Image_5_Construction of an Immune Cell Infiltration Score to Evaluate the Prognosis and Therapeutic Efficacy of Ovarian Cancer Patients.tif

BackgroundOvarian cancer (OC) is an immunogenetic disease that contains tumor-infiltrating lymphocytes (TILs), and immunotherapy has become a novel treatment for OC. With the development of next-generation sequencing (NGS), profiles of gene expression and comprehensive landscape of immune cells can be applied to predict clinical outcome and response to immunotherapy.MethodsWe obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and applied two computational algorithms (CIBERSORT and ESTIMATE) for consensus clustering of immune cells. Patients were divided into two subtypes using immune cell infiltration (ICI) levels. Then, differentially expressed genes (DEGs) associated with immune cell infiltration (ICI) level were identified. We also constructed ICI score after principle-component analysis (PCA) for dimension reduction.ResultsPatients in ICI cluster B had better survival than those in ICI cluster A. After construction of ICI score, we found that high ICI score had better clinical OS and significantly higher tumor mutation burden (TMB). According to the expression of immune checkpoints, the results showed that patients in high ICI group showed high expression of CTLA4, PD1, PD-L1, and PD-L2, which implies that they might benefit from immunotherapy. Besides, patients in high ICI group showed higher sensitivity to two first-line chemotherapy drugs (Paclitaxel and Cisplatin).ConclusionICI score is an effective prognosis-related biomarker for OC and can provide valuable information on the potential response to immunotherapy.</p

Image_3_Construction of an Immune Cell Infiltration Score to Evaluate the Prognosis and Therapeutic Efficacy of Ovarian Cancer Patients.tif

BackgroundOvarian cancer (OC) is an immunogenetic disease that contains tumor-infiltrating lymphocytes (TILs), and immunotherapy has become a novel treatment for OC. With the development of next-generation sequencing (NGS), profiles of gene expression and comprehensive landscape of immune cells can be applied to predict clinical outcome and response to immunotherapy.MethodsWe obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and applied two computational algorithms (CIBERSORT and ESTIMATE) for consensus clustering of immune cells. Patients were divided into two subtypes using immune cell infiltration (ICI) levels. Then, differentially expressed genes (DEGs) associated with immune cell infiltration (ICI) level were identified. We also constructed ICI score after principle-component analysis (PCA) for dimension reduction.ResultsPatients in ICI cluster B had better survival than those in ICI cluster A. After construction of ICI score, we found that high ICI score had better clinical OS and significantly higher tumor mutation burden (TMB). According to the expression of immune checkpoints, the results showed that patients in high ICI group showed high expression of CTLA4, PD1, PD-L1, and PD-L2, which implies that they might benefit from immunotherapy. Besides, patients in high ICI group showed higher sensitivity to two first-line chemotherapy drugs (Paclitaxel and Cisplatin).ConclusionICI score is an effective prognosis-related biomarker for OC and can provide valuable information on the potential response to immunotherapy.</p

Image_6_Construction of an Immune Cell Infiltration Score to Evaluate the Prognosis and Therapeutic Efficacy of Ovarian Cancer Patients.tiff

BackgroundOvarian cancer (OC) is an immunogenetic disease that contains tumor-infiltrating lymphocytes (TILs), and immunotherapy has become a novel treatment for OC. With the development of next-generation sequencing (NGS), profiles of gene expression and comprehensive landscape of immune cells can be applied to predict clinical outcome and response to immunotherapy.MethodsWe obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and applied two computational algorithms (CIBERSORT and ESTIMATE) for consensus clustering of immune cells. Patients were divided into two subtypes using immune cell infiltration (ICI) levels. Then, differentially expressed genes (DEGs) associated with immune cell infiltration (ICI) level were identified. We also constructed ICI score after principle-component analysis (PCA) for dimension reduction.ResultsPatients in ICI cluster B had better survival than those in ICI cluster A. After construction of ICI score, we found that high ICI score had better clinical OS and significantly higher tumor mutation burden (TMB). According to the expression of immune checkpoints, the results showed that patients in high ICI group showed high expression of CTLA4, PD1, PD-L1, and PD-L2, which implies that they might benefit from immunotherapy. Besides, patients in high ICI group showed higher sensitivity to two first-line chemotherapy drugs (Paclitaxel and Cisplatin).ConclusionICI score is an effective prognosis-related biomarker for OC and can provide valuable information on the potential response to immunotherapy.</p

Image_2_Construction of an Immune Cell Infiltration Score to Evaluate the Prognosis and Therapeutic Efficacy of Ovarian Cancer Patients.tif

BackgroundOvarian cancer (OC) is an immunogenetic disease that contains tumor-infiltrating lymphocytes (TILs), and immunotherapy has become a novel treatment for OC. With the development of next-generation sequencing (NGS), profiles of gene expression and comprehensive landscape of immune cells can be applied to predict clinical outcome and response to immunotherapy.MethodsWe obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and applied two computational algorithms (CIBERSORT and ESTIMATE) for consensus clustering of immune cells. Patients were divided into two subtypes using immune cell infiltration (ICI) levels. Then, differentially expressed genes (DEGs) associated with immune cell infiltration (ICI) level were identified. We also constructed ICI score after principle-component analysis (PCA) for dimension reduction.ResultsPatients in ICI cluster B had better survival than those in ICI cluster A. After construction of ICI score, we found that high ICI score had better clinical OS and significantly higher tumor mutation burden (TMB). According to the expression of immune checkpoints, the results showed that patients in high ICI group showed high expression of CTLA4, PD1, PD-L1, and PD-L2, which implies that they might benefit from immunotherapy. Besides, patients in high ICI group showed higher sensitivity to two first-line chemotherapy drugs (Paclitaxel and Cisplatin).ConclusionICI score is an effective prognosis-related biomarker for OC and can provide valuable information on the potential response to immunotherapy.</p

Image_1_Construction of an Immune Cell Infiltration Score to Evaluate the Prognosis and Therapeutic Efficacy of Ovarian Cancer Patients.tiff

BackgroundOvarian cancer (OC) is an immunogenetic disease that contains tumor-infiltrating lymphocytes (TILs), and immunotherapy has become a novel treatment for OC. With the development of next-generation sequencing (NGS), profiles of gene expression and comprehensive landscape of immune cells can be applied to predict clinical outcome and response to immunotherapy.MethodsWe obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and applied two computational algorithms (CIBERSORT and ESTIMATE) for consensus clustering of immune cells. Patients were divided into two subtypes using immune cell infiltration (ICI) levels. Then, differentially expressed genes (DEGs) associated with immune cell infiltration (ICI) level were identified. We also constructed ICI score after principle-component analysis (PCA) for dimension reduction.ResultsPatients in ICI cluster B had better survival than those in ICI cluster A. After construction of ICI score, we found that high ICI score had better clinical OS and significantly higher tumor mutation burden (TMB). According to the expression of immune checkpoints, the results showed that patients in high ICI group showed high expression of CTLA4, PD1, PD-L1, and PD-L2, which implies that they might benefit from immunotherapy. Besides, patients in high ICI group showed higher sensitivity to two first-line chemotherapy drugs (Paclitaxel and Cisplatin).ConclusionICI score is an effective prognosis-related biomarker for OC and can provide valuable information on the potential response to immunotherapy.</p

Image_7_Construction of an Immune Cell Infiltration Score to Evaluate the Prognosis and Therapeutic Efficacy of Ovarian Cancer Patients.tif

BackgroundOvarian cancer (OC) is an immunogenetic disease that contains tumor-infiltrating lymphocytes (TILs), and immunotherapy has become a novel treatment for OC. With the development of next-generation sequencing (NGS), profiles of gene expression and comprehensive landscape of immune cells can be applied to predict clinical outcome and response to immunotherapy.MethodsWe obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and applied two computational algorithms (CIBERSORT and ESTIMATE) for consensus clustering of immune cells. Patients were divided into two subtypes using immune cell infiltration (ICI) levels. Then, differentially expressed genes (DEGs) associated with immune cell infiltration (ICI) level were identified. We also constructed ICI score after principle-component analysis (PCA) for dimension reduction.ResultsPatients in ICI cluster B had better survival than those in ICI cluster A. After construction of ICI score, we found that high ICI score had better clinical OS and significantly higher tumor mutation burden (TMB). According to the expression of immune checkpoints, the results showed that patients in high ICI group showed high expression of CTLA4, PD1, PD-L1, and PD-L2, which implies that they might benefit from immunotherapy. Besides, patients in high ICI group showed higher sensitivity to two first-line chemotherapy drugs (Paclitaxel and Cisplatin).ConclusionICI score is an effective prognosis-related biomarker for OC and can provide valuable information on the potential response to immunotherapy.</p

Image_4_Construction of an Immune Cell Infiltration Score to Evaluate the Prognosis and Therapeutic Efficacy of Ovarian Cancer Patients.tif

BackgroundOvarian cancer (OC) is an immunogenetic disease that contains tumor-infiltrating lymphocytes (TILs), and immunotherapy has become a novel treatment for OC. With the development of next-generation sequencing (NGS), profiles of gene expression and comprehensive landscape of immune cells can be applied to predict clinical outcome and response to immunotherapy.MethodsWe obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and applied two computational algorithms (CIBERSORT and ESTIMATE) for consensus clustering of immune cells. Patients were divided into two subtypes using immune cell infiltration (ICI) levels. Then, differentially expressed genes (DEGs) associated with immune cell infiltration (ICI) level were identified. We also constructed ICI score after principle-component analysis (PCA) for dimension reduction.ResultsPatients in ICI cluster B had better survival than those in ICI cluster A. After construction of ICI score, we found that high ICI score had better clinical OS and significantly higher tumor mutation burden (TMB). According to the expression of immune checkpoints, the results showed that patients in high ICI group showed high expression of CTLA4, PD1, PD-L1, and PD-L2, which implies that they might benefit from immunotherapy. Besides, patients in high ICI group showed higher sensitivity to two first-line chemotherapy drugs (Paclitaxel and Cisplatin).ConclusionICI score is an effective prognosis-related biomarker for OC and can provide valuable information on the potential response to immunotherapy.</p

Image_8_Construction of an Immune Cell Infiltration Score to Evaluate the Prognosis and Therapeutic Efficacy of Ovarian Cancer Patients.tif

BackgroundOvarian cancer (OC) is an immunogenetic disease that contains tumor-infiltrating lymphocytes (TILs), and immunotherapy has become a novel treatment for OC. With the development of next-generation sequencing (NGS), profiles of gene expression and comprehensive landscape of immune cells can be applied to predict clinical outcome and response to immunotherapy.MethodsWe obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and applied two computational algorithms (CIBERSORT and ESTIMATE) for consensus clustering of immune cells. Patients were divided into two subtypes using immune cell infiltration (ICI) levels. Then, differentially expressed genes (DEGs) associated with immune cell infiltration (ICI) level were identified. We also constructed ICI score after principle-component analysis (PCA) for dimension reduction.ResultsPatients in ICI cluster B had better survival than those in ICI cluster A. After construction of ICI score, we found that high ICI score had better clinical OS and significantly higher tumor mutation burden (TMB). According to the expression of immune checkpoints, the results showed that patients in high ICI group showed high expression of CTLA4, PD1, PD-L1, and PD-L2, which implies that they might benefit from immunotherapy. Besides, patients in high ICI group showed higher sensitivity to two first-line chemotherapy drugs (Paclitaxel and Cisplatin).ConclusionICI score is an effective prognosis-related biomarker for OC and can provide valuable information on the potential response to immunotherapy.</p

Image_1_Comprehensive of N1-Methyladenosine Modifications Patterns and Immunological Characteristics in Ovarian Cancer.eps

Backgroundrecently, many researches have concentrated on the relevance between N1-methyladenosine (m1A) methylation modifications and tumor progression and prognosis. However, it remains unknown whether m1A modification has an effect in the prognosis of ovarian cancer (OC) and its immune infiltration.MethodsBased on 10 m1A modulators, we comprehensively assessed m1A modification patterns in 474 OC patients and linked them to TME immune infiltration characteristics. m1Ascore computed with principal component analysis algorithm was applied to quantify m1A modification pattern in OC patients. m1A regulators protein and mRNA expression were respectively obtained by HPA website and RT-PCR in clinical OC and normal samples.ResultsWe finally identified three different m1A modification patterns. The immune infiltration features of these m1A modification patterns correspond to three tumor immune phenotypes, including immune-desert, immune-inflamed and immune-excluded phenotypes. The results demonstrate individual tumor m1A modification patterns can predict patient survival, stage and grade. The m1Ascore was calculated to quantify individual OC patient’s m1A modification pattern. A high m1Ascore is usually accompanied by a better survival advantage and a lower mutational load. Research on m1Ascore in the treatment of OC patients showed that patients with high m1Ascore showed marked therapeutic benefits and clinical outcomes in terms of chemotherapy and immunotherapy. Lastly, we obtained four small molecule drugs that may potentially ameliorate prognosis.ConclusionThis research demonstrates that m1A methylation modification makes an essential function in the prognosis of OC and in shaping the immune microenvironment. Comprehensive evaluation of m1A modifications improves our knowledge of immune infiltration profile and provides a more efficient individualized immunotherapy strategy for OC patients.</p

Image_4_Comprehensive of N1-Methyladenosine Modifications Patterns and Immunological Characteristics in Ovarian Cancer.eps

Backgroundrecently, many researches have concentrated on the relevance between N1-methyladenosine (m1A) methylation modifications and tumor progression and prognosis. However, it remains unknown whether m1A modification has an effect in the prognosis of ovarian cancer (OC) and its immune infiltration.MethodsBased on 10 m1A modulators, we comprehensively assessed m1A modification patterns in 474 OC patients and linked them to TME immune infiltration characteristics. m1Ascore computed with principal component analysis algorithm was applied to quantify m1A modification pattern in OC patients. m1A regulators protein and mRNA expression were respectively obtained by HPA website and RT-PCR in clinical OC and normal samples.ResultsWe finally identified three different m1A modification patterns. The immune infiltration features of these m1A modification patterns correspond to three tumor immune phenotypes, including immune-desert, immune-inflamed and immune-excluded phenotypes. The results demonstrate individual tumor m1A modification patterns can predict patient survival, stage and grade. The m1Ascore was calculated to quantify individual OC patient’s m1A modification pattern. A high m1Ascore is usually accompanied by a better survival advantage and a lower mutational load. Research on m1Ascore in the treatment of OC patients showed that patients with high m1Ascore showed marked therapeutic benefits and clinical outcomes in terms of chemotherapy and immunotherapy. Lastly, we obtained four small molecule drugs that may potentially ameliorate prognosis.ConclusionThis research demonstrates that m1A methylation modification makes an essential function in the prognosis of OC and in shaping the immune microenvironment. Comprehensive evaluation of m1A modifications improves our knowledge of immune infiltration profile and provides a more efficient individualized immunotherapy strategy for OC patients.</p