MétaCan
Menu
Back to cohort
Record W2023617697 · doi:10.1158/1535-7163.targ-11-b25

Abstract B25: Emerging role of heterogeneous ribonucleoproteins (hnRNPs) as early predictive marker and prognosticator for head and neck oral squamous cell carcinoma.

2011· article· en· W2023617697 on OpenAlexaff
S. C. Tripathi, Manish Kumar, Jasbir Kaur, Shyam S. Chauhan, Nootan Kumar Shukla, Alok Thakkar, Ritu Duggal, Siddhartha Dutta Gupta, Ranju Ralhan, KW Michael Siu

Bibliographic record

VenueMolecular Cancer Therapeutics · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsYork University
Fundersnot available
KeywordsBiomarkerImmunohistochemistryHeterogeneous ribonucleoprotein particleRibonucleoproteinMalignancyBiologyPathologyCancer researchHead and neck squamous-cell carcinomaTissue microarrayCancerMedicineRNAHead and neck cancerBiochemistry

Abstract

fetched live from OpenAlex

Abstract Introduction: Recently using iTRAQ-tagging and multidimensional liquid chromatography tandem mass spectrometry (LC-MS/MS), we identified a panel of proteins differentially expressed in head and neck/oral squamous cell carcinoma (HNOSCC) as compared to the non-malignant tissues. Heterogeneous ribonucleoprotein K (hnRNPK) and Heterogeneous ribonucleoprotein D (hnRNPD) are among few of the proteins identified in this panel. These are nuclear protein belonging to the RNA binding protein family. Here, we report the verification of these proteins in an independent set of clinical samples by immunohistochemistry, and investigate their potential as a prognostic biomarker for HNOSCC. The present study also explores their clinical relevance and potential as a biomarker in identification of oral lesions in early stages of malignancy. Experimental Design: Two hundered HNOSCCs, 100 leukoplakias and 100 non-malignant archived tissues were retrieved from the research tissue bank to determine the expression of hnRNPK using immunohistochemistry (IHC). The IHC data were subjected to statistical analyses using the SPSS 15.0 software (Chicago). Receiver operating characteristic (ROC) analysis was used to determine the sensitivity and specificity as a biomarker. The expression in oral lesions was further validated by immunoblotting and RT-PCR analyses in the same tissue and serum samples as used for IHC. Results: hnRNPK and hnRNPD expression was found to be significantly increased from normal mucosa to leukoplakia with or without dysplasia to HNOSCC (ptrend<0.001). Abberant cytoplasmic expression of hnRNPK was also observed in HNOSCCs. ROC curve showed high potential of these proteins as a biomarker for HNOSCC. Cytoplasmic hnRNPK overexpression was significantly associated with dedifferentiation of tumors whereas nuclear hnRNP D was significantly associated with tumor size. In univariate analysis, nuclear as well as cytoplasmic localization of hnRNPK and nuclear hnRNPD were found to be associated with poor survival. There was no correlation between hnRNPK and hnRNPD expression, however, significant reduced disease free survival was obtained for the patients harboring hnRNP D+hnRNP K (p = 0.005; median survival = 11 months) as compared with median disease-free survival of 55 months in the patients showing varied hnRNP D+hnRNP K expression. Cox regression model confirmed that this combination can be a better prognosticator for HNOSCC patients as revealed by multivariate analysis (p=0.013; HR = 2.2, 95% CI = 1.2–4.1). RTPCR and western blotting confirmed our IHC results. hnRNPK was also detected in serum samples of HNOSCC patients. Conclusion: This is the first large scale study that suggests overexpression of hnRNPK and hnRNPD as an early event in development of HNOSCC. Furthermore, their subcellular localization suggests that they may be associated with increased risk of transformation of oral premalignant lesions and recurrence in HNOSCC. As detected in biological fluid, the potential of hnRNPK as a biomarker should be verified on a large scale. The enhanced performance of the combination of hnRNP D and hnRNP K versus either protein individually, in prognosticating the clinical outcome of HNOSCCs needs to be further explored for its implementation in patient care. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr B25.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.259
Teacher spread0.241 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueMolecular Cancer TherapeuticsSame topicRNA modifications and cancerFrench-language works237,207