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Record W2320271319 · doi:10.1158/1538-7445.am2012-4139

Abstract 4139: Clinical evaluation of microRNA expression profiling in non-small cell lung cancer

2012· article· en· W2320271319 on OpenAlexaff
Athina Markou, Panagiotis A. Vorkas, Ioanna Sourvinou, George M. Yousef, Evi Lianidou

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsmicroRNATaqManCarcinogenesisLung cancerOncogeneBiologyGene expression profilingCancer researchReal-time polymerase chain reactionGene expressionCancerComputational biologyGeneOncologyMedicineGeneticsCell cycle

Abstract

fetched live from OpenAlex

Abstract CONTEXT: MicroRNAs (miRNAs) represent a class of small non-coding RNAs that regulate gene expression at the posttranscriptional level. miRNAs play an important role in tumorigenesis since they have functions similar to oncogene or tumor suppressors and represent a new class of powerful tools for cancer prevention and therapeutics. Especially circulating miRNAs in plasma seem to be very promising novel tumor biomarkers, since their expression is correlated to diagnosis, prognosis and prediction of response to treatment. OBJECTIVE: The aim of our study was to evaluate mature miRNAs as novel tumor biomarkers in NSCLC, by exploring global expression profile of miRNAs in NSCLC paired fresh tissues and corresponding plasma samples. METHODS: FlexmiR bead array (Luminex) assay was utilized for miRNA expression profiling in 21 surgically removed NSCLC fresh tissues and their corresponding adjacent non-cancerous tissues. According to our FlexmiR results, evaluated by three different statistical approaches, 23 miRNAs were found to be differentially expressed. Our FlexmiR experiments were validated for four of these microRNAs in 40 paired NSCLC and matched corresponding adjacent non-cancerous tissues by Taqman RT-qPCR microRNA assays (Applied Biosystems). Moreover, total RNA was extracted from 37 corresponding plasma samples and miR-21 expression was quantitated by RT-qPCR. RESULTS: 23 miRNAs were found to be differentially expressed in NSCLC by the FlexmiR assay. Four of these, miR-21, miR-126*, miR-30d and miR-451, were further evaluated by quantitative RT-qPCR. The expression level of miR-21 was significantly higher in NSCLC tissues than in adjacent normal tissues (P=0.002); while miR-126* (P=0.000), miR-30d (P=0.000) and miR-451 (P=0.000) were down-regulated in NSCLC. Interestingly, high miR-21 expression and low miR-126* and miR-30d expression were associated with disease free survival (P=0.027, P= 0.047 and P=0.048 respectively). Levels of circulating miR-21 in plasma of NSCLC patients were significantly higher in NSCLC than in healthy volunteers (P=0.003). Circulating miR-21 in plasma was found to be an independent prognostic factor for NSCLC (P=0.019). Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4139. doi:1538-7445.AM2012-4139

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.107
GPT teacher head0.467
Teacher spread0.359 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2012
Admission routes1
Has abstractyes

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