Identification of a Nine-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival of Pancreatic Cancer.
Journal: 2019/October - Frontiers in Oncology
ISSN: 2234-943X
Abstract:
Background: Pancreatic cancer is highly lethal and aggressive with increasing trend of mortality in both genders. An effective prediction model is needed to assess prognosis of patients for optimization of treatment. Materials and Methods: Seven datasets of mRNA expression and clinical data were obtained from gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas pancreatic ductal adenocarcinoma (TCGA-PAAD) dataset. Differentially expressed genes (DEGs) between pancreatic tumor and normal tissue were identified by integrated analysis of multiple GEO datasets. Univariate and Lasso Cox regression analyses were applied to identify overall survival-related DEGs and establish a prognostic gene signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index (C-index) and calibration curve. GSE62452 and GSE57495 were used for external validation. Gene set enrichment analysis (GSEA) and tumor immunity analysis were applied to elucidate the molecular mechanisms and immune relevance. Multivariate Cox regression analysis was used to identify independent prognostic factors in pancreatic cancer. Finally, a prognostic nomogram was established based on the TCGA PAAD dataset. Results: A nine-gene signature comprising MET, KLK10, COL17A1, CEP55, ANKRD22, ITGB6, ARNTL2, MCOLN3, and SLC25A45 was established to predict overall survival of pancreatic cancer. The ROC curve and C-index indicated good performance of the nine-gene signature at predicting overall survival in the TCGA dataset and external validation datasets relative to classic AJCC staging. The nine-gene signature could classify patients into high- and low-risk groups with distinct overall survival and differentiate tumor from normal tissue. Univariate Cox regression revealed that the nine-gene signature was an independent prognostic factor in pancreatic cancer. The nomogram incorporating the gene signature and clinical prognostic factors was superior to AJCC staging in predicting overall survival. The high-risk group was enriched with multiple oncological signatures and aggressiveness-related pathways and associated with significantly lower levels of CD4+ T cell infiltration. Conclusion: Our study identified a nine-gene signature and established a prognostic nomogram that reliably predict overall survival in pancreatic cancer. The findings may be beneficial to therapeutic customization and medical decision-making.
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Front Oncol 9: 996

Identification of a Nine-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival of Pancreatic Cancer

Supplementary Table 1

234 DEGs identified by integrated analysis using Robust rank aggregation (RRA) method with p < 0.05.

Click here for additional data file.(17K, XLSX)

Supplementary Table 2

Functional enrichment analysis of the DEGs.

Click here for additional data file.(37K, XLSX)

Supplementary Table 3

DEGs associated with overall survival using univariate Cox regression model with p < 0.01.

Click here for additional data file.(20K, XLSX)

Supplementary Table 4

Mean cross-validation error(CVM)and Standard deviation of CVM (CVSD) for each lambda in the LASSO regression.

Click here for additional data file.(202K, XLSX)

Supplementary Table 5

The reasons for each case excluded from univariate- and multivariate Cox regression analyses.

Click here for additional data file.(14K, XLSX)

Supplementary Table 6

Gene set enrichment analyses between high and low risk group in 165 TCGA PAAD Samples.

Click here for additional data file.(63K, XLSX)

Supplementary Figure 1

Differential expression of mRNA between tumor and normal tissue in seven GEO datasets.

Click here for additional data file.(2.5M, TIF)

Supplementary Figure 2

The expression of DEGs identified after integrated analysis in {"type":"entrez-geo","attrs":{"text":"GSE71729","term_id":"71729"}}GSE71729, {"type":"entrez-geo","attrs":{"text":"GSE62165","term_id":"62165"}}GSE62165, {"type":"entrez-geo","attrs":{"text":"GSE62452","term_id":"62452"}}GSE62452, {"type":"entrez-geo","attrs":{"text":"GSE28735","term_id":"28735"}}GSE28735, {"type":"entrez-geo","attrs":{"text":"GSE15471","term_id":"15471"}}GSE15471, and {"type":"entrez-geo","attrs":{"text":"GSE32676","term_id":"32676"}}GSE32676.

Click here for additional data file.(5.4M, TIF)

Supplementary Figure 3

Functional enrichment analyses of the DEGs and the identification of hub genes.

Click here for additional data file.(1.5M, TIF)

Supplementary Figure 4

Lasso analysis of the prognostic DEGs in pancreatic cancer.

Click here for additional data file.(2.1M, TIF)

Supplementary Figure 5

ROC curves for overall survival predictions of the nine gene signature in compare with 3 previously defined gene signatures.

Click here for additional data file.(637K, TIF)

Supplementary Figure 6

Subgroup analyses of the nine gene signature.

Click here for additional data file.(3.9M, TIF)

Supplementary Figure 7

Analyses of response to treatment for patients in high risk and low risk group.

Click here for additional data file.(865K, TIF)

Supplementary Figure 8

External validation of the nine gene signature in {"type":"entrez-geo","attrs":{"text":"GSE57495","term_id":"57495"}}GSE57495 dataset.

Click here for additional data file.(963K, TIF)

Supplementary Figure 9

Distribution of the risk score between metastases and the primary tumors and in different AJCC stages.

Click here for additional data file.(290K, TIF)
Department of General Surgery, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
Edited by: Qingfeng Zhu, Johns Hopkins Medicine, United States
Reviewed by: Ning Pu, Zhongshan Hospital, Fudan University, China; Emilia Andersson, Unilabs AB, Sweden
*Correspondence: Ziwen Liu moc.361@hcmupnewizuil
Yupei Zhao ten.362@8208oahz
This article was submitted to Gastrointestinal Cancers, a section of the journal Frontiers in Oncology
Department of General Surgery, Peking Union Medical College, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
Edited by: Qingfeng Zhu, Johns Hopkins Medicine, United States
Reviewed by: Ning Pu, Zhongshan Hospital, Fudan University, China; Emilia Andersson, Unilabs AB, Sweden
Received 2019 Jul 6; Accepted 2019 Sep 17.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Abstract

Background: Pancreatic cancer is highly lethal and aggressive with increasing trend of mortality in both genders. An effective prediction model is needed to assess prognosis of patients for optimization of treatment.

Materials and Methods: Seven datasets of mRNA expression and clinical data were obtained from gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas pancreatic ductal adenocarcinoma (TCGA-PAAD) dataset. Differentially expressed genes (DEGs) between pancreatic tumor and normal tissue were identified by integrated analysis of multiple GEO datasets. Univariate and Lasso Cox regression analyses were applied to identify overall survival-related DEGs and establish a prognostic gene signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index (C-index) and calibration curve. {"type":"entrez-geo","attrs":{"text":"GSE62452","term_id":"62452"}}GSE62452 and {"type":"entrez-geo","attrs":{"text":"GSE57495","term_id":"57495"}}GSE57495 were used for external validation. Gene set enrichment analysis (GSEA) and tumor immunity analysis were applied to elucidate the molecular mechanisms and immune relevance. Multivariate Cox regression analysis was used to identify independent prognostic factors in pancreatic cancer. Finally, a prognostic nomogram was established based on the TCGA PAAD dataset.

Results: A nine-gene signature comprising MET, KLK10, COL17A1, CEP55, ANKRD22, ITGB6, ARNTL2, MCOLN3, and SLC25A45 was established to predict overall survival of pancreatic cancer. The ROC curve and C-index indicated good performance of the nine-gene signature at predicting overall survival in the TCGA dataset and external validation datasets relative to classic AJCC staging. The nine-gene signature could classify patients into high- and low-risk groups with distinct overall survival and differentiate tumor from normal tissue. Univariate Cox regression revealed that the nine-gene signature was an independent prognostic factor in pancreatic cancer. The nomogram incorporating the gene signature and clinical prognostic factors was superior to AJCC staging in predicting overall survival. The high-risk group was enriched with multiple oncological signatures and aggressiveness-related pathways and associated with significantly lower levels of CD4 T cell infiltration.

Conclusion: Our study identified a nine-gene signature and established a prognostic nomogram that reliably predict overall survival in pancreatic cancer. The findings may be beneficial to therapeutic customization and medical decision-making.

Keywords: gene expression omnibus, nomogram, overall survival, pancreatic cancer, The Cancer Genome Atlas
Abstract

Footnotes

Funding. This research was supported by the National Nature Science Foundation of China (2015, 81572459) and the CAMS Innovation Fund for Medical Sciences (CIFMS) (2016-12M-3-005).

Footnotes
Click here for additional data file.(17K, XLSX)Click here for additional data file.(37K, XLSX)Click here for additional data file.(20K, XLSX)Click here for additional data file.(202K, XLSX)Click here for additional data file.(14K, XLSX)Click here for additional data file.(63K, XLSX)Click here for additional data file.(2.5M, TIF)Click here for additional data file.(5.4M, TIF)Click here for additional data file.(1.5M, TIF)Click here for additional data file.(2.1M, TIF)Click here for additional data file.(637K, TIF)Click here for additional data file.(3.9M, TIF)Click here for additional data file.(865K, TIF)Click here for additional data file.(963K, TIF)Click here for additional data file.(290K, TIF)

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