Interplay of long non-coding RNAs and TGF/SMAD signaling in different cancers
Why this work is in the frame
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Bibliographic record
Abstract
Based on the exciting insights gleaned from decades of ground-breaking research, it has become evident that deregulated signaling pathways play instrumental role in cancer development and progression. Interestingly discovery of non-coding RNAs has revolutionized our understanding related to transcription, post-transcription and translation. Modern era has witnessed landmark discoveries in the field of molecular cancer and non-coding RNA biology has undergone tremendous broadening. There has been an exponential growth in the list of publications related to non-coding RNAs and overwhelmingly increasing classes of non-coding RNAs are adding new layers of complexity to already complicated nature of cancer. Regulation of TGF/SMAD signaling by miRNAs and LncRNAs has opened new horizons for therapeutic targeting of TGF/SMAD pathway. In this review we have set spotlight on central role of LncRNAs in modulation of TGF/SMAD pathway. Major proportion of the available evidence is underlining positive role of LncRNAs in contextual regulation of TGF/SMAD pathway. LncRNAs are vital to these regulatory networks because they provide a background support to make the TGF/SMAD mediated intracellular signaling more smooth or make transduction cascade more flexible in response to cues from extracellular environment. Therefore, in accordance with this notion, MALAT1, OIP5-AS1, MIR100HG, HOTAIR, ANRIL, PVT1, AFAP1-AS1, SPRY4-IT, ZEB2NAT, TUG1 and Lnc-SNHG1 have been reported to positively regulate TGF/SMAD signaling. In this review, we have focused on the regulation of TGF/SMAD signaling by LncRNAs and how these non-coding RNAs can be therapeutically exploited. Short-interfering RNA (siRNA) and natural products are currently being tested for efficacy against different LncRNAs. Nanotechnological strategies to efficiently deliver LncRNA-targeting siRNAs are also currently being investigated in different cancers.
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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) | 0.000 | 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 it