MCAM is associated with metastasis and poor prognosis in osteosarcoma by modulating tumor cell migration

BackgroundAlthough there are standard treatment options for osteosarcoma (OS), the prognoses of patients with OS remain varied. Therefore, it is important to profile OS patients at a high risk of mortality to develop focused interventions. Although tumor biomarkers are closely associated with clinical outcomes, data on prognostic biomarkers for OS remain scarce.MethodsWe collected RNA expression profiles and clinical data of 90 OS patients from the GEO database (dataset {"type":"entrez-geo","attrs":{"text":"GSE21257","term_id":"21257"}}GSE21257 and {"type":"entrez-geo","attrs":{"text":"GSE39055","term_id":"39055"}}GSE39055) and 96 patients in the TARGET program. The data were analyzed using univariate Kaplan‐Meier survival analysis to screen candidate gene sets that might be associated with OS survival.ResultsOur analysis demonstrated that melanoma cell adhesion molecule (MCAM) was associated with overall survival of patients with OS in the three cohorts. The data showed that MCAM was upregulated in OS patients who had metastases within 5 years compared to those without metastases. GO analysis revealed that genes correlated with MCAM were mainly involved in cell migration and wound healing processes. In addition, wound healing assays and gene set enrichment analysis results from RNA sequencing data of small interfering (si)‐MCAM‐transfected OS cells demonstrated that MCAM modulated tumor cell migration.ConclusionsOur data demonstrate that MCAM may be a novel prognostic biomarker for OS. MCAM is associated with increased cell migration ability and risk of metastasis, thus leading to poor prognoses in OS patients.     

Ferroptosis‐based molecular prognostic model for adrenocortical carcinoma based on least absolute shrinkage and selection operator regression

BackgroundThis study aimed to find ferroptosis‐related genes linked to clinical outcomes of adrenocortical carcinoma (ACC) and assess the prognostic value of the model.MethodsWe downloaded the mRNA sequencing data and patient clinical data of 78 ACC patients from the TCGA data portal. Candidate ferroptosis‐related genes were screened by univariate regression analysis, machine‐learning least absolute shrinkage, and selection operator (LASSO). A ferroptosis‐related gene‐based prognostic model was constructed. The effectiveness of the prediction model was accessed by KM and ROC analysis. External validation was done using the {"type":"entrez-geo","attrs":{"text":"GSE19750","term_id":"19750"}}GSE19750 cohort. A nomogram was generated. The prognostic accuracy was measured and compared with conventional staging systems (TNM stage). Functional analysis was conducted to identify biological characterization of survival‐associated ferroptosis‐related genes.ResultsSeventy genes were identified as survival‐associated ferroptosis‐related genes. The prognostic model was constructed with 17 ferroptosis‐related genes including STMN1, RRM2, HELLS, FANCD2, AURKA, GABARAPL2, SLC7A11, KRAS, ACSL4, MAPK3, HMGB1, CXCL2, ATG7, DDIT4, NOX1, PLIN4, and STEAP3. A RiskScore was calculated for each patient. KM curve indicated good prognostic performance. The AUC of the ROC curve for predicting 1‐, 3‐, and 5‐ year(s) survival time was 0.975, 0.913, and 0.915 respectively. The nomogram prognostic evaluation model showed better predictive ability than conventional staging systems.ConclusionWe constructed a prognosis model of ACC based on ferroptosis‐related genes with better predictive value than the conventional staging system. These efforts provided candidate targets for revealing the molecular basis of ACC, as well as novel targets for drug development.     

ITGA5 is an independent prognostic biomarker and potential therapeutic target for laryngeal squamous cell carcinoma

BackgroundIntegrin α5 (ITGA5) was involved in a variety of cancers. However, the role of ITGA5 in laryngeal squamous cell carcinoma (LSCC) remains unknown.MethodsThe expression of ITGA5 and the corresponding clinicopathological parameters of LSCC patients the TCGA database. Five datasets ({"type":"entrez-geo","attrs":{"text":"GSE51985","term_id":"51985"}}GSE51985, {"type":"entrez-geo","attrs":{"text":"GSE59102","term_id":"59102"}}GSE59102, {"type":"entrez-geo","attrs":{"text":"GSE84957","term_id":"84957"}}GSE84957, {"type":"entrez-geo","attrs":{"text":"GSE27020","term_id":"27020"}}GSE27020, and {"type":"entrez-geo","attrs":{"text":"GSE65858","term_id":"65858"}}GSE65858) were downloaded from the GEO database as validation sets. Kaplan–Meier plotter, Cox regression analysis, and nomogram were performed to determine the prognostic value of ITGA5 in LSCC. GO, KEGG, and GSEA were used to explore the underlying biological functions of ITGA5 in LSCC. The algorithms ESTIMATE and CIBERSORT were adopted to evaluate the association between ITGA5 and the infiltration of the immune cells. The algorithm pRRophetic was used to estimate the response to chemotherapeutic drugs.ResultsThe expression of ITGA5 was higher in the LSCC samples and linked to poor overall survival and recurrence‐free survival. Further, the Cox regression analysis confirmed that high expression of ITGA5 was an independent unfavorable prognostic factor. The predictive performance of nomogram based on the expression of ITGA5 was accurate and practical. The functional enrichment analysis confirmed that ITGA5 was related to the construction of the components and structures of the extracellular matrix. Finally, patients with high ITGA5 expression were more likely to benefit from docetaxel and gemcitabine.ConclusionThe expression of ITGA5 was elevated in the LSCC and was a predictor for prognosis and chemotherapeutic response in LSCC patients.     

A novel model based on liquid‐liquid phase separation–Related genes correlates immune microenvironment profiles and predicts prognosis of lung squamous cell carcinoma

ObjectiveThe aim of the study was to construct and validate a robust prognostic model based on liquid‐liquid phase separation (LLPS)–related genes in lung squamous cell carcinoma (LUSC).MethodsThe Cancer Genome Atlas dataset was used as the discovery set to identify the LLPS‐related differentially expressed genes (DEGs) between LUSC and normal tissue. These DEGs were screened by the LASSO Cox regression analysis to identify the genes with nonzero coefficient, which were next included in the multivariate Cox regression analysis to construct the prediction model. The dataset {"type":"entrez-geo","attrs":{"text":"GSE41271","term_id":"41271"}}GSE41271 was adopted as the validation set to verify the efficacy of the model. Enrichment analysis and the CIBERSORT were performed to illustrate potential immune mechanisms underlying the prediction model.ResultsA total of 48 LLPS‐related genes were aberrantly expressed in LUSC. Among them, 7 genes were selected by the LASSO Cox regression analysis to construct the prediction model. Risk index (RI) was calculated according to the model for each patient. The prognosis was significantly different between the patients with high and low RI in the discovery set and the validation set (p < 0.001 and p = 0.028, respectively). The multivariate survival analysis confirmed RI as an independent prognostic factor in LUSC (in the discovery set: p < 0.001, HR = 2.643, 95% CI = 1.986–3.518; in the validation set: p = 0.042, HR = 2.144, 95% CI = 1.026–4.480). A series of pathways involving immune cells were found to be related to RI. The distribution pattern of immune cells and chemokines varied according to the value of RI.ConclusionThe prediction model based on LLPS‐related genes was constructed and validated as a robust prognostic tool for LUSC using multiple datasets. LLPS might have an impact on LUSC through immune pathways.     

miR‐548d‐3p inhibits the invasion and migration of gastric cancer cells by targeting GKN1

BackgroundThe aim of this study was to explore the function and mechanism of GKN1 in gastric cancer (GC) progression.MethodsFirstly, we used GEO2R to perform differential gene analysis on {"type":"entrez-geo","attrs":{"text":"GSE26942","term_id":"26942"}}GSE26942 and {"type":"entrez-geo","attrs":{"text":"GSE79973","term_id":"79973"}}GSE79973 and constructed the protein–protein interaction network of differential genes by STRING. Next, the cytoHubba, Mcode plugins, and GEPIA were used to obtain our follow‐up research object GKN1. Then, the function of GKN1 in GC was verified by scratch and transwell assay in GC cells. We further analyzed the genes related to GKN1 through LinkedOmics, and exported top 100 genes positively or negatively correlated with GKN1. Meanwhile, Metascape was performed on these genes. Finally, we analyzed the miRNAs that bind to GKN1 through the miRDB and verified the correlation between miR‐548d‐3p and GKN1 using dual‐fluorescence and quantitative PCR experiments.ResultsBioinformatics analysis showed that there were 52 differential genes on {"type":"entrez-geo","attrs":{"text":"GSE26942","term_id":"26942"}}GSE26942 and {"type":"entrez-geo","attrs":{"text":"GSE79973","term_id":"79973"}}GSE79973. In addition, the results of functional assays indicated that overexpressed GKN1 can inhibit GC cell migration and invasion, while GKN1 knockdown demonstrated the opposite effect. Additionally, Metascape analysis results showed that the 3′‐UTR region of mRNA is rich in AU sequences, based on which we infer that mRNA may be regulated by miRNA. Dual‐fluorescence and quantitative PCR assays clarified that miR‐548d‐3p may be one of the target miRNAs of GKN1, which was up‐regulated in GC tissues.ConclusionsIn summary, we clarified that miR‐548d‐3p regulates GKN1 to participate in GC cell migration and invasion, and provides a possible target for the prognostic diagnosis and treatment of GC.     

Prognostic score model based on six m6A‐related autophagy genes for predicting survival in esophageal squamous cell carcinoma

BackgroundPrognostic signatures based on autophagy genes have been proposed for esophageal squamous cell carcinoma (ESCC). Autophagy genes are closely associated with m6A genes. Our purpose is to identify m6A‐related autophagy genes in ESCC and develop a survival prediction model.MethodsDifferential expression analyses for m6A genes and autophagy genes were performed based on TCGA and HADd databases followed by constructing a co‐expression network. Uni‐variable Cox regression analysis was performed for m6A‐related autophagy genes. Using the optimal combination of feature genes by LASSO Cox regression model, a prognostic score (PS) model was developed and subsequently validated in an independent dataset.ResultsThe differential expression of 13 m6A genes and 107 autophagy genes was observed between ESCC and normal samples. The co‐expression network contained 13 m6A genes and 96 autophagy genes. Of the 12 m6A‐related autophagy genes that were significantly related to survival, DAPK2, DIRAS3, EIF2AK3, ITPR1, MAP1LC3C, and TP53 were used to construct a PS model, which split the training set into two risk groups with significant different survival ratios (p = 0.015, 1‐year, 3‐year, and 5‐year AUC = 0.873, 0.840, and 0.829). Consistent results of {"type":"entrez-geo","attrs":{"text":"GSE53625","term_id":"53625"}}GSE53625 dataset confirmed predictive ability of the model (p = 0.024, 1‐year, 3‐year, and 5‐year AUC = 0.793, 0.751, and 0.744). The six‐gene PS score was an independent prognostic factor from clinical factors (HR, 2.362; 95% CI, 1.390–7.064; p‐value = 0.012).ConclusionOur study recommends 6 m6A‐related autophagy genes as promising prognostic biomarkers and develops a PS model to predict survival in ESCC.     

IFI27 is a potential therapeutic target for HIV infection

BackgroundTherapeutic studies against human immunodeficiency virus type 1 (HIV-1) infection have become one of the important works in global public health.MethodsDifferential expression analysis was performed between HIV-positive (HIV+) and HIV-negative (HIV-) patients for {"type":"entrez-geo","attrs":{"text":"GPL6947","term_id":"6947"}}GPL6947 and {"type":"entrez-geo","attrs":{"text":"GPL10558","term_id":"10558"}}GPL10558 of {"type":"entrez-geo","attrs":{"text":"GSE29429","term_id":"29429"}}GSE29429. Coexpression analysis of common genes with the same direction of differential expression identified modules. Module genes were subjected to enrichment analysis, Short Time-series Expression Miner (STEM) analysis, and PPI network analysis. The top 100 most connected genes in the PPI network were screened to construct the LASSO model, and AUC values were calculated to identify the key genes. Methylation modification of key genes were identified by the chAMP package. Differences in immune cell infiltration between HIV + and HIV- patients, as well as between antiretroviral therapy (ART) and HIV + patients, were calculated using ssGSEA.ResultsWe obtained 3610 common genes, clustered into nine coexpression modules. Module genes were significantly enriched in interferon signalling, helper T-cell immunity, and HIF-1-signalling pathways. We screened out module genes with gradual changes in expression with increasing time from HIV enrolment using STEM software. We identified 12 significant genes through LASSO regression analysis, especially proteasome 20S subunit beta 8 (PSMB8) and interferon alpha inducible protein 27 (IFI27). The expression of PSMB8 and IFI27 were then detected by quantitative real-time PCR. Interestingly, IFI27 was also a persistently dysregulated gene identified by STEM. In addition, 10 of the key genes were identified to be modified by methylation. The significantly infiltrated immune cells in HIV + patients were restored after ART, and IFI27 was significantly associated with immune cells.ConclusionThe above results provided potential target genes for early diagnosis and treatment of HIV + patients. IFI27 may be associated with the progression of HIV infection and may be a powerful target for immunotherapy.     

Identification of autophagy‐related biomarker and analysis of immune infiltrates in oral carcinoma

BackgroundAutophagy plays a vital role in the progression of the tumor. We aimed to investigate the expression, prognostic value, and immune infiltration of autophagy‐related genes in oral carcinoma via bioinformatics analysis.MethodsThe microarray datasets ({"type":"entrez-geo","attrs":{"text":"GSE146483","term_id":"146483"}}GSE146483 and {"type":"entrez-geo","attrs":{"text":"GSE23558","term_id":"23558"}}GSE23558) of oral carcinoma were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between normal and diseased groups were identified by the Limma package. The screened autophagy‐related gene was further validated by the human protein atlas (HPA) database, TCGA database, and {"type":"entrez-geo","attrs":{"text":"GSE78060","term_id":"78060"}}GSE78060 dataset.ResultsA total of 18 upregulated (top 10: EGFR, TNF, FADD, AURKA, E2F1, CHEK1, BRCA1, BIRC5, EIF2AK2, and CSF2) and 31 downregulated (top 10: MAP1LC3A, PARK2, AGT, IGF1, TP53INP1, CXCL12, IKBKB, SESN1, ULK2, and RRAGD) autophagy‐related (DEGs) were identified, and FADD was found to be related to the prognosis of oral cancer patients. Gene set enrichment analysis indicated that FADD‐associated genes were significantly enriched in immune‐related pathways. Moreover, correlation analysis revealed that FADD expression was associated with immune infiltrates. Upregulation of FADD is associated with poor survival and immune infiltrates in oral cancer.ConclusionWe speculated that FADD is involved in the immune regulation of oral cancer, as well as autophagy.     

Integrated bioinformatics analysis of potential biomarkers for pancreatic cancer

BackgroundPancreatic cancer, particularly pancreatic ductal adenocarcinoma (PDA), is an aggressive malignancy associated with a low 5‐year survival rate. Poor outcomes associated with PDA are attributable to late detection and inoperability. Most patients with PDA are diagnosed with locally advanced and metastatic disease. Such cases are primarily treated with chemotherapy and radiotherapy. Because of the lack of effective molecular targets, early diagnosis and successful therapies are limited. The purpose of this study was to screen key candidate genes for PDA using a bioinformatic approach and to research their potential functional, pathway mechanisms associated with PDA progression. It may help to understand the role of associated genes in the development and progression of PDA and identify relevant molecular markers with value for early diagnosis and targeted therapy.Materials and methodsTo identify novel genes associated with carcinogenesis and progression of PDA, we analyzed the microarray datasets {"type":"entrez-geo","attrs":{"text":"GSE62165","term_id":"62165"}}GSE62165, {"type":"entrez-geo","attrs":{"text":"GSE125158","term_id":"125158"}}GSE125158, and {"type":"entrez-geo","attrs":{"text":"GSE71989","term_id":"71989"}}GSE71989 from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A protein‐protein interaction (PPI) network was constructed using STRING, and module analysis was performed using Cytoscape. Gene Expression Profiling Interactive Analysis (GEPIA) was used to evaluate the differential expression of hub genes in patients with PDA. In addition, we verified the expression of these genes in PDA cell lines and normal pancreatic epithelial cells.ResultsA total of 202 DEGs were identified and these were found to be enriched for various functions and pathways, including cell adhesion, leukocyte migration, extracellular matrix organization, extracellular region, collagen trimer, membrane raft, fibronectin‐binding, integrin binding, protein digestion, and absorption, and focal adhesion. Among these DEGs, 12 hub genes with high degrees of connectivity were selected. Survival analysis showed that the hub genes (HMMR, CEP55, CDK1, UHRF1, ASPM, RAD51AP1, DLGAP5, KIF11, SHCBP1, PBK, and HMGB2) may be involved in the tumorigenesis and development of PDA, highlighting their potential as diagnostic and therapeutic factors in PDA.ConclusionsIn summary, the DEGs and hub genes identified in the present study not only contribute to a better understanding of the molecular mechanisms underlying the carcinogenesis and progression of PDA but may also serve as potential new biomarkers and targets for PDA.     

Identification of key genes in allergic rhinitis by bioinformatics analysis

ObjectiveThis study aimed to explore the potential molecular mechanism of allergic rhinitis (AR) and identify gene signatures by analyzing microarray data using bioinformatics methods.MethodsThe dataset {"type":"entrez-geo","attrs":{"text":"GSE19187","term_id":"19187"}}GSE19187 was used to screen differentially expressed genes (DEGs) between samples from patients with AR and healthy controls. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were applied for the DEGs. Subsequently, a protein–protein interaction (PPI) network was constructed to identify hub genes. {"type":"entrez-geo","attrs":{"text":"GSE44037","term_id":"44037"}}GSE44037 and {"type":"entrez-geo","attrs":{"text":"GSE43523","term_id":"43523"}}GSE43523 datasets were screened to validate critical genes.ResultsA total of 156 DEGs were identified. GO analysis verified that the DEGs were enriched in antigen processing and presentation, the immune response, and antigen binding. KEGG analysis demonstrated that the DEGs were enriched in Staphylococcus aureus infection, rheumatoid arthritis, and allograft rejection. PPI network and module analysis predicted seven hub genes, of which six (CD44, HLA-DPA1, HLA-DRB1, HLA-DRB5, MUC5B, and CD274) were identified in the validation dataset.ConclusionsOur findings suggest that hub genes play important roles in the development of AR.     

Screening and identification of biomarkers associated with the immune infiltration of intracerebral hemorrhage

BackgroundRecent studies showed that inflammation and immunity might play essential roles in the progression of intracerebral hemorrhage (ICH). However, the underlying mechanisms for changes at the cellular and molecular levels after ICH remain unclear.MethodsWe downloaded the microarray dataset of ICH from the Gene Expression Omnibus (GEO) database. The differential expression gene analysis was obtained by weighted gene co‐expression network analysis (WGCNA). We got the hub genes and performed the biological functions and signaling pathways of these genes by Metascape. GSVA algorithm was used to evaluate the potential physical function of time‐varying ICH samples. We used single‐sample gene set enrichment analysis (ssGSEA) to assess the immune signatures infiltration and analyzed the correlation between hub genes and immune signatures.ResultsThe data sets of all 22 ICH samples in {"type":"entrez-geo","attrs":{"text":"GSE125512","term_id":"125512"}}GSE125512 were examined by the WGCNA R package. We finally screened five hub genes (GAPDH, PF4, SELP, APP, and PPBP) in the royal blue module. Metascape analysis displayed the biological processes related to inflammation and immunology. Cell adhesion molecule binding, myeloid leukocyte activation, CXCR chemokine receptor binding, and regulation of cytokine production were the most enriched pathophysiological process. The immune signatures infiltration analyses showed that ICH patients’ early and late samples had different activity and abundance of immune‐related cells and types.ConclusionsGAPDH, PF4, SELP, APP, and PPBP are identified as potential biomarkers for predicting the progression of ICH. This study may help us better understand the immunologic mechanism and shed new light on the promising approaches of immunotherapy for ICH patients.     

Bioinformatics analysis of key biomarkers for retinoblastoma

ObjectiveTo identify key genes involved in occurrence and development of retinoblastoma.MethodsThe microarray dataset, {"type":"entrez-geo","attrs":{"text":"GSE5222","term_id":"5222"}}GSE5222, was downloaded from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between unilateral and bilateral retinoblastoma were identified and functional enrichment analysis performed. The protein–protein interaction (PPI) network was constructed and analysed by STRING and Cytoscape.ResultsDEGs were mainly associated with activation of cysteine-type endopeptidase activity involved in apoptotic process and small molecule catabolic process. Seven genes (WAS, GNB3, PTGER1, TACR1, GPR143, NPFF and CDKN2A) were identified as HUB genes.ConclusionOur research provides more understanding of the mechanisms of the disease at a molecular level and may help in the identification of novel biomarkers for retinoblastoma.     

Identification of potential gene markers in gestational diabetes mellitus

This study aims to investigate underlying mechanisms of gestational diabetes mellitus (GDM). In this work, the {"type":"entrez-geo","attrs":{"text":"GSE70493","term_id":"70493"}}GSE70493 dataset from GDM and control samples was acquired from Gene Expression Omnibus (GEO) database. Afterward, differentially expressed genes (DEGs) were screened between GDM and control samples. Subsequently, functional enrichment analysis and protein–protein interaction (PPI) network analysis of these DEGs were carried out. Furthermore, significant sub‐modules were identified, and the functional analysis was also performed. Finally, we undertook a quantitative real‐time polymerase chain reaction (qRT‐PCR) with the purpose of confirming several key genes in GDM development. There were totally 528 up‐regulated and 684 down‐regulated DEGs between GDM and healthy samples. The functional analyses suggested that the above genes were dramatically enriched in type 1 diabetes mellitus (T1DM) process and immune‐related pathways. Moreover, PPI analysis revealed that several members of human leukocyte antigen (HLA) superfamily, including down‐regulated HLA‐DQA1, HLA‐DRB1, HLA‐DPA1, and HLA‐DQB1 served as hub genes. In addition, six significant sub‐clusters were extracted and functional analysis suggested that these four genes in sub‐module 1 were also associated with immune and T1DM‐related pathways. Finally, they were also confirmed by qRT‐PCR array. Besides, the four members of HLA superfamily might be implicated with molecular mechanisms of GDM, contributing to a deeper understanding of GDM development.     

A novel circRNA–miRNA–mRNA network reveals hsa-circ-0040039 as a biomarker for intervertebral disc degeneration

ObjectiveAlterations in the structure and function of intervertebral discs by multifaceted chronic processes can result in intervertebral disc degeneration (IDD). The mechanisms involved in IDD are still unknown.MethodsWe investigated the possible mechanisms underlying IDD using a bioinformatics analysis of publicly available microarray expression datasets and built a circular RNA–microRNA–mRNA (circRNA–miRNA–mRNA) network based on the results. Datasets {"type":"entrez-geo","attrs":{"text":"GSE67566","term_id":"67566"}}GSE67566 and {"type":"entrez-geo","attrs":{"text":"GSE116726","term_id":"116726"}}GSE116726 were downloaded from the Gene Expression Omnibus (GEO) and analyzed using the limma package in R. The CircInteractome database was used to detect miRNAs related to circRNA, and TargetScan, miRDB, and miRTarBase were used to predict target mRNAs. Key target genes were annotated using Gene Ontology terms.ResultsThe circRNA hsa-circ-0040039 was found to have the top log fold-change score. Analysis using Metascape showed that the associated genes were enriched mainly in the cell cycle. The Cytoscape plugin MCODE predicted that two members of the RAS oncogene family—RAB1A and RAB1B—and multiple coagulation factor deficiency (MCFD2) may play key roles in IDD.ConclusionOur results suggested that hsa-circ-0040039 and the related network may be potential biomarkers for IDD.     

Identification of ITGAX and CCR1 as potential biomarkers of atherosclerosis via Gene Set Enrichment Analysis

ObjectiveAtherosclerosis (AS) is a life-threatening disease in aging populations worldwide. However, the molecular and gene regulation mechanisms of AS are still unclear. This study aimed to identify gene expression differences between atheroma plaques and normal tissues in humans.MethodsThe expression profiling dataset {"type":"entrez-geo","attrs":{"text":"GSE43292","term_id":"43292"}}GSE43292 was obtained from the Gene Expression Omnibus (GEO) dataset. The differentially expressed genes (DEGs) were identified between the atheroma plaques and normal tissues via GEO2R, and functional annotation of the DEGs was performed by GSEA. STRING and MCODE plug-in of Cytoscape were used to construct a protein–protein interaction (PPI) network and analyze hub genes. Finally, quantitative polymerase chain reaction (qPCR) was performed to verify the hub genes.ResultsOverall, 134 DEGs were screened. Functional annotation demonstrated that these DEGs were mainly enriched in sphingolipid metabolism, apoptosis, lysosome, and more. Six hub genes were identified from the PPI network: ITGAX, CCR1, IL1RN, CXCL10, CD163, and MMP9. qPCR analysis suggested that the relative expression levels of the six hub genes were significantly higher in AS samples.ConclusionsWe used bioinformatics to identify six hub genes: ITGAX, CCR1, IL1RN, CXCL10, CD163, and MMP9. These hub genes are potential promising diagnostic and therapeutic targets for AS.     

Identification and construction of a 13‐gene risk model for prognosis prediction in hepatocellular carcinoma patients

We attempted to screen out the feature genes associated with the prognosis of hepatocellular carcinoma (HCC) patients through bioinformatics methods, to generate a risk model to predict the survival rate of patients. Gene expression information of HCC was accessed from GEO database, and differentially expressed genes (DEGs) were obtained through the joint analysis of multi‐chip. Functional and pathway enrichment analyses of DEGs indicated that the enrichment was mainly displayed in biological processes such as nuclear division. Based on TCGA‐LIHC data set, univariate, LASSO, and multivariate Cox regression analyses were conducted on the DEGs. Then, 13 feature genes were screened for the risk model. Also, the hub genes were examined in our collected clinical samples and GEPIA database. The performance of the risk model was validated by Kaplan–Meier survival analysis and receiver operation characteristic (ROC) curves. While its universality was verified in {"type":"entrez-geo","attrs":{"text":"GSE76427","term_id":"76427"}}GSE76427 and ICGC (LIRI‐JP) validation cohorts. Besides, through combining patients’ clinical features (age, gender, T staging, and stage) and risk scores, univariate and multivariate Cox regression analyses revealed that the risk score was an effective independent prognostic factor. Finally, a nomogram was implemented for 3‐year and 5‐year overall survival prediction of patients. Our findings aid precision prediction for prognosis of HCC patients.     

A ten N6‐methyladenosine‐related long non‐coding RNAs signature predicts prognosis of triple‐negative breast cancer

BackgroundPatients with triple‐negative breast cancer (TNBC) face a major challenge of the poor prognosis, and N6‐methyladenosine‐(m6A) mediated regulation in cancer has been proposed. Therefore, this study aimed to explore the prognostic roles of m6A‐related long non‐coding RNAs (LncRNAs) in TNBC.MethodsClinical information and expression data of TNBC samples were collected from TCGA and GEO databases. Pearson correlation, univariate, and multivariate Cox regression analysis were employed to identify independent prognostic m6A‐related LncRNAs to construct the prognostic score (PS) risk model. Receiver operating characteristic (ROC) curve was used to evaluate the performance of PS risk model. A competing endogenous RNA (ceRNA) network was established for the functional analysis on targeted mRNAs.ResultsWe identified 10 independent prognostic m6A‐related LncRNAs (SAMD12‐AS1, BVES‐AS1, LINC00593, MIR205HG, LINC00571, ANKRD10‐IT1, CIRBP‐AS1, SUCLG2‐AS1, BLACAT1, and HOXB‐AS1) and established a PS risk model accordingly. Relevant results suggested that TNBC patients with lower PS had better overall survival status, and ROC curves proved that the PS model had better prognostic abilities with the AUC of 0.997 and 0.864 in TCGA and {"type":"entrez-geo","attrs":{"text":"GSE76250","term_id":"76250"}}GSE76250 datasets, respectively. Recurrence and PS model status were defined as independent prognostic factors of TNBC. These ten LncRNAs were all differentially expressed in high‐risk TNBC compared with controls. The ceRNA network revealed the regulatory axes for nine key LncRNAs, and mRNAs in the network were identified to function in pathways of cell communication, signaling transduction and cancer.ConclusionOur findings proposed a ten‐m6A‐related LncRNAs as potential biomarkers to predict the prognostic risk of TNBC.     

Screening the hub genes and analyzing the mechanisms in discharged COVID‐19 patients retesting positive through bioinformatics analysis

BackgroundAfter encountering COVID‐19 patients who test positive again after discharge, our study analyzed the pathogenesis to further assess the risk and possibility of virus reactivation.MethodsA separate microarray was acquired from the Gene Expression Omnibus (GEO), and its samples were divided into two groups: a “convalescent‐RTP” group consisting of convalescent and “retesting positive” (RTP) patients (group CR) and a “healthy‐RTP” group consisting of healthy control and RTP patients (group HR). The enrichment analysis was performed with R software, obtaining the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, the protein–protein interaction (PPI) networks of each group were established, and the hub genes were discovered using the cytoHubba plugin.ResultsIn this study, 6622 differentially expressed genes were identified in the group CR, among which RAB11B‐AS1, DISP1, MICAL3, PSMG1, and DOCK4 were up‐regulated genes, and ANAPC1, IGLV1‐40, SORT1, PLPPR2, and ATP1A1‐AS1 were down‐regulated. 7335 genes were screened in the group HR, including the top 5 up‐regulated genes ALKBH6, AMBRA1, MIR1249, TRAV18, and LRRC69, and the top 5 down‐regulated genes FAM241B, {"type":"entrez-nucleotide","attrs":{"text":"AC018529.3","term_id":"6653321","term_text":"AC018529.3"}}AC018529.3, {"type":"entrez-nucleotide","attrs":{"text":"AL031963.3","term_id":"5650585","term_text":"AL031963.3"}}AL031963.3, {"type":"entrez-nucleotide","attrs":{"text":"AC006946.1","term_id":"4314404","term_text":"AC006946.1"}}AC006946.1, and FAM149B1. The GO and KEGG analysis of the two groups revealed a significant enrichment in immune response and apoptosis. In the PPI network constructed, group CR and group HR identified 10 genes, respectively, and TP53BP1, SNRPD1, and SNRPD2 were selected as hub genes.ConclusionsUsing the messenger ribonucleic acid (mRNA) expression data from {"type":"entrez-geo","attrs":{"text":"GSE166253","term_id":"166253"}}GSE166253, we found TP53BP1, SNRPD1, and SNRPD2 as hub genes in RTP patients, which is vital to the management and prognostic prediction of RTP patients.     

Constructing a 10‐core genes panel for diagnosis of pediatric sepsis

BackgroundThe lack of sensitivity and specificity of most biomarkers or the lack of relevant studies to demonstrate their effectiveness in sepsis.MethodsDownloaded three sets of sepsis expression data ({"type":"entrez-geo","attrs":{"text":"GSE13904","term_id":"13904"}}GSE13904, {"type":"entrez-geo","attrs":{"text":"GSE25504","term_id":"25504"}}GSE25504, {"type":"entrez-geo","attrs":{"text":"GSE26440","term_id":"26440"}}GSE26440) from GEO. Then, using the R limma package and WGCNA analysis tocore genes. Finally, the value of these core genes was confirmed by clinical samples.ResultsCompared to normal samples, we obtain many abnormally expressed genes in the pediatric sepsis. WGCNA co‐expression analysis showed that genes from blue and turquoise module were close correlation with pediatric sepsis. The top 20 genes (TIMP2, FLOT1, HCK, NCF4, SERPINA1, IL17RA, PGD, PRKCD, GLT1D1, ALOX5, SIRPA, DOK3, ITGAM, S100A11, ZNF438, PLIN3, LTB4R, TSPO, MAPK14, GAS7) of the blue module of pediatric sepsis were mainly enriched in neutrophil degranulation, etc The top 20 genes (TBC1D4, NOL11, NLRC3, ZNF121, DYRK2, ABCE1, MAGEH1, TMEM263, MCUB, MALT1, DDHD2, TRAC, NOC3L, LCK, TRMT61B, ZNF260, ENOPH1, LOC93622, NAE1, TRBC1) for turquoise module were mainly enriched in rRNA‐containing ribonucleoprotein complexes exported from the nucleus, etc The selected hub gene of pediatric sepsis was combined with the markers of cell surface and found 10 core genes (HCK, PRKCD, SIRPA, DOK3, ITGAM, LTB4R, MAPK14, MALT1, NLRC3, LCK). ROC showed that AUC of the 10 core genes for diagnosis of pediatric sepsis was above 0.9.ConclusionThere were many abnormally expressed genes in patients with pediatric sepsis. The panel constructed by the 10 core genes was expected to become a biomarker panel for clinical application of pediatric sepsis.