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. 2019 Feb 26;9(1):2749.
doi: 10.1038/s41598-019-39067-8.

Serum biomarkers identification by iTRAQ and verification by MRM: S100A8/S100A9 levels predict tumor-stroma involvement and prognosis in Glioblastoma

Anjali Arora  1 , Vikas Patil  1 , Paramita Kundu  2 , Paturu Kondaiah  2 , A S Hegde  3 , A Arivazhagan  4 , Vani Santosh  5 , Debnath Pal  6 , Kumaravel Somasundaram  7
Affiliations

"VSports app下载" Serum biomarkers identification by iTRAQ and verification by MRM: S100A8/S100A9 levels predict tumor-stroma involvement and prognosis in Glioblastoma

Anjali Arora et al. Sci Rep. .

Abstract

Despite advances in biology and treatment modalities, the prognosis of glioblastoma (GBM) remains poor. Serum reflects disease macroenvironment and thus provides a less invasive means to diagnose and monitor a diseased condition. By employing 4-plex iTRAQ methodology, we identified 40 proteins with differential abundance in GBM sera. The high abundance of serum S100A8/S100A9 was verified by multiple reaction monitoring (MRM). ELISA and MRM-based quantitation showed a significant positive correlation. Further, an integrated investigation using stromal, tumor purity and cell type scores demonstrated an enrichment of myeloid cell lineage in the GBM tumor microenvironment. Transcript levels of S100A8/S100A9 were found to be independent poor prognostic indicators in GBM. Medium levels of pre-operative and three-month post-operative follow-up serum S100A8 levels predicted poor prognosis in GBM patients who lived beyond median survival. In vitro experiments showed that recombinant S100A8/S100A9 proteins promoted integrin signalling dependent glioma cell migration and invasion up to a threshold level of concentrations. Thus, we have discovered GBM serum marker by iTRAQ and verified by MRM. We also demonstrate interplay between tumor micro and macroenvironment and identified S100A8 as a potential marker with diagnostic and prognostic value in GBM. VSports手机版.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Schematic describing the serum biomarker study work flow. (A) Shotgun discovery: Control pooled sera (n = 10) and GBM pooled sera (n = 10) were subjected to depletion using HU-14 MARS column, to remove high abundant proteins (HAP, H in blue circle) and to obtain low abundant proteins (LAP, L in orange circle). LAP of pooled sera were tryptic digested and labeled using iTRAQ reagents 114 and 115 (yellow and orange) for duplicates of control serum and 116 and 117 (purple and sky blue) for duplicates of GBM serum. iTRAQ labeled control and GBM peptides were mixed together and subjected to PI based offgel fractionation. Fractions obtained (n = 12) were subjected to LC-MS/MS and the data obtained was analyzed by Proteome Discoverer (PD) to obtain differential abundant proteins in GBM sera. (B) Verification by MRM: MRM assay for S100A8 and S100A9, the two most abundant proteins, was developed using two peptides from each protein. Synthetic light peptides were used to optimize the parameters (shown in orange, red, sky blue and purple) and SIS peptides stable-isotope-labeled standard peptide (SIS peptides, shown in orange, red, sky blue and purple with star) were used as internal standards. Developed method was validated to check sensitivity and reproducibility by determining the standard measurements such as, limit of quantitation (LOQ), lower quality control (LQC), middle quality control (MQC), and higher quality control (HQC). This MRM assay was used to determine levels of S100A8 and S100A9 in GBM (n = 36) and control (n = 4) sera. Diagrammatic representation for MRM profile, showing equal SIS peptide in control and GBM (orange, red, sky blue and purple peaks, thick line), but high levels of endogenous peptide (orange, red, sky blue and purple peaks, dotted line) is shown. (C) Validation by ELISA: Serum from a large cohort was subjected to ELISA based measurement. Values obtained were used to perform statistical analysis for obtaining diagnostic and prognostic significance of S100A8 and S100A9. Functional role of higher abundance was investigated by in vitro assays. To evaluate the analytical performance of MRM assay, ELISA and MRM based quantitation of the same cohort was compared.
Figure 2
Figure 2
Proteins with differential abundance in GBM serum identified by iTRAQ. (A,B) Log2 transformed ratios for proteins with at least two unique peptides having high abundance and low abundance, respectively, in GBM sera as compared to control sera are shown. (C,D) Representative tandem mass spectrometry (MS/MS) spectra for peptide sequence GNFHAVY and MLTELEK derived from S100A8. iTRAQ label 114 and 115 were used for control pooled serum sample and 116 and 117 were used for GBM pooled serum sample. (E,F) Representative tandem mass spectrometry (MS/MS) spectra for peptide sequence LGHPDTLNQEEFK and LTWASHEK derived from S100A9. iTRAQ label 114 and 115 were used for control pooled serum sample and 116 and 117 were used for GBM pooled serum sample.
Figure 3
Figure 3
Verification of S100A8 and S100A9 by MRM and Validation by ELISA. (A,B) The concentrations obtained by MRM for two peptides of S100A8 and S100A9, in GBM (n = 36) as compared to control sera (n = 4) are plotted. p values calculated by unpaired t test with Welch’s correction are indicated. p value less than 0.05 is considered significant with *, **, *** representing p value less than 0.05, 0.01 and 0.001 respectively. (C,D) The correlation plots between two peptides of S100A8 and two peptides of S100A9, is shown. Correlation coefficient and p value, calculated by Spearman’s correlation are indicated, dotted lines represent 95% confidence interval. (E,F) Serum levels S100A8 and S100A9 respectively were measured by ELISA on a larger cohort for validation (Control n = 32, GBM n = 87). p values calculated by unpaired t test with Welch’s correction are indicated. (G,H) Concentrations of S100A8 in GBM serum, obtained from MRM (Peptide I and Peptide II) were correlated with ELISA based values in the same cohort. Correlation coefficient and p value, calculated by Spearman’s correlation are indicated, dotted lines represent 95% confidence interval. (I,J) Concentrations of S100A9 in GBM serum, obtained from MRM (Peptide III and Peptide IV) were correlated with ELISA based values in the same cohort. Correlation coefficient and p value, calculated by Spearman’s correlation are indicated, dotted lines represent 95% confidence interval.
Figure 4
Figure 4
Potential involvement of microenvironment in the high levels of S100A8 and S100A9 transcripts in GBM tumor. (A,B) Transcript levels derived from publically available microarray data sets and our cohort (RT-qPCR) for S100A8 and S100A9 respectively, in control (TCGA Agilent n = 10, TCGA Affymetrix n = 10, GSE22866 n = 6, Rembrandt n = 28) and in GBM samples (TCGA Agilent n = 572, TCGA Affymetrix n = 528, GSE22866 n = 40, REMBRANDT Grade II n = 65, Grade III n = 58, GBM n = 227) are shown as scatter plot. Unpaired t-test with Welch’s correction was performed between control brain and GBM samples, p values are indicated with *, **, *** representing p value less than 0.05, 0.01 and 0.001 respectively. (C) Correlation of transcript level of S100A8 in TCGA dataset with ESTIMATE score, xCell microenvironment score and ABSOLUTE-based tumor purity score is shown. Correlation coefficient and p values are indicated, calculated by Spearman’s correlation, dotted lines represent 95% confidence interval. (D) Correlation of transcript level of S100A9 in TCGA dataset with ESTIMATE score, xCell microenvironment score and ABSOLUTE-based tumor purity score is shown. Correlation coefficient and p values are indicated, calculated by Spearman’s correlation, dotted lines represent 95% confidence interval.
Figure 5
Figure 5
S100A8 and S100A9 transcripts are diagnostic and poor prognostic markers for GBM. (A) ROC curve depicting transcript levels of S100A8 and S100A9 discriminate between control and GBM, AUC and p value is indicated. (B) ROC curve depicting transcript levels of S100A8 and S100A9 discriminate between grade III and GBM, AUC and p value is indicated. (C) ROC curve depicting transcript levels of S100A8 and S100A9 discriminate mesenchymal subtype from other GBM subtypes, AUC and p value is indicated. (D) Univariate and Multivariate cox proportional hazard regression analysis was performed using TCGA Affymetrix data for transcript levels of S100A8 and S100A9 along with age, G-CIMP status, IDH1 mutation status, MGMT promoter methylation status. (E,F) Kaplan Meier survival analysis using TCGA Affymetrix cohort (n = 518) for transcript levels of S100A8 and S100A9. Log-rank (Mantel–Cox) test was applied and the p value is indicated.
Figure 6
Figure 6
Serum levels of S100A8 serve as discriminatory and prognostic marker for GBM: (A) ROC curve depicting serum levels of S100A8 discriminate between healthy control and GBM is plotted, AUC and p value is indicated. (B) Scatter plot showing high abundance of serum S100A8 in GBM as compared to grade III as measured by ELISA. (C) ROC curve depicting serum levels of S100A8 discriminate between grade III and GBM, AUC and p value is indicated. (D) Kaplan Meier survival analysis using pre-operative serum levels of GBM patients surviving more than median survival (n = 35). Log-rank (Mantel–Cox) test was applied and the p value is indicated. (E) Kaplan Meier survival analysis using three months post-operative serum levels of GBM patients surviving more than median survival (n = 23). Log-rank (Mantel–Cox) test was applied and the p value is indicated. (F) Concentration dependent role on migratory and invasive property of exogenously added rS100A8 and rS100A9 (recombinant proteins) was measured using trans-well assay. Representative images of U373 cells fixed and stained after migration and invasion respectively are shown. (G,H) Quantitation for migration and invasion capability of U373 in presence of increasing concentration of exogenous rS100A8 and rS100A9. (I) Effect of integrin signaling inhibiting peptide, RGD (20 μM/ml), on migratory and invasive property of U373 cells in presence of exogenously added rS100A8 and rS100A9 (0.5 μg/ml) was measured using trans-well assay. Representative images of U373 cells fixed and stained after migration and invasion respectively are shown. (J,K) Quantitation for migration and invasion capability of U373 cells in presence of exogenous rS100A8 and rS100A9 (0.5 μg/ml) and RGD peptide (20 μM/ml).
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