Identification of key differentially expressed mRNAs and microRNAs in non‑small cell lung cancer using bioinformatics analysis

Wang, Weiwei; Wang, Shanshan; Pan, Lei

doi:10.3892/etm.2020.9105

Cited by 5 publications

(1 citation statement)

References 73 publications

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“…In recent decades, advanced gene microarrays and high-throughput sequencing methods have been used to study CRC gene expression, therapeutic targets, and pathogenesis [ 11 ]. Analysis of gene expression microarray data with bioinformatics provides an effective tool for identifying previously unknown mRNAs and microRNAs (miRNAs) that may have a role in cancer pathogenesis [ 12 ]. CRC is associated with abnormal gene expression and mutations.…”

Section: Introductionmentioning

confidence: 99%

Identification of Key MicroRNAs and Genes between Colorectal Adenoma and Colorectal Cancer via Deep Learning on GEO Databases and Bioinformatics

Zhang

Jin

Liu

et al. 2023

Contrast Media & Molecular Imaging

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Background. Deep learning techniques are gaining momentum in medical research. Colorectal adenoma (CRA) is a precancerous lesion that may develop into colorectal cancer (CRC) and its etiology and pathogenesis are unclear. This study aims to identify transcriptome differences between CRA and CRC via deep learning on Gene Expression Omnibus (GEO) databases and bioinformatics in the Chinese population. Methods. In this study, three microarray datasets from the GEO database were used to identify the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) in CRA and CRC. The FunRich software was performed to predict the targeted mRNAs of DEMs. The targeted mRNAs were overlapped with DEGs to determine the key DEGs. Molecular mechanisms of CRA and CRC were evaluated using enrichment analysis. Cytoscape was used to construct protein–protein interaction (PPI) and miRNA–mRNA regulatory networks. We analyzed the expression of key DEMs and DEGs, their prognosis, and correlation with immune infiltration based on the Kaplan–Meier plotter, UALCAN, and TIMER databases. Results. A total of 38 DEGs are obtained after the intersection, including 11 upregulated genes and 27 downregulated genes. The DEGs were involved in the pathways, including epithelial-to-mesenchymal transition, sphingolipid metabolism, and intrinsic pathway for apoptosis. The expression of has-miR-34c ( P = 0.036 ), hsa-miR-320a ( P = 0.045 ), and has-miR-338 ( P = 0.0063 ) was correlated with the prognosis of CRC patients. The expression levels of BCL2, PPM1L, ARHGAP44, and PRKACB in CRC tissues were significantly lower than normal tissues ( P < 0.001 ), while the expression levels of TPD52L2 and WNK4 in CRC tissues were significantly higher than normal tissues ( P < 0.01 ). These key genes are significantly associated with the immune infiltration of CRC. Conclusion. This preliminary study will help identify patients with CRA and early CRC and establish prevention and monitoring strategies to reduce the incidence of CRC.

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Section: Introductionmentioning

confidence: 99%

Identification of Key MicroRNAs and Genes between Colorectal Adenoma and Colorectal Cancer via Deep Learning on GEO Databases and Bioinformatics

Zhang

Jin

Liu

et al. 2023

Contrast Media & Molecular Imaging

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Research progress of nanocarriers for gene therapy targeting abnormal glucose and lipid metabolism in tumors

Zeng

Zhu

et al. 2021

Drug Delivery

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Bioinformatics analysis reveals three key genes and four survival genes associated with youth-onset NSCLC

Han

Ren

2022

Open Medicine

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Youth-onset non-small cell lung cancer (NSCLC) is a heterogeneous disease. It has a unique clinicopathology and special genetic background. In this study, three key genes, CDC20, CCNB2, and BUB1, have been identified in youth-onset NSCLC tumor tissues based on the TCGA and GEO cohorts. Functional enrichment analysis reveals that the “oocyte meiosis,” “cell cycle,” and the “P53 signaling pathway” are significantly enriched. Additionally, four survival genes, including AKAP12, CRIM1, FEN1, and SLC7A11, that affect the prognosis of youth-onset NSCLC patients are identified in this study. Finally, we construct a risk model to predict the overall survival of youth-onset NSCLC patients, the AUC of the risk model in 1, 3, and 5 years of overall survival is 0.808, 0.844, and 0.728. This study aims to provide a novel idea to explore the pathogenic genes of youth-onset NSCLC.

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Identification of key differentially expressed mRNAs and microRNAs in non‑small cell lung cancer using bioinformatics analysis

Cited by 5 publications

References 73 publications

Identification of Key MicroRNAs and Genes between Colorectal Adenoma and Colorectal Cancer via Deep Learning on GEO Databases and Bioinformatics

Identification of Key MicroRNAs and Genes between Colorectal Adenoma and Colorectal Cancer via Deep Learning on GEO Databases and Bioinformatics

Research progress of nanocarriers for gene therapy targeting abnormal glucose and lipid metabolism in tumors

Bioinformatics analysis reveals three key genes and four survival genes associated with youth-onset NSCLC

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