<abstract xmlns="http://www.w3.org/1999/xhtml">

<bold>Objectives</bold>:To identify key genes common to lung cancer and rheumatoid arthritis through WGCNA co-expression network and MCC algorithm analysis.
<bold>Methods</bold>: Initially, chip data related to lung cancer and rheumatoid arthritis were obtained from the GEO database for data integration and differential analysis, leading to the identification of key differentially expressed genes. Subsequently, WGCNA was utilized to construct a co-expression network, identifying susceptible modules and core genes. Further, common core genes in lung cancer and rheumatoid arthritis were identified through Venn diagrams, assessing their diagnostic accuracy in disease, analyzing differential expression, and constructing a co-expression network. Finally, GO and KEGG enrichment analyses were conducted to understand the functions and pathway enrichment of these core genes, and potential target drugs were predicted.
<bold>Results</bold>: Six lung cancer-related and three rheumatoid arthritis-related gene co-expression modules were constructed using WGCNA. The Turquoise module was identified as the susceptible module for lung cancer, while the Blue module was for rheumatoid arthritis. A total of 953 genes were included in the lung cancer hub genes, and 152 in the rheumatoid arthritis hub genes. Finally, 92 potential target drugs were predicted through the DGIdb database that may regulate the expression of 11 common hub genes.
<bold>Conclusion</bold>: We identified 24 common hub genes for lung cancer and rheumatoid arthritis, with the top 6 ranked by the MCC algorithm being FGR, SLA, GZMH, CSF2RB, PRF1, and CCRL2. This study paves the way for further exploration of the common pathogenesis of lung cancer and rheumatoid arthritis. However, further in vivo and in vitro experiments are required for validation and support.
</abstract>

Objectives:To identify key genes common to lung cancer and rheumatoid arthritis through WGCNA co-expression network and MCC algorithm analysis.
Methods: Initially, chip data related to lung cancer and rheumatoid arthritis were obtained from the GEO database for data integration and differential analysis, leading to the identification of key differentially expressed genes. Subsequently, WGCNA was utilized to construct a co-expression network, identifying susceptible modules and core genes. Further, common core genes in lung cancer and rheumatoid arthritis were identified through Venn diagrams, assessing their diagnostic accuracy in disease, analyzing differential expression, and constructing a co-expression network. Finally, GO and KEGG enrichment analyses were conducted to understand the functions and pathway enrichment of these core genes, and potential target drugs were predicted.
Results: Six lung cancer-related and three rheumatoid arthritis-related gene co-expression modules were constructed using WGCNA. The Turquoise module was identified as the susceptible module for lung cancer, while the Blue module was for rheumatoid arthritis. A total of 953 genes were included in the lung cancer hub genes, and 152 in the rheumatoid arthritis hub genes. Finally, 92 potential target drugs were predicted through the DGIdb database that may regulate the expression of 11 common hub genes.
Conclusion: We identified 24 common hub genes for lung cancer and rheumatoid arthritis, with the top 6 ranked by the MCC algorithm being FGR, SLA, GZMH, CSF2RB, PRF1, and CCRL2. This study paves the way for further exploration of the common pathogenesis of lung cancer and rheumatoid arthritis. However, further in vivo and in vitro experiments are required for validation and support.

Identification of shared hub genes in lung cancer and rheumatoid arthritis patients using bioinformatics approaches

Orthopedics, Jiangxi University of Chinese Medicine

Revista Romana de Medicina de Laborator

This work is licensed under the Creative Commons Attribution 4.0 International License.

Objectives:To identify key genes common to lung cancer and rheumatoid arthritis through WGCNA co-expression network and MCC algorithm analysis.
Methods:...

Identification of shared hub genes in lung cancer and rheumatoid arthritis patients using bioinformatics approaches

Data publikacji: 29 sty 2024

Zakres stron: 41 - 56

Otrzymano: 29 lis 2023

Przyjęty: 15 sty 2024

DOI: https://doi.org/10.2478/rrlm-2024-0007

Słowa kluczowe
WGCNA co-expression network, lung cancer, rheumatoid arthritis, key genes, bioinformatics

© 2024 ChangMao Deng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Identification of shared hub genes in lung cancer and rheumatoid arthritis patients using bioinformatics approaches

Data publikacji: 29 sty 2024

Zakres stron: 41 - 56

Otrzymano: 29 lis 2023

Przyjęty: 15 sty 2024

DOI: https://doi.org/10.2478/rrlm-2024-0007

Słowa kluczoweWGCNA co-expression network, lung cancer, rheumatoid arthritis, key genes, bioinformatics

© 2024 ChangMao Deng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
WGCNA co-expression network, lung cancer, rheumatoid arthritis, key genes, bioinformatics