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Record W2086283096 · doi:10.1371/journal.pone.0113928

Correlation of STAT1 with Apoptosis and Cell-Cycle Markers in Esophageal Squamous Cell Carcinoma

2014· article· en· W2086283096 on OpenAlexafffund
Ying Zhang, Yao-Zhong Zhang, Hailong Yun, Raymond Lai, Min Su

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldMedicine
TopicCytokine Signaling Pathways and Interactions
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchNational Natural Science Foundation of China-Guangdong Joint FundNational Natural Science Foundation of China
KeywordsSurvivinCancer researchCyclin D1Cell cycleBiologyApoptosisMetastasisImmunohistochemistryCyclinOncologyMedicineInternal medicineCancerImmunology

Abstract

fetched live from OpenAlex

We recently found evidence that STAT1 in esophageal squamous carcinoma (ESCC) cells exerts tumor suppressor function, and it regulates five key regulators of apoptosis or cell-cycle progression, including Bcl-2, Bcl-xL, survivin, cyclin D1 and p21. In this study, we confirmed these findings in four ESCC cell lines. Using immunohistochemistry, we also assessed the expression of these proteins in 62 primary tumors. The expression of these markers was heterogeneous, ranging 39 to 69% of the cohort. Significant correlation was found between STAT1 and three proteins (p21, Bcl-xL and survivin), whereas only a trend was identified for cyclin D1 and Bcl-2. We then correlated the expression of these proteins with several clinicopathologic parameters including lymph node metastasis, depth of invasion, clinical stage and overall survival. Significant correlations were found between Bcl-2 and deep invasion (p = 0.033), survivin and lymph node metastasis (p = 0.006), as well as cyclin D1 and clinical stage (p = 0.014). Patients with p21-positive tumors had a significantly longer survival compared to those with p21-negative tumors (p = 0.031). To conclude, our findings support the concept that STAT1 exerts its tumor suppressor effects in ESCC via modulating the expression of key regulators of apoptosis and cell-cycle progression.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.282

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 head0.015
GPT teacher head0.209
Teacher spread0.194 · 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

Citations31
Published2014
Admission routes2
Has abstractyes

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