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TRUVACE RECORD VERSION
record: TRV-2026-1322
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-11T06:54:52.951725Z
status: published
lens: g_space
sector: health
headline: Comprehensive analysis and external validation of a glycolysis-related gene signature as a prognostic index for skin cutaneous melanoma
dek: Skin cutaneous melanoma (SKCM) is the third most common cause of cancer-related deaths globally. Today, precise biomarkers to predict melanoma prognosis are quite limited. Aerobic glycolysis serves as the primary metabolic pathway for malignancies to meet their rapid growth demands. This study built a glycolysis-associated gene (GAG) signature to evaluate prognosis and immunotherapy response of SKCM. Data acquired from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases were evalua…
gain_title: A four-gene glycolysis risk model built with machine learning effectively forecasted survival and treatment efficacy for skin cutaneous melanoma patients.
problem_title: (none)
trace_subject: (none)
gain_reading: A four-gene glycolysis risk model built with machine learning effectively forecasted survival and treatment efficacy for skin cutaneous melanoma patients.
gain_evidence: The risk model could effectively forecast SKCM patient survival and treatment efficacy.
problem_reading: (none)
problem_evidence: (none)
quick_read: By October 2026, researchers had combined GTEx and TCGA data with machine learning to build a four-gene glycolysis signature for skin cutaneous melanoma. The signature, based on GOT2, CLN6, GYS1, and IDUA, was developed into a risk model reported to forecast patient survival and treatment efficacy, with additional in vitro work on GYS1 and CLN6.

The work matters because melanoma lacks precise prognostic biomarkers, and linking metabolic genes to immune infiltration could inform risk stratification and immunotherapy decisions. What remains uncertain is whether the model generalizes beyond database cohorts and in vitro models to prospective clinical use, as validation was limited to laboratory experiments at the time of publication.
limitation: 
tag: Evidence-backed gain
key_points: Study used GTEx and TCGA databases to obtain differentially expressed glycolysis-associated genes. | Machine learning pinpointed four key genes: GOT2, CLN6, GYS1, and IDUA as candidate prognostic biomarkers. | In vitro validation showed overexpression of GYS1 and CLN6 promoted viability, invasion, and proliferation of melanoma cells. | ELISA and western blot showed GYS1 overexpression increased TNF-alpha and IL-1beta secretion and activated NF-kappa B signaling.
rundown: Researchers analyzed differentially expressed glycolysis-associated genes from GTEx and TCGA, then applied machine learning to select GOT2, CLN6, GYS1, and IDUA for a risk model. The model was tested for its ability to forecast survival and immunotherapy response in SKCM.

Functional follow-up focused on GYS1 and CLN6. In vitro overexpression experiments demonstrated increased melanoma cell viability, invasion, and proliferation, with GYS1 linked to higher proinflammatory cytokine secretion and activation of the nuclear factor kappa B pathway promoting an immunosuppressive microenvironment.
sources:
- peer_reviewed | Melanoma Research | https://doi.org/10.1097/cmr.0000000000001143 | 2026-10-06
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