MétaCan
Menu
Back to cohort
Record W2749561870 · doi:10.1111/bju.13931

The utility of micro <scp>RNA</scp> s as biomarkers in predicting progression and survival in patients with clear‐cell renal cell carcinoma

2017· letter· en· W2749561870 on OpenAlexaff
Firas G. Petros, Christopher J.D. Wallis

Bibliographic record

VenueBritish Journal of Urology · 2017
Typeletter
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsClear cell renal cell carcinomamicroRNACarcinogenesisRenal cell carcinomaBiomarkerCancerKidney cancerTumor progressionBiologyStage (stratigraphy)DiseaseNephrectomyMedicineOncologyPathologyInternal medicineGeneKidney

Abstract

fetched live from OpenAlex

RCC constitutes a diverse group of malignancies, yet the clear-cell subtype comprises ~80% of all diagnosed RCC cases 1. The widespread use of abdominal imaging and subsequent stage migration has resulted in improved RCC 5-year cancer-specific survival. However, the overall mortality of RCC remains largely unchanged 2 and one-third of the patients have metastatic disease at the time of presentation 3. Accordingly, the ability to precisely predict patient outcome has become an increasingly significant question in the management of these patients with RCC. Accruing evidence suggests that changes in various biomarkers and their consequent downstream pathways affect cancer initiation and progression. Therefore, accurate prediction of the outcome and prognosis after treatment is necessary 4. MicroRNAs (miRNAs) are small non-coding RNA molecules that can have significant functions in tumorigenesis 5. Because of their ability in post-transcriptional regulation of gene expression, tumour-specific genetic defects in miRNA biogenesis and production correlate with development of human cancers. Thus, the differential expression of specific miRNA signatures in different tumours might become an important tool to help in directing cancer diagnosis and treatment 5. As such, Kowalik et al. 6 report on profiling miRNA to identify biomarker signatures predictive of clear-cell RCC (ccRCC) progression and survival. The authors used 202 formalin-fixed paraffin-embedded samples to isolate RNA from nephrectomy and biopsy specimens (n = 156 and n = 46, respectively) (Fig. 1). The primary analysis of their study 6 focused on the identification of miRNA signatures capable of differentiating between benign and ccRCC, as well as discerning those patients with a non-progressive ccRCC from a progressive clear-cell subtype. The secondary outcome examined the association of miRNA profiles discovered on cancer-specific survival. In their initial microarray screening 20 differentially expressed miRNAs, comparing non-progressive with progressive tumours, were identified. The authors found four miRNA panels (10a-5p, 10b-5p, 106a-5p, and 142-5p) as a potential biomarker signature. This model was validated in nephrectomy specimens and resulted in a sensitivity of 86.7%, a specificity of 92.9%, and an area under the curve (AUC) of 0.930 for detecting ccRCC. Further analysis revealed a second signature of two biomarkers (miR-10a-5p and -223-3p) with 93.8% sensitivity, 83.3% specificity, and an AUC of 0.932 when validated for detecting progressive ccRCC. Similarly, the differential expression of these biomarkers could delineate cancer status in biopsy specimens. For correlation of miRNA expression levels with cancer-specific survival, higher expression levels of (miR-10a-5p and miR-10b-5p) and a lower expression level of (miR-223-3p) were significantly associated with survival (P < 0.001), and the median survival times were not reached. In conclusion, the lack of precise prediction tools has led the authors to explore the potential utility of miRNAs as biomarkers to detect disease presence, biological aggressiveness, and prognosis in ccRCC. However, until future multicentre large prospective studies validate the results of the present work, the transition of miRNA from bench to bedside is emerging on the horizon and has encouraged urologists and scientists to pursue intense translational research in the field. The ability to use miRNAs as biomarkers might be promising for diagnostic and prognostic purposes. These biomarkers may exemplify different aspects of RCC pathogenesis and may potentially have important therapeutic implications to help in a clinical decision-making approach for targeted therapy and RCC personalised treatment. None. None.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.232
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of UrologySame topicRenal cell carcinoma treatmentFrench-language works237,207