Abstract LB-348: Integrated analysis of microRNA and mRNA expression in microsatellite-stable colon cancer using next-generation sequencing and cDNA microarrays
Bibliographic record
Abstract
Abstract Alterations in the expression of microRNAs (miRNAs) contribute to the development and progression of colon cancer, which arises from the accumulation of genetic and epigenetic alterations in the colon tissue. With the advent of next-generation sequencing (NGS), new depths can be reached, allowing for comprehensive profiling of known and novel miRNAs. To profile the miRNAs and their associated target genes in colon cancer, we examined differential expression of miRNAs and predicted target genes between tumor and normal tissue of microsatellite stable (MSS) and CpG island methylator phenotype (CIMP)-negative colon cancer. Ten fresh-frozen colon tumors and 10 adjacent normal mucosa were profiled for miRNAs using the SOLiD NGS platform and mRNA expression analysis was conducted using Affymetrix arrays. To identify differentially expressed miRNAs in tumor versus normal tissue, analysis of variance was conducted that included Bonferroni correction. Integrated expression analysis of miRNAs and their target mRNAs was conducted using Pearson's correlation and the Benjamini and Hochberg's false discovery rate. Both analyses were done using Partek Genomics Suite software. Nineteen miRNAs were significantly differentially expressed in tumor versus normal tissue (fold change ≥ ±2 and p<0.05). Six of these 19 miRNAs have previously been associated with colon cancer (miR-30a, miR-31, miR-135b, miR-182, miR-183 and miR-202). Integrated analysis of miRNA and predicted target mRNA expression yielded 97 significantly correlated miRNA::mRNA pairs for 14 miRNAs with 92 genes (absolute r = 0.71–0.88, q<0.05). Within these miRNA::mRNA pairs, expression levels of the 6 colon cancer-associated miRNAs were correlated with 33 target genes and 8 miRNAs newly identified to be differentially expressed in colon cancer were correlated with 57 target genes. The top functional Ingenuity Pathway Analysis gene ontology categories (p<0.05) for the 92 genes were gastrointestinal disease (18 genes), genetic disorder (21genes), inflammatory disease (15 genes) and cancer (21 genes). The correlated target genes included several genes implicated in colon cancer such as CDH3, NKD1, RGS2, TCFL5 and ZEB2. In summary, deep miRNA sequencing among MSS/CIMP-negative colon tumors confirmed differential expression of 6 colon cancer-associated miRNAs and identified 13 miRNAs not previously reported in colon cancer. Differential expression of most of the discovered miRNAs was correlated with the expression of genes potentially relevant for colon cancer. If confirmed, these miRNAs may serve as new biomarkers for colon cancer and may further our understanding of the epigenetic changes and pathways involved in the development of colon cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-348. doi:10.1158/1538-7445.AM2011-LB-348
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".