The colorectal cancer disease-specific transcriptome may facilitate the discovery of more biologically and clinically relevant information

Colorectal cancer concurrent gene signature based on coherent patterns between genomic and transcriptional alterations

Background

The aim of the study was to enhance colorectal cancer prognostication by integrating single nucleotide polymorphism (SNP) and gene expression (GE) microarrays for genomic and transcriptional alteration detection; genes with concurrent gains and losses were used to develop a prognostic signature.

Methods

The discovery dataset comprised 32 Taiwanese colorectal cancer patients, of which 31 were assayed for GE and copy number variations (CNVs) with Illumina Human HT-12 BeadChip v4.0 and Omni 25 BeadChip v1.1. Concurrent gains and losses were declared if coherent manners were observed between GE and SNP arrays. Concurrent genes were also identified in The Cancer Genome Atlas Project (TCGA) as the secondary discovery dataset (n = 345).

Results

The “universal” concurrent genes, which were the combination of z-transformed correlation coefficients, contained 4022 genes. Candidate genes were evaluated within each of the 10 public domain microarray datasets, and 1655 (2000 probe sets) were prognostic in at least one study. Consensus across all datasets was used to build a risk predictive model, while distinct relapse-free/overall survival patterns between defined risk groups were observed among four out of five training datasets. The predictive accuracy of recurrence, metastasis, or death was between 61 and 86% (cross-validation area under the receiver operating characteristic (ROC) curve: 0.548-0.833) from five independent validation studies.

Conclusion

The colorectal cancer concurrent gene signature is prognostic in terms of recurrence, metastasis, or mortality among 1746 patients. Genes with coherent patterns between genomic and transcriptional contexts are more likely to provide prognostication for colorectal cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09627-9.

Niclosamide's potential direct targets in ovarian cancer†

Recent evidence indicates that niclosamide is an anti-cancer compound that is able to inhibit several signaling pathways. Although niclosamide has previously been identified by high-throughput screening platforms as a potential effective compound against several cancer types, no direct binding interactions with distinct biological molecule(s) has been established. The present study identifies key signal transduction mechanisms altered by niclosamide in ovarian cancer. Using affinity purification with a biotin-modified niclosamide derivative and mass spectrometry analysis, several RNA-binding proteins (RBPs) were identified. We chose the two RBPs, FXR1 and IGF2BP2, for further analysis. A significant correlation exists in which high-expression of FXR1 or IGF2BP2 is associated with reduced survival of ovarian cancer patients. Knockdown of FXR1 or IGF2BP2 in ovarian cancer cells resulted in significantly reduced cell viability, adhesion, and migration. Furthermore, FXR1 or IGF2BP2 deficient ovarian cancer cells exhibited reduced response to most doses of niclosamide showing greater cell viability than those with intact RBPs. These results suggest that FXR1 and IGF2BP2 are direct targets of niclosamide and could have critical activities that drive multiple oncogenic pathways in ovarian cancer.

Use of formalin-fixed paraffin-embedded samples for gene expression studies in breast cancer patients

To obtain gene expression profiles from samples collected in clinical trials, we conducted a pilot study to assess feasibility and estimate sample attrition rates when profiling formalin-fixed, paraffin-embedded specimens. Ten matched fresh-frozen and fixed breast cancer samples were profiled using the Illumina HT-12 and Ref-8 chips, respectively. The profiles obtained with Ref 8, were neither technically nor biologically reliable since they failed to yield the expected separation between estrogen receptor positive and negative samples. With the use of Affymetrix HG-U133 2.0 Plus chips on fixed samples and a quantitative polymerase chain reaction -based sample pre-assessment step, results were satisfactory in terms of biological reliability, despite the low number of present calls (M = 21%±5). Compared with the Illumina DASL WG platform, Affymetrix data showed a wider interquartile range (1.32 vs 0.57, P<2.2 E-16,) and larger fold changes. The Affymetrix chips were used to run a pilot study on 60 fixed breast cancers. By including in the workflow the sample pre-assessment steps, 96% of the samples predicted to give good results (44/46), were in fact rated as satisfactory from the point of view of technical and biological meaningfulness. Our gene expression profiles showed strong agreement with immunohistochemistry data, were able to reproduce breast cancer molecular subtypes, and allowed the validation of an estrogen receptor status classifier derived in frozen samples. The approach is therefore suitable to profile formalin-fixed paraffin-embedded samples collected in clinical trials, provided that quality controls are run both before (sample pre-assessment) and after hybridization on the array.

Identification of clinically relevant molecular subtypes in colorectal cancer: the dawning of a new era

In recent years, a number of protein and genomic-based biomarkers have begun to refine the prognostic information available for colorectal cancer (CRC) and predict defined patient groups that are likely to benefit from systemic treatment or targeted therapies. Of these, KRAS represents the first biomarker integrated into clinical practice for CRC. Microarray-based gene expression profiling has been used to identify prognostic signatures and, to a lesser extent, predictive signatures in CRC. Despite these advances, a number of major challenges remain. This article, which is based on a lecture delivered as part of the 2013 Bob Pinedo Cancer Care Prize, reviews the impact of molecular biomarkers on the management of CRC, emphasizing changes that have occurred in recent years, and focuses on potential mechanisms of patient stratification and opportunities for novel therapeutic development based on enhanced biological understanding of colorectal cancer.

Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network

One of the most important and challenging problems in biomedicine and genomics is how to identify the disease genes. In this study, we developed a computational method to identify colorectal cancer-related genes based on (i) the gene expression profiles, and (ii) the shortest path analysis of functional protein association networks. The former has been used to select differentially expressed genes as disease genes for quite a long time, while the latter has been widely used to study the mechanism of diseases. With the existing protein-protein interaction data from STRING (Search Tool for the Retrieval of Interacting Genes), a weighted functional protein association network was constructed. By means of the mRMR (Maximum Relevance Minimum Redundancy) approach, six genes were identified that can distinguish the colorectal tumors and normal adjacent colonic tissues from their gene expression profiles. Meanwhile, according to the shortest path approach, we further found an additional 35 genes, of which some have been reported to be relevant to colorectal cancer and some are very likely to be relevant to it. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network have more cancer genes than the genes identified from the gene expression profiles alone. Besides, these genes also had greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying colorectal cancer genes. It has not escaped our notice that the method can be applied to identify the genes of other diseases as well.

A systems biology approach identifies SART1 as a novel determinant of both 5-fluorouracil and SN38 drug resistance in colorectal cancer

Chemotherapy response rates for advanced colorectal cancer remain disappointingly low, primarily because of drug resistance, so there is an urgent need to improve current treatment strategies. To identify novel determinants of resistance to the clinically relevant drugs 5-fluorouracil (5-FU) and SN38 (the active metabolite of irinotecan), transcriptional profiling experiments were carried out on pretreatment metastatic colorectal cancer biopsies and HCT116 parental and chemotherapy-resistant cell line models using a disease-specific DNA microarray. To enrich for potential chemoresistance-determining genes, an unsupervised bioinformatics approach was used, and 50 genes were selected and then functionally assessed using custom-designed short interfering RNA (siRNA) screens. In the primary siRNA screen, silencing of 21 genes sensitized HCT116 cells to either 5-FU or SN38 treatment. Three genes (RAPGEF2, PTRF, and SART1) were selected for further analysis in a panel of 5 colorectal cancer cell lines. Silencing SART1 sensitized all 5 cell lines to 5-FU treatment and 4/5 cell lines to SN38 treatment. However, silencing of RAPGEF2 or PTRF had no significant effect on 5-FU or SN38 sensitivity in the wider cell line panel. Further functional analysis of SART1 showed that its silencing induced apoptosis that was caspase-8 dependent. Furthermore, silencing of SART1 led to a downregulation of the caspase-8 inhibitor, c-FLIP, which we have previously shown is a key determinant of drug resistance in colorectal cancer. This study shows the power of systems biology approaches for identifying novel genes that regulate drug resistance and identifies SART1 as a previously unidentified regulator of c-FLIP and drug-induced activation of caspase-8.