The insulin-like growth factor 2 (IGF2) mRNA binding protein IMP2 (IGF2BP2) is an oncogenic protein known to be overexpressed in different tumor types. Pancreatic cancer is a very lethal cancer that requires early diagnosis and new treatment options. The aim of our study was to investigate the role of IMP2 in the initiation and progression of pancreatic ductal adenocarcinoma (PDAC). IMP2 was significantly overexpressed in a human precursor (PanIN) lesions suggesting IMP2 as a marker for early stages of PDAC. In a PDAC cohort of matched normal and tumor samples IMP2 showed overexpression in tumor tissues compared with normal pancreatic tissue. Strict correlation analysis (threshold R² > 0.75) revealed 22 genes highly positively and 9 genes highly negatively correlating with IMP2. Besides genes involved in the inhibition of apoptosis (Bcl-XL), especially factors involved in ubiquitination were strongly correlated with IMP2 expression: SMURF1 and FBXO45. Moreover, protein kinase C (PKC) signaling pathway was distinctly affected: DXS1179E encoding PKC iota, PKC substrate PLEK2, and inositol triphosphate receptor IP3R3 were positively correlated with IMP2 expression. Besides tumor initiation, IMP2 also seemed to have an impact on tumor progression. TGF-β treatment of Panc-1 pancreatic cancer cells to induce epithelial-mesenchymal transition (EMT) was accompanied by increased IMP2 expression. EMT is important for cancer cells to gain migratory and invasive potential, which is essential for metastasis. Concordantly, circulating tumor cells showed higher IMP2 levels as compared with normal tissue from tumor origin and with normal hematological cells. Accordingly, IMP2 protein levels correlated with poor survival. In conclusion, as IMP2 seems to promote tumor progression of PDAC, it might be an interesting diagnostic and prognostic marker as well as a novel target for the treatment of PDAC.

Hepatic lipid deposition and inflammation represent risk factors for hepatocellular carcinoma (HCC). The mRNA-binding protein tristetraprolin (TTP, gene name ZFP36) has been suggested as a tumor suppressor in several malignancies, but it increases insulin resistance. The aim of this study was to elucidate the role of TTP in hepatocarcinogenesis and HCC progression. Employing liver-specific TTP-knockout (lsTtp-KO) mice in the diethylnitrosamine (DEN) hepatocarcinogenesis model, we observed a significantly reduced tumor burden compared to wild-type animals. Upon short-term DEN treatment, modelling early inflammatory processes in hepatocarcinogenesis, lsTtp-KO mice exhibited a reduced monocyte/macrophage ratio as compared to wild-type mice. While short-term DEN strongly induced an abundance of saturated and poly-unsaturated hepatic fatty acids, lsTtp-KO mice did not show these changes. These findings suggested anti-carcinogenic actions of TTP deletion due to effects on inflammation and metabolism. Interestingly, though, investigating effects of TTP on different hallmarks of cancer suggested tumor-suppressing actions: TTP inhibited proliferation, attenuated migration, and slightly increased chemosensitivity. In line with a tumor-suppressing activity, we observed a reduced expression of several oncogenes in TTP-overexpressing cells. Accordingly, ZFP36 expression was downregulated in tumor tissues in three large human data sets. Taken together, this study suggests that hepatocytic TTP promotes hepatocarcinogenesis, while it shows tumor-suppressive actions during hepatic tumor progression.

Recently, many new network paths have been introduced while old paths are still in use. The trade-offs remain vague and should be further addressed. Since last decade, the Internet is playing a major role in people’s lives, and the demand on the Internet in all fields has increased rapidly. In order to get a fast and secure connection to the Internet, the networks providing the service should get faster and more reliable. Many network data paths have been proposed in order to achieve the previous objectives since the 1970s. It started with the Transmission Control Protocol (TCP) and the User Datagram Protocol (UDP) and later followed by several more modern paths including Quick UDP Internet Connections (QUIC), remote direct memory access (RDMA), and the Data Plane Development Kit (DPDK). This raised the question on which data path should be adopted and based on which features. In this work, we try to answer this question using different perspectives such as the protocol techniques, latency and congestion control, head of line blocking, the achieved throughput, middleboxes consideration, loss recovery mechanisms, developer productivity, host resources utilization and targeted application.

Vehicle routing improvement has become a vital topic in modern transport digitalization projects. Presently, there are no fully adapted techniques to offer optimal solutions for finding the best routes that include all visiting locations, considering mandatory transportation constraints. This project explores the application of modern machine learning techniques in solving transportation problems, with a specific focus on Q-learning. We utilize Q-learning to address the traveling salesman and vehicle routing problems. The ability of Q-learning to find optimal solutions in dynamic environments helped overcome the vulnerabilities of traditionally used algorithms. Moreover, this project provides an advanced comparative analysis in terms of accuracy and speed between Q-learning and currently used algorithms in the same scope, using a set of generated routing datasets. Q-learning presented superior performance, generating solutions that were closest to the global optima, exhibiting impressive computational efficiency and fast action even with large-scale problem instances, suggesting that it can serve as a powerful tool for optimizing transportation systems.

Background: In 2022, the World Health Organization announced colorectal cancer as the third most common cancer worldwide and the second leading cause of cancer-related deaths. CRC progression is driven by cumulative genetic and epigenetic alterations which can transformation from benign adenomas into invasive adenocarcinomas. Considering the mortality rate and the challenge of early detection, computational analyses utilizing public datasets provide a powerful approach to suggest novel biomarkers and the underlying molecular basis of the disease. Methods: We applied an integrative gene expression analysis approach to four CRC NCBI GEO gene expression datasets that include human tumor and normal samples. The module detects the common up/downregulated differentially expressed genes (DEGs) across the four datasets. Next, functional enrichment analysis using Gene Ontology (GO) and KEGG pathways was performed to identify key biological processes and potential biomarkers. Then, core candidate biomarkers were evaluated using The Cancer Genome Atlas (TCGA-COAD/READ) datasets, with diagnostic and prognostic utility evaluated via Receiver Operating Characteristic (ROC) curves and a Kaplan–Meier survival analysis. Results: A robust set of 14 downregulated genes and eight upregulated genes common across all four datasets and successfully validated through TCGA datasets. Notably, the batch of the 14 downregulated genes were functionally linked to the regulation of epithelial ion and electrolyte transport. Conclusions: This study provides a robust multi-dataset computational analysis pointing out that reduced epithelial electrolyte transport is a characteristic molecular change in tumor samples. The identified 22 genes are supported by the existing experimental literature and are proposed as potential diagnostic and prognostic biomarkers for future colorectal cancer diagnostics.

Background: Glucose intolerance (GI) is a metabolic disorder that is a consequence of hyperglycemia. Glucose intolerance can, under some conditions, progress to type 2 diabetes mellitus (T2D), where insulin is insufficiently utilized. As a result of genetic and lifestyle effects, the incidence of T2D has increased worldwide. Pathophysiological consequences of the disease may include retinopathy, nephropathy, and neuropathy. Skeletal muscle is one of the major organs that regulates blood sugar homeostasis, both at rest and during exercise. Thus, understanding the molecular and genetic perspectives on the contribution of skeletal muscles to the predisposition to diabetes is a hot topic in diabetes research. In this study, we conducted a differential analysis of gene expression and compared the expression profiles of all the genes in the skeletal muscles of normal, glucose-intolerant, and diabetic individuals via the Affymetrix HGU133plus2 platform. Data were collected from the Gene-Expression Omnibus (GEO) series GSE18732. Gene Ontology enrichment and perturbed pathways were thoroughly analyzed. Results: We found that genes that were significantly differentially expressed between the different tissues contribute to metabolic pathways related to glucose homeostasis, as well as several signaling pathways related to insulin signaling, e.g., the MAPK, mTOR, Toll-like receptor (TLR), p53, WNT and neurotrophin signaling pathways. Furthermore, some genes related to several malignancies were also differentially expressed across the different clinical groups. Additionally, some of these genes are related to epigenetic regulation. Furthermore, other differentially expressed genes were connected to several myopathies. Conclusions: This study may serve as a gene-based analysis that contributes as a basis for further analysis. This investigation may include gene and protein networks that serve in understanding diabetes, the mechanism of action of the involved proteins, and pharmacology and drug design targeting T2D.