Dataset Information

Quantitative Acetylomics Revealed Acetylation-Mediated Molecular Pathway Network Changes in Human Nonfunctional Pituitary Neuroendocrine Tumors

ABSTRACT: Acetylation at lysine residue in a protein mediates multiple cellular biological processes, including tumorigenesis. This study aimed to investigate the acetylated protein profile alterations and acetylation-mediated molecular pathway changes in human nonfunctional pituitary neuroendocrine tumors (NF-PitNETs). The anti-acetyl antibody-based label-free quantitative proteomics was used to analyze the acetylomes between NF-PitNETs (n = 4) and control pituitaries (n = 4). A total of 296 acetylated proteins with 517 acetylation sites was identified, and the majority of which were significantly down-acetylated in NF-PitNETs (p<0.05 or only be quantified in NF-PitNETs/controls). These acetylated proteins widely functioned in cellular biological processes and signaling pathways, including metabolism, translation, cell adhesion, and oxidative stress. The randomly selected acetylated phosphoglycerate kinase 1 (PGK1), which is involved in glycolysis and amino acid biosynthesis, was further confirmed with immunoprecipitation and western blot in NF-PitNETs and control pituitaries. Among these acetylated proteins, 15 lysine residues within 14 proteins were down-acetylated and simultaneously up-ubiquitinated in NF-PitNETs to demonstrate a direct competition relationship between acetylation and ubiquitination. Moreover, the potential effect of protein acetylation alterations on NF-PitNETs invasiveness was investigated. Overlapping analysis between acetylomics data in NF-PitNETs and transcriptomics data in invasive NF-PitNETs identified 26 overlapped molecules. These overlapped molecules were mainly involved in metabolism-associated pathways, which means that acetylation-mediated metabolic reprogramming might be the molecular mechanism to affect NF-PitNET invasiveness. This study provided the first acetylomic profiling and acetylation-mediated molecular pathways in human NF-PitNETs, and offered new clues to elucidate the biological functions of protein acetylation in NF-PitNETs and discover novel biomarkers for early diagnosis and targeted therapy of NF-PitNETs.

SUBMITTER: Wen S

PROVIDER: S-EPMC8546192 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:IntroductionThe invasive behavior of nonfunctioning pituitary neuroendocrine tumors (NF-PitNEts) affects complete resection and indicates a poor prognosis. Cancer immunotherapy has been experimentally used for the treatment of many tumors, including pituitary tumors. The current study aimed to screen the key immune-related genes in NF-PitNEts with invasion.MethodsWe used two cohorts to explore novel biomarkers in NF-PitNEts. The immune infiltration-associated differentially expressed genes (DEGs) were obtained based on high/low immune scores, which were calculated through the ESTIMATE algorithm. The abundance of immune cells was predicted using the ImmuCellAI database. WGCNA was used to construct a coexpression network of immune cell-related genes. Random forest analysis was used to select the candidate genes associated with invasion. The expression of key genes was verified in external validation set using quantitative real-time polymerase chain reaction (qRT‒PCR).ResultsThe immune and invasion related DEGs was obtained based on the first dataset of NF-PitNEts (n=112). The immune cell-associated modules in NF-PitNEts were calculate by WGCNA. Random forest analysis was performed on 81 common genes intersected by immune-related genes, invasion-related genes, and module genes. Then, 20 of these genes with the highest RF score were selected to construct the invasion and immune-associated classification model. We found that this model had high prediction accuracy for tumor invasion, which had the largest area under the receiver operating characteristic curve (AUC) value in the training dataset from the first dataset (n=78), the self-test dataset from the first dataset (n=34), and the independent test dataset (n=73) (AUC=0.732/0.653/0.619). Functional enrichment analysis revealed that 8 out of the 20 genes were enriched in multiple signaling pathways. Subsequently, the 8-gene (BMP6, CIB2, FABP5, HOMER2, MAML3, NIN, PRKG2 and SIDT2) classification model was constructed and showed good efficiency in the first dataset (AUC=0.671). In addition, the expression levels of these 8 genes were verified by qRT‒PCR.ConclusionWe identified eight key genes associated with invasion and immunity in NF-PitNEts that may play a fundamental role in invasive progression and may provide novel potential immunotherapy targets for NF-PitNEts.

Project description:Background and aimAbout a third of Pituitary Neuroendocrine Tumors (PitNETs) may show aggressive behavior. Many efforts have been performed for identifying possible predictive factors to early determine the future behavior of PitNETs. Programmed cell death ligand 1 (PD-L1) expression was associated with a more aggressive biology in different solid tumors, but its role in PitNET is not well-established yet. Our study aims to analyze PD-L1 expression in a surgical cohort of PitNETs to determine its association with radiological invasion and pathology findings, as well as with long-term recurrence rates.MethodsWe performed a retrospective analysis in a series of 86 PitNETs. Clinical presentation and radiological features of the preoperative period were collected, as well as pathological data and follow-up data. The rate of PD-L1 expression was immunohistochemically evaluated and expressed as a tumor proportion score (TPS). We assessed its relationship with cavernous sinus invasion and Trouillas' classification as primary outcomes. Secondary outcomes included the TPS' relationship with histopathological markers of proliferation, hormonal expression, tumor size and long-term recurrence rates. We calculated the optimal cut-point for the primary outcomes while maximizing the product of the sensitivity and specificity and then we evaluated the significance of secondary outcomes with logistic regression analysis.ResultsEighty-six patients were included in the analysis; 50 cases were non-functional PitNETs. The TPS for PD-L1 showed a highly right-skewed distribution in our sample, as 30.2% of patients scored 0. Using Trouillas' classification, we found that "proliferative" cases have a significantly higher probability to express PD-L1 in more than 30% of tumor cells (OR: 5.78; CI 95%: 1.80-18.4). This same cut-point was also associated with p53 expression. A positive association was found between PD-L1 expression and GH expression (p = 0.001; OR: 5.44; CI 95%: 1.98-14.98), while an inverse relationship was found with FSH/LH expression (p = 0.014; OR = 0.27, CI 95%: 0.10-0.76). No association was found with CS invasion, tumor size, bone erosion or dura invasion. We could not find any association between PD-L1 expression and recurrence.ConclusionsPD-L1 expression was associated with proliferative grades of Trouillas' classification and p53 expression. We also confirmed a higher expression of PD-L1 in somatotroph tumors. Larger studies are necessary to investigate the relationship between PD-L1 expression and aggressive behaviors.

Project description:BackgroundEndoscopic ultrasound-guided fine-needle aspiration is associated with the accurate determination of tumor grade. However, because it is an invasive procedure there is a need to explore alternative noninvasive procedures.PurposeTo develop and validate a noncontrast radiomics model for the preoperative prediction of nonfunctional pancreatic neuroendocrine tumor (NF-pNET) grade (G).Study typeRetrospective, single-center study.SubjectsPatients with pathologically confirmed PNETs (139) were included.Field strength/sequence3T/breath-hold single-shot fast-spin echo T2 -weighted sequence and unenhanced and dynamic contrast-enhanced T1 -weighted fat-suppressed sequences.AssessmentTumor features on contrast MR images were evaluated by three board-certified abdominal radiologists.Statistical testsMultivariable logistic regression analysis was used to develop the clinical model. The least absolute shrinkage and selection operator method and linear discriminative analysis (LDA) were used to select the features and to construct a radiomics model. The performance of the models was assessed using the training cohort (97 patients) and the validation cohort (42 patients), and decision curve analysis (DCA) was applied for clinical use.ResultsThe clinical model included 14 imaging features, and the corresponding area under the curve (AUC) was 0.769 (95% confidence interval [CI], 0.675-0.863) in the training cohort and 0.729 (95% CI, 0.568-0.890) in the validation cohort. The LDA included 14 selected radiomics features that showed good discrimination-in the training cohort (AUC, 0.851; 95% CI, 0.758-0.916) and the validation cohort (AUC, 0.736; 95% CI, 0.518-0.874). In the decision curves, if the threshold probability was 0.17-0.84, using the radiomics score to distinguish NF-pNET G1 and G2/3, offered more benefit than did the use of a treat-all-patients or treat-none scheme.Data conclusionThe developed radiomics model using noncontrast MRI could help differentiate G1 and G2/3 tumors, to make the clinical decision, and screen pNETs grade.Level of evidence4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1124-1136.

Project description:ImportanceThe number of patients with small nonfunctional pancreatic neuroendocrine tumors (NF-PanNETs) is increasing. However, the role of surgery for small NF-PanNETs remains unclear.ObjectiveTo evaluate the association between surgical resection for NF-PanNETs measuring 2 cm or smaller and survival.Design, setting, and participantsThis cohort study used data from the National Cancer Database and included patients with NF-pancreatic neuroendocrine neoplasms who were diagnosed between January 1, 2004, and December 31, 2017. Patients with small NF-PanNETs were divided into 2 groups: group 1a (tumor size, ≤1 cm) and group 1b (tumor size, 1.1-2.0 cm). Patients without information on tumor size, overall survival, and surgical resection were excluded. Data analysis was performed in June 2022.ExposuresPatients with vs without surgical resection.Main outcomes and measuresThe primary outcome was overall survival of patients in group 1a or group 1b who underwent surgical resection compared with those who did not, which was evaluated using Kaplan-Meier estimates and multivariable Cox proportional hazards regression models. Interactions between preoperative factors and surgical resection were analyzed with a multivariable Cox proportional hazards regression model.ResultsOf the 10 504 patients with localized NF-PanNETs identified, 4641 were analyzed. These patients had a mean (SD) age of 60.5 (12.7) years and included 2338 males (50.4%). The median (IQR) follow-up time was 47.1 (28.2-71.6) months. In total, 1278 patients were in group 1a and 3363 patients were in group 1b. The surgical resection rates were 82.0% in group 1a and 87.0% in group 1b. After adjustment for preoperative factors, surgical resection was associated with longer survival for patients in group 1b (hazard ratio [HR], 0.58; 95% CI, 0.42-0.80; P < .001) but not for patients in group 1a (HR, 0.68; 95% CI, 0.41-1.11; P = .12). In group 1b, interaction analysis found that age of 64 years or younger, absence of comorbidities, treatment at academic institutions, and distal pancreatic tumors were factors associated with increased survival after surgical resection.Conclusions and relevanceFindings of this study support an association between surgical resection and increased survival in select patients with NF-PanNETs measuring 1.1 to 2.0 cm who were younger than 65 years, had no comorbidities, received treatment at academic institutions, and had tumors of the distal pancreas. Future investigations of surgical resection for small NF-PanNETs that include the Ki-67 index are warranted to validate these findings.

Dataset Information

Quantitative Acetylomics Revealed Acetylation-Mediated Molecular Pathway Network Changes in Human Nonfunctional Pituitary Neuroendocrine Tumors

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets