Gene-disease association with literature based enrichment

HSPA5-mediated glioma hypoxia tolerance promotes M2 macrophage polarization under hypoxic microenvironment

BACKGROUND

The tumor microenvironment (TME), with hallmark features of hypoxia and immunosuppression, plays a crucial role in the progression of various solid tumors. However, the intricate interplay between tumor hypoxia and the formation of tumor immune microenvironment in glioma remains incompletely understood.

METHODS

In the present study, we initially identified genes associated with tumor hypoxia and the immune microenvironment through GSEA and IMMPORT database analysis. We subsequently identified hypoxia- and immune-related genes associated with glioma prognosis through further cross-analysis and multidatabase integrated analysis. HSPA5 was ultimately identified as a potential target gene related to the formation of the hypoxic microenvironment and immune microenvironment in glioma. Furthermore, we conducted MTT, colony formation, EdU, migration and invasion assays and intracranial orthotopic tumor model analysis to further evaluate the impact of interfering with HSPA5 expression on the hypoxic and immune microenvironments of glioma.

RESULTS

We found that HSPA5 is highly expressed in glioma cells and tissues and is associated with a poor prognosis. Further investigation revealed that hypoxia promotes the malignant biological characteristics of glioma and reshaping the Immunosuppressive phenotype of tumor-associated macrophages (TAMs) through upregulation of the HIF-1α/HSPA5 axis. Silencing HSPA5 alleviated glioma hypoxia tolerance and induced the polarization of TAMs toward the M1 phenotype. The induced macrophages could exhibit a tumor-suppressive effect.

CONCLUSION

These observations suggest that HSPA5 upregulation promotes glioma progression by inducing hypoxia tolerance and reshaping the Immunosuppressive phenotype of TAMs. Therefore, targeting HSPA5 may be a novel therapeutic strategy for glioma.

Estimating a Ranked List of Human Genetic Diseases by Associating Phenotype-Gene with Gene-Disease Bipartite Graphs

With vast amounts of medical knowledge available on the Internet, it is becoming increasingly practical to help doctors in clinical diagnostics by suggesting plausible diseases predicted by applying data and text mining technologies. Recently, Genome-Wide Association Studies ( GWAS ) have proved useful as a method for exploring phenotypic associations with diseases. However, since genetic diseases are difficult to diagnose because of their low prevalence, large number, and broad diversity of symptoms, genetic disease patients are often misdiagnosed or experience long diagnostic delays. In this article, we propose a method for ranking genetic diseases for a set of clinical phenotypes. In this regard, we associate a phenotype-gene bipartite graph ( PGBG ) with a gene-disease bipartite graph ( GDBG ) by producing a phenotype-disease bipartite graph ( PDBG ), and we estimate the candidate weights of diseases. In our approach, all paths from a phenotype to a disease are explored by considering causative genes to assign a weight based on path frequency, and the phenotype is linked to the disease in a new PDBG. We introduce the Bidirectionally induced Importance Weight ( BIW ) prediction method to PDBG for approximating the weights of the edges of diseases with phenotypes by considering link information from both sides of the bipartite graph. The performance of our system is compared to that of other known related systems by estimating Normalized Discounted Cumulative Gain ( NDCG ), Mean Average Precision ( MAP ), and Kendall’s tau metrics. Further experiments are conducted with well-known TF · IDF , BM25 , and Jenson-Shannon divergence as baselines. The result shows that our proposed method outperforms the known related tool Phenomizer in terms of NDCG@10, NDCG@20, MAP@10, and MAP@20; however, it performs worse than Phenomizer in terms of Kendall’s tau-b metric at the top-10 ranks. It also turns out that our proposed method has overall better performance than the baseline methods.