Comprehensive Dissection of Transcriptome Data and Regulatory Factors in Pancreatic Cancer Cells
Bibliographic record
Abstract
Features of pancreatic cancers include high mortality rates caused by rapid tumor progression and a lack of effective therapy. Underpinning the molecular mechanisms involved in the alteration of the gene expression program in the pancreatic cancer remains to be understood. In the current study, we performed a comprehensive analysis using 282 pancreatic tumor and normal samples from seven independent expression data sets to provide a better view on the interactions between different transcription factors (TFs) and the most affected biological pathways in pancreatic cancer. We highlighted common differentially expressed genes (DEGs) and common affected processes within pancreatic cancer samples. We revealed 16 main DE-TFs that regulated gene expression alterations as well as the most significant processes in pancreatic cancer compared to normal cells. For example, we found the upregulated FOXM1 to be a top regulator of pancreatic cellular transformation based on results from different analyses, including from its regulation of gene regulatory networks, its presence in protein complex, its significant regulation of genes related to cancer pathways, and its regulation of most of the identified DE-TFs. Furthermore, we provided a model and assessed the role of different DE-TFs in the regulation of the most affected pancreatic- and cancer-specific processes. In conclusion, our bioinformatics meta-analysis of high throughput expression data sets, besides clarifying common affected genes and pathways, also showed the mechanisms involved in regulating these common profiles. Our results, especially for DE-TFs, could potentially be useful for screening for pancreatic cancer, and for confirming or determining novel pharmacological targets. J. Cell. Biochem. 118: 3976-3985, 2017. © 2017 Wiley Periodicals, Inc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".