Hispanic Population: Forgotten Hereditary Breast and Ovarian Cancer High Risk Group?

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.

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

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.807862 ◽

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.

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Comprehensive Analysis of Germline Variants in Mexican Patients with Hereditary Breast and Ovarian Cancer Susceptibility

Cancers ◽

10.3390/cancers10100361 ◽

2018 ◽

Vol 10 (10) ◽

pp. 361 ◽

Cited By ~ 8

Author(s):

Rosalía Quezada Urban ◽

Clara Díaz Velásquez ◽

Rina Gitler ◽

María Rojo Castillo ◽

Max Sirota Toporek ◽

...

Keyword(s):

Ovarian Cancer ◽

High Risk ◽

Cancer Patients ◽

Cancer Susceptibility ◽

High Risk Group ◽

Cancer Genes ◽

Cancer Syndrome ◽

Breast And Ovarian Cancer ◽

Pathogenic Variants ◽

Recurrent Mutations

Hereditary breast and ovarian cancer syndrome (HBOC) represents 5–10% of all patients with breast cancer and is associated with high-risk pathogenic alleles in BRCA1/2 genes, but only for 25% of cases. We aimed to find new pathogenic alleles in a panel of 143 cancer-predisposing genes in 300 Mexican cancer patients with suspicion of HBOC and 27 high-risk patients with a severe family history of cancer, using massive parallel sequencing. We found pathogenic variants in 23 genes, including BRCA1/2. In the group of cancer patients 15% (46/300) had a pathogenic variant; 11% (33/300) harbored variants with unknown clinical significance (VUS) and 74% (221/300) were negative. The high-risk group had 22% (6/27) of patients with pathogenic variants, 4% (1/27) had VUS and 74% (20/27) were negative. The most recurrent mutations were the Mexican founder deletion of exons 9-12 and the variant p.G228fs in BRCA1, each found in 5 of 17 patients with alterations in this gene. Rare VUS with potential impact at the protein level were found in 21 genes. Our results show for the first time in the Mexican population a higher contribution of pathogenic alleles in other susceptibility cancer genes (54%) than in BRCA1/2 (46%), highlighting the high locus heterogeneity of HBOC and the necessity of expanding genetic tests for this disease to include broader gene panels.

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P85 Cox regression versus discriminant techniques: Can we predict a high risk group in stage I epithelial ovarian cancer?

Controlled Clinical Trials ◽

10.1016/0197-2456(93)90217-2 ◽

1993 ◽

Vol 14 (5) ◽

pp. 463

Author(s):

Janet A. Dunn ◽

Catherine B. Finn

Keyword(s):

Ovarian Cancer ◽

High Risk ◽

Epithelial Ovarian Cancer ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Stage I

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

International Journal of Gynecological Cancer ◽

10.1046/j.1525-1438.1993.03040226.x ◽

1993 ◽

Vol 3 (4) ◽

pp. 226-230 ◽

Cited By ~ 8

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%).

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

Frontiers in Immunology ◽

10.3389/fimmu.2021.645839 ◽

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.

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

Frontiers in Immunology ◽

10.3389/fimmu.2021.763791 ◽

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.

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

10.21203/rs.3.rs-28390/v2 ◽

2020 ◽

Author(s):

Yinglian Pan ◽

LiPing 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 two 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.

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A novel defined pyroptosis-related gene signature for predicting the prognosis of ovarian cancer

Cell Death Discovery ◽

10.1038/s41420-021-00451-x ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Ying Ye ◽

Qinjin Dai ◽

Hongbo Qi

Keyword(s):

Ovarian Cancer ◽

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

Differentially Expressed ◽

Related Gene

AbstractOvarian cancer (OC) is a highly malignant gynaecological tumour that has a very poor prognosis. Pyroptosis has been demonstrated in recent years to be an inflammatory form of programmed cell death. However, the expression of pyroptosis-related genes in OC and their correlations with prognosis remain unclear. In this study, we identified 31 pyroptosis regulators that were differentially expressed between OC and normal ovarian tissues. Based on these differentially expressed genes (DEGs), all OC cases could be divided into two subtypes. The prognostic value of each pyroptosis-related gene for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 7-gene signature was built and classified all OC patients in the TCGA cohort into a low- or high-risk group. OC patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P < 0.001). Utilizing the median risk score from the TCGA cohort, OC patients from a Gene Expression Omnibus (GEO) cohort were divided into two risk subgroups, and the low-risk group had increased overall survival (OS) time (P = 0.014). Combined with the clinical characteristics, the risk score was found to be an independent factor for predicting the OS of OC patients. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses indicated that immune-related genes were enriched and that the immune status was decreased in the high-risk group. In conclusion, pyroptosis-related genes play important roles in tumour immunity and can be used to predict the prognosis of OCs.

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