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Integrating multiple 'omics' analyses identifies serological protein biomarkers for preeclampsia.

Liu LY, Yang T, Ji J, Wen Q, Morgan AA, Jin B, Chen G, Lyell DJ, Stevenson DK, Ling XB, Butte AJ - BMC Med (2013)

Bottom Line: In addition to the previously identified biomarkers, the angiogenic and antiangiogenic factors (soluble fms-like tyrosine kinase (sFlt-1) and placental growth factor (PIGF)), we found 3 up-regulated and 6 down-regulated biomakers in PE sera.Both early and late onset PE diagnostic panels, constructed with our PE biomarkers, were superior over sFlt-1/PIGF ratio in PE discrimination.The functional significance of these PE biomarkers and their associated pathways were analyzed which may provide new insights into the pathogenesis of PE.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pediatrics, Stanford University, 1265 Welch Road, Room X-163 MS-5415, Stanford, CA 94305, USA. bxling@stanford.edu.

ABSTRACT

Background: Preeclampsia (PE) is a pregnancy-related vascular disorder which is the leading cause of maternal morbidity and mortality. We sought to identify novel serological protein markers to diagnose PE with a multi-'omics' based discovery approach.

Methods: Seven previous placental expression studies were combined for a multiplex analysis, and in parallel, two-dimensional gel electrophoresis was performed to compare serum proteomes in PE and control subjects. The combined biomarker candidates were validated with available ELISA assays using gestational age-matched PE (n=32) and control (n=32) samples. With the validated biomarkers, a genetic algorithm was then used to construct and optimize biomarker panels in PE assessment.

Results: In addition to the previously identified biomarkers, the angiogenic and antiangiogenic factors (soluble fms-like tyrosine kinase (sFlt-1) and placental growth factor (PIGF)), we found 3 up-regulated and 6 down-regulated biomakers in PE sera. Two optimal biomarker panels were developed for early and late onset PE assessment, respectively.

Conclusions: Both early and late onset PE diagnostic panels, constructed with our PE biomarkers, were superior over sFlt-1/PIGF ratio in PE discrimination. The functional significance of these PE biomarkers and their associated pathways were analyzed which may provide new insights into the pathogenesis of PE.

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Early or late onset biomarker panel scores were plotted as a function of the gestational age. Different panel scores were scaled to the same scoring metric such that they can be directly compared. For either PE or control data points, a loess curve was fitted to represent the overall trend of biomarker scoring as a function of gestation. PE, preeclampsia.
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Figure 3: Early or late onset biomarker panel scores were plotted as a function of the gestational age. Different panel scores were scaled to the same scoring metric such that they can be directly compared. For either PE or control data points, a loess curve was fitted to represent the overall trend of biomarker scoring as a function of gestation. PE, preeclampsia.

Mentions: Using data from the ELISA assays, we constructed different panels with various subsets of the assays. We sought to identify biomarker panels of optimal feature number, balancing the need for small panel size, accuracy of classification, goodness of class separation (PE versus control), and sufficient sensitivity and specificity. With the aim of developing a multiplexed antibody-based assay for PE diagnosis, we used a genetic algorithm method to construct biomarker panels from the nine validated PE protein biomarkers for early and late gestational age PE, comparing to the sFlt-1/PIGF ratio in assessing PE. The algorithm guided panel construction processes, leading to early and late gestational age biomarker panels, which had complete separation between PE and control subjects (Table 6). These chosen biomarker panels are non-redundant, indicating non-inclusive relationships. The sFlt-1/PIGF ratio’s PE assessment utility (panel 0: early onset, ROC area under the curve (AUC) 1.00, p value 4.35 × 10-4; late onset, ROC AUC 0.86, p value 2.94 × 10-4; Additional file 2: Figure S3), previously through the multicenter trial validation [24], was confirmed in this study and used as a benchmark for our newly derived biomarker panels. Panel 1 (early onset, ROC AUC 1.00, p value 1.43 × 10-4) has three proteins, HPX, APO A-I and pikachurin. Panel 2 (late onset, ROC AUC 1.00, p value 3.65 × 10-5) has six proteins, HPX, HP, APO C-III, APO A-I, RBP4 and pikachurin. To demonstrate the efficacy of the biomarker panel as a classifier for PE disease activity according to disease onset, the biomarker panel scores were plotted as a function of time of the gestational age (composite summary in Additional file 2: Figure S2, details shown in Figure 3). According to the scatter plot analysis, the performance of our early-onset PE biomarker panel was comparable to the sFlt-1/PIGF ratio. For gestational age >34 weeks samples, performance of our biomarker panel is better than the sFlt-1/PIGF ratio that has several errors of diagnosis around week 36. Among the early and late gestational age biomarker panels, HPX, APO A-I, and pikachurin are present in both panels, indicating their critical role in the diagnosis.


Integrating multiple 'omics' analyses identifies serological protein biomarkers for preeclampsia.

Liu LY, Yang T, Ji J, Wen Q, Morgan AA, Jin B, Chen G, Lyell DJ, Stevenson DK, Ling XB, Butte AJ - BMC Med (2013)

Early or late onset biomarker panel scores were plotted as a function of the gestational age. Different panel scores were scaled to the same scoring metric such that they can be directly compared. For either PE or control data points, a loess curve was fitted to represent the overall trend of biomarker scoring as a function of gestation. PE, preeclampsia.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4226208&req=5

Figure 3: Early or late onset biomarker panel scores were plotted as a function of the gestational age. Different panel scores were scaled to the same scoring metric such that they can be directly compared. For either PE or control data points, a loess curve was fitted to represent the overall trend of biomarker scoring as a function of gestation. PE, preeclampsia.
Mentions: Using data from the ELISA assays, we constructed different panels with various subsets of the assays. We sought to identify biomarker panels of optimal feature number, balancing the need for small panel size, accuracy of classification, goodness of class separation (PE versus control), and sufficient sensitivity and specificity. With the aim of developing a multiplexed antibody-based assay for PE diagnosis, we used a genetic algorithm method to construct biomarker panels from the nine validated PE protein biomarkers for early and late gestational age PE, comparing to the sFlt-1/PIGF ratio in assessing PE. The algorithm guided panel construction processes, leading to early and late gestational age biomarker panels, which had complete separation between PE and control subjects (Table 6). These chosen biomarker panels are non-redundant, indicating non-inclusive relationships. The sFlt-1/PIGF ratio’s PE assessment utility (panel 0: early onset, ROC area under the curve (AUC) 1.00, p value 4.35 × 10-4; late onset, ROC AUC 0.86, p value 2.94 × 10-4; Additional file 2: Figure S3), previously through the multicenter trial validation [24], was confirmed in this study and used as a benchmark for our newly derived biomarker panels. Panel 1 (early onset, ROC AUC 1.00, p value 1.43 × 10-4) has three proteins, HPX, APO A-I and pikachurin. Panel 2 (late onset, ROC AUC 1.00, p value 3.65 × 10-5) has six proteins, HPX, HP, APO C-III, APO A-I, RBP4 and pikachurin. To demonstrate the efficacy of the biomarker panel as a classifier for PE disease activity according to disease onset, the biomarker panel scores were plotted as a function of time of the gestational age (composite summary in Additional file 2: Figure S2, details shown in Figure 3). According to the scatter plot analysis, the performance of our early-onset PE biomarker panel was comparable to the sFlt-1/PIGF ratio. For gestational age >34 weeks samples, performance of our biomarker panel is better than the sFlt-1/PIGF ratio that has several errors of diagnosis around week 36. Among the early and late gestational age biomarker panels, HPX, APO A-I, and pikachurin are present in both panels, indicating their critical role in the diagnosis.

Bottom Line: In addition to the previously identified biomarkers, the angiogenic and antiangiogenic factors (soluble fms-like tyrosine kinase (sFlt-1) and placental growth factor (PIGF)), we found 3 up-regulated and 6 down-regulated biomakers in PE sera.Both early and late onset PE diagnostic panels, constructed with our PE biomarkers, were superior over sFlt-1/PIGF ratio in PE discrimination.The functional significance of these PE biomarkers and their associated pathways were analyzed which may provide new insights into the pathogenesis of PE.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pediatrics, Stanford University, 1265 Welch Road, Room X-163 MS-5415, Stanford, CA 94305, USA. bxling@stanford.edu.

ABSTRACT

Background: Preeclampsia (PE) is a pregnancy-related vascular disorder which is the leading cause of maternal morbidity and mortality. We sought to identify novel serological protein markers to diagnose PE with a multi-'omics' based discovery approach.

Methods: Seven previous placental expression studies were combined for a multiplex analysis, and in parallel, two-dimensional gel electrophoresis was performed to compare serum proteomes in PE and control subjects. The combined biomarker candidates were validated with available ELISA assays using gestational age-matched PE (n=32) and control (n=32) samples. With the validated biomarkers, a genetic algorithm was then used to construct and optimize biomarker panels in PE assessment.

Results: In addition to the previously identified biomarkers, the angiogenic and antiangiogenic factors (soluble fms-like tyrosine kinase (sFlt-1) and placental growth factor (PIGF)), we found 3 up-regulated and 6 down-regulated biomakers in PE sera. Two optimal biomarker panels were developed for early and late onset PE assessment, respectively.

Conclusions: Both early and late onset PE diagnostic panels, constructed with our PE biomarkers, were superior over sFlt-1/PIGF ratio in PE discrimination. The functional significance of these PE biomarkers and their associated pathways were analyzed which may provide new insights into the pathogenesis of PE.

Show MeSH
Related in: MedlinePlus