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Record W4296804188 · doi:10.1002/ctm2.1056

Sphingosine 1‐phosphate lyase facilitates cancer progression through converting sphingolipids to glycerophospholipids

2022· article· en· W4296804188 on OpenAlexaff
Baasanjav Uranbileg, Makoto Kurano, Kuniyuki Kano, Eri Sakai, Junichi Arita, Kiyoshi Hasegawa, Takeshi Nishikawa, Soichiro Ishihara, Hiroharu Yamashita, Yasuyuki Seto, Hitoshi Ikeda, Junken Aoki, Yutaka Yatomi

Bibliographic record

VenueClinical and Translational Medicine · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSphingolipid Metabolism and Signaling
Canadian institutionsPancreas Centre (Canada)
FundersJapan Society for the Promotion of ScienceJapan Agency for Medical Research and Development
KeywordsSphingolipidSphingosineColorectal cancerSphingosine-1-phosphateCancerCancer researchCancer cellChemistryBiologyReceptorBiochemistryMedicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In addition to potent agonist properties for sphingosine 1-phosphate (S1P) receptors, intracellularly, S1P is an intermediate in metabolic conversion pathway from sphingolipids to glycerolysophospholipids (glyceroLPLs). We hypothesized that this S1P metabolism and its products might possess some novel roles in the pathogenesis of cancer, where S1P lyase (SPL) is a key enzyme. METHODS: The mRNA levels of sphingolipid-related and other cancer-related factors were measured in human hepatocellular carcinoma (HCC), colorectal cancer, and esophageal cancer patients' tumours and in their adjacent non-tumour tissues. Phospholipids (PL) and glyceroLPLs were measured by using liquid chromatography-tandem mass spectrometry (LC-MS/MS). In-vitro experiments were performed in Colon 26 cell line with modulation of the SPL and GPR55 expressions. Xenograft model was used for determination of the cancer progression and for pharmacological influence. RESULTS: Besides high SPL levels in human HCC and colon cancer, SPL levels were specifically and positively linked with levels of glyceroLPLs, including lysophosphatidylinositol (LPI). Overexpression of SPL in Colon 26 cells resulted in elevated levels of LPI and lysophosphatidylglycerol (LPG), which are agonists of GPR55. SPL overexpression-enhanced cell proliferation was inhibited by GPR55 silencing. Conversely, inhibition of SPL led to the opposite outcome and reversed by adding LPI, LPG, and metabolites generated during S1P degradation, which is regulated by SPL. The xenograft model results suggested the contribution of SPL and glyceroLPLs to tumour progression depending on levels of SPL and GPR55. Moreover, the pharmacological inhibition of SPL prevented the progression of cancer. The underlying mechanisms for the SPL-mediated cancer progression are the activation of p38 and mitochondrial function through the LPI, LPG-GPR55 axis and the suppression of autophagy in a GPR55-independent manner. CONCLUSION: A new metabolic pathway has been proposed here in HCC and colon cancer, SPL converts S1P to glyceroLPLs, mainly to LPI and LPG, and facilitates cancer development.

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 score0.001
Threshold uncertainty score0.005

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)0.0010.000

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 head0.041
GPT teacher head0.375
Teacher spread0.334 · 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

Citations37
Published2022
Admission routes1
Has abstractyes

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