Yu Xiao, Zhang Qiyao, Zhang Shuijun, He Yuting, Guo Wenzhi
Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Key Laboratory of Hepatobiliary and Pancreatic Surgery and Digestive Organ Transplantation of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Front Oncol. 2022 Sep 28;12:1000447. doi: 10.3389/fonc.2022.1000447. eCollection 2022.
Single-cell sequencing (SCS) technologies enable analysis of gene structure and expression data at single-cell resolution. However, SCS analysis in pancreatic cancer remains largely unexplored.
We downloaded pancreatic cancer SCS data from different databases and applied appropriate dimensionality reduction algorithms. We identified 10 cell types and subsequently screened differentially expressed marker genes of these 10 cell types using FindAllMarkers analysis. Also, we evaluated the tumor immune microenvironment based on ESTIMATE and MCP-counter. Statistical enrichment was evaluated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used all candidate gene sets in KEGG database to perform gene set enrichment analysis. We used LASSO regression to reduce the number of genes in the pancreatic risk model by R package , followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines.
We identified 15 cell subpopulations by dimension reduction and data clustering. We divided the 15 subpopulations into 10 distinct cell types based on marker gene expression. Then, we performed functional enrichment analysis for the 352 marker genes in pancreatic cancer cells. Based on RNA expression data and prognostic information from TCGA and GEO datasets, we identified 42 prognosis-related genes, including 5 protective genes and 37 high-risk genes, which we used to identified two molecular subtypes. C1 subtype was associated with a better prognosis, whereas C2 subtype was associated with a worse prognosis. Moreover, chemokine and chemokine receptor genes were differentially expressed between C1 and C2 subtypes. Functional and pathway enrichment uncovered functional differences between C1 and C2 subtype. We identified eight genes that could serve as potential biomarkers for prognosis prediction in pancreatic cancer patients. These genes were used to establish an 8-gene pancreatic cancer prognostic model.
We established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.
单细胞测序（SCS）技术能够在单细胞分辨率下分析基因结构和表达数据。然而，胰腺癌中的SCS分析在很大程度上仍未得到充分探索。
我们从不同数据库下载了胰腺癌SCS数据，并应用了适当的降维算法。我们鉴定出10种细胞类型，随后使用FindAllMarkers分析筛选这10种细胞类型的差异表达标记基因。此外，我们基于ESTIMATE和MCP-counter评估肿瘤免疫微环境。使用基因本体论和京都基因与基因组百科全书通路分析评估统计富集情况。我们使用KEGG数据库中的所有候选基因集进行基因集富集分析。我们使用R包中的LASSO回归来减少胰腺癌风险模型中的基因数量，随后通过rtPCR验证特征基因在不同胰腺癌细胞系中的表达。
我们通过降维和数据聚类鉴定出15个细胞亚群。基于标记基因表达，我们将这15个亚群分为10种不同的细胞类型。然后，我们对胰腺癌细胞中的352个标记基因进行了功能富集分析。基于来自TCGA和GEO数据集的RNA表达数据和预后信息，我们鉴定出42个与预后相关的基因，包括5个保护基因和37个高危基因，我们用这些基因鉴定出两种分子亚型。C1亚型与较好的预后相关，而C2亚型与较差的预后相关。此外，趋化因子和趋化因子受体基因在C1和C2亚型之间差异表达。功能和通路富集揭示了C1和C2亚型之间的功能差异。我们鉴定出8个基因可作为胰腺癌患者预后预测的潜在生物标志物。这些基因被用于建立一个8基因胰腺癌预后模型。
我们建立了一个8基因胰腺癌预后模型。该模型能够有效地预测胰腺癌患者的预后和治疗反应。
