Mitochondria and Cancer
Notice bibliographique
Résumé
The future of mitochondrial DNA research has the potential to uncover new insights on genetic diseases and open new opportunities to discover ways to control mitochondria and their influence on the human health and cancer. The outcomes of this work will expand the understanding of cellular respiration and disease risk. In this special issue, we give examples of strategies for the measurement of oxidative stress; a critical factor in tumor progression. While the mitochondrial genome has been well characterized, the associations of the broad spectrum of mitochondrial genotypes remains a relatively rich field of study. Genetic tools are beginning to be realized which characterize mitochondrial populations and link their associations with normal and malignant cells. There are many large-scale deletions which require further investigation. There are populations of genotypes that rise and fall with tissue field effects. Mutations in the mitochondrial genome can alter the cellular biochemical behavior, changing the conditions for potential tumor growth. In this special issue we also address roles of mitochondrial interactions which are central to the physiological processes involved in malignant transformation. Measurements of DNA damage associated with prostate and other cancers can be normalized by comparative measurements of mitochondrial subpopulations. This can be used to assess DNA damage and somatic mutations under physiological and pathological conditions and can serve as a strategy to measure cell toxicity as a guide for devising innovative cancer preventions and treatments. On account that mitochondrial DNA is accessible across various tissues, noninvasive collection and analyses are possible. From such investigations, it has been demonstrated that there is a progression of change in mitochondria through the tissues, as a field effect, that is, associated with the tumor tissue progression. Tumorigenic effects related to increasing ROS are well known in prostate and other solid tumor cancers. Mapping mutations across the entire mitochondrial genome are fundamental to the future work on mitochondrial “omic” investigations. Mitochondrial whole genome sequencing pioneered the concepts of conducting whole genome analyses to understand forensics. This has resulted in efforts to go beyond simple STR typing and to type the entire chromosome. It is because of the increased resolution achieved by sequencing whole metagenomes that, by extension, other fields of diagnostics, personalized medicine, and bacterial and viral forensics have emerged (J. P. Jakupciak unpublished data). Through the study of mitochondria, mechanisms of cancer are emerging. The influence of mitochondria on the metastatic potential of cancer cell lines points to a promising future and in vivo characterization of populations of mitochondria will function as a “looking-glass” into monitoring the modulation events and even predicting changes in metastatic capabilities. Mitochondrial genomics is poised to enhance the over all field of omics and contribute significantly to the advent of personalized medicine [1]. In this special issue, the authors present some of the latest findings in this exciting and rapidly expanding area of genomic research: specific heteroplasmic somatic alterations in the mitochondrial genome contribute to the cell proliferation; a new paradigm for oxidative stress and cell and DNA damage has important implications for both cancer prevention and treatment; an assay for gauging systemic oxidative stress using peripheral blood; upregulation of a nuclear gene whose molecular interactions contribute to mitochondrial dysfunction, promoting cell proliferation; a cancerization field effect described by progressive mitochondrial mutations in noninvasiveand invasive breast cancer. Ryan Parr Andrew Harbottle John P. Jakupciak Gurmit Singh
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».