Comprehensive review on leucine-rich pentatricopeptide repeat-containing protein (LRPPRC, PPR protein): A burgeoning target for cancer therapy
Bibliographic record
Abstract
Leucine-rich pentatricopeptide repeat-containing ( LRPPRC ), known as the gene mutations that cause Leigh Syndrome French Canadian, encodes a high molecular weight PPR protein (157,905 Da), LRPPRC. LRPPRC binds to DNA, RNA, and proteins to regulate transcription and translation, leading to changes in cell fate. Increasing evidence indicates that LRPPRC plays a pivotal role in various human diseases, particularly cancer in recent years. Here, we review the structure, function, molecular mechanism, as well as inhibitors of LRPPRC. LRPPRC expression elevates in most cancer types and high expression of LRPPRC predicts the poor prognosis of cancer patients. Targeting LRPPRC suppresses tumor progression by affecting several cancer hallmarks, including signal transduction, cancer metabolism, and immune regulation. LRPPRC is a promising target in cancer research, serving as both a biomarker and therapeutic target. Further studies are required to extend the understanding of LRPPRC function and molecular mechanism, as well as to refine novel therapeutic strategies targeting LRPPRC in cancer therapy. A review on significant progress regarding the structure, function, mechanism, and inhibition of LRPPRC in human cancers has been made since its discovery. Ongoing efforts are expected in elucidating LRPPRC and its interaction partners' structure; investigating its role in posttranslational modification; examining the crosstalk between LRPPRC and cancer hallmarks; and screening novel direct LRPPRC inhibitors. (Green: already known function, other colors: remain elucidated).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".