综合分析SF3B1突变在葡萄膜黑色素瘤中的功能、预后及免疫浸润特征

doi:10.3969/j.issn.1674-8115.2026.04.007

摘要/Abstract

摘要：

目的·综合分析剪接因子3B亚基1（splicing factor 3B subunit 1，SF3B1）突变在葡萄膜黑色素瘤（uveal melanoma，UVM）中的作用、预后及免疫浸润特征。方法·纳入2018—2021年期间于上海交通大学医学院附属第九人民医院确诊为原发性UVM且接受眼球摘除术的患者20例。提取患者的肿瘤组织样本DNA及RNA，分别用于Sanger测序和RNA测序。同时，从癌症基因组图谱（The Cancer Genome Atlas，TCGA）数据库获取80例UVM患者的基因表达数据、突变信息及临床资料，对基因表达数据的原始计数（STAR-counts）数据进行每百万转录本数（transcripts per million，TPM）格式转换及log₂（TPM+1）归一化处理后用于后续分析。根据SF3B1基因突变与否，将TCGA-UVM队列分为2组，即SF3B1突变组与SF3B1野生型对照组。比较该中心队列样本与TCGA-UVM队列样本的SF3B1突变发生率，并使用Kaplan-Meier曲线对TCGA-UVM队列患者的预后数据进行生存分析。从分子特征数据库（Molecular Signatures Database，MSigDB）收集常见致癌通路相关基因集，并采用单样本基因集富集分析（single-sample gene set enrichment analysis，ssGSEA）评估TCGA-UVM队列样本中相关生物学通路的活性变化。采用R语言limma包对TCGA-UVM队列样本进行差异表达分析，并使用基因本体论（Gene Ontology，GO）和京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes，KEGG）对差异表达结果进行功能富集与信号通路分析。采用肿瘤免疫评估资源库（Tumor Immune Estimation Resource，TIMER）对TCGA-UVM队列样本的免疫细胞浸润水平进行评估，结合免疫检查点相关基因［如程序性细胞死亡蛋白1（programmed cell death protein 1，PDCD1）、细胞毒性T淋巴细胞相关蛋白4（cytotoxic T-lymphocyte-associated protein 4，CTLA4）等］的转录组数据分析SF3B1突变状态与肿瘤免疫微环境特征的关联，并通过肿瘤免疫功能障碍与排斥（Tumor Immune Dysfunction and Exclusion，TIDE）算法预测SF3B1突变对免疫治疗响应的影响。对该中心队列的RNA测序数据进行可变剪切（alternative splicing，AS）事件分析，识别SF3B1突变相关的差异可变剪切事件及转录本异构体变化。结果·通过对比该中心队列样本与TCGA-UVM队列样本的数据发现，SF3B1突变在中国UVM患者中的发生率明显高于TCGA-UVM队列。生存分析的结果显示，与SF3B1野生型对照组相比，SF3B1突变组患者的总体生存率和疾病特异性生存率均较高（均P<0.05）。ssGSEA分析显示，SF3B1突变与肿瘤炎症反应、缺氧反应、上皮-间质转化、血管生成等多条肿瘤相关通路活性升高相关。GO功能富集分析和KEGG信号通路分析的结果显示，差异表达基因主要富集于核受体活性等分子功能及过氧化物酶体增殖物激活受体（peroxisome proliferator-activated receptor，PPAR）信号通路等。免疫浸润分析的结果显示SF3B1突变与B细胞浸润程度较高、免疫检查点相关基因的低表达相关，TIDE算法提示SF3B1突变与免疫治疗响应率较高相关（P<0.05）。AS分析结果显示，SF3B1突变与多种差异可变剪切事件及转录本异构体改变相关。结论·SF3B1突变在中国UVM患者中的发生率较高，与较好的生存预后及特定免疫浸润特征相关，可作为预测UVM患者预后及免疫治疗反应的潜在生物标志物。

关键词: 剪接因子3B亚基1, 葡萄膜黑色素瘤, 突变景观, 免疫浸润, 预后分析, 生物信息学分析

Abstract:

Objective ·To comprehensively analyze the role, prognosis, and immune infiltration characteristics of splicing factor 3B subunit 1 (SF3B1) mutations in uveal melanoma (UVM). Methods ·A total of 20 patients diagnosed with primary UVM who underwent enucleation at Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, between 2018 and 2021 were included. DNA and RNA were extracted from tumor tissue samples and used for Sanger sequencing and RNA sequencing, respectively. In addition, gene expression data, mutation information, and clinical data from 80 UVM patients were obtained from The Cancer Genome Atlas (TCGA) database. Raw gene expression data (STAR-counts) were converted into transcripts per million (TPM) and subsequently normalized using log₂(TPM+1) for downstream analyses. According to SF3B1 mutation status, the TCGA-UVM cohort was divided into the SF3B1 mutant group and the SF3B1 wild-type group. The mutation frequency of SF3B1 was compared between the local cohort and the TCGA-UVM cohort, and Kaplan-Meier survival analysis was performed to evaluate patient prognosis in the TCGA-UVM cohort. Gene sets of common oncogenic pathways were obtained from the Molecular Signatures Database (MSigDB), and single-sample gene set enrichment analysis (ssGSEA) was used to evaluate the activity of relevant biological pathways in the TCGA-UVM cohort. Differential expression analysis was conducted using the R package limma, followed by functional enrichment and signaling pathway analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immune cell infiltration levels in the TCGA-UVM cohort were evaluated using the Tumor Immune Estimation Resource (TIMER) database. Combined with transcriptomic data of immune checkpoint-related genes, such as programmed cell death protein 1 (PDCD1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4), the association between SF3B1 mutation status and tumor immune microenvironment characteristics was further analyzed. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was applied to predict the impact of SF3B1 mutation on responsiveness to immune checkpoint inhibitor therapy. Alternative splicing (AS) events were analyzed based on RNA-sequencing data from local cohort to identify SF3B1 mutation-associated differential AS events and transcript isoform alterations. Results ·Comparison between the local cohort and the TCGA-UVM cohort showed that the mutation frequency of SF3B1 in Chinese patients was markedly higher than that in the TCGA-UVM cohort. Survival analysis indicated that patients in the SF3B1 mutant group had significantly better overall survival and disease-specific survival than those in the SF3B1 wild-type group (both P<0.05). ssGSEA analysis indicated that SF3B1 mutation was associated with increased activities of several tumor-related pathways, including inflammatory response, hypoxia response, epithelial-mesenchymal transition, and angiogenesis. GO and KEGG analyses indicated that differentially expressed genes were primarily enriched in nuclear receptor activity and the peroxisome proliferator-activated receptor (PPAR) signaling pathway. Immune infiltration analysis showed that SF3B1 mutation was associated with increased B-cell infiltration and lower expression of immune checkpoint-related genes (P<0.05). The TIDE algorithm suggested that SF3B1 mutation was associated with a higher response rate to immunotherapy (P<0.05). AS analysis further revealed that SF3B1 mutation was associated with multiple differential alternative splicing events and transcript isoform changes. Conclusion ·SF3B1 mutation occurs at a relatively high frequency in Chinese patients with UVM, and is associated with favorable survival outcomes and distinct immune infiltration characteristics, suggesting its potential as a biomarker for predicting prognosis and immunotherapy response in UVM.

Key words: splicing factor 3B subunit 1 (SF3B1), uveal melanoma (UVM), mutational landscape, immune infiltration, prognostic analysis, bioinformatics analysis

中图分类号:

R773.4

王一然, 张哲, 沈键锋. 综合分析SF3B1突变在葡萄膜黑色素瘤中的功能、预后及免疫浸润特征[J]. 上海交通大学学报（医学版）, 2026, 46(4): 475-485.

Wang Yiran, Zhang Zhe, Shen Jianfeng. Comprehensive analysis of the function, prognosis, and immune infiltration characteristics of SF3B1 mutations in uveal melanoma[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2026, 46(4): 475-485.

图/表 6

图1 UVM的突变景观Note： A. The top ten genes with the highest mutation rates in the TCGA-UVM cohort. B. Sanger sequencing results of primary UVM clinical samples from our center. C. Mutation types and SNP classifications in the TCGA-UVM cohort.

Fig 1 Mutation landscape of UVM

图2 SF3B1 突变对患者预后的影响Note： A. OS curve. B. DSS curve.

Fig 2 Impact of SF3B1 mutation on patient prognosis

图3 SF3B1 突变相关分子特征分析Note： A. Effect of SF3B1 mutation on the mutational burden of UVM. B. Effect of SF3B1 mutation on the stemness of UVM cells. mRNAsi—mRNA expression-based stemness index. C. mRNA expression levels of SF3B1 in the mutant and wild-type groups. D. Correlation between SF3B1 expression and common pathways. TGFB—transforming growth factor β.

Fig 3 Analysis of SF3B1 mutation-associated molecular characteristics

图4 SF3B1 突变组和 SF3B1 野生型对照组DEGs的GO功能富集和KEGG信号通路分析Note： A. GO functional enrichment analysis. B. KEGG signaling pathway analysis. Up—up-regulated genes; down—down-regulated genes.

Fig 4 GO functional enrichment analysis and KEGG signaling pathway analysis of DEGs between the SF3B1 mutant group and the SF3B1 wild-type control group

图5 SF3B1 突变与免疫细胞浸润、免疫检查点基因表达及免疫治疗反应的分析Note： A. TIMER analysis of the association between SF3B1 mutation and immune cell infiltration scores. B. Correlation between SF3B1 mutation and immune checkpoint gene expression. C. Relationship between SF3B1 mutation and responsiveness to ICIs therapy. ①P=0.001, ②P=0.010, ③P=0.006, ④P<0.001, ⑤P=0.008.

Fig 5 Analysis of SF3B1 mutation-associated immune cell infiltration, immune checkpoint gene expression, and immunotherapy response

图6 不同基因突变状态及免疫相关基因表达水平下的AS事件及转录本异构体分析Note： The comparison of the number and proportion of AS types and isoforms between groups classified by the mutation status (upper) of key genes (GNAQ, GNA11, and SF3B1), and by the expression levels (lower) of key molecules (PDCD1, CD274, IFNG, LAG3,and HAVCR2). Statistically significant differences (P<0.05) are highlighted in red boxes. Mut—mutant; WT—wild-type.

Fig 6 Analysis of AS events and transcript isoforms associated with different gene mutation statuses and immune-related gene expression levels

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