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Upregulated Ly-6 gene expression is associated with poor overall survival in uterine corpus endometrial carcinoma patients

2022· article· en· W4313427065 on OpenAlexaff
Luke Rathbun, Anthony M. Magliocco, Anil Bamezai

Bibliographic record

VenueThe Journal of Immunology · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsNational Research Council Institute for Biodiagnostics
Fundersnot available
KeywordsEndometrial cancerGeneCancerUterine cancerCancer researchBiologyIn silicoCarcinomaGene expressionOncologyInternal medicineMedicineGenetics

Abstract

fetched live from OpenAlex

Abstract The human Ly-6 (hLy-6) gene family has recently gained interest for its possible role in tumor progression. High expression of hLy-6 genes is associated with poor overall survival in pancreatic ductal adenocarcinoma patients. To expand upon this work, we have carried out in silico analyses of all known hLy-6 gene expression in other cancers using TNMplot which shows differential gene expression in cancer tissues. We have also analyzed the patient survival by Kaplan-Meier plotter after mining the TCGA database. We report that upregulated expression of several hLy-6 genes is associated with poor cancer patient survival in many other cancer types, especially uterine corpus endometrial carcinoma (UCEC). Importantly, the expression of a number of hLy-6 genes is elevated in UCEC tissue when compared to the expression in normal uterine tissue. Further analysis of tumor-specific expression of the hLy-6 gene family is needed to uncover the function of Ly-6 proteins as well as the signaling pathways these proteins trigger that endow tumor survival and poor patient prognosis. Human Ly-6 gene products can potentially be used as biomarkers for cancer detection and as tumor associated antigens to identify high-risk cancer patients and appropriately design and/or calibrate cancer treatments for such patients.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.219
Teacher spread0.208 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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