Pancreatic Cancer Health Disparity: Pharmacologic Anthropology

Wall, Nathan R.; Fuller, Ryan N.; Morcos, Ann; De Leon, Marino

doi:10.3390/cancers15205070

Cited by 4 publications

(3 citation statements)

References 256 publications

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“…Despite advances in diagnostics and therapeutics, PC remains a very challenging condition to treat, with consistently high mortality rates and limited available treatments 40,41 . Recently, research has focused on identifying prognostic markers for PC, and preclinical studies have identified several prognostic lncRNA signatures 8,[42][43][44] .…”

Section: Discussionmentioning

confidence: 99%

Machine learning predicts metastatic progression using novel differentially expressed lncRNAs as potential markers in pancreatic cancer

Alsharoh

2023

Preprint

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Pancreatic cancer (PC) is associated with high mortality overall. Recent literature has focused on investigating long noncoding RNAs (lncRNAs) in several cancers, but studies on their functions in PC are lacking. The purpose of this study was to identify novel lncRNAs and utilize machine learning to techniques to predict metastatic cases of PC using the identified lncRNAs. To identify significantly altered expression of lncRNA in PC, data was collected from The Cancer Genome Atlas (TCGA) and RNA-sequencing (RNA-seq) transcriptomic profiles of pancreatic carcinomas were extracted for differential gene expression analysis. To assess the contribution of these lncRNAs to metastatic progression, different ML algorithms were used, including logistic regression (LR), support vector machine (SVM), random forest classifier (RFC) and eXtreme Gradient Boosting Classifier (XGBC). To improve the predictive accuracy of these models, hyperparameter tuning was performed, in addition to reducing bias through the synthetic minority oversampling technique. Out of 60,660 gene transcripts shared between 151 PC patients, 38 lncRNAs that were significantly differentially expressed were identified. To further investigate the functions of the novel lncRNAs, gene set enrichment analysis (GSEA) was performed on the population lncRNA panel. GSEA results revealed enrichment of several terms implicated in proliferation. Moreover, using the 4 ML algorithms to predict metastatic progression returned 76% accuracy for both SVM and RFC, explicitly based on the novel lncRNA panel. To the best of my knowledge, this is the first study of its kind to identify this lncRNA panel to differentiate between non-metastatic PC and metastatic PC, with many novel lncRNAs previously unmapped to PC. The ML accuracy score reveals important involvement of the detected RNAs. Based on these findings, I suggest further investigations of this lncRNA panelin vitroandin vivo, as they could be targeted for improved outcomes in PC patients, as well as assist in the diagnosis of metastatic progression based on RNA-seq data of primary pancreatic tumors.

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Section: Discussionmentioning

confidence: 99%

Machine learning predicts metastatic progression using novel differentially expressed lncRNAs as potential markers in pancreatic cancer

Alsharoh

2023

Preprint

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“…Socio-cultural dynamics play a significant role in shaping health behaviors. Strategic community engagement involves understanding and navigating these dynamics (Wall, et al,2023). This includes addressing cultural norms, language barriers, and socio-economic factors that may influence vaccine acceptance.…”

Section: Strategic Approaches In Vaccinationmentioning

confidence: 99%

Ethics and strategy in vaccination: A review of public health policies and practices

Jane Osareme Ogugua,

Evangel Chinyere Anyanwu,

Tolulope Olorunsogo

et al. 2024

Int. J. Sci. Res. Arch.

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The paper provides a succinct overview of the comprehensive review on Ethics and Strategy in Vaccination, exploring the intricate interplay between public health policies and practices. The review delves into the ethical considerations that underpin vaccination strategies, emphasizing the critical need for a balance between individual autonomy and the collective responsibility for public health. Examining a spectrum of vaccination policies, the review navigates through the complexities of decision-making, addressing issues such as vaccine mandates, informed consent, and the equitable distribution of vaccines. Within the framework of public health ethics, the review analyzes the ethical principles guiding vaccination strategies, including autonomy, beneficence, and justice. It explores how these principles are applied in formulating policies that aim to achieve widespread immunization while respecting individual rights. Additionally, the review delves into the global context, discussing the ethical implications of vaccine distribution on an international scale, especially in addressing disparities between developed and developing nations. Strategic considerations in vaccination, including communication strategies, public awareness campaigns, and community engagement, are integral aspects of the review. It assesses the ethical dimensions of communication strategies, ensuring transparency, trust-building, and the dissemination of accurate information to foster informed decision-making. The review underscores the inseparable connection between ethics and strategy in vaccination. It provides insights into the ethical dilemmas faced by policymakers, the principles guiding their decisions, and the strategic approaches employed to navigate the complex landscape of public health. This comprehensive analysis contributes to a nuanced understanding of the ethical foundations that underscore vaccination policies and practices, offering valuable insights for future policymaking and public health endeavors.

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“…CR in PCa research plays a crucial role in addressing health disparities by enabling the study of patient-derived samples. Through CR methodology, researchers can investigate the molecular mechanisms underlying these disparities, identifying genetic, epigenetic, and environmental factors contributing to differential outcomes [ 84 ]. By understanding the root causes of health disparities, CR facilitates the development of targeted interventions and precision medicine approaches tailored to high-risk populations, ultimately aiming to reduce the unequal burden of PCa.…”

Section: Applications Of Cr Cells In Pca Initiation and Progressionmentioning

confidence: 99%

Patient-Derived Conditionally Reprogrammed Cells in Prostate Cancer Research

Elbialy,

Kappala,

Desai

et al. 2024

Cells

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Prostate cancer (PCa) remains a leading cause of mortality among American men, with metastatic and recurrent disease posing significant therapeutic challenges due to a limited comprehension of the underlying biological processes governing disease initiation, dormancy, and progression. The conventional use of PCa cell lines has proven inadequate in elucidating the intricate molecular mechanisms driving PCa carcinogenesis, hindering the development of effective treatments. To address this gap, patient-derived primary cell cultures have been developed and play a pivotal role in unraveling the pathophysiological intricacies unique to PCa in each individual, offering valuable insights for translational research. This review explores the applications of the conditional reprogramming (CR) cell culture approach, showcasing its capability to rapidly and effectively cultivate patient-derived normal and tumor cells. The CR strategy facilitates the acquisition of stem cell properties by primary cells, precisely recapitulating the human pathophysiology of PCa. This nuanced understanding enables the identification of novel therapeutics. Specifically, our discussion encompasses the utility of CR cells in elucidating PCa initiation and progression, unraveling the molecular pathogenesis of metastatic PCa, addressing health disparities, and advancing personalized medicine. Coupled with the tumor organoid approach and patient-derived xenografts (PDXs), CR cells present a promising avenue for comprehending cancer biology, exploring new treatment modalities, and advancing precision medicine in the context of PCa. These approaches have been used for two NCI initiatives (PDMR: patient-derived model repositories; HCMI: human cancer models initiatives).

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Pancreatic Cancer Health Disparity: Pharmacologic Anthropology

Cited by 4 publications

References 256 publications

Machine learning predicts metastatic progression using novel differentially expressed lncRNAs as potential markers in pancreatic cancer

Machine learning predicts metastatic progression using novel differentially expressed lncRNAs as potential markers in pancreatic cancer

Ethics and strategy in vaccination: A review of public health policies and practices

Patient-Derived Conditionally Reprogrammed Cells in Prostate Cancer Research

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