Mathematical simulation of tumour angiogenesis: angiopoietin balance is a key factor in vessel growth and regression

A three-dimensional model with two-body interactions for endothelial cells in angiogenesis

We introduce a three-dimensional mathematical model for the dynamics of vascular endothelial cells during sprouting angiogenesis. Angiogenesis is the biological process by which new blood vessels form from existing ones. It has been the subject of numerous theoretical models. These models have successfully replicated various aspects of angiogenesis. Recent studies using particle-based models have highlighted the significant influence of cell shape on network formation, with elongated cells contributing to the formation of branching structures. While most mathematical models are two-dimensional, we aim to investigate whether ellipsoids also form branch-like structures and how their shape affects the pattern. In our model, the shape of a vascular endothelial cell is represented as a spheroid, and a discrete dynamical system is constructed based on the simple assumption of two-body interactions. Numerical simulations demonstrate that our model reproduces the patterns of elongation and branching observed in the early stages of angiogenesis. We show that the pattern formation of the cell population is strongly dependent on the cell shape. Finally, we demonstrate that our current mathematical model reproduces the cell behaviours, specifically cell-mixing, observed in sprouts.

Colorectal cancer tumor stem cells mediate bevacizumab resistance through the signal IL-22-STAT3 signaling pathway

Bevacizumab is the standard treatment for colorectal cancer (CRC) in the advanced stage. However, poor diagnosis identified due to the bevacizumab resistance in many CRC patients. Previous studies have found that CRC stem cells (CCSCs) and interleukin 22 (IL-22) are involved in the resistance of bevacizumab, however, the mechanism of remains unclear. In this study, we established the bevacizumab drug-resistant cell line HCT-116-R by concentration gradient method, and the cell viability was detected by CCK-8 assay. The resistance of bevacizumab in CRC cell lines HCT-116-R was identified by characterizing epithelial-mesenchymal transition (EMT). Additionally, HCT-116-R cell lines were isolated from CCSCs and their tumorigenicity was validated in nude mice. We observed that that compared with the matched group, the expression of IL-22, IL-22R, STAT3, and GP130 in drug-resistant cells increased distinctly, with blocked IL-22 cells were successfully constructed by lentiviral interference. The level of proteins in stem cell landmarks (EpCAM, CD133), and stem cell landmarks (Oct4, Sox2) was identified by western blotting. Furthermore, the IL-22 role was evaluated by xenograft model. We found that short hairpin RNA (shRNA) suppression of IL-22 expression can restore the sensitivity of drug-resistant CCSCs to bevacizumab, Moreover, xenograft tumor models show that suppression of IL-22 can increase the anti-tumor influence of bevacizumab. In summary, we demonstrated that CCSCs play a major part in bevacizumab-resistant CRC. Inhibiting the signaling pathway of IL-22/STAT3 can improve the anti-tumor influence on bevacizumab in vitro and in vivo. Thus, IL-22 may represent a new anti-bevacizumab target in CRC.

Development of a coupled modeling for tumor growth, angiogenesis, oxygen delivery, and phenotypic heterogeneity

Analysis of the evolution and growth dynamics of tumors is crucial for understanding cancer and the development of individually optimized therapies. During tumor growth, a hypoxic microenvironment around cancer cells caused by excessive non-vascular tumor growth induces tumor angiogenesis that plays a key role in the ensuing tumor growth and its progression into higher stages. Various mathematical simulation models have been introduced to simulate these biologically and physically complex hallmarks of cancer. Here, we developed a hybrid two-dimensional computational model that integrates spatiotemporally different components of the tumor system to investigate both angiogenesis and tumor growth/proliferation. This spatiotemporal evolution is based on partial diffusion equations, the cellular automation method, transition and probabilistic rules, and biological assumptions. The new vascular network provided by angiogenesis affects tumor microenvironmental conditions and drives individual cells to adapt themselves to spatiotemporal conditions. Furthermore, some stochastic rules are involved besides microenvironmental conditions. Overall, the conditions promote some commonly observed cellular states, i.e., proliferative, migrative, quiescent, and cell death, depending on the condition of each cell. Altogether, our results offer a theoretical basis for the biological evidence that regions of the tumor tissue near blood vessels are densely populated by proliferative phenotypic variants, while poorly oxygenated regions are sparsely populated by hypoxic phenotypic variants.

Identification and validation of a novel angiogenesis-related gene signature for predicting prognosis in gastric adenocarcinoma

Background

Angiogenesis is a major promotor of tumor progression and metastasis in gastric adenocarcinoma (STAD). We aimed to develop a novel lncRNA gene signature by identifying angiogenesis-related genes to better predict prognosis in STAD patients.

Methods

The expression profiles of angiogenesis-related mRNA and lncRNA genes were collected from The Cancer Genome Atlas (TCGA). Then, the "limma" package was used to identify differentially expressed genes (DEGs). The expression profiles of angiogenesis-related genes were clustered by consumusclusterplus. The Pearson correlation coefficient was further used to identify lncRNAs coexpressed with angiogenesis-related clustere genes. We used Lasso Cox regression analysis to construct the angiogenesis-related lncRNAs signature. Furthermore, the diagnostic accuracy of the prognostic risk signature were validated by the TCGA training set, internal test sets and external test set. We used multifactor Cox analysis to determine that the risk score is an independent prognostic factor different from clinical characteristics. Nomogram has been used to quantitatively determine personal risk in a clinical environment. The ssGSEA method or GSE176307 data were used to evaluate the infiltration state of immune cells or predictive ability for the benefit of immunotherapy by angiogenesis-related lncRNAs signature. Finally, the expression and function of these signature genes were explored by RT-PCR and colony formation assays.

Results

Among angiogenesis-related genes clusters, the stable number of clusters was 2. A total of 289 DEGs were identified and 116 lncRNAs were screened to have a significant coexpression relationship with angiogenic DEGs (P value<0.001 and |R| >0.5). A six-gene signature comprising LINC01579, LINC01094, RP11.497E19.1, AC093850.2, RP11.613D13.8, and RP11.384P7.7 was constructed by Lasso Cox regression analysis. The multifactor Cox analysis and Nomogram results showed that our angiogenesis-related lncRNAs signature has good predictive ability for some different clinical factors. For immune, angiogenesis-related lncRNAs signature had the ability to efficiently predict infiltration state of 23 immune cells and immunotherapy. The qPCR analysis showed that the expression levels of the six lncRNA signature genes were all higher in gastric adenocarcinoma tissues than in adjacent tissues. The functional experiment results indicated that downregulation of the expression of these six lncRNA signature genes suppressed the proliferation of ASG and MKN45 cells.

Conclusion

Six angiogenesis-related genes were identified and integrated into a novel risk signature that can effectively assess prognosis and provide potential therapeutic targets for STAD patients.

Computational Simulation of Tumor-Induced Angiogenesis

Cancer cells require higher oxygen levels and nutrition than normal cells. Cancer cells induce angiogenesis (the development of new blood vessels) from preexisting vessels. This biological process depends on the special, chemical, and physical properties of the microenvironment surrounding tumor tissues. The complexity of these properties hinders an understanding of their mechanisms. Various mathematical models have been developed to describe quantitative relationships related to angiogenesis. We developed a three-dimensional mathematical model that incorporates angiogenesis and tumor growth. We examined angiopoietin, which regulates the spouting and branching events in angiogenesis. The simulation successfully reproduced the transient decrease in new vessels during vascular network formation. This chapter describes the protocol used to perform the simulations.