Wang Fengdi, Cheng Fanjun, Zheng Fang
Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Department of Pediatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Front Oncol. 2023 Jun 19;13:1166373. doi: 10.3389/fonc.2023.1166373. eCollection 2023.
Recent discoveries uncovered the complex cancer-nerve interactions in several cancer types including skin cutaneous melanoma (SKCM). However, the genetic characterization of neural regulation in SKCM is unclear.
Transcriptomic expression data were collected from the TCGA and GTEx portal, and the differences in cancer-nerve crosstalk-associated gene expressions between normal skin and SKCM tissues were analyzed. The cBioPortal dataset was utilized to implement the gene mutation analysis. PPI analysis was performed using the STRING database. Functional enrichment analysis was analyzed by the R package clusterProfiler. K-M plotter, univariate, multivariate, and LASSO regression were used for prognostic analysis and verification. The GEPIA dataset was performed to analyze the association of gene expression with SKCM clinical stage. ssGSEA and GSCA datasets were used for immune cell infiltration analysis. GSEA was used to elucidate the significant function and pathway differences.
A total of 66 cancer-nerve crosstalk-associated genes were identified, 60 of which were up- or downregulated in SKCM and KEGG analysis suggested that they are mainly enriched in the calcium signaling pathway, Ras signaling pathway, PI3K-Akt signaling pathway, and so on. A gene prognostic model including eight genes (GRIN3A, CCR2, CHRNA4, CSF1, NTN1, ADRB1, CHRNB4, and CHRNG) was built and verified by independent cohorts GSE59455 and GSE19234. A nomogram was constructed containing clinical characteristics and the above eight genes, and the AUCs of the 1-, 3-, and 5-year ROC were 0.850, 0.811, and 0.792, respectively. Expression of CCR2, GRIN3A, and CSF1 was associated with SKCM clinical stages. There existed broad and strong correlations of the prognostic gene set with immune infiltration and immune checkpoint genes. CHRNA4 and CHRNG were independent poor prognostic genes, and multiple metabolic pathways were enriched in high CHRNA4 expression cells.
Comprehensive bioinformatics analysis of cancer-nerve crosstalk-associated genes in SKCM was performed, and an effective prognostic model was constructed based on clinical characteristics and eight genes (GRIN3A, CCR2, CHRNA4, CSF1, NTN1, ADRB1, CHRNB4, and CHRNG), which were widely related to clinical stages and immunological features. Our work may be helpful for further investigation in the molecular mechanisms correlated with neural regulation in SKCM, and in searching new therapeutic targets.
最近的研究发现揭示了包括皮肤黑色素瘤（SKCM）在内的几种癌症类型中复杂的癌症-神经相互作用。然而，SKCM中神经调节的基因特征尚不清楚。
从TCGA和GTEx数据库收集转录组表达数据，分析正常皮肤和SKCM组织之间癌症-神经串扰相关基因表达的差异。利用cBioPortal数据集进行基因突变分析。使用STRING数据库进行蛋白质-蛋白质相互作用（PPI）分析。通过R包clusterProfiler进行功能富集分析。使用K-M plotter、单变量、多变量和LASSO回归进行预后分析和验证。利用GEPIA数据集分析基因表达与SKCM临床分期的关联。使用单样本基因集富集分析（ssGSEA）和基因集细胞分析（GSCA）数据集进行免疫细胞浸润分析。使用基因集富集分析（GSEA）阐明显著的功能和通路差异。
共鉴定出66个癌症-神经串扰相关基因，其中60个在SKCM中上调或下调，京都基因与基因组百科全书（KEGG）分析表明它们主要富集在钙信号通路、Ras信号通路、PI3K-Akt信号通路等。构建了一个包含8个基因（GRIN3A、CCR2、CHRNA4、CSF1、NTN1、ADRB1、CHRNB4和CHRNG）的基因预后模型，并通过独立队列GSE59455和GSE19234进行了验证。构建了一个包含临床特征和上述8个基因的列线图，1年、3年和5年的受试者工作特征曲线（ROC）下面积分别为0.850、0.811和0.792。CCR2、GRIN3A和CSF1的表达与SKCM临床分期相关。预后基因集与免疫浸润和免疫检查点基因存在广泛而强烈的相关性。CHRNA4和CHRNG是独立的不良预后基因，高CHRNA4表达细胞中富集了多种代谢途径。
对SKCM中癌症-神经串扰相关基因进行了全面的生物信息学分析，并基于临床特征和8个基因（GRIN3A、CCR2、CHRNA4、CSF1、NTN1、ADRB1、CHRNB4和CHRNG）构建了一个有效的预后模型，这些基因与临床分期和免疫特征广泛相关。我们的工作可能有助于进一步研究SKCM中与神经调节相关的分子机制，并寻找新的治疗靶点。
