Repeat Element Activation-Driven Inflammation: Role of NFκB and Implications in Normal Development and Cancer?
Bibliographic record
Abstract
The human genome is composed of unique DNA sequences that encode proteins and unique sequence noncoding RNAs that are essential for normal development and cellular differentiation. The human genome also contains over 50% of genome sequences that are repeat in nature (tandem and interspersed repeats) that are now known to contribute dynamically to genetic diversity in populations, to be transcriptionally active under certain physiological conditions, and to be aberrantly active in disease states including cancer, where consequences are pleiotropic with impact on cancer cell phenotypes and on the tumor immune microenvironment. Repeat element-derived RNAs play unique roles in exogenous and endogenous cell signaling under normal and disease conditions. A key component of repeat element-derived transcript-dependent signaling occurs via triggering of innate immune receptor signaling that then feeds forward to inflammatory responses through interferon and NFκB signaling. It has recently been shown that cancer cells display abnormal transcriptional activity of repeat elements and that this is linked to either aggressive disease and treatment failure or to improved prognosis/treatment response, depending on cell context and the amplitude of the so-called 'viral mimicry' response that is engaged. 'Viral mimicry' refers to a cellular state of active antiviral response triggered by endogenous nucleic acids often derived from aberrantly transcribed endogenous retrotransposons and other repeat elements. In this paper, the literature regarding transcriptional activation of repeat elements and engagement of inflammatory signaling in normal (focusing on hematopoiesis) and cancer is reviewed with an emphasis on the role of innate immune receptor signaling, in particular by dsRNA receptors of the RIG-1 like receptor family and interferons/NFκB. How repeat element-derived RNA reprograms cell identity through RNA-guided chromatin state modulation is also discussed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".