Zhang Yuming, Hou Helei, Zhang Xuchen, Lan Hongwei, Huo Xingfa, Duan Xueqin, Li Yufeng, Zhang Xiaochun, Zhou Na
Precision Medicine Center of Oncology, The Affiliated Hospital of Qingdao University, Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China.
Department of Medicine, Qingdao University, No. 308 Ningxia Road, Qingdao, 266000, Shandong, China.
Dig Dis Sci. 2025 Jan;70(1):262-284. doi: 10.1007/s10620-024-08715-z. Epub 2024 Nov 27.
Because of the unique tumor microenvironment (TME), immunotherapy and targeted therapies have shown limited efficacy in treating pancreatic adenocarcinoma (PAAD). CD8 + T cells play crucial roles in regulating the TME in PAAD; therefore, exploring the function of CD8 + T-cell-related genes (CD8RGs) in PAAD has high potential clinical value and could provide a comprehensive understanding of the microenvironment of PAAD.
We employed the weighted gene coexpression network analysis and CIBERSORT algorithms to assess PAAD transcriptome data from The Cancer Genome Atlas (TCGA) dataset and identify modules strongly associated with CD8 + T cell infiltration. Using least absolute shrinkage and selection operator regression analysis and Kaplan-Meier curves, we developed a prognostic risk score model for patients with PAAD. We validated this model using single-cell and transcriptome datasets obtained from the Gene Expression Omnibus (GEO). We also examined the correlations between the risk score and factors such as the TME, clinical characteristics, and tumor mutation burden (TMB). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed on differentially expressed genes between the high- and low-risk groups. In addition, the Tumor Immune Dysfunction and Exclusion website and "pRRophetic" R package were used to predict response to immunotherapy and chemotherapy in the high- and low-risk groups, respectively. Finally, we analyzed the expressions of hub genes at the cellular level with quantitative real-time PCR.
A risk model based on five CD8RGs was established and validated using TCGA and GEO datasets. The low-risk group exhibited significantly longer overall and progression-free survival. A positive correlation between the TMB and the risk score was observed. The TME analysis revealed a significant correlation between the risk score and immune function, as well as immune checkpoints. The expression of hub genes was significantly correlated with the infiltration level of CD8 + T cells. The high-risk group responded better to immunotherapy, paclitaxel, cisplatin, mitomycin C, afatinib (BIBW2992), and gefitinib. In contrast, the low-risk group showed higher sensitivity to sunitinib, MK.2206, palbociclib (PD.0332991), and axitinib. Compared with that in normal pancreatic epithelial cells, the expression levels of BCL11A, PHOSPHO1, and GNG7 were significantly decreased, while those of KLK11 and VCAM1 were significantly increased in pancreatic tumor cells.
CD8RGs play an important role in regulating the TME of PAAD. Five hub genes-BCL11A, KLK11, GNG7, PHOSPHO1, and VCAM1-are closely associated with the prognosis of PAAD patients, providing new references for the exploration of biomarkers. Furthermore, our findings offer novel insights for clinical decision-making.
由于独特的肿瘤微环境(TME),免疫疗法和靶向疗法在治疗胰腺腺癌(PAAD)方面疗效有限。CD8 + T细胞在PAAD的TME调节中起关键作用;因此,探索PAAD中CD8 + T细胞相关基因(CD8RGs)的功能具有很高的潜在临床价值,并有助于全面了解PAAD的微环境。
我们采用加权基因共表达网络分析和CIBERSORT算法来评估来自癌症基因组图谱(TCGA)数据集的PAAD转录组数据,并识别与CD8 + T细胞浸润密切相关的模块。使用最小绝对收缩和选择算子回归分析以及Kaplan-Meier曲线,我们为PAAD患者开发了一种预后风险评分模型。我们使用从基因表达综合数据库(GEO)获得的单细胞和转录组数据集对该模型进行了验证。我们还研究了风险评分与TME、临床特征和肿瘤突变负荷(TMB)等因素之间的相关性。对高风险组和低风险组之间的差异表达基因进行了基因本体论和京都基因与基因组百科全书富集分析。此外,分别使用肿瘤免疫功能障碍和排除网站以及“pRRophetic”R包来预测高风险组和低风险组对免疫疗法和化疗的反应。最后,我们通过定量实时PCR在细胞水平分析了枢纽基因的表达。
基于五个CD8RGs建立了风险模型,并使用TCGA和GEO数据集进行了验证。低风险组的总生存期和无进展生存期明显更长。观察到TMB与风险评分之间呈正相关。TME分析显示风险评分与免疫功能以及免疫检查点之间存在显著相关性。枢纽基因的表达与CD8 + T细胞的浸润水平显著相关。高风险组对免疫疗法、紫杉醇、顺铂、丝裂霉素C、阿法替尼(BIBW2992)和吉非替尼反应更好。相比之下,低风险组对舒尼替尼、MK.2206、帕博西尼(PD.0332991)和阿昔替尼更敏感。与正常胰腺上皮细胞相比,胰腺肿瘤细胞中BCL11A、PHOSPHO1和GNG7的表达水平显著降低而KLK11和VCAM1的表达水平显著升高。
CD8RGs在调节PAAD的TME中起重要作用。五个枢纽基因——BCL11A、KLK11、GNG7、PHOSPHO1和VCAM1——与PAAD患者的预后密切相关,为生物标志物的探索提供了新的参考。此外,我们的研究结果为临床决策提供了新的见解。