Abstract B45: A search for ideal siRNA targets involved in pathway cross-talks for combinational silencing in human cancer cells
Bibliographic record
Abstract
Abstract The heterogeneity in the pathways involved in enhanced cell proliferation and survival mechanisms, as well as the mechanisms playing a major role in development of drug resistance, is an important obstacle in cancer treatment. Signaling axes such as PI3K-AKT, Ras-Raf, MEK-ERK, and JAK-STAT pathways have not only been established as major processes involved in enhanced proliferation and activation of the transcription of multiple anti-apoptosis proteins, but are also shown to be interconnected in forming a vast intracellular signaling network. RNA interference, and more specifically, small interfering RNA (siRNA), is a post-transcriptional down-regulation of the expression of a specific protein, and has been studied extensively in the last decade as not only an investigational tool, but also as a therapeutic approach especially in cancer treatment. In the present study, we undertook a systematic approach to simultaneous silencing of two proteins involved in intracellular signaling network in order to inhibit more than one pathway involved in proliferation and survival of cancer cells. After carefully selecting the proteins with pivotal roles in cell survival through diverse pathways, we studied silencing each protein individually and in all possible dual combinations, and evaluated the cell response as the mRNA level of the selected proteins as well as the viable cell number. Our studies reveled that silencing JAK2, STAT3, and JUN have a significant effect on the expression level of anti-apoptotic proteins, e.g., Mcl-1 and survivin, and could negatively impact the survival of MDA435 cells. These results indicate a promising potential for combinational siRNA silencing as an effective anticancer strategy. Citation Format: Hamidreza Montazeri Aliabadi, Parvin Mahdipoor, Hasan Uludag. A search for ideal siRNA targets involved in pathway cross-talks for combinational silencing in human cancer cells. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Drug Sensitivity and Resistance: Improving Cancer Therapy; Jun 18-21, 2014; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(4 Suppl): Abstract nr B45.
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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) | 0.001 | 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) | 0.002 | 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".