m6A-related metabolism molecular classification with distinct prognosis and immunotherapy response in soft tissue sarcoma.

N6-methyladenosine (m6A) methylation, one of the most crucial RNA modifications, has been proven to play a key role that affect prognosis of soft tissue sarcoma (STS). However, m6A methylation potential role in STS metabolic processes remains unknown. We comprehensively estimated the m6A metabolic molecular subtypes and corresponding survival, immunity, genomic and stemness characteristics based on 568 STS samples and m6A related metabolic pathways. Then, to quantify the m6A metabolic subtypes, machine learning algorithms were used to develop the m6A-metabolic Scores of individual patients. Finally, two distinct m6A metabolic subtypes (Cluster A and Cluster B) among the STS patients were identified. Compared to Cluster B subtype, the Cluster A subtype was mainly characterized by better survival advantages, activated anti-tumor immune microenvironment, lower gene mutation frequency and higher anti-PD-1 immunotherapy response rates. We also found that the m6A-metabolic Scores could accurately predict the molecular subtype of STS, prognosis, the abundance of immune cell infiltration, tumor metastasis status, sensitivity to chemotherapeutics and immunotherapy response. In general, this study revealed that m6A-regulated tumor metabolism processes played a key role in terms of prognosis of STS, tumor progression, and immune microenvironment. The identification of metabolic molecular subtypes and the construction of m6A-metabolic Score will help to more effectively guide immunotherapy, metabolic therapy and chemotherapy in STS.

[Potential Ecological Suitability Regionalization Analysis of Angelica sinensis Based on GIS and Fuzzy Matter Element Model].

OBJECTIVE: To study the potential ecological suitability regionalization of Angelica sinensis, for protecting wild resources and selecting cultivation location and designing rational production layout. METHODS: Based on fuzzy matter element model, the relationship of fuzzy membership function between ferulic acid content and 14 ecological factors, including climate, topography and soil,were established. Then information entropy theory was used to determine the relative importance of each environmental factor, and thus to determine the most limiting habitat criteria. Finally, the probable spatial distribution of Angelica sinensis across ten provinces in Western China was determined based on GIS spatial analysis of habitat conditions. Meanwhile, the optimal index range of ecological factors was quantified. RESULTS: It was showed that the percentage of moderately and highly suitable habitats for Angelica sinensis in the study area was 9. 64%, its area was 306,768. 01 km2. The moderately and highly suitable habitats were mainly located in the southeast of Gansu ind Tibet,the north of Sichuan and the northwest of Yunnan. The results also showed that six dominant ecological factors controlling the distribution of Angelica sinensis. These six dominant features were as follows: (1) mean temperature of wettest quarter, (2) altitude, (3) precipitation of growth, (4) annual relative humidity, (5) average temperature of growth period, and (6) annual )recipitation. CONCLUSION: The habitat suitability assessment model based on GIS and fuzzy matter element model theory can accurately valuate the habitat suitability of Angelica sinensis, quantify the area of suitable habitat and analyze the spatial distribution. This informaion is of value to provide insight for choosing the most suitable cultivation sites,as well as the habitat protection zones.