Investigation of molecular biomarker candidates for diagnosis and prognosis of chronic periodontitis by bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus (GEO)

Identification of Potential Biomarkers and Pathways in Acute Myeloid Leukemia: Correlation Between the Calcineurin Signaling Pathway and Vascular Brittleness in Acute Myeloid Leukemia

OBJECTIVE

In this study, clinical bioinformatics analysis was used to identify potential biomarkers of acute myeloid leukemia (AML) occurrence and development, drug resistance, and poor prognosis to provide a theoretical basis for the treatment of AML.

METHODS

On the basis of the TCGA, GEO, and GTEx databases, an AML secondary database was established, and differential expression analysis and WGCNA were carried out to identify genes related to the prognosis of AML patients. Survival analysis was carried out for internal verification of key genes, and GEO data were used for external verification to obtain core genes related to prognosis. For differentially expressed genes, the EpiMed platform independently developed by the team was used for drug prediction.

RESULTS

A total of 36 overlapping genes were obtained via difference analysis and WGCNA. Enrichment analysis revealed that the overlapping genes were associated with neutrophil activation, transcription dysregulation, AML, apoptosis, and other biological indicators. A protein interaction network was constructed for NCOA4, ACSL4, DPP4, ATL1, MT1G, ALOX15, and SLC7A11, which are key genes. Survival analysis revealed that NCOA4, ACSL4, DPP4, and ATL1 significantly affected the survival of patients with AML. The GSE142698 dataset verified that MPO, BCL2A1, and STMN1 had a statistically significant impact on the survival of AML patients.

CONCLUSION

NCOA4, ACSL4, DPP4, and ATL1 may be potential biomarkers related to the survival and prognosis of patients with AML, and the calcineurin signaling pathway is associated with the risk of vascular fragility in AML patients, which can provide a reference for further research and optimization of treatment regimens.

Predicting interactome networks of up/down regulated proteins and drug-gene interaction analysis associated with peri-implantitis

Background

Peri-implantitis is implant-associated inflammation that leads to irreversible loss of surrounding bone. Early diagnosis increases the success of peri-implantitis treatment. Despite various studies associated with this most common complication, early detection of the onset of peri-implantitis remains a major challenge. Molecular biomarkers are applicable detectors for the early detection of numerous diseases and monitoring their development. The present study aimed to predict interactome networks of up/down regulated proteins and analyze drug-gene interaction in peri-implantitis to identify the diagnostic and druggable genes.

Materials and Methods

In this in silico study, a suitable gene expression profile related to peri-implantitis was retrieved from Gene Expression Omnibus. Screening differentially expressed genes (DEGs) was carried out based on the cut-off criteria |log2 (fold change)|>2 and P < 0.05. Interactome networks were constructed and analyzed by the STRING database (Version: 12.0) and the Cytoscape software (version: 3.9.1). Finally, to investigate drug-gene interaction, detected hub genes were analyzed by DGIdb (version: 5.0.6).

Results

A total of 216 genes were identified as DEGs (129 down-regulated and 87 up-regulated genes) in peri-implantitis. Regarding Cytoscape analysis, FCGR3B, CSF3R, AQP9, TREM1, and P2RY13 were the top 5 hub nodes of up-regulated DEGs, and CXCL10, OASL, IFIT1, RSAD2, and ISG15 were the top 5 hub nodes of down-regulated DEGs. Among these key nods, AQP9, CSF3R, CXCL10, IFIT1, ISG15, OASL, and, FCGR3B were therapeutic targets and had approved drugs.

Conclusion

In this research, seven genes have been identified as druggable genes in peri-implantitis which can be used to treat and diagnose this disease. However, these results and identified genes need to be validated by clinical or experimental methods.

Gradient boosting-based classification of interactome hub genes in periimplantitis with periodontitis - an integrated bioinformatic approach

INTRODUCTION

Peri-implantitis, a destructive inflammatory condition affecting the tissues surrounding dental implants, shares pathological similarities with periodontitis, a chronic inflammatory disease that impacts the supporting structures of natural teeth. This study utilizes a network-based approach to classify interactome hub genes associated with peri-implantitis and periodontitis, aiming to improve understanding of disease mechanisms and identify potential therapeutic targets.

METHODS

We employed gradient boosting and Weighted Gene Co-expression Network Analysis (WGCNA) to predict and classify these interactome hub genes. Gene expression data related to these diseases were sourced from the NCBI GEO dataset GSE223924, and differential gene expression analysis was conducted using the NCBI GEO R tool. Through WGCNA, we constructed a co-expression network to identify key hub genes, while gradient boosting was used to predict these hub genes.

RESULTS

Our analysis revealed a co-expression network comprising 216 genes, including prominent hub genes such as IL17RC, CCN2, BMP7, TPM1, and TIMP1, which are implicated in periodontal disease. The gradient boosting model achieved an 88.2% accuracy in classifying interactome hub genes in samples related to peri-implantitis and periodontitis.

DISCUSSION

These identified genes play roles in inflammation, osteoclast genesis, angiogenesis, and immune response regulation. This study highlights essential hub genes and molecular pathways associated with peri-implantitis and periodontitis, suggesting potential therapeutic targets for developing innovative treatment strategies.

Genetics Affecting the Prognosis of Dental Treatments

Genetics plays a significant role in determining an individual's susceptibility to dental diseases, the response to dental treatments, and the overall prognosis of dental interventions. Here, the authors explore the various genetic factors affecting the prognosis of dental treatments focusing on dental caries, orthodontic treatment, oral cancer, prosthodontic treatment, periodontal disease, developmental disorders, pharmacogenetics, and genetic predisposition to faster wound healing. Understanding the genetic underpinnings of dental health can help personalize treatment plans, predict outcomes, and improve the overall quality of dental care.

Potential correlation between chronic periodontitis and Parkinson's disease

OBJECTIVES

This study aims to investigate possible hub genes, associated pathways, and transcription factors between chronic periodontitis (CP) and Parkinson's disease (PD).

METHODS

Gene expression profiles of CP (GSE16134, GSE23586, and GSE10334) and PD (GSE20141 and GSE49036) were downloaded from the gene expression omnibus (GEO) database for differential expression analysis and functional clustering analysis. The protein-protein interaction (PPI) network was constructed, and hub genes were screened by four topological analysis algorithms and modular segmentation. Functional clustering analysis was performed. The hub genes were validated by external datasets of CP and PD, and causal relation was further assessed by Mendelian randomization (MR).

RESULTS

After merging the data, 1 211 differentially expressed genes (DEGs) were screened in the CP datasets; of which, 551 were upregulated and 660 were downregulated. A total of 2 407 DEGs were screened in the PD dataset, of which, 1 438 were upregulated and 969 were downregulated. The PPI network included 145 nodes and 126 edges. Four hub genes (FCGR3B, PRF1, IL18, and CD33) and three transcription factors (HSF1, HSF2, and HSF4) were finally screened. The relevant pathway was predominantly natural killer (NK) cell-mediated toxic effects. The MR results suggest a possible positive causal relationship between CP and the risk of developing PD.

CONCLUSIONS

This study indicated the probably shared pathophysiology and possible causal relationship between CP and PD and may offer novel concepts and therapeutic targets for future mechanistic investigations.

Uncovering periodontitis-associated markers through the aggregation of transcriptomics information from diverse sources

Introduction

Periodontitis, a common chronic inflammatory disease, significantly impacted oral health. To provide novel biological indicators for the diagnosis and treatment of periodontitis, we analyzed public microarray datasets to identify biomarkers associated with periodontitis.

Method

The Gene Expression Omnibus (GEO) datasets GSE16134 and GSE106090 were downloaded. We performed differential analysis and robust rank aggregation (RRA) to obtain a list of differential genes. To obtain the core modules and core genes related to periodontitis, we evaluated differential genes through enrichment analysis, correlation analysis, protein-protein interaction (PPI) network and competing endogenous RNA (ceRNA) network analysis. Potential biomarkers for periodontitis were identified through comparative analysis of dual networks (PPI network and ceRNA network). PPI network analysis was performed in STRING. The ceRNA network consisted of RRA differentially expressed messenger RNAs (RRA_DEmRNAs) and RRA differentially expressed long non-coding RNAs (RRA_DElncRNAs), which regulated each other's expression by sharing microRNA (miRNA) target sites.

Results

RRA_DEmRNAs were significantly enriched in inflammation-related biological processes, osteoblast differentiation, inflammatory response pathways and immunomodulatory pathways. Comparing the core ceRNA module and the core PPI module, C1QA, CENPK, CENPU and BST2 were found to be the common genes of the two core modules, and C1QA was highly correlated with inflammatory functionality. C1QA and BST2 were significantly enriched in immune-regulatory pathways. Meanwhile, LINC01133 played a significant role in regulating the expression of the core genes during the pathogenesis of periodontitis.

Conclusion

The identified biomarkers C1QA, CENPK, CENPU, BST2 and LINC01133 provided valuable insight into periodontitis pathology.

Loricrin and Cytokeratin Disorganisation in Severe Forms of Periodontitis

OBJECTIVE

The aim of this research was to investigate the role of the cornified epithelium, the outermost layer of the oral mucosa, engineered to prevent water loss and microorganism invasion, in severe forms of periodontitis (stage III or IV, grade C).

METHODS

Porphyromonas gingivalis, a major periodontal disease pathogen, can affect cornified epithelial protein expression through chronic activation of signal transducer and activator of transcription 6 (Stat6). We used a mouse model, Stat6VT, that mimics this to determine the effects of barrier defect on P gingivalis-induced inflammation, bone loss, and cornified epithelial protein expression, and compared histologic and immunohistologic findings with tissues obtained from human controls and patients with stage III and IV, grade C disease. Alveolar bone loss in mice was assessed using micro-computerised tomography, and soft tissue morphology was qualitatively and semi-quantitatively assessed by histologic examination for several proteins, including loricrin, filaggrin, cytokeratin 1, cytokeratin 14, a proliferation marker, a pan-leukocyte marker, as well as morphologic signs of inflammation. Relative cytokine levels were measured in mouse plasma by cytokine array.

RESULTS

In the tissues from patients with periodontal disease, there were greater signs of inflammation (rete pegs, clear cells, inflammatory infiltrates) and a decrease and broadening of expression of loricrin and cytokeratin 1. Cytokeratin 14 expression was also broader and decreased in stage IV. P gingivalis-infected Stat6VT mice showed greater alveolar bone loss in 9 out of 16 examined sites, and similar patterns of disruption to human patients in expression of loricrin and cytokeratins 1 and 14. There were also increased numbers of leukocytes, decreased proliferation, and greater signs of inflammation compared with P gingivalis-infected control mice.

CONCLUSIONS

Our study provides evidence that changes in epithelial organisation can exacerbate the effects of P gingivalis infection, with similarities to the most severe forms of human periodontitis.

Collagen Synthesis as a Prominent Process During the Interval between Two Laser Sessions

Introduction: Many people suffer from skin photodamage, especially photoaging. The application of a laser to repair damages is a common therapeutic method that is used widely. In the present study, the effectiveness and molecular mechanism of an Er:Glass non-ablative fractional laser on the human skin was assessed via bioinformatics and network analysis. Methods: The gene expression profiles of 17 white female forearm skins which received an Er:Glass non-ablative fractional laser before and after laser treatment in two sessions were extracted from Gene Expression Omnibus (GEO). Data were evaluated via GEO2R and the significant differentially expressed genes (DEGs) were assessed via protein-protein interaction (PPI) network analysis. The central nodes were identified and discussed for the compared set of samples. Results: Five classes of samples were clustered in two categories: first, baseline, 7 and 14 days after the first session of laser treatment, and second, one day after the first laser session, 29 days after the first laser session, and 1 day after the second laser session. The gross cell functions such as cell division and cell cycle and immune response were highlighted as the early affected targets of the laser. Collagen synthesis was resulted after the first laser session. Conclusion: In conclusion, the time interval between laser sessions plays a critical role in the effectiveness of laser therapy. Findings indicate that the gross effect of laser application appears in a short time, and important processes such as collagen synthesis happen later.

Clinical importance and PI3K/Akt pathway-dependent anti-proliferative role of PALMD and DPT in breast cancer

This study aimed to identify novel differentially expressed genes in breast cancer and to explore the clinical value and the anti-tumor or oncogenic effects of the identified genes using bioinformatics analysis and in vitro experiments. The differentially expressed genes in breast cancer patients were identified using Gene Expression Omnibus (GEO) database with the cut-off criteria p < 0.05 and |logFC| > 1. The expression levels of palmdelphin (PALMD) and dermatopontin (DPT) in normal tissues and breast cancer tissues were evaluated based on GEPIA and UALCAN databases. PALMD and DPT expression levels in clinical subgroups of patients with breast cancer were analyzed to assess the association of PALMD and DPT expression with clinical characteristics. The prognostic and diagnostic values of PALMD and DPT in breast cancer were evaluated from Kaplan-Meier (K-M) survival curves and receiver operating characteristic (ROC) curves. Pearson's correlation coefficient was performed using LinkedOmics. KEGG pathway enrichment analysis was performed using DAVID. The protein levels were evaluated using western blot analysis. Cell proliferation was assessed using MTT and EdU assays. Two important genes, PALMD and DPT, were identified in breast cancer. The expression levels of PALMD and DPT were significantly lower in breast cancer tissues. The expression levels of PALMD were closely related to age, histological type, and T stage of breast cancer patients. The expression levels of DPT were closely related to age, histological type, T stage, N stage, estrogen receptor status, and progesterone receptor status of breast cancer patients. The K-M survival curves showed that PALMD or DPT was not an independent prognostic factor for breast cancer. The ROC curves showed that both PALMD and DPT had good diagnostic potential for breast cancer. KEGG pathway enrichment results showed that PI3K/Akt pathway was an important overlapping signaling for PALMD and DPT. Further studies proved that overexpression of PALMD and DPT inhibited proliferation in MCF-7 and MDA-MB-231 cells by suppressing the PI3K/Akt pathway. PALMD and DPT knockdown promoted proliferation in MCF-7 and MDA-MB-231 cells by activating the PI3K/Akt pathway. These results collectively suggested that PALMD and DPT might serve as potential diagnostic biomarkers and therapeutic targets for breast cancer.

Development of a classification model and an immune-related network based on ferroptosis in periodontitis

BACKGROUND AND OBJECTIVES

Periodontitis is an immunoinflammatory disease characterized by irreversible periodontal attachment loss and bone destruction. Ferroptosis is a kind of immunogenic cell death that depends on the participation of iron ions and is involved in various inflammatory and immune processes. However, information regarding the relationship between ferroptosis and immunomodulation processes in periodontitis is extremely limited. The purpose of this study was to investigate the correlation between ferroptosis and immune responses in periodontitis.

METHODS

Gene expression profiles of gingivae were collected from the Gene Expression Omnibus data portal. After detecting differentially expressed ferroptosis-related genes (FRGs), we used univariate logistic regression analysis followed by logistic least absolute shrinkage and selection operator (LASSO) regression to establish a ferroptosis-related classification model in an attempt to accurately distinguish periodontitis gingival tissues from healthy samples. The infiltration level of immunocytes in periodontitis was then assessed through single-sample gene-set enrichment analysis. Subsequently, we screened out immune-related genes by weighted correlation network analysis and protein-protein interaction (PPI) analysis and constructed an immune-related network based on FRGs and immune-related genes.

RESULTS

A total of 24 differentially expressed FRGs were detected, and an 8-FRG combined signature constituted the classification model. The established model showed outstanding discriminating ability according to the results of receiver operating characteristic (ROC) curve analysis. In addition, the periodontitis samples had a higher degree of immunocyte infiltration. Activated B cells had the strongest positive correlation while macrophages had a strong negative correlation with certain FRGs, and we found that XBP1, ALOX5 and their interacting genes might be crucial genes in the immune-related network.

CONCLUSIONS

The FRG-based classification model had a satisfactory determination ability, which could bring new insights into the pathogenesis of periodontitis. Those genes in the immune-related network, especially hub genes along with XBP1 and ALOX5, would have the potential to serve as promising targets of immunomodulatory treatments for periodontitis.

Integrated analysis of miRNA-mRNA regulatory network and functional verification of miR-338-3p in coronary heart disease

Coronary heart disease is a cardiovascular disease with high morbidity and mortality. Although great progress has been made in treatment, the prognosis is still very poor. Therefore, this project aims to screen potential diagnostic markers and therapeutic targets related to the progression of coronary heart disease. A total of 94 overlapping differentially expressed mRNAs and 70 differentially expressed miRNAs were identified from GSE20681, GSE12288, GSE49823, and GSE105449. Through a series of bioinformatics methods and experiment, we obtained 5 core miRNA-mRNA regulatory pairs, and selected miR-338-3p/RPS23 for functional analysis. Moreover, we found that RPS23 directly targets miR-338-3p by dual luciferase assay, western, and qPCR. And the expression of miR-338-3p and RPS23 is negatively correlated. The AUC value of miR-338-3p is 0.847. Downregulation of miR-338-3p can significantly inhibit the proliferation and migration of HUVEC. On the contrary, overexpression of miR-338-3p promoted the proliferation and migration of HUVEC. In addition, the interference of RPS23 expression can reverse the regulation of miR-338-3p on HUVEC proliferation. In conclusion, miR-338-3p/RPS23 may be involved in the progression of coronary heart disease, and miR-338-3p may be a diagnostic biomarker and therapeutic target for coronary heart disease.

Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis

Background: Periodontitis is a chronic inflammatory disease leading to tooth loss in severe cases, and early diagnosis is essential for periodontitis prevention. This study aimed to construct a diagnostic model for periodontitis using a random forest algorithm and an artificial neural network (ANN). Methods: Gene expression data of two large cohorts of patients with periodontitis, GSE10334 and GSE16134, were downloaded from the Gene Expression Omnibus database. We screened for differentially expressed genes in the GSE10334 cohort, identified key periodontitis biomarkers using a Random Forest algorithm, and constructed a classification artificial neural network model, using receiver operating characteristic curves to evaluate its diagnostic utility. Furthermore, patients with periodontitis were classified using a consensus clustering algorithm. The immune infiltration landscape was assessed using CIBERSOFT and single-sample Gene Set Enrichment Analysis. Results: A total of 153 differentially expressed genes were identified, of which 42 were downregulated. We utilized 13 key biomarkers to establish a periodontitis diagnostic model. The model had good predictive performance, with an area under the receiver operative characteristic curve (AUC) of 0.945. The independent cohort (GSE16134) was used to further validate the model's accuracy, showing an area under the receiver operative characteristic curve of 0.900. The proportion of plasma cells was highest in samples from patients with period ontitis, and 13 biomarkers were closely related to immunity. Two molecular subgroups were defined in periodontitis, with one cluster suggesting elevated levels of immune infiltration and immune function. Conclusion: We successfully identified key biomarkers of periodontitis using machine learning and developed a satisfactory diagnostic model. Our model may provide a valuable reference for the prevention and early detection of periodontitis.

The association of healthy eating index with periodontitis in National Health and Nutrition Examination Study 2011–2012

Aim

Periodontitis is a chronic inflammatory disorder caused by periodontopathic bacteria that causes inflammation of the supporting tissues around teeth. Previous studies have found that daily dietary nutritional intake can influence the development of periodontal disease. However, research on the Healthy Eating Index’s involvement in periodontitis is limited. The purpose of this study was to look at the link between the Healthy Eating Index and periodontitis.

Methods and design

We examined data from the National Health and Nutrition Examination Study (NHANES), a nationally representative survey that was performed in 2-year cycles from 2011 to 2012. As part of our investigation, we used multivariate logistic regression models to investigate the independent relationship between the Healthy Eating Index and periodontitis. We used odds ratios (OR) with 95% confidence intervals to assess the significance of the connection (95% CI).

Results

Individuals with a lower total healthy eating index were more likely to have periodontitis. A higher healthy diet index was associated with a lower prevalence of periodontitis (OR = 0.69; 95%CI: 0.49–0.97), according to adjusted multivariate regression models. The restricted cubic spline (RCS) analysis revealed that the non-linear relationship between HEI-2015 and periodontitis was statistically significant and that high HEI-2015 reduced periodontitis prevalence.

Conclusion

The study’s findings revealed that dietary structure was linked to the prevalence of periodontitis. Patients with a higher Healthy Eating Index were less likely to have periodontitis. There is a need for future prospective longitudinal studies to confirm causality.

Identification and validation of potential hub genes in rheumatoid arthritis by bioinformatics analysis

OBJECTIVE

Rheumatoid arthritis (RA) is considered to be a chronic immune disease pathologically characterized by synovial inflammation and bone destruction. At present, the potential pathogenesis of RA is still unclear. Hub genes are recognized to play a pivotal role in the occurrence and progression of RA.

METHODS

Firstly, we attempted to screen hub genes that are associated with RA, to clarify the underlying pathological mechanisms of RA, and to offer potential treatment methods for RA. We acquired these datasets (GSE12021, GSE55235, and GSE55457) of RA patients and healthy samples from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were recognized via R software. Then, Gene ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were utilized to deeply explore the underlying biological functions and pathways closely associated with RA. In addition, a protein-protein interaction (PPI) network was built to further evaluate and screen for hub genes. Finally, on the basis of the results of PPI analysis, we confirmed the mRNA expression levels of five hub genes in the synovial tissue of rats modeled with RA.

RESULTS

In the human microarray datasets, LCK, JAK2, SOCS3, STAT1, and EGFR were identified as hub genes associated with RA by bioinformatics analysis. Furthermore, we verified the differential expression levels of hub genes in rat synovial tissues via qRT-PCR (P < 0.05).

CONCLUSIONS

Our findings suggest that the hub genes LCK, JAK2, SOCS3, STAT1, and EGFR might have vital roles in the progression of RA and may offer novel therapeutic treatments for RA.

Identification of Key Genes and Pathways Associated with Oxidative Stress in Periodontitis

Background and Objective. Oxidative stress has been associated with the progression of periodontitis. However, oxidative stress-related genes (OS-genes) have not been used as disease-specific biomarkers that correlate with periodontitis progression. This study is aimed at screening the key OS-genes and pathways in periodontitis by bioinformatics methods. Methods. The differentially expressed genes (DEGs) were identified using periodontitis-related microarray from the GEO database, and OS-genes were extracted from GeneCards database. The intersection of the OS-genes and the DEGs was considered as oxidative stress-related DEGs (OS-DEGs) in periodontitis. The Pearson correlation and protein-protein interaction analyses were used to screen key OS-genes. Gene set enrichment, functional enrichment, and pathway enrichment analyses were performed in OS-genes. Based on key OS-genes, a risk score model was constructed through logistic regression, receiver operating characteristic curve, and stratified analyses. Results. In total, 74 OS-DEGs were found in periodontitis, including 65 upregulated genes and 9 downregulated genes. Six of them were identified as key OS-genes (CXCR4, SELL, FCGR3B, FCGR2B, PECAM1, and ITGAL) in periodontitis. All the key OS-genes were significantly upregulated and associated with the increased risk of periodontitis. Functional enrichment analysis showed that these genes were mainly associated with leukocyte cell-cell adhesion, phagocytosis, and cellular extravasation. Pathway analysis revealed that these genes were involved in several signaling pathways, such as leukocyte transendothelial migration and osteoclast differentiation. Conclusion. In this study, we screened six key OS-genes that were screened as risk factors of periodontitis. We also identified multiple signaling pathways that might play crucial roles in regulating oxidative stress damage in periodontitis. In the future, more experiments need to be carried out to validate our current findings.

Identification of nine signature proteins involved in periodontitis by integrated analysis of TMT proteomics and transcriptomics

Recently, there are many researches on signature molecules of periodontitis derived from different periodontal tissues to determine the disease occurrence and development, and deepen the understanding of this complex disease. Among them, a variety of omics techniques have been utilized to analyze periodontitis pathology and progression. However, few accurate signature molecules are known and available. Herein, we aimed to screened and identified signature molecules suitable for distinguishing periodontitis patients using machine learning models by integrated analysis of TMT proteomics and transcriptomics with the purpose of finding novel prediction or diagnosis targets. Differential protein profiles, functional enrichment analysis, and protein-protein interaction network analysis were conducted based on TMT proteomics of 15 gingival tissues from healthy and periodontitis patients. DEPs correlating with periodontitis were screened using LASSO regression. We constructed a new diagnostic model using an artificial neural network (ANN) and verified its efficacy based on periodontitis transcriptomics datasets (GSE10334 and GSE16134). Western blotting validated expression levels of hub DEPs. TMT proteomics revealed 5658 proteins and 115 DEPs, and the 115 DEPs are closely related to inflammation and immune activity. Nine hub DEPs were screened by LASSO, and the ANN model distinguished healthy from periodontitis patients. The model showed satisfactory classification ability for both training (AUC=0.972) and validation (AUC=0.881) cohorts by ROC analysis. Expression levels of the 9 hub DEPs were validated and consistent with TMT proteomics quantitation. Our work reveals that nine hub DEPs in gingival tissues are closely related to the occurrence and progression of periodontitis and are potential signature molecules involved in periodontitis.

Classifying Integrated Signature Molecules in Macrophages of Rheumatoid Arthritis, Osteoarthritis, and Periodontal Disease: An Omics-Based Study

Rheumatoid arthritis (RA), osteoarthritis (OA), and periodontal disease (PD) are chronic inflammatory diseases that are globally prevalent, and pose a public health concern. The search for a potential mechanism linking PD to RA and OA continues, as it could play a significant role in disease prevention and treatment. Recent studies have linked RA, OA, and PD to Porphyromonas gingivalis (PG), a periodontal bacterium, through a similar dysregulation in an inflammatory mechanism. This study aimed to identify potential gene signatures that could assist in early diagnosis as well as gain insight into the molecular mechanisms of these diseases. The expression data sets with the series IDs GSE97779, GSE123492, and GSE24897 for macrophages of RA, OA synovium, and PG stimulated macrophages (PG-SM), respectively, were retrieved and screened for differentially expressed genes (DEGs). The 72 common DEGs among RA, OA, and PG-SM were further subjected to gene–gene correlation analysis. A GeneMANIA interaction network of the 47 highly correlated DEGs comprises 53 nodes and 271 edges. Network centrality analysis identified 15 hub genes, 6 of which are DEGs (API5, ATE1, CCNG1, EHD1, RIN2, and STK39). Additionally, two significantly up-regulated non-hub genes (IER3 and RGS16) showed interactions with hub genes. Functional enrichment analysis of the genes showed that “apoptotic regulation” and “inflammasomes” were among the major pathways. These eight genes can serve as important signatures/targets, and provide new insights into the molecular mechanism of PG-induced RA, OA, and PD.

Network pharmacology combined with GEO database identifying the mechanisms and molecular targets of Polygoni Cuspidati Rhizoma on Peri-implants

Peri-implants is a chronic disease leads to the bone resorption and loss of implants. Polygoni Cuspidati Rhizoma (PCRER), a traditional Chinese herbal has been used to treat diseases of bone metabolism. However, its mechanism of anti-bone absorption still remains unknown. We aimed to identify its molecular target and the mechanism involved in PCRER potential treatment theory to Peri-implants by network pharmacology. The active ingredients of PCRER and potential disease-related targets were retrieved from TCMSP, Swiss Target Prediction, SEA databases and then combined with the Peri-implants disease differential genes obtained in the GEO microarray database. The crossed genes were used to protein–protein interaction (PPI) construction and Gene Ontology (GO) and KEGG enrichment analysis. Using STRING database and Cytoscape plug-in to build protein interaction network and screen the hub genes and verified through molecular docking by AutoDock vina software. A total of 13 active compounds and 90 cross targets of PCRER were selected for analysis. The GO and KEGG enrichment analysis indicated that the anti-Peri-implants targets of PCRER mainly play a role in the response in IL-17 signaling, Calcium signaling pathway, Toll-like receptor signaling pathway, TNF signaling pathway among others. And CytoHubba screened ten hub genes (MMP9, IL6, MPO, IL1B, SELL, IFNG, CXCL8, CXCL2, PTPRC, PECAM1). Finally, the molecular docking results indicated the good binding ability with active compounds and hub genes. PCRER’s core components are expected to be effective drugs to treat Peri-implants by anti-inflammation, promotes bone metabolism. Our study provides new thoughts into the development of natural medicine for the prevention and treatment of Peri-implants.

Systems Biology in Periodontitis

Systems biology is a promising scientific discipline that allows an integrated investigation of host factors, microbial composition, biomarkers, immune response and inflammatory mediators in many conditions such as chronic diseases, cancer, neurological disorders, and periodontitis. This concept utilizes genetic decoding, bioinformatic, flux-balance analysis in a comprehensive approach. The aim of this review is to better understand the current literature on systems biology and identify a clear applicability of it to periodontitis. We will mostly focus on the association between this condition and topics such as genomics, transcriptomics, proteomics, metabolomics, as well as contextualize delivery systems for periodontitis treatment, biomarker detection in oral fluids and associated systemic conditions.

Autophagy-Related Genes Predict the Progression of Periodontitis Through the ceRNA Network

Purpose

The goal of this study was to identify the crucial autophagy-related genes (ARGs) in periodontitis and construct mRNA-miRNA-lncRNA networks to further understand the pathogenesis of periodontitis.

Methods

We used the Gene Expression Omnibus (GEO) database and Human Autophagy Database (HADb) to identify differentially expressed mRNAs, miRNAs, and ARGs. These ARGs were subjected to Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, and PPI (protein–protein interaction) network analysis. Two databases (miRDB and StarBase v2.0) were used to reverse-predict miRNAs while the miRNA-lncRNA interaction was predicted using the StarBase v2.0 and LncBase Predicted v.2 databases. After excluding the lncRNAs only present in the nucleus, a competing endogenous RNA (ceRNA) network was built. Finally, we used quantitative real-time PCR (qRT-PCR) to confirm the levels of mRNA expression in the ceRNA network.

Results

The differential expression analysis revealed 10 upregulated and 10 downregulated differentially expressed ARGs. After intersecting the reverse-predicted miRNAs with the differentially expressed miRNAs, a ceRNA network consisting of 4 mRNAs (LAMP2, NFE2L2, NCKAP1, and EGFR), 3 miRNAs (hsa-miR-140-3p, hsa-miR-142-5p, and hsa-miR-671-5p), and 30 lncRNAs was constructed. In addition, qRT-PCR results revealed that EGFR expression was downregulated in diseased gingival tissue of periodontitis patients.

Conclusion

Four autophagy-related genes, especially EGFR, may play a key role in periodontitis progression. The novel ceRNA network may aid in elucidating the role and the mechanism of autophagy in periodontitis, which could be important in developing new therapeutic options.

Pleckstrin Levels Are Increased in Patients with Chronic Periodontitis and Regulated via the MAP Kinase-p38α Signaling Pathway in Gingival Fibroblasts

Chronic periodontitis (CP) is a bacteria-driven inflammatory disease characterized by the breakdown of gingival tissue, the periodontal ligament, and alveolar bone, leading ultimately to tooth loss. We previously reported the pleckstrin gene (PLEK) to be highly upregulated in gingival tissue of patients with CP and the only gene concurrently upregulated in other inflammatory diseases including rheumatoid arthritis and cardiovascular diseases. Using saliva from 169 individuals diagnosed with CP and healthy controls, we investigated whether pleckstrin could serve as a novel biomarker of periodontitis. Additionally, we explored signal pathways involved in the regulation of PLEK using human gingival fibroblasts (HGFs). Pleckstrin levels were significantly higher (p < 0.001) in the saliva samples of patients with CP compared to controls and closely associated with CP severity. Immunohistochemical analysis revealed the expression of pleckstrin in inflammatory cells and gingival fibroblasts of CP patients. To explore the signal pathways involved in pleckstrin regulation, we stimulated HGFs with either interleukin-1β (IL-1β) or lipopolysaccharides (LPS) alone, or in combination with inhibitors targeting c-Jun N-terminal kinase, tyrosine kinase, protein kinase C, or p38 MAP kinase. Results showed that IL-1β and LPS significantly increased PLEK mRNA and pleckstrin protein levels. VX-745, the p38 MAP kinase inhibitor significantly decreased IL-1β- and LPS-induced pleckstrin levels at both the mRNA and the protein level. Together, these findings show that pleckstrin could serve as a salivary biomarker for the chronic inflammatory disease periodontitis and a regulator of inflammation via the p38 MAP kinase pathway.

Oral Dysbiosis in Severe Forms of Periodontitis Is Associated With Gut Dysbiosis and Correlated With Salivary Inflammatory Mediators: A Preliminary Study

Inflammation is a driven force in modulating microbial communities, but little is known about the interplay between colonizing microorganisms and the immune response in periodontitis. Since local and systemic inflammation may play a whole role in disease, we aimed to evaluate the oral and fecal microbiome of patients with periodontitis and to correlate the oral microbiome data with levels of inflammatory mediator in saliva. Methods: Nine patients with periodontitis (P) in Stage 3/Grade B and nine age-matched non-affected controls (H) were evaluated. Microbial communities of oral biofilms (the supra and subgingival from affected and non-affected sites) and feces were determined by sequencing analysis of the 16SrRNA V3-V4 region. Salivary levels of 40 chemokines and cytokines were correlated with oral microbiome data. Results: Supragingival microbial communities of P differed from H (Pielou's evenness index, and Beta diversity, and weighted UniFrac), since relative abundance (RA) of Defluviitaleaceae, Desulfobulbaceae, Mycoplasmataceae, Peptostreococcales-Tissierellales, and Campylobacteraceae was higher in P, whereas Muribaculaceae and Streptococcaceae were more abundant in H. Subgingival non-affected sites of P did not differ from H, except for a lower abundance of Gemellaceae. The microbiome of affected periodontitis sites (PD ≥ 4 mm) clustered apart from the subgingival sites of H. Oral pathobionts was more abundant in sub and supragingival biofilms of P than H. Fecal samples of P were enriched with Acidaminococcus, Clostridium, Lactobacillus, Bifidobacterium, Megasphaera, and Romboutsia when compared to H. The salivary levels of interleukin 6 (IL-6) and inflammatory chemokines were positively correlated with the RA of several recognized and putative pathobionts, whereas the RA of beneficial species, such as Rothia aeria and Haemophilus parainfluenzae was negatively correlated with the levels of Chemokine C-C motif Ligand 2 (CCL2), which is considered protective. Dysbiosis in patients with periodontitis was not restricted to periodontal pockets but was also seen in the supragingival and subgingival non-affected sites and feces. Subgingival dysbiosis revealed microbial signatures characteristic of different immune profiles, suggesting a role for candidate pathogens and beneficial organisms in the inflammatory process of periodontitis.

A Novel Four-Gene Signature as a Potential Prognostic Biomarker for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor with high incidence and mortality rates. However, a reliable prognostic signature has not yet been confirmed. Thus, the purpose of the present study was to develop a biomarker with high specificity and sensitivity for the diagnosis and prognosis of patients with HCC. The mRNA expression profiles of HCC were obtained from the GSE19665, GSE41804, and TCGA databases. Subsequently, 193 differentially expressed genes (DEGs) were identified from the intersection of the data from the three datasets. Bioinformatics analysis showed that the identified DEGs are related to the cell cycle, oocyte meiosis, and p53 signaling pathway, among other factors, in cancers. A protein-protein interaction (PPI) and a functional analysis were performed to investigate the biological function of the DEGs and obtain the candidate genes using the MCODE of Cytoscape. The candidate genes were introduced into the TCGA database for survival analysis, and the four candidate genes that were hub genes and meaningful for survival were retained for further verification. We validated the gene and protein expression and determined the prognosis of our patient cohort. In addition, we evaluated the biological functions regulating tumor cell proliferation and metastasis in vitro. According to the ROC curve analysis of gene expression in clinical samples, it was found that the four genes can be used to predict the diagnosis. A survival analysis based on data from the TCGA database and clinical samples showed that the four genes may be used as biomarkers for providing prognoses for patients. The cell functional experiments revealed that these four genes were related to tumor proliferation, migration, and invasion. In conclusion, the genes identified in the present study could be used as markers to diagnose and predict the prognosis of patients with HCC and guide targeted therapy.

Identification of potential genes related to breast cancer brain metastasis in breast cancer patients

Brain metastases (BMs) usually develop in breast cancer (BC) patients. Thus, the molecular mechanisms of breast cancer brain metastasis (BCBM) are of great importance in designing therapeutic strategies to treat or prevent BCBM. The present study attempted to identify novel diagnostic and prognostic biomarkers of BCBM. Two datasets (GSE125989 and GSE100534) were obtained from the Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs) in cases of BC with and without brain metastasis (BM). A total of 146 overlapping DEGs, including 103 up-regulated and 43 down-regulated genes, were identified. Functional enrichment analysis showed that these DEGs were mainly enriched for functions including extracellular matrix (ECM) organization and collagen catabolic fibril organization. Using protein-protein interaction (PPI) and principal component analysis (PCA) analysis, we identified ten key genes, including LAMA4, COL1A1, COL5A2, COL3A1, COL4A1, COL5A1, COL5A3, COL6A3, COL6A2, and COL6A1. Additionally, COL5A1, COL4A1, COL1A1, COL6A1, COL6A2, and COL6A3 were significantly associated with the overall survival of BC patients. Furthermore, COL6A3, COL5A1, and COL4A1 were potentially correlated with BCBM in human epidermal growth factor 2 (HER2) expression. Additionally, the miR-29 family might participate in the process of metastasis by modulating the cancer microenvironment. Based on datasets in the GEO database, several DEGs have been identified as playing potentially important roles in BCBM in BC patients.

Identification of a lncRNA/circRNA-miRNA-mRNA network to explore the effects of ricin toxin-induced inflammation in RAW264.7 cells

Ricin toxin (RT) is a ribosome-inactivating protein derived from the beans of the castor oil plant. Our previous studies have reported that RT can induce the production of inflammatory cytokines and cause inflammatory injury in RAW264.7 cells. In order to explore the various biological processes that long noncoding RNA (lncRNA), circular RNA (circRNA) and micro RNA (miRNA) as endogenous non-coding RNAs (ceRNAs) may participate in the pro-inflammatory mechanism, RT (20 ng/mL) treated and normal RAW264.7 cells were firstly sequenced by RNA-seq. By comparing the differentially expressed genes, we obtained 10 hub genes and enriched the inflammatory-related signaling pathways. Based on our results, we concluded a lncRNA/circRNA-miRNA-mRNA network. Finally, we verified the key genes and pathways by qRT-PCR, WB and ELISA. From the experiment results, an opening MAPK signaling pathway in TNF signaling pathway via TNFR2 was found involved in RT-induced inflammation. This work provides a reference for searching for ceRNA targets or therapeutic drugs in RT-induced inflammatory injury in the future.

Mendelian randomization analysis identified genes potentially pleiotropically associated with periodontitis

OBJECTIVE

To prioritize genes that were pleiotropically or potentially causally associated with periodontitis.

METHODS

We applied the summary data-based Mendelian randomization (SMR) method integrating genome-wide association study (GWAS) for periodontitis and expression quantitative trait loci (eQTL) data to identify genes that were pleiotropically associated with periodontitis. We performed separate SMR analysis using CAGE eQTL data and GTEx eQTL data. SMR analysis were done for participants of European and East Asian ancestries, separately.

RESULTS

We identified multiple genes showing pleiotropic association with periodontitis in participants of European ancestry and participants of East Asian ancestry. PDCD2 (corresponding probe: ILMN_1758915) was the top hit showing pleotropic association with periodontitis in the participants of European ancestry using CAGE eQTL data, and BX093763 (corresponding probe: ILMN_1899903) and AC104135.3 (corresponding probe: ENSG00000204792.2) were the top hits in the participants of East Asian ancestry using CAGE eQTL data and GTEx eQTL data, respectively.

CONCLUSION

We identified multiple genes that may be involved in the pathogenesis of periodontitis in participants of European ancestry and participants of East Asian ancestry. Our findings provided important leads to a better understanding of the mechanisms underlying periodontitis and revealed potential therapeutic targets for the effective treatment of periodontitis.

Reliability of microarray analysis for studying periodontitis: low consistency in 2 periodontitis cohort data sets from different platforms and an integrative meta-analysis

PURPOSE

The aim of this study was to compare the characteristic expression patterns of advanced periodontitis in 2 cohort data sets analyzed using different microarray platforms, and to identify differentially expressed genes (DEGs) through a meta-analysis of both data sets.

METHODS

Twenty-two patients for cohort 1 and 40 patients for cohort 2 were recruited with the same inclusion criteria. The 2 cohort groups were analyzed using different platforms: Illumina and Agilent. A meta-analysis was performed to increase reliability by removing statistical differences between platforms. An integrative meta-analysis based on an empirical Bayesian methodology (ComBat) was conducted. DEGs for the integrated data sets were identified using the limma package to adjust for age, sex, and platform and compared with the results for cohorts 1 and 2. Clustering and pathway analyses were also performed.

RESULTS

This study detected 557 and 246 DEGs in cohorts 1 and 2, respectively, with 146 and 42 significantly enriched gene ontology (GO) terms. Overlapping between cohorts 1 and 2 was present in 59 DEGs and 18 GO terms. However, only 6 genes from the top 30 enriched DEGs overlapped, and there were no overlapping GO terms in the top 30 enriched pathways. The integrative meta-analysis detected 34 DEGs, of which 10 overlapped in all the integrated data sets of cohorts 1 and 2.

CONCLUSIONS

The characteristic expression pattern differed between periodontitis and the healthy periodontium, but the consistency between the data sets from different cohorts and metadata was too low to suggest specific biomarkers for identifying periodontitis.

Interleukin-1 receptor-associated kinase 4 as a potential biomarker: Overexpression predicts poor prognosis in patients with glioma

The undetectable onset of glioma and the difficulty of surgery lead to a poor prognosis. Appropriate biomarkers for diagnosis and treatment need to be identified. Interleukin-1 receptor-associated kinase 4 (IRAK4) is involved in the initiation and progression of cancer. However, up until now, no report has revealed the relationship between IRAK4 and glioma. The present study aimed to examine the expression of IRAK4 in glioma, and to determine if there was a relationship between IRAK4 expression and clinical outcomes or survival prognosis. Thousands of glioma tissue samples and corresponding clinical information were obtained from various databases. Then a series of bioinformatics methods were used to reveal the role of IRAK4 in glioma. Finally, reverse transcription-quantitative PCR technology was used to verify the bioinformatics results. The study found that the expression of IRAK4 was significantly increased in glioma compared with the control brain tissue samples, and IRAK4, as an independent prognostic factor, shortened the overall survival time of patients with glioma. Gene Set Enrichment Analysis showed that IRAK4 promoted the activation of cell signalling pathways, such as NOD-like and Toll-like receptor signalling pathways. Co-expression analysis showed that the expression of IRAK4 was correlated with CMTM6, MOB1A and other genes. The present study demonstrated the role of IRAK4 as an oncogene in the pathological process of glioma for the first time, and highlights the potential of IRAK4 as a biomarker for prognostic evaluation and treatment of glioma.

Differential expression of inflammatory responsive genes between chronic periodontitis and periodontally affected bronchiectasis patients

The study aimed to investigate differential expression of targeted inflammatory-immune responsive genes [LTA, LTB, TNFSF4, TNFSF11/RANKL, TNFSF13, TNFSF13B, TNFRSF11B/ Osteoprotegerin; OPG and GFPT1/GFA ] in gingival tissues of bronchiectasis patients having chronic periodontitis in North central Indian population. Gingival tissues were collected from 30 systemically healthy chronic periodontitis patients (CP), 30 bronchiectasis patients with chronic periodontitis (B+CP), 3 systemically healthy with healthy gingiva (healthy control; HC) and 3 bronchiectasis with healthy gingiva (bronchiectasis control; BC). Statistical analysis revealed 7 genes to be significantly upregulated on comparing CP with B+CP i.e LTA (P<0.0001) in B+CP while LTB (P<0.0001), TNFSF4 (P=0.0003), TNFSF11 (P<0.0001), TNFSF13 (P=0.0003), TNFSF13B (P<0.0001) and TNFRSF11B (P=0.0004) in CP group. LTA (Lymphotoxin A) gene could be a potential genetic marker in bronchiectasis patients with chronic periodontitis.