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Colorectal cancer driver genes identified by patient specific comparison of cytogenetic microarray.

ABSTRACT: Colorectal cancer (CRC), which has high prevalence in Saudi Arabia and worldwide, needs better understanding by exploiting the latest available cytogenetic microarrays. We used biopsy tissue from consenting colorectal cancer patients to extract DNA and carry out microarray analysis using a CytoScan HD platform from Affymetrix. Patient specific comparisons of tumor-normal pairs were carried out. To find out the high probability key players, we performed Genomic Identification of Significant Targets in Cancer analysis and found 144 genes to form the list of driver genes. Of these, 24 genes attained high GISTIC scores and suggest being significantly associated with CRC. Loss of heterozygosity and uniparental disomy were found to affect 9 genes and suggest different mechanisms associated with CRC in every patient. Here we present the details of the methods used in carrying out the above analyses. Also, we provide some additional data on biomarker analysis that would complement the findings.

SUBMITTER: Aziz MA

PROVIDER: S-EPMC4535873 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Colorectal cancer driver genes identified by patient specific comparison of cytogenetic microarray.

Aziz Mohammad Azhar MA Periyasamy Sathish S Yousef Zeyad Z Deeb Ahmad A AlOtaibi Majed M

Genomics data 20140313

Colorectal cancer (CRC), which has high prevalence in Saudi Arabia and worldwide, needs better understanding by exploiting the latest available cytogenetic microarrays. We used biopsy tissue from consenting colorectal cancer patients to extract DNA and carry out microarray analysis using a CytoScan HD platform from Affymetrix. Patient specific comparisons of tumor-normal pairs were carried out. To find out the high probability key players, we performed Genomic Identification of Significant Targe ...[more]

PMID: 26484062

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Project description:Colorectal cancer (CRC) is one of the most prevalent cancers, with over one million new cases per year. Overall, prognosis of CRC largely depends on the disease stage and metastatic status. As precision oncology for patients with CRC continues to improve, this study aimed to integrate genomic, transcriptomic, and proteomic analyses to identify significant differences in expression during CRC progression using a unique set of paired patient samples while considering tumour heterogeneity.We analysed fresh-frozen tissue samples prepared under strict cryogenic conditions of matched healthy colon mucosa, colorectal carcinoma, and liver metastasis from the same patients. Somatic mutations of known cancer-related genes were analysed using Illumina's TruSeq Amplicon Cancer Panel; the transcriptome was assessed comprehensively using Clariom D microarrays. The global proteome was evaluated by liquid chromatography-coupled mass spectrometry (LC‒MS/MS) and validated by two-dimensional difference in-gel electrophoresis. Subsequent unsupervised principal component clustering, statistical comparisons, and gene set enrichment analyses were calculated based on differential expression results.Although panomics revealed low RNA and protein expression of CA1, CLCA1, MATN2, AHCYL2, and FCGBP in malignant tissues compared to healthy colon mucosa, no differentially expressed RNA or protein targets were detected between tumour and metastatic tissues. Subsequent intra-patient comparisons revealed highly specific expression differences (e.g., SRSF3, OLFM4, and CEACAM5) associated with patient-specific transcriptomes and proteomes.Our research results highlight the importance of inter- and intra-tumour heterogeneity as well as individual, patient-paired evaluations for clinical studies. In addition to changes among groups reflecting CRC progression, we identified significant expression differences between normal colon mucosa, primary tumour, and liver metastasis samples from individuals, which might accelerate implementation of precision oncology in the future.

Dataset Information

Colorectal cancer driver genes identified by patient specific comparison of cytogenetic microarray.

Publications

Colorectal cancer driver genes identified by patient specific comparison of cytogenetic microarray.

Similar Datasets

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