Gene Expression Differences and Similarities between Bipolar Disorder and Gliomas Studied by Brain Expression Data

Authors

  • Miao Wang

DOI:

https://doi.org/10.61173/j66q5f51

Keywords:

bipolar disorder, glioma, differential expression analysis, enrichment analysis

Abstract

Both bipolar disorder and glioma are highly prevalent diseases. Bipolar disorder is a significant mental illness, while glioma constitutes 46% of intracranial tumors. By conducting differential expression analysis, Weighted Gene Coexpression Network Analysis, and Gene Ontology enrichment analysis on brain expression data of bipolar disorder and glioma, it was found that these two conditions share three terms: protein localization to organelle, organelle localization, and protein maturation. Exploring targeted therapy and drug-targeting research on these three terms can offer novel insights for the treatment of both bipolar disorder and glioma.

References

[1] Agarwal, A., Koppstein, D., Rozowsky, J., Sboner, A., Habegger, L., Hillier, L. W., . . . Gerstein, M. (2010). Comparison and calibration of transcriptome data from RNA- Seq and tiling arrays. BMC Genomics, 11(1), 1-16.

[2] Bowling KM, R. R., Lasseigne BN, Cooper SJ, Myers RM. (Jun 22, 2017). RNA-sequencing of human post-mortem brain tissues. Retrieved from: https://www.ncbi.nlm.nih.gov/geo/ query/acc.cgi?acc=GSE80655

[3] Grande, I., Berk, M., Birmaher, B., & Vieta, E. (2016). Bipolar disorder. The Lancet, 387(10027), 1561-1572. doi:10.1016/S0140-6736(15)00241-X

[4] Hung, M.-C., & Link, W. (2011). Protein localization in disease and therapy. Journal of cell science, 124(20), 3381-3392.

[5] Langfelder, P., & Horvath, S. (2008). WGCNA is an R package for weighted correlation network analysis. BMC Bioinformatics, 9(1), 1-13.

[6] Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014). voom: Precision weights unlock linear model analysis tools for RNA- seq read counts. Genome biology, 15(2), 1-17.

[7] Marguerat, S., & Bähler, J. (2010). RNA-seq: from technology to biology. Cellular and molecular life sciences, 67, 569-579.

[8] NIH. TCGA-GBM [Gene Level Copy Number]. Retrieved from: https://portal.gdc.cancer.gov/repository

[9] Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. bioinformatics, 26(1), 139-140.

[10] Saraogi, I., & Shan, S.-o. (2014). Co-translational protein targeting the bacterial membrane. Biochimica et Biophysica Acta (BBA)-Molecular Cell Research, 1843(8), 1433-1441.

[11] Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical applications in genetics and molecular biology, 4(1).

Downloads

Published

2024-01-03