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Serum Glycopatterns as Novel Potential Biomarkers for Diagnosis of Acute-on-Chronic Hepatitis B Liver Failure

View Article: PubMed Central - PubMed

ABSTRACT

Acute-on-chronic hepatitis B liver failure (ACHBLF) is an increasingly recognized distinct disease entity encompassing an acute deterioration of liver function in patients with cirrhosis, so little is known about the alterations of protein glycopatterns in serum with its development. We aimed to identify the alterations of serum glycopatterns in ACHBLF and probe the possibility of them as novel potential biomarkers for diagnosis of ACHBLF. As a result, there were 18 lectins (e.g., WFA, GSL-II, and PNA) to give significantly alterations of serum glycopatterns in ACHBLF compared with healthy controls (HC) (all p ≤ 0.0386). Meanwhile, among these lectins, there were 12 lectins (e.g., WFA, GAL-II, and EEL) also exhibited significantly alterations of serum glycopatterns in ACHBLF compared with HBV-infected chronic hepatitis (cHB) (all p ≤ 0.0252). The receiver-operating characteristic (ROC) curve analysis indicated there were 5 lectins (PHA-E + L, BS-I, ECA, ACA, and BPL) had the greatest discriminatory power for distinguishing ACHBLF and HC or cHB, respectively (all p ≤ 0.00136). We provided a new basic insight into serum glycopatterns in ACHBLF and investigated the correlation of alterations in serum glycopatterns as novel potential biomarkers for diagnosis of ACHBLF.

No MeSH data available.


The diagnosis accuracy of the candidate lectins analyzed by PCA, and ROC curve analysis, respectively.(A) The normalized glycopattern abundances responses to 3 pools were visualized by PCA. HC, cHB, and ACHBLF were indicated by a blue dotted line, green dotted line, indigo dotted line, respectively. (B) AUC of the 17 candidate lectins for HC, cHB, and ACHBLF. (C) The detailed information of the selected lectins analyzed by ROC analysis. —: Lectins with an AUC value lower than 0.80.
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f3: The diagnosis accuracy of the candidate lectins analyzed by PCA, and ROC curve analysis, respectively.(A) The normalized glycopattern abundances responses to 3 pools were visualized by PCA. HC, cHB, and ACHBLF were indicated by a blue dotted line, green dotted line, indigo dotted line, respectively. (B) AUC of the 17 candidate lectins for HC, cHB, and ACHBLF. (C) The detailed information of the selected lectins analyzed by ROC analysis. —: Lectins with an AUC value lower than 0.80.

Mentions: Based on 25 above candidate lectins (e.g., ECA, WFA, and GSL-II) that exhibited significantly alterations of protein glycopatterns in serum with ACHBLF, and to assess the serum glycopatterns as potential biomarkers for diagnosis of ACHBLF, serum samples of another cohort of ACHBLF (n = 16), cHB (n = 16), and HC (n = 20) were tested using the lectin microarrays independently. There were 18 lectins (e.g., WFA, GSL-II, and PNA) to give significantly alterations of serum glycopatterns in ACHBLF compared with HC (all p ≤ 0.0386). Meanwhile, among these lectins, there were 12 lectins (e.g., WFA, GAL-II, and EEL) also exhibited significantly alterations of serum glycopatterns in ACHBLF compared with cHB (all p ≤ 0.0252). Notably, ECA showed only significantly up-regulation of serum glycopatterns (p < 0.0001), however, MAL-I only showed significantly down-regulation (p = 0.0039) of serum glycopatterns in ACHBLF compared with cHB (Fig. 2). The principal component analysis (PCA) was used to provide graphical representations of the relationships between ACHBLF, cHB, and HC, which was generated based on the data of each lectin response patterns together for 52 serum samples. The PCA results showed that the subjects assigned to scatterplots tended to cluster separately to form ACHBLF, cHB, and HC pools with different symbols for each pool in Fig. 3A. Interestingly, it was seen that there were no overlapping area among them, indicating that it was possible to distinguish between ACHBLF, cHB, and HC based on precise alterations in serum glycopatterns.


Serum Glycopatterns as Novel Potential Biomarkers for Diagnosis of Acute-on-Chronic Hepatitis B Liver Failure
The diagnosis accuracy of the candidate lectins analyzed by PCA, and ROC curve analysis, respectively.(A) The normalized glycopattern abundances responses to 3 pools were visualized by PCA. HC, cHB, and ACHBLF were indicated by a blue dotted line, green dotted line, indigo dotted line, respectively. (B) AUC of the 17 candidate lectins for HC, cHB, and ACHBLF. (C) The detailed information of the selected lectins analyzed by ROC analysis. —: Lectins with an AUC value lower than 0.80.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5382696&req=5

f3: The diagnosis accuracy of the candidate lectins analyzed by PCA, and ROC curve analysis, respectively.(A) The normalized glycopattern abundances responses to 3 pools were visualized by PCA. HC, cHB, and ACHBLF were indicated by a blue dotted line, green dotted line, indigo dotted line, respectively. (B) AUC of the 17 candidate lectins for HC, cHB, and ACHBLF. (C) The detailed information of the selected lectins analyzed by ROC analysis. —: Lectins with an AUC value lower than 0.80.
Mentions: Based on 25 above candidate lectins (e.g., ECA, WFA, and GSL-II) that exhibited significantly alterations of protein glycopatterns in serum with ACHBLF, and to assess the serum glycopatterns as potential biomarkers for diagnosis of ACHBLF, serum samples of another cohort of ACHBLF (n = 16), cHB (n = 16), and HC (n = 20) were tested using the lectin microarrays independently. There were 18 lectins (e.g., WFA, GSL-II, and PNA) to give significantly alterations of serum glycopatterns in ACHBLF compared with HC (all p ≤ 0.0386). Meanwhile, among these lectins, there were 12 lectins (e.g., WFA, GAL-II, and EEL) also exhibited significantly alterations of serum glycopatterns in ACHBLF compared with cHB (all p ≤ 0.0252). Notably, ECA showed only significantly up-regulation of serum glycopatterns (p < 0.0001), however, MAL-I only showed significantly down-regulation (p = 0.0039) of serum glycopatterns in ACHBLF compared with cHB (Fig. 2). The principal component analysis (PCA) was used to provide graphical representations of the relationships between ACHBLF, cHB, and HC, which was generated based on the data of each lectin response patterns together for 52 serum samples. The PCA results showed that the subjects assigned to scatterplots tended to cluster separately to form ACHBLF, cHB, and HC pools with different symbols for each pool in Fig. 3A. Interestingly, it was seen that there were no overlapping area among them, indicating that it was possible to distinguish between ACHBLF, cHB, and HC based on precise alterations in serum glycopatterns.

View Article: PubMed Central - PubMed

ABSTRACT

Acute-on-chronic hepatitis B liver failure (ACHBLF) is an increasingly recognized distinct disease entity encompassing an acute deterioration of liver function in patients with cirrhosis, so little is known about the alterations of protein glycopatterns in serum with its development. We aimed to identify the alterations of serum glycopatterns in ACHBLF and probe the possibility of them as novel potential biomarkers for diagnosis of ACHBLF. As a result, there were 18 lectins (e.g., WFA, GSL-II, and PNA) to give significantly alterations of serum glycopatterns in ACHBLF compared with healthy controls (HC) (all p&thinsp;&le;&thinsp;0.0386). Meanwhile, among these lectins, there were 12 lectins (e.g., WFA, GAL-II, and EEL) also exhibited significantly alterations of serum glycopatterns in ACHBLF compared with HBV-infected chronic hepatitis (cHB) (all p&thinsp;&le;&thinsp;0.0252). The receiver-operating characteristic (ROC) curve analysis indicated there were 5 lectins (PHA-E&thinsp;+&thinsp;L, BS-I, ECA, ACA, and BPL) had the greatest discriminatory power for distinguishing ACHBLF and HC or cHB, respectively (all p&thinsp;&le;&thinsp;0.00136). We provided a new basic insight into serum glycopatterns in ACHBLF and investigated the correlation of alterations in serum glycopatterns as novel potential biomarkers for diagnosis of ACHBLF.

No MeSH data available.