Transcriptional Profiling Reveals the Regulatory Role of DNER in Promoting Pancreatic Neuroendocrine Neoplasms

Pancreatic Neuroendocrine Tumors: Signaling Pathways and Epigenetic Regulation

Pancreatic neuroendocrine tumors (PNETs) are characterized by dysregulated signaling pathways that are crucial for tumor formation and progression. The efficacy of traditional therapies is limited, particularly in the treatment of PNETs at an advanced stage. Epigenetic alterations profoundly impact the activity of signaling pathways in cancer development, offering potential opportunities for drug development. There is currently a lack of extensive research on epigenetic regulation in PNETs. To fill this gap, we first summarize major signaling events that are involved in PNET development. Then, we discuss the epigenetic regulation of these signaling pathways in the context of both PNETs and commonly occurring-and therefore more extensively studied-malignancies. Finally, we will offer a perspective on the future research direction of the PNET epigenome and its potential applications in patient care.

The uprise of RNA biology in neuroendocrine neoplasms: altered splicing and RNA species unveil translational opportunities

Neuroendocrine neoplasms (NENs) comprise a highly heterogeneous group of tumors arising from the diffuse neuroendocrine system. NENs mainly originate in gastrointestinal, pancreatic, and pulmonary tissues, and despite being rare, show rising incidence. The molecular mechanisms underlying NEN development are still poorly understood, although recent studies are unveiling their genomic, epigenomic and transcriptomic landscapes. RNA was originally considered as an intermediary between DNA and protein. Today, compelling evidence underscores the regulatory relevance of RNA processing, while new RNA molecules emerge with key functional roles in core cell processes. Indeed, correct functioning of the interrelated complementary processes comprising RNA biology, its processing, transport, and surveillance, is essential to ensure adequate cell homeostasis, and its misfunction is related to cancer at multiple levels. This review is focused on the dysregulation of RNA biology in NENs. In particular, we survey alterations in the splicing process and available information implicating the main RNA species and processes in NENs pathology, including their role as biomarkers, and their functionality and targetability. Understanding how NENs precisely (mis)behave requires a profound knowledge at every layer of their heterogeneity, to help improve NEN management. RNA biology provides a wide spectrum of previously unexplored processes and molecules that open new avenues for NEN detection, classification and treatment. The current molecular biology era is rapidly evolving to facilitate a detailed comprehension of cancer biology and is enabling the arrival of personalized, predictive and precision medicine to rare tumors like NENs.

TRIM50 Inhibits Proliferation and Metastasis of Gastric Cancer via Promoting β-Catenin Degradation

Background

Gastric cancer (GC) is a common malignancy with a poor prognosis. Tripartite motif-containing 50 (TRIM50) belongs to the TRIM family and is reported to be related to numerous cancers. This study aimed to investigate the function of TRIM50 in GC.

Methods

Three microarray datasets (GSE13911, GSE79973, and GSE19826) containing GC and adjacent nontumor tissues were used for bioinformatics analysis to screen GC-related genes and assess the associations between GC development and TRIM50 expression. Then, TRIM50 expression in GC cells was detected at mRNA and protein levels. After TRIM50 was knockdown or overexpressed, the effect of TRIM50 on the proliferation and metastasis of GC cells was analyzed using Cell Counting Kit-8 (CCK-8), flow cytometry, scratch, and Transwell assays. The interaction between TRIM50 and β-catenin was analyzed. The expression of cell cycle-, migration-, invasion-, and Wnt/β-catenin signaling pathway-related proteins was detected by Western blot. Furthermore, we measured the role of TRIM50 overexpression on tumor growth as well as the Wnt/β-catenin signaling pathway in vivo. In addition, XAV939 (a WNT/β-catenin signaling pathway inhibitor) was used to clarify the mechanism of TRIM50 on GC.

Results

Bioinformatics revealed that TRIM50 expression was decreased in GC samples and associated with GC development. In vitro study revealed that TRIM50 overexpression impeded the GC cell proliferation and metastasis, while TRIM50 knockdown presented the opposite results. In addition, TRIM50 interacted with β-catenin to induce the degradation of β-catenin. In in vivo assay, TRIM50 overexpression inhibited tumor growth and blocked the Wnt/β-catenin signaling pathway. In addition, TRIM50 knockdown-promoted cell proliferation and metastasis in GC cells were inverted by XAV939.

Conclusion

TRIM50 overexpression may inhibit cell proliferation and metastasis in GC via β-catenin degradation, indicating that TRIM50 could be a target for the treatment of GC.

A machine learning model for predicting patients with major depressive disorder: A study based on transcriptomic data

Background

Identifying new biomarkers of major depressive disorder (MDD) would be of great significance for its early diagnosis and treatment. Herein, we constructed a diagnostic model of MDD using machine learning methods.

Methods

The GSE98793 and GSE19738 datasets were obtained from the Gene Expression Omnibus database, and the limma R package was used to analyze differentially expressed genes (DEGs) in MDD patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify potential molecular functions and pathways. A protein-protein interaction network (PPI) was constructed, and hub genes were predicted. Random forest (RF) and artificial neural network (ANN) machine-learning algorithms were used to select variables and construct a robust diagnostic model.

Results

A total of 721 DEGs were identified in peripheral blood samples of patients with MDD. GO and KEGG analyses revealed that the DEGs were mainly enriched in cytokines, defense responses to viruses, responses to biotic stimuli, immune effector processes, responses to external biotic stimuli, and immune systems. A PPI network was constructed, and CytoHubba plugins were used to screen hub genes. Furthermore, a robust diagnostic model was established using a RF and ANN algorithm with an area under the curve of 0.757 for the training model and 0.685 for the test cohort.

Conclusion

We analyzed potential driver genes in patients with MDD and built a potential diagnostic model as an adjunct tool to assist psychiatrists in the clinical diagnosis and treatment of MDD.