Non-coding RNA transcripts, incredible modulators of cisplatin chemo-resistance in bladder cancer through operating a broad spectrum of cellular processes and signaling mechanism
Bibliographic record
Abstract
Bladder cancer (BC) is a highly frequent neoplasm in correlation with significant rate of morbidity, mortality, and cost. The onset of BC is predominantly triggered by environmental and/or occupational exposures to carcinogens, such as tobacco. There are two distinct pathways by which BC can be developed, including non-muscle-invasive papillary tumors (NMIBC) and non-papillary (or solid) muscle-invasive tumors (MIBC). The Cancer Genome Atlas project has further recognized key genetic drivers of MIBC along with its subtypes with particular properties and therapeutic responses; nonetheless, NMIBC is the predominant BC presentation among the suffering individuals. Radical cystoprostatectomy, radiotherapy, and chemotherapy have been verified to be the common therapeutic interventions in metastatic tumors, among which chemotherapeutics are more conventionally utilized. Although multiple chemo drugs have been broadly administered for BC treatment, cisplatin is reportedly the most effective chemo drug against the corresponding malignancy. Notwithstanding, tumor recurrence is usually occurred following the consumption of cisplatin regimens, particularly due to the progression of chemo-resistant trait. In this framework, non-coding RNAs (ncRNAs), as abundant RNA transcripts arise from the human genome, are introduced to serve as crucial contributors to tumor expansion and cisplatin chemo-resistance in bladder neoplasm. In the current review, we first investigated the best-known ncRNAs, i.e. microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), correlated with cisplatin chemo-resistance in BC cells and tissues. We noticed that these ncRNAs could mediate the BC-related cisplatin-resistant phenotype through diverse cellular processes and signaling mechanisms, reviewed here. Eventually, diagnostic and prognostic potential of ncRNAs, as well as their therapeutic capabilities were highlighted in regard to BC management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".