An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer

Nguyen Phuoc Long, Kyung Hee Jung, Nguyen Hoang Anh, Hong Hua Yan, Tran Diem Nghi, Seongoh Park, Sang Jun Yoon, Jung Eun Min, Hyung Min Kim, Joo Han Lim, Joon Mee Kim, Johan Lim, Sanghyuk Lee, Soon-Sun Hong, Sung Won Kwon, Nguyen Phuoc Long, Kyung Hee Jung, Nguyen Hoang Anh, Hong Hua Yan, Tran Diem Nghi, Seongoh Park, Sang Jun Yoon, Jung Eun Min, Hyung Min Kim, Joo Han Lim, Joon Mee Kim, Johan Lim, Sanghyuk Lee, Soon-Sun Hong, Sung Won Kwon

Abstract

Substantial alterations at the multi-omics level of pancreatic cancer (PC) impede the possibility to diagnose and treat patients in early stages. Herein, we conducted an integrative omics-based translational analysis, utilizing next-generation sequencing, transcriptome meta-analysis, and immunohistochemistry, combined with statistical learning, to validate multiplex biomarker candidates for the diagnosis, prognosis, and management of PC. Experiment-based validation was conducted and supportive evidence for the essentiality of the candidates in PC were found at gene expression or protein level by practical biochemical methods. Remarkably, the random forests (RF) model exhibited an excellent diagnostic performance and LAMC2, ANXA2, ADAM9, and APLP2 greatly influenced its decisions. An explanation approach for the RF model was successfully constructed. Moreover, protein expression of LAMC2, ANXA2, ADAM9, and APLP2 was found correlated and significantly higher in PC patients in independent cohorts. Survival analysis revealed that patients with high expression of ADAM9 (Hazard ratio (HR)OS = 2.2, p-value < 0.001), ANXA2 (HROS = 2.1, p-value < 0.001), and LAMC2 (HRDFS = 1.8, p-value = 0.012) exhibited poorer survival rates. In conclusion, we successfully explore hidden biological insights from large-scale omics data and suggest that LAMC2, ANXA2, ADAM9, and APLP2 are robust biomarkers for early diagnosis, prognosis, and management for PC.

Keywords: diagnostic biomarker; machine learning; meta-analysis; next-generation sequencing; pancreatic ductal adenocarcinoma; prognostic biomarker; systems biology; transcriptomics.

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Screening process of innovative biomarker candidates for pancreatic cancer diagnosis and treatment. (a) Combined effect size distributions in transcriptome meta-analysis. (b) Selection of membrane protein-coding genes. (c) Selection of secretory protein-coding genes. The left circle of the Venn diagram (orange) represents the candidates from meta-analysis and the right circle of the Venn diagram (blue) represents the candidates curated from [17].
Figure 2
Figure 2
Kaplan–Meier (KM) plots of the overall survival of four promising prognostic candidates. (a) KM plot of ADAM9. (b) KM plot of ANXA2. (c) KM plot of ITGA2. (d) KM plot of MET.
Figure 3
Figure 3
Data exploration and diagnostic performance of 11 biomarker candidates in the Random Forests model. (a) The gene expression of CDH3 is higher in PDAC than in normal controls (n = 96), ****: p < 0.0001. (b) The gene expression of LAMC2 is higher in PDAC than in normal controls (n = 96). (c) Principal component analysis of PDAC versus normal controls. (d) Heatmap analysis of 11 biomarker candidates. (e) ROC curve of the random forests model in the test set (n = 28). (f) The importance scores of 11 biomarker candidates in the random forests model.
Figure 4
Figure 4
Immunohistochemical analysis for determination of LAMC2, ADAM9, and ANXA2 (Annexin A2) gene expression. (a) Scoring system for the three proteins in pancreatic tissues. (b) IHC scores of LAMC2, ADAM9, and ANXA2 in pancreatic tumors and normal controls (n = 86). ***: p < 0.001; ****: p < 0.0001. (c) Pairwise correlation of LAMC2, ADAM9, and ANXA2 expression. (d) Pairwise correlation of LAMC2, ADAM9, and ANXA9 at gene expression level (log2 of transcripts per million) in TGCA PC, TCGA normal pancreas, and Genotype-Tissue Expression Project (GTEx) derived normal pancreas.
Figure 4
Figure 4
Immunohistochemical analysis for determination of LAMC2, ADAM9, and ANXA2 (Annexin A2) gene expression. (a) Scoring system for the three proteins in pancreatic tissues. (b) IHC scores of LAMC2, ADAM9, and ANXA2 in pancreatic tumors and normal controls (n = 86). ***: p < 0.001; ****: p < 0.0001. (c) Pairwise correlation of LAMC2, ADAM9, and ANXA2 expression. (d) Pairwise correlation of LAMC2, ADAM9, and ANXA9 at gene expression level (log2 of transcripts per million) in TGCA PC, TCGA normal pancreas, and Genotype-Tissue Expression Project (GTEx) derived normal pancreas.

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