Objective:In order to find a genetic marker to predict the prognosis of patients with ovarian cancer based on multi-omics data. Methods:We download RNA-Seq SNP, CNV data and clinical follow-up information from TCGA da...Objective:In order to find a genetic marker to predict the prognosis of patients with ovarian cancer based on multi-omics data. Methods:We download RNA-Seq SNP, CNV data and clinical follow-up information from TCGA database and randomly divide them into training set and test set. GSE17260 dataset in GEO is taken as an external validation set. Prognosis-related genes, copy number difference genes and mutant genes are screened in the training set. After the integration of genes, the random forest algorithm is further used for feature selection, ultimately obtaining a robust biomarker. On this basis, a gene-related prognostic model is established and verified in the test set and verification set. Results:We have obtained 2097 prognostic related genes, 447 copy amplification genes, 1069 copy deletion genes and 654 significant mutations genes. Through the feature selection of random forest algorithm, five feature genes (PSMB1, COL6A6, SLC22A2, KLHL23 and CD3G) are obtained by integrating these genes, some of which have been reported to be related to tumor progress. Furthermore, the prognostic risk assessment model of 5-gene signature is established by Cox regression analysis. The model can evaluate the risk of patient samples in training set, test set and external verification set. 5-gene signature shows strong robustness and clinical independence. The results of GSEA analysis also show that the pathway of 5-gene signature enrichment is significantly related to the pathway and biological process of the occurrence and development of ovarian cancer. Conclusion:In this study, 5-gene signature is constructed as a new prognostic marker to predict the survival of patients with ovarian cancer.展开更多
In social insects, workers of different morphological castes and age are known to act differently. Yet, it is unclear how body size and ovarian development influence worker personalities （i.e. consistent behavioral v...In social insects, workers of different morphological castes and age are known to act differently. Yet, it is unclear how body size and ovarian development influence worker personalities （i.e. consistent behavioral variation） and task allocation in similar aged ant workers of monomorphic species. Behavioral variation is thought to be a key element of division of labor, but few studies have linked worker personality to task allocation. We investigated individual behavior in Leptothorax acervorum ant workers at two time points during the first three months of their life and in two different settings. We observed worker behavior in the nest （i.e. task allocation） and in standardized aggression, exploration and brood care experiments （i.e. personality） and found behavioral repeatability in foraging and exploration. Further, workers acted consistently across settings： workers with a more ag gressive and exploratory personality type were more active in the nest. Moreover, ovarian development was associated with worker personality and task allocation： older workers with welldeveloped ovaries foraged less, but were more aggressive and exploratory. In accordance with the typical agepolyethism of social insects, workers became more active and foraged more as they grew older. Consequently, our study suggests that task allocation in Leptothorax acervorum is not only influenced by ovari an development and age, but moreover by the personalities of its workers .展开更多
文摘Objective:In order to find a genetic marker to predict the prognosis of patients with ovarian cancer based on multi-omics data. Methods:We download RNA-Seq SNP, CNV data and clinical follow-up information from TCGA database and randomly divide them into training set and test set. GSE17260 dataset in GEO is taken as an external validation set. Prognosis-related genes, copy number difference genes and mutant genes are screened in the training set. After the integration of genes, the random forest algorithm is further used for feature selection, ultimately obtaining a robust biomarker. On this basis, a gene-related prognostic model is established and verified in the test set and verification set. Results:We have obtained 2097 prognostic related genes, 447 copy amplification genes, 1069 copy deletion genes and 654 significant mutations genes. Through the feature selection of random forest algorithm, five feature genes (PSMB1, COL6A6, SLC22A2, KLHL23 and CD3G) are obtained by integrating these genes, some of which have been reported to be related to tumor progress. Furthermore, the prognostic risk assessment model of 5-gene signature is established by Cox regression analysis. The model can evaluate the risk of patient samples in training set, test set and external verification set. 5-gene signature shows strong robustness and clinical independence. The results of GSEA analysis also show that the pathway of 5-gene signature enrichment is significantly related to the pathway and biological process of the occurrence and development of ovarian cancer. Conclusion:In this study, 5-gene signature is constructed as a new prognostic marker to predict the survival of patients with ovarian cancer.
文摘In social insects, workers of different morphological castes and age are known to act differently. Yet, it is unclear how body size and ovarian development influence worker personalities （i.e. consistent behavioral variation） and task allocation in similar aged ant workers of monomorphic species. Behavioral variation is thought to be a key element of division of labor, but few studies have linked worker personality to task allocation. We investigated individual behavior in Leptothorax acervorum ant workers at two time points during the first three months of their life and in two different settings. We observed worker behavior in the nest （i.e. task allocation） and in standardized aggression, exploration and brood care experiments （i.e. personality） and found behavioral repeatability in foraging and exploration. Further, workers acted consistently across settings： workers with a more ag gressive and exploratory personality type were more active in the nest. Moreover, ovarian development was associated with worker personality and task allocation： older workers with welldeveloped ovaries foraged less, but were more aggressive and exploratory. In accordance with the typical agepolyethism of social insects, workers became more active and foraged more as they grew older. Consequently, our study suggests that task allocation in Leptothorax acervorum is not only influenced by ovari an development and age, but moreover by the personalities of its workers .
