Additional file 1 of Role of tumor mutation burden-related signatures in the prognosis and immune microenvironment of pancreatic ductal adenocarcinoma

Additional file 1: Figure S1. GO analysis and GSEA of the TRGs. (A) GO analysis showed the function of TRGs. (B) GSEA showed differentially enriched pathways of TRGs using the GO gene-sets. Figure S2. Mutational landscape of the TMB_high and TMB_low groups. Figure S3. Lasso regression identifies 16 TRGs for model construction. (A) The curve shows that the partial likelihood deviance changed along with the lambda value. The lambda value is determined when the partial likelihood deviance is at its minimum value. (B) When the lambda value is determined, the corresponding coefficient of each gene can be determined. Figure S4. Validation of the constructed model using the same coefficients and cutoff values. (A) PDAC patients were divided into high- and low-risk groups based on their scores calculated by the model. (B) The survival time and status of PDAC patients varied with increasing risk scores. (C) Patients with low lasso risk scores had prolonged OS. (D) Patients with high lasso risk scores featured increased cumulative hazards. (E) ROC curve to assess the accuracy of the model. Figure S5. The differential expression of four genes in the prognostic model based on bulk sequencing and qPCR validation. Target genes from left to right were GBP1, HIST1H1C, MMP28 and PPP1R15A, respectively. Cell-lines from top to bottom were capan-1, panc-1, Mia-paca-2 and SW1990. Figure S6. Correlation analysis between ANKRD55 expression and immune checkpoints (A) DNA transmethylases (B) and DNA repair-related proteins (C). Table S1. Primer sequencing of the genes that need to be validate by PCR. Table S2. A summary of survival-associated TRGs in PDAC. Table S3. Genes used in the construction of prognosis model. Table S4. Characterization of validation cohorts. Table S5. The association between CD8 + T cells infiltration and TMB score.