A Signature of Three Apoptosis-Related Genes Predicts Overall Survival in Breast Cancer.

Background: The commonest malignancy in women is known as breast cancer (BC). Numerous studies demonstrated that apoptosis appears to be critical to the management and clinical outcome of BC patients. The purpose of this study is to explore the potential connection between apoptosis and BC and establish the apoptosis-associated gene signature in BC. Methods: The data of BC patient transcripts and related clinical information comes from the Cancer Genome Atlas Database (TCGA), and the genes related to apoptosis come from the Molecular Characterization Database (MSigDB). We identified the abnormally expressed apoptosis-related genes in BC samples. The optimal apoptosis-related genes screened by Cox regression analysis were designed to construct a prognostic model for predicting BC patients. Using the Nom Chart to Predict 1-Year, 3-Year, and 5-Year overall survival for BC patients. The gene signature-related functional pathways were explored by gene set enrichment analysis (GSEA). Results: Three genes [alpha subunit of the interleukin 3 receptor (IL3RA), apoptosis-inducing factor mitochondrial-associated 1 (AIFM1), and phosphatidylinositol-3 kinase catalytic alpha (PIK3CA)] correlated with apoptosis were shown to be strongly linked to the overall survival of BC. Survival analysis shows that the risk score is directly proportional to the poor prognosis of BC patients. Risk assessment based on three genetic characteristics (age, pathological stage N, and pathological stage M) can independently predict the prognosis of patients with BC. The Nom chart is most suitable for assessing the long-term survival rate of BC patients. The results of GSEA demonstrated that numerous cell cycle-related pathways were abundant in the high-risk group. Conclusion: We constructed an apoptosis-associated gene signature in BC, which had a potential clinical application prospect for BC patients.

[A new category system of China nature reserves based on their attributes].

Based on the numeric attributes of 114 typical nature reserves in China, a new category system was established by using cluster analysis method. Compared with the China national category system of nature reserves, the new category system was simpler and more applicable. The 4 categories in the new system matched well to the categories I , II , IV, and V in IUCN category system, respectively. The new category system, characterized by the consistency of category criteria and scale, could avoid the deviation caused by subjective category to some degree, and provide a quantitative standard for the classification of nature reserves in China.