Timed-averaged blood pressure showed a J-curve association with stroke in elderly chronic kidney disease patients

The risk factors for stroke in elderly patients with chronic kidney disease (CKD) are not well understood. This study aimed to explore the influence of systolic blood pressure (SBP) on the risk of stroke in a large cohort of elderly patients with stage 3–5 CKD. We retrospectively identified 665 patients hospitalized in Beijing Friendship Hospital from January 2000 to December 2021. Patients were followed up until the occurrence of stroke or death. Multivariate logistic regression analysis and Cox proportional hazard models were used to analyze the risk factors for stroke according to the presence or absence of CKD. The association between CKD and stroke was further evaluated regarding the role of SBP in the hypertensive population. In individuals with CKD, a J-shaped relationship was observed between SBP levels and the risk of stroke. Participants with CKD and an SBP less than 125 mmHg had a significantly higher cumulative stroke survival rate than those whose SBP was between 125 and 139 mmHg. The cumulative stroke survival rate increased progressively for those with SBP higher than 140 mmHg. This J-shaped relationship was not found in patients without CKD. In elderly patients with CKD, those with the lowest BP are at increased risk for incident stroke. This phenomenon could be different from that in the general population.</p

Table_2_Hepatitis C virus infection is associated with high risk of breast cancer: a pooled analysis of 68,014 participants.doc

IntroductionBreast cancer is the most common malignancy among women. Previous studies had shown that hepatitis C virus (HCV) infection might serve as a risk factor for breast cancer, while some studies failed to find such an association.MethodsIn this study, we presented a first attempt to capture and clarify this clinical debate via a cumulative analysis (registration ID: CRD42023445888). ResultsAfter systematically searching and excluding the irrelevant publications, five case-control or cohort studies were finally included. The synthetic effect from the eligible studies showed that patients with HCV infection had a significantly higher prevalence of breast cancer than non-HCV infected general population (combined HR= 1.382, 95%CI: 1.129 to 1.692, P=0.002). There was no evidence of statistical heterogeneity during this pooled analysis (I2 = 13.2%, P=0.33). The sensitivity analyses confirmed the above findings. No significant publication bias was observed among the included studies. The underlying pathophysiological mechanisms for this relationship might be associated with persistent infection/inflammation, host immune response, and the modulation of HCV-associated gene expression. DiscussionThough the causal association between HCV infection and breast cancer did not seem quite as strong, screening for HCV might enable the early detection of breast cancer and help to prevent the progression of the disease. Since the topic of this study remains a matter of clinical debate, further studies are still warranted to validate this potential association.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023445888</p

Table_1_Hepatitis C virus infection is associated with high risk of breast cancer: a pooled analysis of 68,014 participants.docx

IntroductionBreast cancer is the most common malignancy among women. Previous studies had shown that hepatitis C virus (HCV) infection might serve as a risk factor for breast cancer, while some studies failed to find such an association.MethodsIn this study, we presented a first attempt to capture and clarify this clinical debate via a cumulative analysis (registration ID: CRD42023445888). ResultsAfter systematically searching and excluding the irrelevant publications, five case-control or cohort studies were finally included. The synthetic effect from the eligible studies showed that patients with HCV infection had a significantly higher prevalence of breast cancer than non-HCV infected general population (combined HR= 1.382, 95%CI: 1.129 to 1.692, P=0.002). There was no evidence of statistical heterogeneity during this pooled analysis (I2 = 13.2%, P=0.33). The sensitivity analyses confirmed the above findings. No significant publication bias was observed among the included studies. The underlying pathophysiological mechanisms for this relationship might be associated with persistent infection/inflammation, host immune response, and the modulation of HCV-associated gene expression. DiscussionThough the causal association between HCV infection and breast cancer did not seem quite as strong, screening for HCV might enable the early detection of breast cancer and help to prevent the progression of the disease. Since the topic of this study remains a matter of clinical debate, further studies are still warranted to validate this potential association.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023445888</p

sj-tiff-3-evb-10.1177_11769343231212078 – Supplemental material for Multi-omics Analysis of Prognostic Significance and Immune Infiltration of FASTK Family Members in Kidney Renal Clear Cell Carcinoma

Supplemental material, sj-tiff-3-evb-10.1177_11769343231212078 for Multi-omics Analysis of Prognostic Significance and Immune Infiltration of FASTK Family Members in Kidney Renal Clear Cell Carcinoma by Guanghui Zhong, Dali Wu, Haiping Chen, Lingfei Yan, Qi Xiang, Yufeng Liu and Tao Wang in Evolutionary Bioinformatics</p

Table3_Construction and verification of a novel prognostic risk model for kidney renal clear cell carcinoma based on immunity-related genes.XLSX

Background: Currently, there are no useful biomarkers or prognostic risk markers for the diagnosis of kidney renal clear cell carcinoma (KIRC), although recent research has shown that both, the onset and progression of KIRC, are substantially influenced by immune-associated genes (IAGs).Objective: This work aims to create and verify the prognostic value of an immune risk score signature (IRSS) based on IAGs for KIRC using bioinformatics and public databases.Methods: Differentially expressed genes (DEGs) related to the immune systems (IAGs) in KIRC tissues were identified from The Cancer Genome Atlas (TCGA) databases. The DEGs between the tumor and normal tissues were identified using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a prognostic IRSS model was constructed and its prognostic and predictive performance was analyzed using survival analyses and nomograms. Kidney renal papillary cell carcinoma (KIRP) sets were utilized to further validate this model.Results: Six independent immunity-related genes (PAEP, PI3, SAA2, SAA1, IL20RB, and IFI30) correlated with prognosis were identified and used to construct an IRSS model. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer prognoses than those of patients in the low-risk group in both, the verification set (p Conclusion: This work investigated the association between immune infiltration, immunity-related gene expression, and severity of KIRC to construct and verify a prognostic risk model for KIRC and KIRP.</p

Table2_Construction and verification of a novel prognostic risk model for kidney renal clear cell carcinoma based on immunity-related genes.XLSX

Background: Currently, there are no useful biomarkers or prognostic risk markers for the diagnosis of kidney renal clear cell carcinoma (KIRC), although recent research has shown that both, the onset and progression of KIRC, are substantially influenced by immune-associated genes (IAGs).Objective: This work aims to create and verify the prognostic value of an immune risk score signature (IRSS) based on IAGs for KIRC using bioinformatics and public databases.Methods: Differentially expressed genes (DEGs) related to the immune systems (IAGs) in KIRC tissues were identified from The Cancer Genome Atlas (TCGA) databases. The DEGs between the tumor and normal tissues were identified using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a prognostic IRSS model was constructed and its prognostic and predictive performance was analyzed using survival analyses and nomograms. Kidney renal papillary cell carcinoma (KIRP) sets were utilized to further validate this model.Results: Six independent immunity-related genes (PAEP, PI3, SAA2, SAA1, IL20RB, and IFI30) correlated with prognosis were identified and used to construct an IRSS model. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer prognoses than those of patients in the low-risk group in both, the verification set (p Conclusion: This work investigated the association between immune infiltration, immunity-related gene expression, and severity of KIRC to construct and verify a prognostic risk model for KIRC and KIRP.</p

Image4_Construction and verification of a novel prognostic risk model for kidney renal clear cell carcinoma based on immunity-related genes.TIFF

Background: Currently, there are no useful biomarkers or prognostic risk markers for the diagnosis of kidney renal clear cell carcinoma (KIRC), although recent research has shown that both, the onset and progression of KIRC, are substantially influenced by immune-associated genes (IAGs).Objective: This work aims to create and verify the prognostic value of an immune risk score signature (IRSS) based on IAGs for KIRC using bioinformatics and public databases.Methods: Differentially expressed genes (DEGs) related to the immune systems (IAGs) in KIRC tissues were identified from The Cancer Genome Atlas (TCGA) databases. The DEGs between the tumor and normal tissues were identified using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a prognostic IRSS model was constructed and its prognostic and predictive performance was analyzed using survival analyses and nomograms. Kidney renal papillary cell carcinoma (KIRP) sets were utilized to further validate this model.Results: Six independent immunity-related genes (PAEP, PI3, SAA2, SAA1, IL20RB, and IFI30) correlated with prognosis were identified and used to construct an IRSS model. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer prognoses than those of patients in the low-risk group in both, the verification set (p Conclusion: This work investigated the association between immune infiltration, immunity-related gene expression, and severity of KIRC to construct and verify a prognostic risk model for KIRC and KIRP.</p

Table8_Construction and verification of a novel prognostic risk model for kidney renal clear cell carcinoma based on immunity-related genes.XLSX

Background: Currently, there are no useful biomarkers or prognostic risk markers for the diagnosis of kidney renal clear cell carcinoma (KIRC), although recent research has shown that both, the onset and progression of KIRC, are substantially influenced by immune-associated genes (IAGs).Objective: This work aims to create and verify the prognostic value of an immune risk score signature (IRSS) based on IAGs for KIRC using bioinformatics and public databases.Methods: Differentially expressed genes (DEGs) related to the immune systems (IAGs) in KIRC tissues were identified from The Cancer Genome Atlas (TCGA) databases. The DEGs between the tumor and normal tissues were identified using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a prognostic IRSS model was constructed and its prognostic and predictive performance was analyzed using survival analyses and nomograms. Kidney renal papillary cell carcinoma (KIRP) sets were utilized to further validate this model.Results: Six independent immunity-related genes (PAEP, PI3, SAA2, SAA1, IL20RB, and IFI30) correlated with prognosis were identified and used to construct an IRSS model. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer prognoses than those of patients in the low-risk group in both, the verification set (p Conclusion: This work investigated the association between immune infiltration, immunity-related gene expression, and severity of KIRC to construct and verify a prognostic risk model for KIRC and KIRP.</p

Table9_Construction and verification of a novel prognostic risk model for kidney renal clear cell carcinoma based on immunity-related genes.XLSX

Background: Currently, there are no useful biomarkers or prognostic risk markers for the diagnosis of kidney renal clear cell carcinoma (KIRC), although recent research has shown that both, the onset and progression of KIRC, are substantially influenced by immune-associated genes (IAGs).Objective: This work aims to create and verify the prognostic value of an immune risk score signature (IRSS) based on IAGs for KIRC using bioinformatics and public databases.Methods: Differentially expressed genes (DEGs) related to the immune systems (IAGs) in KIRC tissues were identified from The Cancer Genome Atlas (TCGA) databases. The DEGs between the tumor and normal tissues were identified using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a prognostic IRSS model was constructed and its prognostic and predictive performance was analyzed using survival analyses and nomograms. Kidney renal papillary cell carcinoma (KIRP) sets were utilized to further validate this model.Results: Six independent immunity-related genes (PAEP, PI3, SAA2, SAA1, IL20RB, and IFI30) correlated with prognosis were identified and used to construct an IRSS model. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer prognoses than those of patients in the low-risk group in both, the verification set (p Conclusion: This work investigated the association between immune infiltration, immunity-related gene expression, and severity of KIRC to construct and verify a prognostic risk model for KIRC and KIRP.</p

Image5_Construction and verification of a novel prognostic risk model for kidney renal clear cell carcinoma based on immunity-related genes.TIFF

Background: Currently, there are no useful biomarkers or prognostic risk markers for the diagnosis of kidney renal clear cell carcinoma (KIRC), although recent research has shown that both, the onset and progression of KIRC, are substantially influenced by immune-associated genes (IAGs).Objective: This work aims to create and verify the prognostic value of an immune risk score signature (IRSS) based on IAGs for KIRC using bioinformatics and public databases.Methods: Differentially expressed genes (DEGs) related to the immune systems (IAGs) in KIRC tissues were identified from The Cancer Genome Atlas (TCGA) databases. The DEGs between the tumor and normal tissues were identified using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a prognostic IRSS model was constructed and its prognostic and predictive performance was analyzed using survival analyses and nomograms. Kidney renal papillary cell carcinoma (KIRP) sets were utilized to further validate this model.Results: Six independent immunity-related genes (PAEP, PI3, SAA2, SAA1, IL20RB, and IFI30) correlated with prognosis were identified and used to construct an IRSS model. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer prognoses than those of patients in the low-risk group in both, the verification set (p Conclusion: This work investigated the association between immune infiltration, immunity-related gene expression, and severity of KIRC to construct and verify a prognostic risk model for KIRC and KIRP.</p