MUC13- A Novel Cancer Biomarker for Early Pancreatic Cancer Diagnosis and Imaging

Pancreatic adenocarcinoma (PDAC) has a very poor survival rate due to late diagnosis. Therefore, identification approaches that aid in early diagnosis are highly desirable. MUC13 is a recently identified high molecular weight glycoprotein that is aberrantly expressed in PDAC and allows its progression via alterations of multiple tumor signaling pathways. Overexpression of MUC13 in PDAC cells leads to enhanced tumorigenic and metastatic phenotypes. These characteristics of PDAC cells are mediated by physical interactions between MUC13 and HER2/Neu. This study elucidates significance of MUC13, as a diagnostic and imaging marker of PDAC. Additionally, a collaborative effort has commenced to investigate the targeting ability of a novel anti-MUC13 monoclonal antibody (MAb) “C14” by radiolabeling with 89Zr for in vivo microPET/CT imaging

eDWaaS: A Scalable Educational Data Warehouse as a Service

The university management is perpetually in the process of innovating policies to improve the quality of service. Intellectual growth of the students, the popularity of university are some of the major areas that management strives to improve upon. Relevant historical data is needed in support of taking any decision. Furthermore, providing data to various university ranking frameworks is a frequent activity in recent years. The format of such requirement changes frequently which requires efficient manual effort. Maintaining a data warehouse can be a solution to this problem. However, both in-house and outsourced implementation of a dedicated data warehouse may not be a cost-effective and smart solution. This work proposes an educational data warehouse as a service (eDWaaS) model to store historical data for multiple universities. The proposed multi-tenant schema facilitates the universities to maintain their data warehouse in a cost-effective solution. It also addresses the scalability issues in implementing such data warehouse as a service model.Comment: 17th International Conference on Intelligent Systems Design and Applications (ISDA 2017). Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. 7th World Congress on Information and Communication Technologies (WICT 2017). December 14-16, 2017. \copyright 2018 Springer International Publishing AG, part of Springer Natur

Further Decoding the Molecular Relationship Between Pancreatic Ductal Adenocarcinoma and Diabetes Mellitus

Background: Pancreatic ductal adenocarcinoma (PDAC) is a devastating malignancy, especially as there are no current reliable methods of screening. A significant relationship between PDAC and Diabetes Mellitus (DM), specifically a new onset of diabetes mellitus (NOD). The molecular network of PDAC and new onset DM is not completely understood. We sought to investigate the molecular network of these two diseases with the ultimate goal of identifying potential biomarkers to aid in the screening of PDAC. Methods: We conducted a review for relevant articles concerning the molecular relationship between PDAC and DM. We compiled a list of 74 genes which have been implicated in the relationship between PDAC and DM. These genes were used for the construction of gene interaction network (GIN) by using GeneMANIA on the bases of genetic interactions, co-expression, co-localization, pathway, physical interactions, predicted interaction and shared protein domains. The GIN input file was imported in the cytoscape for the pathways enrichment analyses by using KEGG plugin. The cytoscape was used for the construction of the final GIN of both normal and cancer genes separately. Results: GIN and pathways enrichment analyses of genes known to be altered during NOD/DM and PDAC indicate their association with different pathways. in this study we have mentioned around 20 enriched pathways in the associated tables and figures which promptly show the direct and indirect association with pancreatic cancer. The major signaling pathways that were observed to be upregulated include NABA Matrisome, protein phosphorylation, metabolic processes and proteins upregulated a s a result of hormone response. Out of all pathways, proteins that are more involved in metabolic processes were found most influenced. Conclusion: In conclusion, we have contributed to identifying the molecular network relating PDAC and DM. Our future aim is to investigate the genes associated in this pathway. We will use this data to design a panel for next generation sequencing in tissue samples of patients diagnosed with PDAC

Update on the Role of MUC13 in Pancreatic Cancer: A Promising Early Detection Biomarker

Background: With the rise in pancreatic cancer (PanCa) prevalence and mortality rate, by 2030 it will secure second position among leading causes of cancer-related deaths. Due to poor prognosis of PanCa only 11% of PanCa patients have a 5-year survival rate, resulting in an equal mortality rate and incidence rate. 85% of PanCa are Pancreatic ductal adenocarcinoma (PDAC). The main clinical challenge with PanCa is poor treatment outcomes due the late diagnosis. Currently, there are traditional biomarkers panels available for diagnosis, however, these biomarkers do not have optimal sensitivity and specificity for PanCa. Considering this alarming unmet clinic need, our team has identified a novel transmembrane glycoprotein, MUC13, as a potential biomarker of PanCa by using integrative big data mining and transcriptomic approaches. Methods: The current study used big transcriptomic data analysis. MUC13 structure was elucidated using SPARKS-X and ConSurf server followed by GTEx server to analyze protein expression coverage & tissue specific gene expression. PDAC patient’s gene data was downloaded from TCGA dataset for DEG analysis and R packages “DEseq2 package” was used for the count data normalization and visualization. Furthermore, ONCOMINE and GEPIA2 were used for analyzing and predicting CNV, pathological staging, disease-free survival plot, MUC13 isoforms and phosphorylation sites. Lastly, LinkedOmics was employed for exploring the genes that exhibited disparity in association with MUC13 in Pancreatic Cancer. Results: We have modeled the structure of MUC13 to visualize its various domains, exposed and functional residues, as its crystal structure is unavailable in public domain. Interestingly, we identified approximately 63 highly conserved, exposed and functionally active residues. It was observed via DEGseq2 of TCGA-PAAD data set that MUC13 had a better expression profile (∼ 3.73-fold) as compared to MUC1 (∼ 2.52-fold) in PanCa condition which suggests better specificity of MUC13 over MUC1. The higher expression of MUC13 correlated to a lower diseasefree survival in PanCa. Isoform analysis suggested that MUC13 has 5 transcripts, among which only 2 transcripts (ENST00000616727.4 & ENST00000478191.1) of MUC13 are coding. Interestingly, ENST00000616727.4 transcript which encodes for long form of MUC13 (L-MUC13 & 512 residues), is tumorigenic (tMUC13). While ENST00000478191.1 transcript encodes for the short form of the MUC13 (s-MUC13 &184 residues) and has shown less expression in tumors. Socio-behavioral & demographic studies on MUC13 show that ethnicity, age, and gender are important factors for higher expression of MUC13 in PanCa. Our analysis suggests that AfroAmerican and Asian PanCa patients express relatively higher MUC13 as compared to Caucasian. The higher expression of MUC13 leads to modulation of several important pathways like chemical carcinogenesis, maturity onset diabetes of the young, pancreatic-bile secretion and glucose and lipid metabolism. Conclusion: This investigation sheds light on MUC13 as a potential early diagnostic biomarker for PanCa, and it also has prospective to upgrade the effectiveness of the current biomarker panel. This kind of methodology will enhance the conception of the role of MUC13 in PanCa. Additionally, the big data analysis methodology is releasing a significant opportunity for the discoveries of specific and significant biomarkers not only for PanCa but also for other malignancies

CEACAM7 emerges as a promising early detection biomarker in pancreatic cancer.

Background: According to the key statistics from the American Cancer Society in 2021, Pancreatic Cancer (PanCa) impacts approximately 60,430 individuals annually in the U.S., affecting 31,950 men and 28,480 women. Diagnosis and treatment of PanCa pose significant challenges. It stands as the fourth leading cause of cancer-related deaths, boasting a mere 9% 5-year survival rate and an overall grim prognosis. Adenocarcinomas, particularly PDAC, constitute the majority (around 85-90%) of PanCa cases, contributing to its highly aggressive nature and low survival rates. The absence of an early tumor-specific biomarker for PDAC underscores the urgent need for a novel strategy to enhance the limited diagnostic options available for PanCa. Recognizing this critical situation, our research group has identified a promising oncogenic protein, Carcinoembryonic antigen-related cell adhesion molecule 7 (CEACAM7). Our studies indicate elevated CEACAM7 expression in pancreatic ductal adenocarcinoma (PDAC) tumors and its correlation with patient survival. Methodology: The research commenced with bioinformatics screening, involving the assessment of CEACAM7 expression across various cancer types, overall survival analysis, correlation with genes, association analysis, spot prediction, and evaluation of immune cell infiltration capability in the context of pancreatic ductal adenocarcinoma (PDAC). Subsequently, guided by the insights gained from the bioinformatics approach, molecular biology techniques were employed to meticulously examine the progressive cell line panel of PDAC (HPNE, HPAF-2, SU86.86/BxPc3, and Panc-1) in terms of both mRNA and protein expression levels of CEACAM7. Confocal microscopy was utilized to recognize the intensity and localization of CEACAM7 protein expression in various cell lines. Additionally, immunohistochemistry (IHC) analysis was conducted to identify protein expressions in human tissue microarray (TMA) cores, along with relevant location and grading data. Results: Bioinformatic results clearly cited the relevance of CEACAM7 as a potential prognostic biomarker of PDAC, followed by molecular biology approaches revealed the positioning of CEACAM7 as an early detection biomarker. Conclusion: Our observations clearly cited that CEACAM7 can be investigated as an early detection biomarker for PDAC

Facile synthesis of diverse N-Acyl Anthranilamides and quinazolin-4-ones as HMG-CoA reductase inhibitor via Pd-catalyzed cascade reaction

This manuscript describes the design and synthesis of a series of diverse N-Acyl Anthranilamides and quinazolin-4-ones derivatives (3a-3n, and 4a-4d) inhibitors of HMG-CoA reductase for the treatment of hypercholesterolemia. A series of N-Acyl Anthranilamides and quinazolin-4-ones derivatives (3a-3n, and 4a-4d) were synthesized and their chemical structures were confirmed by 1 H, 13C NMR and mass spectral data. Analogs were optimized using structure-based design and physical property considerations resulting in the identification of 4b and 3d, a hepatoselective HMG-CoA reductase inhibitor with excellent acute and chronic efficacy in a vitro model

Synthesis and biological evaluation of 2,4-diaminopyrimidine-5-carbonitrile and N-(2-amino-5-cyanopyrimidin-4-yl)benzamide derivatives as EGFR inhibitors

A series of 2,4-diaminopyrimidine-5-carbonitrile and N-(2-amino-5-cyanopyrimidin-4-yl) benzamide derivatives (5–14) were synthesized and their chemical structures were confirmed by 1 H, 13C NMR and mass spectral data. Anticancer activity of all the synthesized compounds were evaluated for in vitro cytotoxic activity against a panel of four human cancer cell lines i.e., human breast (MCF-7,), cervical cancer (C33A), oral (KB) and prostrate (DU-145). All the examined compounds, demonstrated potent to moderate anticancer activity. Among all the synthesized compounds, 6 and 11 were exhibited more potent activity. Docking studies for 6 and 11 into EGFR active site was carried out to investigate their potential binding modes. Therefore, compounds 6 and 11 can be considered as fascinating candidates for further expansion of more potent anticancer agents

Diversity-Oriented Synthesis of Novel Benzimidazoles as Antimalarial agents via post Ugi MCR

An efficient strategy for the syntheses of highly diverse benzimidazoles in exceptional yields via post Ugi reactions has been described. In our methodology we have utilized isocyanide based Ugi-reaction followed by acid catalyzed condensation cyclization reaction under microwave irradiations. All benzimidazole derivatives showed moderate to good antimalarial activity when compared with chloroquine as reference compound. Among these compounds, three of them (8b), (8c), (8d) were found to be most potent towards antimalarial activity. The synthesized hybrids were examined for their purity with the help of thin layer chromatography. Different analytical techniques were employed for further characterization like Mass studies, NMRs (1H and 13C) and FT-IR

Exploration of potential natural inhibitors against KRAS-G12D in PanCan: Protein centered pharmacophore HTVS approach.

Background: As per key statistics of American Cancer Society 2021, Pancreatic Cancer (PanCan) affects around 60,430 persons a year in the U.S. and is tricky to diagnose & treat. Studies revealed that African Americans have a 50–90% higher incidence of PanCan compared to other ethnic groups. Oncogenic KRAS mutation is the signature genetic incident in the progression and development of PDAC. KRAS is the most common protein which is 95% times mutated in PDAC condition. By considering this alarming situation our group is now focused on to develop therapeutic portfolio against KRAS-G12D mutation associated PanCan by using high through-put virtual screening (HTVS) approach. Methodology: In this study, prompt HTVS for vetting the best possible drug candidates from natural compound (NCs) databases has been implemented. Herein, time tested rigorous multi-layered drug screening process to narrow down 66,969 NCs for the identification of potential lead(s) is implemented. Druggability parameters, protein centered pharmacophore-based drug selections & different docking approaches (Rigid & Flexible) were employed in this study. Result: By using different NCs databases around 66,969 NCs were screened based on protein-centered pharmacophore fit score & binding energies. Less than 0.001% of potential NCs were selected against the known & reference KRAS-G12D inhibitor (BI2852). Conclusion: By using HTVS approach we have identified a pool of natural inhibitors against KRAS G12D

CEACAM7 expression contributes to early events of pancreatic cancer

Highlights The trends of pancreatic cancer (PanCa) incidence and mortality are on rising pattern, and it will a second leading cause of cancer related deaths by 2030. Approximately 85–90% PanCa are pancreatic ductal adenocarcinoma (PDAC), which is one of the most challenging and aggressive malignancy. PDAC exhibits with grim prognosis as mortality rate is very close to the incidence due lack of early detection methods and effective therapeutic regimen. Our team has identified a novel oncogenic protein, carcinoembryonic antigen-related cell adhesion molecule 7 (CEACAM7), that can be useful for early PDAC diagnosis and predictor of patient survival. We also observed an increase of CEACAM7 expression in PDAC cell line panel model. However, poorly differentiated, and normal cell lines did not show any expression. Commercially available human tissue analysis also strengthened our data by showing strong and positive IHC staining in early-stage tumors. Abstract Background The trends of pancreatic cancer (PanCa) incidence and mortality are on rising pattern, and it will be a second leading cause of cancer related deaths by 2030. Pancreatic ductal adenocarcinoma (PDAC), major form of PanCa, exhibits a grim prognosis as mortality rate is very close to the incidence rate, due to lack of early detection methods and effective therapeutic regimen. Considering this alarming unmet clinic need, our team has identified a novel oncogenic protein, carcinoembryonic antigen-related cell adhesion molecule 7 (CEACAM7), that can be useful for spotting early events of PDAC. Methodology This study includes bioinformatics pre-screening using publicly available cancer databases followed by molecular biology techniques in PDAC progressive cell line panel and human tissues to evaluate CEACAM7 expression in early events of pancreatic cancer. Results PanCa gene and protein expression analysis demonstrated the significantly higher expression of CEACAM7 in PDAC, compared to other cancers and normal pancreas. Overall survival analysis demonstrated an association between the higher expression of CEACAM7 and poor patients’ prognosis with high hazard ratio. Additionally, in a performance comparison analysis CEACAM7 outperformed S100A4 in relation to PDAC. We also observed an increase of CEACAM7 in PDAC cell line panel model. However, poorly differentiated, and normal cell lines did not show any expression. Human tissue analysis also strengthened our data by showing strong and positive IHC staining in early-stage tumors. Conclusion Our observations clearly cite that CEACAM7 can serve as a potential early diagnostic and/or prognostic marker of PDAC and may also potentiate the sensitivity of the existing biomarker panel of PDAC. However, further studies are warranted to determine its clinical significance