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Record W4405510703 · doi:10.1038/s41419-024-07288-z

CPNE7 promotes colorectal tumorigenesis by interacting with NONO to initiate ZFP42 transcription

2024· article· en· W4405510703 on OpenAlexaff
Liangbo Zhao, Xiao Sun, Chenying Hou, Yanmei Yang, Pei‐Wen Wang, Zhao-Yuan Xu, Zhenzhen Chen, Xiangrui Zhang, Guanghua Wu, Hong Chen, Hao Xing, Huimin Xie, Luyun He, Shuiling Jin, Benyu Liu

Bibliographic record

VenueCell Death and Disease · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsInstitute of Infection and Immunity
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Henan ProvinceNational Natural Science Foundation of China
KeywordsCarcinogenesisGene knockdownColorectal cancerCancer researchMetastasisBiologyDownregulation and upregulationCell growthSmall hairpin RNATranscription factorApoptosisCancerGeneGenetics

Abstract

fetched live from OpenAlex

Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related death globally. Also, there is still a lack of effective therapeutic strategies for CRC patients owing to a poor understanding of its pathogenesis. Here, we analysed differentially expressed genes in CRC and identified CPNE7 as a novel driver of colorectal tumorigenesis. CPNE7 is highly expressed in CRC and negatively correlated with patients' prognosis. Upregulation of CPNE7 promotes proliferation and metastasis of cancer cells in vitro and in vivo, and vice versa. Mechanistically, CPNE7 interacts with NONO to initiate ZFP42 transcription, thus promoting CRC progression. Moreover, ZFP42 knockdown inhibits tumor cell proliferation and migration while promoting apoptosis. Notably, delivery of CPNE7 shRNA or the small molecule gramicidin, which blocks the interaction between CPNE7 and NONO, hinders tumor growth in vivo. In conclusion, our findings demonstrate that the CPNE7-NONO-ZFP42 axis promotes colorectal tumorigenesis and may be a new potential therapeutic target.

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 score0.150
Threshold uncertainty score0.317

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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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
Published2024
Admission routes1
Has abstractyes

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