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INTRODUCTION: Gastric cancer (GC) is the fifth most diagnosed neoplasia and the third leading cause of cancer-related deaths. A substantial number of patients exhibit an advanced GC stage once diagnosed. Therefore, the search for biomarkers contributes to the improvement and development of therapies. OBJECTIVE: This study aimed to identify potential GC biomarkers making use of in silico tools. METHODS: Gastric tissue microarray data available in Gene Expression Omnibus and The Cancer Genome Atlas Program was extracted. We applied statistical tests in the search for differentially expressed genes between tumoral and non-tumoral adjacent tissue samples. The selected genes were submitted to an in-house tool for analyses of functional enrichment, survival rate, histological and molecular classifications, and clinical follow-up data. A decision tree analysis was performed to evaluate the predictive power of the potential biomarkers. RESULTS: In total, 39 differentially expressed genes were found, mostly involved in extracellular structure organization, extracellular matrix organization, and angiogenesis. The genes SLC7A8, LY6E, and SIDT2 showed potential as diagnostic biomarkers considering the differential expression results coupled with the high predictive power of the decision tree models. Moreover, GC samples showed lower SLC7A8 and SIDT2 expression, whereas LY6E was higher. SIDT2 demonstrated a potential prognostic role for the diffuse type of GC, given the higher patient survival rate for lower gene expression. CONCLUSION: Our study outlines novel biomarkers for GC that may have a key role in tumor progression. Nevertheless, complementary in vitro analyses are still needed to further support their potential.
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Stomach Neoplasms/diagnosis , Biomarkers, Tumor , Computational Biology , Prognosis , Computer Simulation , Gene Expression , Tissue Array AnalysisABSTRACT
ABSTRACT Background: The depth of response to platinum in urothelial neoplasm tissues varies greatly. Biomarkers that have practical value in prognosis stratification are increasingly needed. Our study aimed to select a set of BC (bladder cancer)-related genes involved in both platinum resistance and survival, then use these genes to establish the prognostic model. Materials and Methods: Platinum resistance-related DEGs (differentially expressed genes) and tumorigenesis-related DEGs were identified. Ten most predictive co-DEGs were acquired followed by building a risk score model. Survival analysis and ROC (receiver operating characteristic) plot were used to evaluate the predictive accuracy. Combined with age and tumor stages, a nomogram was generated to create a graphical representation of survival rates at 1-, 3-, 5-, and 8-year in BC patients. The prognostic performance was validated in three independent BC datasets with platinum-based chemotherapy. The potential mechanism was explored by enrichment analysis. Results: PPP2R2B, TSPAN7, ATAD3C, SYT15, SAPCD1, AKR1B1, TCHH, AKAP12, AGLN3, and IGF2 were selected for our prognostic model. Patients in high- and low-risk groups exhibited a significant survival difference with HR (hazard ratio) = 2.7 (p < 0.0001). The prognostic nomogram of predicting 3-year OS (overall survival) for BC patients could yield an AUC (area under the curve) of 0.819. In the external validation dataset, the risk score also has a robust predictive ability. Conclusion: A prognostic model derived from platinum resistance-related genes was constructed, we confirmed its value in predicting platinum-based chemotherapy benefits and overall survival for BC patients. The model might assist in therapeutic decisions for bladder malignancy.
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Resumo Fundamento Apesar das evidências crescentes de que pacientes com insuficiência cardíaca (IC) são suscetíveis à sarcopenia, o motivo da associação não é bem compreendido. Objetivo O objetivo deste estudo é explorar ainda mais o mecanismo molecular de ocorrência desta complicação. Métodos Conjuntos de dados de expressão gênica para HF (GSE57345) e Sarcopenia (GSE1428) foram obtidos do banco de dados Gene Expression Omnibus (GEO). Genes diferencialmente expressos (DEGs) foram identificados usando pacotes 'edgeR' e "limma" de R, e suas funções foram analisadas usando Gene Ontology (GO) e a Enciclopédia de Genes e Genomas de Kyoto (KEGG). Redes de interação proteína-proteína (PPI) foram construídas e visualizadas usando Search Tool for the Retrieval of Interacting Genes (STRING) e Cytoscape. Os genes hub foram selecionados usando o plugin cytoHubba e validados com GSE76701 para IC e GSE136344 para Sarcopenia. As vias relacionadas e os mecanismos moleculares dos genes hub foram realizados pela análise de enriquecimento de genes (GSEA). As análises estatísticas foram realizadas no software R. P < 0,05 foi considerado estatisticamente significativo. Resultados Foram encontrados 114 DEGs comuns. As vias relacionadas ao fator de crescimento, secreção de insulina e cGMP-PKG estavam enriquecidas tanto na IC quanto na sarcopenia. Descobriu-se que CYP27A1, KCNJ8, PIK3R5, TIMP2, CXCL12, KIT e VCAM1 são genes hub significativos após validação com GSEA enfatizando a importância dos genes hub na regulação da resposta inflamatória. Conclusão Nosso estudo revela que a IC e a Sarcopenia compartilham vias e mecanismos patogênicos comuns. Estes achados podem sugerir novas direções para pesquisas futuras sobre a patogênese subjacente.
Abstract Background Despite increasing evidence that patients with heart failure (HF) are susceptible to sarcopenia, the reason for the association is not well understood. Objective The purpose of this study is to explore further the molecular mechanism of the occurrence of this complication. Methods Gene expression datasets for HF (GSE57345) and Sarcopenia (GSE1428) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using 'edgeR' and "limma" packages of R, and their functions were analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-protein interaction (PPI) networks were constructed and visualized using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. Hub genes were selected using the plugin cytoHubba and validation with GSE76701 for HF and GSE136344 for Sarcopenia. The related pathways and molecular mechanisms of the hub genes were performed by Gene set enrichment analysis (GSEA). The statistical analyses were performed using R software. P < 0.05 was considered statistically significant. Results A total of 114 common DEGs were found. Pathways related to growth factor, Insulin secretion and cGMP-PKG were enriched in both HF and Sarcopenia. CYP27A1, KCNJ8, PIK3R5, TIMP2, CXCL12, KIT, and VCAM1 were found to be significant hub genes after validation, with GSEA emphasizing the importance of the hub genes in the regulation of the inflammatory response. Conclusion Our study reveals that HF and Sarcopenia share common pathways and pathogenic mechanisms. These findings may suggest new directions for future research into the underlying pathogenesis.
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Objective:To explore the prognostic biomarkers of glioblastoma (GBM) in the tumor microenvironment (TME) and its function.Methods:A total of 169 GBM samples of 161 GBM patients were collected from the Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm in R4.1.0 software was used to calculate the proportion of immune components and stromal components in TME, which were expressed as immune score and stromal score, respectively. According to the median value of the two scores, 169 GBM samples were divided into the high score group and the low score group, respectively, 84 each in each group (those whose scores were equal to the median were not involved in the grouping). The differentially expressed genes (DEG) [false discovery rate (FDR) < 0.05] between the high score group and the low score group of the two scores were obtained by using limma package, and the co-up-regulated and co-down-regulated DEG of the two scores were obtained by using Venn program. Based on the STRING database, the protein interaction (PPI) network of co-up-regulated and down-regulated DEG of immune score and stromal score was constructed, and the top 30 genes with connectivity were selected. Univariate Cox proportional hazard model analysis of overall survival (OS) of 161 GBM patients in the TCGA database was performed on co-up-regulated and down-regulated DEG between immune score and stromal score by using R4.1.0 software to obtain the DEG affecting OS. The intersection of the DEG obtained from PPI analysis and Cox analysis was taken as the prognostic core genes. According to the median expression value of prognostic core genes in GBM samples from the TCGA database, 161 patients were divided into prognostic core genes high expression group and low expression group (patients whose scores were equal to the median were not involved in the grouping), with 80 cases in each group. Kaplan-Meier survival analysis of OS was performed by using R4.1.0 software. GSEA 4.2.1 software was used to perform gene set enrichment analysis (GSEA) on all genes with transcriptome data of GBM patients in the two groups of the TCGA databases, and the main enriched functions of the two groups of genes were obtained. The CIBERSORT algorithm was used to test the accuracy of the proportion of tumor infiltrating immune cell (TIC) subsets in 169 GBM samples from the TCGA database, and 57 GBM samples were finally obtained. Immune cells with differential expression levels and immune cells related to the expression of prognostic core genes among the samples with different expression levels of prognostic core genes were analyzed; Venn program was used to obtain the intersection of immune cells with differential levels and related immune cells, and differentially expressed TIC related to expressions of prognostic core genes in GBM were obtained.Results:Based on the immune score and stromal score of GBM samples in the TCGA database, a total of 693 co-up-regulated and co-down-regulated DEG of both scores were screened out. After the intersection of 78 DEG related to OS obtained by univariate Cox regression analysis and 30 DEG obtained by PPI network results, CC motif chemokine receptor 2 (CCR2) was identified as the prognostic core gene ( HR = 1.294, 95% CI 1.060-1.579, P = 0.011). GBM patients with CCR2 high expression had worse OS compared with those with CCR2 low expression ( P = 0.009). GSEA analysis showed that genes in the CCR2 high expression group were mainly enriched in immune-related pathways, while genes in the CCR2 low expression group were mainly enriched in metabolism-related pathways. Among 57 screened GBM samples, there were differences in the levels of 3 immune cells between the CCR2 high expression group and the CCR2 low expression group ( P < 0.05). CCR2 expression was correlated with the levels of 9 immune cells (all P < 0.05). Venn program analysis showed that differentially expressed 3 TIC in GBM related to CCR2 gene expression were obtained; among them, M2 macrophages were positively correlated with CCR2 expression, while T follicular helper cell and activated NK cells were negatively correlated with CCR2 expression. Conclusions:CCR2 may be the core gene related to the prognosis in the TME of GBM. As reference, the level of CCR2 can help to predict the status of TME and prognosis in GBM patients, which is expected to provide a new direction for the treatment of GBM.
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Abstract Background: UVB irradiation can cause acute damage such as sunburn, or photoaging and melanoma, all of which are major health threats. Objective: This study was designed to investigate the mechanism of skin photoaging induced by UVB radiation in mice through the analysis of the differential expression of miRNAs. Methods: A UVB irradiation photoaging model was constructed. HE and Masson special stains were used to examine the modifications in the epidermis and dermis of mice. The miRNA expression profiles of the mouse skin model exposed to UVB radiation and the normal skin of mice were analyzed using miRNA-sequence analysis. GO and Pathway analysis were employed for the prediction of miRNA targets. Results: A total of 23 miRNAs were evaluated for significantly different expressions in comparison to normal skin. Among them, 7 miRNAs were up-regulated and 16 were down-regulated in the skin with photoaging of mice exposed to UVB irradiation. The differential expression of miRNA is related to a variety of signal transduction pathways, among which mmu-miR-195a-5p and mitogen-activated protein kinase (MAPK) signal pathways are crucial. There was a significant differential expression of miRNA in the skin of normal mice in comparison with the skin with photoaging induced by UVB irradiation. Study limitations: Due to time and energy constraints, the specific protein level verification, MAPK pathway exploration, and miR-195a-5p downstream molecular mechanism need to be further studied in the future. Conclusions: UVB-induced skin photoaging can be diagnosed and treated using miRNA.
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RESUMEN Objetivo. Evaluar in silico y a nivel serológico el potencial antigénico del dominio extracelular recombinante de la proteína de ensamblaje de lipopolisacáridos - D (LptD) de Bartonella bacilliformis (dexr_LptD). Materiales y métodos. Mediante el análisis in silico se realizó la selección de una proteína de B. bacilliformis con potencial antigénico e inmunogénico. El gen de la proteína seleccionada se clonó en Escherichia coli TOP10 y se expresó en Escherichia coli BL21 (DE3) pLysS. La proteína recombinante fue expresada usando isopropil-β-D-1-tiogalactopiranósido (IPTG) y se optimizaron las condiciones de inducción. Por último, se purificó con resina Ni-IDA (His60 Ni Superflow) y se realizó un ensayo de Western Blot. Resultados. In silico, la proteína seleccionada fue LptD por estar localizada en la membrana externa y ser antigénica e inmunogénica. Las condiciones optimizadas para la inducción del dexr_LptD fueron 0,5 mM IPTG, 16 h, medio TB (Terrific Broth), etanol al 3% (v/v), 28 ºC, OD600: 1-1,5 y 200 r.p.m. La purificación se realizó en condiciones denaturantes a pequeña escala y se obtuvo 2,6 µg/mL de dexr_LptD parcialmente purificada. El ensayo de Western Blot mostró una reacción positiva entre los sueros provenientes de pacientes con la enfermedad de Carrión y dexr_LptD, ello evidencia la antigenicidad del dexr_LptD. Conclusiones. El dexr_LptD muestra antigenicidad in silico y a nivel serológico, estos resultados son base para posteriores estudios sobre candidatos vacunales contra la enfermedad de Carrión.
ABSTRACT Objective. To evaluate in silico and at the serological level the antigenic potential of the recombinant extracellular domain of the lipopolysaccharide assembly protein - D (LptD) of Bartonella bacilliformis (dexr_LptD). Materials and Methods. Through in silico analysis, we selected a B. bacilliformis protein with antigenic and immunogenic potential. The selected protein gene was cloned into Escherichia coli TOP10 and expressed in Escherichia coli BL21 (DE3) pLysS. Recombinant protein was expressed using isopropyl-β-D-1-thiogalactopyranoside (IPTG) and induction conditions were optimized. Finally, it was purified with Ni-IDA resin (His60 Ni Superflow) and a Western Blot assay was conducted. Results. In silico, the selected protein was LptD because it is located in the outer membrane and is antigenic and immunogenic. Optimized conditions for dexr_LptD induction were 0.5 mM IPTG, 16 hours, TB (Terrific Broth) medium, 3% (v/v) ethanol, 28 ºC, OD600: 1-1.5 and 200 rpm. Purification was carried out under denaturating conditions on a small scale and we obtained 2.6 μg/mL of partially purified dexr_LptD. The Western Blot assay showed a positive reaction between the sera from patients with Carrión's Disease and dexr_LptD, which shows the antigenicity of dexr_LptD. Conclusions. The dexr_LptD shows antigenicity both in silico and at the serological level, these results are the basis for further studies on vaccine candidates against Carrion's Disease.
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Recombinant Proteins , Cloning, Organism , Bartonella bacilliformis , Bartonella Infections , Computational Biology , Immunogenicity, VaccineABSTRACT
Objective:To explore the key pathways and genes involved in microglia inflammation through transcriptome sequencing and bioinformatics analysis.Methods:BV2 cells were stimulated by lipopolysaccharide to establish microglia inflammation model. The levels of IL-6 and TNF-α were detected by ELISA and RT-qPCR. The established microglia inflammation model was sequenced by transcriptome sequencing, and the differentially expressed genes were screened by bioinformatics method. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed genes were performed. The protein-protein interaction network of differentially expressed genes was constructed by using string database, and the protein-protein interaction network was visualized by using Cytoscape software. The protein interaction network module was extracted by using MCODE app. The hub gene was extracted by using cytohubba app and was verified through RT-qPCR. We conducted enrichment analysis of hub genes, predicted their targeted miRNAs and interacting drugs.Results:The microglia inflammation model was successfully established and verified by ELISA and RT-qPCR. We screened 434 differentially expressed genes by bioinformatics analysis of transcriptome sequencing results. GO analysis showed that these differentially expressed genes were mainly concentrated in cellular response to cytokine stimulus, inflammatory response, regulation of response to external stimulation. KEGG analysis showed that these differentially expressed genes were mainly concentrated in Chemokine signaling pathway, TNF signaling pathway, IL-17 signaling pathway. We constructed the protein interaction network of these differentially expressed genes, and carried out module analysis and extraction of hub genes. Most of hub genes are located in module 1, and the seed gene of module 1 is S1pr1. Hub genes include S1pr1, Cxcr4, Cx3cl1, Cx3cr1, Cxcl10, Cxcl2, Ccl4, Ccl5, Ccl9, Fpr1. RT-qPCR results showed that compared with the culture medium group, the mRNA expressions of S1pr1, Cxcr4, Cx3cl1 and Cx3cr1 were down-regulated, and the mRNA expressions of Cxcl10, Cxcl2, Ccl4, Ccl5, Ccl9 and Fpr1 were up-regulated in the LPS group. The enrichment analysis of hub genes mainly focused on chemokine-mediated signaling pathway, Class A/1 (Rhodopsin-like receptors), cell chemotaxis and so on. Drugs and miRNAs that may interact with hub genes were predicted. Conclusion:Through transcriptome sequencing and bioinformatics analysis of microglia inflammation model, differentially expressed genes were screened, hub genes and seed genes were extracted, which will help us further understand the molecular mechanism of microglia inflammation and provide potential targets for the treatment of related diseases.
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Objective:To investigate the value of computational omics biology model (CBM) in treatment of refractory acute myeloid leukemia (AML) patients.Methods:The clinical data of a refractory AML patient who received personalized therapy regimen predicted by Cellworks tumor response index (TRI) test in November 2018 were retrospectively analyzed. The diagnosis, treatment and the therapeutic efficacy were summarized. The literature related to CBM in AML was reviewed.Results:The patient, a 43-year-old female, was diagnosed as AML accompanied with t(6;11)(q27;q23). She failed to respond after 2 courses of induction therapy, and had poor tolerance of chemotherapy. And then the Cellworks TRI test recommended the 3-drug combination regimen of cladribine, trametinib and cytarabine as the optimal chemotherapy regimen. After 1 course of treatment, the patient achieved complete remission and minimal residual disease negative. After remission, the patient successfully underwent haplo-hematopoietic stem cell transplantation. She experienced a prolonged disease-free survival of 19 months and relapsed in November 2020, and passed away in April 2021. The overall survival time was 28.5 months.Conclusions:Cellworks TRI test based on CBM provides a new therapeutic approach for refractory AML patients, and its personalized treatment regimen based on genomics may improve the survival of patients.
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Abstract Ameloblastoma is a highly aggressive odontogenic tumor, and its pathogenesis is associated with many participating genes. Objective We aimed to identify and validate new critical genes of conventional ameloblastoma using microarray and bioinformatics analysis. Methodology Gene expression microarray and bioinformatic analysis were performed using CHIP H10KA and DAVID software for enrichment. Protein-protein interactions (PPI) were visualized using STRING-Cytoscape with MCODE plugin, followed by Kaplan-Meier and GEPIA analyses that were used for the candidate's postulation. RT-qPCR and IHC assays were performed to validate the bioinformatic approach. Results 376 upregulated genes were identified. PPI analysis revealed 14 genes that were validated by Kaplan-Meier and GEPIA resulting in PDGFA and IL2RA as candidate genes. The RT-qPCR analysis confirmed their intense expression. Immunohistochemistry analysis showed that PDGFA expression is parenchyma located. Conclusion With bioinformatics methods, we can identify upregulated genes in conventional ameloblastoma, and with RT-qPCR and immunoexpression analysis validate that PDGFA could be a more specific and localized therapeutic target.
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Chagas disease is an enduring public health issue in many Latin American countries, receiving insufficient investment in research and development. Strategies for disease control and management currently lack efficient pharmaceuticals, commercial diagnostic kits with improved sensitivity, and vaccines. Genetic heterogeneity of Trypanosoma cruzi is a key aspect for novel drug design since pharmacological technologies rely on the degree of conservation of parasite target proteins. Therefore, there is a need to expand the knowledge regarding parasite genetics which, if fulfilled, could leverage Chagas disease research and development, and improve disease control strategies. The growing capacity of whole-genome sequencing technology and its adoption as disease surveillance routine may be key for solving this long-lasting problem.
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Objective:To investigate the differential expression of four-jointed box kinase 1 (FJX1) gene in colorectal cancer and its relationship with prognosis and the related mechanisms.Methods:On July 16, 2021, the transcriptome data and clinical data of colorectal cancer were downloaded from The Cancer Genome Atlas (TCGA) database to analyze the expressions of FJX1 mRNA in colorectal cancer tissues and paracancerous tissues, and the relationship between FJX1 mRNA and clinicopathological characteristics and prognosis of patients. Receiver operating characteristic (ROC) curve was drawn to evaluate the value of FJX1 mRNA in predicting the survival of patients with colorectal cancer. Cox proportional hazards model was used to evaluate whether FJX1 mRNA was an independent influencing factor for prognosis of colorectal cancer. The overall survival (OS) time and survival status of colorectal cancer patients were downloaded from the Gene Expression Omnibus (GEO) database, and the relationship between FJX1 mRNA and prognosis of patients was analyzed. The methylation data of colorectal cancer was downloaded from the University of California, Santa Cruz (UCSC xena) database to determine the degree of methylation at each site of FJX1 mRNA and the correlation between the expression of FJX1 mRNA and the degree of methylation at each site. Signaling pathways associated with FJX1 mRNA in colorectal cancer were analyzed by using the Gene Set Enrichment Analysis (GSEA) (4.1.0). The correlation between FJX1 mRNA and tumor-infiltrating immune cells was investigated by using the Tumor Immunity Evaluation Resource (TIMER) database. Spearman analysis and small molecule/drug sensitivity analysis were used to explore the correlation between FJX1 mRNA expression and drug sensitivity.Results:In the transcriptome data of 612 colorectal cancer cases in TCGA database, the expression of FJX1 mRNA in colorectal cancer tissues was higher than that in the paracancerous tissues ( P < 0.001). In 549 colorectal cancer patients with complete data, FJX1 mRNA expression was correlated with M stage ( P = 0.007), pathological stage (stage Ⅳ vs. stage Ⅰ, P = 0.016; stage Ⅳ vs. stage Ⅱ, P = 0.03; stage Ⅳ vs. stage Ⅲ, P = 0.012), but it was not correlated with age, gender, T stage and N stage (all P > 0.05). In TCGA database and GEO database, the patients were divided into high expression group and low expression group according to the median expression of FJX1 mRNA. The OS in FJX1 mRNA high expression group was worse than that in low expression group (all P<0.05). The ROC curve of FJX1 mRNA expression on the 1-, 3-, and 5-year OS rates of colorectal cancer patients was drawn by using the data in TCGA database, and the areas under the curve (AUC) were 0.595, 0.625 and 0.764, respectively. Multivariate Cox regression analysis showed that age ( HR = 1.050, 95% CI 1.028-1.073, P < 0.001), T stage ( HR = 1.787, 95% CI 1.090-2.927, P = 0.021) and high FJX1 mRNA expression ( HR = 1.160, 95% CI 1.049-1.282, P = 0.004) were independent influencing factors for poor OS in colorectal cancer. The gene set enrichment analysis found that FJX1 mRNA was related to colorectal cancer, TGF-β signaling pathway, VEGF signaling pathway, Wnt signaling pathway, etc. The expression of FJX1 mRNA in colon cancer was negatively correlated with the degree of methylation of FJX1 mRNA ( r = -0.16, P < 0.001), and the expression of FJX1 mRNA in rectal cancer was positively correlated with the degree of methylation of FJX1 mRNA ( r = 0.33, P < 0.001). The expression of FJX1 mRNA was related to the infiltration of resting memory CD4 + T cells, M0 macrophages and resting dendritic cells. FJX1 mRNA was significantly associated with the resistance of various chemotherapeutic drugs and tumor-targeted drugs such as methotrexate, 5-fluorouracil, gefitinib, etc. Conclusions:FJX1 mRNA may be a potential biomarker of colorectal cancer and is associated with the infiltration of immune cells.
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Objective:To construct a prognostic model for lung squamous cell carcinoma (SqCLC) based on autophagy-related genes analyzed by bioinformatics and validate it.Methods:Expression profile data and clinical information of 268 SqCLC patients were downloaded from The Cancer Genome Atlas (TCGA) database and a dataset of normal lung tissues of 336 healthy people was downloaded from the Genotype Tissue Expression (GTEx) database; the autophagy-related genome was obtained from the GO_AUTOPHAGY genome of the Human Autophagy Database (HADb) and the Molecular Signature Database (MSigDB) 6.2. R 4.0.3 software was applied to analyze the differentially expressed genes between SqCLC tissues in TCGA database and normal lung tissues in GTEx database. Screening of autophagy-related genes differentially expressed between SqCLC tissues and normal lung tissues in the TCGA database (referred to as differentially expressed autophagy genes) was performed using R 4.0.3 software. The Cox proportional risk model was applied to analyze the relationship between the differentially expressed autophagy genes and prognosis of SqCLC patients in TCGA database, and a prognostic model was constructed. The SqCLC patients in TCGA database were divided into high-risk group and low-risk group based on the median risk score of the prognostic model, and the Kaplan-Meier method was used to compare the overall survival of the two groups; the time-dependent receiver operating characteristic (ROC) curve of the 3-, 5- and 10-year overall survival rates of 268 patients in TCGA database predicted by the prognostic model was plotted. Cox regression was used to analyze the independent influencing factors of overall survival of SqCLC patients in TCGA database, and the prognostic index formula was established. Based on the consistency index and restricted mean survival (RMS) curve, the predictive efficacy for the survival of patients in TCGA database between prognostic index of prognostic model risk score alone and prognostic index of risk score combined with independent influencing factors was compared. R 4.0.3 software was used to construct the nomogram for predicting patients' 3-, 5- and 10-year overall survival rates.Results:Six prognosis related differentially expressed autophagy genes were screened, and a prognostic model was constructed as: risk score=PEX14×0.337+CASPASE-8×(-0.280)+TM9SF1×0.292+UBB×0.472+P4HB×0.163+CTSA×0.173. In TCGA database, the overall survival of high-risk group was worse than that of low-risk group ( P < 0.001). Time-dependent ROC curve analysis showed that the area under the curve (AUC) of the prognostic model risk score for predicting the 3-, 5- and 10-year overall survival rates of 268 patients in TCGA database was 0.715, 0.715 and 0.831, respectively. Multivariate Cox regression analysis showed that age, staging and prognostic model risk score were independent factors affecting the overall survival of SqCLC patients in TCGA database, and the prognostic index=0.998×risk score+0.725×staging+0.559×age. The RMS curve showed that compared with the prognostic model risk score, the prognostic index combined with 3 independent prognostic factors had a better effect on predicting the overall survival (consistency index: 0.68 vs. 0.65, P =0.045). Using age, staging and prognostic model risk score, a nomogram was constructed to predict the survival of patients with SqCLC, and its calibration curve was close to the ideal curve. Conclusions:A prognostic model of SqCLC based on 6 characteristic differentially expressed autophagy-related genes has been successfully established. Internal validation shows that this model combined with other clinicopathological factors could be helpful in predicting the survival of SqCLC patients.
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Objective:To screen and analyze ferroptosis-related genes (FRG) impacting the prognosis of colorectal adenocarcinoma patients based on bioinformatics.Methods:RNA sequencing data including the clinical information of 545 colorectal adenocarcinoma patients and 602 data sets were downloaded from the Cancer Genome Atlas (TCGA) database. FRG gene sets were downloaded from FerrDb database. FRG expression dataset could be obtained after taking the intersection between FRG gene sets and TCGA database gene sets. Differentially expressed FRG and prognosis-related genes between colorectal adenocarcinoma tissues and the adjacent tissues were screened by using R software, and finally FRG influencing the prognosis of colorectal adenocarcinoma were obtained. According to protein-protein interaction networks, the interaction and the expression association of proteins were analyzed. LASSO regression analysis was used to build a risk model for patients' 5-year overall survival rate. The risk value was calculated for 509 colorectal adenocarcinoma samples in the TCGA database, and then the median risk value was taken as the cut-off value. All patients were divided into the high-risk group (≥ median risk value) and the low-risk group (< median risk value), and the survival curves of the two groups were drawn. The receiver operating characteristic (ROC) curve was drawn for predicting the 5-year overall survival rate of colorectal adenocarcinoma patients in a time-dependent way in TCGA database according to the risk value of FRG prognosis model. Cox proportional risk model was used to make univariate and multivariate survival analysis in order to screen factors affecting the prognosis. The pathway enrichment analysis of prognosis-related FRG of colorectal adenocarcinoma was performed based on gene ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) database.Results:The clinical information of 545 patients and 602 datasets were extracted from the database. A total of differential expressed 199 FRG in colorectal adenocarcinoma and 28 prognosis-related FRG were identified. After taking the intersection, 21 FRG affecting the prognosis of colorectal adenocarcinoma patients were identified. DUOX2, NOX4, NOX1, DDIT3, JDP2, ATP6V1G2, ULK1, ATG3 were probably associated with WIPI1; expressions of NOX4, NOX5, PLIN4 were positively correlated with ATP6V1G2, while the expression of ULK1 was negatively correlated with MAPK1, MYB, FANCD2, ATG3 and ATP5MC3. LASSO regression analysis showed that 15 FRG were finally screened out (ATP5MC3, NOX4, NOX5, ALOX12B, ATG3, WIPI1, MAPK1, MYB, AKR1C1, DDIT3, JDP2, ATP6V1G2, DRD4, SLC2A3, PLIN4), and the risk model was constructed by calculating the risk value, and the risk value = NOX4×0.139-ATP5M3×0.108+NOX5×1.486+ALOX12B×0.475-ATG3×0.030-WIPI1×0.170-MAPK1×0.271-MYB×0.063+AKR1C1×0.021+DDIT3×0.186+JDP2×0.292+ATP6V1G2×0.777+DRD4×0.294+SLC2A3×0.059+PLIN4×0.113. The overall survival of patients in the high-risk group was worse than that in the low-risk group ( P < 0.001). The 5-year overall survival rate was 48.2% in the high-risk group and 76.8% in the low-risk group. Multivariate survival showed that the age and risk value were independent affecting factors of the prognosis. ROC curves revealed that the risk model constructed by using prognosis-related FRG could well predict the 5-year overall survival rate of patients (the area under the curve was 0.728). The differential expressed genes of both groups may be associated with genetic pathways such as extracellular matrix composition, extracellular structure composition and focal adhesion. Conclusions:The prognostic risk model constructed by the screened FRG can better evaluate the prognosis of colorectal adenocarcinoma patients. These FRG are expected to become new candidate biomarkers related to the prognosis of colorectal adenocarcinoma.
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Objective:To identify the key genes for neuropathic pain in rats.Methods:The genomic data of spinal cord tissues of rats (GSE18803) were downloaded from the Gene Expression Database at the American Center for Biotechnology Information to identify differentially expressed genes associated with neuropathic pain, and key genes were obtained by further analysis of the protein-protein interaction networks.Single-cell localization and expression of the key genes were analyzed by the Tabula Muris database.Results:The protein-protein interaction networks identified 10 hub genes, including Tyrobp, Clec4a3, C1qc, Ptprc, Laptm5, Csf1r, C1qa, C1qb, Fcgr3a, Cd53. Cd53, Laptm5 and Ptprc were mainly expressed in macrophages, B cells, NK cells, monocytes and granulocytes. Clec4a3 and Csf1r were mainly expressed in monocytes, Fcgr3a in monocytes and granulocytes, and Tyrobp in macrophages, monocytes, granulocytes, and pluripotent progenitor cells. Conclusions:Ten target genes associated with neuropathic pain are identified using bioinformatics, and their distribution and expression in immune inflammatory cells are obtained through comprehensive analysis.
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RESUMEN El filtrado de secuencias es un paso esencial sin importar el tipo de tecnología aplicada para la secuenciación de un genoma, en el cual las lecturas de baja calidad o una parte son eliminadas. En un ensamblado la construcción de un genoma se realiza a partir de la unión de lecturas cortas en cóntigos. Algunos ensambladores miden la relación que existe entre secuencias de una longitud fija (k-mer) que puede verse afectada por la presencia de secuencias de baja calidad. Un enfoque común para evaluar los ensamblados se basa en el análisis del número de cóntigos, la longitud del cóntigo más largo y el valor del N50, definido como la longitud del cóntigo que representa el 50 % de la longitud del conjunto. En este contexto, el presente estudio tuvo como objetivo evaluar el efecto del uso de lecturas crudas y filtradas en los valores de los parámetros de calidad obtenidos en el ensamblado del genoma de Bacillus altitudinis 19RS3 aislada de Ilex paraguariensis. Se realizó el análisis de calidad de ambos archivos de partida con el software FastqC y se filtraron las lecturas con el softwareTrimmomatic. Para el ensamblado se utilizó el software SPAdes y para su evaluación la herramienta QUAST. El mejor ensamblado para B. altitudinis 19RS3 se obtuvo a partir de las lecturas filtradas con el valor de k-mer 79, que generó 16 cóntigos mayores a 500 pb con un N50 de 931 914 pb y el cóntigo más largo de 966 271 pb.
ABSTRACT Sequence filtering is an essential step regardless of the type of technology applied for sequencing a genome, in which low-quality readings or a portion are eliminated. In an assembly, the construction of a genome is carried out from the union of short reads in contigs. Some assemblers measure the relationship between sequences of a fixed length (k-mer) that can be affected by the presence of low-quality sequences. A common approach to evaluating assemblies is based on the analysis of the number of contigs, the length of the longest contig, and the value of N50 defined as the length of the contig representing 50 % of the length of the assembly. In this context, the objective of this study was to evaluate the effect of the use of crude and filtered reads on the values of the quality parameters obtained from the genome assembly of Bacillus altituidinis 19RS3 isolated from Ilex paraguariensis. The quality analysis of both starting files was performed with the FastqC software and the readings were filtered with the Trimmomatic software. The SPAdes software was used for the assembly and the QUAST tool for its evaluation. The best assembly for B. altitudinis 19RS3 was obtained from the filtered readings with the value of k-mer 79, which generated 16 contigs greater than 500 bp with a N50 of 931 914 bp and the longest contig of 966 271 bp.
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ABSTRACT Objective: Interstitial cystitis (IC)/bladder pain syndrome (BPS) is a chronic inflammatory disease that can cause bladder pain and accompanying symptoms, such as long-term urinary frequency and urgency. IC/BPS can be ulcerative or non-ulcerative. The aim of this study was to explore the core genes involved in the pathogenesis of ulcerative IC, and thus the potential biomarkers for clinical treatment. Materials and Methods: First, the gene expression dataset GSE11783 was downloaded using the Gene Expression Omnibus (GEO) database and analyzed using the limma package in R to identify differentially expressed genes (DEGs). Then, the Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for Gene Ontology (GO) functional analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for pathway enrichment analysis. Finally, the protein-protein interaction (PPI) network was constructed, and key modules and hub genes were determined using the STRING and Cytoscape software. The resulting key modules were then analyzed for tissue-specific gene expression using BioGPS. Results: A total of 216 up-regulated DEGs and 267 down-regulated genes were identified, and three key modules and nine hub genes were obtained. Conclusion: The core genes (CXCL8, CXCL1, IL6) obtained in this study may be potential biomarkers of interstitial cystitis with guiding significance for clinical treatment.
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Humans , Cystitis, Interstitial/genetics , Software , Gene Expression Profiling , Protein Interaction Maps/genetics , Gene OntologyABSTRACT
RESUMEN Introducción: La restricción del crecimiento intrauterino es una alteración del desarrollo fetal que se caracteriza por una tasa de crecimiento durante la etapa fetal que es menor al potencial genético de crecimiento para la edad gestacional. Esta condición plantea una carga importante para la salud pública, ya que aumenta la morbimortalidad de la descendencia, a corto y a largo plazo, particularmente, por asociarse al desarrollo de enfermedad cardiovascular y metabólica en la vida adulta. Objetivos: Mediante el uso de herramientas bioinformáticas nos propusimos identificar posibles genes cardinales involucrados en la restricción del crecimiento intrauterino asociados al desarrollo de obesidad, hipertensión arterial y síndrome metabólico. Material y métodos: Obtuvimos un total de 343 genes involucrados en los fenotipos de interés e identificamos 20 genes que resultaron significativamente relevantes en el análisis de la red de interacción. Particularmente, cuatro de estos genes identificados codifican para factores de crecimiento o sus receptores, VEGFA, PDGFRB, IGF1R y EGFR. Además, identificamos genes relacionados con la insulina y el control de la homeostasis cardiovascular, como son el CTNNB1, APP, MYC y MDMD2. Por otra parte, el análisis de clústeres permitió reconocer los términos de ontología genética más significativos, entre los que se destacan aquellos relacionados con procesos biológicos de proliferación y muerte celular programada, de comunicación intercelular, del metabolismo proteico, y de desarrollo del sistema cardiovascular. Conclusiones: Los genes hallados en este estudio podrían ser de utilidad como biomarcadores putativos de la presencia de alteraciones cardiovasculares y metabólicas asociadas a la restricción del crecimiento intrauterino o potenciales blancos terapéuticos de estrategias de tratamiento orientadas al genotipo del paciente.
ABSTRACT Background: Intrauterine growth restriction is an abnormal fetal development characterized by a fetal growth rate lower than the potential genetic growth for the gestational age. This condition represents a major burden for public health systems, as it increases short and long-term morbidity and mortality in the offspring, particularly because of its association with the development of cardiovascular and metabolic disease in adult life. Objectives: The aim of the present study was to identify possible cardinal genes involved in intrauterine growth restriction associated with the development of obesity, hypertension and metabolic syndrome using bioinformatics tools. Methods: A total of 343 genes involved in the phenotypes of interest were obtained and 20 genes were identified as significantly relevant in the interaction network analysis. Specifically, four of these identified genes encode for growth factors or their receptors, VEGFA, PDGFRB, IGF1R and EGFR. We also identified genes related to insulin and cardiovascular homeostasis as CTNNB1, APP, MYC and MDMD2. Cluster analysis provided the most significant gene ontology terms, including those related to the biological processes of proliferation and programmed cell death, intercellular communication, protein metabolism and development of the cardiovascular system. Conclusions: The genes found in this study could be useful as putative biomarkers for the presence of cardiovascular and metabolic disorders associated with intrauterine growth restriction, or as potential therapeutic targets for treatment strategies directed to the patient's genotype.
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Abstract Introduction Gaucher's disease (GD) is an autosomal-recessive lysosomal storage disorder that results from hereditary deficiency of the acid glucocerebrosidase enzyme, encoded by the GBA gene necessary for the degradation of glucosylceramide. Objective molecularly characterize the variants found in the GBA gene present in patients from the Southwest of Colombia with GD. Material and methods 19 patients were included in the study, clinically and enzymatically diagnosed with GD. A molecular analysis of the GBA gene was performed and the variants were subsequently searched in different population and clinical databases. A bioinformatic analysis was performed. Results The variants in the GBA gene reported were classified into: 14/19 homozygous patients, 4/19 compound heterozygote and 1/19 heterozygous. The presence of 7 variants coding for 8 different genotypes was reported. Also the known mutations like Asn409Ser, p.Leu483Pro, p.Lys237Glu, p.Glu427Lys, and p.Arg535His were identified in these patients. The most frequent genotype was p. Asn409Ser / Asn409Ser (36%). All the variants presented a pathogenic clinical significance. Conclusion The given study will make it possible to understand the susceptibility to GD in the population. This can help maintain the health quotient of the population through premarital counseling and therefore minimize the burden of disease among the population.
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Abstract Mucopolysaccharidoses are lysosomal storage diseases characterized by the excessive accumulation of glycosaminoglycan sulfate in organs and tissues. To determine the population allelic frequency of the MPS complex variants in a population without clinical and molecular diagnosis of MPS. An observational descriptive study was carried out where the allelic frequency of variants presents in the IDUA, IDS, SGSH, NAGLU, HGSNAT, GNS, GALNS, GLB1, ARSB, GUSB, HYAL1 genes was determined by means of the sequencing of 320 exomes from patients without a clinical diagnosis of MPS; the results were tabulated, and allelic frequency formulas were used to determine the values associated with each of the genes. 509 alleles associated with the MPS complex were reported, of which 262 have not been previously reported. Genes with the most frequent allelic presence were IDUA, GLB1 and GALNS, involved in MPS I and MPS IV A / B. The total frequencies ranged between 0.00393 (2 alleles) and 0.47937 (248 alleles). These studies make it possible to determine polymorphisms that circulate in the country, present in patients not affected with MPS, allowing to expand the knowledge about the characteristics of the alleles that are present in affected patients.