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Record W2040672289 · doi:10.1158/1538-7445.am2014-2094

Abstract 2094: Regulation of site-specific liver metastasis by collagen IV-conveyed signals

2014· article· en· W2040672289 on OpenAlexaffabout
Roni Rayes, Ni Wang, Julia V. Burnier, France Bourdeau, Pnina Brodt

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsAmphiregulinMetastasisCancer researchTranscriptomeBlotSmall hairpin RNAPathologyType IV collagenLungBiologyChemokineCarcinomaGene expressionMedicineCancerGeneImmunologyInternal medicineExtracellular matrixRNAInflammationCell biology

Abstract

fetched live from OpenAlex

Abstract The liver is a primary site of metastasis for some of the most common human malignancies. At present, surgical resection is the most effective curative option for liver metastases, but most patients with liver metastases still succumb to their disease. A better understanding of the underlying biology is essential for design of more effective therapy. We previously identified basement membrane type IV collagen α1/α2 expression levels as determinants of the liver colonizing potential in a murine lung carcinoma model (1) and observed high collagen IV expression levels in surgical specimens of liver metastases from diverse tumor types. The aim of this study was to elucidate the functional relevance of increased collagen IV expression to liver metastases formation. We reported that collagen IV α1/α2 overexpression in non-metastatic lung carcinoma M-27 (M27colIV) cells increased specifically their liver (but not lung) metastasizing potential. A transcriptome analysis was therefore performed on these cells in order to identify changes to gene expression that could account for the newly acquired metastatic potential. This analysis revealed that type IV collagen levels regulated the expression of multiple genes. Prominent among them were genes encoding for chemokines such as CCL-5 and CCL-7 and for growth factors such as amphiregulin (AREG) that were all upregulated by ≥ 3 fold. These changes were validated at the RNA and protein levels using qPCR and Western blotting, respectively. When the expression of these genes in M27colIV cells was subsequently silenced using shRNA, a significant decrease (5-10 folds) was observed in their ability to generate experimental liver metastases, as compared to wild type or mock-transfected cells and they failed to develop metastases in 40-50 % of injected mice. A similar correlation between CCL-5 and CCL-7 expression levels and liver metastases formation was also noted in human colon carcinoma cell lines. The results identify a type IV collagen-regulated gene expression signature that promotes liver metastasis and suggest that collagen IV-induced changes in chemokine and growth factor production levels mediate this effect. They provide a rationale for further exploration of the clinical utility of CCL-5, CCL-7 and AREG as targets in the management of liver metastases. Supported by a grant from the Canadian Institute for Health Research (to PB) and a Henry R. Shibata Cedars Cancer Fellowship (to RR). REFERENCES 1. Burnier JV, Wang N, Michel RP, Hassanain M, Li S, Lu Y, et al. Type IV collagen-initiated signals provide survival and growth cues required for liver metastasis. Oncogene. 2011;30:3766-83. Citation Format: Roni F. Rayes, Ni Wang, Julia V. Burnier, France Bourdeau, Pnina Brodt. Regulation of site-specific liver metastasis by collagen IV-conveyed signals. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2094. doi:10.1158/1538-7445.AM2014-2094

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.049
GPT teacher head0.342
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2014
Admission routes2
Has abstractyes

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