scholarly journals Integrative network analysis identifies an immune-based prognostic signature as the determinant for the mesenchymal subtype in epithelial ovarian cancer

Medicine ◽  
2020 ◽  
Vol 99 (41) ◽  
pp. e22549
Author(s):  
Mingyan Sheng ◽  
Haofei Tong ◽  
Xiaoyan Lu ◽  
Ni Shanshan ◽  
Xingguo Zhang ◽  
...  
2020 ◽  
Author(s):  
tiefeng cao ◽  
huimin shen

Abstract Background:Chemotherapeutic resistance is responsible for treatment failure. Immunotherapy is important in ovarian cancer (OC). Systematic exploration of immunogenic landscape and reliable immune gene-based prognostic biomarkers or signature is necessary to be identified. This study aims to identify the immune gene-based prognostic biomarkers and regulatory factors, further to develop an individualized prediction signature.Methods: This study systematically explored the gene expression profiles from RNA-seq data set for The Cancer Genome Atlas (TCGA) ovarian cancer. Differentially expressed and survival-associated immune genes and transcription factors (TFs) were identified using immune genes from ImmPort dataset and TFs from Cistoma database. We developed the prognostic signature based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, Network analysis was performed to uncover the potential molecular mechanisms of immune-related genes with the help of computational biology. Results: The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected infiltration of some immune cell subtypes.Conclusions: We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, prognosis, even immunotherapy response of OC patients.


2020 ◽  
Vol 13 (S9) ◽  
Author(s):  
Tianyu Zhang ◽  
Liwei Zhang ◽  
Fuhai Li

Abstract Background Though accounts for 2.5% of all cancers in female, the death rate of ovarian cancer is high, which is the fifth leading cause of cancer death (5% of all cancer death) in female. The 5-year survival rate of ovarian cancer is less than 50%. The oncogenic molecular signaling of ovarian cancer are complicated and remain unclear, and there is a lack of effective targeted therapies for ovarian cancer treatment. Methods In this study, we propose to investigate activated signaling pathways of individual ovarian cancer patients and sub-groups; and identify potential targets and drugs that are able to disrupt the activated signaling pathways. Specifically, we first identify the up-regulated genes of individual cancer patients using Markov chain Monte Carlo (MCMC), and then identify the potential activated transcription factors. After dividing ovarian cancer patients into several sub-groups sharing common transcription factors using K-modes method, we uncover the up-stream signaling pathways of activated transcription factors in each sub-group. Finally, we mapped all FDA approved drugs targeting on the upstream signaling. Results The 427 ovarian cancer samples were divided into 3 sub-groups (with 100, 172, 155 samples respectively) based on the activated TFs (with 14, 25, 26 activated TFs respectively). Multiple up-stream signaling pathways, e.g., MYC, WNT, PDGFRA (RTK), PI3K, AKT TP53, and MTOR, are uncovered to activate the discovered TFs. In addition, 66 FDA approved drugs were identified targeting on the uncovered core signaling pathways. Forty-four drugs had been reported in ovarian cancer related reports. The signaling diversity and heterogeneity can be potential therapeutic targets for drug combination discovery. Conclusions The proposed integrative network analysis could uncover potential core signaling pathways, targets and drugs for ovarian cancer treatment.


Author(s):  
LI CHEN ◽  
JIANHUA XUAN ◽  
JINGHUA GU ◽  
YUE WANG ◽  
ZHEN ZHANG ◽  
...  

2021 ◽  
Author(s):  
tiefeng cao ◽  
huimin shen

Abstract Background: Various components of the immune system play a critical role in the prognosis and treatment response in ovarian cancer (OC). Immunotherapy has been recognized as a hallmark of cancer but the effect is contradictional. Reliable immune gene-based prognostic biomarkers or regulatory factors are necessary to be systematically explored to develop an individualized prediction signature.Methods: This study systematically explored the gene expression profiles in patients with ovarian cancer from RNA-seq data set for The Cancer Genome Atlas (TCGA). Differentially expressed immune genes and transcription factors (TFs) were identified using the collected immune genes from ImmPort dataset and TFs from Cistoma database. Survival associated immune genes and TFs were identified in terms of overall survival. The prognostic signature was developed based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, we performed network analysis to uncover the potential regulators of immune-related genes with the help of computational biology. Results: The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected the immune cells landscape and infiltration of some immune cell subtypes.Conclusions: We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, and prognosis of OC patients.


Gene ◽  
2019 ◽  
Vol 709 ◽  
pp. 56-64 ◽  
Author(s):  
Gui Hong Zhang ◽  
Miao Miao Chen ◽  
Jin Yan Kai ◽  
Qian Ma ◽  
Ai Ling Zhong ◽  
...  

Genomics ◽  
2020 ◽  
Vol 112 (6) ◽  
pp. 4827-4841
Author(s):  
Jinhui Liu ◽  
Huangyang Meng ◽  
Sipei Nie ◽  
Ying Sun ◽  
Pinping Jiang ◽  
...  

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Tiefeng Cao ◽  
Huimin Shen

Abstract Background Various components of the immune system play a critical role in the prognosis and treatment response in ovarian cancer (OC). Immunotherapy has been recognized as a hallmark of cancer but the effect is contradictional. Reliable immune gene-based prognostic biomarkers or regulatory factors are necessary to be systematically explored to develop an individualized prediction signature. Methods This study systematically explored the gene expression profiles in patients with ovarian cancer from RNA-seq data set for The Cancer Genome Atlas (TCGA). Differentially expressed immune genes and transcription factors (TFs) were identified using the collected immune genes from ImmPort dataset and TFs from Cistoma database. Survival associated immune genes and TFs were identified in terms of overall survival. The prognostic signature was developed based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, we performed network analysis to uncover the potential regulators of immune-related genes with the help of computational biology. Results The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected the immune cells landscape and infiltration of some immune cell subtypes. Conclusions We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, and prognosis of OC patients.


2014 ◽  
Vol 8 (1) ◽  
Author(s):  
Qingyang Zhang ◽  
Joanna E Burdette ◽  
Ji-Ping Wang

Sign in / Sign up

Export Citation Format

Share Document