Vascular Normalization to Improve Treatment of COVID-19: Lessons from Treatment of Cancer

A bibliometric analysis of the research landscape on vascular normalization in cancer

Tumor vascular normalization profoundly affects the advancement of cancer therapy. Currently, with the rapid increase in research on tumor vascular normalization, few analytical and descriptive studies have investigated the trends in its development, key research power, present research hotspots, and future outlooks. In this study, articles and reviews published between January 1, 2003, and October 29, 2022 were retrieved from Web of Science database. Subsequently, published research trends, countries/regions, institutions, authors, journals, references, and keywords were analyzed based on traditional bibliometric laws (such as Price's exponential growth, Bradford's, Lotka's, and Zipf's). Our results showed that the last two decades have seen an increase in tumor vascular normalization research. USA emerged as the preeminent contributor to the field, boasting the highest H-index and accruing the greatest quantity of publications and citations. Among institutions, Massachusetts General Hospital and Harvard University made significant contributions, and Professor RK Jain was identified as a key leader in this field. Out of 583 academic journals, Cancer Research and Clinical Cancer Research published the most articles on vascular normalization. The research focal points in the field primarily include immunotherapy, tumor microenvironments, nanomedicine, and emerging frontier themes such as metabolism and mechanomedicine. Concurrently, the challenges of vascular normalization in cancer are discussed as well. In conclusion, the study presented a thorough analysis of the literature covering the past 20 years on vascular normalization in cancer, highlighting leading countries, institutions, authors, journals, and the emerging research focal points in this field. Future studies will advance the ongoing efforts in the field of tumor vascular normalization, aiming to enhance our ability to effectively manage and treat cancer.

DeepHisCoM: deep learning pathway analysis using hierarchical structural component models

Many statistical methods for pathway analysis have been used to identify pathways associated with the disease along with biological factors such as genes and proteins. However, most pathway analysis methods neglect the complex nonlinear relationship between biological factors and pathways. In this study, we propose a Deep-learning pathway analysis using Hierarchical structured CoMponent models (DeepHisCoM) that utilize deep learning to consider a nonlinear complex contribution of biological factors to pathways by constructing a multilayered model which accounts for hierarchical biological structure. Through simulation studies, DeepHisCoM was shown to have a higher power in the nonlinear pathway effect and comparable power for the linear pathway effect when compared to the conventional pathway methods. Application to hepatocellular carcinoma (HCC) omics datasets, including metabolomic, transcriptomic and metagenomic datasets, demonstrated that DeepHisCoM successfully identified three well-known pathways that are highly associated with HCC, such as lysine degradation, valine, leucine and isoleucine biosynthesis and phenylalanine, tyrosine and tryptophan. Application to the coronavirus disease-2019 (COVID-19) single-nucleotide polymorphism (SNP) dataset also showed that DeepHisCoM identified four pathways that are highly associated with the severity of COVID-19, such as mitogen-activated protein kinase (MAPK) signaling pathway, gonadotropin-releasing hormone (GnRH) signaling pathway, hypertrophic cardiomyopathy and dilated cardiomyopathy. Codes are available at https://github.com/chanwoo-park-official/DeepHisCoM.