Research ArticleKidney Disease

Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker

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Science Translational Medicine  02 Dec 2015:
Vol. 7, Issue 316, pp. 316ra193
DOI: 10.1126/scitranslmed.aac7071
  • Fig. 1. Schematic overview of the tissue transcriptome-driven strategy to identify urinary biomarkers for CKD progression.
  • Fig. 2. Transcript-eGFR association and EGF expression in the kidney in patients with CKD.

    (A) Correlation between observed and predicted eGFR (continuous line) on the basis of intrarenal transcripts integrated by ridge regression analysis (top 1 to 30 transcripts shown), providing eGFR prediction in the validation cohort (n = 55). Gray area represents 95% confidence interval (CI). Black arrow indicates the highest correlation provided by a six-marker set, which is depicted in (B). (B) Maximal correlation (r = 0.77, P < 0.0001) was demonstrated between observed eGFR and that predicted by gene expression values of six transcripts (n = 55). Dotted line represents 95% CI. (C) Intrarenal EGF mRNA showed a significant correlation with patients’ eGFR in discovery (n = 164) and validation cohorts (n = 55 and 42). TLDA, TaqMan Low Density Array. (D) EGF mRNA expression in major human organs/tissues (a selection out of a panel of 84 human organs/tissues and cell lines), derived from BioGPS (http://biogps.org), indicating a highly kidney-specific expression pattern. For full data set, see fig. S2. (E) EGF mRNA expression pattern in adult kidney (glomeruli, inner and outer cortex, inner and outer medulla, papillary tips, and renal pelvis); data were extracted from Higgins et al. (10). (F) In situ hybridization demonstrates tubule-specific EGF mRNA in both cortex (I and III) and medulla (II and IV), with reduced expression in CKD (III and IV) compared to healthy controls (I and II). Black arrows indicate positive staining in pink. Scale bars, 50 μM.

  • Fig. 3. Correlation of uEGF/Cr with intrarenal EGF mRNA and eGFR.

    (A and B) uEGF/Cr is correlated with intrarenal EGF mRNA in patients with matching urine enzyme-linked immunosorbent assay (ELISA) data and tissue mRNA expression data in C-PROBE (A) (n = 34) and NEPTUNE (B) (n = 85) cohorts. uEGF/Cr is correlated significantly (P < 0.0001) with eGFR at the time of biopsy in patients from C-PROBE [(C) n = 349], NEPTUNE [(D) n = 141], and PKU-IgAN [(E) n = 452].

  • Fig. 4. Association of uEGF/Cr with tubulointerstitial damage.

    (A and B) Tubulointerstitial damage is reflected by the percentage of cortex affected by IF/TA. Scoring of IF/TA was based on evaluation of silver, periodic acid–Schiff (PAS), and trichrome-stained kidney sections (n = 102) by six readers who were blinded to the uEGF/Cr value. For each section, the average scores of the six readers for IF and TA were calculated as indicators of tubulointerstitial damage. uEGF/Cr is significantly correlated with IF (A) and TA (B) scores (Spearman correlation, P < 0.001).

  • Fig. 5. Association of EGF with eGFR slope.

    (A) Intrarenal EGF RNA expression correlated significantly (P < 0.001) with eGFR slope in C-PROBE patients (n = 29). (B and C) Correlation of the observed eGFR slope of CKD patients in C-PROBE (n = 344) with slope predicted by uEGF/Cr (B) or ACR (C) using a regression model (adjusted for age and gender). eGFR slope predicted by uEGF/Cr (B) exhibited a higher correlation with the observed value than slope predicted by ACR (C).

  • Fig. 6. Multivariable-adjusted HRs for predicting the composite end point on the basis of uEGF/Cr.

    HRs were adjusted by age, gender, eGFR, and ACR. Adjusted HRs and 95% CIs were obtained by separate Cox regression models in each study cohort. A one-unit decrease of uEGF/Cr (in log scale) was associated with an increased risk of CKD progression of 3.73 (1.85 to 7.69)–fold, 3.43 (1.72 to 6.67)–fold, and 1.96 (1.45 to 2.70)–fold in these three cohorts. The unadjusted HRs for EGF were 0.33 (0.21 to 0.51) (C-PROBE), 0.33 (0.21 to 0.52) (NEPTUNE), and 0.57 (0.46 to 0.70) (PKU-IgAN).

  • Table 1 Demographic characteristics of CKD patients in the gene expression discovery and validation cohorts.

    Age and eGFR are presented as means ± SD. SLE, lupus nephropathy; RPGN, rapidly progressive glomerulonephritis; HTN, hypertensive nephropathy; MGN, membranous glomerulonephritis; DN, diabetic nephropathy; FSGS, focal segmental glomerulosclerosis; MCD, minimal change disease; TMD, thin basement membrane disease.

    Disease typeCKD patientseGFR (ml/min per 1.73 m2)Age (years)Gender (% female)
    Discovery cohort: ERCB
    SLE3063.7 ± 29.434.7 ± 13.376.7
    IgAN2475.9 ± 37.936.4 ± 14.625
    RPGN2146.6 ± 31.558.5 ± 14.142.9
    HTN2043.9 ± 25.157.2 ± 12.125
    MGN1888.9 ± 41.453.4 ± 19.344.4
    DN1744.3 ± 24.958.3 ± 10.729.4
    FSGS1673.4 ± 38.446.2 ± 17.656.2
    MCD12100.7 ± 33.935.8 ± 16.833.3
    TMD693.4 ± 29.446.0 ± 14.533.3
    Total16466.4 ± 37.246.9 ± 17.643.3
    Validation cohort 1: ERCB
    SLE1060.1 ± 31.537.1 ± 13.860
    IgAN1750.8 ± 34.146.0 ± 17.529.4
    RPGN520.9 ± 15.050.4 ± 21.720
    MGN454.4 ± 32.458.7 ± 23.650
    DN197.344.8100
    FSGS855.9 ± 36.043.6 ± 18.625
    MCD486.0 ± 44.544.4 ± 19.30
    Other668.4 ± 60.745.2 ± 16.733.3
    Total5555.7 ± 38.345.1 ± 17.632.7
    Validation cohort 2: C-PROBE
    SLE16106.4 ± 60.131.3 ± 9.987.5
    IgAN1110.423100
    HTN1146.5290
    MGN251.9 ± 21.953.0 ± 5.750
    DN150.080.8100
    FSGS1151.2 ± 21.041.9 ± 14.027.2
    MCD1124.722100
    Other941.3 ± 23.152.4 ± 18.955.6
    Total4276.3 ± 50.839.6 ± 15.761.9
  • Table 2 Baseline characteristics of patients whose urine samples were analyzed in urine studies.
    Baseline characteristicsC-PROBE (n = 349)NEPTUNE (n = 141)PKU-IgAN (n = 452)
    Age54.8 ±16.145.7 ± 17.336.0 ± 12.0
    Gender (% female)59.335.551.3
    Race (% black)47.923.40
    eGFR (ml/min per 1.73 m2)55.4 ± 32.267.7 ± 37.184.8 ± 34.1
    ACR (mg/mg)0.8 ± 1.22.3 ± 2.10.6 ± 0.6
    Log2 uEGF/Cr (ng/mg)2.5 ± 1.13.0 ± 1.33.5 ± 1.0
  • Table 3 The association of uEGF with time to composite end point.

    The association was evaluated by a Cox proportional hazards regression analysis in C-PROBE, NEPTUNE, and PKU-IgAN cohorts. Renal event is defined as the presence of composite end point of ESKD or 40% reduction in baseline eGFR. The follow-up lengths (in years) for this analysis for the three cohorts were 1.8 ± 0.8, 2.0 ± 0.8, and 3.6 ± 2.2, respectively. AIC, Akaike information criterion; LR, likelihood ratio test.

    CohortModelAICC-statisticsP value (LR test)
    C-PROBE (25 events/189 patients)
    M1eGFR + ACR*206.30.75 (0.58–0.91)
    M2eGFR + ACR* + EGF192.10.87 (0.77–0.97)<0.0001
    NEPTUNE (26 events/110 patients)
    M1eGFR + ACR*213.980.74 (0.61–0.86)
    M2eGFR + ACR* + EGF204.140.80 (0.68–0.92)0.0006
    PKU-IgAN (68 events/428 patients)
    M1eGFR + ACR*660.180.71 (0.59–0.83)
    M2eGFR + ACR* + EGF647.940.75 (0.64–0.86)0.00016

    *Adjusted for age and gender.

    Supplementary Materials

    • www.sciencetranslationalmedicine.org/cgi/content/full/7/316/316ra193/DC1

      Materials and Methods

      Fig. S1. Baseline eGFR prediction by a three-marker panel in C-PROBE.

      Fig. S2. EGF and two other candidate mRNAs’ expression patterns in a panel of 84 human organs, tissues, and cell lines.

      Fig. S3. Correlation of EGF mRNA and uEGF/Cr with eGFR and eGFR slope in DN patients and CKD patients with diabetes.

      Fig. S4. Correlation of uEGF/Cr with ACR.

      Fig. S5. EGF as the top upstream regulator of genes correlated with eGFR slope.

      Fig. S6. Correlation of uEGF/Cr with eGFR slope.

      Fig. S7. ROC curve and corresponding AUC statistics for models with and without uEGF.

      Fig. S8. mRNA localization by in situ hybridization: Negative and positive control images.

      Table S1. Significantly enriched canonical pathways in intrarenal marker set.

      Table S2. qRT-PCR assays used to validate expression of the intrarenal transcripts.

      Table S3. Correlations of identified intrarenal transcripts with log2 eGFR of patients from the discovery and two validation cohorts.

      Table S4. Demographic characteristics of NEPTUNE patients with intrarenal EGF expression data available.

      Table S5. Top 10 upstream regulators of transcripts correlated with eGFR change over time (eGFR slope).

      References (4553)

    • Supplementary Material for:

      Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker

      Wenjun Ju,* Viji Nair, Shahaan Smith, Li Zhu, Kerby Shedden, Peter X. K. Song, Laura H. Mariani, Felix H. Eichinger, Celine C. Berthier, Ann Randolph, Jennifer Yi-Chun Lai, Yan Zhou, Jennifer J. Hawkins, Markus Bitzer, Matthew G. Sampson, Martina Thier, Corinne Solier, Gonzalo C. Duran-Pacheco, Guillemette Duchateau-Nguyen, Laurent Essioux, Brigitte Schott, Ivan Formentini, Maria C. Magnone, Maria Bobadilla, Clemens D. Cohen, Serena M. Bagnasco, Laura Barisoni, Jicheng Lv, Hong Zhang, Hai-Yan Wang, Frank C. Brosius, Crystal A. Gadegbeku, Matthias Kretzler,* for the ERCB, C-PROBE, NEPTUNE, and PKU-IgAN Consortium

      *Corresponding author. E-mail: wenjunj{at}med.umich.edu (W.J.); kretzler{at}umich.edu (M.K.)

      Published 2 December 2015, Sci. Transl. Med. 7, 316ra193 (2015)
      DOI: 10.1126/scitranslmed.aac7071

      This PDF file includes:

      • Materials and Methods
      • Fig. S1. Baseline eGFR prediction by a three-marker panel in C-PROBE.
      • Fig. S2. EGF and two other candidate mRNAs’ expression patterns in a panel of 84 human organs, tissues, and cell lines.
      • Fig. S3. Correlation of EGF mRNA and uEGF/Cr with eGFR and eGFR slope in DN patients and CKD patients with diabetes.
      • Fig. S4. Correlation of uEGF/Cr with ACR.
      • Fig. S5. EGF as the top upstream regulator of genes correlated with eGFR slope.
      • Fig. S6. Correlation of uEGF/Cr with eGFR slope.
      • Fig. S7. ROC curve and corresponding AUC statistics for models with and without uEGF.
      • Fig. S8. mRNA localization by in situ hybridization: Negative and positive control images.
      • Table S1. Significantly enriched canonical pathways in intrarenal marker set.
      • Table S2. qRT-PCR assays used to validate expression of the intrarenal transcripts.
      • Table S3. Correlations of identified intrarenal transcripts with log2 eGFR of patients from the discovery and two validation cohorts.
      • Table S4. Demographic characteristics of NEPTUNE patients with intrarenal EGF expression data available.
      • Table S5. Top 10 upstream regulators of transcripts correlated with eGFR change over time (eGFR slope).
      • References (4553)

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