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2022 ◽  
Vol 12 ◽  
Author(s):  
Zhifeng Lin ◽  
Xiaohui Ji ◽  
Nana Tian ◽  
Yu Gan ◽  
Li Ke

Background: Emerging research suggests that long non-coding RNAs (lncRNAs) play an important role in a variety of developmental or physiological processes of hepatocellular carcinoma (HCC). Various differentially expressed lncRNAs have been identified in HCC. Thus, a deeper analysis of recent research concerning lncRNA and HCC development could provide scientists with a valuable reference for future studies.Methods: Related publications were retrieved from the Web of Science Core Collection database. CiteSpace version 5.6.R4 was employed to conduct bibliometric analysis. Several network maps were constructed to evaluate the collaborations between different countries, institutions, authors, journals, and keywords.Results: A total of 2,667 records were initially found from the year of 2010–2020. The annual related publications output had increased dramatically during these years. Although China was the most prolific country in terms of research publication, the United States played a leading role in collaborative network. The Nanjing Medical University was the most productive institute in the field of lncRNAs in HCC development. Gang Chen was the most prolific researcher, while Yang F was the most frequently co-cited author. Oncotarget, Cell, and Oncogene were the most highly co-cited journals. The most recent burst keywords were interaction, database, and pathway.Conclusion: This study provides a comprehensive overview for the field of lncRNAs in HCC development based on bibliometric and visualized methods. The results would provide a reference for scholars focusing on this field.


2022 ◽  
pp. 103985
Author(s):  
Siun Kim ◽  
Yoona Choi ◽  
Jung-Hyun Won ◽  
Jung Mi Oh ◽  
Howard Lee

2021 ◽  
Author(s):  
Liu Fu ◽  
Zhe Wang ◽  
Fengxiang Jiang ◽  
Guohua Wei ◽  
Longe Sun ◽  
...  

Abstract Background: Eukaryotic translation initiation factor 4 gamma 2 (EIF4G2) is involved in the occurrence and development of various tumors. However, the effect of EIF4G2 in gastric cancer (GC) has not been fully explored. The purpose of this study was to explore the function and mechanism of EIF4G2 in GC.Methods: The Tumor Immune Estimation Resource 2.0 database was used to analyze EIF4G2 expression in various cancers and the relationship between EIF4G2 expression and tumor-infiltrating immune cells. Gene Expression Profiling Interactive Analysis was utilized to assess the EIF4G2 expression level and its effect on survival in GC. UALCAN was conducted to analyze EIF4G2 expression in various sub-groups of GC. The Kaplan–Meier plotter was employed for survival analysis. Receiver Operator Characteristic (ROC) curve analysis was applied to evaluate the diagnostic role of EIF4G2 in GC. LinkedOmics was used to identify the co-expressed genes and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. The Tumor-Immune System Interaction database was employed to analyze the correlation between EIF4G2 expression and tumor-infiltrating lymphocytes. The starBase web platform was used to predict the upstream microRNAs and long non-coding RNAs.Results: EIF4G2 expression was upregulated in GC tissues compared to normal controls. High expression of EIF4G2 indicated poor prognosis in GC. ROC analysis revealed that EIF4G2 had good diagnostic ability to distinguish GC from normal tissues. Immune infiltration analysis indicated that EIF4G2 expression may be involved in the modulation of tumor immune infiltration in GC. Finally, we determined that the Taurine Up-Regulated 1 (TUG1)/hsa-miR-26a-5p/EIF4G2 axis was the most likely regulatory pathway involved in GC development.Conclusions: EIF4G2 was upregulated in GC and elevated expression of EIF4G2 indicated unfavorable prognosis. Moreover, EIF4G2 expression may be involved in the regulation of tumor immune cell infiltration. The TUG1/hsa-miR-26a-5p axis is a likely upstream regulatory mechanism of EIF4G2 in GC. EIF4G2 may thus serve as a prognosis biomarker and present a new therapeutic target.


2021 ◽  
Author(s):  
SOUVIK CHAKRABORTY ◽  
Sajal Dey ◽  
Sushmita Bhowmick

Nowadays, neurological conditions are a major concern as it not only preys on a patients health but also is a huge economic burden that is placed on the patients family. The diagnosis and treatment of disease sometimes cause methodological limitations. This is mainly common for individuals who have the signs of MS and schizophrenia (SZ). Patients suffering from multiple sclerosis are more likely to develop schizophrenia. Besides, a significant portion of patients who have been diagnosed with Autism Spectrum Disorder (ASD) later acquire the symptoms of Schizophrenia. In this study, we used bioinformatics tools to determine differentially expressed genes (DEGs) in all these diseases, and then we created a protein-protein interaction network using the online software STRING and identified 15 significant genes with the help of Cytohubba a plug-in tool in Cytoscape, the offline software (version3.8.2). We then used a drug-gene interaction database to conduct a drug-gene interaction study of the 15 hub genes and from there we showed that there are 37 existing FDA-approved drugs were obtained. These findings may provide a new and common therapeutic approach for MS, SZ, and ASD therapy.


2021 ◽  
Vol 62 (4) ◽  
pp. 316-324
Author(s):  
Susan Omar Rasool ◽  
Ata Mirzaei Nahr ◽  
Sania Eskandari ◽  
Milad Hosseinzadeh ◽  
Soheila Asoudeh Moghanloo ◽  
...  

While COVID-19 liver injuries have been reported in various studies, concerns are raised about disease-drug reactions in COVID-19 patients. In this study, we examined the hypothesis of gene-disease interactions in an in-silico model of gene expression to seek changes in cytochrome P450 genes. The Gene Expression Omnibus dataset of the liver autopsy in deceased COVID-19 patients (GSE150316) was used in this study. Non-alcoholic fatty liver biopsies were used as the control (GSE167523). Besides, gene expression analysis was performed using the DESeq/EdgeR method. The GO databases were used, and the paths were set at p<0.05. The drug-gene interaction database (DGIdb) was searched for interactions. According to the results, 5,147 genes were downregulated, and 5,122 genes were upregulated in SARS-CoV-2 compared to healthy livers. Compared to the cytochromes, 34 cytochromes were downregulated, while 4 cytochromes were upregulated among the detected differentially expressed genes (DEG). The drug-gene interaction database (DGIdb) provided a list of medications with potential interactions with COVID-19 as well as metacetamol, phenethyl isocyanate, amodiaquine, spironolactone, amiloride, acenocoumarol, clopidogrel, phenprocoumon, trimipramine, phenazepam, etc. Besides, dietary compounds of isoflavones, valerian, and coumarin, as well as caffeine metabolism were shown to have possible interactions with COVID-19 disease. Our study showed that expression levels of cytochrome P450 genes could get altered following COVID-19. In addition, a drug-disease interaction list is recommended to be used for evaluations in clinical considerations in further studies.


2021 ◽  
Author(s):  
Laura Mudge ◽  
John Bruno

Abstract The frequency and intensity of Atlantic cyclonic storms are projected to increase as climate change warms the ocean 1,2. These changing disturbance dynamics, paired with simultaneous changes in the condition and composition of Caribbean coral reefs, could be altering reef resilience to storms in unexpected ways. For example, the observed decline of fast-growing, disturbance-sensitive species could promote resistance to and decrease recovery from storms3,4, increasingly locking reefs into a state dominated by weedy taxa. To test this hypothesis, we combined data from coral reef monitoring studies and historical hurricane records to develop a regional reef-storm interaction database. We found that as the living cover of Caribbean corals declined over the past 40 years, while resistance to storms increased, despite a concurrent increase in cyclonic storm frequency and intensity. Because storms selectively damaged branching coral species and had no measurable effect on the cover of “weedy” corals, reef composition shifted towards greater weedy dominance and reduced ecological function. Additionally, storms accelerated the loss rate of threatened acroporid corals, already in pre-storm decline, suggesting a worrisome synergism with other climate-related disturbances.


2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Ali Janbain ◽  
Christelle Reynès ◽  
Zainab Assaghir ◽  
Hassan Zeineddine ◽  
Robert Sabatier ◽  
...  

Abstract A comprehensive, accurate functional annotation of genes is key to systems-level approaches. As functionally related genes tend to be co-expressed, one possible approach to identify functional modules or supplement existing gene annotations is to analyse gene co-expression. We describe TopoFun, a machine learning method that combines topological and functional information to improve the functional similarity of gene co-expression modules. Using LASSO, we selected topological descriptors that discriminated modules made of functionally related genes and random modules. Using the selected topological descriptors, we performed linear discriminant analysis to construct a topological score that predicted the type of a module, random-like or functional-like. We combined the topological score with a functional similarity score in a fitness function that we used in a genetic algorithm to explore the co-expression network. To illustrate the use of TopoFun, we started from a subset of the Gene Ontology Biological Processes (GO-BPs) and showed that TopoFun efficiently retrieved genes that we omitted, and aggregated a number of novel genes to the initial GO-BP while improving module topology and functional similarity. Using an independent protein-protein interaction database, we confirmed that the novel genes gathered by TopoFun were functionally related to the original gene set.


2021 ◽  
Vol 12 ◽  
Author(s):  
Gita A. Pathak ◽  
Frank R. Wendt ◽  
Aranyak Goswami ◽  
Dora Koller ◽  
Flavio De Angelis ◽  
...  

Angiotensin-converting enzyme-2 (ACE2) receptor has been identified as the key adhesion molecule for the transmission of the SARS-CoV-2. However, there is no evidence that human genetic variation in ACE2 is singularly responsible for COVID-19 susceptibility. Therefore, we performed an integrative multi-level characterization of genes that interact with ACE2 (ACE2-gene network) for their statistically enriched biological properties in the context of COVID-19. The phenome-wide association of 51 genes including ACE2 with 4,756 traits categorized into 26 phenotype categories, showed enrichment of immunological, respiratory, environmental, skeletal, dermatological, and metabolic domains (p &lt; 4e-4). Transcriptomic regulation of ACE2-gene network was enriched for tissue-specificity in kidney, small intestine, and colon (p &lt; 4.7e-4). Leveraging the drug-gene interaction database we identified 47 drugs, including dexamethasone and spironolactone, among others. Considering genetic variants within ± 10 kb of ACE2-network genes we identified miRNAs whose binding sites may be altered as a consequence of genetic variation. The identified miRNAs revealed statistical over-representation of inflammation, aging, diabetes, and heart conditions. The genetic variant associations in RORA, SLC12A6, and SLC6A19 genes were observed in genome-wide association study (GWAS) of COVID-19 susceptibility. We also report the GWAS-identified variant in 3p21.31 locus, serves as trans-QTL for RORA and RORC genes. Overall, functional characterization of ACE2-gene network highlights several potential mechanisms in COVID-19 susceptibility. The data can also be accessed at https://gpwhiz.github.io/ACE2Netlas/.


2021 ◽  
Vol 8 ◽  
Author(s):  
Junhao Wang ◽  
Qizheng Han ◽  
Huizi Liu ◽  
Haihua Luo ◽  
Lei Li ◽  
...  

Radiotherapy (RT) plays an important role in the prognosis of lung adenocarcinoma (LUAD) patients, but the radioresistance (RR) of LUAD is still a challenge that needs to be overcome. The current study aimed to investigate LUAD patients with RR to illuminate the underlying mechanisms. We utilized gene set variation analysis (GSVA) and The Cancer Immunome Atlas (TCIA) database to characterize the differences in biological functions and neoantigen-coding genes between RR and radiosensitive (RS) patients. Weighted Gene co-expression network analysis (WGCNA) was used to explore the relationship between RT-related traits and hub genes in two modules, i.e., RR and RS; two representative hub genes for RR (MZB1 and DERL3) and two for RS (IFI35 and PSMD3) were found to be related to different RT-related traits. Further analysis of the hub genes with the Lung Cancer Explorer (LCE), PanglaoDB and GSVA resources revealed the differences in gene expression levels, cell types and potential functions. On this basis, the Tumor and Immune System Interaction Database (TISIDB) was used to identify the potential association between RR genes and B cell infiltration. Finally, we used the Computational Analysis of Resistance (CARE) database to identify specific gene-associated drugs for RR patients and found that GSK525762A and nilotinib might be promising candidates for RR treatment. Taken together, these results demonstrate that B cells in TME may have a significant impact on the RT and that these two drug candidates, GSK525762A and nilotinib, might be helpful for the treatment of RR patients.


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