Single-cell sequencing and establishment of an 8-gene prognostic model for pancreatic cancer patients

Xiao Yu; Qiyao Zhang; Shuijun Zhang; Yuting He; Wenzhi Guo

doi:10.3389/fonc.2022.1000447

Single-cell sequencing and establishment of an 8-gene prognostic model for pancreatic cancer patients

Front Oncol. 2022 Sep 28:12:1000447. doi: 10.3389/fonc.2022.1000447. eCollection 2022.

Authors

Xiao Yu^{1

2

3

4}, Qiyao Zhang^{1

2

3

4}, Shuijun Zhang^{1

2

3

4}, Yuting He^{1

2

3

4}, Wenzhi Guo^{1

2

3

4}

Affiliations

¹ Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
² Key Laboratory of Hepatobiliary and Pancreatic Surgery and Digestive Organ Transplantation of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
³ Open and Key Laboratory of Hepatobiliary & Pancreatic Surgery and Digestive Organ Transplantation at Henan Universities, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
⁴ Henan Key Laboratory of Digestive Organ Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Abstract

Background: 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.

Methods: 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 glmnet, followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines.

Results: 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.

Conclusions: We established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.

Keywords: chemokines; immune microenvironment; pancreatic cancer; prognostic model; single-cell sequencing.