目的以雌性和雄性非肥胖糖尿病(nonobese diabetic,NOD)小鼠以及健康对照癌症研究所(institute for cancer research,ICR)小鼠为研究对象,比较分析初始、效应、记忆、耗竭以及调节性CD8^(+)T细胞分化亚群差异,探讨1型糖尿病(type 1 diab...目的以雌性和雄性非肥胖糖尿病(nonobese diabetic,NOD)小鼠以及健康对照癌症研究所(institute for cancer research,ICR)小鼠为研究对象,比较分析初始、效应、记忆、耗竭以及调节性CD8^(+)T细胞分化亚群差异,探讨1型糖尿病(type 1 diabetes,T1D)背景下的性别因素对CD8^(+)T细胞分化命运的影响。方法采用流式细胞术检测雌雄NOD小鼠脾脏、胰腺引流淋巴结(pancreatic draining lymph nodes,pLN)、胰腺浸润淋巴细胞(pancreas-infiltrating lymphocytes,PIL)、初始T细胞(naive T cells,T_(N))、中枢记忆T细胞(central memory T cells,T_(CM))、效应T细胞(effector T cells,T_(EFF))、效应前体样T细胞(effector precursor T cells,T_(EP))、耗竭T细胞(exhausted T cells,T_(EX))、耗竭前体T细胞(precursor exhausted T cells,T_(PEX))以及调节性T细胞(regulatory T cells,Tregs)等CD8^(+)T细胞分化亚群的频率和表型差异。结果与雄性NOD小鼠比较,雌性NOD小鼠pLN及PIL中IFN-γ^(+)、CD107a^(+)和CCL5^(+)CD8^(+)T_(EFF)频率显著升高(P<0.01,P<0.05,P<0.05),同时脾脏中CD8^(+)T_(N)、CD8^(+)T_(CM)、CD8^(+)T_(EX)、CD8^(+)T_(PEX)和CD122^(+)CD8^(+)Tregs亚群的频率均显著降低(P<0.01,P<0.01,P<0.01,P<0.01,P<0.001);而雌性和雄性ICR小鼠体内除CD122^(+)CD8^(+)Tregs差异变化与NOD小鼠一致(P<0.05),其余上述各CD8^(+)T细胞分化亚群无显著差异。结论雄激素可能通过抑制记忆T细胞向效应T细胞分化,同时促进效应T细胞功能耗竭,导致雌雄发病率差异。展开更多
Objective Triple-negative breast cancer(TNBC)poses a significant challenge for treatment efficacy.CD8+T cells,which are pivotal immune cells,can be effectively analyzed for differential gene expression across diverse ...Objective Triple-negative breast cancer(TNBC)poses a significant challenge for treatment efficacy.CD8+T cells,which are pivotal immune cells,can be effectively analyzed for differential gene expression across diverse cell populations owing to rapid advancements in sequencing technology.By leveraging these genes,our objective was to develop a prognostic model that accurately predicts the prognosis of patients with TNBC and their responsiveness to immunotherapy.Methods Sample information and clinical data of TNBC were sourced from The Cancer Genome Atlas and METABRIC databases.In the initial stage,we identified 67 differentially expressed genes associated with immune response in CD8+T cells.Subsequently,we narrowed our focus to three key genes,namely CXCL13,GBP2,and GZMB,which were used to construct a prognostic model.The accuracy of the model was assessed using the validation set data and receiver operating characteristic(ROC)curves.Furthermore,we employed various methods,including Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway,immune infiltration,and correlation analyses with CD274(PD-L1)to explore the model's predictive efficacy in immunotherapeutic responses.Additionally,we investigated the potential underlying biological pathways that contribute to divergent treatment responses.Results We successfully developed a model capable of predicting the prognosis of patients with TNBC.The areas under the curve(AUC)values for the 1-,3-,and 5-year survival predictions were 0.618,0.652,and 0.826,respectively.Employing this risk model,we stratified the samples into high-and low-risk groups.Through KEGG enrichment analysis,we observed that the high-risk group predominantly exhibited enrichment in metabolism-related pathways such as drug and chlorophyll metabolism,whereas the low-risk group demonstrated significant enrichment in cytokine pathways.Furthermore,immune landscape analysis revealed noteworthy variations between(PD-L1)expression and risk scores,indicating that our model effectively predicted the response of patients to immune-based treatments.Conclusion Our study demonstrates the potential of CXCL13,GBP2,and GZMB as prognostic indicators of clinical outcomes and immunotherapy responses in patients with TNBC.These findings provide valuable insights and novel avenues for developing immunotherapeutic approaches targeting TNBC.展开更多
文摘目的研究长链非编码RNA(long non-coding RNA,LncRNA)LINC01137在非小细胞肺癌(nonsmall cell lung cancer,NSCLC)免疫逃逸中的生物学功能及其潜在的调节机制。方法采集24例健康志愿者和24例NSCLC患者血液样本,并收集NSCLC肿瘤组织和癌旁组织检测LINC01137水平。利用Starbase数据库预测LINC01137与miR-22-3p的结合位点,荧光素酶报告基因分析进行验证。采用A549细胞来源的外泌体和/或sh-LINC01137干扰序列转染A549细胞,检测细胞增殖和侵袭能力;收集转染后的细胞上清液培养CD8^(+)T细胞,检测CD8^(+)T细胞耗竭标志物干扰素-γ(interfereron-γ,IFN-γ)、肿瘤坏死因子-α(tumor necrosis factor-α,TNF-α)、颗粒霉素B(granzyme B)和白细胞介素-2(interleukin-2,IL-2)水平,以及PD-1+Tim3^(+)CD8^(+)T细胞百分比。采用外泌体和/或miR-22-3p模拟物(miR-22-3p mimic)转染CD8^(+)T细胞,检测PD-1蛋白水平。结果与癌旁组织相比,NSCLC肿瘤组织中LINC01137表达(3.357±0.548 vs 1.011±0.371)明显升高;与健康志愿者相比,NSCLC患者外周血LINC01137表达(3.216±0.342 vs 1.007±0.313)亦明显升高,差异具有统计学意义(t=-17.367,-17.147,均P<0.001)。肿瘤组织LINC01137表达与外周血中LINC01137表达呈正相关(r=0.755,P<0.05)。在A549细胞来源的外泌体中LINC01137显著富集。与Exo+sh-NC组相比,Exo+sh-LINC01137组细胞活力(65.852%±4.715%vs 100.153%±11.934%)及细胞侵袭(21.464%±3.481%vs 43.126%±1.447%)能力显著降低,差异具有统计学意义(t=4.630,9.953,均P<0.01)。NSCLC患者外周血中LINC01137表达和CD8^(+)T细胞百分比呈负相关(r=-0.520,P<0.05)。与Exo+sh-NC组相比,Exo+sh-LINC01137组IFN-γ(3865.314±543.852 pg/ml vs 1786.971±105.982 pg/ml),TNF-α(4631.930±510.715pg/ml vs 1973.242±379.623pg/ml),Granzyme B(3876.496±312.438pg/ml vs 1879.439±287.584pg/ml)和IL-2 mRNA水平(3.286±0.437 vs 1.015±0.314)升高,PD-1+Tim3^(+)CD8^(+)T细胞百分比(7.680%±2.185%vs 18.952%±3.216%)降低,差异具有统计学意义(t=-6.497,-7.237,-8.146,-7.310,5.021,均P<0.01)。miR-22-3p是LINC01137的靶基因。与Exo+NC mimic组相比,Exo+miR-22-3p组PD-1蛋白水平(0.384±0.087 vs 1.003±0.147)显著降低,差异有统计学意义(t=6.277,P<0.01)。结论NSCLC患者肿瘤组织及外周血中LINC01137表达显著上调;NSCLC细胞来源的外泌体中LINC01137通过靶向CD8^(+)T细胞中miR-22-3p并抑制其表达,诱导CD8^(+)T细胞耗竭,促进NSCLC细胞免疫逃逸。
文摘目的以雌性和雄性非肥胖糖尿病(nonobese diabetic,NOD)小鼠以及健康对照癌症研究所(institute for cancer research,ICR)小鼠为研究对象,比较分析初始、效应、记忆、耗竭以及调节性CD8^(+)T细胞分化亚群差异,探讨1型糖尿病(type 1 diabetes,T1D)背景下的性别因素对CD8^(+)T细胞分化命运的影响。方法采用流式细胞术检测雌雄NOD小鼠脾脏、胰腺引流淋巴结(pancreatic draining lymph nodes,pLN)、胰腺浸润淋巴细胞(pancreas-infiltrating lymphocytes,PIL)、初始T细胞(naive T cells,T_(N))、中枢记忆T细胞(central memory T cells,T_(CM))、效应T细胞(effector T cells,T_(EFF))、效应前体样T细胞(effector precursor T cells,T_(EP))、耗竭T细胞(exhausted T cells,T_(EX))、耗竭前体T细胞(precursor exhausted T cells,T_(PEX))以及调节性T细胞(regulatory T cells,Tregs)等CD8^(+)T细胞分化亚群的频率和表型差异。结果与雄性NOD小鼠比较,雌性NOD小鼠pLN及PIL中IFN-γ^(+)、CD107a^(+)和CCL5^(+)CD8^(+)T_(EFF)频率显著升高(P<0.01,P<0.05,P<0.05),同时脾脏中CD8^(+)T_(N)、CD8^(+)T_(CM)、CD8^(+)T_(EX)、CD8^(+)T_(PEX)和CD122^(+)CD8^(+)Tregs亚群的频率均显著降低(P<0.01,P<0.01,P<0.01,P<0.01,P<0.001);而雌性和雄性ICR小鼠体内除CD122^(+)CD8^(+)Tregs差异变化与NOD小鼠一致(P<0.05),其余上述各CD8^(+)T细胞分化亚群无显著差异。结论雄激素可能通过抑制记忆T细胞向效应T细胞分化,同时促进效应T细胞功能耗竭,导致雌雄发病率差异。
基金supported by Joint Funds for the Innovation of Science and Technology,Fujian Province[Grant number:2020Y9039]Fujian Provincial Health Technology Project[Grant number:2022GGA032].
文摘Objective Triple-negative breast cancer(TNBC)poses a significant challenge for treatment efficacy.CD8+T cells,which are pivotal immune cells,can be effectively analyzed for differential gene expression across diverse cell populations owing to rapid advancements in sequencing technology.By leveraging these genes,our objective was to develop a prognostic model that accurately predicts the prognosis of patients with TNBC and their responsiveness to immunotherapy.Methods Sample information and clinical data of TNBC were sourced from The Cancer Genome Atlas and METABRIC databases.In the initial stage,we identified 67 differentially expressed genes associated with immune response in CD8+T cells.Subsequently,we narrowed our focus to three key genes,namely CXCL13,GBP2,and GZMB,which were used to construct a prognostic model.The accuracy of the model was assessed using the validation set data and receiver operating characteristic(ROC)curves.Furthermore,we employed various methods,including Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway,immune infiltration,and correlation analyses with CD274(PD-L1)to explore the model's predictive efficacy in immunotherapeutic responses.Additionally,we investigated the potential underlying biological pathways that contribute to divergent treatment responses.Results We successfully developed a model capable of predicting the prognosis of patients with TNBC.The areas under the curve(AUC)values for the 1-,3-,and 5-year survival predictions were 0.618,0.652,and 0.826,respectively.Employing this risk model,we stratified the samples into high-and low-risk groups.Through KEGG enrichment analysis,we observed that the high-risk group predominantly exhibited enrichment in metabolism-related pathways such as drug and chlorophyll metabolism,whereas the low-risk group demonstrated significant enrichment in cytokine pathways.Furthermore,immune landscape analysis revealed noteworthy variations between(PD-L1)expression and risk scores,indicating that our model effectively predicted the response of patients to immune-based treatments.Conclusion Our study demonstrates the potential of CXCL13,GBP2,and GZMB as prognostic indicators of clinical outcomes and immunotherapy responses in patients with TNBC.These findings provide valuable insights and novel avenues for developing immunotherapeutic approaches targeting TNBC.