Interplay of long non-coding RNAs and TGF/SMAD signaling in different cancers
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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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".