Advances in RNA-based cancer therapeutics: pre-clinical and clinical implications
Bibliographic record
Abstract
Cancer therapy has been revolutionised by the emergence of RNA-based therapeutics, providing several strategies and mechanisms to regulate gene expression via messenger RNA (mRNA), small interfering RNA (siRNA), microRNAs (miRNA), antisense oligonucleotides (ASOs), and RNA aptamers. The present review highlights the recent advances in the preclinical development and clinical applications of RNA-based therapeutics, focusing on the delivery strategies, biological targets, and pharmacological optimisation, together with key clinical data. mRNA therapeutics, especially those adapted from vaccine platforms are being developed for the cancer immunotherapy and protein replacement, while siRNAs and ASOs enable highly specific gene silencing and splice correction. miRNA therapies show potential for diverse oncogenic pathway control, despite ongoing challenges in the delivery and specificity. RNA aptamers are obtaining attention as tumor-targeting agents in the drug delivery systems. Progress in lipid nanoparticles, chemical modifications, and tissue-specific delivery has improved the stability and efficacy of these agents. Early-phase clinical trials report encouraging outcomes in both solid tumours and haematologic malignancies, particularly in overcoming resistance and modulating the tumor microenvironment (TME). Although challenges remain in scalability, immune activation, and deep-tumour penetration, RNA-based strategies are advancing towards integration into clinical oncology. Continued refinement of delivery technologies and targeted trial designs will be critical for translating these therapies into effective, personalized cancer treatments. • RNA-based therapies allow for precise intervention at the genetic and molecular levels of cancer. RNA-based therapies enable targeted intervention at the genetic and molecular levels of cancer. • Distinct RNA modalities including mRNA, siRNA, miRNA, ASOs, and aptamers offer provide complementary mechanisms for tumor modulation. • Advances in delivery technologies, particularly lipid nanoparticles (LNPs), have significantly improved RNA stability, targeting, and intracellular uptake. • Clinical trials report encouraging promising efficacy and tolerability stability of RNA therapeutics in both solid tumours and haematologic malignancies. • Novel approaches such as self-amplifying RNA (saRNA) and synthetic lethality are emerging as precision strategies to address tumour heterogeneity and drug resistance. Questions • How do different types of RNA therapeutics function in cancer treatment? • What are the major challenges in delivering the delivery of RNA molecules effectively to tumor sites? • How do chemical modifications improve the performance of RNA-based drugs? • What clinical evidence supports the use of RNA therapeutics in oncology? • In what ways can RNA therapies be integrated into personalized cancer care strategies?
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".