Exploring potential roles of long non-coding RNAs in cancer immunotherapy: a comprehensive review
Bibliographic record
Abstract
Cancer treatment has long been fraught with challenges, including drug resistance, metastasis, and recurrence, making it one of the most difficult diseases to treat effectively. Traditional therapeutic approaches often fall short due to their inability to target cancer stem cells and the complex genetic and epigenetic landscape of tumors. In recent years, cancer immunotherapy has revolutionized the field, offering new hope and viable alternatives to conventional treatments. A particularly promising area of research focuses on non-coding RNAs (ncRNAs), especially long non-coding RNAs (lncRNAs), and their role in cancer resistance and the modulation of signaling pathways. To address these challenges, we performed a comprehensive review of recent studies on lncRNAs and their impact on cancer immunotherapy. Our review highlights the crucial roles that lncRNAs play in affecting both innate and adaptive immunity, thereby influencing the outcomes of cancer treatments. Key observations from our review indicate that lncRNAs can modify the tumor immune microenvironment, enhance immune cell infiltration, and regulate cytokine production, all of which contribute to tumor growth and resistance to therapies. These insights suggest that lncRNAs could serve as potential targets for precision medicine, opening up new avenues for developing more effective cancer immunotherapies. By compiling recent research on lncRNAs across various cancers, this review aims to shed light on their mechanisms within the tumor immune microenvironment.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".