Pseudotime‐Derived Seven‐Gene Prognostic Signature and TYRP1 in Melanoma
- 1Department of Phase I Clinical Trial Tianjin Medical University Cancer Institute and Hospital Tianjin China tmucih.com
Abstract
Background: Melanoma is characterized by high malignancy and complex immune microenvironment heterogeneity. A robust prognostic framework that integrates tumor cell state diversity with clinical outcome remains lacking.
Methods: Integrative scRNA‐seq analysis of public melanoma datasets was performed using Harmony batch correction, unsupervised clustering, and Monocle 3 pseudotime trajectory reconstruction to characterize tumor cell state transitions. Pseudotime‐dynamic genes were subjected to univariate Cox screening across four independent cohorts (GSE19234, GSE22153, GSE65904, TCGA), followed by LASSO‐Cox regression to construct a multigene prognostic model. Validation encompassed immune infiltration estimation, pathway activity scoring, tumor mutational burden (TMB) analysis, and protein‐level expression assessment.
Results: scRNA‐seq identified 14 major cell populations, within which tumor cell reclustering resolved seven functional states: melanocytic, neural crest‐like, MHC‐II high, stress/transitional, proliferating, IFN‐stimulated, and invasive. Pseudotime analysis reconstructed a directional evolution trajectory originating from the melanocytic state, with the stress/transitional state acting as a central hub. Gene module enrichment further delineated the core transcriptional programs of each state. A seven‐gene prognostic model (GBP4, ITGB4, GBP2, TNFSF13B, TSC22D3, CIITA, TYRP1) achieved consistent survival stratification across all four cohorts (all p < 0.01) and outperformed 76 published signatures by C‐index ranking. High‐risk patients exhibited an immunosuppressive microenvironment, activation of protumorigenic pathways, and broad suppression of the cancer–immunity cycle. Risk score and TMB were statistically independent, yet their combined stratification significantly refined prognostic resolution (p < 0.001). TYRP1, the sole risk gene in the model, demonstrated tumor‐specific protein upregulation by IHC and consistently predicted poor overall survival across three independent transcriptomic cohorts.
Conclusion: This study presents an integrated single‐cell and multicohort prognostic framework for melanoma, revealing the mechanistic link between tumor cell state evolution and immune microenvironment remodeling, and establishing TYRP1 as an independent prognostic biomarker to guide precision immunotherapy decision‐making.
Related
- An Oxidative Stress-Associated Seven-Gene Prognostic Signature in Lung Adenocarcinoma: Integrative Transcriptomic Analysis Across Public Cohorts
- Characterization of the crotonylation landscape in gastric cancer: a machine learning-derived prognostic signature and the oncogenic role of TIMP1
- Construction of a prognostic model and immune landscape analysis for lung adenocarcinoma utilizing a bioinformatics signature derived from acrolein-related genes, and identification of associated subtypes.
- MGP_HNSC32: A H&E Morphology‐Derived 32‐Gene Prognostic Signature in Head and Neck Squamous Cell Carcinoma
- A Literature-Derived Muscle Wasting-Related Prognostic Signature Identifies ZBTB7C as a Candidate Associated with Tumor-Related Skeletal Muscle Alterations in Esophageal Cancer
BibTeX
@article{li2026pseudotimederived,
title = {{Pseudotime‐Derived Seven‐Gene Prognostic Signature and TYRP1 in Melanoma}},
author = {Xiaoqing Li and Yilin Deng and Yue Song and Xiaohui Liu and Hongli Li},
journal = {International Journal of Genomics},
year = {2026},
month = nov,
doi = {10.1155/ijog/5922810},
url = {https://doi.org/10.1155/ijog/5922810},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Pseudotime‐Derived Seven‐Gene Prognostic Signature and TYRP1 in Melanoma
AU - Xiaoqing Li
AU - Yilin Deng
AU - Yue Song
AU - Xiaohui Liu
AU - Hongli Li
PY - 2026
DA - 2026/11/02
JO - International Journal of Genomics
DO - 10.1155/ijog/5922810
UR - https://doi.org/10.1155/ijog/5922810
AB - Background: Melanoma is characterized by high malignancy and complex immune microenvironment heterogeneity. A robust prognostic framework that integrates tumor cell state diversity with clinical outcome remains lacking. Methods: Integrative scRNA‐seq analysis of public melanoma datasets was performed using Harmony batch correction, unsupervised clustering, and Monocle 3 pseudotime trajectory reconstruction to characterize tumor cell state transitions. Pseudotime‐dynamic genes were subjected to univariate Cox screening across four independent cohorts (GSE19234, GSE22153, GSE65904, TCGA), followed by LASSO‐Cox regression to construct a multigene prognostic model. Validation encompassed immune infiltration estimation, pathway activity scoring, tumor mutational burden (TMB) analysis, and protein‐level expression assessment. Results: scRNA‐seq identified 14 major cell populations, within which tumor cell reclustering resolved seven functional states: melanocytic, neural crest‐like, MHC‐II high, stress/transitional, proliferating, IFN‐stimulated, and invasive. Pseudotime analysis reconstructed a directional evolution trajectory originating from the melanocytic state, with the stress/transitional state acting as a central hub. Gene module enrichment further delineated the core transcriptional programs of each state. A seven‐gene prognostic model (GBP4, ITGB4, GBP2, TNFSF13B, TSC22D3, CIITA, TYRP1) achieved consistent survival stratification across all four cohorts (all p < 0.01) and outperformed 76 published signatures by C‐index ranking. High‐risk patients exhibited an immunosuppressive microenvironment, activation of protumorigenic pathways, and broad suppression of the cancer–immunity cycle. Risk score and TMB were statistically independent, yet their combined stratification significantly refined prognostic resolution (p < 0.001). TYRP1, the sole risk gene in the model, demonstrated tumor‐specific protein upregulation by IHC and consistently predicted poor overall survival across three independent transcriptomic cohorts. Conclusion: This study presents an integrated single‐cell and multicohort prognostic framework for melanoma, revealing the mechanistic link between tumor cell state evolution and immune microenvironment remodeling, and establishing TYRP1 as an independent prognostic biomarker to guide precision immunotherapy decision‐making.
SN - 2314-436X
SN - 2314-4378
ER -