Purpose: To explore the underlying molecular mechanism of pterygium and identify the key genes regulating the development of pterygium.
Methods: Differentially expressed mRNAs were obtained from the Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID (http://david.abcc.ncifcrf.gov/). The differential expressions of hub genes were verified using the reverse transcription-real-time fluorescent quantitative PCR (RT-qPCR). The function of the hub genes was further confirmed based on associations between the single nucleotide polymorphisms (SNPs) in hub genes and pterygium. The genotyping results were analyzed using SNPStats online software in five gene models, including codominant, dominant, recessive, overdominant, and log-additive. Five gene models were analyzed using SNPStats.
Results: We found that 240 genes were significantly differentially expressed. Functional enrichment analysis showed that focal adhesion pathway is extremely meaningful, among which JUN, FN1, and LAMB1 were verified to significantly differentially express in pterygium (P = 0.0011, P = 0.0018, and P = 0.0050, respectively). However, the all nine candidate SNPs (rs11688, rs3748814 in JUN; rs1263, rs1132741, rs1250259 in FN1; rs20556, rs35710474, rs25659, rs4320486 in LAMB1), were not statistically associated with pterygium.
Conclusion: Our results demonstrated that JUN, FN1, and LAMB1 polymorphisms were not associated with susceptibility to pterygium in Chinese Han population. Considering the fact that these three genes are differentially expressed in pterygium, further research is needed to explain its involvement in pterygium.
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http://dx.doi.org/10.1080/13816810.2022.2065511 | DOI Listing |
Postgrad Med J
December 2024
Neck-Shoulder and Lumbocrural Pain Hospital of Shandong First Medical University, Shandong First Medical University (Shandong Academy of Medical Sciences), No. 18877, Jing 10 Road, Jinan 250000, Shandong, China.
Background: The mechanisms underlying osteoarthritis (OA) remain unclear, and effective treatments are lacking. This study aims to identify OA-related genes and explore their potential in drug repositioning for OA treatment.
Methods: Transcriptome-wide association studies (TWAS) were performed using genome-wide association studies summary data and expression quantitative trait loci data from the Genotype-Tissue Expression project.
Asian Pac J Cancer Prev
November 2024
Proteomics Research Center, System Biology Institute, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Med J Islam Repub Iran
June 2024
Surgical Oncology, Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: Breast cancer is a complex and heterogeneous disease, and understanding its regulatory mechanisms and network characteristics is essential for identifying therapeutic targets and developing effective treatment strategies. This study aimed to unravel the intricate network of interactions involving differentially expressed genes, microribonucleic acid (miRNAs), and proteins in breast cancer through an integrative analysis of multi-omic data from Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset.
Methods: The TCGA-BRCA dataset was used for data acquisition, which included RNA sequencing data for gene expression, miRNA sequencing data for miRNA expression, and protein expression quantification data.
Sci Rep
August 2024
Bioinformatics Lab (Dry), Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
J Proteome Res
September 2024
Peking University Fifth School of Clinical Medicine, Beijing 100730, China.
Colorectal cancer (CRC) involves a complex interaction between tumor cells and immune cells, notably monocytes, leading to immunosuppression. This study explored these interactions using in vitro coculture systems of THP-1 cells and CRC cell lines, employing quantitative proteomics to analyze protein changes in monocytes. Multiple analytical methods were utilized to delineate the altered proteomic landscape, identify key proteins, and their associated functional pathways for comprehensive data analysis.
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