Identification and Validation of IFI44 as Key Biomarker in Lupus Nephritis.
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ABSTRACT:
SUBMITTER: Shen L
PROVIDER: S-EPMC8574154 | biostudies-literature | 2021
REPOSITORIES: biostudies-literature
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Identification and Validation of IFI44 as Key Biomarker in Lupus Nephritis.
Shen Lingling L Lan Lan L Zhu Tingting T Chen Hongjun H Gu Haifeng H Wang Cuili C Chen Ying Y Wang Minmin M Tu Haiyan H Enghard Philipp P Jiang Hong H Chen Jianghua J
Frontiers in medicine 20211025
Lupus nephritis (LN) is a common and severe organ manifestation of systemic lupus erythematosus (SLE) and is a major cause of SLE related deaths. Early diagnosis is essential to improve the prognosis of patients with LN. To screen the potential biomarkers associated with LN, we downloaded the gene expression profile of GSE99967 from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was utilized to construct a gene co-expression network and identify ...[more]
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Project description:BackgroundLupus nephritis (LN) emerges as a severe complication of systemic lupus erythematosus (SLE), significantly affecting patient survival. Despite improvements in treatment reducing LN's morbidity and mortality, existing therapies remain suboptimal, emphasizing the necessity for early detection to improve patient outcomes.MethodsThis study employs bioinformatics and machine learning to identify and validate potential LN biomarkers using immunohistochemistry (IHC). It explores the relationship between these biomarkers and the clinical and pathological characteristics of LN, assessing their prognostic significance. The research provides deeper mechanistic insights by employing Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Additionally, the study characterizes the immune profiles of LN patients through the CIBERSORT algorithm, focusing on the role of interferon-inducible protein 44 (IFI44) as a key biomarker.ResultsIFI44 shows elevated expression in LN-affected kidneys, compared to healthy controls. The levels of IFI44 positively correlate with serum creatinine and the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) and inversely with serum complement C3 and initial estimated glomerular filtration rate (eGFR).ConclusionIFI44 is identified as a promising biomarker for LN, offering potential to refine the assessment of disease progression and predict clinical outcomes. This facilitates the development of more personalized treatment strategies for LN patients.
| S-EPMC11921169 | biostudies-literature
Project description:BackgroundConventional markers of juvenile-onset systemic lupus erythematosus (JSLE) disease activity fail to adequately identify lupus nephritis (LN). While individual novel urine biomarkers are good at detecting LN flares, biomarker panels may improve diagnostic accuracy. The aim of this study was to assess the performance of a biomarker panel to identify active LN in two international JSLE cohorts.MethodsNovel urinary biomarkers, namely vascular cell adhesion molecule-1 (VCAM-1), monocyte chemoattractant protein 1 (MCP-1), lipocalin-like prostaglandin D synthase (LPGDS), transferrin (TF), ceruloplasmin, alpha-1-acid glycoprotein (AGP) and neutrophil gelatinase-associated lipocalin (NGAL), were quantified in a cross-sectional study that included participants of the UK JSLE Cohort Study (Cohort 1) and validated within the Einstein Lupus Cohort (Cohort 2). Binary logistic regression modelling and receiver operating characteristic curve analysis [area under the curve (AUC)] were used to identify and assess combinations of biomarkers for diagnostic accuracy.ResultsA total of 91 JSLE patients were recruited across both cohorts, of whom 31 (34 %) had active LN and 60 (66 %) had no LN. Urinary AGP, ceruloplasmin, VCAM-1, MCP-1 and LPGDS levels were significantly higher in those patients with active LN than in non-LN patients [all corrected p values (p c) < 0.05] across both cohorts. Urinary TF also differed between patient groups in Cohort 2 (p c = 0.001). Within Cohort 1, the optimal biomarker panel included AGP, ceruloplasmin, LPGDS and TF (AUC 0.920 for active LN identification). These results were validated in Cohort 2, with the same markers resulting in the optimal urine biomarker panel (AUC 0.991).ConclusionIn two international JSLE cohorts, urinary AGP, ceruloplasmin, LPGDS and TF demonstrate an 'excellent' ability for accurately identifying active LN in children.
| S-EPMC5203828 | biostudies-literature
Project description:IntroductionLupus nephritis (LN) is one of the most frequent and serious organic manifestations of systemic lupus erythematosus (SLE). Autophagy, a new form of programmed cell death, has been implicated in a variety of renal diseases, but the relationship between autophagy and LN remains unelucidated.MethodsWe analyzed differentially expressed genes (DEGs) in kidney tissues from 14 LN patients and 7 normal controls using the GSE112943 dataset. Key modules and their contained genes were identified utilizing weighted gene co-expression network analysis (WGCNA). Differentially expressed autophagy-related genes (DE-ARGs) among DEGs, key module genes and autophagy-related genes (ARGs) were obtained by venn plot, and subjected to protein-protein interaction network construction. Two machine learning methods were applied to identify signature genes. The area under the receiver operating characteristic (ROC) curves was used to assess the accuracy of the signature genes. We also analyzed immune cell infiltration in LN. Additionally, the association between key genes and kidney diseases was predicted. Finally, key genes expression in kidney was verified by clinical samples and animal experiments.ResultsA total of 10304 DEGs were identified in GSE1129943 and 29 modules were identified in WGCNA. Among them, the brown module and coral 2 module exhibited significant correlation with LN (cor = 0.86, -0.84, p<0.001). Machine learning techniques identified 5 signature genes, but only 2 were validated in the external dataset GSE32591, namely MAP1LC3B (AUC = 0.920) and TNFSF10 (AUC = 0.937), which are involved in autophagy and apoptosis. Immune infiltration analysis suggested that these key genes may be associated with immune cell infiltration in LN. In addition, these genes have been linked to a variety of renal diseases, and their expression was verified in kidney tissues in LN patients and lupus mice.ConclusionMAP1LC3B and TNFSF10 may be key autophagy-related genes in LN. These key genes have the potential to provide new insights into the molecular diagnosis and treatment of LN.
| S-EPMC11771862 | biostudies-literature
Project description:BackgroundWe have previously shown that 15 urinary biomarkers (of 129 tested by Luminex), discriminate between active Lupus Nephritis (ALN) and non-LN patients. The aim of this study was to evaluate the ability of these 15 previously-identified urinary biomarkers to predict treatment responses to conventional therapy, and for the most predictive of these biomarkers to validate their utility to identify ALN patients in an independent prospectively-acquired lupus cohort.MethodsOur study had a 3-stage approach. In stage 1, we used Luminex to examine whether our previously identified urinary biomarkers at the time of the renal flare ( ± 3 months) or 12 ± 3 months after treatment of biopsy-proven ALN could predict treatment responses. In stage 2, a larger prospectively-acquired cross-sectional cohort was used to further validate the utility of the most predictive urinary biomarkers (identified in stage 1) to detect ALN patients. In this 2nd stage, cut-offs with the best operating characteristics to detect ALN patients were produced for each biomarker and different combinations and/or numbers of elevated biomarkers needed to accurately identify ALN patients were analyzed. In stage 3, we aimed to further corroborate the sensitivity of the cut-offs created in stage 2 to detect ALN patients in a biopsy-proven ALN cohort who had a urine sample collection within 3 months of their biopsy.ResultsTwenty-one patients were included in stage 1. Twelve (57.1%), 4 (19.1%), and 5 (23.8%) patients had a complete (CR), partial (PR) and no (NR) remission at 24 ± 3 months, respectively. The percentage decrease following 12 ± 3 months of treatment for Adiponectin, MCP-1, sVCAM-1, PF4, IL-15 and vWF was significantly higher in patients with CR in comparison to those with PR/NR. In stage 2, a total of 247 SLE patients were included, of which 24 (9.7%) had ALN, 79 (31.9%) had LN in remission (RLN) and 144 (58.3%) were non-LN (NLN) patients. Based on the combinations of biomarkers with the best operating characteristics we propose "rule out" and "rule in" ALN criteria. In stage 3, 53 biopsy-proven ALN patients were included, 35 with proliferative LN and 18 with non-proliferative ALN, demonstrating that our "rule in ALN" criteria operate better in detecting active proliferative than non-proliferative classes.ConclusionsOur results provide further evidence to support the role of Adiponectin, MCP-1, sVCAM-1 and PF4 in the detection of proliferative ALN cases. We further show the clinical utility of measuring multiple rather than a single biomarker and we propose novel "rule in" and "rule out" criteria for the detection of proliferative ALN with excellent operating characteristics.
| S-EPMC9196040 | biostudies-literature
Project description:BackgroundLupus nephritis (LN) is the most common and severe clinical manifestation of systemic lupus erythematosus (SLE) with considerable morbidity/mortality and limited treatment options. Since kidney biopsy is a relative hysteretic indicator, it is indispensable to investigate potential biomarkers for early diagnosis and predicting clinical outcomes of LN patients. Extracellular proteins may become the promising biomarkers by the secretion into body fluid. Our study linked extracellular proteins with lupus nephritis to identify the emerging biomarkers.MethodsThe expression profiling data were acquired from the Gene Expression Omnibus (GEO) database. Meanwhile, the two gene lists encoding extracellular proteins were collected from the Human Protein Atlas (HPA) and UniProt database. Subsequently, the extracellular protein-differentially expressed genes (EP-DEGs) were screened out, and the key EP-DEGs were determined by MCODE, MCC, and Degree methods via the protein-protein interaction (PPI) network. The expression level, immune characteristics, and diagnostic value of these candidate biomarkers were investigated. Finally, the Nephroseq V5 tool was applied to evaluate the clinical significance of the key EP-DEGs.ResultsA total of 164 DEGs were acquired by comparing LN samples with healthy controls based on GSE32591 datasets. Then, 38 EP-DEGs were screened out through the intersection between DEGs and extracellular protein gene lists. Function enrichment analysis indicated that these EP-DEGs might participate in immune response and constitute the extracellular matrix. Four key EP-DEGs (LUM, TGFBI, COL1A2, and POSTN) were eventually identified as candidate biomarkers, and they were all overexpressed in LN samples. Except that LUM expression was negatively correlated with most of the immune regulatory genes, there was a positive correlation between the remaining three biomarkers and the immune regulatory genes. In addition, these biomarkers had high diagnostic value, especially the AUC value of the LUM-TGFBI combination which reached almost 1 (AUC = 0.973), demonstrating high accuracy in distinguishing LN from controls. Finally, we found a meaningful correlation of these biomarkers with sex, WHO class, and renal function such as glomerular filtration rate (GFR), serum creatinine level, and proteinuria.ConclusionIn summary, our study comprehensively identified four key EP-DEGs exerting a vital role in LN diagnosis and pathogenesis and serving as promising therapeutic targets.
| S-EPMC9366080 | biostudies-literature
Project description:In search for new targets for the diagnosis and treatment of lupus nephritis (LN), we employed TMT-liquid chromatography-triple quadrupole mass spectrometry (TMT-LC-MS/MS) combined with RNA-seq and identified a panel of proteins that was dysregulated both at protein level and mRNA level in active LN patients compared with healthy controls. We chose to study the role of IGFBP2 since it is a relatively understudied protein in the context of LN. We further validated that IGFBP2 significantly increased and correlated with SLE activity index in active LN patients. The receiver operator characteristic (ROC) curve suggested that plasma IGFBP2 had a high diagnostic efficiency for distinguishing between inactive and active LN patients (AUC = 0.992; 95% CI = 0.974-1.000; P < 0.001). We demonstrated neutralizing IGFBP2-downregulated CD4+ T cell activation, upregulated the ratio of Treg, downregulated AKT/mTOR/4E-BP1 pathway, and significantly improved nephritis in MRL/lpr mice. In all, our work demonstrated IGFBP2 as a biomarker specific for active LN and blocking IGFBP2 could be a new target for treating LN. KEY MESSAGES : Plasma IGFBP2 is a promising diagnostic marker for distinguishing stable LN from active LN, and it is also a predictor for the poor prognosis of LN. Blockade of IGFBP2 can significantly improve the pathological damage of LN. IGFBP2 may regulate activation of CD4+ T and Treg ratio. Neutralizing IGFBP2 downregulates AKT/mTOR/4E-BP1 pathway.
| S-EPMC9470718 | biostudies-literature
Project description:A noninvasive means to predict the onset and recurrence of lupus nephritis (LN) before overt renal injury is needed to optimize and individualize treatment. Colony-stimulating factor-1 (CSF-1) is expressed by kidney tubules at the onset of LN, increases with disease progression, and spills into the circulation in lupus-prone mice. We tested the hypothesis that amplified expression of CSF-1 detected in the serum or urine correlates with intrarenal CSF-1 expression and histopathology (increased macrophage accumulation, activity indices) and clinical kidney disease activity and predicts the onset and recurrence of nephritis in patients with systemic lupus erythematosus (SLE). We found increased serum or urine CSF-1 levels in patients with cutaneous, serositis, and musculoskeletal disease; however, the increase in CSF-1 levels was far greater in LN. Moreover, an elevation in serum or urine CSF-1 levels correlated with increasing intrarenal CSF-1 expression and histopathology. By longitudinally tracking patients, we found that elevated serum CSF-1 heralded the initial onset of disease, and a rise in serum or urine CSF-1 predicted recurrences of LN before clinical evidence of glomerular dysfunction and conventional serologic measures, even in patients with other manifestations of SLE. These findings indicate that serial monitoring for a rise in serum or urine CSF-1 levels in patients with SLE reflects kidney histopathology and may predict renal disease activity and the onset and recurrence of LN more accurately than conventional laboratory measures.
| S-EPMC4310658 | biostudies-literature
Project description:ObjectiveThe American College of Rheumatology guidelines for the treatment of lupus nephritis recommend change in induction therapy when response to therapy has not occurred within 6 months. Response is not defined, and renal fibrosis can occur while waiting for this end point. Therefore, a decision support tool to better define response is needed to guide clinicians when starting patients on therapy. This study was undertaken to identify biomarker models with sufficient predictive power to develop such a tool.MethodsUrine samples from 140 patients with biopsy-proven lupus nephritis who had not yet started induction therapy were analyzed for a panel of urinary biomarkers. Univariate receiver operating characteristic (ROC) curves were generated for each individual biomarker and compared to the ROC area under the curve values from machine learning models developed using random forest algorithms. Biomarker models of outcome developed with novel markers in addition to clinical markers were compared to those developed with traditional clinical markers alone.ResultsModels developed with the combined traditional and novel biomarker panels demonstrated clinically meaningful predictive power. Markers most predictive of response were chemokines, cytokines, and markers of cellular damage.ConclusionThis is the first study to demonstrate the power of low-abundance biomarker panels and machine learning algorithms for predicting lupus nephritis outcomes. This is a critical first step in research to develop clinically meaningful decision support tools.
| S-EPMC5201110 | biostudies-literature
Project description:ObjectiveTo validate clinical indices of lupus nephritis activity and damage when used in children against the criterion standard of kidney biopsy findings.MethodsIn 83 children requiring kidney biopsy, the Systemic Lupus Erythematosus Disease Activity Index renal domain (SLEDAI-R), British Isles Lupus Assessment Group index renal domain (BILAG-R), Systemic Lupus International Collaborating Clinics (SLICC) renal activity score (SLICC-RAS), and SLICC Damage Index renal domain (SDI-R) were measured. Fixed effects and logistic models were calculated to predict International Society of Nephrology/Renal Pathology Society (ISN/RPS) class; low-to-moderate versus high lupus nephritis activity (National Institutes of Health [NIH] activity index [AI]) score: ≤10 versus >10; tubulointerstitial activity index (TIAI) score: ≤5 versus >5; or the absence versus presence of lupus nephritis chronicity (NIH chronicity index) score: 0 versus ≥1.ResultsThere were 10, 50, and 23 patients with ISN/RPS class I/II, III/IV, and V, respectively. Scores of the clinical indices did not differentiate among patients by ISN/RPS class. The SLEDAI-R and SLICC-RAS but not the BILAG-R differed with lupus nephritis activity status defined by NIH-AI scores, while only the SLEDAI-R scores differed between lupus nephritis activity status based on TIAI scores. The sensitivity and specificity of the SDI-R to capture lupus nephritis chronicity was 23.5% and 91.7%, respectively. Despite being designed to measure lupus nephritis activity, SLICC-RAS and SLEDAI-R scores significantly differed with lupus nephritis chronicity status.ConclusionCurrent clinical indices of lupus nephritis fail to discriminate ISN/RPS class in children. Despite its shortcomings, the SLEDAI-R appears best for measuring lupus nephritis activity in a clinical setting. The SDI-R is a poor correlate of lupus nephritis chronicity.
| S-EPMC4720587 | biostudies-literature
Project description:ObjectiveTo validate clinical indices of lupus nephritis activity and damage when used in children against the criterion standard of kidney biopsy findings.MethodsIn 83 children requiring kidney biopsy, the Systemic Lupus Erythematosus Disease Activity Index renal domain (SLEDAI-R), British Isles Lupus Assessment Group index renal domain (BILAG-R), Systemic Lupus International Collaborating Clinics (SLICC) renal activity score (SLICC-RAS), and SLICC Damage Index renal domain (SDI-R) were measured. Fixed effects and logistic models were calculated to predict International Society of Nephrology/Renal Pathology Society (ISN/RPS) class; low-to-moderate versus high lupus nephritis activity (National Institutes of Health [NIH] activity index [AI]) score: ≤10 versus >10; tubulointerstitial activity index (TIAI) score: ≤5 versus >5; or the absence versus presence of lupus nephritis chronicity (NIH chronicity index) score: 0 versus ≥1.ResultsThere were 10, 50, and 23 patients with ISN/RPS class I/II, III/IV, and V, respectively. Scores of the clinical indices did not differentiate among patients by ISN/RPS class. The SLEDAI-R and SLICC-RAS but not the BILAG-R differed with lupus nephritis activity status defined by NIH-AI scores, while only the SLEDAI-R scores differed between lupus nephritis activity status based on TIAI scores. The sensitivity and specificity of the SDI-R to capture lupus nephritis chronicity was 23.5% and 91.7%, respectively. Despite being designed to measure lupus nephritis activity, SLICC-RAS and SLEDAI-R scores significantly differed with lupus nephritis chronicity status.ConclusionCurrent clinical indices of lupus nephritis fail to discriminate ISN/RPS class in children. Despite its shortcomings, the SLEDAI-R appears best for measuring lupus nephritis activity in a clinical setting. The SDI-R is a poor correlate of lupus nephritis chronicity.
| S-EPMC4720587 | biostudies-literature