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创建皮肤黑色素瘤的多维度预后模型：单细胞测序和批量测序与机器学习的融合

Creating a multifaceted prognostic model for cutaneous melanoma: the convergence of single-cell and bulk sequencing with machine learning.

作者信息

Mao Fei, Wan Neng

机构信息

Department of Urology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, China.

Department of Plastic Surgery, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, China.

出版信息

Front Cell Dev Biol. 2024 May 6;12:1401945. doi: 10.3389/fcell.2024.1401945. eCollection 2024.

DOI:10.3389/fcell.2024.1401945
PMID:38770150
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11102988/
Abstract

BACKGROUND

Cutaneous melanoma is a highly heterogeneous cancer, and understanding the role of inflammation-related genes in its progression is crucial.

METHODS

The cohorts used include the TCGA cohort from TCGA database, and GSE115978, GSE19234, GSE22153 cohort, and GSE65904 cohort from GEO database. Weighted Gene Coexpression Network Analysis (WGCNA) identified key inflammatory modules. Machine learning techniques were employed to construct prognostic models, which were validated across multiple cohorts, including the TCGA cohort, GSE19234, GSE22153, and GSE65904. Immune cell infiltration, tumor mutation load, and immunotherapy response were assessed. The hub gene STAT1 was validated through cellular experiments.

RESULTS

Single-cell analysis revealed heterogeneity in inflammation-related genes, with NK cells, T cells, and macrophages showing elevated inflammation-related scores. WGCNA identified a module highly associated with inflammation. Machine learning yielded a CoxBoost + GBM prognostic model. The model effectively stratified patients into high-risk and low-risk groups in multiple cohorts. A nomogram and Receiver Operating Characteristic (ROC) curves confirmed the model's accuracy. Low-risk patients exhibited increased immune cell infiltration, higher Tumor Mutational Burden (TMB), and potentially better immunotherapy response. Cellular experiments validated the functional role of STAT1 in melanoma progression.

CONCLUSION

Inflammation-related genes play a critical role in cutaneous melanoma progression. The developed prognostic model, nomogram, and validation experiments highlight the potential clinical relevance of these genes and provide a basis for further investigation into personalized treatment strategies for melanoma patients.

摘要

背景

皮肤黑色素瘤是一种高度异质性的癌症，了解炎症相关基因在其进展中的作用至关重要。

方法

使用的队列包括来自TCGA数据库的TCGA队列，以及来自GEO数据库的GSE115978、GSE19234、GSE22153队列和GSE65904队列。加权基因共表达网络分析（WGCNA）确定了关键的炎症模块。采用机器学习技术构建预后模型，并在多个队列中进行验证，包括TCGA队列、GSE19234、GSE22153和GSE65904。评估了免疫细胞浸润、肿瘤突变负荷和免疫治疗反应。通过细胞实验验证了枢纽基因STAT1。

结果

单细胞分析揭示了炎症相关基因的异质性，自然杀伤细胞、T细胞和巨噬细胞显示出升高的炎症相关评分。WGCNA确定了一个与炎症高度相关的模块。机器学习产生了一个CoxBoost + GBM预后模型。该模型在多个队列中有效地将患者分为高风险和低风险组。列线图和受试者工作特征（ROC）曲线证实了模型的准确性。低风险患者表现出免疫细胞浸润增加、肿瘤突变负担（TMB）更高以及潜在的更好的免疫治疗反应。细胞实验验证了STAT1在黑色素瘤进展中的功能作用。

结论

炎症相关基因在皮肤黑色素瘤进展中起关键作用。所开发的预后模型、列线图和验证实验突出了这些基因潜在的临床相关性，并为进一步研究黑色素瘤患者的个性化治疗策略提供了依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/a66dacf16c7e/fcell-12-1401945-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/8255db29a5ad/fcell-12-1401945-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/6773cd0e41b1/fcell-12-1401945-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/6be219ec6627/fcell-12-1401945-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/89f202709d11/fcell-12-1401945-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/420ff1bd9700/fcell-12-1401945-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/6868d0567ebe/fcell-12-1401945-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/a73c6de98e33/fcell-12-1401945-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/a66dacf16c7e/fcell-12-1401945-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/8255db29a5ad/fcell-12-1401945-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/6773cd0e41b1/fcell-12-1401945-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/c5489af8128c/fcell-12-1401945-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/6be219ec6627/fcell-12-1401945-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/89f202709d11/fcell-12-1401945-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/420ff1bd9700/fcell-12-1401945-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/6868d0567ebe/fcell-12-1401945-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/a73c6de98e33/fcell-12-1401945-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4acb/11102988/a66dacf16c7e/fcell-12-1401945-g009.jpg

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