Identification of potential core genes and pathways predicting pathogenesis in head and neck squamous cell carcinoma

The mechanism of action of myricetin against lung adenocarcinoma based on bioinformatics, in silico and in vitro experiments

Myricetin is a natural flavonoid with anti-cancer and anti-inflammatory effects, but its mechanism for treating lung adenocarcinoma (LUAD) remains unclearly. Therefore, bioinformatics, in silico and in vitro experiments were employed to elucidate this issue in this study. The core targets of myricetin against LUAD were screened by PharmaMapper (v2017), Assistant for Clinical Bioinformatics, STRING (v11.5) and Cytoscape (v3.8.1). Using Kaplan-Meier Plotter (v2022.04.20), UALCAN (v2021.12.13) and GEPIA (v2.0) databases, the correlation between core genes and the prognosis of LUAD patients were analyzed, and the expression levels of core genes were verified. In silico studies were used to analyze the binding energies and sites of myricetin with core genes. The effects of myricetin on H1975 cells were explored through thiazolyl blue (MTT), cell migration, colony formation and western blot assays. A total of 72 potential targets of myricetin against LUAD were identified through bioinformatics. Among the four core targets obtained by multiple networks and in silico assays, the up-regulated MMP9 (HR = 1.14 (1-1.29), logrank P = 0.046) and down-regulated PIK3R1 (HR = 0.58 (0.51-0.66), logrank P < 1E-16) were positively correlated with poor survival outcomes in LUAD patients. In vitro experiments demonstrated that myricetin inhibited the proliferation and migration of H1975 cells, promoting their apoptosis. Myricetin inhibits the proliferation of H1975 cells and induces cell apoptosis through its influence on the expression levels of MMP1, MMP3, MMP9, and PIK3R1 and regulating the multiple pathways these genes participate in. Both MMP9 and PIK3R1 are potential biomarkers for LUAD.

KIF2C is a critical regulator for malignant progression of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a significant cause of mortality, while the underlying mechanism remains unclear. Our studies have revealed that KIF2C plays a crucial role in tumor proliferation and metastasis in HNSCC. The results demonstrate that KIF2C is highly expressed at both the mRNA and protein levels and is closely associated with lymph node metastasis. The gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses indicate that the differentially expressed genes are enriched in processes or pathways related to cell adhesion and cell mitosis in HNSCC. Moreover, the established protein-protein interaction network identifies KIF2C as a potential hub gene in HNSCC. Knockdown of KIF2C has been demonstrated to significantly reduce cell migration and invasion ability, leading to cell cycle arrest, a high proportion of abnormal cell apoptosis, and cell chromosome division mismatches in the HNSCC cell line. Downstream genes such as PDGFA, EGFR, TP63, SNAI2, KRT5, and KRT14 were found to be down-regulated, and multiple critical pathways, including mTOR, ERK, and PI3K-AKT pathways, were inactivated as a result of KIF2C knockdown. These findings provide strong evidence for the crucial role of KIF2C in HNSCC and suggest that targeting KIF2C may be a promising therapeutic strategy for this disease. Knockdown of KIF2C has been shown to significantly inhibit tumor proliferation in nude mice, demonstrating the potential therapeutic role of KIF2C in HNSCC treatment.

Head and Neck Squamous Cell Carcinoma Biopsies Maintained Ex Vivo on a Perfusion Device Show Gene Changes with Time and Clinically Relevant Doses of Irradiation

Advancements in 3-Dimensional (3D) culture models for studying disease have increased significantly over the last two decades, but fully understanding how these models represent in vivo still requires further investigation. The current study investigated differences in gene expression between a baseline sample and that maintained on a tissue-on-chip perfusion device for up to 96 h, with and without clinically-relevant doses of irradiation, to allow differentiation of model and treatment effects. Tumour tissue samples from 7 Head and Neck Squamous Cell Carcinomas (HNSCC) patients were sub-divided and either fixed immediately upon excision or maintained in a tissue-on-chip device for 48 and 96 h, with or without 2 Gray (Gy) or 10 Gy irradiation. Gene expression was measured using an nCounter® PanCancer Progression Panel. Differentially expressed genes between pre- and post-ex vivo culture, and control and irradiated samples were identified using nSolver software (version 4.0). The secretome from the tumour-on-chip was analysed for the presence of cytokines using a Proteome Profiler™ platform. Significant numbers of genes both increased (n = 6 and 64) and decreased (n = 18 and 58) in expression in the tissue maintained on-chip for 48 and 96 h, respectively, compared to fresh tissue; however, the irradiation schedule chosen did not induce significant changes in gene expression or cytokine secretion. Although HNSCC tissue maintained ex vivo shows a decrease in a large proportion of altered genes, 25% and 53% (48 and 96 h) do show increased expression, suggesting that the tissue remains functional. Irradiation of tumour tissue-on-chip needs to be conducted for longer time periods for specific gene changes to be observed, but we have shown, for the first time, the feasibility of using this perfusion platform for studying the genomic response of HNSCC tissue biopsies.

Exploring the Targets and Molecular Mechanisms of Thalidomide in the Treatment of Ulcerative Colitis: Network Pharmacology and Experimental Validation

BACKGROUND

Ulcerative colitis (UC) is a chronic, nonspecific, inflammatory disease of the intestine with an unknown cause. Thalidomide (THA) has been shown to be an effective drug for the treatment of UC. However, the molecular targets and mechanism of action of THA for the treatment of UC are not yet clear.

OBJECTIVES

Combining network pharmacology with in vitro experiments, this study aimed to investigate the potential targets and molecular mechanisms of THA for the treatment of UC.

METHODS

Firstly, relevant targets of THA against UC were obtained from public databases. Then, the top 10 hub targets and key molecular mechanisms of THA for UC were screened based on the network pharmacology approach and bioinformatics method. Finally, an in vitro cellular inflammation model was constructed using lipopolysaccharide (LPS) induced intestinal epithelial cells (NCM460) to validate the top 10 hub targets and key signaling pathways.

RESULTS

A total of 121 relevant targets of THA against UC were obtained, of which the top 10 hub targets were SRC, LCK, MAPK1, HSP90AA1, EGFR, HRAS, JAK2, RAC1, STAT1, and MAP2K1. The PI3K-Akt pathway was significantly associated with THA treatment of UC. In vitro experiments revealed that THA treatment reversed the expression of HSP90AA1, EGFR, STAT1, and JAK2 differential genes. THA was able to up- regulate the mRNA expression of pro-inflammatory factor IL-10 and decrease the mRNA levels of anti-inflammatory factors IL-6, IL-1β, and TNF-α. Furthermore, THA also exerted anti-inflammatory effects by inhibiting the activation of the PI3K/Akt pathway.

CONCLUSION

THA may play a therapeutic role in UC by inhibiting the PI3K-Akt pathway. HSP90AA1, EGFR, STAT1, and JAK2 may be the most relevant potential therapeutic targets for THA in the treatment of UC.

An oxidative stress-related prognostic signature for indicating the immune status of oral squamous cell carcinoma and guiding clinical treatment

Oral squamous cell carcinoma (OSCC) is the eighth most common cancer worldwide and presents high mortality. Oxidative stress, caused by reactive oxygen species accumulation, plays a crucial role in tumorigenesis, cancer progression, and drug resistance. Nevertheless, the specific prognostic and clinical values of oxidative stress-related genes (OSGs) in OSCC remain unclear. Here, we developed an oxidative stress-related prognostic signature according to mRNA expression data from The Cancer Genome Atlas (TCGA) database and evaluated its connections with the prognosis, clinical features, immune status, immunotherapy, and drug sensitivity of OSCC through a series of bioinformatics analyses. Finally, we filtered out six prognostic OSGs to construct a prognostic signature. On the basis of both TCGA-OSCC and GSE41613 cohorts, the signature was proven to be an independent prognostic factor with high accuracy and was confirmed to be an impactful indicator for predicting the prognosis and immune status of patients with OSCC. Additionally, we found that patients with high-risk scores may obtain greater benefit from immune checkpoint therapy compared to those with low-risk scores, and the risk score presented a close interaction with the tumor microenvironment and chemotherapy sensitivity. The prognostic signature may provide a valid and robust predictive tool that could predict the prognosis and immune status and guide clinicians to develop personalized therapeutic strategies for patients with OSCC.

Comparisons of Forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Machine Learning Models based on Multi-omics

Background: Machine learning methods showed excellent predictive ability in a wide range of fields. For the survival of head and neck squamous cell carcinoma (HNSC), its multi-omics influence is crucial. This study attempts to establish a variety of machine learning multi-omics models to predict the survival of HNSC and find the most suitable machine learning prediction method. Methods: The HNSC clinical data and multi-omics data were downloaded from the TCGA database. The important variables were screened by the LASSO algorithm. We used a total of 12 supervised machine learning models to predict the outcome of HNSC survival and compared the results. In vitro qPCR was performed to verify core genes predicted by the random forest algorithm. Results: For omics of HNSC, the results of the twelve models showed that the performance of multi-omics was better than each single-omic alone. Results were presented, which showed that the Bayesian network(BN) model (area under the curve [AUC] 0.8250, F1 score=0.7917) and random forest(RF) model (area under the curve [AUC] 0.8002,F1 score=0.7839) played good prediction performance in HNSC multi-omics data. The results of in vitro qPCR were consistent with the RF algorithm. Conclusion: Machine learning methods could better forecast the survival outcome of HNSC. Meanwhile, this study found that the BN model and the RF model were the most superior. Moreover, the forecast result of multi-omics was better than single-omic alone in HNSC.

miR-589-3p promoted osteogenic differentiation of periodontal ligament stem cells through targeting ATF1

Background

An increasing number of studies have shown that dysregulated miR-589-3p is associated with multiple diseases. However, the role of miR-589-3p in osteogenic differentiation of periodontal ligament stem cells (PDLSCs) remains unknown. This study aimed to explore the biological function and potential molecular mechanism of miR-589-3p in osteogenic differentiation of PDLSCs.

Methods

GSE159508 was downloaded from Gene Expression Omibus (GEO, http://www.ncbi.nlm.nih.gov/geo/). Differentially expressed miRNAs between osteogenic induction PDLSCs versus non-induction PDLSCs were obtained by R software. miR-589-3p mimic and miR-589-3p inhibitor and corresponding negative control were obtained and to identify the role of miR-589-3p in osteogenic differentiation of PDLSCs. ALP staining and ARS were used to evaluate ALP activity and mineralization, respectively. The targeted binding relationship between miR-589-3p and ATF1 was predicted and verified by target prediction analysis and dual-luciferase assay. Furthermore, the functional mechanism based on miR-589-3p and ATF1 in osteogenic differentiation of PDLSCs was further investigated through rescue experiments.

Results

According to the cut-off criteria with log 2 FC > 1.0 and P < 0.05, 514 differentially expressed miRNAs were identified between osteogenic induction and non-induction PDLSCs, including 309 upregulated miRNAs and 205 downregulated miRNAs. Compared with control PDLSCs, miR-589-3p expression level was notably increased in PDLSCs that underwent osteogenic induction. The overexpression of miR-589-3p promoted the cell viability of PDLSCs, while the low expression of miR-589-3p had the opposite effect. The dual luciferase reporter assay verified that ATF1 was proved to be a direct target of miR-589-3p in PDLSCs. And overexpressed miR-589-3p reduced the expression of ATF1. Overexpression of miR-589-3p enhanced the osteogenic capacity of PDLSCs, as demonstrated by increases in ALP activity, matrix mineralization, and RUNX2, OCN and OSX expression. In addition, the rescue experiments confirmed that overexpressed ATF1 restored the effects of overexpressed miR-589-3p on cell proliferation and osteogenic differentiation of PDLSCs.

Conclusion

miR-589-3p could down-regulate the expression of ATF1, thereby promote the proliferation and osteogenic differentiation of PDLSCs. This finding may provide a new therapeutic target for molecular therapy of periodontitis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-022-03000-z.

Bioinformatics Characterization of Candidate Genes Associated with Gene Network and miRNA Regulation in Esophageal Squamous Cell Carcinoma Patients

The present study aimed to identify potential therapeutic targets for esophageal squamous cell carcinoma (ESCC). The gene expression profile GSE161533 contained 84 samples, in that 28 tumor tissues and 28 normal tissues encoded as ESCC patients were retrieved from the Gene Expression Omnibus database. The obtained data were validated and screened for differentially expressed genes (DEGs) between normal and tumor tissues with the GEO2R tool. Next, the protein–protein network (PPI) was constructed using the (STRING 2.0) and reconstructed with Cytoscape 3.8.2, and the top ten hub genes (HGsT10) were predicted using the Maximal Clique Centrality (MCC) algorithm of the CytoHubba plugin. The identified hub genes were mapped in GSE161533, and their expression was determined and compared with The Cancer Genome Atlas (TCGA.) ESCC patient’s samples. The overall survival rate for HGsT10 wild and mutated types was analyzed with the Gene Expression Profiling Interactive Analysis2 (GEPIA2) server and UCSC Xena database. The functional and pathway enrichment analysis was performed using the WebGestalt database with the reference gene from lumina human ref 8.v3.0 version. The promoter methylation for the HGsT10 was identified using the UALCAN server. Additionally, the miRNA-HGsT10 regulatory network was constructed to identify the top ten hub miRNAs (miRT10). Finally, we identified the top ten novel driving genes from the DEGs of GSE161533 ESCC patient’s sample using a multi-omics approach. It may provide new insights into the diagnosis and treatment for the ESCC affected patients early in the future.

Therapeutic Target Analysis and Molecular Mechanism of Melatonin - Treated Leptin Resistance Induced Obesity: A Systematic Study of Network Pharmacology

BACKGROUND

Obesity is a medical problem with an increased risk for other metabolic disorders like diabetes, heart problem, arthritis, etc. Leptin is an adipose tissue-derived hormone responsible for food intake, energy expenditure, etc., and leptin resistance is one of the significant causes of obesity. Excess leptin secretion by poor diet habits and impaired hypothalamic leptin signaling leads to LR. Melatonin a sleep hormone; also possess antioxidant and anti-inflammatory properties. The melatonin can attenuate the complications of obesity by regulating its targets towards LR induced obesity.

AIM

The aim of this study includes molecular pathway and network analysis by using a systems pharmacology approach to identify a potential therapeutic mechanism of melatonin on leptin resistance-induced obesity.

METHODS

The bioinformatic methods are used to find therapeutic targets of melatonin in the treatment of leptin resistance-induced obesity. It includes target gene identification using public databases, Gene ontology, and KEGG pathway enrichment by 'ClusterProfiler' using the R language, network analysis by Cytoscape, and molecular Docking by Autodock.

RESULTS

We obtained the common top 33 potential therapeutic targets of melatonin and LR-induced obesity from the total melatonin targets 254 and common LR obesity targets 212 using the data screening method. They are involved in biological processes related to sleep and obesity, including the cellular response to external stimulus, chemical stress, and autophagy. From a total of 180 enriched pathways, we took the top ten pathways for further analysis, including lipid and atherosclerosis, endocrine, and AGE-RAGE signaling pathway in diabetic complications. The top 10 pathways interacted with the common 33 genes and created two functional modules. Using Cytoscape network analysis, the top ten hub genes (TP53, AKT1, MAPK3, PTGS2, TNF, IL6, MAPK1, ERBB2, IL1B, MTOR) were identified by the MCC algorithm of the CytoHubba plugin. From a wide range of pathway classes, melatonin can reduce LR-induced obesity risks by regulating the major six classes. It includes signal transduction, endocrine system, endocrine and metabolic disease, environmental adaptation, drug resistance antineoplastic, and cardiovascular disease.

CONCLUSION

The pharmacological mechanism of action in this study shows the ten therapeutic targets of melatonin in LR-induced obesity.

Underlying Mechanism and Active Ingredients of Tianma Gouteng Acting on Cerebral Infarction as Determined via Network Pharmacology Analysis Combined With Experimental Validation

Cerebral infarction (CI), a common cerebrovascular disease worldwide, is caused by unknown factors common to many diseases, including hypokalemia, respiratory diseases, and lower extremity venous thrombosis. Tianma Gouteng (TMGT), a traditional Chinese Medicine (TCM) prescription, has been used for the clinical treatment of CI. In this study, high-performance liquid chromatography (HPLC) fingerprint analysis was used to detect and identify major chemical constituents of TMGT. TCMSP and BATMAN-TCM databases were used to screen for active TMGT constituent compounds, while the GeneCards database was used to screen for protein targets associated with CI. Next, GO and KEGG enrichment analysis of these core nodes were performed to determine the identities of key associated biological processes and signal pathways. Meanwhile, a total of six possible gene targets of TMGT, including NFKBIA, PPARG, IL6, IL1B, CXCL8, and HIF1A, were selected for further study using two cellular models of CI. For one model, PC12 cells were treated under oxygen and glucose deprivation (OGD) conditions to generate an OGD cellular model of CI, while for the other model, BV2 cells were stimulated with lipopolysaccharide (LPS) to generate a cellular model of CI-associated inflammation. Ultimately TMGT treatment increased PPARγ expression and downregulated the expression of p-P65, p-IκBα, and HIF-1α in both OGD-induced and LPS-induced cell models of CI. In addition, molecular docking analysis showed that one TMGT chemical constituent, quercetin, may be a bioactive TMGT compound with activity that may be associated with the alleviation of neuronal damage and neuroinflammation triggered by CI. Moreover, additional data obtained in this work revealed that TMGT could inhibit neuroinflammation and protect brain cells from OGD-induced and LPS-induced damage by altering HIF-1α/PPARγ/NF-κB pathway functions. Thus, targeting this pathway through TMGT administration to CI patients may be a strategy for alleviating nerve injury and neuroinflammation triggered by CI.