Telomere-related prognostic biomarkers for survival assessments in pancreatic cancer

Abstract

Human telomeres are linked to genetic instability and a higher risk of developing cancer. Therefore, to improve the dismal prognosis of pancreatic cancer patients, a thorough investigation of the association between telomere-related genes and pancreatic cancer is required. Combat from the R package "SVA" was performed to correct the batch effects between the TCGA-PAAD and GTEx datasets. After differentially expressed genes (DEGs) were assessed, we constructed a prognostic risk model through univariate Cox regression, LASSO-Cox regression, and multivariate Cox regression analysis. Data from the ICGC, GSE62452, GSE71729, and GSE78229 cohorts were used as test cohorts for validating the prognostic signature. The major impact of the signature on the tumor microenvironment and its response to immune checkpoint drugs was also evaluated. Finally, PAAD tissue microarrays were fabricated and immunohistochemistry was performed to explore the expression of this signature in clinical samples. After calculating 502 telomere-associated DEGs, we constructed a three-gene prognostic signature (DSG2, LDHA, and RACGAP1) that can be effectively applied to the prognostic classification of pancreatic cancer patients in multiple datasets, including TCGA, ICGC, GSE62452, GSE71729, and GSE78229 cohorts. In addition, we have screened a variety of tumor-sensitive drugs targeting this signature. Finally, we also found that protein levels of DSG2, LDHA, and RACGAP1 were upregulated in pancreatic cancer tissues compared to normal tissues by immunohistochemistry analysis. We established and validated a telomere gene-related prognostic signature for pancreatic cancer and confirmed the upregulation of DSG2, LDHA, and RACGAP1 expression in clinical samples, which may provide new ideas for individualized immunotherapy.

Figure 1

Identification of telomere-related DEGs and establishment of telomere-related subtypes (A) DEGs between the 171 normal pancreatic tissues and 178 pancreatic cancer tissues. (B) 502 overlapping genes were identified as telomere-related DEGs. (C) GSEA enrichment results of these telomere-related DEGs. (D) 72 of the 502 telomere-related DEGs were calculated as prognostic genes. (E) LASSO-Cox regression analysis. (F) The scores of patients and their distribution. (G) Patients with higher risk ratings exhibited noticeably lower survival outcomes. (H) ROC analysis.

Figure 2

Patient death (A), advanced grades (B), and relapses (C) were all linked to higher risk scores, while gender (D), age (E), and TNM stage (F) were not associated with risk scores.

Figure 3

Verification of the signature in multiple additional datasets. (A) Kaplan–Meier survival analysis. (B) ROC analysis.

Figure 4

Characterization of ICIs analysis. (A) The tumor mutational burden (TMB) value of each pancreatic cancer patient. (B) The TMB value was greater in patients in the high-risk group and the risk score was strongly positively linked with the TMB value. (C) Patients with lower scores had higher levels of the genes CTLA4, CD4, CXCR4, LL1A, TNFRSF4, and PD1 than patients with higher scores. (D) Scores were strongly correlated with the expression levels of CTLA4, CD4, CXCR4, and LL1A.

Figure 5

Correlation analysis of the signature and TME. (A) In comparison to patients with lower scores, patients with higher scores had lower stromal, immune, and estimate scores. Immune cell infiltration analysis by the TIMER (B), CIBERSORT (C), MCPcounter (D), and xCELL (E).

Figure 6

Potential therapeutic drug sensitivity analysis. (A) Top 16 most important tumor-sensitive drugs. (B) Differential analysis of the IC50 of tumor-sensitive drugs.

Figure 7

Protein expression levels and percentage of positivity for the three genes in clinical samples. (A) DSG2. (B) LDHA. (C) RACGAP1.