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Uncovering the underlying mechanism of cancer tumorigenesis and development under an immune microenvironment from global quantification of the landscape.

ABSTRACT: The study of the cancer-immune system is important for understanding tumorigenesis and the development of cancer and immunotherapy. In this work, we build a comprehensive cancer-immune model including both cells and cytokines to uncover the underlying mechanism of cancer immunity based on landscape topography. We quantify three steady-state attractors, normal state, low cancer state and high cancer state, for the innate immunity and adaptive immunity of cancer. We also illustrate the cardinal inhibiting cancer immunity interactions and promoting cancer immunity interactions through global sensitivity analysis. We simulate tumorigenesis and the development of cancer and classify these into six stages. The characteristics of the six stages can be classified further into three groups. These correspond to the escape, elimination and equilibrium phases in immunoediting, respectively. Under specific cell-cell interactions strength oscillations emerge. We found that tumorigenesis and cancer recovery processes may need to go through cancer-immune oscillation, which consumes more energy. Based on the cancer-immune landscape, we predict three types of cells and two types of cytokines for cancer immunotherapy as well as combination immunotherapy. This landscape framework provides a quantitative way to understand the underlying mechanisms of the interplay between cancer and the immune system for cancer tumorigenesis and development.

SUBMITTER: Wenbo L

PROVIDER: S-EPMC5493794 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIncreasing evdence supports the suggestion that the immune cell infiltration (ICI) patterns play a pivotal role in tumor progression in breast cancer (BRCA). Nonetheless, there has been no comprehensive analysis of the ICI patterns effects on the clinical outcomes and immunotherapy.MethodsMultiomic data for BRCA samples were downloaded from TCGA. ESTIMATE algorithm, ssGSEA method, and CIBERSORT analysis were used to uncover the landscape of the tumor immune microenvironment (TIME). BRCA subtypes based on the ICI pattern were identified by consensus clustering and principal-component analysis was performed to obtain the ICI scores to quantify the ICI patterns in individual tumors. Their prognostic value was validated by the Kaplan-Meier survival curves. Gene set enrichment analysis (GSEA) was applied for functional annotation. Immunophenoscore (IPS) was employed to explore the immunotherapeutic role of the ICI scores. Finally, the mutation data was analyzed by using the "maftools" R package.ResultsThree different immune infiltration patterns with a distinct prognosis and biological signature were recognized among 1,198 BRCA samples. The characteristics of TIME under these three patterns were highly consistent with three known immune profiles: immune- excluded, immune-desert, and immune-inflamed phenotypes, respectively. The identification of the ICI patterns within individual tumors based on the ICI score, developed under the ICI-related signature genes, contributed into dissecting biological processes, clinical outcome, immune cells infiltration, immunotherapeutic effect, and genetic variation. High ICI score subtype, characterized with a suppression of immunity, suggested an immune-exhausted phenotype. Abundant effective immune cells were discovered in the low ICI score patients, which corresponded to an immune-activated phenotype and might present an immunotherapeutic advantage. Immunophenoscore was implemented as a surrogate of immunotherapeutic outcome, low-ICI scores samples obtained a significantly higher immunophenoscore. Enrichment of the JAK/STAT and VEGF signal pathways were activated in the ICI low-score subgroup. Finally, the synergistic effect between the ICI score and the tumor mutation burden (TMB) was confirmed.ConclusionThis work comprehensively elucidated that the ICI patterns served as an indispensable player in complexity and diversity of TIME. Quantitative identification of the ICI patterns in individual tumor will contribute into mapping the landscape of TIME further optimizing precision immunotherapy.

Project description:Gastric cancer is a daunting disease with a tragic impact on global health. It is the fourth most common cancer and has become the second most frequent cause of cancer death in recent times. According to the Lauren classification, gastric cancer can be classified into two types: intestinal and diffuse. Intestinal-type gastric cancer (IGC) is more common in elderly people, and atrophic gastritis (AG) and intestinal metaplasia (IM) have been proven to be the main premalignant causes of intestinal-type gastric cancer. In turn, Helicobacter pylori infection has been identified as the most significant cause of AG and IM. In this study, we determine the mechanism of IGC progression and how H. pylori infection induces IGC. Through researching the relevant literature, we identified the key genes associated with gastric cancer and the specific genes associated with IGC. We then use hese genes to build up a gene regulatory network for IGC. Based on this gene regulatory network, we quantify the IGC landscape. Within this landscape, there are three stable states, which are classified as the normal, AG, and gastric cancer states. Through landscape topography, we can determine the biological features and progression process of IGC. To investigate the influence of H. pylori infection on IGC, we simulated different degrees of H. pylori infection. As the H. pylori infection becomes more serious, the landscape topography changes accordingly. A fourth state, named the intestinal metaplasia (IM) state, emerges on the landscape and is associated with a very high risk of developing gastric cancer. The emergence of this state is due to the interactions/regulations among genes. Through variations in the landscape topography, we can determine the influence of H. pylori infection on IGC. Finally, we use global sensitivity analysis to research the regulations most sensitive to IGC prevention or therapies. This study presents a new approach and a novel model with which to explore the mechanism of IGC. The simulations of different degrees of H. pylori infection can provide us with a systematic view of IGC progression. The key regulations found can give us some insight and guidance for clinical trials and experimental studies.

Project description:BackgroundPreeclampsia is a life-threatening disease of pregnancy that lacks effective pharmaceuticals which can target its pathogenesis. Since preeclampsia involves complex pathological processes, including autophagy, this study aims to explore autophagy-related mechanisms of preeclampsia and to screen potential drugs.MethodsFirstly, the datasets GSE75010, GSE24129, GSE66273, and autophagic genes lists were downloaded from public databases. Then, a weighted gene co-expression network analysis (WGCNA) was applied to filter autophagic-related hub genes of preeclampsia. The differential expression levels of the hub genes were validated with datasets GSE24129 and GSE66273. Next, the GO and KEGG enrichment, protein-protein interacting (PPI) network, as well as the downstream pathways was analyzed via the starBase, STRING and Cytoscape to determine the functions and regulatory network of the hub genes. Additionally, the immune microenvironment of preeclampsia was investigated by the CIBERSORTX database. Finally, three herb ingredients, berberine, baicalein, and luteolin were screened by molecular docking in comparison to pravastatin, metformin, and aspirin, to predict potential drugs for treating preeclampsia.ResultsA total of 54 autophagy-related genes were filtered by WGCNA. After filtering with |GS| > 0.5 and |MM| > 0.8, three hub genes, namely PKM, LEP, and HK2, were identified and validated. Among these genes, PKM and LEP were overexpressed in women older than 35 years old ( p<0.05; p<0.05); the expression of PKM, LEP, and HK2 differed remarkably in women with different BMI (all p<0.05); PKM overexpressed in women with hypertension (p<0.05). The regulatory network of hub genes demonstrated that they were mainly enriched in metabolic pathways, including the AMPK signaling pathway, glucagon signaling pathway, adipocytokine signaling pathway, and central carbon metabolism. Then, immune microenvironment analysis turned out that M2 macrophages were reduced in preeclampsia women (p<0.0001) and were negatively correlated with the expression of PKM (r=-0.2, p<0.05), LEP (r=-0.4, p<0.0001), and HK2 (r=-0.3, p<0.001). Lastly, molecular docking showed baicalein and luteolin could bind intimately to hub genes.ConclusionPKM, LEP, and HK2 could be promising biomarkers for preeclampsia, which might regulate the pathogenesis of preeclampsia via metabolism pathways and immune microenvironment. Baicalein and luteolin could be potential therapeutics for preeclampsia.

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Uncovering the underlying mechanism of cancer tumorigenesis and development under an immune microenvironment from global quantification of the landscape.

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