Abstract 2309: Predicting molecular networks mediating colorectal cancer neoplastic progression by integrative transcriptome-wide meta-analysis

Abstract

Abstract Introduction: Colorectal cancer (CRC) is the third most common cancer in the United States. While survival rates have increased in part due to enhanced detection and targeted therapeutics, mortality remains high, highlighting a need to further investigate novel targetable pathways along the neoplastic axis. To explore potential molecular networks involved in pre- and post-malignant processes, we implemented a bioinformatics-based pipeline to meta-analyze multiple microarray datasets for predicting differentially active/inhibited pathways. Methods: We constructed a curated, batch-corrected Meta-dataset comprising twelve microarray datasets including GSE4183, GSE8671, GSE9348, GSE15960, GSE20916, GSE21510, GSE22598, GSE23194, GSE23878, GSE32323, GSE33113, and GSE37364, harboring normal (n = 221), adenoma (n = 137), and CRC (n = 452) samples. The Meta-dataset expression set was validated against the TCGA-COAD dataset with functional adenoma and CRC signatures confirmed by Ingenuity Pathway Analysis (IPA) and Gene Set Enrichment Analysis (GSEA). Differentially expressed genes (DEGs) were identified for Adenoma:Normal (A v N), CRC:Adenoma (C v A), and CRC:Normal (C v N) comparison groups and submitted to IPA. To identify relevant gene clusters between DEGs, a heatmap was constructed and arranged by Euclidean distance-weighted unsupervised hierarchal clustering. The same was done regarding IPA-predicted mediators with analysis being restricted to upstream regulators with activation Z-scores > I 2 I in at least two of the three comparison groups. Results: Using ComBat, inter-study batch effects were removed from the Meta-dataset whilst preserving normal, adenoma, and CRC signatures. We found a high degree of correlation between the Meta- and the TCGA-COAD dataset (rho = 0.89, p = 2.2e-16) with functional validation of both adenoma (FDR < 0.0001) and CRC (FDR < 0.0001) signatures using IPA and GSEA. Following, three-way analysis of A v N, C v A, and C v N DEGs revealed four distinct clusters associated with adenoma formation and malignant transformation. Clusters were then cross-reference against established networks regulated by KRAS, EGFR, ERBB2, and TP53 in IPA. Predicted networks associated with adenoma formation were enriched in epithelial-mesenchymal transition pathways (AREG, SNAI1, FOXM1, TCF4) while those associated with malignant transformation were inflammatory based (IL17, TNF, CD24, TLR4). Moreover, early and late downregulated networks were primarily controlled by tumor suppressors (CDKN1A, CST5, TRPS1 and NR1H family members). Conclusion: We identified both known and novel pathways involved in the neoplastic progression of CRC through transcriptome-wide pooled analysis. These pathways may be attractive candidates for future investigation of tumorigenic mechanisms, prognostication, and therapeutics. Citation Format: Michael W. Rohr, Deborah Altomare. Predicting molecular networks mediating colorectal cancer neoplastic progression by integrative transcriptome-wide meta-analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2309.