Clinical significance of novel biomarker NGAL in early diagnosis of acute renal injury

Zhang, Jinghua; Han, Jianle; Liu, Jinrui; Liang, Bing; Wang, Xiaobo; Wang, Chang‐An

doi:10.3892/etm.2017.5150

Cited by 23 publications

(20 citation statements)

References 23 publications

(22 reference statements)

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“…LCN2 expression was increased in patients with flares (Figure f). LCN2 encodes neutrophil gelatinase‐associated lipocalin (NGAL), which suggests increased neutrophil‐mediated antibacterial activity; NGAL is also a biomarker of kidney injury . Gene set enrichment analysis of patients with flares suggested increased inflammatory signalling (e.g.…”

Section: Resultsmentioning

confidence: 99%

“…LCN2 encodes neutrophil gelatinase-associated lipocalin (NGAL), which suggests increased neutrophilmediated antibacterial activity; NGAL is also a biomarker of kidney injury. 30 Gene set enrichment analysis of patients with flares suggested increased inflammatory signalling (e.g. IL-6 and TNF-a), increased proliferation signalling (KRAS) and haematological disturbances (haem metabolism, coagulation, complement and platelet-related gene sets) (Supplementary figure 11).…”

Section: Resultsmentioning

confidence: 99%

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Machine learning applied to whole‐blood RNA‐sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Figgett

Monaghan

et al. 2019

Clin & Trans Imm

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Objectives Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus, responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the computational analysis of patients’ whole‐blood transcriptomes. Methods We applied machine learning approaches to RNA‐sequencing (RNA‐seq) data sets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta‐analysis on three recently published whole‐blood RNA‐seq data sets was carried out, and an additional similar data set of 30 patients with SLE and 29 healthy donors was incorporated in this study; a total of 161 patients with SLE and 57 healthy donors were analysed. Results Examination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease‐related genes relative to clinical presentation. Moreover, gene signatures correlated with flare activity were successfully identified. Conclusion Given that SLE disease heterogeneity is a key challenge hindering the design of optimal clinical trials and the adequate management of patients, our approach opens a new possible avenue addressing this limitation via a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy allowing the identification of separate molecular mechanisms underpinning disease in SLE. Further, this approach may have a use in understanding the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Machine learning applied to whole‐blood RNA‐sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Figgett

Monaghan

et al. 2019

Clin & Trans Imm

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“…LCN2 encodes neutrophil gelatinase-associated lipocalin (NGAL), which suggests increased neutrophil-mediated anti-bacterial activity; NGAL is also a biomarker of kidney injury. 45 Gene set enrichment analysis revealed a number of pro-inflammatory gene sets and a neutrophil gene signature are predominant features of flare activity (figure 5G).…”

Section: Resultsmentioning

confidence: 99%

Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Figgett

Monaghan

et al. 2019

Preprint

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16Objective. Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that 17 is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus 18 responses to available treatments are unpredictable. Here, we developed a new stratification 19 scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE. 20Methods. We applied machine learning approaches to RNA-sequencing (RNA-seq) datasets 21 to stratify patients with SLE into four distinct clusters based on their gene expression profiles. 22A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and 23 an additional similar dataset of 30 patients with SLE and 29 healthy donors was contributed in 24 this research; 141 patients with SLE and 51 healthy donors were analysed in total. 25 Results. Examination of SLE clusters, as opposed to unstratified SLE patients, revealed 26 underappreciated differences in the pattern of expression of disease-related genes relative to 27 clinical presentation. Moreover, gene signatures correlated to flare activity were successfully 28 identified. 29 Conclusion. Given that disease heterogeneity has confounded research studies and clinical 30 trials, our approach addresses current unmet medical needs and provides a greater 31 understanding of SLE heterogeneity in humans. Stratification of patients based on gene 32 expression signatures may be a valuable strategy to harness disease heterogeneity and identify 33 patient populations that may be at an increased risk of disease symptoms. Further, this approach 34 can be used to understand the variability in responsiveness to therapeutics, thereby improving 35 the design of clinical trials and advancing personalised therapy. 36 37 Abstract word count: 242 38 39 Keywords 40 SLE, autoimmunity, RNA-seq, transcriptomics, stratification. 41 3 Abbreviations 42 ACR, American College of Rheumatology; ANA, anti-nuclear autoantibodies; BAFF, B cell 43 activating factor of the TNF family; cpm, counts per million; ECOC, error-correcting output 44 codes; ENA, extractible nuclear antigens; FPKM, fragments per kilobase of transcript per 45 million mapped reads; GILZ, glucocorticoid-induced leucine zipper; GO, gene ontology; HPC, 46 high performance computing; ISM, interferon signature metric; KEGG, Kyoto Encyclopedia 47 MATERIALS AND METHODS 85 Human subjects 86Human subjects in Datasets 1 and 3 are previously described (table 1). 12, 13 Patients with SLE 87 and healthy donors in Dataset 2 were recruited from the Monash Medical Centre. 14 Patients 88 with SLE fulfilled the American College of Rheumatology (ACR) classification criteria. 15 The 89 SLE disease activity index 2000 (SLEDAI-2k) 16 and the Physician Global Assessment (PGA; 90 range 0-3) 17 scores were recorded. Blood was collected into PAXgene Blood RNA tubes (BD), 91 which were frozen at -20 °C for later RNA extraction. Patients did not participate in the 92 analysis. 93 94 RNA extraction and RNA-sequencing 95

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“…J. Zhang и соавт. показали значимую положительную корреляцию между концентрациями sCysC и креатинина (r=0,501, p=0,027) у пациентов с ОПП [28]. А. Palazzuoli и соавт.…”

Section: Discussionunclassified

sCysC for early diagnosis of acute kidney injury in patients with acute decompensated heart failure

Яровая¹,

Lediakhova²,

Nasonova³

et al. 2018

Kardio. vestn.

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Диагностика острого повреждения почек (ОПП)-актуальная проблема российского здравоохранения. ОПП часто встречается у пациентов с острой декомпенсацией хронической сердечной недостаточности (ОДХСН) и ассоциируется с высоким уровнем летальности. Своевременная диагностика ОПП с помощью биомаркеров позволит улучшить исходы ОПП. Цель исследования-определить значение концентрации цистатина С в сыворотке крови (sCysC) для ранней диагностики ОПП у пациентов с ОДХСН со сниженной систолической функцией левого желудочка (ЛЖ). Материал и методы. В исследование включены 60 мужчин, госпитализированных с ОДХСН (возраст 62,0±11,1 года) со сниженной систолической функцией ЛЖ. Для количественного определения sCysC при поступлении применялся тест системы Human Cystatin C ELISA, основанный на методе количественного иммуноферментного анализа типа «сэндвич». Результаты. Концентрации sCysC были статистически значимо выше у пациентов с ОПП, чем без него. Для прогнозирования ОПП был проведен ROC-анализ. Площадь под кривой (AUC) для концентрации sCysC составляла 0,82; p<0,001. При отрезном значении sCysС, равном 1364,0 нг/мл, чувствительность составляла 79,3%, специфичность-74,2%. При концентрации sCysC больше, чем 1364 нг/мл, шанс развития ОПП увеличивается в 11 раз. Вывод. sCysC может использоваться в ранней диагностике ОПП у пациентов с ОДХСН со сниженной систолической функцией ЛЖ. Ключевые слова: цистатин C, острая декомпенсация хронической сердечной недостаточности, острое повреждение почек, креатинин, скорость клубочковой фильтрации. sCysC for early diagnosis of acute kidney injury in patients with acute decompensated heart failure

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Clinical significance of novel biomarker NGAL in early diagnosis of acute renal injury

Cited by 23 publications

References 23 publications

Machine learning applied to whole‐blood RNA‐sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Machine learning applied to whole‐blood RNA‐sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

sCysC for early diagnosis of acute kidney injury in patients with acute decompensated heart failure

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