Identification of Potential Biomarkers Related to the Progression and Prognosis of Parkinson's Disease and Melanoma via Combined System Biology Approaches

Parkinson's disease (PD) and melanoma are considered high risk in affecting an individual's health. The association between PD and melanoma has been reported with consistent results by various epidemiological studies. The identification of differentially expressed genes (DEGs) and pathways between the two diseases can support the findings of the epidemiological studies. Transcriptomics studies play a vital role in investigating DEGs with better specificity and sensitivity. Hence, we have performed transcriptomic data analysis to discover the gene expression profiles and significant pathways and provide insights into the relationship between PD and melanoma. The DEG analysis revealed that genes, such as CLU, glial fibrillary acidic protein (GFAP), and bone morphogenetic protein 6 (BMP6), highly expressed in melanoma, were associated with the progression of PD and genes such as BAG6, heat shock protein family A member 1B (HSPA1B), and ubiquitin-conjugating enzyme E2C (UBE2C), highly expressed in PD, were associated with the progression of melanoma based on evidence from previous studies. Out of the significant common KEGG pathways observed between PD and melanoma, tryptophan metabolism, steroid biosynthesis, peroxisome proliferator-activated receptor (PPAR) signaling and arginine biosynthesis were directly related to the pathogenesis and progression of the two diseases. Therefore, these findings have elucidated the involvement of multiple genes and pathways in the association of PD and melanoma.

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Publication Details

Journal
Biotechnology and Applied Biochemistry
Published
2025-08-12
DOI
https://doi.org/10.1002/bab.70036
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Identification of Potential Biomarkers Related to the Progression and Prognosis of Parkinson's Disease and Melanoma via Combined System Biology Approaches

Murugan Sevanan, Ankita Murmu, Muthu Kumar Thirunavukkarasu, Riddhi Upadhyay
1 links
Biotechnology and Applied Biochemistry
Computational Drug Discovery Methods
article

Identification of Potential Biomarkers Related to the Progression and Prognosis of Parkinson's Disease and Melanoma via Combined System Biology Approaches

Murugan Sevanan, Ankita Murmu, Muthu Kumar Thirunavukkarasu, Riddhi Upadhyay
article en

Abstract

Parkinson's disease (PD) and melanoma are considered high risk in affecting an individual's health. The association between PD and melanoma has been reported with consistent results by various epidemiological studies. The identification of differentially expressed genes (DEGs) and pathways between the two diseases can support the findings of the epidemiological studies. Transcriptomics studies play a vital role in investigating DEGs with better specificity and sensitivity. Hence, we have performed transcriptomic data analysis to discover the gene expression profiles and significant pathways and provide insights into the relationship between PD and melanoma. The DEG analysis revealed that genes, such as CLU, glial fibrillary acidic protein (GFAP), and bone morphogenetic protein 6 (BMP6), highly expressed in melanoma, were associated with the progression of PD and genes such as BAG6, heat shock protein family A member 1B (HSPA1B), and ubiquitin-conjugating enzyme E2C (UBE2C), highly expressed in PD, were associated with the progression of melanoma based on evidence from previous studies. Out of the significant common KEGG pathways observed between PD and melanoma, tryptophan metabolism, steroid biosynthesis, peroxisome proliferator-activated receptor (PPAR) signaling and arginine biosynthesis were directly related to the pathogenesis and progression of the two diseases. Therefore, these findings have elucidated the involvement of multiple genes and pathways in the association of PD and melanoma.

Biotechnology and Applied Biochemistry
Karunya University (IN), National Institute of Technology Warangal (IN)
Openalex Percentile: Top 78%
Computational Drug Discovery Methods
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