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

Table4_A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis.XLSX

Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited.Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts.Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.</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_9_Crosstalk of RNA Adenosine Modification-Related Subtypes, Establishment of a Prognostic Model, and Immune Infiltration Characteristics in Ovarian Cancer.tif

BackgroundFour RNA adenosine modifications, including m6A, m1A, alternative polyadenylation, and adenosine-to-inosine RNA editing, have been identified as potentially valuable in influencing colorectal carcinogenesis, immune infiltration, and response to drug therapy. However, the regulatory mechanisms and clinical significance of these four RNA modifications in ovarian cancer (OC) remain unknown.MethodsWe comprehensively described the transcriptional and genetic modifications of 26 RNA modification “writers” in OC and assessed the expression patterns. We identified two RNA modification subtypes using an unsupervised clustering approach. Subsequently, using differentially expressed genes (DEGs) in both subtypes, we calculated RNA modification “writer” scores (RMW scores) to characterize the RNA modifications of single OC patients. RMW score-related gene expression was investigated by qRT-PCR. We explored the correlation between RMW score and clinical features, immune infiltration, and drug sensitivity. We drew a nomogram to more intuitively and accurately describe the application value of the RMW score.ResultsWe found that molecular alterations in “writers” are strongly related to prognostic and immune-infiltrating features in OC patients. We identified two different clusters of RNA modifications. According to the immune infiltration characteristics in the two RNA modification isoforms, cluster A and cluster B can correspond to “hot” and “cold” tumors, respectively. With the median RMW score, we classified the patients into high- and low-score subgroups. A low RMW score was associated with good patient prognosis and lower immune infiltration. In addition, a low RMW score equated with a higher cancer stem cell index and a lower tumor mutation burden, which to some extent affected the sensitivity of patients to therapeutic drugs. Seven RMW score-related gene expressions were investigated by qRT-PCR in three OC cell lines. Compared to previously known models, our established RMW score has higher accuracy in predicting patient survival.ConclusionA comprehensive analysis of four RNA modification patterns in OC reveals their potential value in OC prognosis, immune microenvironment, and drug sensitivity. These results could deepen our knowledge of RNA modification and yield fresh insights for new personalized therapeutic strategies.</p

Image1_A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis.TIF

Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited.Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts.Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.</p

Image3_A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis.TIF

Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited.Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts.Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.</p

Table1_A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis.XLSX

Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited.Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts.Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.</p

Table_1_Crosstalk of RNA Adenosine Modification-Related Subtypes, Establishment of a Prognostic Model, and Immune Infiltration Characteristics in Ovarian Cancer.docx

BackgroundFour RNA adenosine modifications, including m6A, m1A, alternative polyadenylation, and adenosine-to-inosine RNA editing, have been identified as potentially valuable in influencing colorectal carcinogenesis, immune infiltration, and response to drug therapy. However, the regulatory mechanisms and clinical significance of these four RNA modifications in ovarian cancer (OC) remain unknown.MethodsWe comprehensively described the transcriptional and genetic modifications of 26 RNA modification “writers” in OC and assessed the expression patterns. We identified two RNA modification subtypes using an unsupervised clustering approach. Subsequently, using differentially expressed genes (DEGs) in both subtypes, we calculated RNA modification “writer” scores (RMW scores) to characterize the RNA modifications of single OC patients. RMW score-related gene expression was investigated by qRT-PCR. We explored the correlation between RMW score and clinical features, immune infiltration, and drug sensitivity. We drew a nomogram to more intuitively and accurately describe the application value of the RMW score.ResultsWe found that molecular alterations in “writers” are strongly related to prognostic and immune-infiltrating features in OC patients. We identified two different clusters of RNA modifications. According to the immune infiltration characteristics in the two RNA modification isoforms, cluster A and cluster B can correspond to “hot” and “cold” tumors, respectively. With the median RMW score, we classified the patients into high- and low-score subgroups. A low RMW score was associated with good patient prognosis and lower immune infiltration. In addition, a low RMW score equated with a higher cancer stem cell index and a lower tumor mutation burden, which to some extent affected the sensitivity of patients to therapeutic drugs. Seven RMW score-related gene expressions were investigated by qRT-PCR in three OC cell lines. Compared to previously known models, our established RMW score has higher accuracy in predicting patient survival.ConclusionA comprehensive analysis of four RNA modification patterns in OC reveals their potential value in OC prognosis, immune microenvironment, and drug sensitivity. These results could deepen our knowledge of RNA modification and yield fresh insights for new personalized therapeutic strategies.</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_5_Crosstalk of RNA Adenosine Modification-Related Subtypes, Establishment of a Prognostic Model, and Immune Infiltration Characteristics in Ovarian Cancer.tif

BackgroundFour RNA adenosine modifications, including m6A, m1A, alternative polyadenylation, and adenosine-to-inosine RNA editing, have been identified as potentially valuable in influencing colorectal carcinogenesis, immune infiltration, and response to drug therapy. However, the regulatory mechanisms and clinical significance of these four RNA modifications in ovarian cancer (OC) remain unknown.MethodsWe comprehensively described the transcriptional and genetic modifications of 26 RNA modification “writers” in OC and assessed the expression patterns. We identified two RNA modification subtypes using an unsupervised clustering approach. Subsequently, using differentially expressed genes (DEGs) in both subtypes, we calculated RNA modification “writer” scores (RMW scores) to characterize the RNA modifications of single OC patients. RMW score-related gene expression was investigated by qRT-PCR. We explored the correlation between RMW score and clinical features, immune infiltration, and drug sensitivity. We drew a nomogram to more intuitively and accurately describe the application value of the RMW score.ResultsWe found that molecular alterations in “writers” are strongly related to prognostic and immune-infiltrating features in OC patients. We identified two different clusters of RNA modifications. According to the immune infiltration characteristics in the two RNA modification isoforms, cluster A and cluster B can correspond to “hot” and “cold” tumors, respectively. With the median RMW score, we classified the patients into high- and low-score subgroups. A low RMW score was associated with good patient prognosis and lower immune infiltration. In addition, a low RMW score equated with a higher cancer stem cell index and a lower tumor mutation burden, which to some extent affected the sensitivity of patients to therapeutic drugs. Seven RMW score-related gene expressions were investigated by qRT-PCR in three OC cell lines. Compared to previously known models, our established RMW score has higher accuracy in predicting patient survival.ConclusionA comprehensive analysis of four RNA modification patterns in OC reveals their potential value in OC prognosis, immune microenvironment, and drug sensitivity. These results could deepen our knowledge of RNA modification and yield fresh insights for new personalized therapeutic strategies.</p