Positive Caricature Transcriptomic Effects Associated with Broad Genomic Aberrations in Colorectal Cancer

Abstract

We re-examined the correlation between Broad Genomic Aberrations (BGAs) and transcriptomic profiles in Colorectal Cancer (CRC). Two types of BGAs have been examined: Broad Copy-Number Abnormal regions (BCNAs), distinguished in gain- and loss-type, and Copy-Neutral Loss of Heterozygosities (CNLOHs). Transcripts are classified as “OverT” or “UnderT” if overexpressed or underexpressed comparing CRCs bearing a specific BGA to CRCs not bearing it and as “UpT” or “DownT” if upregulated or downregulated in cancer compared to normal tissue. BGA-associated effects were evaluated by changes in the “Chromosomal Distribution Index” (CDI) of different transcript classes. Data show that UpT are more sensitive than DownT to BCNA-associated gene dosage effects. “Over-UpT” genes are upregulated in cancer and further overexpressed by gene dosage, defining the so called “positive caricature transcriptomic effect”. When Over-UpT genes are ranked according to overexpression, top positions are occupied by genes implicated at the functional and therapeutic level in CRC. We show that cancer-upregulated transcripts are sensitive markers of BCNA-induced effects and suggest that analysis of positive caricature transcriptomic effects can provide clues toward the identification of BCNA-associated cancer driver genes.

Chromosomal Density of Cancer Up-Regulated Genes, Aberrant Enhancer Activity and Cancer Fitness Genes Are Associated with Transcriptional Cis-Effects of Broad Copy Number Gains in Colorectal Cancer

“…We called positive transcripts (PositiveT) and negative transcripts (NegativeT) those VT having FC > 1 or FC < 1, respectively. The chromosomal distribution of the up-regulated transcripts (PositiveT) and downregulated transcripts (NegativeT) are reported in Figure 2 (top panels), using the so-called Normalized Chromosomal Distribution Index (NCDI), as previously defined in Condorelli et al [19].…”

“…For each COAD group, four different “indices” were used in order to estimate transcript level differences [19]: (a) Linear fold-changes obtained comparing all COAD samples (Selected + Control COAD) vs. normal colonic mucosae (denominated FC1); (b) linear fold-changes obtained comparing COAD samples bearing a specific arm-level CNA (Selected COAD group) to samples not bearing it (Control COAD group) (denominated FC2); (c) linear fold-changes obtained comparing “Control COAD” group to normal colonic mucosae (denominated FC3); (d) linear fold-changes obtained comparing “Selected COAD” group to normal colonic mucosae (denominated FC4). The number of patients in each “Selected COAD” and corresponding “Control COAD” group are reported in Figure 3 and Supplementary Figure S1.…”

“…These data suggest a differential susceptibility of PositiveT and NegativeT to gene dosage effects. Bearing in mind that positive transcripts are up-regulated transcripts in comparison to normal tissue, and that negative ones are down-regulated transcripts, this phenomenon has been denominated the “positive caricature effect”, based on the definition of “caricature” as an exaggeration of tumour’s up-regulated gene expression features [19]. Of course, this is just a statistical observation, suggesting that the probability of gene-dosage effects in down-regulated genes is lower than in up-regulated ones.…”

“…We have recently reported that genes whose expression is up-regulated in colon cancer cells (in comparison to normal tissue) are more susceptible to the gene-dosage transcriptional effects in comparison to downregulated genes [19]. In the present paper we extend our previous results analysing data on colon cancer samples provided by The Cancer Genome Atlas (TCGA) consortium, showing that the chromosomal density of cancer up-regulated genes in selected aberrant chromosomes is correlated to the frequencies of BCNGs in the same chromosomes.…”

Identification of long non‑coding RNA SCARNA9L as a novel molecular target for colorectal cancer

“…nih.gov/geo/). GSE73360 and GSE84984 datasets were used to screen the differentially expressed lncRNAs in CRC (23)(24)(25)(26)(27). The process included: Comparison analysis by limma R package (28) (version:3.36.5; http://bioinf.wehi.edu.au/limma); Gene Ontology (GO; http://geneontology.org/); analysis through statistical method, by two side Fisher's exact test (29); pathway analysis through two side Fisher's exact test (30) (https://www.genome.…”