Transcriptomic Analyses Revealed Systemic Alterations in Gene Expression in Circulation and Tumor Microenvironment of Colorectal Cancer Patients

Abstract

Colorectal cancer (CRC) is among the leading causes of cancer-related deaths worldwide, underscoring a need for better understanding of the disease and development of novel diagnostic biomarkers and therapeutic interventions. Herein, we performed transcriptome analyses on peripheral blood mononuclear cells (PBMCs), CRC tumor tissue and adjacent normal tissue from 10 CRC patients and PBMCs from 15 healthy controls. Up regulated transcripts from CRC PBMCs were associated with functions related to immune cell trafficking and cellular movement, while downregulated transcripts were enriched in cellular processes related to cell death. Most affected signaling networks were those involved in tumor necrosis factor (TNF) and interleukin signaling. The expression of selected immune-related genes from the RNA-Seq data were further validated using qRT-PCR. Transcriptome analysis of CRC tumors and ingenuity pathway analysis revealed enrichment in several functional categories related to cellular movement, cell growth and proliferation, DNA replication, recombination and repair, while functional categories related to cell death were suppressed. Upstream regulator analysis revealed activation of ERBB2 and FOXM1 networks. Interestingly, there were 18 common upregulated and 36 common downregulated genes when comparing PBMCs and tumor tissue, suggesting transcriptomic changes in the tumor microenvironment could be reflected, in part, in the periphery with potential utilization as disease biomarkers.

Keywords: colorectal cancer (CRC), peripheral blood mononuclear cell (PBMC), transcriptome sequencing; differential expression; disease biomarkers; immune regulation; inflammation.

Figure 1

Transcriptional landscape in peripheral blood mononuclear cells (PBMCs), from colorectal cancer (CRC) and health donors (HD). (a) Hierarchical clustering of CRC (n = 10) and normal (n = 15) based on differentially expressed RNA transcripts in PBMCs from each group. Each column represents one sample and each row represents a transcript. Expression level of each transcript (log2) in a single sample is depicted according to the color scale. (b) Principal component analysis (PCA) for the RNA transcriptome of PBMCs from CRC and HD.

Figure 2

Altered immune signature expression in PBMCS from CRC and healthy donors (HD). (a) Heatmap depicting the expression of several upregulated (left) and downregulated (right) immune regulators in PBMCs from ten CRC and 15 healthy individuals. Data are presented as log2 Transcripts Per Million (TPM) expression value. Expression values are depicted according to the color scale. (b) The expression of six upregulated (CXCL2, IL8, CCL7, CXCL3, IL10 and CCL3) and six downregulated (TLR7, TLR5, TLR10, TLR8, TNFSF10 and CD79B) based on RNA-Seq data in PBMCs from eight CRC compared to eight health subjects (ran in duplicate) using qRT-PCR. Data are presented as scatter plot (x axis) while –delta CT value is presented on the y-axis.

Figure 3

Downstream effector analysis of differentially expressed gene transcripts in PBMCs from CRC and health subjects. (a) Tree map (hierarchical heat map) depicting affected functional categories based on differentially expressed genes where the major boxes represent a category of diseases and functions. Illustration of the Immune cell trafficking (b), cellular movement (c) and cell death and survival (d) is depicted. Each individualy colored rectangle is a particular biological function or disease and the color range indicates its predicted activation state—increasing (orange) or decreasing (blue). Darker colors indicate higher absolute Z-scores. In this default view, the size of the rectangles is correlated with increasing overlap significance.

Figure 4

Mechanistic network analysis predicts multiple altered signaling networks in PBMCs from CRC compared to health subjects. (a) Illustration of the TNF, (b) IL1A and (c) CCL5 mechanistic networks. Figure legend illustrate the relationship between molecules within the network.

Figure 5

Transcriptional landscape in CRC compared to adjacent normal tissue. (a) Hierarchical clustering of CRC (n = 10) and adjacent normal tissue (n = 10) based on differentially expressed RNA transcripts between the two group. Each column represents one sample and each row represents a transcript. Expression level of each transcript (log2) in a single sample is depicted according to the color scale. (b) Principal component analysis (PCA) for the RNA transcriptome of CRC and adjacent normal tissue.

Figure 6

Downstream effector analysis of differentially expressed gene transcripts in Tumor Tissue and adjacent normal tissue. (a) Tree map (hierarchical heat map) depicting affected functional categories based on differentially expressed genes where the major boxes represent a category of diseases and functions. Illustration of cellular movement (b), cell growth and proliferation (c), DNA replication, recombination and repair (d) and cell death and survival (e) is depicted. Each individual colored rectangle is a particular biological function or disease and the color range indicates its predicted activation state—increasing (orange) or decreasing (blue). Darker colors indicate higher absolute Z-scores. In this default view, the size of the rectangles is correlated with increasing overlap significance.

Figure 7

Multiple altered functional categories and signaling networks in CRC. Bar chart depicting activated (a) and inhibited (b) upstream regulators networks in CRC compared to adjacent normal tissue. X axis represent the activation Z score. Illustration of the ERBB2 (c) and FOXM1 (d) networks is shown. Figure legend illustrate the relationship between molecules within the network.

Figure 8

Commonality and differences in gene expression between PBMCs and CRC tumor tissue. Venn diagram showing common differentially expressed transcripts comparing CRC and PBMC data. Upregulated in CRC compared to adjacent normal tissue, downregulated in CRC compared to adjacent normal tissue, upregulated in PBMCs from CRC patients compared to healthy individuals and downregulated in PBMCs from CRC patients compared to healthy individuals (a). Commonly inhibited and activated upstream regulators in CRC versus PBMC (b). Heat map showing the eighteen common up (c) and thirty-six common down (d) regulated transcripts identified from panel a.