scholarly journals Comprehensive Analysis of m6A RNA Methylation Regulators and the Immune Microenvironment to Aid Immunotherapy in Pancreatic Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Yongdong Guo ◽  
Ronglin Wang ◽  
Junqiang Li ◽  
Yang Song ◽  
Jie Min ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Survival Data ◽  
Immune Cell ◽  
Small Sample Size ◽  
Small Sample ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Consensus Clustering ◽  
Immune Microenvironment ◽  
Gene Set

Pancreatic cancer (PAAD) is one of the most malignant cancers and immune microenvironment has been proved to be involved in pathogenesis of PAAD. m6A modification, related to the expression of m6A regulators, participates in the development of multiple cancers. However, the correlation between m6A regulators and immune microenvironment was largely unknown in PAAD. And because of the small sample size of pancreatic cancer in the TCGA database, it is not enough to draw a convincing conclusion. In the present study, we downloaded seven pancreatic cancer datasets with survival data and removed batch effects among these datasets to be used as the PAAD cohort to analyze the immune landscape of PAAD and the expression pattern of m6A regulators and divided the integrated dataset into cluster 1 and cluster 2 by consensus clustering for m6A regulators. Lower m6A regulators were found to be related to higher immune cell infiltration and a better survival. Moreover, we identified six m6A regulators and constructed the prognostic signature of m6A regulators. Patients with low-risk score had a higher response to immune checkpoint inhibitor and a longer overall survival. To figure out the underlying mechanism, we analyzed the cancer immunity cycle, most altered genes, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) in risk subtypes. In summary, the present study proved m6A regulators modulated the PAAD immune microenvironment. And risk scores served as predictive indicator for immunotherapy and played a prognostic role for PAAD patients. Our study provided novel therapeutic targets to improve immunotherapy efficacy.

scholarly journals Construction of a Novel Signature and Prediction of the Immune Landscape in Soft Tissue Sarcomas Based on N6-Methylandenosine-Related LncRNAs

2021 ◽  
Vol 8 ◽  
Author(s):  
Li Zhang ◽  
Xianzhe Tang ◽  
Jia Wan ◽  
Xianghong Zhang ◽  
Tao Zheng ◽  
...  
Keyword(s):  
High Risk ◽  
Soft Tissue ◽  
Cox Regression ◽  
Soft Tissue Sarcomas ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Consensus Clustering ◽  
Immune Microenvironment

Background: N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) are critically involved in cancer development. However, the roles and clinical significance of m6A-related lncRNAs in soft tissue sarcomas (STS) are inconclusive, thereby warranting further investigations.Methods: Transcriptome profiling data were extracted from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx). Consensus clustering was employed to divide patients into clusters and Kaplan–Meier analysis was used to explore the prognostic differences between the subgroups. Gene set enrichment analysis (GSEA) was conducted to identify the biological processes and signaling pathways associated with m6A-Related lncRNAs. Finally, patients were randomly divided into training and validation cohorts, and least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to establish the m6A-related lncRNA-based risk signature.Results: A total of 259 STS patients from TCGA-SARC dataset were enrolled in our study. Thirteen m6A-Related lncRNAs were identified to be closely related to the prognosis of STS patients. Patients were divided into two clusters, and patients in cluster 2 had a better overall survival (OS) than those in cluster 1. Patients in different clusters also showed differences in immune scores, infiltrating immune cells, and immune checkpoint expression. Patients were further classified into high-risk and low-risk subgroups according to risk scores, and high-risk patients were found to have a worse prognosis. The receiver operating characteristic (ROC) curve indicated that the risk signature displayed excellent performance at predicting the prognosis of patients with STS. Further, the risk signature was remarkably connected with the immune microenvironment and chemosensitivity in STS.Conclusion: Our study demonstrated that m6A-related lncRNAs were significantly associated with prognosis and tumor immune microenvironment and could function as independent prognosis-specific predictors in STS, thereby providing novel insights into the roles of m6A-related lncRNAs in STS.

Identification of novel immunomodulatory tumor biology through comprehensive characterization of a metastases-specific epigenome in patients with metachronous primary and metastatic urothelial carcinoma (UC) tumor pairs.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 452-452
Author(s):  
Noah M. Hahn ◽  
Harvey M Cramer ◽  
Sunil S. Badve ◽  
Liang Cheng ◽  
Yesim Gokmen-Polar ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Small Sample Size ◽  
Tumor Biology ◽  
Small Sample ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Illumina Hiseq ◽  
Primary Tumors ◽  
Gene Set

452 Background: The Cancer Genome Atlas project identified a distinct cluster of hypermethylated muscle-invasive UC tumors in one third of patients. It is unknown if this epigenetic biology persists or changes in UC metastases. The present study aimed to identify uniquely hypermethylated regions in UC metastases compared to their matched primary tumors. Methods: UC patients with paired formalin fixed paraffin embedded tissue samples ( > 75% tumor) from primary and metachronous metastatic tumors were identified. DNA methylation was analyzed on the Illumina HiSeq platform by double-enzyme reduced representation bisulfite sequencing (dRRBS). Results were analyzed by BS-seq within BSmooth open source software. Alignment and methylation estimation was done using Bismark open source software. CpG regions with coverage < 2 in more than 66% of samples were removed to reduce false positive results. Differentially methylated regions (DMRs) in metastases compared to primary tumors were determined by paired t-test. Gene set enrichment analysis (GSEA) of the top 5% DMRs was performed utilizing multiple gene set collections including c7IMMUNO. A false discovery rate of < 10% defined significant DMRs. Results: 15 UC primary/metachronous metastases pairs were analyzed. After filtering for low coverage, 1,781,762 loci remained for analysis. After merging loci within 2500 bp of each other, the top 5% DMRs resulted in 18,452 DMRs. GSEA including the c7IMMUNO gene set identified multiple previously undescribed hypermethylated genes modulating immune function through TGF-beta and Treg signaling including: TGFBR2, TGFBR3, SMAD1, SMAD3, SMAD4, BACH1, BACH2, and VDR all q < 0.05. Conclusions: We identified numerous genes with immunomodulatory functions significantly hypermethylated in UC metastases compared to their matched primary tumors. Our findings provide rationale to examine epigenetic approaches as a means to improve clinical outcomes of UC patients treated with immunotherapy. Our small sample size limits definitive conclusions and warrants validation in independent data sets.

scholarly journals An m6A-Related Prognostic Biomarker Associated With the Hepatocellular Carcinoma Immune Microenvironment

2021 ◽  
Vol 12 ◽  
Author(s):  
Yingxi Du ◽  
Yarui Ma ◽  
Qing Zhu ◽  
Tongzheng Liu ◽  
Yuchen Jiao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Checkpoint ◽  
Immune Cell ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Survival Prediction ◽  
Consensus Clustering ◽  
Immune Microenvironment ◽  
Cluster 2

Background: N6-methyladenosine (m6A) is related to the progression of multiple cancers. However, the underlying influences of m6A-associated genes on the tumor immune microenvironment in hepatocellular carcinoma (HCC) remain poorly understood. Therefore, we sought to construct a survival prediction model using m6A-associated genes to clarify the molecular and immune characteristics of HCC.Methods: HCC case data were downloaded from The Cancer Genome Atlas (TCGA). Then, by applying consensus clustering, we identified two distinct HCC clusters. Next, four m6A-related genes were identified to construct a prognostic model, which we validated with Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) datasets. Additionally, the molecular and immune characteristics in different subgroups were analyzed.Results: m6A RNA methylation regulators were differentially expressed between HCC and normal samples and linked with immune checkpoint expression. Using consensus clustering, we divided HCC samples into two subtypes with distinct clinical features. Cluster 2 was associated with unfavorable prognosis, higher immune checkpoint expression and immune cell infiltration levels. In addition, the immune and carcinogenic signaling pathways were enriched in cluster 2. Furthermore, we constructed a risk model using four m6A-associated genes. Patients with different risk scores had distinct survival times, expression levels of immunotherapy biomarkers, TP53 mutation rates, and sensitivities to chemotherapy and targeted therapy. Similarly, the model exhibited an identical impact on overall survival in the validation cohorts.Conclusion: The constructed m6A-based signature may be promising as a biomarker for prognostics and to distinguish immune characteristics in HCC.

scholarly journals Landscape of Immune Microenvironment Under Immune Cell Infiltration Pattern in Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Qianhui Xu ◽  
Shaohuai Chen ◽  
Yuanbo Hu ◽  
Wen Huang
Keyword(s):  
Breast Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
Principal Component ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Signal Pathways ◽  
Immune Cell Infiltration ◽  
Consensus Clustering ◽  
Immune Microenvironment

BackgroundIncreasing evdence supports the suggestion that the immune cell infiltration (ICI) patterns play a pivotal role in tumor progression in breast cancer (BRCA). Nonetheless, there has been no comprehensive analysis of the ICI patterns effects on the clinical outcomes and immunotherapy.MethodsMultiomic data for BRCA samples were downloaded from TCGA. ESTIMATE algorithm, ssGSEA method, and CIBERSORT analysis were used to uncover the landscape of the tumor immune microenvironment (TIME). BRCA subtypes based on the ICI pattern were identified by consensus clustering and principal-component analysis was performed to obtain the ICI scores to quantify the ICI patterns in individual tumors. Their prognostic value was validated by the Kaplan-Meier survival curves. Gene set enrichment analysis (GSEA) was applied for functional annotation. Immunophenoscore (IPS) was employed to explore the immunotherapeutic role of the ICI scores. Finally, the mutation data was analyzed by using the “maftools” R package.ResultsThree different immune infiltration patterns with a distinct prognosis and biological signature were recognized among 1,198 BRCA samples. The characteristics of TIME under these three patterns were highly consistent with three known immune profiles: immune- excluded, immune-desert, and immune-inflamed phenotypes, respectively. The identification of the ICI patterns within individual tumors based on the ICI score, developed under the ICI-related signature genes, contributed into dissecting biological processes, clinical outcome, immune cells infiltration, immunotherapeutic effect, and genetic variation. High ICI score subtype, characterized with a suppression of immunity, suggested an immune-exhausted phenotype. Abundant effective immune cells were discovered in the low ICI score patients, which corresponded to an immune-activated phenotype and might present an immunotherapeutic advantage. Immunophenoscore was implemented as a surrogate of immunotherapeutic outcome, low-ICI scores samples obtained a significantly higher immunophenoscore. Enrichment of the JAK/STAT and VEGF signal pathways were activated in the ICI low-score subgroup. Finally, the synergistic effect between the ICI score and the tumor mutation burden (TMB) was confirmed.ConclusionThis work comprehensively elucidated that the ICI patterns served as an indispensable player in complexity and diversity of TIME. Quantitative identification of the ICI patterns in individual tumor will contribute into mapping the landscape of TIME further optimizing precision immunotherapy.

scholarly journals Integrative Analysis of m6A Regulator-Mediated RNA Methylation Modification Patterns and Immune Characteristics in Lupus Nephritis

2021 ◽  
Vol 9 ◽  
Author(s):  
Huanhuan Zhao ◽  
Shaokang Pan ◽  
Jiayu Duan ◽  
Fengxun Liu ◽  
Guangpu Li ◽  
...  
Keyword(s):  
Lupus Nephritis ◽  
Immune Responses ◽  
Kidney Tissue ◽  
Gene Set Enrichment Analysis ◽  
Consensus Clustering ◽  
Immune Microenvironment ◽  
Rna Methylation ◽  
Gene Set ◽  
Healthy Donors ◽  
Functional Pathways

BackgroundThere is growing evidence to demonstrate that the epigenetic regulation of immune characteristics, especially for N6-methyladenosine (m6A) RNA methylation. However, how m6A methylation is involved in lupus nephritis (LN) is still unclear. This study aimed to determine the role of m6A RNA methylation and their association with the immune microenvironment in LN.MethodsIn total, 87 glomeruli (73 LN, 14 living healthy donors), 110 tubulointerstitium (95 LN, 15 living healthy donors), and 21 kidney whole tissue samples (14 LN, 7 controls) were included in our research to evaluate the expression levels of m6A regulators. CIBERSORT was used to assess the abundance of infiltrating immunocytes. The m6A regulator gene signature for LN was identified using LASSO-logistic regression and verified with external data. Consensus clustering algorithms were used for the unsupervised cluster analysis of m6A modification patterns in LN. Single-sample gene-set enrichment analysis and gene set variation analysis algorithms were employed to assess the activity of immune responses and other functional pathways. Weighted gene co-expression network analysis and protein-protein interaction networks were used to identify m6A methylation markers. Lastly, the Nephroseq V5 tool was used to analyze the correlation between m6A markers and renal function.ResultsWe found that the expression of m6A regulators was more significantly different in the glomeruli in LN compared with tubulointerstitium and whole kidney tissue. We established an m6A regulator signature, comprised of METTL3, WTAP, YTHDC2, YTHDF1, FMR1, and FTO, that can easily distinguish LN and healthy individuals. Two distinct m6A modification patterns based on 18 m6A regulators were determined, with significant differences in m6A regulator expression, immune microenvironment, biological functional pathways, and clinical characteristics. Activated NK cells, most immune responses, and HLA genes had strong correlations with m6A regulators. Seven m6A markers were identified and demonstrated a meaningful correlation with GFR, indicating that they are potential prognostic biomarkers.ConclusionThis study emphasized that m6A RNA methylation and the immune microenvironment are closely linked in LN. A better understanding of m6A modification patterns provide a basis for the development of novel therapeutic options for LN.

scholarly journals A Novel Prognostic Model Based On Cytosolic DNA Sensing-Related Gene Signature for Pancreatic Cancer

2021 ◽  
Author(s):  
Chuan-Qi Xu ◽  
Kui-Sheng Yang ◽  
Shu-Xian Zhao ◽  
Jian Lv
Keyword(s):  
Pancreatic Cancer ◽  
Prognostic Factor ◽  
Prognostic Model ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Independent Prognostic Factor ◽  
Gene Set Enrichment Analysis ◽  
Dna Sensing ◽  
Gene Set Enrichment ◽  
Gene Set

Abstract Objective: Pancreatic cancer (PC) is one of the most malignant tumors. Cytosolic DNA sensing have been found to play an essential role in tumor. In this study, a cytosolic DNA sensing-related genes (CDSRGs) signature was constructed and the potential mechanisms also been discussed.Methods: The RNA expression and clinical data of PC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Subsequently, univariate (UCR) and multivariate Cox regression (MCR) analyses were conducted to establish a prognostic model in the TCGA patients, which was verified by GEO patients. Cancer immune infiltrates were investigated via single sample gene set enrichment analysis (ssGSEA) and Tumor Immune Estimation Resource (TIMER). Finally, Gene Set Enrichment Analysis (GSEA) was used to investigate the related signaling pathways.Results: A prognostic model comprising four genes (POLR2E,IL18, MAVS, and FADD) was established. The survival rate of patients in the low-risk group was significantly higher than that of patients in the high-risk group. In addition, CDSRGs-risk score was proved as an independent prognostic factor in PC. Immune infiltrates and drug sensitivity are associated with POLR2E,IL18, MAVS, and FADD expression.Conclusions: In summary, we present and validated a CDSRGs risk model that is an independent prognostic factor and indicates the immune characteristics of PC. This prognostic model may facilitate the personalized treatment and monitoring.

scholarly journals RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

2022 ◽  
Vol 12 ◽  
Author(s):  
Lan-Xin Mu ◽  
You-Cheng Shao ◽  
Lei Wei ◽  
Fang-Fang Chen ◽  
Jing-Wei Zhang
Keyword(s):  
Breast Cancer ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Impact ◽  
Immune Microenvironment ◽  
Model Based ◽  
Tumor Immune Microenvironment

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p &lt; 0.001) and m6aRiskscore (p &lt; 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

scholarly journals Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

2020 ◽  
Author(s):  
Lili Fan ◽  
Han Lei ◽  
Ying Lin ◽  
Zhengwei Zhou ◽  
Guang Shu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Immune Cell ◽  
Good Prognosis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Survival Prognosis ◽  
Immune Activity ◽  
Gene Set ◽  
Immune Infiltration ◽  
Keywords Ovarian Cancer

Abstract Background : Ovarian cancer (OC) is a serious tumor disease in gynecology. Many papers have reported that high tumor mutational burden (TMB) can generate many neoantigens to result in a higher degree of tumor immune infiltration, so our study aims to predict the key molecules in OC immunotherapy by combined TMB with immunoactivity-related gene. Method: We divided OC cases into two groups: the low & high TMB group hinged on the somatic mutation data from the Cancer Genome Atlas (TCGA). We also used single-sample gene set enrichment analysis (ssGSEA) scores of immune cell types to conduct unsupervised clustering of OC patients in the TCGA cohort and some of them were defined as the low & high immunity group. Besides, to further understand the function of these genes, we conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, protein-protein interaction network, survival prognosis analysis and immune infiltration analysis. Finally, the effects on prognosis and immunotherapy in OC patients were explored by the Group on Earth Observations verification the patients' responses to immunotherapy. Results: We found that the higher the TMB was associated with the higher OC grades. Moreover, both high TMB and high immunity were significantly correlated with a good prognosis of OC. Then, 14 up-regulated differential expression genes (Up-DEGs) that were closely related to the prognosis of OC patients were screened according to the high TMB group and the high immunity group. Next, pathway analysis revealed that Up-DGEs were mainly involved in immune response and T cell proliferation. Finally, four genes had a good prognosis and were validated in the GEO dataset which included CXCL13, FCRLA, PLA2G2D, and MS4A1. We also identified that four genes had a good prognosis in melanoma patients treated with anti-PD-L1 and anti-CTLA-4 in the TIDE database. Conclusion: High TMB can promote immune cell infiltration and increases immune activity. And our analysis also demonstrated that the higher the TMB, the higher the immune activity, the better the prognosis of OC. Altogether, we found that CXCL13, FCRLA, PLA2G2D, and MS4A1 may be biomarkers for OC immunotherapy. Keywords: ovarian cancer, TMB, immune cells infiltration, survival prognosis.

scholarly journals Identification and Validation of Three Autophagy-Related Long Noncoding RNAs as Prognostic Signature in Cholangiocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Ya Jun Liu ◽  
Alphonse Houssou Hounye ◽  
Zheng Wang ◽  
Xiaowei Liu ◽  
Jun Yi ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Noncoding Rnas ◽  
Enrichment Analysis ◽  
Long Noncoding Rnas ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Roc Curve Analysis ◽  
Good Reliability ◽  
Normal Tissues

Cholangiocarcinoma (CCA) is featured by common occurrence and poor prognosis. Autophagy is a biological process that has been extensively involved in the progression of tumors. Long noncoding RNAs (lncRNAs) have been discovered to be critical in diagnosing and predicting various tumors. It may be valuable to elaborate autophagy-related lncRNAs (ARlncRNAs) in CCA, and indeed, there are still few studies concerning the role of ARlncRNAs in CCA. Here, a prognostic ARlncRNA signature was constructed to predict the survival outcome of CCA patients. Through identification, three differentially expressed ARlncRNAs (DEARlncRNAs), including CHRM3.AS2, MIR205HG, and LINC00661, were screened and were considered predictive signatures. Furthermore, the overall survival (OS) of patients with high-risk scores was significantly lower than that of patients with low scores. Interestingly, the risk score was an independent factor for the OS of patients with CCA. Moreover, receiver operating characteristic (ROC) curve analysis showed that the screened and constructed prognosis signature for 1 year (AUC = 0.884), 3 years (AUC =0.759), and 5 years (AUC = 0.788) presented a high score of accuracy in predicting OS of CCA patients. Gene set enrichment analysis (GSEA) revealed that the three DEARlncRNAs were significantly enriched in CCA-related signaling pathways, including “pathways of basal cell carcinoma”, “glycerolipid metabolism”, etc. Quantitative real-time PCR (qRT-PCR) showed that expressions of CHRM3.AS2, MIR205HG, and LINC00661 were higher in CCA tissues than those in normal tissues, similar to the trends detected in the CCA dataset. Furthermore, Pearson’s analysis reported an intimate correlation of the risk score with immune cell infiltration, indicating a predictive value of the signature for the efficacy of immunotherapy. In addition, the screened lncRNAs were found to have the ability to modulate the expression of mRNAs by interacting with miRNAs based on the established lncRNA-miRNA-mRNA network. In conclusion, our study develops a novel nomogram with good reliability and accuracy to predict the OS of CCA patients, providing a significant guiding value for developing tailored therapy for CCA patients.

Abstract P139: The Relationship Between Tissue Sodium Storage, Immune Cell Activation And Salt-sensitive Hypertension

Hypertension ◽  
2020 ◽  
Vol 76 (Suppl_1) ◽  
Author(s):  
Melis Sahinoz ◽  
Fernando Elijovich ◽  
Cheryl L Laffer ◽  
Ashley Pitzer ◽  
Thomas G Stewart ◽  
...  
Keyword(s):  
Cell Activation ◽  
Immune Cell ◽  
Small Sample Size ◽  
Salt Sensitivity ◽  
Small Sample ◽  
Sensitivity Index ◽  
Salt Loading ◽  
Immune Cell Activation ◽  
Salt Depletion

Introduction: Salt Sensitivity (SS) of blood pressure (BP) is an independent predictor of death due to cardiovascular disease, but its pathogenesis is poorly understood. Sodium (Na + ) is stored in the skin and muscle interstitium. This hyperosmolar Na + activates monocytes in vitro via oxidative stress with generation of isolevuglandin (isoLG) protein adducts that are immunogenic and activate the adaptive immune system. Methods: Five subjects with essential hypertension discontinued all anti-hypertensive therapy for two weeks before the study. SS was assessed by an inpatient protocol of salt loading (460 mmoL/24h) and salt depletion (10 mmoL/24h, plus furosemide 40 mg x 3). Muscle and skin Na + contents were measured at baseline (BA) by 23 Sodium magnetic resonance imaging ( 23 NaMRI). Urine and serum electrolytes, glomerular filtration rate and the % CD14 + monocytes containing isoLG adducts using flow cytometry were obtained at BA, after salt-loading (HI) and after salt-depletion (LO). All continuous data are displayed as median (interquartile range). Spearman’s correlation was used to test associations. Results: Median age was 54 years (44-55), 60% of subjects were female, screening systolic BP (SBP) was 140 mmHg (134-148), diastolic BP was 88 mmHg (84-99) and BMI was 35 kg/m 2 (30-39). SBP response to salt-depletion (salt-sensitivity index, SSI) varied from -13.8 to +1.8 mmHg. %isoLG + CD14 + cells were 48 (27-65) at BA, 55 (31-56) at HI, and 70 (33-72) at LO (p=0.594, by the Kruskal-Wallis test). The correlation between SSI and delta (Δ) %isoLG LO minus HI, was 0.86, [95% confidence interval (CI), -0.07-0.99] which may suggest conclusively as we gather more data that the greater the SSI, the larger the decrease in isoLGs by salt depletion. Muscle Na + content correlated with 24h urine Na + (BA) (r=0.90, 95% CI, 0.11-0.99), however, the correlation with BP, SSI or isoLGs was inconclusive, potentially due to the small sample size. Skin Na + content correlated with baseline %CD14IsoLG + (r=0.91; 95% CI, 0.12-0.99). Conclusions: Na + intake is a component of the determinants of muscle Na + . Skin Na + is associated with increased isoLGs in monocytes, a marker of immune cell activation. Variability in ΔCD14isoLG may serve as a biomarker for SS of BP in humans.

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