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Identification and Validation of T Cell Exhaustion Signature for Predicting Prognosis and Immune Response in Pancreatic Cancer by Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data
Zhu, Y.; Tan, L.; Luo, D.; Wang, X. Identification and Validation of T-Cell Exhaustion Signature for Predicting Prognosis and Immune Response in Pancreatic Cancer by Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data. Diagnostics 2024, 14, 667, doi:10.3390/diagnostics14060667.
Zhu, Y.; Tan, L.; Luo, D.; Wang, X. Identification and Validation of T-Cell Exhaustion Signature for Predicting Prognosis and Immune Response in Pancreatic Cancer by Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data. Diagnostics 2024, 14, 667, doi:10.3390/diagnostics14060667.
Zhu, Y.; Tan, L.; Luo, D.; Wang, X. Identification and Validation of T-Cell Exhaustion Signature for Predicting Prognosis and Immune Response in Pancreatic Cancer by Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data. Diagnostics 2024, 14, 667, doi:10.3390/diagnostics14060667.
Zhu, Y.; Tan, L.; Luo, D.; Wang, X. Identification and Validation of T-Cell Exhaustion Signature for Predicting Prognosis and Immune Response in Pancreatic Cancer by Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data. Diagnostics 2024, 14, 667, doi:10.3390/diagnostics14060667.
Abstract
Purpose: Pancreatic cancer (PAAD) is one of the most lethal malignancies. Immunotherapy is largely ineffective in PAAD. T-cell exhaustion contributes to the resistance for immunotherapies. We investigated the prognostic potential of T cell exhaustion-related genes (TEXGs). Methods: Single-cell RNA (scRNA) sequencing dataset from TISCH, and bulk sequencing datasets from TCGA and GTEx were applied to screen differently expressed TEXGs. Kaplan-Meier survival, LASSO regression, and Univariate/Multivariate Cox regression analysis were performed to construct TEXGs risk model. This model was used to predict the prognosis, tumor immune microenvironment, and immunotherapy response. PAAD cohorts from ICGC and GSE71729 datasets were used to validate this risk model. Pan-cancer expression of SPOCK2 was performed in TISCH database. Results: A six-gene (SPOCK2, MT1X, LIPH, RARRES3, EMP1, and MEG3) risk model was constructed. Low-risk PAAD patients had longer survival time in both training dataset (TCGA-PAAD, n=178) and validation datasets (ICGC-PACA-CA, ICGC-PAAD-US, and GSE71729, n=412). Multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic variable for PAAD. High-risk patients correlated with cancer-related KEGG pathway enrichment, higher mutation frequency of KRAS and TP53 genes, and immunosuppressive status. Immunohistochemistry staining confirmed the changes of TEXGs in clinical samples. Moreover, pan-cancer scRNA sequencing datasets from TISCH analysis indicated that SPOCK2 might be a novel marker of exhausted CD8+ T cells. Conclusion: In conclusion, we established and validated a T cell exhaustion-related prognostic signature for PAAD patients. Moreover, our study suggests that SPOCK2 may be a novel marker of exhausted CD8+ T cells.
Keywords
pancreatic cancer; T cell exhaustion; immunotherapy; risk model; SPOCK2
Subject
Biology and Life Sciences, Life Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
