ArticleFrontiers in cell and developmental biology2021
Identification of Key Genes With Differential Correlations in Lung Adenocarcinoma.
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 23 citations in OpenAlex.
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- HIGD1B, as a novel prognostic biomarker, is involved in regulating the tumor microenvironment and immune cell infiltration; its overexpression leads to poor prognosis in gastric cancer patients.Frontiers in immunology · 2024Article
- Death-associated protein 3 in cancer-discrepant roles of DAP3 in tumours and molecular mechanisms.Frontiers in oncology · 2023Review
- Death associated protein‑3 (DAP3) and DAP3 binding cell death enhancer‑1 (DELE1) in human colorectal cancer, and their impacts on clinical outcome and chemoresistance.International journal of oncology · 2023Article
- ISG20L2 suppresses bortezomib antimyeloma activity by attenuating bortezomib binding to PSMB5.JCI insight · 2022Article
- Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.Frontiers in immunology · 2022Article
- Identification of Hub Genes With Differential Correlations in Sepsis.Frontiers in genetics · 2022Article
- Transcriptomics Changes in the Peritoneum of Mice with Lipopolysaccharide-Induced Peritonitis.International journal of molecular sciences · 2021Article
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Authors and funding
13 authors at 5 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the advent of large-scale molecular profiling, an increasing number of oncogenic drivers contributing to precise medicine and reshaping classification of lung adenocarcinoma (LUAD) have been identified. However, only a minority of patients archived improved outcome under current standard therapies because of the dynamic mutational spectrum, which required expanding susceptible gene libraries. Accumulating evidence has witnessed that understanding gene regulatory networks as well as their changing processes was helpful in identifying core genes which acted as master regulators during carcinogenesis. The present study aimed at identifying key genes with differential correlations between normal and tumor status.
methodsWeighted gene co-expression network analysis (WGCNA) was employed to build a gene interaction network using the expression profile of LUAD from The Cancer Genome Atlas (TCGA). R package DiffCorr was implemented for the identification of differential correlations between tumor and adjacent normal tissues. STRING and Cytoscape were used for the construction and visualization of biological networks.
resultsA total of 176 modules were detected in the network, among which yellow and medium orchid modules showed the most significant associations with LUAD. Then genes in these two modules were further chosen to evaluate their differential correlations. Finally, dozens of novel genes with opposite correlations including ATP13A4-AS1, HIGD1B, DAP3, and ISG20L2 were identified. Further biological and survival analyses highlighted their potential values in the diagnosis and treatment of LUAD. Moreover, real-time qPCR confirmed the expression patterns of ATP13A4-AS1, HIGD1B, DAP3, and ISG20L2 in LUAD tissues and cell lines.
conclusionOur study provided new insights into the gene regulatory mechanisms during transition from normal to tumor, pioneering a network-based algorithm in the application of tumor etiology.
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