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BackgroundGlioblastoma (GBM) is the most prominent and aggressive primary brain tumor in adults. Anoikis is a specific form of programmed cell death that plays a key role in tumor invasion and metastasis. The presence of anti-anoikis factors is associated with tumor aggressiveness and drug resistance.MethodsThe non-negative matrix factorization algorithm was used for effective dimension reduction for integrated datasets. Differences in the tumor microenvironment (TME), stemness indices, and clinical characteristics between the two clusters were analyzed. Difference analysis, weighted gene coexpression network analysis (WGCNA), univariate Cox regression, and least absolute shrinkage and selection operator regression were leveraged to screen prognosis-related genes and construct a risk score model. Immunohistochemistry was performed to evaluate the expression of representative genes in clinical specimens. The relationship between the risk score and the TME, stemness, clinical traits, and immunotherapy response was assessed in GBM and pancancer.ResultsTwo definite clusters were identified on the basis of anoikis-related gene expression. Patients with GBM assigned to C1 were characterized by shortened overall survival, higher suppressive immune infiltration levels, and lower stemness indices. We further constructed a risk scoring model to quantify the regulatory patterns of anoikis-related genes. The higher risk score group was characterized by a poor prognosis, the infiltration of suppressive immune cells and a differentiated phenotype, whereas the lower risk score group exhibited the opposite effects. In addition, patients in the lower risk score group exhibited a higher frequency of isocitrate dehydrogenase (IDH) mutations and a more sensitive response to immunotherapy. Drug sensitivity analysis was performed, revealing that the higher risk group may benefit more from drugs targeting the PI3K/mTOR signaling pathway.ConclusionWe revealed potential relationships between anoikis-related genes and clinical features, TME, stemness, IDH mutation, and immunotherapy and elucidated their therapeutic value.

Compared with individual events, compound weather and climate extremes may impose more serious influences on natural systems and human society, especially in populated areas. In this study, we examine the changes in the compound precipitation events that follow extremely hot weather within several days during 1961–2017 in South China by taking the Guangdong Province as an example. Additionally, we assess the impacts of urbanization on these changes. It is found that extreme precipitation events in Guangdong are often preceded by hot weather, with an average fraction of 28.25%. The fraction of such compound events is even larger in more populated and urbanized areas such as the Pearl River Delta (PRD) region. Moreover, our results reveal significant increases in the frequency and fraction of the compound extreme heat and precipitation events. These increases are especially stronger in more developed areas (e.g., PRD), and their increasing trends tend to accelerate in recent decades. Furthermore, the local urbanization contributes to 40.91 and 49.38% of the increases in the frequency and fraction of the compound events, respectively. Our findings provide scientific references for policy-makers and urban planners to mitigate the influences of the compound heat and precipitation extremes by considering their increasing risks under the context of global climate change and local urbanization.