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Gan T, Qu LX, Qu S, Qi YY, Zhang YM, Wang YN, Li Y, Liu LJ, Shi SF, Lv JC, Zhang H, Peng YJ, Zhou XJ. Unveiling biomarkers and therapeutic targets in IgA nephropathy through large-scale blood transcriptome analysis. Int Immunopharmacol 2024; 132:111905. [PMID: 38552291 DOI: 10.1016/j.intimp.2024.111905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 03/12/2024] [Accepted: 03/18/2024] [Indexed: 05/01/2024]
Abstract
INTRODUCTION IgA nephropathy (IgAN) is the most prevalent form of glomerulonephritis. Unfortunately, molecular biomarkers for IgAN derived from omics studies are still lacking. This research aims to identify critical genes associated with IgAN through large-scale blood transcriptome analysis. METHODS We constructed novel blood transcriptome profiles from peripheral blood mononuclear cells (PBMCs) of 53 Chinese IgAN patients and 28 healthy individuals. Our analysis included GO, KEGG, and GSEA for biological pathways. We analyzed immune cell profiles with CIBERSORT and constructed PPI networks with STRING, visualized in Cytoscape. Key differentially expressed genes (DEGs) were identified using CytoHubba and MCODE. We assessed the correlation between gene expressions and clinical data to evaluate clinical significance and identified hub genes through machine learning, validated with an open-access dataset. Potential drugs were explored using the CMap database. RESULTS We identified 333 DEGs between IgAN patients and healthy controls, mainly related to immune response and inflammation. Key pathways included NK cell mediated cytotoxicity, complement and coagulation cascades, antigen processing, and B cell receptor signaling. Cytoscape revealed 16 clinically significant genes (including KIR2DL1, KIR2DL3, VISIG4, C1QB, and C1QC, associated with sub-phenotype and prognosis). Machine learning identified two hub genes (KLRC1 and C1QB) for a diagnostic model of IgAN with 0.92 accuracy, validated at 1.00 against the GSE125818 dataset. Sirolimus, calcifediol, and efaproxiral were suggested as potential therapeutic agents. CONCLUSION Key DEGs, particularly VISIG4, KLRC1, and C1QB, emerge as potential specific markers for IgAN, paving the way for future targeted personalized treatment options.
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Affiliation(s)
- Ting Gan
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Lu-Xi Qu
- Guanghua School of Management, Peking University, Beijing 100871, China
| | - Shu Qu
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Yuan-Yuan Qi
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Yue-Miao Zhang
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Yan-Na Wang
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Yang Li
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Li-Jun Liu
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Su-Fang Shi
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Ji-Cheng Lv
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Hong Zhang
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Yi-Jie Peng
- National Institute of Health Data Science, Peking University, Beijing 100191, China; Xiangjiang Laboratory, Changsha 410205, China.
| | - Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Beijing 100034, China; Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China; Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, China; Research Units of Diagnosis and Treatment of Immune-mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China.
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Mohammadpour RA, Yazdani-Charati J, Faghani SZ, Alizadeh A, Barzegartahamtan M. Radiation dose-response (a Bayesian model) in the radiotherapy of the localized prostatic adenocarcinoma: the reliability of PSA slope changes as a response surrogate endpoint. PeerJ 2019; 7:e7172. [PMID: 31304057 PMCID: PMC6610535 DOI: 10.7717/peerj.7172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 05/23/2019] [Indexed: 11/20/2022] Open
Abstract
Purpose One of the characteristics of Prostate-Specific Antigen (PSA) is PSA slope. It is the rate of diminishing PSA marker over time after radiotherapy (RT) in prostate cancer (PC) patients. The purpose of this study was to evaluate the relationship between increasing RT doses and PSA slope as a potential surrogate for PC recurrence. Patients and Methods This retrospective study was conducted on PC patients who were treated by radiotherapy in the Cancer Institute of Iran during 2007–2012. By reviewing the records of these patients, the baseline PSA measurement before treatment (iPSA), Gleason score (GS), clinical T stage (T. stage), and periodic PSA measurements after RT and the total radiation dose received were extracted for each patient separately. We used a Bayesian dose-response model, analysis of variance, Kruskal–Wallis test, Kaplan–Meier product-limit method for analysis. Probability values less 0.05 were considered statistically significant. Results Based on the D’Amico risk assessment system, 13.34% of patients were classified as “Low Risk”, 51.79% were “Intermediate Risk”, and 34.87% were “High Risk”. In terms of radiation doses, 12.31% of the patients received fewer than 50 Gy, 15.38% received 50 to 69 Gy, 61.03% received 70 Gy, and 11.28% received more than 70 Gy. The PSA values decreased after RT for all dose levels. The slope of PSA changes was negative for 176 of 195 patients. By increasing the dosage of radiation, the PSA decreased but these changes were not statistically significant (p = 0.701) and PSA slope as a surrogate end point cannot met the Prentice’s criteria for PC recurrence. Conclusion Significant changes in the dose-response relationship were not observed when the PSA slope was considered as the response criterion. Therefore, although the absolute value of the PSA decreased with increasing doses of RT, the relationship between PSA slope changes and increasing doses was not clear and cannot be used as a reliable response surrogate endpoint.
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Affiliation(s)
- Reza Ali Mohammadpour
- Department of Biostatistics, Faculty of Health, Mazandaran University of Medical Sciences, Sari, Iran
| | - Jamshid Yazdani-Charati
- Department of Biostatistics, Faculty of Health, Mazandaran University of Medical Sciences, Sari, Iran
| | - SZahra Faghani
- Department of Biostatistics, Faculty of Health, Mazandaran University of Medical Sciences, Sari, Iran
| | - Ahad Alizadeh
- Department of Epidemiology and Reproductive Health, Reproductive Epidemiology Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
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