Abstract
Superoxide Dismutase 3 (SOD3) scavenges extracellular superoxide giving a hydrogen peroxide metabolite. Both Reactive Oxygen Species diffuse through aquaporins causing oxidative stress and biomolecular damage. SOD3 is differentially expressed in cancer and this research utilises Gene Expression Omnibus data series GSE2109 with 2,158 cancer samples. Genome-wide expression correlation analysis was conducted with SOD3 as the seed gene. Categorical SOD3 Pearson Correlation gene lists incrementing in correlation strength by 0.01 from ρ≥|0.34| to ρ≥|0.41| were extracted from the data. Positively and negatively SOD3 correlated genes were separated for each list and checked for significance against disease overlapping genes in the ClinVar and Orphanet databases via Enrichr. Disease causal genes were added to the relevant gene list and checked against Gene Ontology, Phenotype Ontology, and Elsevier Pathways via Enrichr before the significant ontologies containing causal and non-overlapping genes were reviewed with a literature search for possible disease and oxidative stress associations. 12 significant individually discriminated disorders were identified: Autosomal Dominant Cutis Laxa (p = 6.05x10-7), Renal Tubular Dysgenesis of Genetic Origin (p = 6.05x10-7), Lethal Arteriopathy Syndrome due to Fibulin-4 Deficiency (p = 6.54x10-9), EMILIN-1-related Connective Tissue Disease (p = 6.54x10-9), Holt-Oram Syndrome (p = 7.72x10-10), Multisystemic Smooth Muscle Dysfunction Syndrome (p = 9.95x10-15), Distal Hereditary Motor Neuropathy type 2 (p = 4.48x10-7), Congenital Glaucoma (p = 5.24x210-9), Megacystis-Microcolon-Intestinal Hypoperistalsis Syndrome (p = 3.77x10-16), Classical-like Ehlers-Danlos Syndrome type 1 (p = 3.77x10-16), Retinoblastoma (p = 1.9x10-8), and Lynch Syndrome (p = 5.04x10-9). 35 novel (21 unique) genes across 12 disorders were identified: ADNP, AOC3, CDC42EP2, CHTOP, CNN1, DES, FOXF1, FXR1, HLTF, KCNMB1, MTF2, MYH11, PLN, PNPLA2, REST, SGCA, SORBS1, SYNPO2, TAGLN, WAPL, and ZMYM4. These genes are proffered as potential biomarkers or therapeutic targets for the corresponding rare diseases discussed.
| Original language | English |
|---|---|
| Article number | e0313139 |
| Pages (from-to) | 1-31 |
| Number of pages | 31 |
| Journal | PLoS One |
| Volume | 19 |
| Issue number | 10 |
| Early online date | 31 Oct 2024 |
| DOIs | |
| Publication status | Published online - 31 Oct 2024 |
Bibliographical note
Publisher Copyright:© 2024 Stanworth, Zhang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability Statement
The raw data for the gene expressions that this research is based on are available in the Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/ and can be accessed with the accession number GSE2109. The gene list data underlying this article are available in Enrichr at https://maayanlab.cloud/Enrichr/ (unique URLs can be found inSupplementary File 2). Software coding is available in Supplementary File 1. The methodology and 24 2 / 31
code used are also available at doi.org/10.17504/protocols.io.rm7vz.
Funding
The author(s) received no specific funding for this work.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Humans
- Computational Biology/methods
- Reactive Oxygen Species/metabolism
- Superoxide Dismutase/genetics
- Rare Diseases/genetics
- Gene Ontology
- Oxidative Stress/genetics
- Rare Diseases - genetics
- Superoxide Dismutase - genetics - metabolism
- Computational Biology - methods
- Reactive Oxygen Species - metabolism
- Oxidative Stress - genetics
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