKI components and key targets. The anti-gastric cancer effect of CKI and its key targets was verified by in vivo and in vitro experiments. The analysis of ceRNA network of CKI on GC revealed that the potential molecular mechanism of CKI can regulate PI3K/AKT and Toll-like receptor signaling pathways by interfering with hub genes such as AKR1B1, MMP2 and PTGERR3. In conclusion, this study not only partially highlighted the molecular mechanism of CKI in GC therapy but also provided a novel and advanced systems pharmacology strategy to explore the mechanisms of traditional Chinese medicine formulations.

Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.

Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients' overall survival (OS). The turquoise module (cor = 0.67; P < 0.001) and its genes (n = 1092) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome (P < 0.0001). Also, this IRRS model was found to be an independent prognostic indicator of gliomas' survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

An overview on the biological activity and anti-cancer mechanism of lovastatin

Lovastatin, a secondary metabolite isolated from fungi, is often used as a representative drug to reduce blood lipid concentration and treat hypercholesterolemia. Its structure is similar to that of HMG-CoA. Lovastatin inhibits the binding of the substrate to HMG-CoA reductase, and strongly competes with HMG-CoA reductase (HMGR), thereby exerting a hypolipidemic effect. Further, its safety has been confirmed in vivo and in vitro. Lovastatin also has anti-inflammatory, anti-cancer, and neuroprotective effects. Therefore, the biological activity of lovastatin, especially its anti-cancer effect, has garnered research attention. Several in vitro studies have confirmed that lovastatin has a significant inhibitory effect on cancer cell viability in a variety of cancers (such as breast, liver, cervical, lung, and colon cancer). At the same time, lovastatin can also increase the sensitivity of some types of cancer cells to chemotherapeutic drugs and strengthen their therapeutic effect. Lovastatin inhibits cell proliferation and regulates cancer cell signaling pathways, thereby inducing apoptosis and cell cycle arrest. This article reviews the structure, biosynthetic pathways, and applications of lovastatin, focusing on the anti-cancer effects and mechanisms of action.

Histone deacetylase 2 selective inhibitors: A versatile therapeutic strategy as next generation drug target in cancer therapy

Acetylation and deacetylation of histone and several non-histone proteins are the two important processes amongst the different modes of epigenetic modulation that are involved in regulating cancer initiation and development. Abnormal expression of histone deacetylases (HDACs) is often reported in various types of cancers. Few pan HDAC inhibitors have been approved for use as therapeutic interventions for cancer treatment including vorinostat, belinostat and panobinostat. However, not all the HDAC isoforms are abnormally expressed in certain cancers, such as in the case of, ovarian cancer where overexpression of HDAC1-3, lung cancer where overexpression of HDAC 1 and 3 and gastric cancer where overexpression of HDAC2 is seen. Therefore, pan-inhibition of HDAC is not an efficient way to combat cancer via HDAC inhibition. Hence, isoform-selective HDAC inhibition can be one of the best therapeutic strategies in the treatment of cancer. In this context since aberrant expression of HDAC2 largely contributes to cancer progression by silencing pro-apoptotic protein expressions such as NOXA and APAF1 (caspase 9-activating proteins) and inactivation of tumor suppressor p53, HDAC2 specific inhibitors may help to develop not only the direct targets but also indirect targets that are crucial for tumor development. However, to develop a HDAC2 specific and potent inhibitor, extensive knowledge of its structure and specific functions is essential. The present review updates details on the structural features, physiological functions, and roles of HDAC2 in different types of cancer, emphasizing the challenges and status of the development of HDAC2 selective inhibitors against various types of cancer.

Identification of potential histone deacetylase inhibitory biflavonoids from Garcinia kola (Guttiferae) using in silico protein-ligand interaction

Overactivity of histone deacetylases (HDACs) is the underlying cause of some cancers, thus, inhibiting their overactivities is a rational treatment option. However, endeavors to employ current anti-HDACs agents in cancer treatment have yielded limited success. Consequently, there is need to explore anti-HDACs natural products, especially from plants sources, because of the intimate relationship plant products and drug discovery have enjoyed over the centuries. To identify possible HDACs inhibitors, Garcinia kola (Guttiferae) seed-derived compounds were screened in silico for HDAC-inhibitory tendencies because of their reported anticancer potentials. Fifteen G. kola-derived compounds and givinostat were docked with five selected HDACs using AutodockVina, while the binding interactions of the compounds with high binding affinities for the five HDACs were viewed with Discovery Studio Visualizer BIOVIA, 2016. Results indicated that four of the compounds studied, including amentoflavone, Garcinia biflavonoid 1, Garcinia biflavonoid 2 and kolaflavanone have higher binding propensity for all the five HDACs relative to givinostat, the standard HDAC inhibitor. This study indicated that inhibition of HDAC might be another key mechanism accountable for the bioactivities of G. kola and its intrinsic compounds. The results from this study implied that the compounds could be further investigated as drugable HDAC inhibitors with potential pharmacological applications in the treatment of cancers.

The role of neuronal apoptosis in Valproic Acid brain-related teratogenesis: a histochemical and immunohistochemical study in BALB/c mice

OBJECTIVES

The purpose of this study was to examine the teratogenic effects of Valproic Acid (VPA) and to investigate the role of apoptosis in neural tissue development. Although an apoptotic activity due to VPA has been reported, a direct connection of VPA-induced apoptosis with embryonic brain and∕or spine malformations and teratogenesis has not yet been established.

MATERIALS AND METHODS

VPA was administered to BALB∕c mice, from the 7th to the 10th gestational days. Macroscopical congenital anomalies were registered under a stereomicroscope and were further histologically studied. Immunohistochemistry was performed with terminal deoxynucleotidyl transferase (TdT)-mediated deoxyuridine triphosphate (dUTP) nick-end labeling (TUNEL) staining.

RESULTS

Birth defects were described and an increase of the apoptotic activity in the brain was immunohistochemically identified.

CONCLUSIONS

Considering the increased and very intense TUNEL expression of the neural cells of treated animals' fetuses, it is suggested that VPA triggers a pathological increase of apoptosis resulting in an imbalance between cell proliferation and cell death, the final result of which is malformation.

The Advantages of Connectivity Map Applied in Traditional Chinese Medicine

Amid the establishment and optimization of Connectivity Map (CMAP), the functional relationships among drugs, genes, and diseases are further explored. This biological database has been widely used to identify drugs with common mechanisms, repurpose existing drugs, discover the molecular mechanisms of unknown drugs, and find potential drugs for some diseases. Research on traditional Chinese medicine (TCM) has entered a new era in the wake of the development of bioinformatics and other subjects including network pharmacology, proteomics, metabolomics, herbgenomics, and so on. TCM gradually conforms to modern science, but there is still a torrent of limitations. In recent years, CMAP has shown its distinct advantages in the study of the components of TCM and the synergetic mechanism of TCM formulas; hence, the combination of them is inevitable.

Histone Deacetylases (HDACs) in Gastric Cancer: An Update of their Emerging Prognostic and Therapeutic Role

Chemotherapy resistance is a rising concern in Gastric Cancer (GC) and has led to the investigation of various cellular compounds. Α functional equilibrium of histone acetylation and deacetylation was discovered in all cells, regulated by Histone Acetyltransferases and Deacetylases (HDACs), controlling chromatin coiling status and changing gene expression appropriately. In accordance with recent research, this equilibrium can be dysregulated in cancer cells aiding in the process of carcinogenesis and tumor progression by altering histone and non-histone proteins affecting gene expression, cell cycle control, differentiation, and apoptosis in various malignancies. In addition, increased HDAC expression in GC cells has been associated with increased stage, tumor invasion, nodal metastases, increased distant metastatic potential, and decreased overall survival. HDAC inhibitors could be used as treatment regimens for GC patients and could develop important synergistic interactions with chemotherapy drugs. The aim of this article is to review the molecular identity and mechanism of action of HDAC inhibitors, as well as highlight their potential utility as anti-cancer agents in GC.

Novel therapeutic compounds for prostate adenocarcinoma treatment: An analysis using bioinformatic approaches and the CMap database

INTRODUCTION

Prostate adenocarcinoma is the most frequently diagnosed malignancy, particularly for people >70 years old. The main challenge in the treatment of advanced neoplasm is bone metastasis and therapeutic resistance for known oncology drugs. Novel treatment methods to prolong the survival time and improve the life quality of these specific patients are required. The present study attempted to screen potential therapeutic compounds for the tumor through bioinformatics approaches, in order to provide conceptual treatment for this malignant disease.

METHODS

Differentially expressed genes were obtained from the Gene Expression Omnibus database and submitted into the Connectivity Map database for the detection of potentially associated compounds. Target genes were extracted from the search results. Functional annotation and pathway enrichment were performed for the confirmation. Survival analysis was used to measure potential therapeutic effects.

RESULTS

It was revealed that 3 compounds (vanoxerine, tolnaftate, and gabexate) may help to prolong the disease-free survival time from tumor metastasis of patients with the tumor. A total of 6 genes [also-keto reductase family 1 member C3 (AKR1C3), collagen type III α 1 chain (COL3A1), lipoprotein lipase (LPL), glucuronidase, β pseudogene 11 (GUSBP11), apolipoprotein E (APOE), and collagen type I α 1 chain (COL1A1)] were identified to be the potential therapeutic targets for the aforementioned compounds.

CONCLUSION

In the present study, it was speculated that 3 compounds may function as the potential therapeutic drugs of bone metastatic prostate adenocarcinoma; however, further studies verifying vitro and in vivo are necessary.

TAF1A and ZBTB41 serve as novel key genes in cervical cancer identified by integrated approaches

Cervical cancer (CC) is the second most common cancer and the leading cause of cancer mortality in women. Numerous studies have found that the development of CC was associated with multiple genes. However, the mechanisms on gene level are enigmatic, hindering the understanding of its functional roles. This study sought to identify prognostic biomarkers of CC, and explore their biological functions. Here we conducted an integrated analysis to screen potential vital genes. Candidate genes were further tested by experiments in clinical specimens and cancer cell line. Then, molecular modeling was used to predict the three-dimensional structure of candidate genes’ proteins, and the interaction pattern was analyzed by docking simulation technique. Among the potential genes identified, we found that TAF1A and ZBTB41 were highly correlated. Furthermore, there was a definite interaction between the proteins of TAF1A and ZBTB41, which was affected by the activity of the p53 signaling pathway. In conclusion, our findings identified TAF1A and ZBTB41 could serve as biomarkers of CC. We confirmed their biological function and deciphered their interaction for the first time, which may be helpful for developing further researches.

A connectivity map-based drug repurposing study and integrative analysis of transcriptomic profiling of SARS-CoV-2 infection

AIMS

The recent outbreak of COVID-19 has become a global health concern. There are currently no effective treatment strategies and vaccines for the treatment or prevention of this fatal disease. The current study aims to determine promising treatment options for the COVID-19 through a computational drug repurposing approach.

MATERIALS AND METHODS

In this study, we focus on differentially expressed genes (DEGs), detected in SARS-CoV-2 infected cell lines including "the primary human lung epithelial cell line NHBE" and "the transformed lung alveolar cell line A549". Next, the identified DEGs are used in the connectivity map (CMap) analysis to identify similarly acting therapeutic candidates. Furthermore, to interpret lists of DEGs, pathway enrichment and protein network analysis are performed. Genes are categorized into easily interpretable pathways based on their biological functions, and overrepresentation of each pathway is tested in comparison to what is expected randomly.

KEY FINDINGS

The results suggest the effectiveness of lansoprazole, folic acid, sulfamonomethoxine, tolnaftate, diclofenamide, halcinonide, saquinavir, metronidazole, ebselen, lidocaine and benzocaine, histone deacetylase (HDAC) inhibitors, heat shock protein 90 (HSP90) inhibitors, and many other clinically approved drugs as potent drugs against COVID-19 outbreak.

SIGNIFICANCE

Making new drugs remain a lengthy process, so the drug repurposing approach provides an insight into the therapeutics that might be helpful in this pandemic. In this study, pathway enrichment and protein network analysis are also performed, and the effectiveness of some drugs obtained from the CMap analysis has been investigated according to previous researches.

Protein acetylation and deacetylation: An important regulatory modification in gene transcription (Review)

Cells primarily rely on proteins to perform the majority of their physiological functions, and the function of proteins is regulated by post-translational modifications (PTMs). The acetylation of proteins is a dynamic and highly specific PTM, which has an important influence on the functions of proteins, such as gene transcription and signal transduction. The acetylation of proteins is primarily dependent on lysine acetyltransferases and lysine deacetylases. In recent years, due to the widespread use of mass spectrometry and the emergence of new technologies, such as protein chips, studies on protein acetylation have been further developed. Compared with histone acetylation, acetylation of non-histone proteins has gradually become the focus of research due to its important regulatory mechanisms and wide range of applications. The discovery of specific protein acetylation sites using bioinformatic tools can greatly aid the understanding of the underlying mechanisms of protein acetylation involved in related physiological and pathological processes.

Identification of subtype-specific genes signature by WGCNA for prognostic prediction in diffuse type gastric cancer

BACKGROUND

Gastric cancer is a common malignancy and had poor response to treatment due to its strong heterogeneity. This study aimed to identify essential genes associated with diffuse type gastric cancer and construct a powerful prognostic model.

RESULTS

We conducted a weighted gene co-expression network analysis (WGCN) using transcripts per million (TPM) expression data from The Cancer Genome Atlas (TCGA) to find out the module related with diffuse type gastric cancer. Combining Least Absolute Shrinkage and Selection Operator (LASSO) with multi-cox regression, the 10 specific genes risk score model of diffuse type gastric cancer was established. The concordance index (0.97), the area under the respective ROC curves (AUCs) (1-years: 0.98; 3-years: 1; 5-years: 1) and survival difference of high- and low risk groups (p=2.84e-10) of this model in TCGA dataset were obtained. The moderate predicting performance was observed in the independent cohort of GSE15459 and GSE62254. The results of the gene set enrichment analysis (GSEA) using high-and low risk group as phenotype indicated differential expression of tumor-related pathways.

CONCLUSION

Thus, we constructed a reliable prognostic model for diffuse type gastric cancer, which should be beneficial for clinical therapeutic decision-making.

Statin Use Improves Overall Survival of Patients with Gastric Cancer after Surgery and Adjuvant Chemotherapy in Taiwan: A Nationwide Matched Cohort Study

Background: Numerous studies have revealed that statins have antitumor effects in vivo and in vitro. However, few studies have explored the relationship between statin use and the mortality of gastric cancer (GC) patients after treatments. This study examines the relationship between statin use and the overall survival (OS) of GC patients after surgery and adjuvant chemotherapy, using data from the nationwide cohort database of Taiwan. Methods: All patients newly diagnosed with GC from 1999 to 2008 in Taiwan were identified from the Registry of Catastrophic Illness Patients Database. Through propensity score matching, statin users were matched to statin non-users at a 1:4 ratio. The relationship between statin use and the OS of patients with GC was estimated through Cox regression models. Results: The study cohort included 1835 patients with GC who had received therapies during the study period. The death numbers among statin users (defined as those who used more than 28 cumulative defined daily doses (cDDDs)) and statin non-users were 138 and 895, respectively. A dose–response association was noted between statin use and the OS of patients with GC after treatments. The adjusted hazard ratios were 0.62 (95% confidence intervals (CI), 0.50–0.78) and 0.34 (95% CI, 0.26–0.45) for statin users administered 28–167 cDDDs and >168 cDDDs, respectively, compared with no statin use (<28 cDDDs). Conclusions: This study highlights that statin use may dose-dependently improve the OS of patients with GC after surgery and adjuvant chemotherapy in Taiwan. Additional studies are required to confirm the efficacy and safety of statin use.

Clinical Features and Prognostic Impact of Coexpression Modules Constructed by WGCNA for Diffuse Large B-Cell Lymphoma

Objective

Diffuse large B-cell lymphoma (DLBCL) is a highly aggressive malignant tumor, accounting for 30-40% of non-Hodgkin's lymphoma. Our aim was to construct novel prognostic models of candidate genes based on clinical features.

Methods

RNA-seq and clinical data of DLBCL were retrieved from TCGA database. Coexpression modules were constructed by WGCNA. Then, we investigated the interactions between modules and clinical features. By overall survival analysis, prognostic candidate genes from modules of interest were identified. A coexpression network of prognostic candidate genes was then constructed through WGCNA. GEPIA was used to analyze the expression of a candidate gene between DLBCL and normal samples.

Results

19 coexpression modules were constructed by 12813 genes from 52 DLBCL samples. The number of genes in modules ranged from 34 to 5457. We found that the purple module was significantly related with histological type (p value = 1e-04). Overall survival analysis revealed that MAFA-AS1, hsa-mir-338, and hsa-mir-891a were related with prognosis of DLBCL (p value = 0.027, 0.039, and 0.022, respectively). A coexpression network was constructed for the three prognostic genes. MAFA-AS1 was interacted with 36 genes, hsa-mir-891a was interacted with 11 genes, while no gene showed interaction with hsa-mir-338. Using GEPIA, we found that MAFA-AS1 showed low expression in DLBCL samples (p < 0.01).

Conclusion

We constructed a coexpression module related with histological type and identified three candidate genes (MAFA-AS1, hsa-mir-338, and hsa-mir-891a) that possessed potential value as prognostic biomarkers and therapeutic targets of DLBCL.

Identification of critical genes associated with the development of asthma by co-expression modules construction

INTRODUCTION

Asthma is a worldwide problem that is caused by complex underlying immune dysregulation. The identification of potential prognostic markers of asthma may provide information for treatment. The purpose of this study was to explore the key mechanisms involved in the development of asthma on the basis of microarray analysis.

METHODS

The expression profile data of GSE43696, which contains 20 endobronchial epithelial brushing samples from healthy patients and 88 from asthma patients, were obtained from Gene Expression Omnibus. For the present study, we built co-expression modules by weighted gene co-expression network analysis (WGCNA). This new analysis strategy was applied to the data set to investigate the relationships underlying the modules and the pathogenesis of asthma. Functional enrichment analysis was performed on these co-expression genes from the modules, and a gene network was then constructed. In addition, mouse models of HDM-induced and OVA-induced asthma were established, and the expression of hub genes was measured.

RESULTS

First, using WGCNA, 20 co-expression modules were constructed with 19,596 genes obtained from 108 human endobronchial epithelial brushing samples. The number of genes within the modules ranged from 41 to 845. According to the colours assigned by the system, the module positively correlated with asthma status was named 'red module', and the module positively correlated with asthma severity was named 'purple module'. The results of a functional enrichment analysis showed that the red module was mainly enriched in intracellular calcium-activated chloride channel activity, intracellular chloride channel activity and endopeptidase inhibitor activity. The purple module was mainly enriched in microtubule motor activity and microtubule-binding and motor activity. Moreover, the mRNA expression levels of the 15 hub genes were confirmed to be significantly upregulated in the HDM mouse model, and 12 hub genes were upregulated in the OVA model.

CONCLUSIONS

The hub genes ANO7, PYCR1 and UBE2C might play potential roles in the pathogenesis of asthma. Our findings provided a framework of co-expression gene modules of asthma and led to the identification of some new markers that might be potential targets for the development of new drugs and diagnostic markers.

Different Cardiotoxicity of Palbociclib and Ribociclib in Breast Cancer: Gene Expression and Pharmacological Data Analyses, Biological Basis, and Therapeutic Implications

Breast cancer is the most frequent tumor in women. The recent advent of cyclin-dependent kinase (CDK) 4/6 inhibitors palbociclib and ribociclib has represented a major step forward for patients with hormone receptor-positive breast cancer. These two agents have showed similar efficacy in terms of breast cancer outcome but different cardiotoxic effects. In particular, ribociclib, but not palbociclib, has been associated with QT interval prolongation, and the mechanisms underlying this event are still unclear. In order to clarify such difference, we matched the candidate genes associated with QT interval prolongation with genes whose expression is altered following palbociclib or ribociclib treatment. We also investigated whether pharmacokinetic and pharmacodynamic characteristics, such as IC₅₀ (hERG) [concentration of drug producing 50% inhibition (human ether-à-go-go related gene)] and maximum concentration (C_max), could justify the different effects on QT interval prolongation. Our results show that ribociclib, but not palbociclib, could act by down-regulating the expression of KCNH2 (encoding for potassium channel hERG) and up-regulating SCN5A and SNTA1 (encoding for sodium channels Nav1.5 and syntrophin-α1, respectively), three genes associated with long QT syndrome. Consistent with the cardiotoxicity induced by ribociclib, its IC₅₀ (hERG)/free concentration (C_{max free}) ratio is closer to the safety threshold than that of palbociclib. In summary, we hypothesize that the different cardiotoxicity associated with ribociclib and palbociclib could be due to the alteration of potassium and sodium channels induced by ribociclib. A better comprehension of the mechanisms of cardiac channelopathies and drug-induced QT interval prolongation will be fundamental to avoid serious and potentially lethal adverse events and, as a consequence, optimize the management of breast cancer patients.

Identification of gene modules associated with survival of diffuse large B-cell lymphoma treated with CHOP-based chemotherapy

Diffuse Large B-cell Lymphoma (DLBCL), a heterogeneous disease, is influenced by complex network of gene interactions. Most previous studies focused on individual genes, but ignored the importance of intergenic correlations. In current study, we aimed to explore the association between gene networks and overall survival (OS) of DLBCL patients treated with CHOP-based chemotherapy (cyclophosphamide combination with doxorubicin, vincristine and prednisone). Weighted gene co-expression network analysis was conducted to obtain insights into the molecular characteristics of DLBCL. Ten co-expression gene networks (modules) were identified in training dataset (n = 470), and their associations with patients' OS after chemotherapy were tested. The results were validated in four independent datasets (n = 802). Gene ontology (GO) biological function enrichment analysis was conducted with Metascape. Three modules (purple, brown and red), which were enriched in T-cell immune, cell-cell adhesion and extracellular matrix (ECM), respectively, were found to be related to longer OS. Higher expression of several hub genes within these three co-expression modules, for example, LCP2 (HR = 0.77, p = 5.40 × 10^-2), CD2 (HR = 0.87, p = 6.31 × 10^-2), CD3D (HR = 0.83, p = 6.94 × 10^-3), FYB (HR = 0.82, p = 1.40 × 10^-2), GZMK (HR = 0.92, p = 1.19 × 10^-1), FN1 (HR = 0.88, p = 7.06 × 10^-2), SPARC (HR = 0.82, p = 2.06 × 10^-2), were found to be associated with favourable survival. Moreover, the associations of the modules and hub genes with OS in different molecular subtypes and different chemotherapy groups were also revealed. In general, our research revealed the key gene modules and several hub genes were upregulated correlated with good survival of DLBCL patients, which might provide potential therapeutic targets for future clinical research.

Rapid Discovery of Potential Drugs for Osteonecrosis of Femoral Head Based on Gene Expression Omnibus Database and Connectivity Map

Objective

To use Gene Expression Omnibus (GEO) database coupled with Connectivity Map (CMap) databases to screen potential therapeutic drugs for osteonecrosis of femoral head (ONFH) rapidly.

Methods

Raw genetic data with accession number GSE74089 that contained eight hip articular cartilage specimens from four ONFH patients and four healthy controls were obtained from the Gene Expression Omnibus (GEO) database and were then integrated using R to identify differentially expressed genes (DEGs). Subsequently, to identify several potential small molecular compounds that were most strongly negatively correlated with ONFH, a search query of DEGs was explored by using CMap.

Results

Filtering revealed 1937 DEGs with log (fold‐change) ≥1 and adjust P value < 0.001. Finally, a network of candidate targets for ONFH with 135 nodes and 660 edges was constructed through network topology analysis, including 96 up‐regulated genes and 39 down‐regulated genes. Several significant gene functions and signaling pathways associated with pathological processes of ONFH were identified via gene enrichment analysis. Based on the CMap database, some potential small molecular components that may be possible to counteract the effects of molecular signal imbalance for ONFH were identified. Neostigmine bromide with low CMap score and P value and specificity score was predicted to be the most candidate compound, involved in the “positive regulation of stem cell proliferation,” “regulation of protein autophosphorylation,” “VEGF signaling pathway,” and “ECM‐receptor interaction.”

Conclusions

The GEO and CMap databases can be effectively used in understanding the molecular changes in ONFH and provide a systematic manner to identify potential drugs for ONFH prevention and treatment. However, additional clinical and experimental research of the candidate compound is warranted.

Systems biology based drug repositioning for development of cancer therapy

Drug repositioning is a powerful method that can assists the conventional drug discovery process by using existing drugs for treatment of a disease rather than its original indication. The first examples of repurposed drugs were discovered serendipitously, however data accumulated by high-throughput screenings and advancements in computational biology methods have paved the way for rational drug repositioning methods. As chemotherapeutic agents have notorious side effects that significantly reduce quality of life, drug repositioning promises repurposed noncancer drugs with little or tolerable adverse effects for cancer patients. Here, we review current drug-related data types and databases including some examples of web-based drug repositioning tools. Next, we describe systems biology approaches to be used in drug repositioning for effective cancer therapy. Finally, we highlight examples of mostly repurposed drugs for cancer treatment and provide an overview of future expectations in the field for development of effective treatment strategies.

Trichostatin a inhibits phenotypic transition and induces apoptosis of the TAF-treated normal colonic epithelial cells through regulation of TGF-β pathway

Tumor-associated fibroblasts (TAFs) contribute to transdifferentiation of stromal cells in tumor microenvironment. Epithelial-mesenchymal transition (EMT) is a procedure of phenotypic remodeling of epithelial cells and extensively exists in local tumoral stroma. Histone deacetylase (HDAC) inhibitor Tricostatin A (TSA) and sodium butyrate (SB) are reported to play important roles in the regulation of biological behaviour of cancer cells. However, whether TSA or SB is involved in control of EMT in colon epithelial cells induced by TAFs remains unidentified. In present study, we used conditioned medium (CM) form TAF-like CCD-18Co cells to stimulate 2D- and 3D-cultured colon epithelial HCoEpiC cells for 24 h and 4 d. We found that the CCD-18Co CM triggered multiple morphological changes in HCoEpiCs including prolonged cell diameters, down-regulation of E-cadherin and up-regulation of vimentin and α-SMA. Besides, ZEB1 and Snail expression and migration were also promoted by the CM. These phenomena were abolised by 5 μg/ml LY364947, a TGF-β receptor inhibitor. CCD-18Co induced up-regulation of HDAC1 and HDAC2 in the 2D and 3D models, while no change of HDAC4 exprerssion was found. Treatment of 2 μg/ml TSA reversed the CCD-18Co-induced morphological changes and migration of the HCoEpiCs, and suppressed the downregulation of E-cadherin and upregulation of vimentin, α-SMA, ZEB1 and Snail. However, the suppressive effect of 4 mg/ml SB on the EMT was not observed. TSA down-regulated the expressions of Smad2/3, p-Smad2/3 amd HDAC4. Besides, TSA promoted the apoptosis rate (36.84 ± 6.52%) comparing with the CCD-18Co-treated HCoEpiCs (3.52 ± 0.85%, P < 0.05), with promotion of Bax (0.5893±0.0498 in 2D and 0.8867±0.0916 in 3D) and reduction of Bcl-2 (0.0476±0.0053 in 2D and 0.0294±0.0075 in 3D). TSA stimulated expression of phosphorylated-p38 MAPK in 2D (0.3472±0.0249) and 3D (0.3188±0.0248). After pre-treatment with p38 MAPK inhibitor VX-702 (0.5 mg/ml), the apoptosis rate of TSA was decreased in 2D (10.32%) and 3D (5.26%). Our observations demonstrate that epigenetic treatment with HDAC inhibitor TSA may be a useful therapeutic tool for the reversion of TAF-induced EMT in colon epithelium through mediating canonical Smads pathway and non-canonical p38 MAPK signalling.

Molecular profiling of mucinous epithelial ovarian cancer by weighted gene co-expression network analysis

PURPOSE

In order to identify the molecular characteristics and improve the efficacy of early diagnosis of mucinous epithelial ovarian cancer (mEOC), here, the transcriptome profiling by weighted gene co-expression network analysis (WGCNA) has been proposed as an effective method.

METHODS

The gene expression dataset GSE26193 was reanalyzed with a systematical approach, WGCNA. mEOC-related gene co-expression modules were detected and the functional enrichments of these modules were performed at GO and KEGG terms. Ten hub genes in the mEOC-related modules were validated using two independent datasets GSE44104 and GSE30274.

RESULTS

11 co-expressed gene modules were identified by WGCNA based on 4917 genes and 99 epithelial ovarian cancer samples. The turquoise module was found to be significantly associated with the subtype of mEOC. KEGG pathway enrichment analysis showed genes in the turquoise module significantly enriched in metabolism of xenobiotics by cytochrome P450 and steroid hormone biosynthesis. Ten hub genes (LIPH, BCAS1, FUT3, ZG16B, PTPRH, SLC4A4, MUC13, TFF1, HNF4G and TFF2) in the turquoise module were validated to be highly expressed in mEOC using two independent gene expression datasets GSE44104 and GSE30274.

CONCLUSION

Our work proposed an applicable framework of molecular characteristics for patients with mEOC, which may help us to obtain a precise and comprehensive understanding on the molecular complexities of mEOC. The hub genes identified in our study, as potential specific biomarkers of mEOC, may be applied in the early diagnosis of mEOC in the future.

Drug Solubility Enhancement through the Preparation of Multicomponent Organic Materials: Eutectics of Lovastatin with Carboxylic Acids

Lovastatin (LOV) is a drug used to treat hypercholesterolemia. Recent studies have identified its antioxidant effects and potential use in the treatment of some types of cancer. However, the low bioavailability related to its poor water solubility limits its use in solid oral dosage forms. Therefore, to improve the solubility of LOV three eutectic systems of LOV with the carboxylic acids benzoic (BEN), salicylic (SAL) and cinnamic (CIN) were obtained. Both binary phase and Tammann diagrams were constructed using differential scanning calorimetry (DSC) data of mixtures prepared from 0.1 to 1.0 molar ratios. Binary mixtures and eutectics were prepared by liquid-assisted grinding. The eutectics were further characterized by DSC and powder X-ray diffraction (PXRD), Fourier-transform infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM). The LOV-BEN, LOV-SAL and LOV-CIN system formed a eutectic at an LOV mole fraction of 0.19, 0.60 and 0.14, respectively. The systems exhibited improvements in LOV solubility, becoming more soluble by five-fold in the LOV-SAL system and approximately four-fold in the other two systems. Considering that the solubility enhancements and the carboxylic acids used are generally recognized as safe by the U.S. Food and Drug Administration (FDA), the LOV eutectic systems are promising materials to be used in a solubility enhancement strategy for pharmaceutical product formulation.