Bioinformatic analysis of key pathways and genes involved in pediatric atopic dermatitis

The initiation of atopic dermatitis (AD) typically happens very early in life, but most of our understanding of AD is derived from studies on AD patients in adult. The aim of the present study was to identify gene signature speficic to pediatric AD comapred with adult AD. The gene expression profiles of four datasets ({"type":"entrez-geo","attrs":{"text":"GSE32924","term_id":"32924"}}GSE32924, {"type":"entrez-geo","attrs":{"text":"GSE36842","term_id":"36842"}}GSE36842, {"type":"entrez-geo","attrs":{"text":"GSE58558","term_id":"58558"}}GSE58558, and {"type":"entrez-geo","attrs":{"text":"GSE107361","term_id":"107361"}}GSE107361) were downloaded from the GEO database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and protein–protein interaction (PPI) network was constructed by Cytoscape software. Total 654 differentially expressed genes (DEGs) (394 up-regulated and 260 down-regulated) were identified in pediatric AD samples with adult AD samples as control. The up-regulated DEGs were significantly enriched in the migration and chemotaxis of granulocyte and neutrophil, while down-regulated DEGs were significantly enriched in biological adhesion. KEGG pathway analysis showed that up-regulated DEGs participated in chemokine signaling pathway while down-regulated DEGs participated in adherens junction, focal adhesion, and regulation of actin cytoskeleton. The top 10 hub genes GAPDH, EGFR, ACTB, ESR1, CDK1, CXCL8, CD44, KRAS, PTGS2, and SMC3 were involved in chemokine signaling pathway, cytokine–cytokine receptor interaction, interleukin-17 signaling pathway, and regulation of actin cytoskeleton. In conclusion, we identified DEGs and hub genes involved in pediatric AD, which might be used as therapeutic targets and diagnostic biomarkers for pediatric AD.     

Mining the key genes for ventilator-induced lung injury using co-expression network analysis

Mechanical ventilation is extensively adopted in general anesthesia and respiratory failure management, but it can also induce ventilator-induced lung injury (VILI). Therefore, it is of great urgency to explore the mechanisms involved in the VILI pathogenesis, which might contribute to its future prevention and treatment. Four microarray datasets from the GEO database were selected in our investigation, and were subjected to the Weighted Gene Co-Expression Network Analysis (WGCNA) to identify the VILI-correlated gene modules. The limma package in R software was used to identify the differentially expressed genes (DEGs) between the VILI and control groups. WGCNA was constructed by merging the {"type":"entrez-geo","attrs":{"text":"GSE9314","term_id":"9314"}}GSE9314, {"type":"entrez-geo","attrs":{"text":"GSE9368","term_id":"9368"}}GSE9368, {"type":"entrez-geo","attrs":{"text":"GSE11434","term_id":"11434"}}GSE11434 and {"type":"entrez-geo","attrs":{"text":"GSE11662","term_id":"11662"}}GSE11662 datasets. A total of 49 co-expression network modules were determined as associated with VILI. The intersected genes between hub genes screened from DEGs for VILI and those identified using WGCNA were as follows: Tlr2, Hmox1, Serpine1, Mmp9, Il6, Il1b, Ptgs2, Fos and Atf3, which were determined to be key genes for VILI. Those key genes were validated by {"type":"entrez-geo","attrs":{"text":"GSE86229","term_id":"86229"}}GSE86229 and quantitative PCR (qPCR) experiment to have significantly statistical difference in their expression between the VILI and control groups. In a nutshell, nine key genes with expression differences in VILI were screened by WGCNA by integrating multiple datasets.     

FRZB as a key molecule in abdominal aortic aneurysm progression affecting vascular integrity

Abdominal aortic aneurysm (AAA), when ruptured, results in high mortality. The identification of molecular pathways involved in AAA progression is required to improve AAA prognosis. The aim of the present study was to assess the key genes for the progression of AAA and their functional role. Genomic and clinical data of three independent cohorts were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) ({"type":"entrez-geo","attrs":{"text":"GSE57691","term_id":"57691"}}GSE57691, {"type":"entrez-geo","attrs":{"text":"GSE7084","term_id":"7084"}}GSE7084, and {"type":"entrez-geo","attrs":{"text":"GSE98278","term_id":"98278"}}GSE98278). To develop AAA diagnosis and progression-related differentially expressed genes (DEGs), we used a significance analysis of microarray (SAM). Spearman correlation test and gene set analysis were performed to identify potential enriched pathways for DEGs. Only the Frizzled-related protein (FRZB) gene and chromosome 1 open reading frame 24 (C1orf24) exhibited significant down-regulation in all analyses. With FRZB, the pathways were associated with RHO GTPase and elastin fiber formation. With C1orf24, the pathways were elastic fiber formation, extracellular matrix organization, and cell–cell communication. Since only FRZB was evolutionally conserved in the vertebrates, function of FRZB was validated using zebrafish embryos. Knockdown of frzb remarkably reduced vascular integrity in zebrafish embryos. We believe that FRZB is a key gene involved in AAA initiation and progression affecting vascular integrity.     

Identification of novel biomarkers and candidate small molecule drugs in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis of high‐throughput data

Background: Rheumatoid arthritis (RA) and osteoarthritis (OA) are two major types of joint diseases. The present study aimed to identify hub genes involved in the pathogenesis and further explore the potential treatment targets of RA and OA.Methods: The gene expression profile of {"type":"entrez-geo","attrs":{"text":"GSE12021","term_id":"12021"}}GSE12021 was downloaded from Gene Expression Omnibus (GEO). Total 31 samples (12 RA, 10 OA and 9 NC samples) were used. The differentially expressed genes (DEGs) in RA versus NC, OA versus NC and RA versus OA groups were screened using limma package. We also verified the DEGs in {"type":"entrez-geo","attrs":{"text":"GSE55235","term_id":"55235"}}GSE55235 and {"type":"entrez-geo","attrs":{"text":"GSE100786","term_id":"100786"}}GSE100786. Functional annotation and protein–protein interaction (PPI) network construction of OA‐ and RA‐specific DEGs were performed. Finally, the candidate small molecules as potential drugs to treat RA and OA were predicted in CMap database.Results: 165 up-regulated and 163 down-regulated DEGs between RA and NC samples, 73 up-regulated and 293 down-regulated DEGs between OA and NC samples, 92 up-regulated and 98 down-regulated DEGs between RA and OA samples were identified. Immune response and TNF signaling pathway were significantly enriched pathways for RA‐ and OA‐specific DEGs, respectively. The hub genes were mainly associated with ‘Primary immunodeficiency’ (RA vs. NC group), ‘Ribosome’ (OA vs. NC group), and ‘Chemokine signaling pathway’ (RA vs. OA group). Arecoline and Cefamandole were the most promising small molecule to reverse the RA and OA gene expression.Conclusion: Our findings suggest new insights into the underlying pathogenesis of RA and OA, which may improve the diagnosis and treatment of these intractable chronic diseases.     

Bioinformatics Analysis of Key micro-RNAs and mRNAs under the Hand,Foot, and Mouth Disease Virus Infection

To clarify crucial key micro-RNAs and mRNAs associated with hand, foot, and mouth disease (HFMD) virus infection, we conducted this bioinformatics analysis from four GEO datasets. The following datasets were used for the analysis: {"type":"entrez-geo","attrs":{"text":"GSE85829","term_id":"85829"}}GSE85829, {"type":"entrez-geo","attrs":{"text":"GSE94551","term_id":"94551"}}GSE94551, {"type":"entrez-geo","attrs":{"text":"GSE52780","term_id":"52780"}}GSE52780, and {"type":"entrez-geo","attrs":{"text":"GSE45589","term_id":"45589"}}GSE45589. Differentially expressed genes (DEGs) were acquired, and the analysis of functional and pathway enrichment and the relative regulatory network were conducted. After screening common differentially expressed miRNAs (DE-miRNAs), five key miRNAs were acquired: miR-100-3p, miR-125a-3p, miR-1273g-3p, miR-5585-3p, and miR-671-5p. There were three common enriched GO terms between miRNA-derived prediction and mRNA-derived analysis: biosynthetic process, cytosol, and nucleoplasm. There was one common KEGG pathway, i.e., cell cycle shared between miRNA-based and mRNA-based enrichment. Using TarBase V8 in DIANA tools, we acquired 1,520 potential targets (mRNA) from the five key DE-miRNAs, among which the159 DE-mRNAs also included 11 DEGs. These common DEGs showed a PPI network mainly connected by SMC1A, SMARCC1, SF3B3, LIG1, and BRMS1L. Together, changes in five key miRNAs and 11 key mRNAs may play crucial roles in HFMD progression. A combination of these roles may benefit the early diagnosis and treatment of HFMD.Key words: HFMD, micro-RNA, protein-protein interaction, microarray, regulatory network     

Bioinformatics analysis identifies COL1A1, THBS2 and SPP1 as potential predictors of patient prognosis and immunotherapy response in gastric cancer

Background: The present study aimed to use bioinformatics tools to explore pivotal genes associated with the occurrence of gastric cancer (GC) and assess their prognostic significance, and link with clinicopathological parameters. We also investigated the predictive role of COL1A1, THBS2, and SPP1 in immunotherapy.Materials and methods: We identified differential genes (DEGs) that were up- and down-regulated in the three datasets ({"type":"entrez-geo","attrs":{"text":"GSE26942","term_id":"26942"}}GSE26942, {"type":"entrez-geo","attrs":{"text":"GSE13911","term_id":"13911"}}GSE13911, and {"type":"entrez-geo","attrs":{"text":"GSE118916","term_id":"118916"}}GSE118916) and created protein–protein interaction (PPI) networks from the overlapping DEGs. We then investigated the potential functions of the hub genes in cancer prognosis using PPI networks, and explored the influence of such genes in the immune environment.Results: Overall, 268 overlapping DEGs were identified, of which 230 were up-regulated and 38 were down-regulated. CytoHubba selected the top ten hub genes, which included SPP1, TIMP1, SERPINE1, MMP3, COL1A1, BGN, THBS2, CDH2, CXCL8, and THY1. With the exception of SPP1, survival analysis using the Kaplan–Meier database showed that the levels of expression of these genes were associated with overall survival. Genes in the most dominant module explored by MCODE, COL1A1, THBS2, and SPP1, were primarily enriched for two KEGG pathways. Further analysis showed that all three genes could influence clinicopathological parameters and immune microenvironment, and there was a significant correlation between COL1A1, THBS2, SPP1, and PD-L1 expression, thus indicating a potential predictive role for GC response to immunotherapy.Conclusion: ECM–receptor interactions and focal adhesion pathways are of great significance in the progression of GC. COL1A1, THBS2, and SPP1 may help predict immunotherapy response in GC patients.     

High expression of COL5A2, a member of COL5 family,indicates the poor survival and facilitates cell migration in gastric cancer

Background: Gastric cancer (GC) metastasis determines the prognosis of patients, and exploring the molecular mechanism of GC metastasis is expected to provide a theoretical basis for clinical treatment. Recent studies have shown that extracellular matrix protein is closely related to GC metastasis. The present study aimed to explore the expression profile and role of COL5A2, as an extracellular matrix protein, in GC.Methods: The expression, overall survival, and progression-free survival data of COL5 family members were extracted from The Cancer Genome Atlas (TCGA) database, respectively. Weighted gene co-expression network analysis of the {"type":"entrez-geo","attrs":{"text":"GSE62229","term_id":"62229"}}GSE62229 database was performed out to identify modules and associated genes.Results: COL5A2 was selected as our research target in the TCGA database, and was also verified in the {"type":"entrez-geo","attrs":{"text":"GSE62229","term_id":"62229"}}GSE62229 and {"type":"entrez-geo","attrs":{"text":"GSE15459","term_id":"15459"}}GSE15459 datasets. COL5A2 was up-regulated in GC tissues by paraffin immunohistochemistry and RT-qPCR. The prognosis of patients with low COL5A2 expression was better than that of patients with high COL5A2 expression. Scratch and migration experiments showed that knockdown of COL5A2 decreased the migration ability of gastric cancer cells compared with the control group. In vivo, mice with tail vein injection COL5A2 knockdown had fewer and smaller metastatic nodules in liver. GSEA results showed that the TCGA and {"type":"entrez-geo","attrs":{"text":"GSE62229","term_id":"62229"}}GSE62229 samples were significantly enriched in several well-known cancer-related pathways, such as the TGF-β, MAPK, and JAK2 signaling pathways.Conclusion: COL5A2 was most closely related to advanced GC among COL5 family members. High COL5A2 expression is associated with a poor prognosis, and may be a novel therapeutic target for GC.     

Epigenetic alternations of microRNAs and DNA methylation contribute to gestational diabetes mellitus

This study aimed to identify epigenetic alternations of microRNAs and DNA methylation for gestational diabetes mellitus (GDM) diagnosis and treatment using in silico approach. Data of mRNA and miRNA expression microarray ({"type":"entrez-geo","attrs":{"text":"GSE103552","term_id":"103552"}}GSE103552 and {"type":"entrez-geo","attrs":{"text":"GSE104297","term_id":"104297"}}GSE104297) and DNA methylation data set ({"type":"entrez-geo","attrs":{"text":"GSE106099","term_id":"106099"}}GSE106099) were obtained from the GEO database. Differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs) and differentially methylated genes (DMGs) were obtained by limma package. Functional and enrichment analyses were performed with the DAVID database. The protein‐protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. Simultaneously, a connectivity map (CMap) analysis was performed to screen potential therapeutic agents for GDM. In GDM, 184 low miRNA‐targeting up‐regulated genes and 234 high miRNA‐targeting down‐regulated genes as well as 364 hypomethylation–high‐expressed genes and 541 hypermethylation–low‐expressed genes were obtained. They were mainly enriched in terms of axon guidance, purine metabolism, focal adhesion and proteasome, respectively. In addition, 115 genes (67 up‐regulated and 48 down‐regulated) were regulated by both aberrant alternations of miRNAs and DNA methylation. Ten chemicals were identified as putative therapeutic agents for GDM and four hub genes (IGF1R, ATG7, DICER1 and RANBP2) were found in PPI and may be associated with GDM. Overall, this study identified a series of differentially expressed genes that are associated with epigenetic alternations of miRNA and DNA methylation in GDM. Ten chemicals and four hub genes may be further explored as potential drugs and targets for GDM diagnosis and treatment, respectively.     

An autophagy-related prognostic signature associated with immune microenvironment features of uveal melanoma

Autophagy is involved in cancer initiation and progression but its role in uveal melanoma (UM) was rarely investigated. Herein, we built an autophagy-related gene (ARG) risk model of UM patients by univariate Cox regression and least absolute shrinkage and selection operator (Lasso) regression model and filtrated out nine prognostic ARGs in The Cancer Genome Atlas (TCGA) cohort. Survival and Receiver Operating Characteristic (ROC) Curve analysis in the TCGA and other four independent UM cohorts ({"type":"entrez-geo","attrs":{"text":"GSE22138","term_id":"22138"}}GSE22138, {"type":"entrez-geo","attrs":{"text":"GSE27831","term_id":"27831"}}GSE27831, {"type":"entrez-geo","attrs":{"text":"GSE44295","term_id":"44295"}}GSE44295 and {"type":"entrez-geo","attrs":{"text":"GSE84976","term_id":"84976"}}GSE84976) proved that the ARG-signature possessed robust and steady prognosis predictive ability. We calculated risk scores for patients included in our study and patients with higher risk scores showed worse clinical outcomes. We found the expressions of the nine ARGs were significantly associated with clinical and molecular features (including risk score) and overall survival (OS) of UM patients. Furthermore, we utilized univariate and multivariate Cox regression analyses to determine the independent prognostic ability of the ARG-signature. Functional enrichment analysis showed the ARG-signature was correlated with several immune-related processes and pathways like T-cell activation and T-cell receptor signaling pathway. Gene set enrichment analysis (GSEA) found tumor hallmarks including angiogenesis, IL6-JAK-STAT3-signaling, reactive oxygen species pathway and oxidative phosphorylation were enriched in high-risk UM patients. Finally, infiltrations of several immune cells and immune-related scores were found significantly associated with the ARG-signature. In conclusion, the ARG-signature might be a strong predictor for evaluating the prognosis and immune infiltration of UM patients.     

Exploring the Novel Computational Drug Target and Associated Key Pathways of Oral Cancer

Oral cancer (OC) is a serious health concern that has a high fatality rate. The oral cavity has seven kinds of OC, including the lip, tongue, and floor of the mouth, as well as the buccal, hard palate, alveolar, retromolar trigone, and soft palate. The goal of this study is to look into new biomarkers and important pathways that might be used as diagnostic biomarkers and therapeutic candidates in OC. The publicly available repository the Gene Expression Omnibus (GEO) was to the source for the collection of OC-related datasets. {"type":"entrez-geo","attrs":{"text":"GSE74530","term_id":"74530"}}GSE74530, {"type":"entrez-geo","attrs":{"text":"GSE23558","term_id":"23558"}}GSE23558, and {"type":"entrez-geo","attrs":{"text":"GSE3524","term_id":"3524"}}GSE3524 microarray datasets were collected for analysis. Minimum cut-off criteria of |log fold-change (FC)| > 1 and adjusted p < 0.05 were applied to calculate the upregulated and downregulated differential expression genes (DEGs) from the three datasets. After that only common DEGs in all three datasets were collected to apply further analysis. Gene ontology (GO) and pathway analysis were implemented to explore the functional behaviors of DEGs. Then protein–protein interaction (PPI) networks were built to identify the most active genes, and a clustering algorithm was also implemented to identify complex parts of PPI. TF-miRNA networks were also constructed to study OC-associated DEGs in-depth. Finally, top gene performers from PPI networks were used to apply drug signature analysis. After applying filtration and cut-off criteria, 2508, 3377, and 670 DEGs were found for {"type":"entrez-geo","attrs":{"text":"GSE74530","term_id":"74530"}}GSE74530, {"type":"entrez-geo","attrs":{"text":"GSE23558","term_id":"23558"}}GSE23558, and {"type":"entrez-geo","attrs":{"text":"GSE3524","term_id":"3524"}}GSE3524 respectively, and 166 common DEGs were found in every dataset. The GO annotation remarks that most of the DEGs were associated with the terms of type I interferon signaling pathway. The pathways of KEGG reported that the common DEGs are related to the cell cycle and influenza A. The PPI network holds 88 nodes and 492 edges, and CDC6 had the highest number of connections. Four clusters were identified from the PPI. Drug signatures doxorubicin and resveratrol showed high significance according to the hub genes. We anticipate that our bioinformatics research will aid in the definition of OC pathophysiology and the development of new therapies for OC.     

Tumor microenvironment characterization in stage IV gastric cancer

Immunotherapy is remarkably affected by the immune environment of the principal tumor. Nonetheless, the immune environment’s clinical relevance in stage IV gastric cancer (GC) is largely unknown. The gene expression profiles of 403 stage IV GC patients in the three cohorts: GEO (Gene Expression Omnibus, {"type":"entrez-geo","attrs":{"text":"GSE84437","term_id":"84437"}}GSE84437 (n=292) and {"type":"entrez-geo","attrs":{"text":"GSE62254","term_id":"62254"}}GSE62254 (n=77), and TCGA (The Cancer Genome Atlas, n=34) were used in the present study. Using four publicly available stage IV GC expression datasets, 29 immune signatures were expression profiled, and on this basis, we classified stage IV GC. The classification was conducted using the hierarchical clustering method. Three stage IV GC subtypes L, M, and H were identified representing low, medium, and high immunity, respectively. Immune correlation analysis of these three types revealed that Immune H exhibited a better prognostic outcome as well as a higher immune score compared with other subtypes. There was a noticeable difference in the three subgroups of HLA genes. Further, on comparing with other subtypes, CD86, CD80, CD274, CTLA4, PDCD1, and PDCD1LG2 had higher expression in the Immunity H subtype. In stage IV GC, potentially positive associations between immune and pathway activities were displayed, due to the enrichment of pathways including TNF signaling, Th-17 cell differentiation, and JAK-STAT signaling pathways in Immunity H vs Immunity L subtypes. External cohorts from TCGA cohort ratified these results. The identification of stage IV GC subtypes has potential clinical implications in stage IV GC treatment.     

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

Rheumatoid arthritis (RA), osteoarthritis (OA), and periodontal disease (PD) are chronic inflammatory diseases that are globally prevalent, and pose a public health concern. The search for a potential mechanism linking PD to RA and OA continues, as it could play a significant role in disease prevention and treatment. Recent studies have linked RA, OA, and PD to Porphyromonas gingivalis (PG), a periodontal bacterium, through a similar dysregulation in an inflammatory mechanism. This study aimed to identify potential gene signatures that could assist in early diagnosis as well as gain insight into the molecular mechanisms of these diseases. The expression data sets with the series IDs {"type":"entrez-geo","attrs":{"text":"GSE97779","term_id":"97779"}}GSE97779, {"type":"entrez-geo","attrs":{"text":"GSE123492","term_id":"123492"}}GSE123492, and {"type":"entrez-geo","attrs":{"text":"GSE24897","term_id":"24897"}}GSE24897 for macrophages of RA, OA synovium, and PG stimulated macrophages (PG-SM), respectively, were retrieved and screened for differentially expressed genes (DEGs). The 72 common DEGs among RA, OA, and PG-SM were further subjected to gene–gene correlation analysis. A GeneMANIA interaction network of the 47 highly correlated DEGs comprises 53 nodes and 271 edges. Network centrality analysis identified 15 hub genes, 6 of which are DEGs (API5, ATE1, CCNG1, EHD1, RIN2, and STK39). Additionally, two significantly up-regulated non-hub genes (IER3 and RGS16) showed interactions with hub genes. Functional enrichment analysis of the genes showed that “apoptotic regulation” and “inflammasomes” were among the major pathways. These eight genes can serve as important signatures/targets, and provide new insights into the molecular mechanism of PG-induced RA, OA, and PD.     

Identification of potential biomarkers in dengue via integrated bioinformatic analysis

Dengue fever virus (DENV) is a global health threat that is becoming increasingly critical. However, the pathogenesis of dengue has not yet been fully elucidated. In this study, we employed bioinformatics analysis to identify potential biomarkers related to dengue fever and clarify their underlying mechanisms. The results showed that there were 668, 1901, and 8283 differentially expressed genes between the dengue-infected samples and normal samples in the {"type":"entrez-geo","attrs":{"text":"GSE28405","term_id":"28405"}}GSE28405, {"type":"entrez-geo","attrs":{"text":"GSE38246","term_id":"38246"}}GSE38246, and {"type":"entrez-geo","attrs":{"text":"GSE51808","term_id":"51808"}}GSE51808 datasets, respectively. Through overlapping, a total of 69 differentially expressed genes (DEGs) were identified, of which 51 were upregulated and 18 were downregulated. We identified twelve hub genes, including MX1, IFI44L, IFI44, IFI27, ISG15, STAT1, IFI35, OAS3, OAS2, OAS1, IFI6, and USP18. Except for IFI44 and STAT1, the others were statistically significant after validation. We predicted the related microRNAs (miRNAs) of these 12 target genes through the database miRTarBase, and finally obtained one important miRNA: has-mir-146a-5p. In addition, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out, and a protein–protein interaction (PPI) network was constructed to gain insight into the actions of DEGs. In conclusion, our study displayed the effectiveness of bioinformatics analysis methods in screening potential pathogenic genes in dengue fever and their underlying mechanisms. Further, we successfully predicted IFI44L and IFI6, as potential biomarkers with DENV infection, providing promising targets for the treatment of dengue fever to a certain extent.     

A ferroptosis-related gene signature for overall survival prediction and immune infiltration in lung squamous cell carcinoma

Background: Ferroptosis is associated with cancer initiation and progression. However, the molecular mechanism and prognostic value of ferroptosis-related genes in lung squamous cell carcinoma (LUSC) are poorly understood.Methods: The mRNA expression profiles, methylation data, and clinical information of patients with LUSC were downloaded from TCGA and GEO database. Ferroptosis-related differentially expressed genes (DEGs) were identified between cancerous and non-cancerous tissues, and their prognostic value was systemically investigated by bioinformatic analyses.Results: A ferroptosis-related gene signature (ALOX5, TFRC, PHKG2, FADS2, NOX1) was constructed using multivariate Cox regression analysis and represented as a risk score. Overall survival (OS) probability was significantly lower in the high-risk group than in the low-risk group (P<0.001), and receiver operating characteristic curve showed a good predictive capacity (AUC = 0.739). The risk score was an independent prognostic factor for LUSC. A nomogram was constructed to predict the OS probabilities at 1, 3, and 5 years. High-risk score was associated with increased immune infiltration, lower methylation levels, higher immune checkpoint genes expression levels, and better chemotherapy response. Cell adhesion molecules, focal adhesion, and extracellular matrix receptor interaction were the main pathways in the high-risk group. The signature was validated using the TCGA test cohort, entire TCGA cohort, {"type":"entrez-geo","attrs":{"text":"GSE30219","term_id":"30219"}}GSE30219, {"type":"entrez-geo","attrs":{"text":"GSE157010","term_id":"157010"}}GSE157010, {"type":"entrez-geo","attrs":{"text":"GSE73403","term_id":"73403"}}GSE73403, and {"type":"entrez-geo","attrs":{"text":"GSE4573","term_id":"4573"}}GSE4573 datasets. The gene disorders in patients with LUSC were validated using real-time PCR and single-cell RNA sequencing analysis.Conclusions: A ferroptosis-related gene signature was constructed to predict OS probability in LUSC. This could facilitate novel therapeutic methods and guide individualized therapy.     

Role of PCK2 in the proliferation of vascular smooth muscle cells in neointimal hyperplasia

Vascular smooth muscle cell (VSMC) proliferation is a hallmark of neointimal hyperplasia (NIH) in atherosclerosis and restenosis post-balloon angioplasty and stent insertion. Although numerous cytotoxic and cytostatic therapeutics have been developed to reduce NIH, it is improbable that a multifactorial disease can be successfully treated by focusing on a preconceived hypothesis. We, therefore, aimed to identify key molecules involved in NIH via a hypothesis-free approach. We analyzed four datasets ({"type":"entrez-geo","attrs":{"text":"GSE28829","term_id":"28829"}}GSE28829, {"type":"entrez-geo","attrs":{"text":"GSE43292","term_id":"43292"}}GSE43292, {"type":"entrez-geo","attrs":{"text":"GSE100927","term_id":"100927"}}GSE100927, and {"type":"entrez-geo","attrs":{"text":"GSE120521","term_id":"120521"}}GSE120521), evaluated differentially expressed genes (DEGs) in wire-injured femoral arteries of mice, and determined their association with VSMC proliferation in vitro. Moreover, we performed RNA sequencing on platelet-derived growth factor (PDGF)-stimulated human VSMCs (hVSMCs) post-phosphoenolpyruvate carboxykinase 2 (PCK2) knockdown and investigated pathways associated with PCK2. Finally, we assessed NIH formation in Pck2 knockout (KO) mice by wire injury and identified PCK2 expression in human femoral artery atheroma. Among six DEGs, only PCK2 and RGS1 showed identical expression patterns between wire-injured femoral arteries of mice and gene expression datasets. PDGF-induced VSMC proliferation was attenuated when hVSMCs were transfected with PCK2 siRNA. RNA sequencing of PCK2 siRNA-treated hVSMCs revealed the involvement of the Akt-FoxO-PCK2 pathway in VSMC proliferation via Akt2, Akt3, FoxO1, and FoxO3. Additionally, NIH was attenuated in the wire-injured femoral artery of Pck2-KO mice and PCK2 was expressed in human femoral atheroma. PCK2 regulates VSMC proliferation in response to vascular injury via the Akt-FoxO-PCK2 pathway. Targeting PCK2, a downstream signaling mediator of VSMC proliferation, may be a novel therapeutic approach to modulate VSMC proliferation in atherosclerosis.     

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

Brain metastases (BMs) usually develop in breast cancer (BC) patients. Thus, the molecular mechanisms of breast cancer brain metastasis (BCBM) are of great importance in designing therapeutic strategies to treat or prevent BCBM. The present study attempted to identify novel diagnostic and prognostic biomarkers of BCBM. Two datasets ({"type":"entrez-geo","attrs":{"text":"GSE125989","term_id":"125989"}}GSE125989 and {"type":"entrez-geo","attrs":{"text":"GSE100534","term_id":"100534"}}GSE100534) were obtained from the Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs) in cases of BC with and without brain metastasis (BM). A total of 146 overlapping DEGs, including 103 up-regulated and 43 down-regulated genes, were identified. Functional enrichment analysis showed that these DEGs were mainly enriched for functions including extracellular matrix (ECM) organization and collagen catabolic fibril organization. Using protein–protein interaction (PPI) and principal component analysis (PCA) analysis, we identified ten key genes, including LAMA4, COL1A1, COL5A2, COL3A1, COL4A1, COL5A1, COL5A3, COL6A3, COL6A2, and COL6A1. Additionally, COL5A1, COL4A1, COL1A1, COL6A1, COL6A2, and COL6A3 were significantly associated with the overall survival of BC patients. Furthermore, COL6A3, COL5A1, and COL4A1 were potentially correlated with BCBM in human epidermal growth factor 2 (HER2) expression. Additionally, the miR-29 family might participate in the process of metastasis by modulating the cancer microenvironment. Based on datasets in the GEO database, several DEGs have been identified as playing potentially important roles in BCBM in BC patients.     

Screening and identification of LMNB1 and DLGAP5, two key biomarkers in gliomas

Glioma is the most common primary cancer in the central nervous system. Despite advances in surgery, radiotherapy and chemotherapy over the past decades, the prognosis of glioblastoma patients remains poor. We aim to identify robust gene signatures to better understand the complex molecular mechanisms and to discover potential novel molecular biomarkers for glioma. By exploring {"type":"entrez-geo","attrs":{"text":"GSE16011","term_id":"16011"}}GSE16011, {"type":"entrez-geo","attrs":{"text":"GSE4290","term_id":"4290"}}GSE4290 and {"type":"entrez-geo","attrs":{"text":"GSE50161","term_id":"50161"}}GSE50161 data in Gene Expression Omnibus (GEO) database, we screened out 380 differentially expressed genes between non-tumor and glioma tissues, and further selected 30 hub genes through the Molecular Complex Detection (MCODE) plug-in in Cytoscape. In addition, LMNB1 and DLGAP5 were selected for further analyses due to their high expression in gliomas and were verified by using our cohort. Our study confirmed that LMNB1 and DLGAP5 were up-regulated in gliomas, and patients with high expression of LMNB1 or DLGAP5 had poor survival rate. Furthermore, silence of LMNB1 and DLGAP5 inhibited the proliferation of glioma cells. Together, LMNB1 and DLGAP5 were two potentially novel molecular biomarkers for diagnosis and prognosis of glioma.     

Expression of the Genetic Suppressor Element 24.2 (GSE24.2) Decreases DNA Damage and Oxidative Stress in X-Linked Dyskeratosis Congenita Cells

The predominant X-linked form of Dyskeratosis congenita results from mutations in DKC1, which encodes dyskerin, a protein required for ribosomal RNA modification that is also a component of the telomerase complex. We have previously found that expression of an internal fragment of dyskerin ({"type":"entrez-geo","attrs":{"text":"GSE24","term_id":"24"}}GSE24.2) rescues telomerase activity in X-linked dyskeratosis congenita (X-DC) patient cells. Here we have found that an increased basal and induced DNA damage response occurred in X-DC cells in comparison with normal cells. DNA damage that is also localized in telomeres results in increased heterochromatin formation and senescence. Expression of a cDNA coding for {"type":"entrez-geo","attrs":{"text":"GSE24","term_id":"24"}}GSE24.2 rescues both global and telomeric DNA damage. Furthermore, transfection of bacterial purified or a chemically synthesized {"type":"entrez-geo","attrs":{"text":"GSE24","term_id":"24"}}GSE24.2 peptide is able to rescue basal DNA damage in X-DC cells. We have also observed an increase in oxidative stress in X-DC cells and expression of {"type":"entrez-geo","attrs":{"text":"GSE24","term_id":"24"}}GSE24.2 was able to diminish it. Altogether our data indicated that supplying {"type":"entrez-geo","attrs":{"text":"GSE24","term_id":"24"}}GSE24.2, either from a cDNA vector or as a peptide reduces the pathogenic effects of Dkc1 mutations and suggests a novel therapeutic approach.