scholarly journals Construction of a Macrophage Infiltration Regulatory Network and Related Prognostic Model of High-Grade Serous Ovarian Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hua Chang ◽  
Yuyan Zhu ◽  
Jiahui Zheng ◽  
Lian Chen ◽  
Jiaxing Lin ◽  
...  

Background. High-grade serous ovarian cancer (HGSOC) carries the highest mortality in the gynecological cancers; however, therapeutic outcomes have not significantly improved in recent decades. Macrophages play an essential role in the occurrence and development of ovarian cancer, so the mechanisms of macrophage infiltration should be elucidated. Method. We downloaded transcriptome data of ovarian cancers from the Gene Expression Omnibus and The Cancer Genome Atlas. After rigorous screening, 1566 HGSOC were used for data analysis. CIBERSORT was used to estimate the level of macrophage infiltration and WGCNA was used to identify macrophage-related modules. We constructed a macrophage-related prognostic model using machine learning LASSO algorithm and verified it using multiple HGSOC cohorts. Results. In the GPL570-OV cohort, high infiltration level of M1 macrophages was associated with a good outcome, while high infiltration level of M2 macrophages was associated with poor outcomes. We used WGCNA to select genes correlated with macrophage infiltration. These genes were used to construct protein-protein interaction maps of macrophage infiltration. IFL44L, RSAD2, IFIT3, MX1, IFIH1, IFI44, and ISG15 were the hub genes in the network. We then constructed a macrophage-related prognostic model composed of CD38, ACE2, BATF2, HLA-DOB, and WARS. The model had the ability to predict the overall survival rate of HGSOC patients in GPL570-OV, GPL6480-OV, TCGA-OV, GSE50088, and GSE26712. In exploring the immune microenvironment, we found that CD4 memory T cells and activated mast cells showed that the degree of infiltration was higher in the high-risk group, while M1 macrophages were the opposite, and HLA molecules were overexpressed in the high-risk group. Conclusion. We constructed a macrophage infiltration-related protein interaction network that provides a basis for studying macrophages in HGSOC. Our macrophage-related prognostic model is robust and widely applicable. It predicts overall survival in HGSOC patients and may improve HGSOC treatment.

2021 ◽  
Author(s):  
Yahui Jiang ◽  
Tianjiao Lyu ◽  
Tianyu Zhou ◽  
Yiwen Shi ◽  
Weiwei Feng

Abstract Background: Recently, immune system has been shown to be indispensable for ovarian cancer progression. The key immune-related genes (IRGs) related to the overall survival of ovarian cancer patients should be taken seriously. Here, we screened 9 survival-related IRGs in high-grade serous ovarian cancer (HGSOC) and build a prognostic signature to predict the outcome of HGSOC patients.Methods: We downloaded RNA-sequence profiles from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed genes between normal fallopian tube and HGSOC. Among these genes, IRGs were filtered based on the Immunology Database and Analysis Portal (ImmPort). Using univariate Cox regression, Lasso regression and multivariate Cox regression, we selected 9 survival-related IRGs and established a prognostic signature to compute the risk score. Patients were divided into a low-risk group and a high-risk group, and the immunological feature differences between them were analysed with the ESTIMATE R package, TIMER and GSEA software. Moreover, the prognostic signature was validated by data from Gene Expression Omnibus (GEO) datasets.Results: We obtained 1544 differentially expressed genes in HGSOC compared with normal fallopian tube, among which 99 genes were related to immunology. After univariate Cox regression, Lasso regression and multivariate Cox regression, nine IRGs (HLA-F, PSMC1, PI3, CXCL10, CXCL9, CXCL11, LRP1, STAT1 and OGN) were identified as optimal survival-related IRGs and used to establish a prognostic signature for calculating the risk scores of HGSOC patients. The prognostic signature showed its efficiency in predicting the overall survival of HGSOC patients in TCGA training cohort (p=1.018e-8) and GEO test cohort (p=2.632e-2). Age and risk scores were independent risk factors for overall survival. As the risk scores increased, the proportions of neutrophil, dendritic cells, CD8+ T cells, CD4+ T cells and B cells decreased (p values were 0.026, 1.909e-4, 9.165e-10, 0.003 and 2.658e-4, respectively). In addition, 21 out of 24 HLA-related genes were highly expressed in the low-risk group than in the high-risk group. The above might prompt a stronger immune response in the low-risk group.Conclusions: Our study constructed a nine-IRG-based prognostic signature that could effectively predict the overall survival of HGSOC patients and become a promising therapeutic target for HGSOC treatments.


2021 ◽  
Author(s):  
Debao Li ◽  
Lei Wang ◽  
Guanghui Wang ◽  
Yaowen Yang ◽  
Weiyu Yang ◽  
...  

Abstract Background: Ewing sarcoma (ES) is a malignant bone or soft-tissue cancer that mainly arises in children and young adults. However, the prognosis of Ewing sarcoma remains very poor, and there is no effective prediction method. The aim of our study was to identify a prognostic model for ES patients based on prognosis-associated mRNA expression profiles. Methods: The GSE17679 dataset was downloaded from the Gene Expression Omnibus (GEO) database. Differently expressed genes (DEGs) between ES and normal control were identified using R package “limma”. A weighted gene co-expression network analysis (WGCNA) was used to screen gene modules associated with recurrence/metastasis and survival status based on DEGs. Results: The prognostic model was constructed based on genes in MEbrown module, which was most associated with recurrence/metastasis and survival status, using Kaplan-Meier survival and lasso regression analysis. Sixteen genes were screened to construct the prognostic model. ES patients were grouped into high- and low-risk groups based on the median of risk score calculated for each of them. ES patients in high-risk group have worse survival than patients in low-risk group. The AUCs (Area under the ROC curve) for 1-year, 3-year, and 6-year overall survival were 0.903, 0.995, 0.953. Conclusions: Taken together, our research constructed a prognostic model which has excellent prediction performance for overall survival of ES patients.


Author(s):  
Marta De Donato ◽  
Gabriele Babini ◽  
Simona Mozzetti ◽  
Marianna Buttarelli ◽  
Alessandra Ciucci ◽  
...  

Abstract Background In spite of great progress in the surgical and clinical management, until now no significant improvement in overall survival of High-Grade Serous Ovarian Cancer (HGSOC) patients has been achieved. Important aspects for disease control remain unresolved, including unclear pathogenesis, high heterogeneity and relapse resistance after chemotherapy. Therefore, further research on molecular mechanisms involved in cancer progression are needed to find new targets for disease management. The Krüppel-like factors (KLFs) are a family of transcriptional regulators controlling several basic cellular processes, including proliferation, differentiation and migration. They have been shown to play a role in various cancer-relevant processes, in a context-dependent way. Methods To investigate a possible role of KLF family members as prognostic biomarkers, we carried out a bioinformatic meta-analysis of ovarian transcriptome datasets in different cohorts of late-stage HGSOC patients. In vitro cellular models of HGSOC were used for functional studies exploring the role of KLF7 in disease development and progression. Finally, molecular modelling and virtual screening were performed to identify putative KLF7 inhibitors. Results Bioinformatic analysis highlighted KLF7 as the most significant prognostic gene, among the 17 family members. Univariate and multivariate analyses identified KLF7 as an unfavourable prognostic marker for overall survival in late-stage TCGA-OV and GSE26712 HGSOC cohorts. Functional in vitro studies demonstrated that KLF7 can play a role as oncogene, driving tumour growth and dissemination. Mechanistic targets of KLF7 included genes involved in epithelial to mesenchymal transition, and in maintaining pluripotency and self-renewal characteristics of cancer stem cells. Finally, in silico analysis provided reliable information for drug-target interaction prediction. Conclusions Results from the present study provide the first evidence for an oncogenic role of KLF7 in HGSOC, suggesting it as a promising prognostic marker and therapeutic target.


2016 ◽  
Vol 26 (4) ◽  
pp. 671-679 ◽  
Author(s):  
Hans-Christian Bösmüller ◽  
Philipp Wagner ◽  
Janet Kerstin Peper ◽  
Heiko Schuster ◽  
Deborah Lam Pham ◽  
...  

ObjectiveIncreased numbers of tumor-infiltrating lymphocytes (TILs) in high-grade serous ovarian cancer (HGSC) are associated with improved clinical outcome. Intraepithelial localization of TILs might be regulated by specific homing receptors, such as CD103, which is widely expressed by intraepithelial lymphocytes. Given the emerging role of CD103+ TILs, we aimed to assess their contribution to the prognostic value of immunoscoring in HGSC.MethodsThe density of intratumoral CD3+ and CD103+ lymphocytes was examined by immunohistochemistry on a tissue microarray of a series of 135 patients with advanced HGSC and correlated with CD4+, CD8+, CD56+, FoxP3+, and TCRγ+ T-cell counts, as well as E-cadherin staining and conventional prognostic parameters and clinical outcome.ResultsBoth the presence of CD103+ cells, as well as high numbers of intraepithelial CD3+ lymphocytes (CD3E), showed a significant correlation with overall survival, in the complete series, as well as in patients with optimal debulking and/or platinum sensitivity. Combining CD3 and CD103 counts improved prognostication and identified 3 major subgroups with respect to overall survival. The most pronounced effect was demonstrated for patients with optimally resected and platinum-sensitive tumors. Patients with CD3high/CD103high tumors showed a 5-year survival rate at 90%, CD3low/CD103high at 63%, and CD3low/CD103low at 0% (P < 0.001).ConclusionsThese results suggest that combined assessment of CD103 and CD3 counts improves the prognostic value of TIL counts in HGSC and might identify patients with early relapse or long-term survival based on the type and extent of the immune response.


2020 ◽  
Vol 21 (14) ◽  
pp. 995-1010
Author(s):  
Sara Gagno ◽  
Michele Bartoletti ◽  
Chiara Romualdi ◽  
Elena Poletto ◽  
Simona Scalone ◽  
...  

Aim: To define the impact of polymorphisms in genes involved in platinum-taxane and estrogen activity in the outcome of platinum-based treated ovarian cancer patients (OCP). Patients & Methods: Two hundred and thirty OCP were analyzed for 124 germ-line polymorphisms to generate a prognostic score for overall survival (OS), progression-free survival (PFS) and platinum-free interval (PFI). Results: ABCG2 rs3219191D>I, UGT1A rs10929302G>A and UGT1A rs2741045T>C polymorphisms were significantly associated with all three parameters (OS, PFS and PFI) and were used to generate a score. Patients in high-risk group had a poorer OS (hazard ratio [HR]: 1.8; 95% CI: 1.3–2.7; p = 0.0019), PFS (HR: 2.0; 95% CI: 1.4–2.9; p < 0.0001) and PFI (HR: 1.9; 95% CI: 1.4–2.8; p = 0.0002) compared with those in low-risk group. Conclusion: The prognostic-score including polymorphisms involved in drug and estrogen pathways stratifies OCP according to OS, PFS and PFI.


PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11911
Author(s):  
Lei Liu ◽  
Huayu He ◽  
Yue Peng ◽  
Zhenlin Yang ◽  
Shugeng Gao

Background The prognosis of patients for lung adenocarcinoma (LUAD) is known to vary widely; the 5-year overall survival rate is just 63% even for the pathological IA stage. Thus, in order to identify high-risk patients and facilitate clinical decision making, it is vital that we identify new prognostic markers that can be used alongside TNM staging to facilitate risk stratification. Methods We used mRNA expression from The Cancer Genome Atlas (TCGA) cohort to identify a prognostic gene signature and combined this with clinical data to develop a predictive model for the prognosis of patients for lung adenocarcinoma. Kaplan-Meier curves, Lasso regression, and Cox regression, were used to identify specific prognostic genes. The model was assessed via the area under the receiver operating characteristic curve (AUC-ROC) and validated in an independent dataset (GSE50081) from the Gene Expression Omnibus (GEO). Results Our analyses identified a four-gene prognostic signature (CENPH, MYLIP, PITX3, and TRAF3IP3) that was associated with the overall survival of patients with T1-4N0-2M0 in the TCGA dataset. Multivariate regression suggested that the total risk score for the four genes represented an independent prognostic factor for the TCGA and GEO cohorts; the hazard ratio (HR) (high risk group vs low risk group) were 2.34 (p < 0.001) and 2.10 (p = 0.017). Immune infiltration estimations, as determined by an online tool (TIMER2.0) showed that CD4+ T cells were in relative abundance in the high risk group compared to the low risk group in both of the two cohorts (both p < 0.001). We established a composite prognostic model for predicting OS, combined with risk-grouping and clinical factors. The AUCs for 1-, 3-, 5- year OS in the training set were 0.750, 0.737, and 0.719; and were 0.645, 0.766, and 0.725 in the validation set. The calibration curves showed a good match between the predicted probabilities and the actual probabilities. Conclusions We identified a four-gene predictive signature which represents an independent prognostic factor and can be used to identify high-risk patients from different TNM stages of LUAD. A new prognostic model that combines a prognostic gene signature with clinical features exhibited better discriminatory ability for OS than traditional TNM staging.


2020 ◽  
Author(s):  
Jiaxing Lin ◽  
Dan Sun ◽  
Tianren Li

Abstract Background: High-grade serous ovarian cancer (HGSOC) is a common cause of death from gynecological cancer, with an overall survival rate that has not significantly improved in decades. Reliable bio-markers are needed to identify high-risk HGSOC to assist in the selection and development of treatment options.Method: The study included ten HGSOC cohorts, which were merged into four separate cohorts including a total of 1526 samples. We used the relative expression of immune genes to construct the gene-pair matrix, and the Least absolute shrinkage and selection operator regression was performed to build the prognosis model using the training set. The prognosis of the model was verified in the training set (363 cases) and three validation sets (of 251, 354, and 558 cases). Finally, the differences in immune cell infiltration and gene enrichment pathways between high and low score groups were identified.Results: A prognosis model of HGSOC overall survival rate was constructed in the training set, and included data for 35 immune gene-related gene pairs and the regression coefficients. The risk stratification of HGSOC patients was successfully performed using the training set, with a p-value of Kaplan-Meier of < 0.001. A score from this model is an independent prognostic factor of HGSOC, and prognosis was evaluated in different clinical subgroups. This model was also successful for the other three validation sets, and the results of Kaplan-Meier analysis were statistically significant. The model can also predict patient progression-free survival with HGSOC to reflect tumor growth status. There were differences in some immune cells between the high-risk and low-risk groups as defined by the model. There was a lower infiltration level of M1 macrophages in the high-risk group compared to that in the low-risk group (p < 0.001). Finally, many of the immune-related pathways were enriched in the low-risk group, with antigen processing and presentation identified as the most enriched pathways.Conclusion: The prognostic model based on immune-related gene pairs developed is a potential prognostic marker for high-grade serous ovarian cancer treated with platinum. The model has robust prognostic ability and wide applicability. More prospective studies will be needed to assess the practical application of this model for precision therapy.


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