The clinical prognostic value of lncRNA FOXP4-AS1 in cancer patients: A meta-analysis and bioinformatics analysis based on TCGA datasets

Shu, Qiang; Liu, Xiaoling; Yang, Jushu; Mou, Tinggang; Xie, Fei

doi:10.1097/md.0000000000031439

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…Bioinformatics is an emerging interdisciplinary field that studies a variety of omics data, including transcriptomics, proteomics, and other biological data. The research methods mainly include the collection (collection and screening), processing (editing, sorting, management, and display), utilization (calculation and simulation), and analysis of biological data ( 9 , 10 ). Experience has shown that bioinformatics technology has great application value in the screening of disease biomarkers, which is of great significance for the diagnosis, treatment, and prognosis of diseases, and can provide a more comprehensive and profound understanding of diseases ( 11 - 14 ).…”

Section: Introductionmentioning

confidence: 99%

Screening and identification of hub genes for ischemic cardiomyopathy and construction and validation of a clinical prognosis model using bioinformatics analysis

Wang,

Tang,

Bai

et al. 2024

J Thorac Dis

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Background Myocardial ischemia and hypoxia may result in myocardial cell necrosis, scar formation, and hyperplasia. We aim to explore the differentially expressed genes (DEGs) in ischemic cardiomyopathy (ICM), construct and identify a clinical prognosis model using bioinformatics methods, so as to screen potential biomarkers of ICM to provide a basis for the early diagnosis and treatment of ICM. Methods Based on the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, R language was used to screen DEGs in healthy myocardial (n=5) and ICM myocardial tissues (n=12). DEGs were analyzed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI). Receiver operating characteristic (ROC) curves were drawn to verify the target genes. Results A total of 259 genes with significantly changed fold change (FC) values were obtained through conditional screening, including up-regulated genes and down-regulated genes. The first two hub genes [interleukin-6 ( IL-6 ) and Ras homologous gene family member A ( RHOA )] with the largest degree value among the above up-regulated and down-regulated genes were selected and their expression values were combined in the gene chip to draw the ROC curve based on the pROC package of R language. The area under the ROC curve (AUC) values of IL-6 and RHOA were 0.956 and 0.995, respectively. The expression levels of Sqstm1, Nos2, IL-6, RHOA , and Zfp36 genes in the ICM group are lower than those in the blank control group and the difference was statistically significant (P<0.05). RHOA and Stat3 were identified as the key genes controlling the occurrence and development of ICM. Conclusions ICM is closely related to the changes of extracellular matrix (ECM) and oxidoreductase activity. The IL-6 and RHOA are expected to become potential targets for ICM treatment.

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