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. 2021 Dec 21;12(26):2500-2513.
doi: 10.18632/oncotarget.28161.

Comparative transcriptome analysis between patient and endometrial cancer cell lines to determine common signaling pathways and markers linked to cancer progression

Madelaine J Cho-Clark  1 , Gauthaman Sukumar  2 , Newton Medeiros Vidal  3 , Sorana Raiciulescu  4 , Mario G Oyola  1 , Cara Olsen  4 , Leonardo Mariño-Ramírez  5 , Clifton L Dalgard  2   6 , T John Wu  1
Affiliations

Comparative transcriptome analysis between patient and endometrial cancer cell lines to determine common signaling pathways and markers linked to cancer progression

Madelaine J Cho-Clark et al. Oncotarget. .

Abstract

The rising incidence and mortality of endometrial cancer (EC) in the United States calls for an improved understanding of the disease's progression. Current methodologies for diagnosis and treatment rely on the use of cell lines as models for tumor biology. However, due to inherent heterogeneity and differential growing environments between cell lines and tumors, these comparative studies have found little parallels in molecular signatures. As a consequence, the development and discovery of preclinical models and reliable drug targets are delayed. In this study, we established transcriptome parallels between cell lines and tumors from The Cancer Genome Atlas (TCGA) with the use of optimized normalization methods. We identified genes and signaling pathways associated with regulating the transformation and progression of EC. Specifically, the LXR/RXR activation, neuroprotective role for THOP1 in Alzheimer's disease, and glutamate receptor signaling pathways were observed to be mostly downregulated in advanced cancer stage. While some of these highlighted markers and signaling pathways are commonly found in the central nervous system (CNS), our results suggest a novel function of these genes in the periphery. Finally, our study underscores the value of implementing appropriate normalization methods in comparative studies to improve the identification of accurate and reliable markers.

Keywords: cancer stage; comparative transcriptome analysis; endometrial cancer; normalization; signaling pathways.

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Conflict of interest statement

CONFLICTS OF INTEREST The authors report no conflicts of interest. The views expressed herein are those of the authors and do not reflect the official policy or opinion of the Department of Defense or the United States Army or Navy. The contents and views in this manuscript are those of the authors and should not be construed to represent the views of the National Institutes of Health.

Figures

Figure 1
Figure 1. Relative log expression (RLE) and principal component analysis (PCA) plots of overall transcriptome profiles of TCGA tumor samples and endometrial cancer cell lines.
(A) The RLE boxplot distributions of datasets normalized using RUVg k = 3 or k = 10 resulted in improved log counts centered around zero; demonstrating lowered magnitude in variability and higher resilience toward outliers between tumor samples and cell lines. (B) PCA plot axes represents major sources of variation based on genes profiles in the first two dimensions, PC1 and PC2 (centered, log scale)). Scatter plots indicates normalization by RUVg methods leads to better clustering between TCGA tumor samples and cell lines. Normalization with library size, as seen by a distinct separation in scatter plots between TCGA patients and cell lines, suggests similarities in expression are more dependent on sample type.
Figure 2
Figure 2. Differential expression analysis of transcriptomes in TCGA tumor samples and endometrial cancer cell lines.
(A) Hierarchiral clustering analysis of all DEGs between TCGA tumors and cell lines indicates that early stage comparisons show higher degree of clustering between sample types. However, comparisons between early to a later cancer stage demonstrates clustering between stages suggesting clear distinctive expression differences that is stage dependent. All DEGs shown are significant (p value <0.05) (top panel). (B) Respective volcano plot and bar charts highlighting up- or downregulated DEGs (red dots) suggests downregulation of DEGs with advanced cancer stage (p < 0.05; at least |log2 fold change (FC)| ≥ 1), non-significant (NS, grey), log2 fold change (FC)| ≥ 1 (logFC, green), p < 0.05 (p-value, blue) (bottom panel).
Figure 3
Figure 3. Top signaling pathways and enriched gene sets associated to stage comparisons.
(A) Top five signaling pathways that are altered between stages. (B) Respective z-scores and color intensity that is correlated to expected relationship direction (gene expression from knowledge base) and observed gene expression (C) Venn diagram identifying DEGs that are unique or commonly regulated across or between all stages. (D) GO analysis of all dysregulated gene sets (FDR <0.05).
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