Gene signature and connectivity mapping to assist with drug prediction for pancreatic ductal adenocarcinoma

Machine Learning-Based Radiomics in Malignancy Prediction of Pancreatic Cystic Lesions: Evidence from Cyst Fluid Multi-Omics

The malignant potential of pancreatic cystic lesions (PCLs) varies dramatically, leading to difficulties when making clinical decisions. This study aimed to develop noninvasive clinical-radiomic models using preoperative CT images to predict the malignant potential of PCLs. It also investigates the biological mechanisms underlying these models. Patients from two retrospective and one prospective cohort, all undergoing surgical resection for PCLs, are divided into four datasets: training, internal test, external test, and prospective application sets. Eleven machine learning classifiers are employed to construct radiomic models based on selected features. Cyst fluid from the prospective cohort is collected for proteomic and lipidomic analysis. The radiomic models demonstrated high accuracy, with area under the receiver operating characteristic curves (AUCs) > 0.93 across the training (n = 262), internal test (n = 50), and external test (n = 50) sets. AUCs ranged from 0.92 to 0.96 for the prospective cohort (n = 34). Meanwhile, differentially-expressed proteins and lipid molecules, along with their associated signaling pathways, are identified between high and low groups of clinical-radiomic scores. This models can effectively and accurately predict the malignant potential of PCLs, with multi-omics evidence suggesting the biological mechanisms involving secretion function and lipid metabolism underlying clinical-radiomic models.

Investigating the function and targeting of MET protein as an oncogene kinase in pancreatic ductal adenocarcinoma: A microarray data integration

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with a poor prognosis. Kinase proteins are essential regulators of cellular processes and potential targets for drug development.

Methods

Integration of multiple microarray datasets was screened to find differentially expressed kinases (DE-Kinases) across adjacent normal and tumor tissue samples in PDAC. The most effective kinase for drug design and docking in this study was selected by investigating biological mechanisms and survival analyses. Forty phytochemicals were extracted from the yellow sweet clover, Melilotus officinalis (Linn.) Pall, and were then subjected to in silico screening and molecular docking studies against a specific potent kinase.

Results

MET, PAK3, and PDK4 were identified as the DE-Kinases. After examining the pathways and biological processes, up-regulated MET had the most significant survival analysis and became our primary kinase for drug design and docking in this study. Four of the extracted phytocompounds of Melilotus officinalis (Linn.) Pall that exhibited high binding affinities with MET and were selected for toxicity analysis. Finally, the stability and mobility of the two nontoxic compounds that passed the toxicity test (dicumarol PubChem CID: 54676038 and melilotigenin PubChem CID: 14059499) were studied by molecular dynamics simulation.

Conclusion

This study's results identified two phytochemicals in yellow sweet clover that could be used to develop an anticancer drug, but experimental evaluation is necessary to confirm their efficacy.

The Status Quo of Pharmacogenomics of Tyrosine Kinase Inhibitors in Precision Oncology: A Bibliometric Analysis of the Literature

Precision oncology and pharmacogenomics (PGx) intersect in their overarching goal to institute the right treatment for the right patient. However, the translation of these innovations into clinical practice is still lagging behind. Therefore, this study aimed to analyze the current state of research and to predict the future directions of applied PGx in the field of precision oncology as represented by the targeted therapy class of tyrosine kinase inhibitors (TKIs). Advanced bibliometric and scientometric analyses of the literature were performed. The Scopus database was used for the search, and articles published between 2001 and 2023 were extracted. Information about productivity, citations, cluster analysis, keyword co-occurrence, trend topics, and thematic evolution were generated. A total of 448 research articles were included in this analysis. A burst of scholarly activity in the field was noted by the year 2005, peaking in 2017, followed by a remarkable decline to date. Research in the field was hallmarked by consistent and impactful international collaboration, with the US leading in terms of most prolific country, institutions, and total link strength. Thematic evolution in the field points in the direction of more specialized studies on applied pharmacokinetics of available and novel TKIs, particularly for the treatment of lung and breast cancers. Our results delineate a significant advancement in the field of PGx in precision oncology. Notwithstanding the practical challenges to these applications at the point of care, further research, standardization, infrastructure development, and informed policymaking are urgently needed to ensure widespread adoption of PGx.