DataSheet_1_Exploring KRAS-mutant pancreatic ductal adenocarcinoma: a model validation study.docx

IntroductionPancreatic ductal adenocarcinoma (PDAC) has the highest mortality rate among all solid tumors. Tumorigenesis is promoted by the oncogene KRAS, and KRAS mutations are prevalent in patients with PDAC. Therefore, a comprehensive understanding of the interactions between KRAS mutations and PDAC may expediate the development of therapeutic strategies for reversing the progression of malignant tumors. Our study aims at establishing and validating a prediction model of KRAS mutations in patients with PDAC based on survival analysis and mRNA expression.MethodsA total of 184 and 412 patients with PDAC from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC), respectively, were included in the study.ResultsAfter tumor mutation profile and copy number variation (CNV) analyses, we established and validated a prediction model of KRAS mutations, based on survival analysis and mRNA expression, that contained seven genes: CSTF2, FAF2, KIF20B, AKR1A1, APOM, KRT6C, and CD70. We confirmed that the model has a good predictive ability for the prognosis of overall survival (OS) in patients with KRAS-mutated PDAC. Then, we analyzed differential biological pathways, especially the ferroptosis pathway, through principal component analysis, pathway enrichment analysis, Gene Ontology (GO) enrichment analysis, and gene set enrichment analysis (GSEA), with which patients were classified into low- or high-risk groups. Pathway enrichment results revealed enrichment in the cytokine-cytokine receptor interaction, metabolism of xenobiotics by cytochrome P450, and viral protein interaction with cytokine and cytokine receptor pathways. Most of the enriched pathways are metabolic pathways predominantly enriched by downregulated genes, suggesting numerous downregulated metabolic pathways in the high-risk group. Subsequent tumor immune infiltration analysis indicated that neutrophil infiltration, resting CD4 memory T cells, and resting natural killer (NK) cells correlated with the risk score. After verifying that the seven gene expression levels in different KRAS-mutated pancreatic cancer cell lines were similar to that in the model, we screened potential drugs related to the risk score.DiscussionThis study established, analyzed, and validated a model for predicting the prognosis of PDAC based on risk stratification according to KRAS mutations, and identified differential pathways and highly effective drugs.</p

Table_3_Exploring KRAS-mutant pancreatic ductal adenocarcinoma: a model validation study.xlsx

IntroductionPancreatic ductal adenocarcinoma (PDAC) has the highest mortality rate among all solid tumors. Tumorigenesis is promoted by the oncogene KRAS, and KRAS mutations are prevalent in patients with PDAC. Therefore, a comprehensive understanding of the interactions between KRAS mutations and PDAC may expediate the development of therapeutic strategies for reversing the progression of malignant tumors. Our study aims at establishing and validating a prediction model of KRAS mutations in patients with PDAC based on survival analysis and mRNA expression.MethodsA total of 184 and 412 patients with PDAC from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC), respectively, were included in the study.ResultsAfter tumor mutation profile and copy number variation (CNV) analyses, we established and validated a prediction model of KRAS mutations, based on survival analysis and mRNA expression, that contained seven genes: CSTF2, FAF2, KIF20B, AKR1A1, APOM, KRT6C, and CD70. We confirmed that the model has a good predictive ability for the prognosis of overall survival (OS) in patients with KRAS-mutated PDAC. Then, we analyzed differential biological pathways, especially the ferroptosis pathway, through principal component analysis, pathway enrichment analysis, Gene Ontology (GO) enrichment analysis, and gene set enrichment analysis (GSEA), with which patients were classified into low- or high-risk groups. Pathway enrichment results revealed enrichment in the cytokine-cytokine receptor interaction, metabolism of xenobiotics by cytochrome P450, and viral protein interaction with cytokine and cytokine receptor pathways. Most of the enriched pathways are metabolic pathways predominantly enriched by downregulated genes, suggesting numerous downregulated metabolic pathways in the high-risk group. Subsequent tumor immune infiltration analysis indicated that neutrophil infiltration, resting CD4 memory T cells, and resting natural killer (NK) cells correlated with the risk score. After verifying that the seven gene expression levels in different KRAS-mutated pancreatic cancer cell lines were similar to that in the model, we screened potential drugs related to the risk score.DiscussionThis study established, analyzed, and validated a model for predicting the prognosis of PDAC based on risk stratification according to KRAS mutations, and identified differential pathways and highly effective drugs.</p

Table_2_Exploring KRAS-mutant pancreatic ductal adenocarcinoma: a model validation study.csv

IntroductionPancreatic ductal adenocarcinoma (PDAC) has the highest mortality rate among all solid tumors. Tumorigenesis is promoted by the oncogene KRAS, and KRAS mutations are prevalent in patients with PDAC. Therefore, a comprehensive understanding of the interactions between KRAS mutations and PDAC may expediate the development of therapeutic strategies for reversing the progression of malignant tumors. Our study aims at establishing and validating a prediction model of KRAS mutations in patients with PDAC based on survival analysis and mRNA expression.MethodsA total of 184 and 412 patients with PDAC from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC), respectively, were included in the study.ResultsAfter tumor mutation profile and copy number variation (CNV) analyses, we established and validated a prediction model of KRAS mutations, based on survival analysis and mRNA expression, that contained seven genes: CSTF2, FAF2, KIF20B, AKR1A1, APOM, KRT6C, and CD70. We confirmed that the model has a good predictive ability for the prognosis of overall survival (OS) in patients with KRAS-mutated PDAC. Then, we analyzed differential biological pathways, especially the ferroptosis pathway, through principal component analysis, pathway enrichment analysis, Gene Ontology (GO) enrichment analysis, and gene set enrichment analysis (GSEA), with which patients were classified into low- or high-risk groups. Pathway enrichment results revealed enrichment in the cytokine-cytokine receptor interaction, metabolism of xenobiotics by cytochrome P450, and viral protein interaction with cytokine and cytokine receptor pathways. Most of the enriched pathways are metabolic pathways predominantly enriched by downregulated genes, suggesting numerous downregulated metabolic pathways in the high-risk group. Subsequent tumor immune infiltration analysis indicated that neutrophil infiltration, resting CD4 memory T cells, and resting natural killer (NK) cells correlated with the risk score. After verifying that the seven gene expression levels in different KRAS-mutated pancreatic cancer cell lines were similar to that in the model, we screened potential drugs related to the risk score.DiscussionThis study established, analyzed, and validated a model for predicting the prognosis of PDAC based on risk stratification according to KRAS mutations, and identified differential pathways and highly effective drugs.</p

Additional file 1 of Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

Additional file 1: Fig S1. Distribution of DMPs’ median-∆β values between tumor and normal tissues at the pan-cancer level. Fig S2. Top 10 significant Hyper-DMPs across 9 cancer types. Fig S3. Top 10 significant Hypo-DMPs across 9 cancer types. Fig S4. Identification of conserved differentially methylated probes at the pan-cancer level. Fig S5. NMF identifies three hypermethylation signatures and seven hypomethylation signatures. Fig S6. Comparison of Hypo-MSs using β or 1-β values as input. Fig S7. Characterization of DNA methylation signatures. Fig S8. Methylation signature activities’ association with age. Fig S9. Analysis of the correlations between overall survival, cancer stages and methylation signature activities. Fig S10. The relationship between methylation signature activities and tumor immune microenvironment in cancers. Fig S11. The relationship between Tumor mutation burden, neoantigen load, tumor progression and Hypo-MS4 activity. Fig S12. Analysis of the correlations between deterministic genes and Hypo-MS4 activity. Fig S13. Analysis of the intersection of deterministic genes and Hypo-MS4 activity. Fig S14. Analysis of overall survival and ICI response of Hypo-MS4 with deterministic genes status

Additional file 12: of Germline and somatic variations influence the somatic mutational signatures of esophageal squamous cell carcinomas in a Chinese population

Figure S7 Correlation of the somatic events with the genetic burdens of the risk-associated genes in ESCC. The genetic burdens of CHEK2 and HECTD4 are associated with the frequencies of C > G substitution (a). The genetic burdens of HEATR3 are associated with the frequencies of C > T substitution (b). The genetic burdens of CHEK2, HEATR3 and SMG6 are associated with the “AID/APOBEC-1” signature (c). The genetic burdens of DNAH11, HAP1, HECTD4 and HLA-DQA1 are associated with the “AID/APOBEC-2” signature (d). FDR is based on the adjusted SKAT P values, in which the age, the clinical stage and ancestry are considered as covariates. (PDF 407 kb

Additional file 1: of Germline and somatic variations influence the somatic mutational signatures of esophageal squamous cell carcinomas in a Chinese population

Table S1. Somatic mutations in 302 ESCC patients. (XLSX 785 kb

Additional file 6: of Germline and somatic variations influence the somatic mutational signatures of esophageal squamous cell carcinomas in a Chinese population

Figure S2. Significantly modified subnetworks in ESCC. The subnetworks are identified by HotNet2 from public protein-protein interactions databases of HINT+HI2012 (a), HPRD (b), iRefIndex (c) and MultiNet (d). The colored nodes represent the genes with different types of somatic alterations in ESCC, the sizes of the nodes correspond to the frequency of alteration in the population. All the subnetworks are identified with the minimum edge weight (δ), the minimum size of subnetwork (k) and the P less than 0.05. (PDF 158 kb

Additional file 9: of Germline and somatic variations influence the somatic mutational signatures of esophageal squamous cell carcinomas in a Chinese population

Figure S4. Comparison of total SNV counts and the frequencies of specific base substitutions with the somatic statuses of certain genes. (a) The total SNV counts are compared to the somatic statuses of TP53, ZNF750, FAT1, FBXW7 and PIK3CA. (b) The frequencies of substitutions are compared to the somatic statuses of TP53. (c) The total SNV counts are compared to the somatic copy-number statuses of MYC, CDC27 and FHIT. (d) The frequency of C > A substitution is compared to the somatic copy-number statuses of CDC27. FDR is based on the adjusted Wilcoxon rank-sum test P values. (PDF 418 kb

Additional file 8: of Germline and somatic variations influence the somatic mutational signatures of esophageal squamous cell carcinomas in a Chinese population

Figure S3. Population stratification of 302 ESCC patients. Two hundred eight genotyped reference individuals are obtained from TGP including 103 CHB and 105 CHS. After filtering 20 outliers, the remaining 282 samples are classified into CHB and CHS at a threshold level of 0 for PC2. (PDF 165 kb

Additional file 3: of Germline and somatic variations influence the somatic mutational signatures of esophageal squamous cell carcinomas in a Chinese population

Table S2. The functional annotations of in-frame mutations of TNRC6A and FAM90A1. (XLSX 10 kb