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He Q, Wei C, Cao L, Zhang P, Zhuang W, Cai F. Blood cell indices and inflammation-related markers with kidney cancer risk: a large-population prospective analysis in UK Biobank. Front Oncol 2024; 14:1366449. [PMID: 38846978 PMCID: PMC11153768 DOI: 10.3389/fonc.2024.1366449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 05/01/2024] [Indexed: 06/09/2024] Open
Abstract
Background Kidney cancer is a prevalent malignancy with an increasing incidence worldwide. Blood cell indices and inflammation-related markers have shown huge potential as biomarkers for predicting cancer incidences, but that is not clear in kidney cancer. Our study aims to investigate the correlations of blood cell indices and inflammation-related markers with kidney cancer risk. Methods We performed a population-based cohort prospective analysis using data from the UK Biobank. A total of 466,994 participants, free of kidney cancer at baseline, were included in the analysis. The hazard ratios (HRs) and 95% confidence intervals (CIs) for kidney cancer risk were calculated using Cox proportional hazards regression models. Restricted cubic spline models were used to investigate nonlinear longitudinal associations. Stratified analyses were used to identify high-risk populations. The results were validated through sensitivity analyses. Results During a mean follow-up of 12.4 years, 1,710 of 466,994 participants developed kidney cancer. The Cox regression models showed that 13 blood cell indices and four inflammation-related markers were associated with kidney cancer incidence. The restricted cubic spline models showed non-linear relationships with kidney cancer. Finally, combined with stratified and sensitivity analyses, we found that the mean corpuscular hemoglobin concentration (MCHC), red blood cell distribution width (RDW), platelet distribution width (PDW), systemic immune-inflammation index (SII), and product of platelet count and neutrophil count (PPN) were related to enhanced kidney cancer risk with stable results. Conclusion Our findings identified that three blood cell indices (MCHC, RDW, and PDW) and two inflammation-related markers (SII and PPN) were independent risk factors for the incidence of kidney cancer. These indexes may serve as potential predictors for kidney cancer and aid in the development of targeted screening strategies for at-risk individuals.
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Affiliation(s)
- Qingliu He
- Department of Urology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Chengcheng Wei
- Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Cao
- Department of Orthopaedic, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Pu Zhang
- Department of Urology, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Zhuang
- Department of Urology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Fangzhen Cai
- Department of Urology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
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Pham HN, Pham L, Sato K. Deconvolution analysis identified altered hepatic cell landscape in primary sclerosing cholangitis and primary biliary cholangitis. Front Med (Lausanne) 2024; 11:1327973. [PMID: 38818402 PMCID: PMC11138208 DOI: 10.3389/fmed.2024.1327973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 04/29/2024] [Indexed: 06/01/2024] Open
Abstract
Introduction Primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC) are characterized by ductular reaction, hepatic inflammation, and liver fibrosis. Hepatic cells are heterogeneous, and functional roles of different hepatic cell phenotypes are still not defined in the pathophysiology of cholangiopathies. Cell deconvolution analysis estimates cell fractions of different cell phenotypes in bulk transcriptome data, and CIBERSORTx is a powerful deconvolution method to estimate cell composition in microarray data. CIBERSORTx performs estimation based on the reference file, which is referred to as signature matrix, and allows users to create custom signature matrix to identify specific phenotypes. In the current study, we created two custom signature matrices using two single cell RNA sequencing data of hepatic cells and performed deconvolution for bulk microarray data of liver tissues including PSC and PBC patients. Methods Custom signature matrix files were created using single-cell RNA sequencing data downloaded from GSE185477 and GSE115469. Custom signature matrices were validated for their deconvolution performance using validation data sets. Cell composition of each hepatic cell phenotype in the liver, which was identified in custom signature matrices, was calculated by CIBERSORTx and bulk RNA sequencing data of GSE159676. Deconvolution results were validated by analyzing marker expression for the cell phenotype in GSE159676 data. Results CIBERSORTx and custom signature matrices showed comprehensive performance in estimation of population of various hepatic cell phenotypes. We identified increased population of large cholangiocytes in PSC and PBC livers, which is in agreement with previous studies referred to as ductular reaction, supporting the effectiveness and reliability of deconvolution analysis in this study. Interestingly, we identified decreased population of small cholangiocytes, periportal hepatocytes, and interzonal hepatocytes in PSC and PBC liver tissues compared to healthy livers. Discussion Although further studies are required to elucidate the roles of these hepatic cell phenotypes in cholestatic liver injury, our approach provides important implications that cell functions may differ depending on phenotypes, even in the same cell type during liver injury. Deconvolution analysis using CIBERSORTx could provide a novel approach for studies of specific hepatic cell phenotypes in liver diseases.
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Affiliation(s)
- Hoang Nam Pham
- Department of Life Sciences, University of Science and Technology of Hanoi, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Linh Pham
- Department of Science and Mathematics, Texas A&M University—Central Texas, Killeen, TX, United States
| | - Keisaku Sato
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
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Mueller FB, Yang H, Li C, Dadhania DM, Xiang JZ, Salvatore S, Seshan SV, Sharma VK, Suthanthiran M, Muthukumar T. RNA-sequencing of Human Kidney Allografts and Delineation of T-Cell Genes, Gene Sets, and Pathways Associated With Acute T Cell-mediated Rejection. Transplantation 2024; 108:911-922. [PMID: 38291584 PMCID: PMC10963156 DOI: 10.1097/tp.0000000000004896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
BACKGROUND Delineation of T-cell genes, gene sets, pathways, and T-cell subtypes associated with acute T cell-mediated rejection (TCMR) may improve its management. METHODS We performed bulk RNA-sequencing of 34 kidney allograft biopsies (16 Banff TCMR and 18 no rejection [NR] biopsies) from 34 adult recipients of human kidneys. Computational analysis was performed to determine the differential intragraft expression of T-cell genes at the level of single-gene, gene set, and pathways. RESULTS T-cell signaling pathway gene sets for plenary T-cell activation were overrepresented in TCMR biopsies compared with NR biopsies. Heightened expression of T-cell signaling genes was validated using external TCMR biopsies. Pro- and anti-inflammatory immune gene sets were enriched, and metabolism gene sets were depleted in TCMR biopsies compared with NR biopsies. Gene signatures of regulatory T cells, Th1 cells, Th2 cells, Th17 cells, T follicular helper cells, CD4 tissue-resident memory T cells, and CD8 tissue-resident memory T cells were enriched in TCMR biopsies compared with NR biopsies. T-cell exhaustion and anergy were also molecular attributes of TCMR. Gene sets associated with antigen processing and presentation, and leukocyte transendothelial migration were overexpressed in TCMR biopsies compared with NR biopsies. Cellular deconvolution of graft infiltrating cells by gene expression patterns identified CD8 T cell to be the most abundant T-cell subtype infiltrating the allograft during TCMR. CONCLUSIONS Our delineation of intragraft T-cell gene expression patterns, in addition to yielding new biological insights, may help prioritize T-cell genes and T-cell subtypes for therapeutic targeting.
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Affiliation(s)
- Franco B. Mueller
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Hua Yang
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Carol Li
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Darshana M. Dadhania
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
- Department of Transplantation Medicine, NewYork Presbyterian Hospital-Weill Cornell Medical College, New York, NY
| | - Jenny Z. Xiang
- Genomics Resources Core Facility, Department of Microbiology and Immunology, Weill Cornell Medical College, New York, NY
| | - Steven Salvatore
- Division of Renal Pathology, Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY
| | - Surya V. Seshan
- Division of Renal Pathology, Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY
| | - Vijay K. Sharma
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Manikkam Suthanthiran
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
- Department of Transplantation Medicine, NewYork Presbyterian Hospital-Weill Cornell Medical College, New York, NY
| | - Thangamani Muthukumar
- Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medical College, New York, NY
- Department of Transplantation Medicine, NewYork Presbyterian Hospital-Weill Cornell Medical College, New York, NY
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Vathrakokoili Pournara A, Miao Z, Beker OY, Nolte N, Brazma A, Papatheodorou I. CATD: a reproducible pipeline for selecting cell-type deconvolution methods across tissues. BIOINFORMATICS ADVANCES 2024; 4:vbae048. [PMID: 38638280 PMCID: PMC11023940 DOI: 10.1093/bioadv/vbae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/20/2024] [Accepted: 03/21/2024] [Indexed: 04/20/2024]
Abstract
Motivation Cell-type deconvolution methods aim to infer cell composition from bulk transcriptomic data. The proliferation of developed methods coupled with inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Additionally, the growing accessibility of single-cell RNA sequencing datasets, often accompanied by bulk expression from related samples enable the benchmark of existing methods. Results In this study, we conduct a comprehensive assessment of 31 methods, utilizing single-cell RNA-sequencing data from diverse human and mouse tissues. Employing various simulation scenarios, we reveal the efficacy of regression-based deconvolution methods, highlighting their sensitivity to reference choices. We investigate the impact of bulk-reference differences, incorporating variables such as sample, study and technology. We provide validation using a gold standard dataset from mononuclear cells and suggest a consensus prediction of proportions when ground truth is not available. We validated the consensus method on data from the stomach and studied its spillover effect. Importantly, we propose the use of the critical assessment of transcriptomic deconvolution (CATD) pipeline which encompasses functionalities for generating references and pseudo-bulks and running implemented deconvolution methods. CATD streamlines simultaneous deconvolution of numerous bulk samples, providing a practical solution for speeding up the evaluation of newly developed methods. Availability and implementation https://github.com/Papatheodorou-Group/CATD_snakemake.
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Affiliation(s)
- Anna Vathrakokoili Pournara
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Zhichao Miao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
- GMU-GIBH Joint School of Life Sciences, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, 511436, China
| | - Ozgur Yilimaz Beker
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla 34956, Turkey
| | - Nadja Nolte
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 121-1000, Slovenia
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Open Targets, Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, United Kingdom
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