Analysis of the SH3-Domain Kinase Binding Protein 1 Predictive Model for Pancreatic Ductal Adenocarcinoma and CCCTC-Binding Factor Transcriptional Regulatory Study
Notice bibliographique
Résumé
Background: Pancreatic ductal adenocarcinoma (PDAC) is among the most aggressive malignancies with extremely poor prognosis. This study systematically evaluates the expression patterns, biological functions, and regulatory mechanisms of the SH3-domain kinase binding protein 1 (SH3KBP1) in PDAC through multi-omics integrative analysis strategies. Methods: Key predictive genes were identified using machine learning algorithms, including support vector machine (SVM), least absolute shrinkage and selection operator (LASSO) regression, and SHapley Additive exPlanations (SHAP), based on multi-omics datasets. Validation analysis was performed using mRNA expression data from 179 PDAC tumor samples and 171 adjacent non-tumor samples (including four adjacent tissues and 167 normal controls), combined with proteomics data from 300 pairs of PDAC and non-tumor samples. Single-cell RNA sequencing and spatial transcriptomics analyzed cell-type-specific distribution and spatial localization characteristics of SH3KBP1. Clustered regularly interspaced short palindromic repeats (CRISPR) knockout experiments assessed functional dependency, and transcription factor binding site prediction explored upstream regulatory mechanisms. All analyses were performed on the R platform using specialized packages. Results: Machine learning analysis successfully identified SH3KBP1 as a key predictive biomarker for pancreatic cancer. The gene was consistently selected by multiple algorithms. The constructed predictive model demonstrated excellent performance, with a maximum area under the curve (AUC) value of 0.994, and SHAP analysis further confirmed its important contribution to pancreatic cancer prediction. Validation analysis showed significant overexpression of SH3KBP1 in PDAC tumor tissues, with a diagnostic receiver operating characteristic curve AUC value reaching 0.985, demonstrating excellent diagnostic capability. Survival analysis revealed significant associations between high SH3KBP1 expression and poorer overall survival, disease-specific survival, and progress-free interval. Proteomics and immunohistochemistry analysis further confirmed significant upregulation of SH3KBP1 at the protein level. CRISPR knockout experiments revealed functional dependency of some PDAC cell lines on SH3KBP1. Single-cell RNA sequencing data showed that SH3KBP1 was mainly enriched in malignant epithelial cell populations with obvious copy number variation characteristics, and spatial transcriptomics analysis further confirmed its significant enrichment in tumor core regions. Transcriptional regulatory analysis suggested that transcription factor CCCTC-binding factor (CTCF) may regulate SH3KBP1 transcriptional activity by binding to its promoter region, with both showing significant positive correlations in expression levels. Conclusions: Via multi-omics integrative analysis, this study systematically elucidated the expression characteristics, spatial distribution, biological functions, and transcriptional regulatory networks of SH3KBP1 in PDAC, confirming its excellent diagnostic/prognostic value and potential as a PDAC molecular target, particularly involving the CTCF regulatory axis. It also provides a key theoretical basis for understanding how SH3KBP1-mediated signaling pathways (regulated by CTCF) drive pancreatic cancer development and progression.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| 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 source (Gemma direct ou Codex distillé), 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 ».