Biomarkers in renal cell carcinoma and their targeted therapies: a review
Bibliographic record
Abstract
Renal cell carcinoma (RCC) is one of the most life-threatening urinary malignancies displaying poor response to radiotherapy and chemotherapy. Although in the recent past there have been tremendous advancements in using targeted therapies for RCC, despite that it remains the most lethal urogenital cancer with a 5-year survival rate of roughly 76%. Timely diagnosis is still the key to prevent the progression of RCC into metastatic stages as well as to treat it. But due to the lack of definitive and specific diagnostic biomarkers for RCC and its asymptomatic nature in its early stages, it becomes very difficult to diagnose it. Reliable and distinct molecular markers can not only refine the diagnosis but also classifies the tumors into thier sub-types which can escort subsequent management and possible treatment for patients. Potential biomarkers can permit a greater degree of stratification of patients affected by RCC and help tailor novel targeted therapies. The review summarizes the most promising epigenetic [DNA methylation, microRNA (miRNA; miR), and long noncoding RNA (lncRNA)] and protein biomarkers that have been known to be specifically involved in diagnosis, cancer progression, and metastasis of RCC, thereby highlighting their utilization as non-invasive molecular markers in RCC. Also, the rationale and development of novel molecular targeted drugs and immunotherapy drugs [such as tyrosine kinase inhibitors and immune checkpoint inhibitors (ICIs)] as potential RCC therapeutics along with the proposed implication of these biomarkers in predicting response to targeted therapies will be 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".