Limits...
Three serum metabolite signatures for diagnosing low-grade and high-grade bladder cancer

View Article: PubMed Central - PubMed

ABSTRACT

To address the shortcomings of cystoscopy and urine cytology for detecting and grading bladder cancer (BC), ultrahigh performance liquid chromatography (UHPLC) coupled with Q-TOF mass spectrometry in conjunction with univariate and multivariate statistical analyses was employed as an alternative method for the diagnosis of BC. A series of differential serum metabolites were further identified for low-grade(LG) and high-grade(HG) BC patients, suggesting metabolic dysfunction in malignant proliferation, immune escape, differentiation, apoptosis and invasion of cancer cells in BC patients. In total, three serum metabolites including inosine, acetyl-N-formyl-5-methoxykynurenamine and PS(O-18:0/0:0) were selected by binary logistic regression analysis, and receiver operating characteristic (ROC) test based on their combined use for HG BC showed that the area under the curve (AUC) was 0.961 in the discovery set and 0.950 in the validation set when compared to LG BC. Likewise, this composite biomarker panel can also differentiate LG BC from healthy controls with the AUC of 0.993 and 0.991 in the discovery and validation set, respectively. This finding suggested that this composite serum metabolite signature was a promising and less invasive classifier for probing and grading BC, which deserved to be further investigated in larger samples.

No MeSH data available.


Multivariate data analysis based on the data from UHPLC-Q-TOFMS spectra of HG BC (), LG BC (), and HC (▲). (A) PCA score plot from HG BC, LG BC and HC, (B) PLS-DA score plot from HG BC, LG BC and HC, (C) Validation plot of PLS-DA model obtained using 99 permutation tests from HG BC, LG BC and HC, (D) PLS-DA score plot from HG BC and LG BC, (E) Validation plot of PLS-DA model obtained using 99 permutation tests from HG BC and LG BC, and (F) OPLS-DA score plot from HG BC and LG BC.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5382774&req=5

f1: Multivariate data analysis based on the data from UHPLC-Q-TOFMS spectra of HG BC (), LG BC (), and HC (▲). (A) PCA score plot from HG BC, LG BC and HC, (B) PLS-DA score plot from HG BC, LG BC and HC, (C) Validation plot of PLS-DA model obtained using 99 permutation tests from HG BC, LG BC and HC, (D) PLS-DA score plot from HG BC and LG BC, (E) Validation plot of PLS-DA model obtained using 99 permutation tests from HG BC and LG BC, and (F) OPLS-DA score plot from HG BC and LG BC.

Mentions: In this work, the BC patients were classified into two subgroups according to the histopathological evaluation of transurethral-resected tissue specimens, i.e., LG BC group and HG BC group. As an unsupervised multivariate statistical model, PCA was first performed to explore the metabolic differences among healthy subjects, LG BC patients and HG BC patients (12PCs, R2X = 0.882, Q2 = 0.532). A tendency in the PCA scores plot to separate BC patients and healthy subjects into the two classes was detected (Fig. 1A), indicating a significantly different serum metabolome between BC patients and healthy subjects. However, we did not observe an obvious difference between the LG BC and HG BC patients in the PCA scores plot. Furthermore, the supervised PLS-DA model was conducted (3PCs,R2Y = 0.816,Q2 = 0.773). A dramatic difference between BC patients and healthy subjects was observed in the PLS-DA score plot (Fig. 1B). Meanwhile, an obvious separation trend between LG BC and HG BC patients was also observed. Model validation with the number of permutations equalling 99 generated intercepts of R2 = 0.146 and Q2 = −0.310, which meant that the PLS-DA model was non-overfitting and reliable (Fig. 1C).


Three serum metabolite signatures for diagnosing low-grade and high-grade bladder cancer
Multivariate data analysis based on the data from UHPLC-Q-TOFMS spectra of HG BC (), LG BC (), and HC (▲). (A) PCA score plot from HG BC, LG BC and HC, (B) PLS-DA score plot from HG BC, LG BC and HC, (C) Validation plot of PLS-DA model obtained using 99 permutation tests from HG BC, LG BC and HC, (D) PLS-DA score plot from HG BC and LG BC, (E) Validation plot of PLS-DA model obtained using 99 permutation tests from HG BC and LG BC, and (F) OPLS-DA score plot from HG BC and LG BC.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5382774&req=5

f1: Multivariate data analysis based on the data from UHPLC-Q-TOFMS spectra of HG BC (), LG BC (), and HC (▲). (A) PCA score plot from HG BC, LG BC and HC, (B) PLS-DA score plot from HG BC, LG BC and HC, (C) Validation plot of PLS-DA model obtained using 99 permutation tests from HG BC, LG BC and HC, (D) PLS-DA score plot from HG BC and LG BC, (E) Validation plot of PLS-DA model obtained using 99 permutation tests from HG BC and LG BC, and (F) OPLS-DA score plot from HG BC and LG BC.
Mentions: In this work, the BC patients were classified into two subgroups according to the histopathological evaluation of transurethral-resected tissue specimens, i.e., LG BC group and HG BC group. As an unsupervised multivariate statistical model, PCA was first performed to explore the metabolic differences among healthy subjects, LG BC patients and HG BC patients (12PCs, R2X = 0.882, Q2 = 0.532). A tendency in the PCA scores plot to separate BC patients and healthy subjects into the two classes was detected (Fig. 1A), indicating a significantly different serum metabolome between BC patients and healthy subjects. However, we did not observe an obvious difference between the LG BC and HG BC patients in the PCA scores plot. Furthermore, the supervised PLS-DA model was conducted (3PCs,R2Y = 0.816,Q2 = 0.773). A dramatic difference between BC patients and healthy subjects was observed in the PLS-DA score plot (Fig. 1B). Meanwhile, an obvious separation trend between LG BC and HG BC patients was also observed. Model validation with the number of permutations equalling 99 generated intercepts of R2 = 0.146 and Q2 = −0.310, which meant that the PLS-DA model was non-overfitting and reliable (Fig. 1C).

View Article: PubMed Central - PubMed

ABSTRACT

To address the shortcomings of cystoscopy and urine cytology for detecting and grading bladder cancer (BC), ultrahigh performance liquid chromatography (UHPLC) coupled with Q-TOF mass spectrometry in conjunction with univariate and multivariate statistical analyses was employed as an alternative method for the diagnosis of BC. A series of differential serum metabolites were further identified for low-grade(LG) and high-grade(HG) BC patients, suggesting metabolic dysfunction in malignant proliferation, immune escape, differentiation, apoptosis and invasion of cancer cells in BC patients. In total, three serum metabolites including inosine, acetyl-N-formyl-5-methoxykynurenamine and PS(O-18:0/0:0) were selected by binary logistic regression analysis, and receiver operating characteristic (ROC) test based on their combined use for HG BC showed that the area under the curve (AUC) was 0.961 in the discovery set and 0.950 in the validation set when compared to LG BC. Likewise, this composite biomarker panel can also differentiate LG BC from healthy controls with the AUC of 0.993 and 0.991 in the discovery and validation set, respectively. This finding suggested that this composite serum metabolite signature was a promising and less invasive classifier for probing and grading BC, which deserved to be further investigated in larger samples.

No MeSH data available.