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Record W4313475622 · doi:10.1186/s13046-022-02584-y

PCSK9 facilitates melanoma pathogenesis via a network regulating tumor immunity

2023· article· en· W4313475622 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Experimental & Clinical Cancer Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsUniversité de MontréalMontreal Clinical Research InstituteUniversity of GuelphSt. Joseph’s Healthcare HamiltonMcMaster UniversitySt. Joseph's Hospital
FundersCanadian Institutes of Health Research
KeywordsCancer researchMelanomaBiologyCarcinogenesisImmune systemImmune checkpointCD8Cell growthCancerImmunologyImmunotherapyGenetics

Abstract

fetched live from OpenAlex

Abstract Background PCSK9 regulates cholesterol homeostasis and promotes tumorigenesis. However, the relevance of these two actions and the mechanisms underlying PCSK9’s oncogenic roles in melanoma and other cancers remain unclear. Methods PCSK9’s association with melanoma was analysed using the TCGA dataset. Empty vector (EV), PCSK9, gain-of-function (D374Y), and loss-of-function (Q152H) PCSK9 mutant were stably-expressed in murine melanoma B16 cells and studied for impact on B16 cell-derived oncogenesis in vitro and in vivo using syngeneic C57BL/6 and Pcsk9 −/− mice. Intratumoral accumulation of cholesterol was determined. RNA-seq was performed on individual tumor types. Differentially-expressed genes (DEGs) were derived from the comparisons of B16 PCSK9, B16 D374Y, or B16 Q152H tumors to B16 EV allografts and analysed for pathway alterations. Results PCSK9 expression and its network negatively correlated with the survival probability of patients with melanoma. PCSK9 promoted B16 cell proliferation, migration, and growth in soft agar in vitro, formation of tumors in C57BL/6 mice in vivo, and accumulation of intratumoral cholesterol in a manner reflecting its regulation of the low-density lipoprotein receptor (LDLR): Q152H, EV, PCSK9, and D374Y. Tumor-associated T cells, CD8 + T cells, and NK cells were significantly increased in D374Y tumors along with upregulations of multiple immune checkpoints, IFNγ, and 143 genes associated with T cell dysfunction. Overlap of 36 genes between the D374Y DEGs and the PCSK9 DEGs predicted poor prognosis of melanoma and resistance to immune checkpoint blockade (ICB) therapy. CYTH4, DENND1C, AOAH, TBC1D10C, EPSTI1, GIMAP7, and FASL (FAS ligand) were novel predictors of ICB therapy and displayed high level of correlations with multiple immune checkpoints in melanoma and across 30 human cancers. We observed FAS ligand being among the most robust biomarkers of ICB treatment and constructed two novel and effective multigene panels predicting response to ICB therapy. The profiles of allografts produced by B16 EV, PCSK9, D374Y, and Q152H remained comparable in C57BL/6 and Pcsk9 −/− mice. Conclusions Tumor-derived PCSK9 plays a critical role in melanoma pathogenesis. PCSK9’s oncogenic actions are associated with intratumoral cholesterol accumulation. PCSK9 systemically affects the immune system, contributing to melanoma immune evasion. Novel biomarkers derived from the PCSK9-network effectively predicted ICB therapy responses.

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.

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)0.005
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 score0.664
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 integrity0.0000.001
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 head0.120
GPT teacher head0.472
Teacher spread0.352 · 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