2. Academic Validation
  3. A Metabolism-Driven Prognostic Model and PSMD14-SP1-GYS1 Axis Reveal Therapeutic Vulnerabilities in Melanoma

A Metabolism-Driven Prognostic Model and PSMD14-SP1-GYS1 Axis Reveal Therapeutic Vulnerabilities in Melanoma

  • J Invest Dermatol. 2025 Sep 16:S0022-202X(25)02430-3. doi: 10.1016/j.jid.2025.08.044.
Jiaheng Xie 1,  Songyun Zhao 2,  Dan Wu 3,  Chenfeng Ma 4,  Wei Yan 5,  Pengpeng Zhang 6,  Qingyu Lu 4,  Zeyu Wan 4,  Qikai Tang 4,  Liqun Li 2,  Ming Wang 7,  Yucang He 8
Affiliations

Affiliations

  • 1 Department of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha 410008, P. R. China; Department of Plastic and Aesthetic Surgery, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, Jiangsu, China.
  • 2 Department of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • 3 Department of Dermatology, Huashan Hospital, Fudan University, No. 12 of Wulumuqi Mild Street, Jingan District, Shanghai, 200040, China.
  • 4 Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, Jiangsu, China.
  • 5 Department of Burn and Plastic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • 6 Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China.
  • 7 Department of Plastic and Aesthetic Surgery, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, Jiangsu, China. Electronic address: wangming@jsph.org.cn.
  • 8 Department of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. Electronic address: heyucang0@wmu.edu.cn.
PMID: 40967300 DOI: 10.1016/j.jid.2025.08.044
Abstract

Melanoma is a highly aggressive cutaneous malignancy characterised by a strong propensity for metastasis and therapy resistance, with its progression being closely linked to metabolic reprogramming. This study integrated multi-omics data (TCGA, GEO, ENA) and advanced machine learning to develop prognostic and immunotherapy prediction models for melanoma, focusing on 114 metabolism-related pathways. COX regression identified 70 genes linked to survival, with functional enrichment revealing key metabolic pathway alterations. A Metabolism-Related Prognostic Model (MRPM) was constructed using 101 combinations of machine learning algorithms, demonstrating superior predictive accuracy across four cohorts. High-risk patients showed worse survival and immunotherapy response in melanoma and Other cancers. Tumor microenvironment analysis revealed MRPM's negative correlation with immune infiltration and positive association with tumor purity. Single-cell Sequencing highlighted MRPM gene enrichment in melanocytes. Mechanistically, GYS1 (the key gene in MRPM) emerged as a pivotal prognostic gene promoting melanoma proliferation and metastasis. Regulatory studies uncovered SP1's transcriptional control of GYS1 and PSMD14-mediated stabilisation of SP1 through K48-linked ubiquitination removal. In vivo validation confirmed that PSMD14 knockdown suppressed tumor growth via SP1-GYS1 axis disruption. This work establishes MRPM as a robust predictive tool and elucidates the PSMD14-SP1-GYS1 regulatory network as a potential therapeutic target in melanoma metabolism.

Keywords

Deubiquitination; GYS1; Melanoma; Metabolism; Multi-omics.

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