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Improved insight into the molecular mechanisms of triple negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify key genes which may affect the prognosis of TNBC patients by bioinformatic analysis. In our study, the RNA sequencing (RNA-seq) expression data of 116 breast cancer lacking ER, PR, and HER2 expression and 113 normal tissues were downloaded from The Cancer Genome Atlas (TCGA). We screened out 147 differentially co-expressed genes in TNBC compared to non-cancerous tissue samples by using weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were constructed, revealing that 147 genes were mainly enriched in nuclear division, chromosomal region, ATPase activity, and cell cycle signaling. After using Cytoscape software for protein-protein interaction (PPI) network analysis and LASSO feature selection, a total of fifteen key genes were identified. Among them, BUB1 and CENPF were significantly correlated with the overall survival rate (OS) difference of TNBC patients (p value < 0.05). In addition, BUB1, CCNA2, and PACC1 showed significant poor disease-free survival (DFS) in TNBC patients (p value < 0.05), and may serve as candidate biomarkers in TNBC diagnosis. Thus, our results collectively suggest that BUB1, CCNA2, and PACC1 genes could play important roles in the progression of TNBC and provide attractive therapeutic targets.

Background/Aims: Long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) play significant roles in the development of tumors, but the functions of specific lncRNAs and lncRNA-related ceRNA networks have not been fully elucidated for colon adenocarcinoma (COAD). In this study, we aimed to clarify the lncRNA-microRNA (miRNA)-mRNA ceRNA network and potential lncRNA biomarkers in COAD. Methods: We extracted data from The Cancer Genome Atlas (TCGA) and identified COAD-specific mRNAs, miRNAs, and lncRNAs. The biological processes in Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed for COAD-specific mRNAs. We then constructed a ceRNA network of COAD-specific mRNAs, miRNAs and lncRNAs and analyzed the correlation between expression patterns and clinical features of the lncRNAs involved. After identifying potential mRNA targets of 4 lncRNAs related to overall survival (OS), we conducted stepwise analysis of these targets through GO and KEGG. Using tissue samples from our own patients, we also verified certain analytical results using quantitative real-time PCR (qRT-PCR). Results: Data from 521 samples (480 tumor tissue and 41 adjacent non-tumor tissue samples) were extracted from TCGA. A total of 258 specific lncRNAs, 206 specific miRNAs, and 1467 specific mRNAs were identified (absolute log 2 [fold change] > 2, false discovery rate < 0.01). Analysis of KEGG revealed that specific mRNAs were enriched in cancer-related pathways. The ceRNA network was constructed with 64 lncRNAs, 18 miRNAs, and 42 mRNAs. Among these lncRNAs involved in the network, 3 lncRNAs (LINC00355, HULC, and IGF2-AS) were confirmed to be associated with certain clinical features and 4 lncRNAs (HOTAIR, LINC00355, KCNQ1OT1, and TSSC1-IT1) were found to be negatively linked to OS (log-rank p < 0.05). KEGG showed that the potential mRNA targets of these 4 lncRNAs may be concentrated in the MAPK pathway. Certain results were validated by qRT-PCR. Conclusion: This study providing novel insights into the lncRNA-miRNA-mRNA ceRNA network and reveals potential lncRNA biomarkers in COAD.

Trihelix transcription factors play important roles in triggering plant growth and imparting tolerance against biotic and abiotic stresses. However, a systematical analysis of the trihelix transcription factor family under heat and drought stresses in maize has not been reported. PlantTFDB and TBtools were employed to identify the trihelix domain-containing genes in the maize genome. The heat-regulated transcriptome data for maize were obtained from NCBI to screen differentially expressed genes through statistical analysis. The basic protein sequences, chromosomal localization, and subcellular localization were analyzed using Maize GDB, Expasy, SOMPA, TBtools, and Plant-mPLoc. The conserved motifs, evolutionary relationships, and -elements, were analyzed by MEME, MEGA7.0 and PlantCARE software, respectively. The tissue expression patterns of and their expression profiles under heat and drought stress were detected using quantitative real-time PCR (qRT-PCR). A total of 44 trihelix family members were discovered, and members were distributed over 10 chromosomes in the maize genome. A total of 11 genes were identified that were regulated by heat stress; these were unevenly distributed on chromosomes 1, 2, 4, 5, and 10. encoded a total of 16 proteins, all of which were located in the nucleus; however, ZmTH04.1 was also distributed in the chloroplast. The protein length varied from 206 to 725 amino acids; the molecular weight ranged from 22.63 to 76.40 kD; and the theoretical isoelectric point (pI) ranged from 5.24 to 11.2. The protein's secondary structures were mainly found to be random coils and α-helices, with fewer instances of elongation chains and β-rotations. Phylogenetic relationship analysis showed that these can be divided into five sub-groups. The conserved domain of was GT1 or MyB_DNA-Bind_4. The protein and gene structure of differed greatly among the subfamilies, while the structures within the subfamilies were similar. The promoter of contained abundant tissue-specific expression -acting elements and abiotic stress response elements. qRT-PCR analysis showed that expression levels were significantly different in different tissues. Furthermore, the expression of was dramatically up-regulated by heat stress, while the expression of , , , , , , , and were down-regulated by heat stress. Upon PEG-simulated drought stress, was significantly up-regulated, while and were down-regulated. We performed a genome-wide, systematic identification and analysis of differentially expressed trihelix genes under heat and drought stresses in maize.

Researches have indicated that major depressive disorder (MDD) and anorexia may have overlapping pathogenic mechanisms. In light of this, we endeavored to identify diagnostic gene candidates for MDD in individuals with comorbid anorexia. Gene expression data pertaining to MDD and healthy controls (HC) were retrieved from the Gene Expression Omnibus (GEO) database. Utilizing Limma and weighted gene co-expression network analysis (WGCNA), key MDD-associated genes were meticulously screened. Subsequently, these genes were cross-referenced with established anorexia-related genes to pinpoint common genetic factors shared between MDD and anorexia. Further refinement was conducted using Cytoscape software to identify hub-genes. Ultimately, animal experiments were conducted to validate and confirm the findings. We identified a total of 214 genes that are commonly associated with both MDD and anorexia. These genes were found to be enriched in inflammatory and immune-related pathways. Among these 214 genes, six were distinguished as hub-genes: IL10, ITGAM, PTPRC, IL13, STAT5B, and JAK2. Immune infiltration analysis further revealed that all these hub-genes exhibited associations with multiple immune cell types. Animal experiments demonstrated that, compared to control mice, the model group exhibited significantly elevated expression levels of PTPRC, STAT5B, and JAK2. Through the application of bioinformatics analysis and animal experimentation, we have pinpointed three hub-genes (PTPRC, STAT5B, and JAK2) that hold potential as both diagnostic biomarkers and therapeutic targets for MDD accompanied with anorexia.

Neuroinflammation is considered an important step in the progression of secondary brain injury (SBI) induced by cerebral hemorrhage (ICH). The nucleotide-binding and oligomerization structural domain-like receptor family of pyridine structural domain-containing 3 (NLRP3) inflammasomes play an important role in the immune pathophysiology of SBI. Febuxostat (Feb) is a xanthine oxidase inhibitor that is approved for the treatment of gout and has been found to have potent anti-inflammatory effects. However, it has been less studied after ICH and we aimed to explore its protective role in ICH. We established an autologous blood-brain hemorrhage model in C57BL/6 mice. Functions of co-expressed genes were analyzed by trend analysis and bioinformatics analysis. Enzyme-linked immunosorbent assay were used to assess the inflammatory factor levels. Fluoro-Jade B histochemistry and TUNEL staining were used to detect neuronal apoptosis. Immunofluorescence staining and western blotting were used to detect the expression of NLRP3 inflammasomes. Pretreatment with Feb reduced neuronal cell death and degeneration and alleviated neurobehavioral disorders . Feb was found to modulate inflammation-related pathways by trend analysis and bioinformatics analysis. In addition, Feb inhibited microglia activation and elevated cytokine levels after ICH. Furthermore, double immunofluorescence staining showed that co-localization of NLRP3 with Iba1 positive cells was reduced after treatment with Feb. Finally, we found that Feb inhibited the activation of the NLRP3/ASC/caspase-1 pathway after ICH. By inhibiting the NLRP3 inflammasome, preconditioning Feb attenuates inflammatory injury after ICH. Our findings may provide new insights into the role of Feb in neuroprotection.

The peripheral nervous system has the potential to regenerate after nerve injury owing to the intrinsic regrowth ability of neurons and the permissive microenvironment. The regenerative process involves numerous gene expression changes, in which transcription factors play a critical role. Previously, we profiled dysregulated genes in dorsal root ganglion neurons at different time points (0, 3 and 9 hours, and 1, 4 and 7 days) after sciatic nerve injury in rats by RNA sequencing. In the present study, we investigated differentially expressed transcription factors following nerve injury, and we identified enriched molecular and cellular functions of these transcription factors by Ingenuity Pathway Analysis. This analysis revealed the dynamic changes in the expression of transcription factors involved in cell death at different time points following sciatic nerve injury. In addition, we constructed regulatory networks of the differentially expressed transcription factors in cell death and identified some key transcription factors (such as STAT1, JUN, MYC and IRF7). We confirmed the changes in expression of some key transcription factors (STAT1 and IRF7) by quantitative reverse transcription-polymerase chain reaction. Collectively, our analyses provide a global overview of transcription factor changes in dorsal root ganglia after sciatic nerve injury and offer insight into the regulatory transcription factor networks involved in cell death.

The mechanism through which death-associated protein kinase 1 (DAPK1) causes hepatocellular carcinoma (HCC) progression remains unclear. In this study, we aimed to identify key proteins that were altered after DAPK1 knockout. Stable DAPK1 knockout HCC cell lines were established, then the differentially expressed genes (DEGs) of HCC were screened using the NetworkAnalyst database and enriched using the Metascape software. Protein-protein interaction networks (PPIs) were analyzed and visualized using the STRING database expansion. In total, 732 differentially expressed genes were identified, including 415 upregulated genes and 317 downregulated genes. Through Cytoscape software scoring, 10 pivotal genes were found to be closely related to changes in DAPK1 expression; Kininogen-1 (KNG1), Complement C3 (C3), Metalloproteinase inhibitor 1 (TIMP1), and Alpha-2-HS-glycoprotein (AHSG) were the most strongly associated with DAPK1 expression changes. Moreover, western blot analysis results revealed that changes in the levels of proteins encoded by the four key genes after DAPK1 knockout were consistent with those seen in the database screening. These results provide a direction for further studies on the DAPK1 gene and on the mechanism through which DAPK1 leads to hepatocellular carcinoma development.

Objective: The mechanisms underlying the chronic rhinosinusitis with nasal polyps (CRSwNP) remained unclear. This study aimed to identify differentially expressed genes (DEGs) in nasal polyps from CRSwNP patients compared to healthy controls and explore key genes and pathways associated with CRSwNP pathophysiology and prognosis. Methods: Three datasets were obtained from the Gene Expression Omnibus database and the intersecting DEGs were identified in CRSwNP patients. Gene Ontology (GO) and protein-protein interaction (PPI) network analysis were applied to investigate the function of DEGs. Nasal specimens from 90 CRSwNP and 45 controls were further collected and qRT-PCR was applied to verify the mRNA expression of hub genes, and moreover, their association with tissue eosinophilia and clinical characteristics in CRSwNP were analyzed. Results: Sixty-eight co-DEGs including 8 upregulated and 60 downregulated genes were identified and GO analyses identified the terms including positive regulation of ERK1 and ERK2 cascade, transforming growth factor beta receptor signaling pathway. PPI networks identified hub genes including EGF, ERBB4, AZGP1, CRISP3 and PIP which were validated to be significantly down-regulated in CRSwNP and showed well diagnostic prediction quality. In addition, lower mRNA expressions level of EGF and AZGP1 in eosinophilic CRSwNP compared with non-eosinophilic CRSwNP were found. Aberrant low expressions of EGF and AZGP1 protein in CRSwNP were identified, and there was good consistency between their mRNA expression level and protein relative expression level. Furthermore, the expressions of EGF and AZGP1 mRNA were significantly correlated with clinical severity parameters. Conclusion: Integrated analysis revealed 68 co-DEGs between nasal polyps and controls and identified hub genes, of which EGF and AZGP1 expression was significantly downregulated in eosinophilic CRSwNP and correlated with disease severity. Downregulation of EGF and AZGP1 may contribute to epithelial barrier dysfunction and type 2 inflammation in CRSwNP, suggesting them as potential diagnostic biomarkers and therapeutic targets. Keywords: chronic rhinosinusitis with nasal polyps, bioinformatics analysis, differentially expressed genes, EGF, AZGP1

This study aimed to predict and explore the possible clinical value and mechanism of genetic markers in prostate cancer (PCa) using a bioinformatics analysis method. The RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database to identify the differentially expressed genes (DEGs). The hub genes were screened by building protein-protein interaction (PPI) subnetworks with four topological analysis methods. The overall survival analysis of hub genes was conducted using Kaplan-Meier curves. Furthermore, the bioinformatics results were confirmed in 102 PCa samples collected in our hospital. Gene Set Enrichment Analysis (GSEA) was performed to provide information about the molecular mechanisms underlying PCa. Among 13 hub genes, the high expression of GTSE1 or KIF18B was associated with worse overall survival according to the TCGA samples. Immunoreactive scores for GTSE1 staining were significantly higher in PCa tissues than in paracancerous tissues (P<0.01). The overall survival time of patients with high GTSE1 expression was shorter than that of patients with low GTSE1 expression (P=0.015). GSEA demonstrated that high GTSE1 expression was mainly enriched in the cell cycle (P<0.001), DNA replication (P<0.001), mismatch repair (P<0.001), and p53 signaling pathway (P<0.001). GTSE1 expression was significantly high in PCa and associated with poor prognosis. GTSE1 may serve as a potential biomarker and therapeutic target in PCa patients.

Chamaemelum nobile is a traditional Chinese herbal medicine, whose secondary metabolites used in the pharmacology of Chinese medicine. Among them, the flavonoids have great research value. Flavanone 3-hydroxylase (F3H) is one of the core enzymes in the early steps of flavonoid biosynthesis. This study aimed to elucidate the structures, functions, and expression levels of F3H families from C. nobile. Four members of the F3H family were screened from C. nobile transcriptome data and performed bioinformatics analysis. Results showed that CnF3H1~4 had a high similarity with the other F3H plants, and all genes contained two conserved isopenicillin N synthase-like and oxoglutarate/iron-dependent dioxygenase domains. Further analysis revealed that the four CnF3H proteins contained some differences in binding sites. The results of secondary and 3-D structures displayed that the composition and proportion of the four CnF3H secondary structures were basically the same, and their 3D structures were consistent with the secondary structures. The phylogenetic tree displayed that CnF3H2, CnF3H3, and CnF3H4 were grouped with Asteraceae. The expression patterns of CnF3Hs in the roots, stems, leaves, and flowers of C. nobile were evaluated using the value of RPKM. The results indicated that CnF3Hs had significant difference in the expression of different tissues. Especially, CnF3H1~3 and CnF3H4 had the highest expression levels in the flowers and roots, respectively. Hence, CnF3Hs played a significant role in the flavonoid metabolism.