An integrative bioinformatics analysis of microarray data for identifying hub genes as diagnostic biomarkers of preeclampsia

Analyzing molecular signatures in preeclampsia and fetal growth restriction: Identifying key genes, pathways, and therapeutic targets for preterm birth

Background

Intrauterine growth restriction (IUGR) and preeclampsia (PE) are intricately linked with specific maternal health conditions, exhibit shared placental abnormalities, and play pivotal roles in precipitating preterm birth (PTB) incidences. However, the molecular mechanism underlying the association between PE and IUGR has not been determined. Therefore, we aimed to analyze the data of females with PE and those with PE + IUGR to identify the key gene(s), their molecular pathways, and potential therapeutic interactions.

Methods

In this study, a comprehensive relationship analysis of both PE and PE + IUGR was conducted using RNA sequence datasets. Using two datasets (GSE148241 and GSE114691), differential gene expression analysis via DESeq2 through R-programming was performed. Gene set enrichment analysis was performed using ClusterProfiler, protein‒protein interaction (PPI) networks were constructed, and cluster analyses were conducted using String and MCODE in Cytoscape. Functional enrichment analyses of the resulting subnetworks were performed using ClueGO software. The hub genes were identified under both conditions using the CytoHubba method. Finally, the most common hub protein was docked against a library of bioactive flavonoids and PTB drugs using the PyRx AutoDock tool, followed by molecular dynamic (MD) simulation analysis. Pharmacokinetic analysis was performed to determine the ADMET properties of the compounds using pkCSM.

Results

We identified eight hub genes highly expressed in the case of PE, namely, PTGS2, ENG, KIT, MME, CGA, GAPDH, GPX3, and P4HA1, and the network of the PE + IUGR gene set demonstrated that nine hub genes were overexpressed, namely, PTGS2, FGF7, FGF10, IL10, SPP1, MPO, THBS1, CYBB, and PF4. PTGS2 was the most common hub gene found under both conditions (PE and PEIUGR). Moreover, the greater (-9.1 kcal/mol) molecular binding of flavoxate to PTGS2 was found to have satisfactory pharmacokinetic properties compared with those of other compounds. The flavoxate-bound PTGS2 protein complex remained stable throughout the simulation; with a ligand fit to protein, i.e., a RMSD ranging from ∼2.0 to 4.0 Å and a RMSF ranging from ∼0.5 to 2.9 Å, was observed throughout the 100 ns analysis.

Conclusion

The findings of this study may be useful for treating PE and IUGR in the management of PTB.

CDH1 overexpression sensitizes TRAIL resistant breast cancer cells towards rhTRAIL induced apoptosis

PURPOSE

Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) is well known for its unique ability to induce apoptosis in cancer cells but not normal cells. However, a subpopulation of cancer cells exist that does not respond to toxic doses of TRAIL. In this study, we aimed to identify key factors regulating TRAIL resistance in breast cancer.

METHODS

rhTRAIL (recombinant human TRAIL) resistant cells (TR) isolated from TRAIL sensitive MDA-MB-231 parental cells (TS) were confirmed using trypan blue assay, cell viability assay and AO/EtBr (acridine orange/ethidium bromide) staining. Microarray was performed followed by analysis using DAVID and Cytoscape bioinformatics software to identify the candidate hub gene. Gene expression of the candidate gene was confirmed using real-time PCR and western blot. Candidate gene was overexpressed via transient transfection to identify its significance in the context of rhTRAIL. Breast cancer patient data was obtained from The Cancer Genome Atlas (TCGA) database.

RESULTS

Whole transcriptome analysis identified 4907 differentially expressed genes (DEGs) between TS and TR cells. CDH1 was identified as the candidate hub gene, with 18-degree centrality. We further observed CDH1 protein to be downregulated, overexpression of which increased apoptosis in TR cells after rhTRAIL treatment. TCGA patient data analysis also showed CDH1 mRNA to be low in TRAIL resistant patient group compared to TRAIL sensitive group.

CONCLUSION

CDH1 overexpression sensitizes TR cells towards rhTRAIL induced apoptosis. Therefore, we can hypothesize that CDH1 expression should be taken into account while performing TRAIL therapy in breast cancer.

Decreased PPP1R3G in pre-eclampsia impairs human trophoblast invasion and migration via Akt/MMP-9 signaling pathway

Pre-eclampsia (PE) is a severe pregnancy complication characterized by impaired trophoblast invasion and spiral artery remodeling and can have serious consequences for both mother and child. Protein phosphatase 1 regulatory subunit 3G (PPP1R3G) is involved in numerous tumor-related biological processes. However, the biological action and underlying mechanisms of PPP1R3G in PE progression remain unclear. We used western blotting and immunohistochemistry to investigate PPP1R3G expression in gestational age-matched pre-eclamptic and normal placental tissues. After lentivirus transfection, wound-healing, Transwell, cell-counting kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU), and TdT mediateddUTP Nick End Labeling (TUNEL) assays were used to assess trophoblast migration, invasion, proliferation, and apoptosis, respectively. The relative expression levels of PPP1R3G and the proteins involved in the Akt signaling pathway were determined using western blotting. The results showed that PPP1R3G levels were significantly lower in the placental tissues and GSE74341 microarray of the PE group than those of the healthy control group. We also found that neonatal weight and Apgar score were lower at birth, and peak systolic blood pressure and diastolic blood pressure were higher in the PE group than in the non-PE group. In addition, PPP1R3G knockdown decreased p-Akt/Akt expression and inhibited migration, invasion, and proliferation in HTR-8/SVneo trophoblasts but had no discernible effect on cell apoptosis. Furthermore, PPP1R3G positively regulated matrix metallopeptidase 9 (MMP-9), which was downregulated in placental tissues of pregnant women with PE. These results provided the first evidence that the reduced levels of PPP1R3G might contribute to PE by suppressing the invasion and migration of trophoblasts and targeting the Akt/MMP-9 signaling pathway.

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

BACKGROUND

IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR.

METHODS

We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures.

RESULTS

We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80-0.88), specificity = 0.87 (95% CI 0.83-0.90), positive predictive value = 0.78 (95% CI 0.68-0.86), negative predictive value = 0.91 (95% CI 0.86-0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34-49.59). In detail, the RF-SVM (Random Forest-Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG.

CONCLUSIONS

our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized.

Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications

Introduction

Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.

Aim

To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.

Methodology

Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.

Current state

ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.

Future challenges

To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.

Conclusion

The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.

Bioinformatics methods in biomarkers of preeclampsia and associated potential drug applications

Background

Preeclampsia is a pregnancy-related condition that causes high blood pressure and proteinuria after 20 weeks of pregnancy. It is linked to increased maternal mortality, organ malfunction, and foetal development limitation. In this view, there is a need critical to identify biomarkers for the early detection of preeclampsia. The objective of this study is to discover critical genes and explore medications for preeclampsia treatment that may influence these genes.

Methods

Four datasets, including GSE10588, GSE25906, GSE48424 and GSE60438 were retrieved from the Gene Expression Omnibus database. The GSE10588, GSE25906, and GSE48424 datasets were then removed the batch effect using the “sva” R package and merged into a complete dataset. The differentially expressed genes (DEGs) were identified using the “limma” R package. The potential small-molecule agents for the treatment of PE was further screened using the Connective Map (CMAP) drug database based on the DEGs. Further, Weight gene Co-expression network (WGNCA) analysis was performed to identified gene module associated with preeclampsia, hub genes were then identified using the logistic regression analysis. Finally, the immune cell infiltration level of genes was evaluated through the single sample gene set enrichment analysis (ssGSEA).

Results

A total of 681 DEGs (376 down-regulated and 305 up-regulated genes) were identified between normal and preeclampsia samples. Then, Dexamethasone, Prednisone, Rimexolone, Piretanide, Trazodone, Buflomedil, Scoulerin, Irinotecan, and Camptothecin drugs were screened based on these DEGs through the CMAP database. Two modules including yellow and brown modules were the most associated with disease through the WGCNA analysis. KEGG analysis revealed that the chemokine signaling pathway, Th1 and Th2 cell differentiation, B cell receptor signalling pathway and oxytocin signalling pathway were significantly enriched in these modules. Moreover, two key genes, PLEK and LEP were evaluated using the univariate and multivariate logistic regression analysis from the hub modules. These two genes were further validated in the external validation cohort GSE60438 and qRT-PCR experiment. Finally, we evaluated the relationship between immune cell and two genes.

Conclusion

In conclusion, the present study investigated key genes associated with PE pathogenesis that may contribute to identifying potential biomarkers, therapeutic agents and developing personalized treatment for PE.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08937-3.

A transcriptomic analysis of neuropathic pain in the anterior cingulate cortex after nerve injury

The anterior cingulate cortex (ACC) is a core brain region processing pain emotion. In this study, we performed RNA sequencing analysis to reveal transcriptomic profiles of the ACC in a rat chronic constriction injury (CCI) model. A total of 1628 differentially expressed genes (DEGs) were identified by comparing sham-operated rats with rats of 12 hours, 1, 3, 7, and 14 days after surgery, respectively. Although these inflammatory-related DEGs were generally increased after CCI, different kinetics of time-series expression were observed with the development of neuropathic pain affection. Specifically, the expression of Ccl5, Cxcl9 and Cxcl13 continued to increase following CCI. The expression of Ccl2, Ccl3, Ccl4, Ccl6, and Ccl7 were initially upregulated after CCI and subsequently decreased after 12 hours. Similarly, the expression of Rac2, Cd68, Icam-1, Ptprc, Itgb2, and Fcgr2b increased after 12 hours but reduced after 1 day. However, the expression of the above genes increased again 7 days after CCI, when the neuropathic pain affection had developed. Furthermore, gene ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment and interaction network analyses further showed a high connectivity degree among these chemokine targeting genes. Similar expressional changes in these genes were found in the rat spinal dorsal horn responsible for nociception processing. Taken together, our results indicated chemokines and their targeting genes in the ACC may be differentially involved in the initiation and maintenance of neuropathic pain affection. These genes may be a target for not only the nociception but also the pain affection following nerve injury.

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

BACKGROUND

Identification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis.

METHODS

In this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues.

RESULTS

A total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues.

CONCLUSIONS

The hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.

Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review

Introduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications.

Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications.

Methods: A total of 98 articles were obtained with the keywords “machine learning,” “deep learning,” “artificial intelligence,” and accordingly as they related to perinatal complications (“complications in pregnancy,” “pregnancy complications”) from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method.

Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy.

Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women’s health.

OUP accepted manuscript

Preeclampsia is a pregnancy-specific disease that can have serious effects on the health of both mothers and their offspring. Predicting which women will develop preeclampsia in early pregnancy with high accuracy will allow for improved management. The clinical symptoms of preeclampsia are well recognized, however, the precise molecular mechanisms leading to the disorder are poorly understood. This is compounded by the heterogeneous nature of preeclampsia onset, timing and severity. Indeed a multitude of poorly defined causes including genetic components implicates etiologic factors, such as immune maladaptation, placental ischemia and increased oxidative stress. Large datasets generated by microarray and next-generation sequencing have enabled the comprehensive study of preeclampsia at the molecular level. However, computational approaches to simultaneously analyze the preeclampsia transcriptomic and network data and identify clinically relevant information are currently limited. In this paper, we proposed a control theory method to identify potential preeclampsia-associated genes based on both transcriptomic and network data. First, we built a preeclampsia gene regulatory network and analyzed its controllability. We then defined two types of critical preeclampsia-associated genes that play important roles in the constructed preeclampsia-specific network. Benchmarking against differential expression, betweenness centrality and hub analysis we demonstrated that the proposed method may offer novel insights compared with other standard approaches. Next, we investigated subtype specific genes for early and late onset preeclampsia. This control theory approach could contribute to a further understanding of the molecular mechanisms contributing to preeclampsia.

Identification of potential biomarkers with colorectal cancer based on bioinformatics analysis and machine learning

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Biomarker discovery is critical to improve CRC diagnosis, however, machine learning offers a new platform to study the etiology of CRC for this purpose. Therefore, the current study aimed to perform an integrated bioinformatics and machine learning analyses to explore novel biomarkers for CRC prognosis. In this study, we acquired gene expression microarray data from Gene Expression Omnibus (GEO) database. The microarray expressions GSE103512 dataset was downloaded and integrated. Subsequently, differentially expressed genes (DEGs) were identified and functionally analyzed via Gene Ontology (GO) and Kyoto Enrichment of Genes and Genomes (KEGG). Furthermore, protein protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software to identify hub genes; however, the hub genes were subjected to Support Vector Machine (SVM), Receiver operating characteristic curve (ROC) and survival analyses to explore their diagnostic values. Meanwhile, TCGA transcriptomics data in Gene Expression Profiling Interactive Analysis (GEPIA) database and the pathology data presented by in the human protein atlas (HPA) database were used to verify our transcriptomic analyses. A total of 105 DEGs were identified in this study. Functional enrichment analysis showed that these genes were significantly enriched in biological processes related to cancer progression. Thereafter, PPI network explored a total of 10 significant hub genes. The ROC curve was used to predict the potential application of biomarkers in CRC diagnosis, with an area under ROC curve (AUC) of these genes exceeding 0.92 suggesting that this risk classifier can discriminate between CRC patients and normal controls. Moreover, the prognostic values of these hub genes were confirmed by survival analyses using different CRC patient cohorts. Our results demonstrated that these 10 differentially expressed hub genes could be used as potential biomarkers for CRC diagnosis.

Comprehensive transcriptome mining identified the gene expression signature and differentially regulated pathways of the late-onset preeclampsia

Preeclampsia (PE) is categorized as a pregnancy-related hypertensive disorder and is a serious concern in pregnancies. Several factors, including genetic factors (placenta gene expression, and imprinting), oxidative stress, the inaccurate immune response of the mother, and the environmental factors are responsible for PE development, but still, the exact mechanism of the pathogenesis has remained unknown. The main aim of the present study is to identify the gene expression signature in placenta tissue, to unveil disease etiology mechanisms. The GEO, PubMed, and ArrayExpress databases have selected to identify gene expression datasets on placenta samples of both preeclampsia and the normotensive controls. A comprehensive gene expression meta-analysis of fourteen publicly available microarray data of preeclampsia disease has performed to identify gene expression signature and responsible biological pathways and processes. Using two different meta-analysis pipeline (in-house and INMEX) we have identified a total of 1234 differentially expressed genes (DEGs) with in-house method, including 713 overexpressed and 356 under-expressed genes whereas 272 DEGs (131 over and 141 under-expressed) have identified with INMEX, across PEs and healthy controls. Comprehensive functional enrichment and pathway analysis was performed by EnrichR library, whic revealed "Asparagine N-linked glycosylation Homo sapiens", "Nef and signal transduction", "Hemostasis", and "immune system" among the most enriched terms. The present study sets out to explain a novel database of candidate genetic markers and biological pathways that play a critical role in PE development, which might aid in the identification of diagnostic, prognostic, and therapeutic informative molecules.

Gene Transcript Alterations in the Spinal Cord, Anterior Cingulate Cortex, and Amygdala in Mice Following Peripheral Nerve Injury

Chronic neuropathic pain caused by nerve damage is a most common clinical symptom, often accompanied by anxiety- and depression-like symptoms. Current treatments are very limited at least in part due to incompletely understanding mechanisms underlying this disorder. Changes in gene expression in the dorsal root ganglion (DRG) have been acknowledged to implicate in neuropathic pain genesis, but how peripheral nerve injury alters the gene expression in other pain-associated regions remains elusive. The present study carried out strand-specific next-generation RNA sequencing with a higher sequencing depth and observed the changes in whole transcriptomes in the spinal cord (SC), anterior cingulate cortex (ACC), and amygdala (AMY) following unilateral fourth lumbar spinal nerve ligation (SNL). In addition to providing novel transcriptome profiles of long non-coding RNAs (lncRNAs) and mRNAs, we identified pain- and emotion-related differentially expressed genes (DEGs) and revealed that numbers of these DEGs displayed a high correlation to neuroinflammation and apoptosis. Consistently, functional analyses showed that the most significant enriched biological processes of the upregulated mRNAs were involved in the immune system process, apoptotic process, defense response, inflammation response, and sensory perception of pain across three regions. Moreover, the comparisons of pain-, anxiety-, and depression-related DEGs among three regions present a particular molecular map among the spinal cord and supraspinal structures and indicate the region-dependent and region-independent alterations of gene expression after nerve injury. Our study provides a resource for gene transcript expression patterns in three distinct pain-related regions after peripheral nerve injury. Our findings suggest that neuroinflammation and apoptosis are important pathogenic mechanisms underlying neuropathic pain and that some DEGs might be promising therapeutic targets.

Identification of key pathways and core genes involved in atherosclerotic plaque progression

Background

Atherosclerosis leads to the occurrence of cardiovascular diseases. However, the molecular mechanisms that contribute to atherosclerotic plaque rupture are incompletely characterized. We aimed to identify the genes related to atherosclerotic plaque progression that could serve as novel biomarkers and interventional targets for plaque progression.

Methods

The datasets of GSE28829 in early vs. advanced atherosclerotic plaques and those of GSE41571 in stable vs. ruptured plaques from Gene Expression Omnibus (GEO) were analyzed by using bioinformatics methods. In addition, we used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the expression level of core genes in a mouse atherosclerosis model.

Results

There were 29 common differentially expressed genes (DEGs) between the GSE28829 and GSE41571 datasets, and the DEGs were mainly enriched in the chemokine signaling pathway and the Staphylococcus aureus infection pathway (P<0.05). We identified 6 core genes (FPR3, CCL18, MS4A4A, CXCR4, CXCL2, and C1QB) in the protein-protein interaction (PPI) network, 3 of which (CXCR4, CXCL2, and CCL18) were markedly enriched in the chemokine signaling pathway. qRT-PCR analysis showed that the messenger RNA levels of two core genes (CXCR4 and CXCL2) increased significantly during plaque progression in the mouse atherosclerosis model.

Conclusions

In summary, bioinformatics techniques proved useful for the screening and identification of novel biomarkers of disease. A total of 29 DEGs and 6 core genes were linked to atherosclerotic plaque progression, in particular the CXCR4 and CXCL2 genes.

Identification of KIF4A and its effect on the progression of lung adenocarcinoma based on the bioinformatics analysis

BACKGROUND

Lung adenocarcinoma (LUAD) is the most frequent histological type of lung cancer, and its incidence has displayed an upward trend in recent years. Nevertheless, little is known regarding effective biomarkers for LUAD.

METHODS

The robust rank aggregation method was used to mine differentially expressed genes (DEGs) from the gene expression omnibus (GEO) datasets. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to extract hub genes from the protein-protein interaction (PPI) network. The expression of the hub genes was validated using expression profiles from TCGA and Oncomine databases and was verified by real-time quantitative PCR (qRT-PCR). The module and survival analyses of the hub genes were determined using Cytoscape and Kaplan-Meier curves. The function of KIF4A as a hub gene was investigated in LUAD cell lines.

RESULTS

The PPI analysis identified seven DEGs including BIRC5, DLGAP5, CENPF, KIF4A, TOP2A, AURKA, and CCNA2, which were significantly upregulated in Oncomine and TCGA LUAD datasets, and were verified by qRT-PCR in our clinical samples. We determined the overall and disease-free survival analysis of the seven hub genes using GEPIA. We further found that CENPF, DLGAP5, and KIF4A expressions were positively correlated with clinical stage. In LUAD cell lines, proliferation and migration were inhibited and apoptosis was promoted by knocking down KIF4A expression.

CONCLUSION

We have identified new DEGs and functional pathways involved in LUAD. KIF4A, as a hub gene, promoted the progression of LUAD and might represent a potential therapeutic target for molecular cancer therapy.

Comprehensive Analysis of Differently Expressed and Methylated Genes in Preeclampsia

Preeclampsia (PE) is one of the mainly caused maternal and infant incidences and mortalities worldwide. However, the mechanisms underlying PE remained largely unclear. The present study identified 1716 high expressions of gene and 2705 low expressions of gene using GSE60438 database, and identified 7087 hypermethylated and 15120 hypomethylated genes in preeclampsia using GSE100197. Finally, 536 upregulated genes with hypomethylation and 322 downregulated genes with hypermethylation were for the first time revealed in PE. Gene Ontology (GO) analysis revealed that these genes were associated with peptidyl-tyrosine phosphorylation, skeletal system development, leukocyte migration, transcription regulation, T cell receptor and IFN-γ-involved pathways, innate immune response, signal transduction, cell adhesion, angiogenesis, and hemopoiesis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis demonstrated that aberrantly methylated differentially expressed genes were involved in regulating adherens junction, pluripotency of stem cell regulation, immune processing, T cell receptor and NF-κB pathways, HTLV-I and HSV infections, leishmaniasis, and NK-induced cytotoxicity. Protein-protein interaction (PPI) network analysis identified several hub networks and key genes, including MAPK8, CCNF, CDC23, ABL1, NF1, UBE2E3, CD44, and PIK3R1. We hope these findings will draw more attention to these hub genes in future PE studies.

Differentially Expressed Genes in the Brain of Aging Mice With Cognitive Alteration and Depression- and Anxiety-Like Behaviors

Despite the great increase in human lifespan with improved medical care, the physiological and pathological changes such as memory and cognitive disorders and associated anxiety and depression are major concern with aging. Molecular mechanisms underlying these changes are little known. The present study examined the differentially expressed genes (DEGs) and the genes with differentially expressed isoforms in three brain regions, anterior cingulate cortex (ACC), amygdala and hippocampus, throughout the lifespan of mice. Compared to 2-month old mice, both 12- and 24-month old mice displayed memory and cognitive impairments in the Morris water maze, Y-maze, and novel object recognition tests and depression- and anxiety-like behaviors in the tail suspension, forced swimming, open field, and elevated plus maze tests. RNA sequencing analysis identified 634 and 1078 DEGs in ACC, 453 and 1015 DEGs in the amygdala and 884 and 1054 DEGs in hippocampus in the 12- and 24-month old mice, respectively. Similarly, many genes with differentially expressed isoforms were also identified in these three brain regions in the 12- and 24-month old mice. Further functional analysis revealed that many DEGs and the genes with differentially expressed isoforms in the ACC and amygdala were mapped to depression- and anxiety-related genes, respectively and that a lot of DEGs and the genes with differentially expressed isoforms in hippocampus were mapped to cognitive dysfunction-related genes from both 12- and 24-month old mice. All of these mapped DEGs and the genes with differentially expressed isoforms were closely related to neuroinflammation. Our findings indicate that these neuroinflammation-related DEGs and the genes with differentially expressed isoforms are likely new targets in the management of memory/cognitive impairment and emotional disorders during the aging.

Identification of Pivotal Genes and Pathways in Osteoarthritic Degenerative Meniscal Lesions via Bioinformatics Analysis of the GSE52042 Dataset

Background

To better understand the process of osteoarthritic degenerative meniscal lesions (DMLs) formation, this study analyzed the dataset GSE52042 using bioinformatics methods to identify the pivotal genes and pathways related to osteoarthritic DMLs.

Material/Methods

The GSE52042 dataset, comprising diseased meniscus samples and healthier meniscus samples, was downloaded and the differentially-expressed genes (DEGs) were extracted. The reactome pathways assessment and functional analysis were performed using the “ClusterProfiler” package and “ReactomePA” package of Bioconductor. The protein–protein interaction network was constructed, followed by the extraction of hub genes and modules.

Results

A set of 154 common DEGs, including 64 upregulated DEGs and 90 downregulated DEGs, were obtained. GO analysis suggested that the DEGs primarily participated in positive regulation of the mitotic cell cycle and extracellular matrix organization. Reactome pathway analysis showed that the DEGs were predominantly enriched in TP53, which regulates transcription of genes involved in G2 cell cycle arrest and extracellular matrix organization. The top 10 hub genes were TYMS, AURKA, CENPN, NUSAP1, CENPM, TPX2, CDK1, UBE2C, BIRC5, and CCNB1. The genes in the 2 modules were primarily associated with M Phase and keratan sulfate degradation.

Conclusions

A series of pivotal genes and reactome pathways were identified elucidate the molecular mechanisms involved in the formation of osteoarthritic DMLs and to discover potential therapeutic targets.