Can we predict a high risk group in stage I epithelial ovarian cancer?

1993 ◽  
Vol 3 (4) ◽  
pp. 226-230 ◽  
Author(s):  
C. B. Finn ◽  
J. Dunn ◽  
E. J. Buxton ◽  
D. M. Luesley ◽  
M. Shafi
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Epithelial Ovarian Cancer ◽  
Risk Group ◽  
High Risk Group ◽  
Stage I ◽  
Independent Effect ◽  
Independent Variables ◽  
Peritoneal Washings ◽  
Invasive Epithelial Ovarian Cancer

A retrospective review of 373 patients with stage I invasive epithelial ovarian cancer was undertaken over a 5 year period to develop a model to characterize the patient at high risk. Actuarial 5-year survival was 70%. To identify factors with an independent effect on 5-year survival, a logistic regression analysis was performed. Adjuvant chemotherapy, histologic grade and peritoneal washings, were identified as independent variables. A model to determine the predictivity of survival was created using a learning sample (2/3 of the cases) and the model was then used to reclassify a validation sample (1/3 of the cases). Using all the independent variables, outcome was predicted correctly in 78% of cases. However the model failed to improve identification of those at risk of recurrence (specificity of 53%).

scholarly journals Prediction of a high-risk group based on postoperative nadir CA-125 levels in patients with advanced epithelial ovarian cancer

2011 ◽  
Vol 22 (4) ◽  
pp. 269 ◽  
Author(s):  
Sokbom Kang ◽  
Tae-Joong Kim ◽  
Sang-Soo Seo ◽  
Byoung-Gie Kim ◽  
Duk-Soo Bae ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Epithelial Ovarian Cancer ◽  
Risk Group ◽  
High Risk Group ◽  
Ca 125 ◽  
Advanced Epithelial Ovarian Cancer

scholarly journals A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yinglian Pan ◽  
Li Ping Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gynecologic Cancer ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment ◽  
Prognostic Signature

Abstract Background In this study we aimed to identify a prognostic signature in BRCA1/2 mutations to predict disease progression and the efficiency of chemotherapy ovarian cancer (OV), the second most common cause of death from gynecologic cancer in women worldwide. Methods Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from data obtained from The Cancer Genome Atlas (TCGA) database. The area under the curve of the receiver operating characteristic curve was assessed, and the sensitivity and specificity of the prediction model were determined. Results A signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as the basis for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The three-year overall survival (OS) rates for the high- and low-risk group were approximately 38 and 100%, respectively. Chemotherapy treatment survival rates indicated that the high-risk group had significantly lower OS rates with adjuvant chemotherapy than the low-risk group. The one-, three-, and five-year OS were 100, 40, and 15% respectively in the high-risk group. The survival rate of the high-risk group declined rapidly after 2 years of OV chemotherapy treatment. Multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development. Conclusion In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with BRCA1/2 mutations, and can be used to predict prognosis and the efficiency of chemotherapy.

scholarly journals Integrated Analysis of Ferroptosis-Related Biomarker Signatures to Improve the Diagnosis and Prognosis Prediction of Ovarian Cancer

2022 ◽  
Vol 9 ◽  
Author(s):  
Huan Wang ◽  
Qi Cheng ◽  
Kaikai Chang ◽  
Lingjie Bao ◽  
Xiaofang Yi
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Group ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
M2 Macrophage ◽  
Gynecological Malignancy

Ovarian cancer remains the most lethal gynecological malignancy. Ferroptosis, a specialized form of iron-dependent, nonapoptotic cell death, plays a crucial role in various cancers. However, the contribution of ferroptosis to ovarian cancer is poorly understood. Here, we characterized the diagnostic, prognostic, and therapeutic value of ferroptosis-related genes in ovarian cancer by analyzing transcriptomic data from The Cancer Genome Atlas and Gene Expression Omnibus databases. A reliable 10-gene ferroptosis signature (HIC1, ACSF2, MUC1, etc.) for the diagnosis of ovarian cancer was identified. Notably, we constructed and validated a novel prognostic signature including three FRGs: HIC1, LPCAT3, and DUOX1. We also further developed a risk score model based on these three genes which divided ovarian cancer patients into two risk groups. Functional analysis revealed that immune response and immune-related pathways were enriched in the high-risk group. Meanwhile, the tumor microenvironment was distinct between the two groups, with more M2 Macrophage infiltration and higher expression of key immune checkpoint molecules in the high-risk group than in the other group. Low-risk patients exhibited more favorable immunotherapy and chemotherapy responses. We conclude that crosstalk between ferroptosis and immunity may contribute to the worse prognosis of patients in the high-risk group. In particular, HIC1 showed both diagnostic and prognostic value in ovarian cancer. In vitro experiments demonstrated that inhibition of HIC1 improved drug sensitivity of chemotherapy and immunotherapy agents by inducing ferroptosis. Our findings provide new insights into the potential role of FRGs in the early detection, prognostic prediction, and individualized treatment decision-making for ovarian cancer patients.

Is There a High-Risk Subgroup of Stage I Epithelial Ovarian Cancer That Is Most Likely to Benefit From 6 Versus 3 Cycles of Adjuvant Chemotherapy?

2010 ◽  
Vol 20 (7) ◽  
pp. 1125-1131 ◽  
Author(s):  
Jamie N. Bakkum-Gamez ◽  
Debra L. Richardson ◽  
Leigh G. Seamon ◽  
Giovanni D. Aletti ◽  
Cecelia A. Powless ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Adjuvant Chemotherapy ◽  
Epithelial Ovarian Cancer ◽  
Stage I

scholarly journals Development of an Oncogenic Driver Alteration Associated Immune-Related Prognostic Model for Stage I-II Lung Adenocarcinoma

2021 ◽  
Vol 10 ◽  
Author(s):  
Jian-Zhao Xu ◽  
Chen Gong ◽  
Zheng-Fu Xie ◽  
Hua Zhao
Keyword(s):  
High Risk ◽  
Mast Cells ◽  
Tumor Microenvironment ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Stage I ◽  
Oncogenic Driver

Lung adenocarcinoma (LUAD) needs to be stratified for its heterogeneity. Oncogenic driver alterations such as EGFR mutation, ALK translocation, ROS1 translocation, and BRAF mutation predict response to treatment for LUAD. Since oncogenic driver alterations may modulate immune response in tumor microenvironment that may influence prognosis in LUAD, the effects of EGFR, ALK, ROS1, and BRAF alterations on tumor microenvironment remain unclear. Immune-related prognostic model associated with oncogenic driver alterations is needed. In this study, we performed the Cox-proportional Hazards Analysis based on the L1-penalized (LASSO) Analysis to establish an immune-related prognostic model (IPM) in stage I-II LUAD patients, which was based on 3 immune-related genes (PDE4B, RIPK2, and IFITM1) significantly enriched in patients without EGFR, ALK, ROS1, and BRAF alterations in The Cancer Genome Atlas (TCGA) database. Then, patients were categorized into high-risk and low-risk groups individually according to the IPM defined risk score. The predicting ability of the IPM was validated in GSE31210 and GSE26939 downloaded from the Gene Expression Omnibus (GEO) database. High-risk was significantly associated with lower overall survival (OS) rates in 3 independent stage I-II LUAD cohorts (all P < 0.05). Moreover, the IPM defined risk independently predicted OS for patients in TCGA stage I-II LUAD cohort (P = 0.011). High-risk group had significantly higher proportions of macrophages M1 and activated mast cells but lower proportions of memory B cells, resting CD4 memory T cells and resting mast cells than low-risk group (all P < 0.05). In addition, the high-risk group had a significantly lower expression of CTLA-4, PDCD1, HAVCR2, and TIGIT than the low-risk group (all P < 0.05). In summary, we established a novel IPM that could provide new biomarkers for risk stratification of stage I-II LUAD patients.

scholarly journals Multi-Omics Profiling Identifies Risk Hypoxia-Related Signatures for Ovarian Cancer Prognosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Xingyu Chen ◽  
Hua Lan ◽  
Dong He ◽  
Runshi Xu ◽  
Yao Zhang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Cancer Prognosis ◽  
High Risk Patients ◽  
Risk Patients

BackgroundOvarian cancer (OC) has the highest mortality rate among gynecologic malignancy. Hypoxia is a driver of the malignant progression in OC, which results in poor prognosis. We herein aimed to develop a validated model that was based on the hypoxia genes to systematically evaluate its prognosis in tumor immune microenvironment (TIM).ResultsWe identified 395 hypoxia-immune genes using weighted gene co-expression network analysis (WGCNA). We then established a nine hypoxia-related genes risk model using least absolute shrinkage and selection operator (LASSO) Cox regression, which efficiently distinguished high-risk patients from low-risk ones. We found that high-risk patients were significantly related to poor prognosis. The high-risk group showed unique immunosuppressive microenvironment, lower antigen presentation, and higher levels of inhibitory cytokines. There were also significant differences in somatic copy number alterations (SCNAs) and mutations between the high- and low-risk groups, indicating immune escape in the high-risk group. Tumor immune dysfunction and exclusion (TIDE) and SubMap algorithms showed that low-risk patients are significantly responsive to programmed cell death protein-1 (PD-1) inhibitors.ConclusionsIn this study, we highlighted the clinical significance of hypoxia in OC and established a hypoxia-related model for predicting prognosis and providing potential immunotherapy strategies.

scholarly journals Identification of an immune prognostic 11-gene signature for lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10749
Author(s):  
Tao Yang ◽  
Lizheng Hao ◽  
Renyun Cui ◽  
Huanyu Liu ◽  
Jian Chen ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Stage Iv ◽  
Gene Signature ◽  
High Risk Group ◽  
Stage I ◽  
Local Progression ◽  
Immune Score

Background The immunological tumour microenvironment (TME) has occupied a very important position in the beginning and progression of non-small cell lung cancer (NSCLC). Prognosis of lung adenocarcinoma (LUAD) remains poor for the local progression and widely metastases at the time of clinical diagnosis. Our objective is to identify a potential signature model to improve prognosis of LUAD. Methods With the aim to identify a novel immune prognostic signature associated with overall survival (OS), we analysed LUADs extracted from The Cancer Genome Atlas (TCGA). Immune scores and stromal scores of TCGA-LUAD were downloaded from Estimation of STromal and Immune cells in MAlignant Tumour tissues Expression using data (ESTIMATE). LASSO COX regression was applied to build the prediction model. Then, the prognostic gene signature was validated in the GSE68465 dataset. Results The data from TCGA datasets showed patients in stage I and stage II had higher stromal scores than patients in stage IV (P < 0.05), and for immune score patients in stage I were higher than patients in stage III and stage IV (P < 0.05). The improved overall survivals were observed in high stromal score and immune score groups. Patients in the high-risk group exhibited the inferior OS (P = 2.501e − 05). By validating the 397 LUAD patients from GSE68465, we observed a better OS in the low-risk group compared to the high-risk group, which is consistent with the results from the TCGA cohort. Nomogram results showed that practical and predicted survival coincided very well, especially for 3-year survival. Conclusion We obtained an 11 immune score related gene signature model as an independent element to effectively classify LUADs into different risk groups, which might provide a support for precision treatments. Moreover, immune score may play a potential valuable sole for estimating OS in LUADs.

scholarly journals Identification and Validation of Immune-Related Gene for Predicting Prognosis and Therapeutic Response in Ovarian Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhao-Cong Zhang ◽  
Jun-Nan Guo ◽  
Ning Zhang ◽  
Zhi-Qiang Wang ◽  
Ge Lou ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Prediction Models ◽  
Cox Regression ◽  
Risk Group ◽  
Checkpoint Inhibitors ◽  
High Risk Group ◽  
Poor Overall Survival ◽  
Number Of Patients

Ovarian cancer (OC) is a devastating malignancy with a poor prognosis. The complex tumor immune microenvironment results in only a small number of patients benefiting from immunotherapy. To explore the different factors that lead to immune invasion and determine prognosis and response to immune checkpoint inhibitors (ICIs), we established a prognostic risk scoring model (PRSM) with differential expression of immune-related genes (IRGs) to identify key prognostic IRGs. Patients were divided into high-risk and low-risk groups according to their immune and stromal scores. We used a bioinformatics method to identify four key IRGs that had differences in expression between the two groups and affected prognosis. We evaluated the sensitivity of treatment from three aspects, namely chemotherapy, targeted inhibitors (TIs), and immunotherapy, to evaluate the value of prediction models and key prognostic IRGs in the clinical treatment of OC. Univariate and multivariate Cox regression analyses revealed that these four key IRGs were independent prognostic factors of overall survival in OC patients. In the high-risk group comprising four genes, macrophage M0 cells, macrophage M2 cells, and regulatory T cells, observed to be associated with poor overall survival in our study, were higher. The high-risk group had a high immunophenoscore, indicating a better response to ICIs. Taken together, we constructed a PRSM and identified four key prognostic IRGs for predicting survival and response to ICIs. Finally, the expression of these key genes in OC was evaluated using RT-qPCR. Thus, these genes provide a novel predictive biomarker for immunotherapy and immunomodulation.

Sign in / Sign up

Export Citation Format

Share Document