切换至 "中华医学电子期刊资源库"
高级检索
中华结直肠疾病电子杂志

中华结直肠疾病电子杂志 ›› 2024, Vol. 13 ›› Issue (04) : 303 -311. doi: 10.3877/cma.j.issn.2095-3224.2024.04.006

论著

M2型巨噬细胞特征基因与结肠癌免疫微环境研究
朱军1, 宋家伟2, 乔一桓2, 郭雅婕2, 刘帅2, 姜玉3, 李纪鹏2,†()   
  1. 1. 710032　西安，中国人民解放军空军军医大学第一附属医院消化外科;510030　广州，中国人民解放军南部战区空军医院普通外科
    2. 710032　西安，中国人民解放军空军军医大学第一附属医院消化外科
    3. 710082　西安大兴医院肝胆外科
  • 收稿日期:2024-04-01 出版日期:2024-08-25
  • 通信作者: 李纪鹏
  • 基金资助:
    国家自然科学基金面上项目(82172781)

M2-type macrophage signature genes and the immune microenvironment of colon cancer

Jun Zhu1, Jiawei Song2, Yihuan Qiao2, Yajie Guo2, Shuai Liu2, Yu Jiang3, Jipeng Li2,†()   

  1. 1. Department of Gastrointestinal Surgery, the First Affiliated Hospital of Air Force Medical University, Xi’an 710032, China;Department of General Surgery, the Southern Theater Air Force Hospital, Guangzhou 510030, China
    2. Department of Gastrointestinal Surgery, the First Affiliated Hospital of Air Force Medical University, Xi’an 710032, China
    3. Department of Hepatobiliary Surgery, Xi'an Daxing Hospital, Xi'an 710082, China
  • Received:2024-04-01 Published:2024-08-25
  • Corresponding author: Jipeng Li
全文 ( ) 下载PDF ( ) 导出引用
引用本文：

朱军, 宋家伟, 乔一桓, 郭雅婕, 刘帅, 姜玉, 李纪鹏. M2型巨噬细胞特征基因与结肠癌免疫微环境研究[J]. 中华结直肠疾病电子杂志, 2024, 13(04): 303-311.

Jun Zhu, Jiawei Song, Yihuan Qiao, Yajie Guo, Shuai Liu, Yu Jiang, Jipeng Li. M2-type macrophage signature genes and the immune microenvironment of colon cancer[J]. Chinese Journal of Colorectal Diseases(Electronic Edition), 2024, 13(04): 303-311.

目的

通过机器学习结合生物信息学技术以筛选核心预后价值的巨噬细胞的特征基因，并且探索其与免疫微环境、肿瘤免疫治疗的关系。

方法

本研究搜集TCGA数据库中COAD数据集和GEO数据库中（GSE39582）数据集。CIBERSORT法计算肿瘤样本中M2型巨噬细胞水平，通过相关性分析、单因素和多因素Cox回归分析和随机生存森林算法筛选特征基因。ESTIMATE算法计算肿瘤样本的免疫微环境评分（间质评分与免疫评分），并且研究特征基因及其关系，最后在免疫治疗队列中进行验证。

结果

本研究确定PPM1M与MRAS作为机器学习确定的核心预后基因。在TCGA数据中，高表达水平MRAS人群拥有更短的无进展生存时间（P=0.0013）。在GEO数据中，高表达PPM1M基因（P=0.031）和MRAS基因（P=0.002）均与复发相关。PPM1M、MRAS基因和肿瘤免疫评分和间质评分均呈正相关、与抑制性调节T淋巴细胞水平呈正相关。最后，在免疫治疗评价中，高表达的PPM1M和MRAS患者接受免疫治疗的预后更好。

结论

机器学习确定的M2型巨噬细胞特征基因与生存、复发和进展相关。在免疫微环境中，PPM1M和MRAS均与抑制性的肿瘤免疫成分和间质成分呈正相关。此外，PPM1M和MRAS可能成为免疫治疗疗效的新型标志物。

关键词: 结肠肿瘤, M2型巨噬细胞, 随机生存森林, 免疫微环境, 生存分析
Objective

We aimed to identify for macrophage-2 (M2) characteristic genes with hub prognostic value through machine learning combined with bioinformatics techniques, and to explore their relationship with the immune microenvironment and tumor immunotherapy.

Methods

This study collected the TCGA-COAD dataset and the dataset (GSE39582) from the GEO database. The CIBERSORT method was used to calculate the levels of M2-type macrophages in tumor samples, and characteristic genes were screened through correlation analysis, univariate and multivariate Cox regression analysis, and the random survival forest algorithm. The ESTIMATE algorithm was employed to calculate the immune microenvironment scores (stromal score and immune score) of tumor samples, and to study the characteristic genes and their relationships, finally validating in an immunotherapy cohort.

Results

This study identified PPM1M and MRAS as core prognostic genes determined by machine learning. In the TCGA data, populations with high expression levels of MRAS had shorter progression-free survival (P=0.0013). In the GEO data, high expression of PPM1M gene (P=0.031) and MRAS gene (P=0.002) were both associated with recurrence. Both PPM1M and MRAS genes were positively correlated with tumor immune score and stromal score, and positively correlated with the levels of suppressive regulatory T cells (Treg). Finally, in the evaluation of immunotherapy, patients with high expression of PPM1M and MRAS had better prognosis after receiving immunotherapy.

Conclusion

Characteristic genes of M2-type macrophages determined by machine learning are related to survival, recurrence, and progression. In the immune microenvironment, PPM1M and MRAS are both positively correlated with suppressive tumor immune components and stromal components. Furthermore, PPM1M and MRAS may serve as novel biomarkers for the efficacy of immunotherapy.

Key words: Colon neoplasms, M2 type macrophage, Random survival forest, Immune microenvironment, Survival analysis
表1 结肠癌的临床基本资料[，例（%）]
特征 TCGA-COAD数据集（n=435） GSE39582数据集（n=585）
年龄（岁） 67.2±13.0 66.9±13.2
性别
女 208（47.82） 263（44.96）
男 227（52.18） 322（55.04）
T分期
T1 8（1.84） 12（2.05）
T2 74（17.01） 49（8.38）
T3 299（68.74） 379（64.79）
T4 54（12.41） 119（20.34）
NA 0（0） 26（4.44）
M分期
M0 330（75.86） 499（85.30）
M1 62（14.25） 61（10.43）
NA 43（9.89） 25（4.27）
N分期
N0 257（59.08） 314（53.68）
N1 101（23.22） 137（23.42）
N2 77（17.70） 100（17.09）
NA 0（0） 34（5.81）
MSI状态
MSI-H 76（17.47） 77（13.16）
MSI-L/MSS 323（74.25） 459（78.46）
NA 36（8.28） 49（8.38）
图1 随机生存森林确定核心预后的M2巨噬细胞特征基因。1A：TCGA-COAD数据集；1B：GSE39582数据集
图2 PPM1M、MRAS基因与疾病复发、进展的关系。2A：PPM1M高低表达组的无复发生存曲线；2B：MRAS高低表达组的无复发生存曲线；2C：PPM1M高低表达组的无进展生存曲线；2D：MRAS高低表达组的无进展生存曲线
图3 PPM1M、MRAS基因与微卫星不稳定、免疫分型的关系。3A：PPM1M在MSI-H组和MSI-L/MSS组表达量比较；3B：MRAS在MSI-H组和MSI-L/MSS组表达量比较；3C：PPM1M与免疫分型的关系；3D：MRAS与免疫分型的关系
表2 PPM1M、MRAS基因与结肠癌免疫微环境的相关性
基因名称 评价指标 评价方法 相关系数 P值
PPM1M M2型巨噬细胞 MCPCOUNTER 0.719 <0.001
PPM1M M2型巨噬细胞 QUANTISEQ 0.544 <0.001
PPM1M M2型巨噬细胞 XCELL 0.312 <0.001
PPM1M 调节性T淋巴细胞 QUANTISEQ 0.492 <0.001
PPM1M 免疫评分 ESTIMATE 0.660 0.066
PPM1M 间质评分 ESTIMATE 0.590 <0.001
PPM1M 肿瘤突变负荷 Varscan 0.144 0.009
PPM1M PDCD1 Spearman 0.540 <0.001
PPM1M CD274 Spearman 0.500 <0.001
MRAS M2型巨噬细胞 MCPCOUNTER 0.815 <0.001
MRAS M2型巨噬细胞 QUANTISEQ 0.533 <0.001
MRAS M2型巨噬细胞 XCELL 0.348 <0.001
MRAS 调节性T淋巴细胞 QUANTISEQ 0.499 <0.001
MRAS 免疫评分 ESTIMATE 0.650 <0.001
MRAS 间质评分 ESTIMATE 0.940 <0.001
MRAS 肿瘤突变负荷 Varscan 0.073 0.191
MRAS PDCD1 Spearman 0.550 <0.001
MRAS CD274 Spearman 0.530 <0.001
MRAS PPM1M Spearman 0.770 <0.001
图4 PPM1M、MRAS基因表达量与免疫治疗预后。4A：PPM1M与免疫治疗预后的关系；4B：MRAS与免疫治疗预后的关系
[1]
Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249.
[2]
Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022[J]. J Natl Cancer Cent, 2024, 4(1): 47-53.
[3]
Hanahan D. Hallmarks of cancer: new dimensions[J]. Cancer Discov, 2022, 12(1): 31-46.
[4]
Galon J, Bruni D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies[J]. Nat Rev Drug Discov, 2019, 18(3): 197-218.
[5]
Liu Z, Xiang Y, Zheng Y, et al. Advancing immune checkpoint blockade in colorectal cancer therapy with nanotechnology[J]. Front Immunol, 2022, 20(13): 1027124.
[6]
Wu K, Lin K, Li X, et al. Redefining tumor-associated macrophage subpopulations and functions in the tumor microenvironment[J]. Front Immunol, 2020, 11(4): 1731.
[7]
Liang ZX, Liu HS, Wang FW, et al. LncRNA RPPH1 promotes colorectal cancer metastasis by interacting with TUBB3 and by promoting exosomes-mediated macrophage M2 polarization [J]. Cell Death Dis, 2019, 10(11): 829.
[8]
Zhao S, Mi Y, Guan B, et al. Tumor-derived exosomal miR-934 induces macrophage M2 polarization to promote liver metastasis of colorectal cancer[J]. J Hematol Oncol, 2020, 13(1): 156.
[9]
Huang C, Ou R, Chen X, et al. Tumor cell-derived SPON2 promotes M2-polarized tumor-associated macrophage infiltration and cancer progression by activating PYK2 in CRC[J]. J Exp Clin Cancer Res, 2021, 40(1): 304.
[10]
Li R, Zhou R, Wang H, et al. Gut microbiota-stimulated cathepsin K secretion mediates TLR4-dependent M2 macrophage polarization and promotes tumor metastasis in colorectal cancer[J]. Cell Death Differ, 2019, 26(11): 2447-2463.
[11]
DeNardo DG, Ruffell B. Macrophages as regulators of tumour immunity and immunotherapy [J]. Nat Rev Immunol, 2019, 19(6): 369-382.
[12]
Wang X, Duanmu J, Fu X, et al. Analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment[J]. J Transl Med, 2020, 18(1): 324.
[13]
李秀勤, 韩腾辉, 朱军, 等. 基于机器学习的结直肠癌微卫星不稳定基因挖掘及其应用价值分析[J]. 中国普通外科杂志, 2022, 31(10): 1355-1362.
[14]
Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles[J]. Nat Methods, 2015, 12(5): 453-457.
[15]
Zhao W, Liu M, Zhang M, et al. Effects of inflammation on the immune microenvironment in gastric cancer[J]. Front Oncol, 2021, 11(13): 690298.
[16]
Wang H, Zhou L. Random survival forest with space extensions for censored data[J]. Artif Intell Med, 2017, 79(6): 52-61.
[17]
Taylor JM. Random survival forests[J]. J Thorac Oncol, 2011, 6(12): 1974-1975.
[18]
陈哲, 许恒敏, 李哲轩, 等. 随机生存森林: 基于机器学习算法的生存分析模型[J]. 中华预防医学杂志, 2021, 55(1): 104-109.
[19]
宋欠欠, 武晓岩, 侯艳, 等. 随机生存森林在高维基因组数据生存分析中的应用[J]. 中国卫生统计, 2013, 30(6): 786-789.
[20]
Lánczky A, Győrffy B. Web-based survival analysis tool tailored for medical research (KMplot): Development and Implementation[J]. J Med Internet Res, 2021, 23(7): e27633.
[21]
Kovács SA, Győrffy B. Transcriptomic datasets of cancer patients treated with immune-checkpoint inhibitors: a systematic review[J]. J Transl Med, 2022, 20(1): 249.
[22]
Jiménez-Sánchez A, Cast O, Miller ML. Comprehensive benchmarking and integration of tumor microenvironment cell estimation methods[J]. Cancer Res, 2019, 79(24): 6238-6246.
[23]
Luo D, Feng W, Ma Y, et al. Identification and validation of a novel prognostic model of inflammation-related gene signature of lung adenocarcinoma[J]. Sci Rep, 2022, 12(1): 14729.
[24]
Li T, Fu J, Zeng Z, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells[J]. Nucleic Acids Res, 2020, 48(W1): W509-w514.
[25]
Li T, Fan J, Wang B, et al. TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells[J]. Cancer Res, 2017, 77(21): e108-e110.
[26]
Lin A, Zhang J, Luo P. Crosstalk between the MSI status and tumor microenvironment in colorectal cancer[J]. Front Immunol, 2020, 11(12): 2039.
[27]
He X, Chen H, Zhong X, et al. BST2 induced macrophage M2 polarization to promote the progression of colorectal cancer [J]. Int J Biol Sci, 2023, 19(1): 331-345.
[28]
Kamada R, Kudoh F, Ito S, et al. Metal-dependent Ser/Thr protein phosphatase PPM family: Evolution, structures, diseases and inhibitors[J]. Pharmacol Ther, 2020, 215(11): 107622.
[29]
Henmi T, Amano K, Nagaura Y, et al. A mechanism for the suppression of interleukin-1-induced nuclear factor kappaB activation by protein phosphatase 2Ceta-2[J]. Biochem J, 2009, 423(1): 71-78.
[30]
Apte RN, Dotan S, Elkabets M, et al. The involvement of IL-1 in tumorigenesis, tumor invasiveness, metastasis and tumor-host interactions[J]. Cancer Metastasis Rev, 2006, 25(3): 387-408.
[31]
Young LC, Rodriguez-Viciana P. MRAS: a close but understudied member of the RAS family[J]. Cold Spring Harb Perspect Med, 2018, 8(12): a033621.
[32]
Hauseman ZJ, Fodor M, Dhembi A, et al. Structure of the MRAS-SHOC2-PP1C phosphatase complex[J]. Nature, 2022, 609(7926): 416-423.
[33]
Kwon JJ, Hajian B, Bian Y, et al. Structure-function analysis of the SHOC2-MRAS-PP1C holophosphatase complex[J]. Nature, 2022, 609(7926): 408-415.
[34]
Gao A, Liu X, Lin W, et al. Tumor-derived ILT4 induces T cell senescence and suppresses tumor immunity [J]. J Immunother Cancer, 2021, 9(3): e001536.
[35]
Osawa J, Karakawa M, Taniguchi A, et al. Functional regulation of the protein phosphatase PPM1M by phosphorylation at multiple sites with Ser/Thr-Pro motifs[J]. Arch Biochem Biophys, 2024, 753(3): 109887.
[1] 李怡泉, 谢宇斌, 胡宏, 张燕茹, 陈图锋. 基于生物信息学分析HDAC8在结肠癌中的临床意义及其与免疫浸润的关系[J]. 中华普通外科学文献(电子版), 2024, 18(04): 275-281.
[2] 刘伟, 安杰, 智亮辉, 陈金辉. 阿帕替尼联合新辅助化疗对局部晚期结肠癌的临床疗效研究[J]. 中华普通外科学文献(电子版), 2024, 18(03): 199-203.
[3] 崔宏帅, 冯丽明, 东维玲, 韩博. 腹腔镜右半结肠癌D3根治术+IGLN清扫术治疗局部进展期结肠肝曲癌的临床效果研究[J]. 中华普外科手术学杂志(电子版), 2024, 18(05): 566-569.
[4] 丁志翔, 于鹏, 段绍斌. 血浆BRAF基因检测对腹腔镜右半结肠癌D3根治术中行幽门淋巴结清扫的指导价值[J]. 中华普外科手术学杂志(电子版), 2024, 18(05): 570-573.
[5] 王维花, 王楠, 乔庆, 罗红. 完全腹腔镜右半结肠癌切除术两种腔内消化道重建方案对比研究[J]. 中华普外科手术学杂志(电子版), 2024, 18(05): 574-577.
[6] 孙龙凤, 侯高峰, 王幼黎, 刘磊. 腹腔镜下右半结肠癌D3根治术中SMA或SMV入路的选择[J]. 中华普外科手术学杂志(电子版), 2024, 18(04): 438-441.
[7] 李晓鸥, 杨鹤鸣, 王国栋, 林海冠, 杨建武. 不同入路腹腔镜左半结肠癌根治术治疗效果对比[J]. 中华普外科手术学杂志(电子版), 2024, 18(04): 377-380.
[8] 金田力, 袁文正, 付涛. 单孔腹腔镜右半结肠癌根治术后尿管拔除时机探讨[J]. 中华普外科手术学杂志(电子版), 2024, 18(03): 271-274.
[9] 杜彦斌, 黄涛, 寇天阔, 石英. 双镜联合根治术与腹腔镜根治术在早期结肠癌患者中的应用效果[J]. 中华普外科手术学杂志(电子版), 2024, 18(03): 275-278.
[10] 张聃, 王毅, 冯文迪, 方兴中. 完整结肠系膜切除术与传统根治术治疗结肠癌对患者生存期的影响观察[J]. 中华普外科手术学杂志(电子版), 2024, 18(03): 279-282.
[11] 胡剑平, 王振乾, 张龙, 尹任其, 陈涵, 赵任, 吕强. 尾侧中间联合入路与尾侧入路在腹腔镜右半结肠癌根治术中的应用对比[J]. 中华普外科手术学杂志(电子版), 2024, 18(02): 196-199.
[12] 李卓骋, 陈羽翔, 高亮, 张宇, 朱许源, 马晓杰, 李涛, 赵甜甜, 蒋鸿涛. 巨噬细胞-肌成纤维细胞转化在肾纤维化过程中的作用[J]. 中华移植杂志(电子版), 2024, 18(03): 181-185.
[13] 程璞, 郑朝旭. 伴有错配修复缺陷的病理Ⅱ期结直肠癌临床病理特征分析及生存分析[J]. 中华结直肠疾病电子杂志, 2024, 13(03): 236-241.
[14] 谢尔盖·叶菲托夫, 曹宇, 关旭, 刘正, 波琳娜·帕诺娃, 王锡山. 单孔经腹膜后入路行保留左结肠动脉的乙状结肠癌根治术(附视频)[J]. 中华结直肠疾病电子杂志, 2024, 13(02): 166-170.
[15] 靳英, 付小霞, 陈美茹, 袁璐, 郝力瑶. CD147调控MAPK信号通路对结肠癌细胞增殖和凋亡的影响及机制研究[J]. 中华临床医师杂志(电子版), 2024, 18(05): 474-480.
阅读次数
全文


摘要

版权所有 中华医学会 中华医学电子音像出版社有限责任公司  京ICP备14006079号-1  网络出版服务许可证：(署)网出证(京)字第075号