Data-Independent Acquisition and Quantification of Extracellular Matrix from Human Lung in Chronic Inflammation-Associated Carcinomas
Notice bibliographique
Résumé
Abstract Early events associated with chronic inflammation and cancer involve significant remodeling of the extracellular matrix (ECM), which greatly affects its composition and functional properties. Using lung squamous cell carcinoma (LSCC), a chronic inflammation-associated cancer (CIAC), we optimized a robust proteomic pipeline to discover potential biomarker signatures and protein changes specifically in the stroma. We combined ECM enrichment from fresh human tissues, data-independent acquisition strategies, and stringent statistical processing to analyze ‘Tumor’ and matched adjacent histologically normal (‘Matched Normal’) tissues from patients with LSCC. Overall, 1,802 protein groups were quantified with at least two unique peptides, and 56% of those proteins were annotated as ‘extracellular’. Confirming dramatic ECM remodeling during CIAC progression, 529 proteins were significantly altered in the ‘Tumor’ compared to ‘Matched Normal’ tissues. The signature was typified by a coordinated loss of basement membrane proteins and small leucine-rich proteins. The dramatic increase in the stromal levels of SERPINH1/heat shock protein 47, that was discovered using our ECM proteomic pipeline, was validated by immunohistochemistry (IHC) of ‘Tumor’ and ‘Matched Normal’ tissues, obtained from an independent cohort of LSCC patients. This integrated workflow provided novel insights into ECM remodeling during CIAC progression, and identified potential biomarker signatures and future therapeutic targets. Statement of significance of the study The extracellular matrix (ECM) is a complex scaffolding network composed of glycoproteins, proteoglycans and collagens, which binds soluble factors and, most importantly, significantly impacts cell fate and function. Alterations of ECM homeostasis create a microenvironment promoting tumor formation and progression, therefore deciphering molecular details of aberrant ECM remodeling is essential. Here, we present a multi-laboratory and refined proteomic workflow, featuring i) the prospective collection of tumor and matched histologically normal tissues from patients with lung squamous cell carcinoma, ii) the enrichment for ECM proteins, and iii) subsequent label-free data-independent acquisition (DIA)-based quantification. DIA is a powerful strategy to comprehensively profile and quantify all detectable precursor ions contained in the biological samples, with high quantification accuracy and reproducibility. When combined with very stringent statistical cutoffs, this unbiased strategy succeeded in capturing robust and highly confident proteins changes associated with cancer, despite biological variability between individuals. This label-free quantification workflow provided the flexibility required for ongoing prospective studies. Discussions with clinicians, surgeons, pathologists, and cancer biologists represent an opportunity to interrogate the DIA digitalized maps of the samples for newly formulated questions and hypotheses, thus gaining insights into the continuum of the disease and opening the path to novel ECM-targeted therapies.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».