Abstract A28: Differentially expressed microRNA profiles in pancreatic ductal and ampullary adenocarcinomas
Bibliographic record
Abstract
Abstract Background: Pancreatic cancer is associated with 6.9% and 4% of all cancer-related deaths in the United States and Brazil, respectively. Pancreatic ductal carcinoma comprises 90% of cases, the majority being of adenocarcinoma subtype. Approximately 12% of periampullary tumors are adenocarcinomas of Vater papilla (ampullary adenocarcinomas); ampullary tumors are often associated with a better prognosis than ductal adenocarcinomas. Although genetic alterations were previously identified in pancreatic carcinomas, there is still a lack of effective treatment strategies. Therefore, the identification of new biomarkers, such as alterations in non-coding RNAs, is urgently needed for the development of novel molecularly targeted therapies for these cancers. microRNAs (miRNAs) are frequently deregulated and contribute to cancer development and progression and have potential prognostic and predictive value. Global miRNA expression profiling analysis in pancreatic cancer, followed by the identification of miRNA target genes may lead to the identification of clinically applicable biomarkers. The novel aspect of our work is the investigation of pancreatic tumors from Brazilian patients, with the inclusion of ampullary adenocarcinomas, a rare subtype. Objectives: To identify global miRNA expression profiles and miRNA target genes in pancreatic ductal and ampullary adenocarcinomas compared to paired histologically normal pancreatic tissue. Patients and Methods: 30 formalin fixed, paraffin embedded (FFPE) pancreatic carcinoma samples were used, including 24 pancreatic ductal adenocarcinomas (PDAC) and 6 ampullary adenocarcinomas (AMP). Paired histologically normal pancreatic tissues were used as controls. All tumor and normal tissues were needle microdissected (Leica EZ4 stereomicroscope). Global miRNA expression profiles were determined using the TaqMan Array Human MicroRNA Cards (TLDA) (card A, v3.0) (Life Technologies) platform. Data analysis was performed using the ExpressionSuite Software v1.0.3. Statistical analysis was performed to correlate miRNA expression with relevant clinical data, using SAS 9.3 software. Computational bioinformatics analysis was performed to identify miRNA target genes, as well as to construct protein-protein interaction and miRNA-gene targets networks. Results and Discussion: We identified 63 significantly deregulated (FC≥2 and p<0.05) miRNAs in PDAC (33 over- and 30 under-expressed) compared to paired histologically normal pancreatic tissue. In AMP, a group of 7 miRNAs was significantly deregulated (4 over- and 3 under-expressed) compared to normal pancreas. Our results showed differentially expressed miRNAs and a complexity of miRNA changes potentially associated to PDAC and AMP tumorigenesis. 3/7 miRNAs (miR-222, 148a and 375) were commonly deregulated in PDAC and AMP tumors. Furthermore, miRNA-gene targets networks were distinct in these different histological subtypes of pancreatic carcinomas. Global miRNA expression profiles showed that PDAC have a significantly higher number of altered miRNAs and a higher number of predicted miRNA target genes than AMP tumors, which could be potentially associated to disease progression and tumor aggressiveness in PDAC compared to AMP. Although these tumors have biological differences, commonly deregulated miRNAs in PDAC and AMP suggest that PDAC and AMP tumorigenesis may share commonly deregulated pathways. Conclusion: miRNAs identified herein may be associated to the biology of PDAC and AMP. Among the miRNAs exclusively deregulated in PDAC, we identified known and not previously reported (novel) miRNAs. In addition, we identified several miRNA target genes associated with tumor invasion, metastasis and poor patient prognosis. Functional in vitro and in vivo validation studies may elucidate the role of identified miRNAs as modulators of oncogenesis mechanisms in PDAC and AMP. T. Felix was funded through São Paulo Research Foundation (FAPESP), MSc. fellowship (2014/00367-4) Citation Format: Tainara F. Felix, Tomas Tokar, Maria A. M. Rodrigues, Rogerio A. Oliveira, Claudia N. Hasimoto, Juan C. Llanos, Robson F. Carvalho, Silvia R. Rogatto, Wan Lam, Igor Jurisica, Sandra A. Drigo, Patricia P. Reis.{Authors}. Differentially expressed microRNA profiles in pancreatic ductal and ampullary adenocarcinomas. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2016 May 12-15; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(24 Suppl):Abstract nr A28.
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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) | 0.002 | 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".