|本期目录/Table of Contents|

[1]苏怡文,陈 刚.基于单细胞转录组数据的口腔鳞状细胞癌免疫微环境时空分析[J].天津医科大学学报,2025,31(06):521-526.[doi:10.20135/j.issn.1006-8147.2025.06.0521]
　SU Yiwen,CHEN Gang.Decoding the immune microenvironment of oral squamous cell carcinoma through the spatiotemporal analysis of single-cell transcriptome sequencing datasets[J].Journal of Tianjin Medical University,2025,31(06):521-526.[doi:10.20135/j.issn.1006-8147.2025.06.0521]
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基于单细胞转录组数据的口腔鳞状细胞癌免疫微环境时空分析(PDF)

《天津医科大学学报》[ISSN:1006-8147/CN:12-1259/R]

卷:
31卷
期数:
2025年06期
页码:
521-526
栏目:
肿瘤疾病专题
出版日期:
2025-11-20

文章信息/Info

Title:
Decoding the immune microenvironment of oral squamous cell carcinoma through the spatiotemporal analysis of single-cell transcriptome sequencing datasets
文章编号:
1006-8147(2025)06-0521-06
作者:
苏怡文; 陈 刚
（天津医科大学口腔医学院，天津 300070）
Author(s):
SU Yiwen;  CHEN Gang
（School of Stomatology， Tianjin Medical University， Tianjin 300070， China）
关键词:
口腔鳞状细胞癌; 单细胞转录组测序; 肿瘤免疫微环境; MAIT; 生物信息学分析
Keywords:
oral squamous cell carcinoma;  single-cell transcriptome sequencing;  tumor immune microenvironment;  MAIT;  bioinformatics analysis
分类号:
R782
DOI:
10.20135/j.issn.1006-8147.2025.06.0521
文献标志码:
A
摘要:
目的：基于单细胞转录组整合分析，解析口腔鳞状细胞癌（OSCC）发生、发展过程中肿瘤免疫微环境（TIME）的细胞组成及变化。方法：本研究纳入分析的单细胞转录组测序数据集来自Gene Expression Omnibus （GEO）公开数据库，利用生物信息学手段，整合OSCC患者正常癌旁组织、癌前病变组织、原发性肿瘤组织和继发性肿瘤转移组织的单细胞转录组测序数据，并从中筛选出免疫细胞进行整合分析；比较 4 种组织类型间的免疫微环境组成及其差异；利用Python和R编程语言对整合数据进行分析。结果：4 种组织类型间的免疫微环境组成存在差异，其中T细胞的比例在原发性肿瘤组织和继发性肿瘤转移组织中升高，基于T细胞差异基因的富集分析发现对病毒的防御反应（P<0.000 1）、T细胞活化的正向调控（P<0.000 1）等生物学过程在原发性肿瘤组织中富集，T细胞受体信号通路（P<0.000 1）、Ⅱ型干扰素介导的信号通路（P=0.001 3）等生物学通路则在继发性肿瘤转移组织中富集；黏膜相关恒定T细胞（MAIT）在转移组织中相对特异且占比较高，且转移组织中MAIT的免疫特性明显更强，NFKB1、IRF1和STAT3可能是MAIT发挥作用的关键调控因子。结论：研究基于包含 117 313 个细胞的单细胞转录组测序数据集，发现OSCC发生、发展进程中的免疫微环境细胞组成和分子特征存在不同。
Abstract:
Objective： To analyze the cellular composition and changes of the tumor immune microenvironment （TIME） during the occurrence and development of oral squamous cell carcinoma （OSCC） based on the integration of single-cell transcriptome analysis. Methods： The single-cell transcriptome sequencing datasets included in this study were obtained from the Gene Expression Omnibus （GEO） public database. Bioinformatics methods were used to integrate the single-cell transcriptome sequencing data of normal adjacent tissues， precancerous lesion tissues， primary tumor tissues， and secondary tumor metastasis tissues of OSCC patients， and immune cells were selected for integrated analysis. The composition and differences of the immune microenvironment in the four tissues were compared. Python and R programming languages were used to analyze the integrated data. Results： The composition of the immune microenvironment in the four tissues was different. The proportion of T cells increased in primary tumor tissues and secondary tumor metastasis tissues. Enrichment analysis of differentially expressed genes in T cells revealed that biological processes such as defense response to virus （P<0.000 1） and positive regulation of T cell activation （P<0.000 1） were enriched in primary tumor tissues， while biological pathways such as T cell receptor signaling pathway （P<0.000 1） and type Ⅱ interferon-mediated signaling pathway （P=0.001 3） were enriched in secondary tumor metastasis tissues. Mucosal-associated invariant T cells （MAIT） were relatively specific and had a relatively high proportion in metastatic tissues， and the immune characteristics of MAIT in metastatic tissues were significantly stronger. NFKB1， IRF1， and STAT3 may be the key regulatory factors for the function of MAIT. Conclusion： Based on the analysis of single-cell transcriptome sequencing dataset containing 117 313 cells， it is found that the cellular compositions and molecular characteristics of the immune microenvironment during the occurrence and development of OSCC are different.Objective： To analyze the cellular composition and changes of the tumor immune microenvironment （TIME） during the occurrence and development of oral squamous cell carcinoma （OSCC） based on the integration of single-cell transcriptome analysis. Methods： The single-cell transcriptome sequencing datasets included in this study were obtained from the Gene Expression Omnibus （GEO） public database. Bioinformatics methods were used to integrate the single-cell transcriptome sequencing data of normal adjacent tissues， precancerous lesion tissues， primary tumor tissues， and secondary tumor metastasis tissues of OSCC patients， and immune cells were selected for integrated analysis. The composition and differences of the immune microenvironment in the four tissues were compared. Python and R programming languages were used to analyze the integrated data. Results： The composition of the immune microenvironment in the four tissues was different. The proportion of T cells increased in primary tumor tissues and secondary tumor metastasis tissues. Enrichment analysis of differentially expressed genes in T cells revealed that biological processes such as defense response to virus （P<0.000 1） and positive regulation of T cell activation （P<0.000 1） were enriched in primary tumor tissues， while biological pathways such as T cell receptor signaling pathway （P<0.000 1） and type Ⅱ interferon-mediated signaling pathway （P=0.001 3） were enriched in secondary tumor metastasis tissues. Mucosal-associated invariant T cells （MAIT） were relatively specific and had a relatively high proportion in metastatic tissues， and the immune characteristics of MAIT in metastatic tissues were significantly stronger. NFKB1， IRF1， and STAT3 may be the key regulatory factors for the function of MAIT. Conclusion： Based on the analysis of single-cell transcriptome sequencing dataset containing 117 313 cells， it is found that the cellular compositions and molecular characteristics of the immune microenvironment during the occurrence and development of OSCC are different.

参考文献/References:

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[17] HIAM-GALVEZ K， ALLEN B， SPITZER M. Systemic immunity in cancer[J]. Nat Rev Cancer，2021，21（6）：345-359.
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[2] JOHNSON D， BURTNESS B， LEEMANS C， et al. Head and neck squamous cell carcinoma[J]. Nat Rev Dis Primers，2020，6（1）：1-22.
[3] BINNEWIES M， ROBERTS E， KERSTEN K， et al. Understanding the tumor immune microenvironment （TIME） for effective therapy[J]. Nat Med，2018，24（5）：541-550.
[4] TIROSH I， SUVA M. Cancer cell states： lessons from ten years of single-cell RNA-sequencing of human tumors[J]. Cancer Cell，2024，42（9）：1497-1506.
[5] GILBERT L. Mapping cancer genetics at single-cell resolution[J]. Sci Transl Med，2020，12（1）：1-3.
[6] CHOI J， LEE B， JANG J， et al. Single-cell transcriptome profiling of the stepwise progression of head and neck cancer[J]. Nat Commun，2023，14（1）：1-13.
[7] K?譈RTEN C， KULKARNI A， CILLO A， et al. Investigating immune and non-immune cell interactions in head and neck tumors by single-cell RNA sequencing[J]. Nat Commun，2021，12（1）：1-16.
[8] PENG Y， XIAO L， RONG H， et al. Single-cell profiling of tumor-infiltrating TCF1/TCF7[J]. Oral Oncol，2021，119（1）：1-10.
[9] CILLO A， K?譈RTEN C， TABIB T， et al. Immune landscape of viral-and carcinogen-driven head and neck cancer[J]. Immunity，2020， 52（1）：183-199.
[10] BILL R， WIRAPATI P， MESSEMAKER M， et al. CXCL9：SPP1： macrophage polarity identifies a network of cellular programs that control human cancers[J]. Science，2023，381（6657）：515-524.
[11] KORSUNSKY I， MILLARD N， FAN J， et al. Fast， sensitive and accurate integration of single-cell data with Harmony[J]. Nat Methods，2019，16（12）：1289-1296.
[12] WOLF F， ANGERER P， THEIS F. Scanpy： large-scale single-cell gene expression data analysis[J]. Genome Biol，2018，19（1）：1-5.
[13] BUTLER A， HOFFMAN P， SMIBERT P， et al. Integrating single-cell transcriptomic data across different conditions， technologies， and species[J]. Nat Biotechnol，2018，36（5）：411-420.
[14] ZHOU Y， ZHOU B， PACHE L， et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets[J]. Nat Commun，2019，10（1）：1-10.
[15] HAN H， CHO J， LEE S， et al. Trrust v2： an expanded reference database of human and mouse transcriptional regulatory interactions[J]. Nucleic Acids Res，2018，46（1）：380-386.
[16] XIE X， LIU M， ZHANG Y， et al. Single-cell transcriptomic landscape of human blood cells[J]. Natl Sci Rev，2021，8（3）：1-11.
[17] HIAM-GALVEZ K， ALLEN B， SPITZER M. Systemic immunity in cancer[J]. Nat Rev Cancer，2021，21（6）：345-359.
[18] ELHANANI O， BEN-URI R， KEREN L. Spatial profiling technologies illuminate the tumor microenvironment[J]. Cancer Cell，2023， 41（3）：404-420.
[19] GODFREY D， KOAY H， MCCLUSKEY J， et al. The biology and functional importance of MAIT cells[J]. Nat Immunol，2019，20（9）：1110-1128.
[20] SANDBERG J， LEEANSYAH E， ELLER M， et al. The emerging role of MAIT cell responses in viral infections[J]. J Immunol，2023， 211（4）：511-517.
[21] JIANG Q， WANG F， YANG J， et al. MAIT cells and their implication in human oral diseases[J]. Inflamm Res，2022，71（9）：1041-1054.
[22] TOPP B， CHANNAVAZZALA M， MAYAWALA K， et al. Tumor dynamics in patients with solid tumors treated with pembrolizumab beyond disease progression[J]. Cancer Cell，2023，41（9）：1680-1688.
[23] FENG H， ZHANG Y， GUI J， et al. Interferon regulatory factor 1 （IRF1） and anti-pathogen innate immune responses[J]. PLoS Pa-thog，2021，17（1）：1-22.
[24] SAMAD M， AHMAD I， HASAN A， et al. STAT3 signaling pathway in health and disease[J]. MedComm，2025，6（4）：1-29.

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备注/Memo

备注/Memo:
作者简介 苏怡文（1994-），女，硕士在读，研究方向：口腔临床医学；通信作者：陈刚，E-mail： doctorchen@tmu.edu.cn。
更新日期/Last Update: 2025-11-20