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Record W2966530663 · doi:10.1101/717793

Galectin-14 expression in ovarian cancer

2019· preprint· en· W2966530663 on OpenAlexaff
Lorenna Oliveira Fernandes de Araujo, Yves St‐Pierre

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsArmand Frappier MuseumInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsGalectinOvarian cancerGalectin-3Cancer researchBiologyGalectin-1In silicoCancerCell biologyImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

Abstract Galectins (gal) are multifunctional proteins whose expression changes under different physiological or pathological conditions, including cancer. However, so far, most studies have focused on gal-1 and gal-3, and to a lesser extent to gal-7 and gal-9. We still know very little about other galectins, especially the recently discovered ones, such as gal-14, a prototype galectin highly expressed at the maternal-fetal interface. Here, using in silico and in vitro approaches, we report a correlation between lgals14 expression and ovarian cancer. We found that high expression of gal-14 mRNA in ovarian cancer cells is associated with a shorter survival. Consistent with this observation, we also found that lgals14 is preferentially expressed in high grade serous adenocarcinoma (HGSA) ovarian cancer. Our in vitro data with ovarian cancer cell lines confirmed that lgals14 is readily expressed in HGSA. Interestingly, de novo expression of gal-14 in HEK-293 cells increased apoptosis, both at the basal level and following exposure to low doses of etoposide. Thus, although the study of this galectin is still in its infancy, we were able to provide novel insights into the expression patterns of this galectin and its involvement in cancer.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
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 score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.002
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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicGalectins and Cancer BiologyFrench-language works237,207