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Objectives: This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune‐associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain–Barré syndrome. Methods: Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour‐MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions. Results: Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players. Conclusion: This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Breast cancer (BC), the most prevalent malignancy in women globally, still lacks comprehensive research on its molecular targets and necessitates further investigation into the underlying molecular mechanisms driving its initiation and progression.The GSE20685 Series Matrix File downloaded from the Gene Expression Omnibus database was divided into a high‐risk group (n = 49) and a low‐risk group (n = 278) to construct the co‐expression network.Four hub genes were identified based on the Weighted Gene Co‐expression Network Analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses were performed. Hub gene immune infiltration was investigated using the Tumor Immune Estimation Resource database, and CD4+ T cell expression levels were substantially correlated with hub gene expression. Based on the CancerRxGene database (Genomics of Drug Sensitivity in Cancer database), it was found that the hub genes were highly sensitive to common chemotherapy drugs such as AKT inhibitor VIII and Erlotinib. The expression of Secreted Frizzled‐Related Protein 1, melanoma‐inhibiting activity (MIA), and Keratin 14 was related to tumor mutation burden, and the expression of MIA also affected the microsatellite instability of the tumor. This study employs multi‐omics analysis to investigate the molecular network associated with the prognosis of BC, highlighting its intricate connection with the immune microenvironment.These findings pinpoint four crucial genes in BC progression, offering targets for further research and therapy. Their connections to immune infiltration and chemotherapy sensitivity underscore complex interactions in the tumor microenvironment. [ABSTRACT FROM AUTHOR]

Peroxisome proliferator‐activated receptors (PPARs) are essential for cellular physiological processes. However, there is less research on the PPAR‐related genes in lung adenocarcinoma (LUAD). Open‐access data were get from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. All the analysis were conducted in the R software based on different R packages. In this study, we gauged the PPAR score employing a set of 72 PPAR‐associated genes and probed the biological impact of this score on lung adenocarcinoma (LUAD). Subsequently, we established a unique signature composed of eight PPAR‐related genes (ANGPTL4, ACSL3, ADIPOQ, FABP1, SLC27A1, ACOX2, PPARD and OLR1) to forecast the prognosis of LUAD. The signature's effectiveness in predicting survival was validated through the receiver operating characteristic curve in the TCGA‐LUAD cohort. As per the pathway enrichment analysis, several crucial oncogenic pathways and metabolic processes were enriched in high‐risk individuals. Further, we observed that these high‐risk patients exhibited heightened genomic instability. Additionally, compared to the low‐risk cohort, high‐risk patients demonstrated diminished immune components and function. Intriguingly, high‐risk patients exhibited a potential heightened sensitivity to immunotherapy and certain drugs, including Gefitinib, Afatinib, Erlotinib, IAP_5620, Sapitinib, LCL161, Lapatinib and AZD3759. The prognosis model based on eight PPAR‐related genes has satisfactory prognosis prediction efficiency. Meanwhile, our results can provide direction for future studies in the relevant aspects. [ABSTRACT FROM AUTHOR]

Background: Intervertebral disc degeneration (IVDD) is a common chronic disease in orthopedics, and its molecular mechanisms are still not well explained. Aim: This study's objective was to bioinformatics‐based discovery of IVDD biomarkers and immune‐inflammatory infiltrates. Materials and Methods: The IVDD illness gene collection was gathered from GeneCards, DisGeNet, and gene expression profiles were chosen from the extensive Gene Expression Omnibus database (GSE124272, GSE150408, and GSE153761). The STRING database was used to create a network of protein–protein interactions, while the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases were used for functional enrichment analysis. Using hub genes, the immune cell infiltration between IVDD patient samples and control tissues was examined. Finally, quantitative polymerase chain reaction and Western blot experiments were used to verify the expression of hub genes. Results: A total of 27 differentially expressed hub genes were identified by bioinformatics. According to GO and KEGG analyses, hub genes were prominent in immunological responses, chemokine‐mediated signaling pathways, and inflammatory responses, with the key signaling pathways engaged in cellular senescence, apoptosis, Th1 and Th2 cell differentiation, and Th17 cell differentiation. Immune cell infiltration research revealed that T cells, lymphocytes, B cells, and NK cells were decreased in IVDD patients while monocytes, neutrophils, and CD8 T cells were increased. The expression levels of the senescence hub genes SP1, VEGFA, IL‐6, and the apoptosis key gene CASP3 were considerably greater in the IVDD model group than in the control group, according to in vitro validation. Conclusion: In conclusion, the cellular senescence signaling pathway, the apoptosis signaling pathway, and associated hub genes play significant roles in the development and progression of IVDD, this finding may help direct future research on the senescence signaling route in IVDD. [ABSTRACT FROM AUTHOR]

Hypertrophic scar (HS) is a chronic inflammatory skin disorder characterized by excessive deposition of extracellular matrix, and the mechanisms underlying their formation remain poorly understood. We analysed scRNA‐seq data from samples of normal skin and HS. Using the hdWGCNA method, key gene modules of fibroblasts in HS were identified. Non‐negative matrix factorization was employed to perform subtype analysis of HS patients using these gene modules. Multiple machine learning algorithms were applied to screen and validate accurate gene signatures for identifying and predicting HS, and a convolutional neural network (CNN) based on deep learning was established and validated. Quantitative reverse transcription‐polymerase chain reaction and western blotting were performed to measure mRNA and protein expression. Immunofluorescence was used for gene localization analysis, and biological features were assessed through CCK8 and wound healing assay. Single‐cell sequencing revealed distinct subpopulations of fibroblasts in HS. HdWGCNA identified key gene characteristics of this population, and pseudotime analysis was conducted to investigate gene variation during fibroblast differentiation. By employing various machine learning algorithms, the gene range was narrowed down to three key genes. A CNN was trained using the expression of these key genes and immune cell infiltration, enabling diagnosis and prediction of HS. Functional experiments demonstrated that THBS2 is associated with fibroblast proliferation and migration in HS and affects the formation and development of HS through the TGFβ1/P‐Smad2/3 pathway. Our study identifies unique fibroblast subpopulations closely associated with HS and provides biomarkers for the diagnosis and treatment of HS. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) poses a significant health challenge. This study aims to investigate the prognostic value of a regulatory T cell (Treg)‐related gene signature in CRC. Methods: We extracted the gene expression and clinical data on CRC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The gene module related to Treg was identified by weighted gene co‐expression network analysis (WGCNA). The genes in the significant module were filtered by univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. A riskscore model was established in terms of the key Treg‐related genes. The reliability of this riskscore model was validated using the external GEO dataset. The association of riskscore with clinical features, mutation patterns and signaling pathways was explored. Results: Genes in the blue module showed the strongest association with Tregs. After a series of filtering cycles, seven Treg‐related key genes, GDE1, GSR, HSPB1, AOC2, TBX19, TAMM41 and TIGD6, were selected to construct a riskscore model. This model performed well in evaluating the patients' survival in TCGA cohort, and was further affirmed by the GSE17536 validation cohort. For precise evaluation of the patients' survival, we established a nomogram in light of riskscore and clinical factors. Patients in different risk groups had distinct clinical features, mutation patterns and signaling pathway activities. The expression of five key genes was significantly associated with Treg infiltration in the CRC samples. Conclusion: We established a useful riskscore model in light of seven Treg‐related genes. This model may contribute to the prognosis evaluation, direct tailored treatment, and hopefully improve clinical outcomes of the CRC patients. [ABSTRACT FROM AUTHOR]

Background and Aims: Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of cancer with a high incidence globally. This study aimed to investigate the role of P2RY13 in the progression of ccRCC and elucidate its mechanism of action. Methods: Gene Expression Omnibus and The Cancer Genome Atlas databases were used to extract gene expression profiles of ccRCC. These profiles were annotated and visualized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, as well as Gene Set Enrichment Analysis (GSEA). The STRING database was used to establish a protein–protein interaction network and to analyze the functional similarity. The GEPIA2 database was used to predict survival associated with hub genes. Meanwhile, the TIMER2.0 database was used to assess immune cell infiltration and its link with the hub genes. Immunohistochemistry (IHC) was used to determine the difference between ccRCC and adjacent normal tissue. Results: We identified 272 differentially expressed genes (DEGs). GO and KEGG analyses suggested that DEGs were primarily involved in lymphocyte activation, inflammatory response, immunological effector mechanism pathways. By cytohubba, the 20 highest‐scoring hub genes were screened to identify critical genes in the protein–protein interaction network linked with ccRCC. Resting dendritic cells, CD8 T cells, and activated mast cells all showed a significant positive correlation with these hub genes. Moreover, a higher immune score was associated with increased prognostic risk scores, which in turn correlated with a poorer prognosis. IHC revealed that P2RY13 was expressed at higher levels in ccRCC compared to para‐cancer tissues. Conclusion: Identifying the DEGs will aid in the understanding of the causes and molecular mechanisms involved in ccRCC. P2RY13 may play a pivotal role in the progression and prognosis of ccRCC, potentially driving carcinogenesis though immune system mechanisms. [ABSTRACT FROM AUTHOR]

Background: Metastasis poses the greatest threat to the lives of individuals with prostate cancer. Therefore, it is imperative to identify the underlying mechanism driving metastasis. Doing so would facilitate the detection of new diagnostic biomarkers and the advancement of treatment options for patients. Methods: Metastasis‐related modules were identified through weighted gene co‐expression network analysis based on microarray GSE6919. Hub genes were confirmed by quantitative real‐time PCR across different prostate cell lines and clinic samples. Pivotal genes were determined through integration of RNA and transcription factor‐target associated interactions. To predict drugs with potential to suppress tumor metastasis, we applied molecular networks using the DrugBank database. Drug repositioning analysis and confirmation of drug screen were conducted using the compound library. Confirmation of selective cytotoxicity of cupric oxide was carried out via invasion, transwell and apoptosis assays. Results: We identified five metastasis‐related modules. Of these modules, two were identified to represent core dysfunction modules in which five hub genes were determined for each module. Five of these 10 genes correlating with prostate cancer progression. Furthermore, our analysis revealed that there are 36 drugs with the potential to be active against tumor metastasis. Finally, we identified four compounds that have not previously been reported to have any association with cancer therapy. Of these, cupric oxide was determined to have the best chemotherapeutic potential in treating prostate cancer metastasis. Conclusions: By combining bioinformatics methods with compound library screening, this study proposes a valuable approach to drug discovery. Cupric oxide showed the potential in the treatment of prostate cancer metastasis and deserves further study. [ABSTRACT FROM AUTHOR]

Endometriosis (EMS) is a prevalent gynecological condition lacking reliable diagnostic biomarkers. This prospective study aimed to analyze the potential of serum heat shock transcription factor 1 (HSF1) as a diagnostic marker for EMS. Clinical features of 92 EMS patients and 52 controls were recorded, revealing significant differences in dysmenorrhea, dyspareunia, pelvic pain, nulliparity, and CA125 levels. Serum HSF1 was upregulated in EMS patients, with higher levels in American Society for Reproductive Medicine (ASRM) III/IV than ASRM I/II. Receiver operating characteristic curve analysis demonstrated good diagnostic function for serum HSF1 (AUC: 0.857, sensitivity: 91.30%, specificity: 63.46%). Serum HSF1, dysmenorrhea, dyspareunia, and nulliparity were independent risk factors for EMS, while dysmenorrhea and serum HSF1 were independent risk factors for EMS severity. Additionally, the GSE25628 dataset was downloaded from the GEO database for differential analysis of gene expression. The HSF1 downstream target genes PTGES3, HSP90AA1, and HSPB1 showed significant differential expression in EMS, suggesting their involvement in the regulatory mechanism of HSF1 in EMS. [ABSTRACT FROM AUTHOR]

Keloids are formed due to abnormal hyperplasia of the skin connective tissue. We explored the relationship between N6‐methyladenosine (m6A)‐related genes and keloids. The transcriptomic datasets (GSE44270 and GSE185309) of keloid and normal skin tissues samples were obtained from the Gene Expression Omnibus database. We constructed the m6A landscape and verified the corresponding genes using immunohistochemistry. We extracted hub genes for unsupervised clustering analysis using protein–protein interaction (PPI) network; gene ontology enrichment analysis was performed to determine the biological processes or functions affected by the differentially expressed genes (DEGs). We performed immune infiltration analysis to determine the relationship between keloids and the immune microenvironment using single‐sample gene set enrichment analysis and CIBERSORT. Differential expression of several m6A genes was observed between the two groups; insulin‐like growth factor 2 mRNA‐binding protein 3 (IGF2BP3) was significantly upregulated in keloid patients. PPI analysis elucidated six genes with significant differences between the two keloid sample groups. Enrichment analysis revealed that the DEGs were mainly enriched in cell division, proliferation, and metabolism. Moreover, significant differences in immunity‐related pathways were observed. Therefore, the results of this study will provide a reference for the elucidation of the pathogenesis and therapeutic targets of keloids. [ABSTRACT FROM AUTHOR]

Objective: This study was conducted to investigate key long noncoding RNAs (lncRNAs) involved in competitive endogenous RNA (ceRNA) network associated with laryngeal squamous cell carcinoma (LSCC). Materials and Methods: Three mRNA datasets, two miRNA datasets, and one lncRNA dataset of LSCC were downloaded from GEO database. Following the identification of differentially expressed mRNAs (DEmRNAs), (microRNAs) miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) in LSCC compared with adjacent tissues, functional enrichment of DEmRNAs was performed. Then, construction of the ceRNA (DElncRNA‐DEmiRNA‐DEmRNA) regulatory network and functional analyses of all DEmRNAs in ceRNA regulatory network were conducted. Quantitative real‐time polymerase chain reactions (qRT‐PCR) were used to detect the expression levels of selected DEmRNAs, DEmiRNAs, and DElncRNAs. Results: A total of 3449 DEmRNAs, 40 DEmiRNAs, and 100 DElncRNAs were identified in LSCC. The ceRNA networks, which contained 132 DElncRNA‐DEmiRNA pairs and 287 DEmiRNA‐DEmRNA pairs, involving 44 lncRNAs, 3 miRNAs, and 271 mRNAs, were obtained. DEmRNAs in ceRNA regulatory networks were significantly enriched in pathways in cancer, prostate cancer, and aldosterone‐regulated sodium reabsorption. Except for HCG22 and hsa‐miR‐1246, expressions of the others in the qRT‐PCR results played the same pattern with that in our integrated analysis, generally. Conclusions: We concluded that HCG22/EGOT‐hsa‐miR‐1275‐FAM107A and HCG22/EGOT‐hsa‐miR‐1246‐Glycerol‐3‐phosphate dehydrogenase 1 like interaction pairs may play a central role in LSCC. [ABSTRACT FROM AUTHOR]

Backgrounds: Erectile dysfunction (ED) is a common andrological disorder that tends to afflict diabetic patients, among others. Pharmacological therapy of diabetes mellitus-induced ED (DMED) is ineffective, as it is linked with smooth muscle cell loss in the corpus cavernosum. Ferroptosis is a recently identified kind of cell death evoked by lipid peroxidation, and it is connected with a number of diabetic complications. Objectives: To investigate the role of ferroptosis in DMED. Materials and methods: We established the rat model of DMED and conducted a combined analysis of RNA sequencing (RNA-seq) and Gene Expression Omnibus (GEO) data to identify differentially expressed genes (DEGs). Next, DMED disease targets were determined by cross-referencing DEGs and DMED-related genes in the DisGeNET,GenCLiP3, andGeneCards databases. Additionally, these targetswere analyzed using "clusterProfiler" in R utilizing Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations. Immunohistochemistry (IHC) staining of rat penile tissues was used to validate several targets. Notably, the Cell Counting Kit-8 assay, Western blotting, oxidative stress (OS) level, and iron concentrationwere tested in corpus cavernosum smooth muscle cells (CCSMCs) stimulated with high glucose (HG), and treated with Ferrostatin-1 (Fer-1). Results: Sixty-nine disease targets of DMED were identified. According to KEGG analysis, these targets were primarily enriched in the ferroptosis pathway. Additionally, IHC results revealed that the expression of GPX4, SLC7A11, and ACSL4 was deregulated in the DMED group compared to the control group. Significantly, HG decreased cell viability and increased OS and iron levels in CCSMCs, which could be reversed by Fer-1 treatment. Discussion and conclusion: Our study revealed that ferroptosis may indeed exist in DMED. GPX4, SLC7A11, and ACSL4 all have a role in controlling the viability of CCSMCs, making them potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: Head and neck squamous cell carcinoma (HNSCC) is a lifethreatening disease with poor prognosis. Pyroptosis has been recently disclosed as a programmed cell death triggered by invasive infection, involved in cancer development. However, the prognosis role of pyroptosis-related genes in HNSCC has not been discussed. Methods: The RNA sequence data of pyroptosis-related genes were obtained from The Cancer Genome Atlas (TCGA) database. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were performed to screen the HNSCC survival-related signature genes. We established a HNSCC risk model with the identified prognostic genes, then divided the HNSCC patients into low- and high-risk subgroups according to median risk score. Moreover, we utilized Gene Expression Omnibus (GEO) dataset to validate the risk model. Go and KEGG analyses were conducted to reveal the potential function of differential expression of genes that identified between low- and high-risk subgroups. ESTIMATE algorithm was performed to investigate the immune infiltration of tumors. Correlation between signature gene expression and drug- sensitivity was disclosed by Spearman's analysis. Results: We constructed a HNSCC risk model with identified seven pyroptosisrelated genes (CASP1, GSDME, IL6, NLRP1, NLRP2, NLRP6, and NOD2) as prognostic signature genes. High-risk subgroup of HNSCC patients in TCGA cohort correlated with lower survival probability than patients from low-risk subgroup (p < .001), and the result is verified with GEO dataset. In addition, 161 genes were identified differentially expressed between the low- and high-risk subgroups in the TCGA cohort, mainly related to immune response. Higher PD-L1 expression level was found in the high-risk subgroup that indicated the possible employment of immune checkpoint inhibitors. IL6 was positively correlated with WZ3105 and MPS-1-IN-1 in the cancer therapeutics response portal database. Conclusion: We built and verified a risk model for HNSCC prognosis using seven pyroptosis-related signature genes, which could predict the overall survival of HNSCC patients and facilitate treatment. [ABSTRACT FROM AUTHOR]

Summary: Hybrid geosynchronous earth orbit (GEO) and low earth orbit (LEO) satellite networks play an important role in future satellite‐assisted internet of things (S‐IoT). However, the limited satellite on‐board communication and computing resource poses a large challenge for the task offloading in the hybrid GEO‐LEO satellite networks. In this paper, the problem of task offloading is formulated as a cooperative user association and resource allocation problem. To tackle the problem, we model it as a Markov decision process and decompose it into two sub‐problems, which are sequential decisions for user association and resource allocation with fixed user association conditions. Deep reinforcement learning (DRL) is adopted to make sequential decisions to achieve long‐term benefits, and convex optimization method is utilized to obtain optimal communication and computing resource allocation. Simulation results show that the proposed method is superior to other referred schemes. [ABSTRACT FROM AUTHOR]

This research attempted to elucidate the molecular components are involved in the pathogenesis of recurrent implantation failure (RIF). We initially identified that 386 mRNAs, 144 miRNAs and 2548 circRNAs were differentially expressed (DE) in RIF and then investigated the genetic cause of the observed abnormal expression by constructing a circRNA-miRNA-mRNA network considering the competing endogenous RNA theory. We further analysed the upstream transcription factors and related kinases of DEmRNAs (DEMs) and demonstrated that SUZ12, AR, TP63, NANOG, and TCF3 were the top five TFs binding to these DEMs. Besides, protein-protein interaction analysis disclosed that ACTB, CXCL10, PTGS2, CXCL12, GNG4, AGT, CXCL11, SST, PENK, and FOXM1 were the top 10 hub genes in the acquired network. Finally, we performed the functional enrichment analysis and found that arrhythmogenic right ventricular cardiomyopathy (ARVC), hypertrophic cardiomyopathy (HCM), pathways in cancer, TNF signalling pathway and steroid hormone biosynthesis were the potentially disrupted pathways in RIF patients. Optimistically, our findings may deepen our apprehensions about the underlying molecular and biological causes of RIF and provide vital clues for future laboratory and clinical experiments that will ultimately bring a better outcome for patients with RIF. [ABSTRACT FROM AUTHOR]

Background: Peripheral blood mononuclear cells (PBMCs) play an important role in the pathogenesis of pulmonary arterial hypertension (PAH). However, the specific roles of PBMCs in the development and progression of idiopathic PAH (IPAH) have not been fully understood. Methods: Here, differentially expressed genes (DEGs) of PBMCs or lung tissues between IPAH patients and healthy controls were identified via bioinformatics analysis of Gene Expression Omnibus (GEO) datasets GSE33463 and GSE48149, respectively. Subsequently, extensive target prediction and network analysis were performed to assess protein–protein interaction (PPI) networks, Gene Ontology (GO) terms, and pathway enrichment for DEGs. Co‐expressed DEGs between PBMCs and lung tissues coupled with corresponding predicted miRNAs involved in PAH were also assessed. We identified 251 DEGs in PBMCs and 151 DEGs in lung tissue samples from IPAH. PDK4, RBPMS2, and PDE5A expression were altered in both PBMCs and lung tissues from IPAH patients compared to healthy control. Results: CXCL8, JUN, TLR8, IL1B, and TLR7 could be implicated as the hub genes in PBMCs, whereas ENO1, STAT1, CXCL10, GPI, and IRF1 in lung tissues. Finally, co‐expressed DEGs of PDK4, RBPMS2, and PDE5A coupled with corresponding predicted miRNAs, especially miR‐103a‐3p, miR‐185‐5p, and miR‐515‐5p, are significantly associated with IPAH. Conclusion: Our findings collectively suggest that the expression levels of PDK4, RBPMS2, and PDE5A in PBMCs are associated with the expression of these genes in lung tissues. Thus, these molecules may serve as potential circulating biomarkers and/or possible therapeutic targets for IPAH. [ABSTRACT FROM AUTHOR]

Background: Oral squamous cell carcinoma (OSCC) is the most common tumor in the oral cavity and maxillofacial region. Increasing evidence suggests that aerobic glycolysis plays an important role in the occurrence, development, and prognosis of OSCC. Therefore, the identification of biomarkers related to glycolysis in OSCC represents considerable potential for improving its treatment. Methods: In the present study, a single‐sample gene‐set enrichment analysis (ssGSEA) algorithm with weighted gene co‐expression network analysis (WGCNA) were used to quantify the degree of glycolysis and identify key modules with the greatest correlation with glycolysis. Results: Glycolytic scores significantly correlated with prognosis. In the key module 5 HUB genes were finally selected, which displayed a robust predictive effect. The expressions of key genes were associated with glycolysis. Conclusions: The research comprehensively analyzed the glycolysis of OSCC and identified several biomarkers related to glycolysis. These biomarkers may represent potential therapeutic targets for future OSCC therapy. [ABSTRACT FROM AUTHOR]

Background: Ovarian cancer (OC) is the most lethal gynecological malignancy. The objective of this study was to establish and validate an individual aging-related gene signature and a clinical nomogram that can powerfully predict independently the overall survival rate of patients with ovarian cancer. Methods: Data on transcriptomic profile and relevant clinical information were retrieved from The Cancer Genome Atlas (TCGA) database as a training group, and the same data from three public Gene Expression Omnibus (GEO) databases as validation groups. Univariate Cox regression analysis, lasso regression analysis, and multiple multivariate Cox analysis were analyzed sequentially to select the genes to be included in the aging-associated signature. A risk scoring model was established and verified, the predictive value of the model was evaluated, and a clinical nomogram was established. Results: We found eight genes that were most relevant to prognosis and constructed an eight-mRNA signature. Based on the model, each OC patient's risk score was able to be calculated and patients were split into groups of low and high risks with a distinct outcome. Survival analysis confirmed that the outcome of patients in the high-risk group was dramatically shorter than that of those in the low-risk group, and the eight-mRNA signature can be considered as a powerful and independent predictor that could predict the outcome of OC patient. Additionally, the risk score and age can be used to construct a clinical nomogram as a simpler tool for predicting prognosis. We also explored the association between the risk score and immunity and drug sensitivity. Conclusion: This study suggested that the aging-related gene signature could be used as an intervention point and latent prognostic predictor in OC, which may provide new perceptions for postoperative treatment strategies. [ABSTRACT FROM AUTHOR]

Hypoxanthine phosphoribosyltransferase (HPRT1), as a salvage pathway enzyme, plays a crucial role in modulating the cell cycle and has been reported to be overexpressed in multiple cancers. Nevertheless, the relationship between the HPRT1 gene and head and neck squamous cell carcinomas (HNSCCs) has not been investigated so far. In this study, we first evaluated the expression and clinical value of HPRT1 mRNA and protein in tumor and healthy control tissues. Then, we examined mutations of the HPRT1 gene and their association with survival outcomes of patients with HNSCC. We also performed functional analyses of HPRT1 coexpressed genes and examined the association between HPRT1 expression and drug sensitivity. Both HPRT1 mRNA and protein were significantly higher in HNSCC compared with normal tissues, and up‐regulation of HPRT1 was also correlated with age, sex, pathological stage and histological grades of patients with HNSCC. Moreover, HPRT1 and its associated genes were observed to be enriched for several cancer‐related pathways, including DNA replication and cell cycle. Finally, patients exhibiting overexpression of the HPRT1 gene may be resistant to abiraterone and sensitive to several drugs, including tozasertib and teniposide. This study demonstrated that the elevated expression of HPRT1 gene is correlated with the progression of HNSCC; thus, this gene may serve as a useful indicator for the early detection, risk stratification and targeted therapy of patients with HNSCC. [ABSTRACT FROM AUTHOR]

Colon adenocarcinoma (COAD) is a common malignant tumor of the digestive tract that threatens human health seriously. Thus, it is urgent to explore biomarkers that can be used to evaluate a patient's survival prognosis overall as a supplementary treatment. RNA‐seq expression profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus, and Lasso and multivariate Cox regression analyses were used for developing the prognostic model. Finally, a nomogram comprising the prognostic model was established to evaluate survival overall. A risk model comprised of a total of 12 immune‐related gene pairs was constructed. Further analysis revealed the model's independent prognostic ability in relation to other clinical characteristics. This model's nomogram could help clinicians choose personalized treatment for COAD patients. This model has significant potential to complement COAD's clinical identifying characteristics, and also provide new insights into the identification of colon cancer patients with a high risk of death. [ABSTRACT FROM AUTHOR]

Prostate cancer, the second most common cancer found in male over the world, was estimated to have 191,930 new cases and 33,330 deaths in 2020 in the United States. Prostate cancer is very common in male, about 12.1% of men will acquire this cancer in their lifetime, and a higher risk was reported in older men and African American men. Gene deregulations have been found to be extensively associated with cancer development. To gain further insight into how gene deregulation affects prostate cancer, we analysed three gene profiling datasets of prostate cancer from Gene Expression Omnibus (GEO) applying bioinformatic tools in our study. Firstly, we identified common differently expressed genes (DEGs) shared by the three gene profiling datasets, constructed protein–protein interaction network and determined top 10 hub genes. Further DEGs validation in TCGA and Human Protein Atlas Database identified AMACR as the core gene. We then analysed the role of AMACR in prostate cancer cell lines and found that AMACR‐knockdown resulted in the decreased cell proliferation and increased apoptosis. These results suggest an oncogenic role of AMACR in prostate cancer, and it could be a potential biomarker for the diagnosis of prostate cancer. [ABSTRACT FROM AUTHOR]

Summary: The first true multitechnology communication system is 5G, which is expected to have a large impact on society and industry. The European Commission funded H2020 5G‐PPP Phase 2 project SaT5G addressed the plug‐and‐play integration of satellite communication into 5G. One of the SaT5G use cases corresponds to the delivery of 5G connectivity services to moving platforms such as aircraft via GEO/MEO satellite backhauling. With focus on this use case, this paper elaborates on the practical implementation and measurement results obtained within the 5G Aero testbed developed as part of the SaT5G project. The 5G Aero testbed activities focus on the next generation of connectivity and content distribution services to airplanes through satellite and terrestrial integration in 5G at the user, control and management planes. Software‐defined networking (SDN) and network functions virtualisation (NFV) are key enablers to develop a powerful end‐to‐end testbed that can accelerate the adoption of multi‐access edge computing (MEC) for the next‐generation in‐flight entertainment and connectivity (IFEC) services, which use geostationary (GEO) and medium Earth orbit (MEO) satellite backhauling technologies. Hence, measurement results obtained from both over‐the‐air demonstration over the O3b MEO satellite constellation and in‐lab validation over an emulated GEO satellite link are presented, towards the next‐generation 5G‐enabled IFEC services. [ABSTRACT FROM AUTHOR]

A primary factor in tumor morbidity and mortality, lung adenocarcinoma (LUAD) is known to be a major subtype of lung cancer, having the lowest survival rate among all other cancers. Using The Cancer Genome Atlas (TCGA) database the relationship between the immune infiltrate and the NUP62CL was explored and the value of the NUP62CL expression in the prognosis and diagnosis LUAD was examined. Using the logistic regression and the Wilcoxon signed‐rank test the relationship between the NUP62CL and the clinico‐pathological features was analyzed. There was a significant correlation between the clinical stage (p = 0.005), the N (p = 0.004), and the decreased expression of NUP62CL. The prognosis of LUAD with high NUP62CL expression was revealed to be worse than that with low NUP62CL expression (p < 0.001) by the Kaplan‐Meier survival analysis. The potentiality of NUP62CL to be a significant factor of prognosis for LUAD was indicated by the analyses of the multivariate and the univariate Cox regression models. In LUAD, the crucial role of recombination and maintenance of telomere as a significant pathway for NUP62CL was suggested by the Gene Set Enrichment Analysis (GSEA). To analyze the correlation between the genes and the tumor infiltrating immune cells the CIBERSORT was used. Moreover the positive correlation with the NUP62CL expression in LUAD of the infiltration level of the tumor infiltrating B lymphocytes and memory CD4+ T cells was exhibited by CIBERSORT. Therefore, NUP62CL may be a new valuable prognostic indicator for LUAD. [ABSTRACT FROM AUTHOR]

RNA binding proteins (RBPs) are increasingly appreciated as being essential for normal hematopoiesis and have a critical role in the progression of hematological malignancies. However, their functional consequences and clinical significance in diffuse large B‐cell lymphoma (DLBCL) remain unknown. Here, we conducted a systematic analysis to identify RBP‐related genes affecting DLBCL prognosis based on the Gene Expression Omnibus database. By univariate and multivariate Cox proportional hazards regression (CPHR) methods, six RBPs‐related genes (CMSS1, MAEL, THOC5, PSIP1, SNIP1, and ZCCHC7) were identified closely related to the overall survival (OS) of DLBCL patients. The RBPs signature could efficiently distinguished low‐risk from high‐risk patients and could serve as an independent and reliable factor for predicting OS. Moreover, Gene Set Enrichment Analysis revealed 17 significantly enriched pathways between high‐ versus low‐risk group, including the regulation of autophagy, chronic myeloid leukemia, NOTCH signaling pathway, and B cell receptor signaling pathway. Then we developed an RBP‐based nomogram combining other clinical risk factors. The receiver operating characteristic curve analysis demonstrated high prognostic predictive efficiency of this model with the area under the curve values were 0.820 and 0.780, respectively, in the primary set and entire set. In summary, our RBP‐based model could be a novel prognostic predictor and had the potential for developing treatment targets for DLBCL. [ABSTRACT FROM AUTHOR]

Aim: Polycystic ovary syndrome (PCOS) is a complicated endocrine and metabolic abnormality diseases common in women of child‐bearing age. This study aims to screen out critical miRNAs and mRNAs associated with PCOS, which may be conducive to offer novel insights and treatment for the diseases. Methods: Three mRNA datasets and one miRNA dataset derived from granulosa cells of patients with PCOS and normal controls were downloaded to obtain the differentially expressed mRNAs (DEmRNAs) and miRNAs (DEmiRNAs). Then, DEmiRNA‐target DEmRNAs analysis and functional annotation of DEmiRNA‐target DEmRNAs were performed. Quantitative real time polymerase chain reaction (qRT‐PCR) validation of the expression of the selected DEmRNAs and DEmiRNAs were performed. Results: A total of 1643 DEmRNAs, 88 DEmiRNAs, 2406 DEmiRNA (down)‐DEmRNA (up), and 2179 DEmiRNA (up)‐DEmRNA (down) pairs were obtained. The functional annotation of DEmiRNA‐target DEmRNAs revealed that C‐type lectin receptor signaling pathway, Steroid biosynthesis and Galactose metabolism were significantly enriched KEGG pathways. Conclusion: These findings may provide make contribution to understanding PCOS pathogenesis, diagnosis, or treatment. [ABSTRACT FROM AUTHOR]

To seek novel prognostic biomarkers for testicular germ cell tumour (TGCT) and investigate the tumour immune microenvironment, we identified critical differentially expressed genes (DEGs) by overlapping GSE1818 dataset from Gene Expression Omnibus (GEO). Protein–protein interaction (PPI) network was used to investigate key modules and hub genes. Functional enrichment analysis was performed to investigate the underlying molecular functions of the DEGs in TGCT development and progression. The following survival analysis based on The Cancer Genome Atlas (TCGA) TGCT dataset indicated that AKAP4, SPA17 and TNP1 are correlated with TGCT prognosis. Immunohistochemistry and quantitative real‐time polymerase chain reaction verified the down‐regulation of the 3 hub genes in TGCT. Gene set enrichment analysis was conducted to further explore the role of the 3 hub genes in TGCT respectively. In addition, TGCT samples had high infiltration of CD8+ T cells, M0 and M1 macrophage cells, and resting myeloid dendritic cells in immune microenvironment. We also constructed the microRNA‐gene regulatory networks to identify the key upstream microRNAs in TGCT. In conclusion, our findings indicated that AKAP4, SPA17 and TNP1 are promising biomarkers of TGCT. AKAP4 and TNP1 might regulate immune cells infiltration in immune microenvironment. [ABSTRACT FROM AUTHOR]

The immunophenotype of bladder cancer plays a pivotal role in the prognosis of cancer, but the effect of different epigenetic factors on different immunophenotypes in bladder tumours remains unclear. This study used multi‐omics data analysis to provide molecular basis support for different immune phenotypes. Unsupervised cluster analysis revealed distinct subclusters with higher (subcluster B2) or lower cytotoxic immune phenotypes (subcluster A1) related to PD‐L1 and IFNG expression. Mutational landscape analyses showed that the mutation level of TP53 in subcluster B1 was highest than other subclusters, and subcluster B1 had a lower frequency of concurrent mutation than subcluster A2. A total of 2364 differentially expressed genes were identified between subclusters A2 and B1, and the main functions of the up‐regulated genes in subcluster B1 were enriched in the activation of T cells and other related pathways. We found that STAT1 was a key gene in a gene regulatory network related to immune phenotypes in bladder cancer. Finally, we constructed a prognostic prediction model by LASSO Cox regression which could distinguish high‐risk and low‐risk cases significantly. In conclusion, the present study addressed a field synopsis between genetic and epigenetic events in immune phenotypes of bladder cancer. [ABSTRACT FROM AUTHOR]

Aims: To investigate novel biomarker for diagnosis of cervical cancer, we analyzed the datasets in Gene Expression Omnibus (GEO) and confirmed the candidate biomarker in patient sample. Materials and methods: We collected major datasets of cervical cancer in GEO, and analyzed the differential expression of normal and cancer samples online with GEO2R and tested the differences, then focus on the GSE63514 to screen the target genes in different histological grades by using the R‐Bioconductor package and R‐heatmap. Then human specimens from the cervix in different histological grades were used to confirm the top 8 genes expression by immunohistochemical staining using Ki67 as a standard control. Results: We identified genes differentially expressed in normal and cervical cancer, 274 upregulated genes and 206 downregulated genes. After intersection with GSE63514, we found the obvious tendency in different histological grades. Then we screened the top 24 genes, and confirmed the top 8 genes in human cervix tissues. Immunohistochemical (IHC) results confirmed that keratin 17 (KRT17) was not expressed in normal cervical tissues and was over‐expressed in cervical cancer. Cysteine‐rich secretory protein‐2 (CRISP2) was less expressed in high‐grade squamous intraepithelial lesions (HSILs) than in other histological grades. Conclusion: For the good repeatability and consistency of KRT17 and CRISP2, they may be good candidate biomarkers. Combined analysis of KRT17, CRISP2 expression at both genetic and protein levels can determine different histological grades of cervical squamous cell carcinoma. Such combined analysis is capable of improving diagnostic accuracy of cervical cancer. [ABSTRACT FROM AUTHOR]

Background: This study set out to determine key lncRNAs correlated with sepsis via constructing competing endogenous RNA (ceRNA) network. Methods: Three septic patients and three healthy controls were recruited to obtain lncRNA profiles in this current study. Combined with the mRNA profiles by RNA‐sequencing, an integrated analysis of mRNA expression profiles downloaded from GEO was performed to obtain the differentially expressed mRNAs (DEmRNAs). Based on differentially expressed lncRNAs (DElncRNAs) and DEmRNAs acquired in this present study and differentially expressed miRNAs (DEmiRNAs) acquired in previous study, a ceRNA network was constructed. Furthermore, LINC00963 was validated in THP‐1 cells by performing loss of function assays. Results: In this analysis, a total of 290 DEmRNAs and 46 DElncRNAs were detected in sepsis. Parkinson's disease, Oxidative phosphorylation and Cardiac muscle contraction were significantly enriched KEGG pathways in sepsis. XPO1, CUL4A, and NEDD8 were three hub proteins of sepsis‐specific PPI network. A ceRNA network, which contained 16 DElncRNA‐DEmiRNA pairs and 82 DEmiRNA‐DEmRNA pairs, involving 5 lncRNAs, 10 miRNAs, and 60 mRNAs, was obtained. The function experiments indicated that knockdown of LINC00963 could promote cell proliferation, reduce cytokine expression, and suppress inflammasome activation and phagocytosis in LPS‐induced THP‐1 cells. Conclusion: This study determined key lncRNAs involved in sepsis, which may contribute to the development for novel treatment strategy of sepsis. [ABSTRACT FROM AUTHOR]

Background: The tumor microenvironment (TME) plays a critical role in tumorigenesis, development, and therapeutic efficacy. Major advances have been achieved in the treatment of various cancers through immunotherapy. Nevertheless, only a minority of patients have positive responses to immunotherapy, which is partly due to conditions of the immunosuppressive microenvironment. Therefore, it is essential to identify prognostic biomarkers that reflect heterogeneous landscapes of the TME. Methods and materials: Based upon the ESTIMATE algorithm, we evaluated the infiltrating levels of immune and stromal components derived from patients afflicted by various types of cancer from The Cancer Genome Atlas database (TCGA). According to respective patient immune and stromal scores, we categorized cases into high‐ and low‐scoring subgroups for each cancer type to explore associations between TME and patient prognosis. Gene Set Enrichment Analyses (GSEA) were conducted and genes enriched in IFNγ response signaling pathway were selected to facilitate establishment of a risk model for predicting overall survival (OS). Furthermore, we investigated the associations between the prognostic signature and tumor immune infiltration landscape by using CIBERSORT algorithm and TIMER database. Results: Among the cancers assessed, the immune scores for skin cutaneous melanoma (SKCM) were the most significantly correlated with patients' survival time (P <.0001). We identified and validated a five‐IFNγ response‐related gene signature (UBE2L6, PARP14, IFIH1, IRF2, and GBP4), which was closely correlated with the prognosis for SKCM afflicted patients. Multivariate Cox regression analysis indicated that this risk model was an independent prognostic factor for SKCM. Tumor‐infiltrating lymphocytes and specific immune checkpoint molecules had notably differential levels of expression in high‐ compared to low‐risk samples. Conclusion: In this study, we established a novel five‐IFNγ response‐related gene signature that provided a better and increasingly comprehensive understanding of tumor immune landscape, and which demonstrated good performance in predicting outcomes for patients afflicted by SKCM. [ABSTRACT FROM AUTHOR]

Background: Coronary atherosclerotic heart disease (CHD) is the most common cardiovascular disease and has become a leading cause of death globally. Various molecular typing methods are available for the diagnosis and treatment of tumors. However, molecular typing results are not routinely used for CHD. Methods and Results: Aiming to uncover the underlying molecular features of different types of CHD, we screened the differentially expressed genes (DEGs) associated with CHD based on the Gene Expression Omnibus (GEO) data and expanded those with the NCBI‐gene and OMIM databases to finally obtain 2021 DEGs. The weighted gene co‐expression analysis (WGCNA) was performed on the candidate genes, and six distinctive WGCNA modules were identified, two of which were associated with CHD. Moreover, DEGs were mined as key genes for co‐expression based on the module network relationship. Furthermore, the differentially expressed miRNAs in CHD and interactions in the database were mined in the GEO data set to build a multifactor regulatory network of key genes for co‐expression. Based on the network, the CHD samples were further classified into five clusters and we defined FTH1, HCAR3, RGS2, S100A9, and TYROBP as the top genes of the five subgroups. Finally, the mRNA levels of FTH1, S100A9, and TYROBP were found to be significantly increased, while the expression of HCAR3 was decreased in the blood of CHD patients. We did not detect measurable levels of RGS2. Conclusion: The screened core clusters of genes may be a target for the diagnosis and treatment of CHD as a molecular typing module. [ABSTRACT FROM AUTHOR]

Abnormal gene expression is an established cause of gastric cancer (GC) initiation and progression. In this study, we aimed to identify several key genes that could be used to effectively predict progression and prognosis in patients with GC. The Cancer Genome Atlas and the Gene Expression Omnibus database were used to identify candidate genes. Fourteen genes were found to associate highly with progress, metastasis, and survival of GC. Five of these genes were overexpressed in tumor tissue compared to adjacent normal tissue. This was confirmed by reverse transcription‐polymerase chain reaction and western blotting for myosin‐Va (MYO5A), phospholipid transfer protein (PLTP), and tripeptidyl peptidase 1 (TPP1), while the CCK8 assay was used to show that these three genes promote GC cell proliferation. In summary, we demonstrate that MYO5A, PLTP, and TPP1 expression may be suitable markers for the progression and prognosis of GC. [ABSTRACT FROM AUTHOR]

Accurate prediction of malignancies is important in choosing therapeutic strategies. Although there are many genetic and cytogenetic prognostic factors for acute myeloid leukemia (AML), prognosis is difficult to predict because of the heterogeneity of AML. Prognostic factors, including messenger RNA (mRNA) expression, have been determined for other malignancies, but not for AML. A total of 402 patients from The Cancer Genome Atlas, GSE12417 (GPL96, 97), and GSE12417 (GPL570) were included in this study. In Kaplan-Meier curve analyses, high expression of family with sequence similarity 213 member A (FAM213A), which activates antioxidant proteins, was associated with worse prognosis of AML. Similar to the results of the survival curve, C-indices and area under the curve values were high. Current prognostic factors of AML, unlike those of other cancers, do not take mRNA expression into consideration. Thus, the development of mRNA-based prognostic factors would be beneficial for accurate prediction of the survival of AML patients. Additionally, in vivo validation using zebrafish revealed that fam213a is important for myelopoiesis at the developmental stage and is a negative regulator of the p53 tumor suppressor gene. The findings implicate fam213a as a novel prognostic factor for AML patients. [ABSTRACT FROM AUTHOR]

The relationship between age and breast cancer is ambiguous. Here, we analyzed the differential expression pattern of long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) in different age groups to provide an effective association between age and breast cancer risk at the molecular level. We integrated the microarray information from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data sets. The patients were divided into young (< 50 years) and old (≥ 50 years) age groups and evaluated by differential gene expression, weighted gene correlation network analysis (WGCNA), functional enrichment analyses, and coexpression analysis. To determine their potential clinical significance, univariate Cox regression analysis and survival assessment were conducted. We identified two lncRNAs (AL139280.1 and AP000851.1) and three mRNAs (MT1M, HBB, and TFPI2) as the risk markers, and Gene set enrichment analysis (GSEA) focusing on a single gene revealed that "pyrimidine metabolism," "cell cycle," and "P53 signaling pathway" were coenriched. These data demonstrated that age may be a risk factor for breast carcinogenesis and prognosis and provide an in‐depth molecular characterization based on the expression patterns of lncRNAs and mRNAs. [ABSTRACT FROM AUTHOR]

Summary: From a geostationary Earth orbit (GEO) satellite's perspective, a low Earth orbit (LEO) satellite is visible on more than half of its orbit. Albeit the free‐space loss of an inter‐satellite link is much higher than the one of a direct ground link, considerable data rates and download volumes can be achieved. In this paper, we describe the system architecture of an integrated approach for a data relay satellite system and the development of LEO satellite and ground station modems. The approach allows serving several small and inexpensive LEO satellites at the same time both with low rate telemetry/telecommand links and with high rate download of sensor data. [ABSTRACT FROM AUTHOR]

As a major disease that threatens the health of women worldwide, breast cancer (BC) lacks effective molecular markers in the clinic at the same time. We aim at finding a new biomarker of BC. In our study, through the Gene Expression Omnibus database chip, a total of 1393 pairs of microRNA‐messenger RNA (miRNA‐mRNA) networks and 35754 pairs of long noncoding RNA‐miRNA networks were obtained. We found out that NEAT1/miR‐21/RRM2 axis may play a role in BC diagnosis and prognosis. The real‐time quantitative reverse transcription‐polymerase chain reaction test was used to analyze the mRNA level of NEAT1, miR‐21, and RRM2. Western blot was used to detect the protein level of RRM2. Through the 5‐ethynyl‐2′‐deoxyuridine assay, the proliferation of MDA‐MB‐231 cells was detected. Through wound healing and transwell assay, the migration of MDA‐MB‐231 cells was detected. Altogether, our data indicated that NEAT1, miR‐21, and RRM2 were upregulated in several BC cell lines. Overexpressed of miR‐21 in MDA‐MB‐231 cells promote proliferation and migration. Besides, our results demonstrated that overexpressed of miR‐21 upregulated the level of RRM2. Accordingly, miR‐21/RRM2 might be a new diagnosis and treatment target of BC. [ABSTRACT FROM AUTHOR]

Background: The role of meteorin (METRN) in colorectal cancer has not been reported previously. We aimed to explore the relationship between METRN and colorectal cancer (CRC) prognosis. Methods: Data were retrieved from the Gene Expression Omnibus database. Gene expression values were log2 transformed and normalized by quantile normalization. Missing values were imputed with the R impute package. Differentially expressed genes were analyzed using the R limma package. METRN expression was compared between normal and CRC tissues and among different stages and subtypes of CRC. We assessed the relationship between METRN and KRAS/BRAF mutations in CRC. Five‐year overall (OS), disease‐free (DFS), and disease‐specific survival (DSS) rates were determined by Kaplan–Meier analysis and analyzed by log‐rank test. Results: METRN was expressed at a higher level in CRC (p =.0011) than in normal tissues, especially in advanced stages (p =.0343). METRN expression levels were higher in the MSI (dMMR) subtype (p <.001) and usually with BRAF mutations (p <.0001). METRN overexpression was associated with poor prognosis and low OS (p =.01014), DFS (p =.0146), and DSS (p <.0001) rates. Conclusion: METRN overexpression is a predictive factor for poor prognosis in patients with CRC. [ABSTRACT FROM AUTHOR]

Traveling ionospheric disturbances (TIDs) are the manifestations of atmospheric gravity waves in the ionosphere. These disturbances have practical importance because they affect satellite navigation technologies such as Global Navigational Satellite System (GNSS), causing degradation in precise positioning applications. They also have scientific significance as their generation mechanisms and propagation are not fully understood. While there are specific instruments that can measure TIDs in certain locations, there is a need for wide‐area observations across extended geographical regions to continuously monitor their onset and spatial and temporal characteristics. This paper evaluates the use of observations from ground‐based geodetic GNSS receivers to image TIDs using ionospheric tomography and data assimilation. Certain GNSS receivers also monitor signals from geostationary (GEO) satellites, which provide a unique perspective on the TID. The advantage of using the GEO data is investigated. A computerized simulation of GNSS observations is used for evaluation of the Multi‐Instrument Data Analysis System (MIDAS) with GEO and regular GNSS geometry. The simulated observations are generated by integrating the electron density through a modeled TID‐perturbed dynamic ionosphere between actual receiver and satellite positions. The output 3‐D electron density image series generated from the synthetic data by the MIDAS ionospheric tomography and data assimilation algorithm are compared with the input model ionosphere. Results show that GEO geometry improves the reconstruction of medium‐scale TIDs (MSTIDs) and smaller LSTIDs in cases where the movement of regular GNSS satellites in Medium Earth orbit (MEO) may otherwise introduce distortions to the observations. Key Points: Ionospheric tomography with GEO geometry is tested by simulationUsing GEO geometry improves tomographic imaging of MSTIDsRegular GNSS‐MEO geometry or a combination of GEO and GNSS‐MEO geometry is preferred for LSTIDs [ABSTRACT FROM AUTHOR]

While hundreds of consistently altered metabolic genes had been identified in hepatocellular carcinoma (HCC), the prognostic role of them remains to be further elucidated. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas—Liver Hepatocellular Carcinoma and GSE14520 data set from the Gene Expression Omnibus database. Univariate Cox regression analysis and lasso Cox regression model established a novel four‐gene metabolic signature (including acetyl‐CoA acetyltransferase 1, glutamic‐oxaloacetic transaminase 2, phosphatidylserine synthase 2, and uridine‐cytidine kinase 2) for HCC prognosis prediction. Patients in the high‐risk group shown significantly poorer survival than patients in the low‐risk group. The signature was significantly correlated with other negative prognostic factors such as higher α‐fetoprotein. The signature was found to be an independent prognostic factor for HCC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. Furthermore, gene set enrichment analyses revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four‐gene metabolic signature for HCC prognosis prediction. The signature might reflect the dysregulated metabolic microenvironment and provided potential biomarkers for metabolic therapy and treatment response prediction in HCC. [ABSTRACT FROM AUTHOR]

Cutaneous malignant melanoma (hereafter called melanoma) is one of the most aggressive cancers with increasing incidence and mortality rates worldwide. In this study, we performed a systematic investigation of the tumor microenvironmental and genetic factors associated with melanoma to identify prognostic biomarkers for melanoma. We calculated the immune and stromal scores of melanoma patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and found that they were closely associated with patients' prognosis. Then the differentially expressed genes were obtained based on the immune and stromal scores, and prognostic immune‐related genes further identified. Functional analysis and the protein–protein interaction network further revealed that these genes enriched in many immune‐related biological processes. In addition, the abundance of six infiltrating immune cells was analyzed using prognostic immune‐related genes by TIMER algorithm. The unsupervised clustering analysis using immune‐cell proportions revealed eight clusters with distinct survival patterns, suggesting that dendritic cells were most abundant in the microenvironment and CD8+ T cells and neutrophils were significantly related to patients' prognosis. Finally, we validated these genes in three independent cohorts from the Gene Expression Omnibus database. In conclusion, this study comprehensively analyzed the tumor microenvironment and identified prognostic immune‐related biomarkers for melanoma. [ABSTRACT FROM AUTHOR]

Background and Aim: Hepatocellular carcinoma (HCC) is the most common types of hepatic malignancies. This study aimed to better understand the pathogenesis of HCC and may help facilitate the improvement of the diagnostic of HCC. Methods: The mRNA and miRNA expression profiles of HCC, which was retrieved from The Cancer Genome Atlas database, and the circRNA expression profiles of HCC, which was retrieved from Gene Expression Omnibus database, were included in this study to perform an integrated analysis. The differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed circRNAs (DEcircRNAs) were identified, and competing endogenous RNA (ceRNA) (DEcircRNA–DEmiRNA–DEmRNA) regulatory network was conducted. Functional annotation of host gene of DEcircRNAs and DEmRNAs in ceRNA regulatory network was performed. Quantitative real‐time polymerase chain reaction validation of the expression of the selected DEmRNAs, DEmiRNAs, and DEcircRNAs was performed. Results: A total of 2982 DEmRNAs, 144 DEmiRNAs, and 264 DEcircRNAs were obtained. The ceRNA network contained 61 circRNA–miRNA pairs and 1149 miRNA–mRNA pairs, including 48 circRNAs, 30 miRNAs, and 1149 mRNAs. Functional annotation of DEmRNAs in ceRNA regulatory network revealed that these DEmRNAs were significantly enriched in tryptophan metabolism, fatty acid metabolism, and pathways in cancer. Except for ARNT2 and hsa‐miR‐214‐3p, expression of the others in the quantitative real‐time polymerase chain reaction results was consistent with that in our integrated analysis, generally. Conclusion: We speculate that hsa_circRNA_104268/hsa‐miR‐214‐3p/E2F2, hsa_circRNA_104168/hsa‐miR‐139‐5p/HRAS, and hsa_circRNA_104769/hsa‐miR‐93‐5p/JUN interaction pairs may play a vital role in HCC. This study expected to provide a novel insight into the pathogenesis and therapy of HCC from the circRNA–miRNA–mRNA network view. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) remains an important malignancy worldwide with poor prognosis. Long noncoding RNAs (lncRNAs) can markedly affect cancer progression. Moreover, lncRNAs have been proposed as diagnostic or prognostic biomarkers of GC. Therefore, the current study aimed to explore lncRNA‐based prognostic biomarkers for GC. LncRNA expression profiles from the Gene Expression Omnibus (GEO) database were first downloaded. After re‐annotation of lncRNAs, a univariate Cox analysis identified 177 prognostic lncRNA probes in the training set GSE62254 (n = 225). Multivariate Cox analysis of each lncRNA with clinical characteristics as covariates identified a total of 46 prognostic lncRNA probes. Robust likelihood‐based survival and least absolute shrinkage and selection operator (LASSO) models were used to establish a 6‐lncRNA signature with prognostic value. Receiver operating characteristic (ROC) curve analyses were employed to compare survival prediction in terms of specificity and sensitivity. Patients with high‐risk scores exhibited a significantly worse overall survival (OS) than patients with low‐risk scores (log‐rank test P‐value <.0001), and the area under the ROC curve (AUC) for 5‐year survival was 0.77. A nomogram and forest plot were constructed to compare the clinical characteristics and risk scores by a multivariable Cox regression analysis, which suggested that the 6‐lncRNA signature can independently make the prognosis evaluation of patients. Single‐sample GSEA (ssGSEA) was used to determine the relationships between the 6‐lncRNA signature and biological functions. The internal validation set GSE62254 (n = 75) and the external validation set GSE57303 (n = 70) were successfully used to validate the robustness of our 6‐lncRNA signature. In conclusion, based on the above results, the 6‐lncRNA signature can effectively make the prognosis evaluation of GC patients. [ABSTRACT FROM AUTHOR]

The transition from non–muscle‐invasive bladder cancer (NMIBC) to muscle‐invasive bladder cancer (MIBC) is detrimental to bladder cancer (BLCA) patients. Here, we aimed to study the underlying mechanism of the subtype transition. Gene set variation analysis (GSVA) revealed the epithelial‐mesenchymal transition (EMT) signalling pathway with the most positive correlation in this transition. Then, we built a LASSO Cox regression model of an EMT‐related gene signature in BLCA. The patients with high risk scores had significantly worse overall survival (OS) and disease‐free survival (DFS) than those with low risk scores. The EMT‐related gene signature also performed favourably in the accuracy of prognosis and in the subtype survival analysis. Univariate and multivariate Cox regression analyses demonstrated that the EMT‐related gene signature, pathological N stage and age were independent prognostic factors for predicting survival in BLCA patients. Furthermore, the predictive nomogram model was able to effectively predict the outcome of BLCA patients by appropriately stratifying the risk score. In conclusion, we developed a novel EMT‐related gene signature that has tumour‐promoting effects, acts as a negative independent prognostic factor and might facilitate personalized counselling and treatment in BLCA. [ABSTRACT FROM AUTHOR]

Thyroid cancer is a common endocrine malignancy with a rapidly increasing incidence worldwide. Although its mortality is steady or declining because of earlier diagnoses, its survival rate varies because of different tumour types. Thus, the aim of this study was to identify key biomarkers and novel therapeutic targets in thyroid cancer. The expression profiles of GSE3467, GSE5364, GSE29265 and GSE53157 were downloaded from the Gene Expression Omnibus database, which included a total of 97 thyroid cancer and 48 normal samples. After screening significant differentially expressed genes (DEGs) in each data set, we used the robust rank aggregation method to identify 358 robust DEGs, including 135 upregulated and 224 downregulated genes, in four datasets. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses of DEGs were performed by DAVID and the KOBAS online database, respectively. The results showed that these DEGs were significantly enriched in various cancer‐related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network, and modules analysis was performed. Finally, we filtered out five hub genes, including LPAR5, NMU, FN1, NPY1R, and CXCL12, from the whole network. Expression validation and survival analysis of these hub genes based on the The Cancer Genome Atlas database suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for thyroid cancer, which will be useful for further clinical applications in thyroid cancer diagnosis, prognosis and targeted therapy. [ABSTRACT FROM AUTHOR]

Cervical squamous cell carcinoma (CSCC) accounts for a significant proportion of cervical cancer; thus, there is a need for novel and noninvasive diagnostic biomarkers for this malignancy. In this study, we performed integrated analysis of a dataset from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRNAs) between CSCC, cervical intraepithelial neoplasia (CIN) and healthy control subjects. We further established protein‐protein interaction and DEmiRNA‐target gene interaction networks, and performed functional annotation of the target genes of DEmiRNAs. In total, we identified 1375 DEGs and 19 DEmiRNAs in CIN versus normal control, and 2235 DEGs and 33 DEmiRNAs in CSCC versus CIN by integrated analysis. Our protein‐protein interaction network indicates that the common DEGs, Cyclin B/cyclin‐dependent kinase 1 (CDK1), CCND1, ESR1 and Aurora kinase A (AURKA), are the top four hub genes. P53 and prostate cancer were identified as significantly enriched signaling pathways of common DEGs and DEmiRNA targets, respectively. We validated that expression levels of three DEGs (TYMS, SASH1 and CDK1) and one DEmiRNA of hsa‐miR‐99a were altered in blood samples of patients with CSCC. In conclusion, a total of four DEGs (TYMS, SASH1, CDK1 and AURKA) and two DEmiRNAs (hsa‐miR‐21 and hsa‐miR‐99a) may be involved in the pathogenesis of CIN and the progression of CIN into CSCC. Of these, TYMS is predicted to be regulated by hsa‐miR‐99a and SASH1 to be regulated by hsa‐miR‐21. [ABSTRACT FROM AUTHOR]

Clear cell renal cell carcinoma (ccRCC) is a common urinary neoplasm, looking for useful candidates to establish scientific foundation for the therapy of ccRCC is urgent. We downloaded genomic profiles of GSE781, GSE6244, GSE53757, and GSE66271 from the Gene Expression Omnibus (GEO) database. GEO2R was used to analyze the derivative genes, while hub genes were screened by protein‐protein interactions and cytoscape. Further, overall survival, gene methylation, gene mutation, and gene expression were all analyzed using bioinformatics tools. Colony formation and cell‐cycle assay were used to detect the biological function of GNG7 in vitro. We found that GNG7 was downregulated in ccRCC tissues and negatively associated with overall survival in ccRCC patients. We also found that promoter methylation and frequent gene mutation were responsible for GNG7 gene suppression. GNG7 low expression was related to upregulation of enhancer of zeste homolog 2 and downregulation of disabled homolog 2‐interacting protein. Further, Gene Set Enrichment Analysis results showed that mTOR1, E2F, G2M, and MYC pathways were all significantly altered in response to GNG7 low expression. In vitro, A498 and 786‐O cells in which GNG7 expression was silenced, exhibited a lower G1 phase when compared to the negative control cells. Taken together, our findings suggest that GNG7 is a tumor suppressor gene in ccRCC progression and represents a novel candidate for ccRCC treatment. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most frequently diagnosed malignancies and a leading cause of cancer death among females. Multiple molecular alterations are observed in breast cancer. LncRNA transcripts were proved to play important roles in the biology of tumorigenesis. In this study, we aimed to identify lncRNA expression signature that can predict breast cancer patient survival. We developed a 10‐lncRNA signature‐based risk score which was used to separate patients into high‐risk and low‐risk groups. Patients in the low‐risk group had significantly better survival than those in the high‐risk group. Receiver operating characteristic analysis indicated that this signature exhibited excellent diagnostic efficiency for 1‐, 3‐ and 5‐year disease‐relapse events. Moreover, multivariate Cox regression analysis demonstrated that this 10‐lncRNA signature was an independent risk factor when adjusting for several clinical signatures such as age, tumour size and lymph node status. The prognostic value of risk scores was validated in the validation set. In addition, a nomogram was established and the calibration plots analysis indicated the good performance and clinical utility of the nomogram. In conclusion, our results demonstrated that this 10‐lncRNA signature effectively grouped patients at low and high risk of disease recurrence. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer‐related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network. The module analysis was performed by the MCODE plug‐in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug‐in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) ranks as one of the most commonly diagnosed malignancies worldwide. Although mortality rates have been decreasing, the prognosis of CRC patients is still highly dependent on the individual. Therefore, identifying and understanding novel biomarkers for CRC prognosis remains crucial. The gene expression profiles of five‐gene expression omnibus (GEO) data sets of CRC were first downloaded. A total of 352 consistent differentially expressed genes (DEGs) were identified for CRC and paired with normal tissues. Functional analysis including gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment revealed that these DEGs were related to metabolic pathways, tight junctions, and the cell cycle. Ten hub DEGs were identified based on the search tool for the retrieval of interacting genes database and protein–protein interaction networks. By using univariate Cox proportional hazard regression analysis, we found 11 survival‐related genes among these DEGs. We finally established a five‐gene signature (kinesin family member 15, N‐acetyltransferase 2, glutathione peroxidase 3, secretogranin II, and chloride channel accessory 1) with prognostic value in CRC by step multivariate Cox regression analysis. Based on this risk scoring system, patients in the high‐risk group had significantly poorer survival results compared with those in the low‐risk group (log‐rank test, p < 0.0001). Finally, we validated our gene signature scoring system in two independent GEO cohorts (GSE17536 and GSE33113). We found all five of the signature genes to be DEGs in The Cancer Genome Atlas database. In conclusion, our findings suggest that our five DEG‐based signature can provide a novel biomarker with useful applications in CRC prognosis. Identification of a five‐gene signature with prognostic value in colorectal cancer [ABSTRACT FROM AUTHOR]

Recent studies have identified certain non‐coding RNAs (ncRNAs) as biomarkers of disease progression. Glioma is the most common primary intracranial cancer, with high mortality. Here, we developed a prognostic signature for prediction of overall survival (OS) of glioma patients by analyzing ncRNA expression profiles. We downloaded gene expression profiles of glioma patients along with their clinical information from the Gene Expression Omnibus and extracted ncRNA expression profiles via a microarray annotation file. Correlations between ncRNAs and glioma patients' OS were first evaluated through univariate Cox regression analysis and a permutation test, followed by random survival forest analysis for further screening of valuable ncRNA signatures. Prognostic signatures could be established as a risk score formula by including ncRNA signature expression values weighted by their estimated regression coefficients. Patients could be divided into high risk and low risk subgroups by using the median risk score as cutoff. As a result, glioma patients with a high risk score tended to have shorter OS than those with low risk scores, which was confirmed by analyzing another set of glioma patients in an independent dataset. Additionally, gene set enrichment analysis showed significant enrichment of cancer development‐related biological processes and pathways. Our study may provide further insights into the evaluation of glioma patients' prognosis. Through integrating univariate and multivariate Cox regression analysis of a glioma expression dataset from the Gene Expression Omnibus, we developed a prognostic model that contains three non‐coding RNAs, through which a risk score was allocated to each sample. Glioma patients with a high risk score tended to have shorter overall survival than those with a low risk score, which was confirmed using another set of glioma patients in an independent dataset. [ABSTRACT FROM AUTHOR]