Screening For Differentially Expressed Genes Research Articles

Construction of a diagnostic model for osteoarthritis based on transcriptomic immune-related genes

BackgroundOsteoarthritis (OA) is a leading cause of disability globally, affecting over 500 million individuals worldwide. However, accurate and early diagnosis of OA is challenging to achieve. Immune-related genes play an essential role in OA development. Therefore, the objective of this study was to develop a diagnostic model for OA based on immune-related genes identified in synovial membrane. MethodsThe gene expression profile of OA were downloaded based on four datasets. The significantly differentially expressed genes (DEGs) between OA and control groups were selected. The differential immune cells were analyzed, followed by immune-related DEGs screening. WGCNA was used to screen module genes and these genes were further selected through optimization algorithm. Then, nomogram model was constructed. Chemical drug small molecule related to OA was predicted. Finally, expression levels of several key genes were validated by qRT-PCR through construction of OA rat models. ResultsThe total 656 DEGs were obtained. Eight immune cells were significantly differential between two groups, and 317 immune-related DEGs were obtained. WGCNA identified three modules. The genes in modules were significantly involved in 15 pathways, involving in 65 genes. Then 12 DEGs were screened as the final optimal combination of DEGs, such as CEBPB, CXCL1, JUND, GABARAPL2 and PDGFC. The Nomogram model was also constructed. Furthermore, the chemical small molecules, such as acetaminophen, aspirin, and caffeine were predicted. The expression levels of CEBPB, CXCL1, GABARAPL2 and PDGFC were validated in OA rat models. ConclusionA diagnostic model based on twelve immune related genes was constructed. These model genes, such as CEBPB, CXCL1, GABARAPL2, and PDGFC, may serve as diagnostic biomarkers and immunotherapeutic targets.

Characterization of candidate factors associated with the metastasis and progression of high-grade serous ovarian cancer.

High-grade serous ovarian cancer (HGSOC) is the biggest cause of gynecological cancer-related mortality because of its extremely metastatic nature. This study aimed to explore and evaluate the characteristics of candidate factors associated with the metastasis and progression of HGSOC. Transcriptomic data of HGSOC patients' samples collected from primary tumors and matched omental metastatic tumors were obtained from three independent studies in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were selected to evaluate the effects on the prognosis and progression of ovarian cancer using data from The Cancer Genome Atlas (TCGA) database. Hub genes' immune landscapes were estimated by the Tumor Immune Estimation Resource (TIMER) database. Finally, using 25 HGSOC patients' cancer tissues and 10 normal fallopian tube tissues, immunohistochemistry (IHC) was performed to quantify the expression levels of hub genes associated with International Federation of Gynecology and Obstetrics (FIGO) stages. Fourteen DEGs, ADIPOQ , ALPK2 , BARX1 , CD37 , CNR2 , COL5A3 , FABP4 , FAP , GPR68 , ITGBL1 , MOXD1 , PODNL1 , SFRP2 , and TRAF3IP3 , were upregulated in metastatic tumors in every database while CADPS , GATA4 , STAR , and TSPAN8 were downregulated. ALPK2 , FAP , SFRP2 , GATA4 , STAR , and TSPAN8 were selected as hub genes significantly associated with survival and recurrence. All hub genes were correlated with tumor microenvironment infiltration, especially cancer-associated fibroblasts and natural killer (NK) cells. Furthermore, the expression of FAP and SFRP2 was positively correlated with the International Federation of Gynecology and Obstetrics (FIGO) stage, and their increased protein expression levels in metastatic samples compared with primary tumor samples and normal tissues were confirmed by IHC ( P = 0.0002 and P = 0.0001, respectively). This study describes screening for DEGs in HGSOC primary tumors and matched metastasis tumors using integrated bioinformatics analyses. We identified six hub genes that were correlated with the progression of HGSOC, particularly FAP and SFRP2 , which might provide effective targets to predict prognosis and provide novel insights into individual therapeutic strategies for HGSOC.

Intestinal flora induces depression by mediating the dysregulation of cerebral cortex gene expression and regulating the metabolism of stroke patients.

Post-stroke depression (PSD) is a common cerebrovascular complication characterized by complex pathogenesis and poor treatment effects. Here, we tested the influence of differentially expressed genes (DEGs), non-targeted metabolites, and intestinal microbes on the occurrence and development of PSD. We acquired gene expression profiles for stroke patients, depression patients, and healthy controls from the Gene Expression Omnibus database. After screening for DEGs using differential expression analysis, we identified common DEGs in stroke and depression patients that were considered to form the molecular basis of PSD. Functional enrichment analysis of DEGs also revealed that the majority of biological functions were closely related to metabolism, immunity, the nervous system, and microorganisms, and we also collected blood and stool samples from healthy controls, stroke patients, and PSD patients and performed 16S rDNA sequencing and untargeted metabolomics. After evaluating the quality of the sequencing data, we compared the diversity of the metabolites and intestinal flora within and between groups. Metabolic pathway enrichment analysis was used to identify metabolic pathways that were significantly involved in stroke and PSD, and a global metabolic network was constructed to explore the pathogenesis of PSD. Additionally, we constructed a global regulatory network based on 16S rDNA sequencing, non-targeted metabolomics, and transcriptomics to explore the pathogenesis of PSD through correlation analysis. Our results suggest that intestinal flora associates the dysregulation of cerebral cortex gene expression and could potentially promote the occurrence of depression by affecting the metabolism of stroke patients. Our findings may be helpful in identifying new targets for the prevention and treatment of PSD.

Plasma exosomal IRAK1 can be a potential biomarker for predicting the treatment response to renin-angiotensin system inhibitors in patients with IgA nephropathy.

BackgroundRenin-angiotensin system inhibitors (RASi) are the first choice and basic therapy for the treatment of IgA nephropathy (IgAN) with proteinuria. However, approximately 40% of patients have no response to RASi treatment. The aim of this study was to screen potential biomarkers for predicting the treatment response of RASi in patients with IgAN.MethodsWe included IgAN patients who were treatment-naive. They received supportive treatment, including a maximum tolerant dose of RASi for 3 months. According to the degree of decrease in proteinuria after 3 months of follow-up, these patients were divided into a sensitive group and a resistant group. The plasma of the two groups of patients was collected, and the exosomes were extracted for high-throughput sequencing. The screening of hub genes was performed using a weighted gene co-expression network (WGCNA) and filtering differentially expressed genes (DEGs). We randomly selected 20 patients in the sensitive group and 20 patients in the resistant group for hub gene validation by real-time quantitative polymerase chain reaction (qRT−PCR). A receiver operating characteristic (ROC) curve was used to evaluate hub genes that predicted the efficacy of the RASi response among the 40 validation patients.ResultsAfter screening 370 IgAN patients according to the inclusion and exclusion criteria and the RASi treatment response evaluation, there were 38 patients in the sensitive group and 32 patients in the resistant group. IRAK1, ABCD1 and PLXNB3 were identified as hub genes by analyzing the high-throughput sequencing of the plasma exosomes of the two groups through WGCNA and DEGs screening. The sequencing data were consistent with the validation data showing that these three hub genes were upregulated in the resistant group compared with the sensitive group. The ROC curve indicated that IRAK1 was a good biomarker to predict the therapeutic response of RASi in patients with IgAN.ConclusionsPlasma exosomal IRAK1 can be a potential biomarker for predicting the treatment response of RASi in patients with IgAN.

Identifying DUSP-1 and FOSB as hub genes in immunoglobulin A nephropathy by WGCNA and DEG screening and validation.

BackgroundThe mechanism of immunoglobulin A nephropathy (IgAN) is still unknown. A bioinformatics analysis is a powerful method to identify the biomarkers and possible therapeutic targets of a certain disease from related datasets.MethodsThe GSE93973 dataset, obtained from the Gene Expression Omnibus (GEO) database, was used to construct a weighted gene co-expression network (WGCNA) and filter differentially expressed genes (DEGs). The biological process (BP) enrichment among all the genes in the key modules was analyzed through a Gene Ontology (GO) enrichment analysis. We selected the overlap of hub genes in the WGCNA and Protein-Protein Interaction (PPI) network as the final hub genes in IgAN. We verified the final hub genes in two other datasets and in clinical kidney tissue specimens. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of hub genes for IgAN.ResultsThe turquoise module, which contained 1,806 genes, was the module with the highest correlation coefficient with IgAN in the GSE93973 dataset. The GO enrichment analysis showed that these 1,806 genes were mainly enriched in inflammation and immune responses. There were five hub genes identified by WGCNA and 34 hub genes identified in a DEG analysis in the GSE93973 dataset. DUSP1 and FOSB were identified as the final hub genes in IgAN. The validation results of the final hub genes in two other databases and clinical kidney tissue specimens validated the result that, compared to the control group, FOSB and DUSP1 were expressed at lower levels in the glomerulus of IgAN patients. The ROC curve indicated that DUSP1 and FOSB were good diagnostic indicators for IgAN.ConclusionsOur analysis identified two hub genes that might be potential targets for the intervention and treatment of IgAN.

Discovering Gene Signature Shared by Prostate Cancer and Neurodegenerative Diseases Based on the Bioinformatics Approach.

Background Prostate cancer (PCa) is one of the highest frequent malignant tumors with very complicated pathogenesis. Genes of neurodegenerative diseases can influence tumor progression. But its role in the progression of PCa remains unclear. The purpose of the present academic work was to identify significant genes with poor outcome and their underlying mechanism. Methods The GSE70768, GSE88808, and GSE134051 datasets were downloaded to screen the differentially expressed genes (DEGs). The DEG screening criteria were as follows: P < 0.05 and differential fold change |logFC| ≥ 1. The common DEGs (co-DEGs) of the three datasets were obtained by the Robust Rank Aggregation (RRA) method. Gene Ontology (GO) function annotation and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis were performed using R software. Protein-protein interaction (PPI) network analysis was performed for co-DEGs using STRING to screen critical genes. Differential expression and prognosis of key genes were analyzed by the online tool Gene Expression Profiling Interactive Analysis 2 (GEPIA2). The intersection gene between key genes and neurodegenerative genes was identified by constructing a Venn diagram. Results A total of 263 co-DEGs were identified from the three datasets. GO analysis showed that co-DEGs were mainly involved in muscle contraction and blood circulation regulation. The top ten key genes were ACTG2, APOE, F5, CALD1, MYH11, MYL9, MYLK, TPM1, TPM2, and CALM1. GEPIA2 analysis showed that APOE, MYH11, and MYLK differ dramatically between tumor and normal tissues. These key genes are related to disease-free survival (DFS) in PCa. APOE was the intersection gene between key genes and Alzheimer-related genes. Conclusion The neurodegenerative gene APOE may be a potential prognostic and diagnostic biomarker for PCa.

Identification of genes related to tipburn resistance in Chinese cabbage and preliminary exploration of its molecular mechanism

BackgroundTipburn, also known as leaf tip necrosis, is a severe issue in Chinese cabbage production. One known cause is that plants are unable to provide adequate Ca2+ to rapidly expanding leaves. Bacterial infection is also a contributing factor. Different cultivars have varying degrees of tolerance to tipburn. Two inbred lines of Chinese cabbage were employed as resources in this research.ResultsWe determined that the inbred line ‘J39290’ was the tipburn resistant material and the inbred line ‘J95822’ was the tipburn sensitive material based on the severity of tipburn, and the integrity of cell membrane structure. Ca2+ concentration measurements revealed no significant difference in Ca2+ concentration between the two materials inner leaves. Transcriptome sequencing technology was also used to find the differentially expressed genes (DEGs) of ‘J95822’ and ‘J39290’, and there was no significant difference in the previously reported Ca2+ uptake and transport related genes in the two materials. However, it is evident through DEG screening and classification that 23 genes are highly linked to plant-pathogen interactions, and they encode three different types of proteins: CaM/CML, Rboh, and CDPK. These 23 genes mainly function through Ca2+-CaM/CML-CDPK signal pathway based on KEGG pathway analysis, protein interaction prediction, and quantitative real-time PCR (qRT-PCR) of key genes.ConclusionsBy analyzing the Ca2+ concentration in the above two materials, the transcription of previously reported genes related to Ca2+ uptake and transport, the functional annotation and KEGG pathway of DEGs, it was found that Ca2+ deficiency was not the main cause of tipburn in ‘J95822’, but was probably caused by bacterial infection. This study lays a theoretical foundation for exploring the molecular mechanism of resistance to tipburn in Chinese cabbage, and has important guiding significance for genetics and breeding.

Identification of Three Potential Prognostic Genes in Platinum-Resistant Ovarian Cancer via Integrated Bioinformatics Analysis.

PurposeOvarian cancer is the most lethal gynecologic malignancy. Resistance to platinum-based chemotherapy affects the overall survival of patients. This study used an integrated bioinformatics to find the poorly understood molecular mechanisms underlying platinum resistance in ovarian cancer.MethodsBased on the RNA-seq data of tissues in The Cancer Genome Atlas (TCGA) and RNA-seq data of cells from the Cancer Cell Encyclopedia (CCLE), we integrated differentially expressed genes (DEGs) in ovarian cancer tissue and cells. After screening for DEGs related to platinum resistance, we conducted survival analysis and built protein interaction networks to identify genes that may affect prognosis and interact with each other. Least absolute shrinkage and selection operator (Lasso) regression analysis was used to construct a predictive model. Immunohistochemistry and Western blot were used to validate the results. Finally, gene set enrichment analysis (GSEA) was performed on the expression of genes individually.ResultsWe found that ATPase Na+/K+ transporting subunit alpha 2 (ATP1A2), calsequestrin 2 (CASQ2) and ryanodine receptor 2 (RYR2) interacted with each other and could predict resistance to platinum-based therapy, correlating negatively with prognosis. Moreover, we constructed a predictive model based on nine genes, including ATP1A2 and CASQ2. Immunohistochemistry and Western blot validated the upregulation of these genes in ovarian cancer tissue samples and cell lines. The immunohistochemistry results also confirmed the prognostic value of ATP1A2, CASQ2 and RYR2. GSEA predicted that ATP1A2, CASQ2 and RYR2 may act on the KRAS and mTORC1 pathways and participate in metabolic reprogramming and regulation of calcium homeostasis in platinum-resistant cells.ConclusionATP1A2, CASQ2 and RYR2 were highly expressed in platinum-resistant ovarian cancer. ATP1A2 and CASQ2 were related to the prognosis of platinum-resistant ovarian cancer patients. These genes might act on KARS and mTORC1 pathways and participate in metabolic reprogramming and regulation of calcium homeostasis in platinum-resistant cells.

Differential Expression Analysis for the Identification of Survival Associated Genes in Primary Bladder Cancer using Microarray Data

Bladder cancer (BC) is a highly malignant and malevolent tumor whose incidence and mortality worldwide remains high. This paper aimed to identify the differentially expressed genes (DEGs) between normal and primary bladder cancer samples, which might be of prognostic value, and evaluate their association to clinical outcomes (survival) in patients with primary bladder cancer. A sample of 256 gene expression profiles of which 10 were normal bladder mucosae whereas 165 were primary bladder cancer tissues were downloaded from the gene expression omnibus (GEO). The limma package in R was used to perform screening for DEGs after variance reduction through quantile normalization and log2 transformation. Gene Ontology (GO), KEGG enrichment analysis, and Network analysis were performed to determine the functions of these DEGs. Cox proportional hazard regression model was then used to determine the association between the top selected DEGs and overall primary bladder cancer survival. Selected genes were then encoded as either low, middle or high expression and Kaplan-Meier plots then used to visualize the effect of these expression levels for the selected five genes on bladder cancer survival probability. This study identified a total of 5, 979 DEGs where 2, 878 were upregulated, 3, 101 were downregulated and 37, 169 were not significantly expressed differently. Cox proportional hazard stepwise regression models revealed a number of DEGs that have a statistically significant association to bladder cancer. The selected five DEGs associated with survival were EDNRB (HR=1.9858, 95%CI 1.4965–2.6350), COL14A1 (HR=1.5335, 95%C1 1.1670–2.0151), MOXD1 (HR=1.4878, 95%CI 1.1070–1.9996), FAM107A (HR=1.4970, 95%CI 1.1162–2.0077), and REM1 (HR=1.5999, 95%CI 1.2422–2.0605). After encoding these genes as either low, middle, or high expression, high expression of both EDNRB, COL14A1, FAM107A, and REM1 were found to result into statistically significant reductions in survival probabilities of patients (p0.05). The genes found in this study could provide useful insight in the understanding of the association between the identified DEGs and primary bladder cancer survival, which can further inform future studies and clinical therapies.

HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

BackgroundSteroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH.MethodsThe GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve.ResultsNine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14.ConclusionsOur study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis

BackgroundThe disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms.Materials and methodsThe expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA.ResultsA total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune.ConclusionThis study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms.

Identification of Key Genes and Pathways Associated with Hepatosplenic T-Cell Lymphoma (HSTCL) by Bioinformatics Analysis.

Prognosis of Hepatosplenic T-Cell Lymphoma (HSTCL) is very poor, while the molecular mechanism of this disease has rarely been investigated and remains mysterious. The aim of the study is to screen differentially expressed genes (DEGs) of patients with HSTCL and normal controls, explore the pathogenesis, and provide guidance for the gene diagnosis and precise treatment of HSTCL. The genetic chip data GSE57520 of HSTCL was searched from the GEO database, and the quality control and DEGs screening were performed through BART online tools. In addition, FunRich software was used to perform gene enrichment and pathway analysis on the screened DEGs. Subsequently protein interaction network (PPI) was constructed via the STRING database and analyzed using the visual module of Cytoscape software. A total of 4,759 DEGs were obtained, including 2,501 up-regulated genes and 2,258 down-regulated genes (p < 0.05). The analysis of gene ontology (GO) showed that DEGs in cytology component (CC) mainly involved cytoplasm, nucleus, plasma membrane, Golgi apparatus, lysosome, and endoplasmic reticulum. Besides, DEGs in molecular function (MF) mainly included transcription factor activity, catalytic activity, transporter activity, transcription regulator activity, receptor signaling pathway complex, receptor activity. Moreover, DEGs in biological processes (BP) are mainly involved in base regulation, transport, energy pathways, metabolism, protein metabolism, and apoptosis. The results of the Kyoto Gene and Genome Encyclopedia (KEGG) analysis showed that the DEGs mainly include TRAIL, Beta1 integrin, integrin family, proteoglycan, S1P, and ErbB. Combined with Cytoscape software cytoHubba plug-in, protein interaction network (PPI) analysis showed that KIF20A, DLGAP5, PBK, TOP2A, ASPM, NEK2, KIF14, and DEPDC1B were the most abundant core genes. Module analysis showed that the three gene modules with the highest scores were mainly related to mitosis, epithelial cell adhesion and signal transduction, and the process of DNA damage. The DEGs of HSTCL patients versus healthy control groups were obtained through a variety of bioinformatics methods. KIF20A and DLGAP5 may become potential therapeutic targets for HSTCL. Also, the most abundant signaling pathway in DEGs was the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) - related pathway. Besides, related genes and expression characteristics of HSTCL pathogenesis were reanalyzed from distinctive perspectives, which might provide specific diagnostic markers and targeted therapy for HSTCL.

Radiosensitivity-Related Genes and Clinical Characteristics of Nasopharyngeal Carcinoma.

Materials and Methods Clinicopathological data of 185 patients with NPC treated at Nanfang Hospital of Southern Medical University between January 2013 and December 2014 were retrospectively analyzed. SPSS statistical software was used to analyze the clinicopathological data related to radiotherapy efficacy. Three patients who achieved complete remission and three with disease progression after CRT were selected. Differentially expressed genes (DEGs) were screened via mRNA microarray analysis of primary diagnostic endoscopy specimens. Results The peripheral blood leukocyte count, platelet count, and EBV-DNA copy number in NPC patients who were resistant to radiotherapy were higher than those in NPC patients who were sensitive to radiotherapy. The RobustRankAggreg (RRA) analysis method identified 392 DEGs, and the 66 most closely related genes among the DEGs were identified from the PPI network. Conclusion The results of this study indicate that screening for DEGs and pathways in NPC using integrated in silico analyses can help identify a series of genetic and clinical signatures for NPC patients treated with neoadjuvant chemotherapy followed by concurrent chemoradiotherapy.

Identification of Key Genes and Potential New Biomarkers for Ovarian Aging: A Study Based on RNA-Sequencing Data.

Ovarian aging leads to reproductive and endocrine dysfunction, causing the disorder of multiple organs in the body and even declined quality of offspring’s health. However, few studies have investigated the changes in gene expression profile in the ovarian aging process. Here, we applied integrated bioinformatics to screen, identify, and validate the critical pathogenic genes involved in ovarian aging and uncover potential molecular mechanisms. The expression profiles of GSE84078 were downloaded from the Gene Expression Omnibus (GEO) database, which included the data from ovarian samples of 10 normal C57BL/6 mice, including old (21–22 months old, ovarian failure period) and young (5–6 months old, reproductive bloom period) ovaries. First, we filtered 931 differentially expressed genes (DEGs), including 876 upregulated and 55 downregulated genes through comparison between ovarian expression data from old and young mice. Functional enrichment analysis showed that biological functions of DEGs were primarily immune response regulation, cell–cell adhesion, and phagosome pathway. The most closely related genes among DEGs (Tyrobp, Rac2, Cd14, Zap70, Lcp2, Itgb2, H2-Ab1, and Fcer1g) were identified by constructing a protein–protein interaction (PPI) network and consequently verified using mRNA and protein quantitative detection. Finally, the immune cell infiltration in the ovarian aging process was also evaluated by applying CIBERSORT, and a correlation analysis between hub genes and immune cell type was also performed. The results suggested that plasma cells and naïve CD4+ T cells may participate in ovarian aging. The hub genes were positively correlated with memory B cells, plasma cells, M1 macrophages, Th17 cells, and immature dendritic cells. In conclusion, this study indicates that screening for DEGs and pathways in ovarian aging using bioinformatic analysis could provide potential clues for researchers to unveil the molecular mechanism underlying ovarian aging. These results could be of clinical significance and provide effective molecular targets for the treatment of ovarian aging.

Radiosensitivity-Related Genes and Clinical Characteristics of Nasopharyngeal Carcinoma

Background and purpose: Radioresistance is one of the main obstacles limiting the therapeutic efficacy of chemoradiotherapy (CRT) and favorable patient prognoses, and the molecular mechanisms underlying this type of resistance remain unclear. The purpose of this study was to identify characteristic genes involved in chemoradiotherapy resistance in nasopharyngeal carcinoma (NPC). Materials and methods: Clinicopathological data of 185 patients with NPC treated at Nanfang Hospital of Southern Medical University between January 2013 and December 2014 were retrospectively analyzed. SPSS statistical software was used to analyze the clinicopathological data related to radiotherapy efficacy. Three patients who achieved complete remission and three with disease progression after CRT were selected. Differentially expressed genes (DEGs) were screened via mRNA microarray analysis of primary diagnostic endoscopy specimens. Results: The peripheral blood leukocyte count, platelet count and EBV-DNA copy number in NPC patients who were resistant to radiotherapy were higher than those in NPC patients who were sensitive to radiotherapy. The RobustRankAggreg (RRA) analysis method identified 392 DEGs, and the 66 most closely related genes among the DEGs were identified from the PPI network. Conclusion: The results of this study indicate that screening for DEGs and pathways in NPC using integrated in silico analyses can help identify a series of genetic and clinical signatures for NPC patients treated with neoadjuvant chemotherapy followed by concurrent chemoradiotherapy. Funding Statement: This work was supported by the Clinical Research Startup Program of Southern Medical University by High-level University Construction Funding of Guangdong Provincial Department of Education (LC2019ZD008); the Clinical Research Startup Program of Nanfang Hospital (2018CR021);the National Natural Science Foundation of China (Grant Nos. 81602685 and 81672992); the Science and Technology Projects in Guangdong Province (Grant Nos. 2018-1201-SF-0019 ); the Health & Medical Collaborative Innovation Project of Guangzhou City, China (201803040003); and the Natural Science Foundation of Guangdong Province (Grant No. 2017A030313486); the Natural Science Foundation of Fujian Province (Grant No. 2017J01245). Declaration of Interests: We declare no competing interests. Ethics Approval Statement: The protocol was approved by the ethics committee of Nanfang Hospital and implemented in accordance with the principles of the Declaration of Helsinki. All participants provided informed consent.

Identification of differentially expressed genes and typical fusion genes associated with three subtypes of breast cancer

This study aimed to identify the differentially expressed genes (DEGs) and the typical fusion genes in different types of breast cancers using RNA-seq. GSE52643 was downloaded from Gene Expression Omnibus, which included 1 normal sample (MCF10A) and 7 breast cancer samples (BT-474, BT-20, MCF7, MDA-MB-231, MDA-MB-468, T47D, and ZR-75-1). The transcript abundance and the DEGs screening were performed by Cufflinks. The functional and pathway enrichment was analyzed by Gostats. SnowShoes-FTD was applied to identify the fusion genes. We screened 430, 445, 397, 417, 369, 557, and 375 DEGs in BT-474, BT-20, MCF7, DA-MB-231, MDA-MB-468, T47D, and ZR-75-1, respectively, compared with MCF10A. DEGs in each comparison group (such as CD40 and CDH1) were significantly enriched in the functions of cell adhesion and extracellular matrix organization and pathways of CAMs and ECM receptor interaction. UCP2 was a common DEG in the 7 comparison groups. SFRP1 and MMP7 were significantly enriched in wnt/-catenin signaling pathway in MDA-MB-231. FAS was significantly enriched in autoimmune thyroid disease pathway in BT-474. Besides, we screened 96 fusion genes, such as ESR1-C6orf97 in ZR-75-1, COBRA1-C9orf167 in BT-20, and VAPB-IKZF3 and ACACA-STAC2 in BT-474. The DEGs such as SFRP1, MMP7, CDH1, FAS, and UCP2 might be the potential biomarkers in breast cancer. Furthermore, some pivotal fusion genes like ESR1-C6orf97 with COBRA1-C9orf167 and VAPB-IKZF3 with ACACA-STAC2 were found in Luminal A and Luminal B breast cancer, respectively.

Identification of differentially expressed genes and signaling pathways in ovarian cancer by integrated bioinformatics analysis.

BackgroundThe mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, the cause and underlying molecular events of ovarian cancer are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in ovarian cancer and reveal potential molecular mechanisms.ResultsThe expression profiles of GDS3592, GSE54388, and GSE66957 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 115 samples, including 85 cases of ovarian cancer samples and 30 cases of normal ovarian samples. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by DAVID and KOBAS online analyses, respectively. The protein–protein interaction (PPI) networks of the DEGs were constructed from the STRING database. A total of 190 DEGs were identified in the three GEO datasets, of which 99 genes were upregulated and 91 genes were downregulated. GO analysis showed that the biological functions of DEGs focused primarily on regulating cell proliferation, adhesion, and differentiation and intracellular signal cascades. The main cellular components include cell membranes, exosomes, the cytoskeleton, and the extracellular matrix. The molecular functions include growth factor activity, protein kinase regulation, DNA binding, and oxygen transport activity. KEGG pathway analysis showed that these DEGs were mainly involved in the Wnt signaling pathway, amino acid metabolism, and the tumor signaling pathway. The 17 most closely related genes among DEGs were identified from the PPI network.ConclusionThis study indicates that screening for DEGs and pathways in ovarian cancer using integrated bioinformatics analyses could help us understand the molecular mechanism underlying the development of ovarian cancer, be of clinical significance for the early diagnosis and prevention of ovarian cancer, and provide effective targets for the treatment of ovarian cancer.

Acupuncture Regulates the Notch Signaling Pathway in Bone Marrow Cells to Alleviate Cyclophosphamide-Induced Myelosuppression in S180 Tumor Bearing Mice

Objective: To evaluate the protein expression and distribution of Notch signaling-related, differentially expressed genes (DEGs) in the bone marrow cells of S180 tumor bearing mice. Additionally, verification of acupuncture regulated protein expression of these DEGs to protect bone marrow stem cells and alleviate cyclophosphamide (CTX) chemotherapy-induced myelosuppression was examined as well. Methods: Healthy male Kunming mice were inoculated with S180 sarcomas and further divided into blank control (group A), CTX group (group B), acupuncture (group C), and moxibustion (group D) groups. Animals in groups B, C, and D were intraperitoneally injected with single dose of 150 mg/kg CTX to generate the myelosuppressive model; while animals in group A were injected with an equivalent volume of sterile saline. After immobilizing the animals, mice in groups C and D received daily acupuncture and moxibustion, respectively at Dazhui, Geshu, Shenshu, and Zusanli acupoints; while mice in groups A and B received no daily treatment. Animals in each group were euthanized by cervical dislocation following five days of treatment and their femurs collected for marrow harvest. Small-throughput cDNA microarray was used for initial DEG screening, followed by immunohistochemistry, real-time quantitative PCR (RT-qPCR), and Western Blot analysis to detect protein expression and quantity of the Notch signaling-related DEGs in the bone marrow cells. Results: Expression of numb proteins (i.e., numb1 and numb2) was found to be up regulated while notch2 and jag1 were down regulated in bone marrow cells of acupuncture treated, tumor bearing mice. Conclusion: Acupuncture regulated the changes of Notch signaling-related DEGs in mouse bone marrow cells. The data indicates that acupuncture treatment may alleviate the CTX chemotherapy-induced myelosuppression associated with tumor burden, suggesting that might be the key mechanism to improve bone marrow function.

Identification of key genes associated with the human abdominal aortic aneurysm based on the gene expression profile.

The present study was aimed at screening the key genes associated with abdominal aortic aneurysm (AAA) in the neck, and to investigate the molecular mechanism underlying the development of AAA. The gene expression profile, GSE47472, including 14 AAA neck samples and eight donor controls, was downloaded from the Gene Expression Omnibus database. The total AAA samples were grouped into two types to avoid bias. Differentially expressed genes (DEGs) were screened in patients with AAA and subsequently compared with donor controls using linear models for microarray data, or the Limma package in R, followed by gene ontology enrichment analysis. Furthermore, a protein-protein interaction (PPI) network based on the DEGs was constructed to detect highly connected regions using a Cytoscape plugin. In total, 388 DEGs in the AAA samples were identified. These DEGs were predominantly associated with limb development, including embryonic limb development and appendage development. Nuclear receptor co-repressor 1 (NCOR1), histone 4 (H4), E2F transcription factor 4 (E2F4) and hepatocyte nuclear factor 4α (HNF4A) were the four transcription factors associated with AAA. Furthermore, HNF4A indirectly interacted with the other three transcription factors. Additionally, six clusters were selected from the PPI network. The DEG screening process and the construction of an interaction network enabled an understanding of the mechanism of AAA to be gleaned. HNF4A may exert an important role in AAA development through its interactions with the three other transcription factors (E2F4, NCOR1 and H4), and the mechanism of this coordinated regulation of the transcription factors in AAA may provide a suitable target for the development of therapeutic intervention strategies.

Biological processes and pathway changes in isoflurane-induced anesthesia revealed by bioinformatics analysis of gene expression profiles.

To identify differentially expressed genes (DEGs) and further analyze potential biological processes and pathways involved in isoflurane-induced anesthesia. Microarray data (ID: GSE64617) from rat brains treated with exposure to either isoflurane in oxygen (2%) (test group) or oxygen alone (control group) for 15 min were downloaded from Gene Expression Omnibus. Data pre-processing was performed using the Affy package, followed by DEG screening using the limma package. Protein-protein interactions (PPIs) among DEGs were obtained from STRING (Search Tool for the Retrieval of Interacting Genes/Proteins), and then visualized by constructing a network using Cytoscape. Functional and pathway enrichment analyses were further implemented to identify the biological processes and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways enriched by DEGs. A total of 240 DEGs were identified between the test and control groups, including 128 up-regulated DEGs and 112 down-regulated DEGs in the test group, of which 17 DEGs with a connectivity degree >4 were identified as hub genes (e.g., Pik3r1 and Pik3r2) in the constructed PPI network. Additionally, Slc17a7 and Camk4 interacted with Syn1, and Pik3r1 interacted with Pik3r2. Enrichment analysis further revealed the significantly enriched biological processes of 'synaptic transmission', 'cell-cell signaling' and 'transmission of nerve impulse' (e.g., Slc17a7, Camk4, Syn1, Gria1, Prkcg and Lphn1), as well as KEGG pathways including 'focal adhesion', 'Fc γ R-mediated phagocytosis' (e.g., Prkcg, Pik3r1 and Pik3r2), and 'regulation of actin cytoskeleton' (e.g., Pik3r1 and Pik3r2). The identified DEGs significantly enriched in biological processes and KEGG pathways might be implicated in isoflurane-induced anesthesia.