Identification of Carcinogenesis and Tumor Progression Processes in Pancreatic Ductal Adenocarcinoma Using High-Throughput Proteomics.
Lucía Trilla-FuertesAngelo Gámez-PozoMaría Isabel Lumbreras-HerreraRocío López-VacasVictoria Heredia-SotoIsmael GhanemElena López-CamachoAndrea Zapater-MorosMaría MiguelEva M Peña-BurgosElena PalaciosMarta De UribeLaura GuerraAntje DittmannMarta MendiolaJuan Ángel Fresno VaraJaime Feliú-BatllePublished in: Cancers (2022)
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with an overall 5-year survival rate of just 5%. A better understanding of the carcinogenesis processes and the mechanisms of the progression of PDAC is mandatory. Fifty-two PDAC patients treated with surgery and adjuvant therapy, with available primary tumors, normal tissue, preneoplastic lesions (PanIN), and/or lymph node metastases, were selected for the study. Proteins were extracted from small punches and analyzed by LC-MS/MS using data-independent acquisition. Proteomics data were analyzed using probabilistic graphical models, allowing functional characterization. Comparisons between groups were made using linear mixed models. Three proteomic tumor subtypes were defined. T1 (32% of patients) was related to adhesion, T2 (34%) had metabolic features, and T3 (34%) presented high splicing and nucleoplasm activity. These proteomics subtypes were validated in the PDAC TCGA cohort. Relevant biological processes related to carcinogenesis and tumor progression were studied in each subtype. Carcinogenesis in the T1 subtype seems to be related to an increase of adhesion and complement activation node activity, whereas tumor progression seems to be related to nucleoplasm and translation nodes. Regarding the T2 subtype, it seems that metabolism and, especially, mitochondria act as the motor of cancer development. T3 analyses point out that nucleoplasm, mitochondria and metabolism, and extracellular matrix nodes could be involved in T3 tumor carcinogenesis. The identified processes were different among proteomics subtypes, suggesting that the molecular motor of the disease is different in each subtype. These differences can have implications for the development of future tailored therapeutic approaches for each PDAC proteomics subtype.
Keyphrases
- mass spectrometry
- lymph node
- label free
- extracellular matrix
- high throughput
- poor prognosis
- cell death
- ejection fraction
- electronic health record
- minimally invasive
- end stage renal disease
- early stage
- neoadjuvant chemotherapy
- newly diagnosed
- coronary artery bypass
- prognostic factors
- artificial intelligence
- big data
- atrial fibrillation
- reactive oxygen species
- radiation therapy
- single cell
- deep learning
- papillary thyroid
- data analysis
- young adults
- patient reported
- endoplasmic reticulum
- free survival
- biofilm formation
- neural network