Exploring the impact of deubiquitination on melanoma prognosis through single-cell RNA sequencing DOI Creative Commons
Peng Su, Jiaheng Xie,

Xiaotong He

et al.

Frontiers in Genetics, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 5, 2024

Background Cutaneous melanoma, characterized by the malignant proliferation of melanocytes, exhibits high invasiveness and metastatic potential. Thus, identifying novel prognostic biomarkers therapeutic targets is essential. Methods We utilized single-cell RNA sequencing data (GSE215120) from Gene Expression Omnibus (GEO) database, preprocessing it with Seurat package. Dimensionality reduction clustering were executed through Principal Component Analysis (PCA) Uniform Manifold Approximation Projection (UMAP). Cell types annotated based on known marker genes, AUCell algorithm assessed enrichment deubiquitination-related genes. Cells categorized into DUB_high DUB_low groups scores, followed differential expression analysis. Importantly, we constructed a robust model utilizing various which was evaluated in TCGA cohort an external validation cohort. Results Our model, developed using Random Survival Forest (RSF) Ridge Regression methods, demonstrated excellent predictive performance, evidenced C-index AUC values across multiple cohorts. Furthermore, analyses immune cell infiltration tumor microenvironment scores revealed significant differences distribution characteristics between high-risk low-risk groups. Functional experiments indicated that TBC1D16 significantly impacts migration melanoma cells. Conclusion This study highlights critical role deubiquitination presents effectively stratifies patient risk. The model’s strong ability enhances clinical decision-making provides framework for future studies potential mechanisms progression. Further exploration this applicability settings are warranted.

Language: Английский

Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer DOI Creative Commons

Longpeng Li,

Jinfeng Zhao, Yaxin Wang

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 14

Published: Jan. 6, 2025

Programmed cell death (PCD) is closely related to the occurrence, development, and treatment of breast cancer. The aim this study was investigate association between various programmed patterns prognosis cancer (BRCA) patients. levels 19 different deaths in were assessed by ssGSEA analysis, these PCD scores summed obtain PCDS for each sample. relationship with immune as well metabolism-related pathways explored. PCD-associated subtypes obtained unsupervised consensus clustering differentially expressed genes analyzed. prognostic signature (PCDRS) constructed best combination 101 machine learning algorithm combinations, C-index PCDRS compared 30 published signatures. In addition, we analyzed relation therapeutic responses. distribution cells explored single-cell analysis spatial transcriptome analysis. Potential drugs targeting key Cmap. Finally, expression clinical tissues verified RT-PCR. showed higher normal. Different groups significant differences pathways. PCDRS, consisting seven genes, robust predictive ability over other signatures datasets. high group had a poorer strongly associated cancer-promoting tumor microenvironment. low exhibited anti-cancer immunity responded better checkpoint inhibitors chemotherapy-related drugs. Clofibrate imatinib could serve potential small-molecule complexes SLC7A5 BCL2A1, respectively. mRNA upregulated tissues. can be used biomarker assess response BRCA patients, which offers novel insights monitoring personalization

Language: Английский

Citations

1
Integrated analysis of multiple programmed cell death-related prognostic genes and functional validation of apoptosis-related genes in osteosarcoma DOI
Zhen Tang, Zhang Zhi,

Jungang Zhao

et al.

International Journal of Biological Macromolecules, Journal Year: 2025, Volume and Issue: unknown, P. 142113 - 142113

Published: March 1, 2025

Language: Английский

Citations

1
Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma DOI Creative Commons

Mengmeng Hua,

Tao Li

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 24, 2025

To integrate machine learning and multiomic data on lactylation-related genes (LRGs) for molecular typing prognosis prediction in lung adenocarcinoma (LUAD). LRG mRNA long non-coding RNA transcriptomes, epigenetic methylation data, somatic mutation from The Cancer Genome Atlas LUAD cohort were analyzed to identify lactylation cancer subtypes (CSs) using 10 multiomics ensemble clustering techniques. findings then validated the GSE31210 GSE13213 cohorts. A model was developed identified hub LRGs divide patients into high- low-risk groups. effectiveness of this validated. We two CSs, which Nine LRGs, namely HNRNPC, PPIA, BZW1, GAPDH, H2AFZ, RAN, KIF2C, RACGAP1, WBP11, used construct model. In subsequent validation, high-risk group included more with stage T3 + 4, N1 2 3, M1, III IV cancer; higher recurrence/metastasis rates; lower 1, 5 year overall survival rates. oncogenic pathway analysis, most mutations detected group. tumor microenvironment analysis illustrated that immune activity notably elevated patients, indicating they might strongly respond immunotherapy than patients. Further, oncoPredict revealed have increased sensitivity chemotherapeutics. Overall, we a combines prognosis. Our represent valuable reference further understanding important function modification pathways progression.

Language: Английский

Citations

0
Linking Parkinson’s disease and melanoma: the impact of copper-driven cuproptosis and related mechanisms DOI Creative Commons
Quan Wang,

Yinghui Duan,

Yu Xu

et al.

npj Parkinson s Disease, Journal Year: 2025, Volume and Issue: 11(1)

Published: April 13, 2025

Patients with Parkinson's disease (PD) exhibit an increased risk of melanoma, implying shared yet incompletely understood molecular mechanisms. This study aimed to delineate these common and distinct pathways by analyzing gene expression profiles from the Gene Expression Omnibus. A total 90 differentially expressed genes (DEGs) were commonly regulated, while 173 DEGs exhibited divergent regulation between PD melanoma. Protein-protein interaction analysis identified SNCA as a central node within 21-protein network. LASSO regression revealed 13 hub (e.g., CCNB1, CCNH, CORO1C, GSN) high diagnostic accuracy (AUC >0.93) across both conditions. set enrichment implicated copper-induced cell death (cuproptosis) in neurons melanoma cells, linking this process genes. RT-qPCR confirmed during cuproptosis. Additional analyses macrophage involvement WNT-β-catenin signaling relevant. These findings suggest cuproptosis potential therapeutic target

Language: Английский

Citations

0
Comprehensive genomic characterization of programmed cell death-related genes to predict drug resistance and prognosis for patients with multiple myeloma DOI Creative Commons
Yan Li, Fuxu Wang, Hongbo Zhao

et al.

Aging, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Multiple myeloma (MM) is a cancer that difficult to be diagnosed and treated. This study aimed identify programmed cell death (PCD)-related molecular subtypes of MM assess their impact on patients' prognosis, immune status, drug sensitivity. We used the ConsensusClusterPlus method classify with prognostically relevant PCD genes from patients screened. A prognostic model nomogram were established applying one-way COX regression analysis LASSO Cox analysis. sensitivity chemotherapeutic agents was predicted for at-risk populations. Six classified employing PCD-related genes, notably, three them had higher tendency escape two correlated worse prognosis MM. Furthermore, C3 subtype activated pathways such as oxidative phosphorylation DNA repair, while C2 C4 related apoptosis. The Risk score showed can correctly predict OS patients, in particular, high-risk group low overall survival (OS). Pharmacovigilance analyses revealed low-risk groups greater IC50 values drugs SB505124_1194 AZD7762_1022, respectively. 12-gene developed accurately patients. Our provided potential targets strategies individualized treatment

Language: Английский

Citations

0
Development of a machine learning-derived programmed cell death index for prognostic prediction and immune insights in colorectal cancer DOI Creative Commons
Jinping Li, Yan Jiang,

S H Nong

et al.

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 24, 2025

Language: Английский

Citations

0
Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy DOI
Çıgır Biray Avci, Bakiye Göker Bağca,

Behrouz Shademan

et al.

Functional & Integrative Genomics, Journal Year: 2024, Volume and Issue: 24(5)

Published: Oct. 1, 2024

Language: Английский

Citations

0
Exploring the impact of deubiquitination on melanoma prognosis through single-cell RNA sequencing DOI Creative Commons
Peng Su, Jiaheng Xie,

Xiaotong He

et al.

Frontiers in Genetics, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 5, 2024

Background Cutaneous melanoma, characterized by the malignant proliferation of melanocytes, exhibits high invasiveness and metastatic potential. Thus, identifying novel prognostic biomarkers therapeutic targets is essential. Methods We utilized single-cell RNA sequencing data (GSE215120) from Gene Expression Omnibus (GEO) database, preprocessing it with Seurat package. Dimensionality reduction clustering were executed through Principal Component Analysis (PCA) Uniform Manifold Approximation Projection (UMAP). Cell types annotated based on known marker genes, AUCell algorithm assessed enrichment deubiquitination-related genes. Cells categorized into DUB_high DUB_low groups scores, followed differential expression analysis. Importantly, we constructed a robust model utilizing various which was evaluated in TCGA cohort an external validation cohort. Results Our model, developed using Random Survival Forest (RSF) Ridge Regression methods, demonstrated excellent predictive performance, evidenced C-index AUC values across multiple cohorts. Furthermore, analyses immune cell infiltration tumor microenvironment scores revealed significant differences distribution characteristics between high-risk low-risk groups. Functional experiments indicated that TBC1D16 significantly impacts migration melanoma cells. Conclusion This study highlights critical role deubiquitination presents effectively stratifies patient risk. The model’s strong ability enhances clinical decision-making provides framework for future studies potential mechanisms progression. Further exploration this applicability settings are warranted.

Language: Английский

Citations

0