Zhang Deyu, Wang Meiqi, Peng Lisi, Yang Xiaoli, Li Keliang, Yin Hua, Xia Chuanchao, Cui Fang, Huang Haojie, Jin Zhendong
Department of Gastroenterology, Changhai Hospital, Shanghai, China.
Department of Gastroenterology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
J Oncol. 2021 Dec 27;2021:4986227. doi: 10.1155/2021/4986227. eCollection 2021.
With the progress of precision medicine treatment in pancreatic ductal adenocarcinoma (PDAC), individualized cancer-related medical examination and prediction are of great importance in this high malignant tumor and tumor-immune microenvironment with changed pathways highly enrolled in the carcinogenesis of PDAC.
High-throughput data of pancreatic ductal adenocarcinoma were downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. After batch normalization, the enrichment pathway and relevant scores were identified by the enrichment of immune-related pathway signature using gene set variation analysis (GSVA). Then, cancerous subtype in TCGA and GEO samples was defined through the NMF methods by cancertypes packages in R software, respectively. Subsequently, the significance between the characteristics of each TCGA sample and cancer type and the significant prognosis-related pathway with risk score formula is calculated through t-test and univariate Cox analysis. Next, the prognostic value of gained risk score formula and each significant prognosis-related pathway were validated in TCGA and GEO samples by survival analysis. The pivotal hub genes in the enriched significant prognosis-related pathway are identified and validated, and the TIMER database was used to identify the potential role of hub genes in the PDAC immune environment. The potential role of hub genes is promoting the transdifferentiation of cancer-associated fibroblasts.
The enrichment pathway and relevant scores were identified by GSVA, and 3 subtypes of pancreatic ductal adenocarcinoma were defined in TCGA and GEO samples. The clinical stage, tumor node metastasis classification, and tumor grade are strongly relative to the subtype above in TCGA samples. A risk formula about GSVA significant pathway "GSE45365_WT_VS_IFNAR_KO_CD11B_DC_MCMV_INFECTION_DN ∗ 0.80 + HALLMARK_GLYCOLYSIS ∗ 16.8 + GSE19888_CTRL_VS_T_CELL_MEMBRANES_ACT_MAST_CELL_DN ∗ 14.4" was identified and validated in TCGA and GEO samples through survival analysis with significance. DCN, VCAN, B4GALT7, SDC1, SDC2, B3GALT6, B3GAT3, SDC3, GPC1, and XYLT2 were identified as hub genes in these GSVA significant pathways and validated in silico.
Three pancreatic ductal adenocarcinoma subtypes are identified, and an individualized GSVA immune pathway score-related prognostic risk score formula with 10 hub genes is identified and validated. The predicted function of the 10 upregulated hub genes in tumor-immune microenvironment was promoting the infiltration of cancer-associated fibroblasts. These findings will contribute to the precision medicine of pancreatic ductal adenocarcinoma treatment and tumor immune-related basic research.
随着胰腺导管腺癌（PDAC）精准医学治疗的进展，在这种高恶性肿瘤以及肿瘤免疫微环境中，个体癌症相关医学检查和预测具有重要意义，该肿瘤免疫微环境中改变的通路在PDAC致癌过程中高度参与。
从基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）数据库下载胰腺导管腺癌的高通量数据。经过批次标准化后，使用基因集变异分析（GSVA）通过免疫相关通路特征富集来识别富集通路和相关分数。然后，分别通过R软件中cancertypes包的非负矩阵分解（NMF）方法定义TCGA和GEO样本中的癌亚型。随后，通过t检验和单变量Cox分析计算每个TCGA样本的特征与癌症类型之间的显著性以及具有风险评分公式的显著预后相关通路。接下来，通过生存分析在TCGA和GEO样本中验证获得的风险评分公式和每个显著预后相关通路的预后价值。识别并验证富集的显著预后相关通路中的关键枢纽基因，并使用TIMER数据库确定枢纽基因在PDAC免疫环境中的潜在作用。枢纽基因的潜在作用是促进癌症相关成纤维细胞的转分化。
通过GSVA识别了富集通路和相关分数，并在TCGA和GEO样本中定义了3种胰腺导管腺癌亚型。在TCGA样本中，临床分期、肿瘤淋巴结转移分类和肿瘤分级与上述亚型密切相关。通过生存分析在TCGA和GEO样本中识别并验证了一个关于GSVA显著通路“GSE45365_WT_VS_IFNAR_KO_CD11B_DC_MCMV_INFECTION_DN ∗ 0.80 + HALLMARK_GLYCOLYSIS ∗ 16.8 + GSE19888_CTRL_VS_T_CELL_MEMBRANES_ACT_MAST_CELL_DN ∗ 14.4”的风险公式，具有显著性。在这些GSVA显著通路中，DCN、VCAN、B4GALT7、SDC1、SDC2、B3GALT6、B3GAT3、SDC3、GPC1和XYLT2被识别为枢纽基因，并在计算机上进行了验证。
识别出3种胰腺导管腺癌亚型，并识别和验证了一个与GSVA免疫通路评分相关的个体化预后风险评分公式，该公式包含10个枢纽基因。肿瘤免疫微环境中10个上调的枢纽基因的预测功能是促进癌症相关成纤维细胞的浸润。这些发现将有助于胰腺导管腺癌治疗的精准医学和肿瘤免疫相关基础研究。
