The Immunome of Colon Cancer: Functional In Silico Analysis of Antigenic Proteins Deduced from IgG Microarray Profiling

Abstract

Characterization of the colon cancer immunome and its autoantibody signature from differentially-reactive antigens (DIRAGs) could provide insights into aberrant cellular mechanisms or enriched networks associated with diseases. The purpose of this study was to characterize the antibody profile of plasma samples from 32 colorectal cancer (CRC) patients and 32 controls using proteins isolated from 15,417 human cDNA expression clones on microarrays. 671 unique DIRAGs were identified and 632 were more highly reactive in CRC samples. Bioinformatics analyses reveal that compared to control samples, the immunoproteomic IgG profiling of CRC samples is mainly associated with cell death, survival, and proliferation pathways, especially proteins involved in EIF2 and mTOR signaling. Ribosomal proteins (e.g., RPL7, RPL22, and RPL27A) and CRC-related genes such as APC, AXIN1, E2F4, MSH2, PMS2, and TP53 were highly enriched. In addition, differential pathways were observed between the CRC and control samples. Furthermore, 103 DIRAGs were reported in the SEREX antigen database, demonstrating our ability to identify known and new reactive antigens. We also found an overlap of 7 antigens with 48 "CRC genes." These data indicate that immunomics profiling on protein microarrays is able to reveal the complexity of immune responses in cancerous diseases and faithfully reflects the underlying pathology.

Keywords: Autoantibody tumor biomarker; Cancer immunology; Colorectal cancer; Immunomics; Protein microarray.

Figure 1

Procedure overview The described procedure exemplifies the methodology used in this study. An expression library consisting of 15,417 cDNA clones was used to produce recombinant human proteins. The recombinant proteins were isolated and used for printing protein microarrays. IgG was isolated from a total of 64 samples (32 CRC samples and 32 healthy control samples) and tested on the protein microarrays. Bioinformatics analyses (t-tests) were performed to identify the DIRAGs between the groups of arrays. Subsequently, the list of DIRAGs were subjected to functional analysis with IPA, hierarchical protein interaction module enrichment analysis with WebGestalt, association of overlapping proteins with the Cancer Immunome Database analysis, and analysis of overlap with known CRC and TAAs. CRC, colorectal cancer; TAA, tumor-associated antigen; GO, Gene Ontology.

Figure 2

Node-link diagram visualization of DIRAG-enriched Module 3 Visualization of higher antigenic reactivity (up-regulated, colored from white to red) and low-antigenic reactivity (down-regulated, colored from blue to white) DIRAGs in CRC samples in comparison with control samples (in the center) and their direct neighbors (at the edge) was obtained using the protein interaction enrichment analysis in WebGestalt. Enrichment analysis was performed using the hypergeometric test, and the Benjamini–Hochberg procedure for multiple test adjustment (P = 0.01). CRC, colorectal cancer; DIRAG, differentially-reactive antigen.

Figure 3

GO Slim classification analysis of the 671 DIRAGs identified Histogram of functional annotations of DIRAGs in CRC samples in comparison with control samples (P = 0.01) was generated based on the WebGestalt derived GO slim charts in the three GO functional categories. A. Molecular function. B. Biological process. C. Cellular component. More than half of the proteins are nuclear proteins. DIRAG, differentially-reactive antigens; CRC, colorectal cancer; GO, Gene Ontology.

Supplementary Figure S3

Node-link diagram visualization of DIRAGs enriched in Module 1Visualization of higher antigenic reactivity (up-regulated, colored from white to red) and low-antigenic reactivity (down-regulated, colored from blue to white) DIRAGs in CRC samples in comparison with control samples (in the center and their direct neighbors (at the edge) with WebGestalt. Enrichment analysis was performed using the hypergeometric test, and the Benjamini–Hochberg procedure was used for multiple test adjustment. A minimum number of two genes for a category was used as cut-off, and pathways significance level was set at P = 0.01 (t-test).

Supplementary Figure S4

Node-link diagram visualization of DIRAGs enriched in Module 2Visualization of higher antigenic reactivity (up-regulated, colored from white to red) and low-antigenic reactivity (down-regulated, colored from blue to white) DIRAGs in CRC samples in comparison with control samples (in the center and their direct neighbors (at the edge) with WebGestalt. Enrichment analysis was performed using the hypergeometric test, and the Benjamini–Hochberg procedure was used for multiple test adjustment. A minimum number of two genes for a category was used as cut-off, and pathways significance level was set at P = 0.01 (t-test).