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Enregistrement W4402786670 · doi:10.3389/fonc.2024.1478011

Editorial: Reviews in cancer metabolism: 2023

2024· editorial· en· W4402786670 sur OpenAlexaff
Béla Ózsvári, Nadia Jacobo‐Herrera

Notice bibliographique

RevueFrontiers in Oncology · 2024
Typeeditorial
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer, Lipids, and Metabolism
Établissements canadiensVancouver Biotech (Canada)
Organismes subventionnairesnon disponible
Mots-clésCancerMedicineCancer researchBioinformaticsComputational biologyChemistryInternal medicineBiology

Résumé

récupéré en direct d'OpenAlex

Dysregulation of lipid metabolism has recently emerged as a hallmark of cancer (Beloribi-Djefaflia S), as well as alterations of the lipid profile in head and neck cancer. Liang J, et al describe in their review on lipid metabolism reprogramming and its potential therapeutic targets in head and neck cancer (Liang J). Figure 1 demonstrates the most important enzymes involved in lipid metabolism in head and neck cancer showing the elevated level of uptake and synthesis of various fatty acids and sterol lipids in cancer cells. 2 lists the potential lipid metabolism-related therapeutic targets, mostly overexpressed enzymes of different fatty acid synthases. They also highlighted the deteriorating effects of prolonged consumption of tobacco, alcohol, and high-fat diets on healthy cells.Non-small cell lung cancer (NSCLC) accounts for most lung cancer histological types, which mainly include squamous cell carcinoma and adenocarcinoma, and a poor survival rate. Wang Z, et al detail in their review about Aryl hydrocarbon receptor (AhR), which is a ligandactivated nuclear transcription factor. AhR overexpression helps tumor cells evade the immune system by sending inhibitory signals to the immune cells through the tumor microenvironment (Sadik A). Figure 1 highlights the clinical applications of metabolic reprogramming in tumors. In Figure 2, the authors revealed that AhR elevation correlates with increased glycolysis in cancer cells. In conclusion, they suggest that AhR is crucial in the regulation of cellular metabolism, especially in tumor metabolic reprogramming.Challenges in cancer treatment are metastasis, chemoresistance, and disease relapse; cancer stem cells (CSC) are known to be related to these phenomena. Wang and colleagues reviewed the impact of studying the cancer stem cells (CSCs) for cancer treatment (Wang H). CSCs can renew themselves, differentiate, and form new tumors, characteristics related to drug resistance, recurrence, and the spread of cancer cells to other parts of the body. Thus, targeting CSCs presents an opportunity for cancer treatment. They also delved into the changes observed in iron metabolism, lipid peroxidation, and the removal of lipid peroxides in CSCs, exploring their implications on ferroptosis. This research investigates the mechanisms governing iron metabolism and ferroptosis regulation in CSCs, extending the discussion to potential treatment tactics and new compounds that target CSCs by promoting ferroptosis.It is well-documented how tumors satisfy their energy, biosynthesis, and redox requirements by undergoing metabolic reprogramming, resulting in an increased lactate level and other metabolites in the tumor microenvironment. According to Xu and colleagues, lactate and lactylation mediate the reprogramming of immune cells and cellular adaptability to enhanced immunosuppression within the tumor microenvironment in hepatocellular carcinoma (HCC) (Xu Y). The alteration of glucose metabolism and the Warburg effect in HCC leads to significant lactate production and accumulation, suggesting abnormal lactate modification in tumor tissue. In this context, Xu reviewed the immune regulation of atypical lactate metabolism and lactate modification in hepatocellular carcinoma and the therapeutic approach of lactate-immunotherapy targeting, aiming to improve guidance for medication and treatment of patients with hepatocellular carcinoma.One of the main problems of monotherapies is chemoresistance. A strategy to target different hallmarks of the cancer cell simultaneously could be an appropriate approach to stop drug resistance, metastasis, and disease recurrence. In leukemia, despite the efforts in drug development, chemoresistance is still of concern with the current chemotherapies, reducing the success of a complete recuperation, especially in elderly patients. Feng, et al, introduced in their review the term "mitotherapy" and emphasized the importance of disordered mitochondrial metabolism and metabolic reprogramming as a therapeutic strategy in leukemia treatment, particularly in addressing chemoresistance (Feng L).In summary, the reviews within this special issue discussed some metabolic alterations observed in cancer cells, highlighting the significance of understanding cancer metabolism for developing targeted therapies and improving patient outcomes. Studies on lipid, iron, and lactate metabolism, and their roles in chemoresistance, as well as the exploration of mitochondria-dependent metabolic reprogramming in addressing chemoresistance, offer valuable insights into potential treatment strategies. By targeting specific metabolic pathways and identifying metabolic vulnerabilities, there is potential for innovative therapeutic approaches in cancer treatment.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,010
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,065
Score d'incertitude au seuil0,217

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,010
Méta-épidémiologie (sens strict)0,0040,001
Méta-épidémiologie (sens large)0,0040,003
Bibliométrie0,0050,003
Études des sciences et des technologies0,0020,002
Communication savante0,0070,005
Science ouverte0,0040,002
Intégrité de la recherche0,0110,010
Charge utile insuffisante (le modèle a refusé de juger)0,0650,054

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,318
Écart entre enseignants0,308 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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