scholarly journals Identification of an Immune-Related Prognostic Gene CLEC5A Based on Immune Microenvironment and Risk Modeling of Ovarian Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiacheng Shen ◽  
Tingwei Liu ◽  
Jia Lv ◽  
Shaohua Xu
Keyword(s):  
Ovarian Cancer ◽  
Immune Response ◽  
Copy Number ◽  
Risk Model ◽  
Copy Number Variant ◽  
Prognostic Indicator ◽  
Immune Checkpoints ◽  
Genomic Alteration ◽  
Risk Modeling ◽  
Immune Microenvironment

Objective: To understand the immune characteristics of the ovarian cancer (OC) microenvironment and explore the differences of immune-related molecules and cells to establish an effective risk model and identify the molecules that significantly affected the immune response of OC, to help guide the diagnosis.Methods: First, we calculate the TMEscore which reflects the immune microenvironment, and then analyze the molecular differences between patients with different immune characteristics, and determine the prognostic genes. Then, the risk model was established by least absolute shrinkage and selection operator (LASSO) analysis and combined with clinical data into a nomogram for diagnosis and prediction. Subsequently, the potential gene CLEC5A influencing the immune response of OC was identified from the prognostic genes by integrative immune-stromal analysis. The genomic alteration was explored based on copy number variant (CNV) and somatic mutation data.Results: TMEscore was a prognostic indicator of OC. The prognosis of patients with high TMEscore was better. The risk model based on immune characteristics was a reliable index to predict the prognosis of patients, and the nomogram could comprehensively evaluate the prognosis of patients. Besides, CLEC5A was closely related to the abundance of immune cells, immune response, and the expression of immune checkpoints in the OC microenvironment. OC cells with high expression of CLEC5A increased the polarization of M2 macrophages. CLEC5A expression was significantly associated with TTN and CDK12 mutations and affected the copy number of tumor progression and immune-related genes.Conclusion: The study of immune characteristics in the OC microenvironment and the risk model can reveal the factors affecting the prognosis and guide the clinical hierarchical treatment. CLEC5A can be used as a potential key gene affecting the immune microenvironment remodeling of OC, which provides a new perspective for improving the effect of OC immunotherapy.

scholarly journals Identification of Hypoxia Signature to Assess the Tumor Immune Microenvironment and Predict Prognosis in Patients with Ovarian Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Chunyan Wei ◽  
Xiaoqing Liu ◽  
Qin Wang ◽  
Qipei Li ◽  
Min Xie
Keyword(s):  
Ovarian Cancer ◽  
Risk Model ◽  
Risk Group ◽  
Plasmacytoid Dendritic Cell ◽  
Gene Signature ◽  
Immune Checkpoints ◽  
Immune Microenvironment ◽  
Cell Immunity ◽  
Tumor Immune Microenvironment ◽  
Hypoxia Signature

Background. The 5-year overall survival rate of ovarian cancer (OC) patients is less than 40%. Hypoxia promotes the proliferation of OC cells and leads to the decline of cell immunity. It is crucial to find potential predictors or risk model related to OC prognosis. This study aimed at establishing the hypoxia-associated gene signature to assess tumor immune microenvironment and predicting the prognosis of OC. Methods. The gene expression data of 378 OC patients and 370 OC patients were downloaded from datasets. The hypoxia risk model was constructed to reflect the immune microenvironment in OC and predict prognosis. Results. 8 genes (AKAP12, ALDOC, ANGPTL4, CITED2, ISG20, PPP1R15A, PRDX5, and TGFBI) were included in the hypoxic gene signature. Patients in the high hypoxia risk group showed worse survival. Hypoxia signature significantly related to clinical features and may serve as an independent prognostic factor for OC patients. 2 types of immune cells, plasmacytoid dendritic cell and regulatory T cell, showed a significant infiltration in the tissues of the high hypoxia risk group patients. Most of the immunosuppressive genes (such as ARG1, CD160, CD244, CXCL12, DNMT1, and HAVCR1) and immune checkpoints (such as CD80, CTLA4, and CD274) were upregulated in the high hypoxia risk group. Gene sets related to the high hypoxia risk group were associated with signaling pathways of cell cycle, MAPK, mTOR, PI3K-Akt, VEGF, and AMPK. Conclusion. The hypoxia risk model could serve as an independent prognostic indicator and reflect overall immune response intensity in the OC microenvironment.

scholarly journals A Combination of Glutaminase Inhibitor 968 and PD-L1 Blockade Boosts the Immune Response against Ovarian Cancer

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1749
Author(s):  
Jing-Jing Wang ◽  
Michelle Kwan-Yee Siu ◽  
Yu-Xin Jiang ◽  
Thomas Ho-Yin Leung ◽  
David Wai Chan ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Immune Response ◽  
T Cells ◽  
Cancer Cells ◽  
Cd8 T Cells ◽  
Granzyme B ◽  
Combined Treatment ◽  
In Vivo Studies ◽  
Immune Checkpoints ◽  
Ovarian Cancer Cells

Programmed cell death 1 ligand (PD-L1) blockade has been used therapeutically in the treatment of ovarian cancer, and potential combination treatment approaches are under investigation to improve the treatment response rate. The increased dependence on glutamine is widely observed in various type of tumors, including ovarian cancer. Kidney-type glutaminase (GLS), as one of the isotypes of glutaminase, is found to promote tumorigenesis. Here, we have demonstrated that the combined treatment with GLS inhibitor 968 and PD-L1 blockade enhances the immune response against ovarian cancer. Survival analysis using the Kaplan–Meier plotter dataset from ovarian cancer patients revealed that the expression level of GLS predicts poor survival and correlates with the immunosuppressive microenvironment of ovarian cancer. 968 inhibits the proliferation of ovarian cancer cells and enhances granzyme B secretion by CD8+ T cells as detected by XTT assay and flow cytometry, respectively. Furthermore, 968 enhances the apoptosis-inducing ability of CD8+ T cells toward cancer cells and improves the treatment effect of anti-PD-L1 in treating ovarian cancer as assessed by Annexin V apoptosis assay. In vivo studies demonstrated the prolonged overall survival upon combined treatment of 968 with anti-PD-L1 accompanied by increased granzyme B secretion by CD4+ and CD8+ T cells isolated from ovarian tumor xenografts. Additionally, 968 increases the infiltration of CD3+ T cells into tumors, possibly through enhancing the secretion of CXCL10 and CXCL11 by tumor cells. In conclusion, our findings provide a novel insight into ovarian cancer cells influence the immune system in the tumor microenvironment and highlight the potential clinical implication of combination of immune checkpoints with GLS inhibitor 968 in treating ovarian cancer.

scholarly journals Landscape of Immune Microenvironment in Epithelial Ovarian Cancer and Establishing Risk Model by Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shi-yi Liu ◽  
Rong-hui Zhu ◽  
Zi-tao Wang ◽  
Wei Tan ◽  
Li Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Immune Cells ◽  
Risk Model ◽  
Dominant Role ◽  
Immune Checkpoints ◽  
Support Vector ◽  
Lasso Regression ◽  
High Infiltration

Background. Epithelial ovarian cancer (EOC) is an extremely lethal gynecological malignancy and has the potential to benefit from the immune checkpoint blockade (ICB) therapy, whose efficacy highly depends on the complex tumor microenvironment (TME). Method and Result. We comprehensively analyze the landscape of TME and its prognostic value through immune infiltration analysis, somatic mutation analysis, and survival analysis. The results showed that high infiltration of immune cells predicts favorable clinical outcomes in EOC. Then, the detailed TME landscape of the EOC had been investigated through “xCell” algorithm, Gene set variation analysis (GSVA), cytokines expression analysis, and correlation analysis. It is observed that EOC patients with high infiltrating immune cells have an antitumor phenotype and are highly correlated with immune checkpoints. We further found that dendritic cells (DCs) may play a dominant role in promoting the infiltration of immune cells into TME and forming an antitumor immune phenotype. Finally, we conducted machine-learning Lasso regression, support vector machines (SVMs), and random forest, identifying six DC-related prognostic genes (CXCL9, VSIG4, ALOX5AP, TGFBI, UBD, and CXCL11). And DC-related risk stratify model had been well established and validated. Conclusion. High infiltration of immune cells predicted a better outcome and an antitumor phenotype in EOC, and the DCs might play a dominant role in the initiation of antitumor immune cells. The well-established risk model can be used for prognostic prediction in EOC.

scholarly journals The Correlation Between Novel Survival-related Alternative Splicing Signature and Prognostic Prediction and Immune Microenvironment in Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
Wengang Jian ◽  
Gang Wang ◽  
Yipeng Yu ◽  
Licheng Cai ◽  
Yongchun Yu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Immune Cell ◽  
Risk Model ◽  
Clear Cell ◽  
Cell Infiltration ◽  
Immune Checkpoints ◽  
Splicing Factors ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Cell Renal Cell Carcinoma

Abstract BackgroundAlthough extensive researches related alternative splicing (AS) as the prognostic markers of patients in cancers, which remains unknown in clear cell renal cell carcinoma (ccRCC), especially in immunotherapy. Therefore, a novel survival-associated AS signature was established to predict prognosis of patients and explored its correlation with immune cell infiltration or immune checkpoint expression to explain the phenomenon of resistance to immunotherapy in ccRCC.Methodsccording to AS data, clinical information and gene expression data in ccRCC, overall survival-related AS events was identified and further the AS-related prognostic risk model established by LASSO regression and multi-Cox regression analysis was evaluated by Kaplan-Meier survival analysis, the ROC curves and a nomogram model. Then we clarified the biological processes and pathways by GSEA, and further measured the immune cell infiltration by ESTIMATE, CIBERSORT and ssGSEA. Finally we analysed the clinical features and immune features of different parental genes, and quested the splicing factors regulating riskScore-related AS events by Spearman correlation analysis.ResultsWe obtained the most significant 5 AS events, including C4orf19|69001|AT, UACA|31438|AP, FAM120C|89237|AT, TRIM16L|39629|AP and SEC31A|100881|ES, to establish the prognostic risk model, and further illustrated the stability and importance of the riskScore prognostic signatures. Then we found that in high risk group, most of the top 10 GO enrichments and the KEGG pathway were closely related to the immunity, and the higher immune cell infiltration, and higher expression of classic immune checkpoints such as PD1 and CTLA4. In addition, 6 different parental genes were obtained, including C4orf19, ARHGAP24 DNASE1L3, P4HA1, SLC39A14 and TAF1D. These 6 genes could not be the independent prognostic signatures, but the expression of these genes was closely related to immune cells infiltration and the expression of immune checkpoints. Finally, we got aberrant 52 splicing factors regulating riskScore-related AS events.ConclusionOur study discovered that overall survival-related AS events mediated by aberrant splicing factors can be constructed a prognostic risk model to predict prognosis of patiens and utilized to index the situation of immune cell infiltration and immune checkpoint expression that impact tumor immune microenvironment in ccRCC.

scholarly journals Role of a Pyroptosis-Related lncRNA Signature in Risk Stratification and Immunotherapy of Ovarian Cancer

2022 ◽  
Vol 8 ◽  
Author(s):  
Zeyu Zhang ◽  
Zhijie Xu ◽  
Yuanliang Yan
Keyword(s):  
Ovarian Cancer ◽  
Risk Stratification ◽  
Survival Data ◽  
Risk Model ◽  
Cox Regression ◽  
Roc Curves ◽  
Immune Checkpoints ◽  
Random Allocation ◽  
Cox Regression Analysis

Background: Pyroptosis is a newly recognized form of cell death. Emerging evidence has suggested the crucial role of long non-coding RNAs (lncRNAs) in the tumorigenesis and progression of ovarian cancer (OC). However, there is still poor understanding of pyroptosis-related lncRNAs in OC.Methods: The TCGA database was accessed for gene expression and clinical data of 377 patients with OC. Two cohorts for training and validation were established by random allocation. Correlation analysis and Cox regression analysis were performed to identify pyroptosis-related lncRNAs and construct a risk model.Results: Six pyroptosis-related lncRNAs were included in the final signature with unfavorable survival data. Subsequent ROC curves showed promising predictive value of patient prognosis. Further multivariate regression analyses confirmed the signature as an independent risk factor in the training (HR: 2.242, 95% CI: 1.598–3.145) and validation (HR: 1.884, 95% CI: 1.204–2.95) cohorts. A signature-based nomogram was also established with a C-index of.684 (95% CI: 0.662–0.705). Involvement of the identified signature in multiple immune-related pathways was revealed by functional analysis. Moreover, the signature was also associated with higher expression of three immune checkpoints (PD-1, B7-H3, and VSIR), suggesting the potential of the signature as an indicator for OC immunotherapies.Conclusion: This study suggests that the identified pyroptosis-related lncRNA signature and signature-based nomogram may serve as methods for risk stratification of OC. The signature is also associated with the tumor immune microenvironment, potentially providing an indicator for patient selection of immunotherapy in OC.

scholarly journals Comprehensive Landscape of Ovarian Cancer Immune Microenvironment Based on Integrated Multi-Omics Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiacheng Shen ◽  
Tingwei Liu ◽  
Qiaoli Bei ◽  
Shaohua Xu
Keyword(s):  
Ovarian Cancer ◽  
Cancer Patients ◽  
Copy Number Variant ◽  
The Cancer Genome Atlas ◽  
Epigenetic Therapy ◽  
Molecular Characteristics ◽  
Immune Microenvironment ◽  
Combination Strategies ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Epithelial ovarian cancer has a low response rate to immunotherapy and a complex immune microenvironment that regulates its treatment outcomes. Understanding the immune microenvironment and its molecular basis is of great clinical significance in the effort to improve immunotherapy response and outcomes. To determine the characteristics of the immune microenvironment in ovarian cancer, we stratified ovarian cancer patients into three immune subtypes (C1, C2, and C3) using immune-related genes based on gene expression data from The Cancer Genome Atlas and found that these three subtypes had significant differences in immune characteristics and prognosis. Methylation and copy number variant analysis showed that the immune checkpoint genes that influenced immune response were significantly hypermethylated and highly deleted in the immunosuppressive C3 subtype, suggesting that epigenetic therapy may be able to reverse the efficacy of immunotherapy. In addition, the mutation frequencies of BRCA2 and CDK12 were significantly higher in the C2 subtype than in the other two subtypes, suggesting that mutation of DNA repair-related genes significantly affects the prognosis of ovarian cancer patients. Our study further elucidated the molecular characteristics of the immune microenvironment of ovarian cancer, which providing an effective hierarchical method for the immunotherapy of ovarian cancer patients, and has clinical relevance to the design of new immunotherapies and a reasonable combination strategies.

scholarly journals Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xingyu Chen ◽  
Hua Lan ◽  
Dong He ◽  
Zhanwang Wang ◽  
Runshi Xu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Signaling Pathways ◽  
Risk Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Clinical Importance ◽  
Clinical Samples ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment ◽  
Cluster A

Ovarian cancer (OC) is one of the most lethal gynecologic malignant tumors. The interaction between autophagy and the tumor immune microenvironment has clinical importance. Hence, it is necessary to explore reliable biomarkers associated with autophagy-related genes (ARGs) for risk stratification in OC. Here, we obtained ARGs from the MSigDB database and downloaded the expression profile of OC from TCGA database. The k-means unsupervised clustering method was used for clustering, and two subclasses of OC (cluster A and cluster B) were identified. SsGSEA method was used to quantify the levels of infiltration of 24 subtypes of immune cells. Metascape and GSEA were performed to reveal the differential gene enrichment in signaling pathways and cellular processes of the subtypes. We found that patients in cluster A were significantly associated with higher immune infiltration and immune-associated signaling pathways. Then, we established a risk model by LASSO Cox regression. ROC analysis and Kaplan-Meier analysis were applied for evaluating the efficiency of the risk signature, patients with low-risk got better outcomes than those with high-risk in overall survival. Finally, ULK2 and GABARAPL1 expression was further validated in clinical samples. In conclusion, Our study constructed an autophagy-related prognostic indicator, and identified two promising targets in OC.

scholarly journals Comprehensive Analysis of the Expression, Prognostic Value and Immune Microenvironment of CMTM Family Members in Ovarian Cancer

2021 ◽  
Author(s):  
Mengjun Zhang ◽  
Yue Yin ◽  
Zhenxing Sun ◽  
Yuan Liu ◽  
Yiru Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Prognostic Value ◽  
Immune Cell ◽  
Progression Free Survival ◽  
Genetic Alterations ◽  
High Expression ◽  
Immune Checkpoints ◽  
Immune Microenvironment ◽  
Protein Levels

Abstract Background: Ovarian cancer (OV) is one of the most common gynecological malignancies worldwide, and its immunotherapy has considerable prospects. Multiple members of the CMTM family were aberrantly expressed in human cancers and controled key malignant biological processes and immune regulation in cancer development. However, little is known about the function of this gene family in ovarian cancer, especially in terms of immunity.Methods: GEPIA, Oncomine, HPA, Kaplan-Meier plotter, cBioPortal, GeneMANIA and TIMER were used to analyze the differential gene expression, prognostic value, genetic alterations and alterations in the immune microenvironment of the CMTM family in patients with ovarian cancer. Importantly, RT-qPCR was used to verify the gene expression of the CMTM family.Results: CMTM1/3/4/6/7/8 showed abnormally high expression at the mRNA and protein levels in OV tissues based on the GEPIA and HPA databases. RT-qPCR showed that CMTM1/6/8 was highly expressed in ovarian cancer cell lines. Survival analysis showed that high expression of CMTM1/2/3/5/8 can lead to a significant reduction in overall survival and progression-free survival. There were many types of genetic alterations in the CMTM family. And CMTM1/2/3/6 had a certain correlation with the changes of immune microenvironment such as immune cell infiltration and immune checkpoint expression, which may be the potential mechanism of the CMTM family in ovarian cancer.Conclusion: This study confirmed that the CMTM family has abnormal expression in ovarian cancer and can be used as a biomarker for prognostic evaluation. And the CMTM family may be used as a potential target for immunotherapy based on the suppression of immune checkpoints.

scholarly journals Comprehensive Analysis of Tumor Immune Microenvironment and Prognosis of m6A-Related lncRNAs in Gastric Cancer

2021 ◽  
Author(s):  
Yi Wang ◽  
Gui-Qi Zhu ◽  
Di Tian ◽  
Chang-Wu Zhou ◽  
Na Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Model ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Immune Checkpoints ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Abstract Background N6-methyladenosine (m6A) modification and long non-coding RNAs (lncRNAs) play pivotal role in gastric cancer (GC) progression. The emergence of immunotherapy in GC has created a paradigm shift in the approach of treatment, whereas there is significant heterogeneity with regard to degree of treatment responses, which results from the variability of tumor immune microenvironment (TIME). How the interplay between them enrolled in the shaping of TIME remains unclear. Methods The RNA sequencing and clinical data of GC patients were collected from TCGA database. Pearson correlation test and univariate Cox analysis were used to screen out m6A-related lncRNAs. Consensus clustering was implemented to classify GC patients into 2 subtypes. Survival analysis, the infiltration of immune cells, Gene set enrichment analysis (GSEA) and the mutation profiles were analyzed and compared between two clusters. Then least absolute shrinkage and selection operator (LASSO) COX regression was implemented to select pivotal genes and risk score model was constructed accordingly. The prognosis value of the risk model was explored. In addition, the discrepancies of response to immune checkpoints inhibitor (ICIs) therapy were compared between different risk groups. Finally, we performed qRT-PCR to detect the expression pattern in 35 tumor tissues and paired adjacent normal tissue, and validated the prognostic value of risk model in the our cohort (N=35). Results The expression profiles of 23 lncRNAs were included to cluster patients into different subtypes. Cluster1 with worse prognosis harbored higher immune score, stromal score, ESTIMATE score and mutation rate of genes. Different immune cell infiltration pattern were also displayed between different clusters. GSEA showed that cluster1 was preferentially enriched with tumor hallmarks and tumor correlated biological pathways. Next, 9 lncRNAs were selected by LASSO regression model to construct risk model. Patients in the high risk group had poor prognosis. The prognosis value of this model was also validated in our cohort. As for predicting responses to the ICIs therapy, we found that patients from high risk group had lower TMB score and lower proportion of MSI-H subtype. Moreover, patients had distinct immunophenoscores in different risk groups. Conclusion Our study revealed that the potential interplay between m6A modification and lncRNAs might have critical role in predicting GC prognosis, sculpting TIME landscape and predicting the responses to immune checkpoints inhibitors therapy.

scholarly journals Model constructions of chemosensitivity and prognosis of high grade serous ovarian cancer based on evaluation of immune microenvironment and immune response

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Han Zhang ◽  
Yijun Wu ◽  
Hao Li ◽  
Liping Sun ◽  
Xiangkai Meng
Keyword(s):  
Ovarian Cancer ◽  
Immune Response ◽  
Prediction Models ◽  
Interaction Network ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Immune Microenvironment ◽  
Chemotherapy Sensitivity ◽  
Protein Protein Interaction ◽  
Sensitivity Score

Abstract Background The prognosis of high grade serous ovarian cancer (HGSOC) patients is closely related to the immune microenvironment and immune response. Based on this, the purpose of this study was to construct a model to predict chemosensitivity and prognosis, and provide novel biomarkers for immunotherapy and prognosis evaluation of HGSOC. Methods GSE40595 (38 samples), GSE18520 (63 samples), GSE26712 (195 samples), TCGA (321 samples) and GTEx (88 samples) were integrated to screen differential expressed genes (DEGs) of HGSOC. The prognosis related DEGs (DEPGs) were screened through overall survival analysis. The DEGs-encoded protein–protein interaction network was constructed and hub genes of DEPGs (DEPHGs) were generated by STRING. Immune characteristics of the samples were judged by ssGSEA, ESTIMATE and CYBERSORT. TIMER was used to analyze the relationship between DEPHGs and tumor-infiltrating immunocytes, as well as the immune checkpoint genes, finally immune-related DEPHGs (IDEPHGs) were determined, and whose expression in 12 pairs of HGSOC tissues and tumor-adjacent tissues were analyzed by histological verification. Furthermore, the chemosensitivity genes in IDEPHGs were screened according to GSE15622 (n = 65). Finally, two prediction models of paclitaxel sensitivity score (PTX score) and carboplatin sensitivity score (CBP score) were constructed by lasso algorithm. The area under curve was calculated to estimate the accuracy of candidate gene models in evaluating chemotherapy sensitivity. Results 491 DEGs were screened and 37 DEGs were identified as DEPGs, and 11 DEPHGs were further identified. Among them, CXCL13, IDO1, PI3, SPP1 and TRIM22 were screened as IDEPHGs and verified in the human tissues. Further analysis showed that IDO1, PI3 and TRIM22 could independently affect the chemotherapy sensitivity of HGSOC patients. The PTX score was significantly better than TRIM22, PI3, SPP1, IDO1 and CXCL13 in predicting paclitaxel sensitivity, so was CBP score in predicting carboplatin sensitivity. What’s more, both of the HGSOC patients with high PTX score or high CBP score had longer survival time. Conclusions Five IDEPHGs identified through comprehensive bioinformatics analysis were closely related with the prognosis, immune microenvironment and chemotherapy sensitivity of HGSOC. Two prediction models based on IDEPHGs might have potential application of chemotherapy sensitivity and prognosis for patients with HGSOC.

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