Serum α2,6-sialylated glycoform of serotransferrin as a glycobiomarker for diagnosis and prediction of clinical severity in cholangiocarcinoma

Prognostic Values of Ferroptosis-Related Proteins ACSL4, SLC7A11, and CHAC1 in Cholangiocarcinoma

BACKGROUND

The epithelial malignant tumor known as cholangiocarcinoma (CCA) is most commonly found in Southeast Asia, particularly in northeastern Thailand. Previous research has indicated that the overexpression of acyl-CoA synthetase long-chain family member 4 (ACSL4), solute carrier family 7 member 11 (SLC7A11), and ChaC glutathione-specific γ-glutamylcyclotransferase (CHAC1) as ferroptosis-related proteins is associated with poorer prognosis in several cancers. The role of these three proteins in CCA is still unclear. The present study aimed to investigate the expression levels of ACSL4, SLC7A11, and CHAC1, all potential ferroptosis biomarkers, in CCA.

METHODS

The ACSL4, SLC7A11, and CHAC1 protein expression levels in 137 CCA tissues were examined using immunohistochemistry, while 61 CCA serum samples were evaluated using indirect ELISA. The associations between the expression levels of ACSL4, SLC7A11, and CHAC1 and patient clinicopathological data were evaluated to determine the clinical significance of these proteins.

RESULTS

The expression levels of ACSL4, SLC7A11, and CHAC1 were assessed in CCA tissues. A significant association was observed between high ACSL4 levels and extrahepatic CCA, tumor growth type, and elevated alanine transferase (ALT). There was also a positive association between elevated SLC7A11 levels and tumor growth type. Additionally, the upregulation of CHAC1 was significantly associated with a shorter survival time in patients. High levels of ACSL4 and SLC7A11 in CCA sera were both significantly associated with advanced tumor stages and abnormal liver function test results, indicating that they could be used as a reliable prognostic biomarker panel in patients with CCA.

CONCLUSIONS

The results of the present study demonstrated that the upregulation of ACSL4, SLC7A11, and CHAC1 could be used as a valuable biomarker panel for predicting prognosis parameters in CCA. Furthermore, ACSL4 and SLC7A11 could potentially serve as complementary markers for improving the accuracy of prognosis prediction when CCA sera is used. These less invasive biomarkers could facilitate effective treatment planning.

Development and validation of a novel preoperative clinical model for predicting lymph node metastasis in perihilar cholangiocarcinoma

BACKGROUD

We aimed to develop a novel preoperative nomogram to predict lymph node metastasis (LNM) in perihilar cholangiocarcinoma (pCCA) patients.

METHODS

160 pCCA patients were enrolled at Lihuili Hospital from July 2006 to May 2022. A novel nomogram model was established to predict LNM in pCCA patients based on the independent predictive factors selected by the multivariate logistic regression model. The precision of the nomogram model was evaluated through internal and external validation with calibration curve statistics and the concordance index (C-index). Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate and determine the clinical utility of the nomogram.

RESULTS

Multivariate logistic regression demonstrated that age (OR = 0.963, 95% CI: 0.930-0.996, P = 0.030), CA19-9 level (> 559.8 U/mL vs. ≤559.8 U/mL: OR = 3.162, 95% CI: 1.519-6.582, P = 0.002) and tumour diameter (OR = 1.388, 95% CI: 1.083-1.778, P = 0.010) were independent predictive factors of LNM in pCCA patients. The C-index was 0.763 (95% CI: 0.667-0.860) and 0.677 (95% CI: 0.580-0.773) in training cohort and validation cohort, respectively. ROC curve analysis indicated the comparative stability and adequate discriminative ability of nomogram. The sensitivity and specificity were 0.820 and 0.652 in training cohort and 0.704 and 0.649 in validation cohort, respectively. DCA revealed that the nomogram model could augment net benefits in the prediction of LNM in pCCA patients.

CONCLUSIONS

The novel prediction model is useful for predicting LNM in pCCA patients and showed adequate discriminative ability and high predictive accuracy.

Diagnostic potential of site-specific serotransferrin N-glycosylation in discriminating different liver diseases

BACKGROUND

Altered glycosylation modulates the structure and function of disease-related proteins. The associations between serotransferrin (STF) N-glycosylation and liver diseases (LDs) have been revealed. However, how intact N-glycopeptides vary among different types of liver diseases remains unclear.

METHODS

Intact STF N-glycopeptides from patients with chronic liver disease (CLD, n = 92), primary liver cancer (PLC, n = 123), metastatic liver cancer (MLC, n = 57), and healthy controls (HCs, n = 59) were determined using high-resolution mass spectrometry.

RESULTS

Significant changes were displayed in STF glycosylation among 4 groups. The LD screening model, including Asn432 G1S/G2S, Asn432 G2S/G2S2, and Asn630 G2NS2/G2FNS2, was constructed to differentiate LDs from HCs, with a AUC of 0.92. The liver cancer (LC) diagnostic model, a combination of Asn432 G1-N/G1S-N, Asn432 G1/G2, Asn432 G2FS/G2FS2, and Asn630 G1S-N /G1S, showed good performance in discriminating LC from CLD (AUC = 0.93). Moreover, AFP-negative LC patients (93 %) were successfully predicted by the LC diagnostic model. Furthermore, the MLC triage model, composed of Asn432 G1/G2, Asn432 G3F/G3FS, Asn630 G2/G2S, Asn630 G2S2/G2NS2, and Asn630 G3FS/G3FS2, yielded an AUC of 0.98 between PLC and MLC.

CONCLUSIONS

STF N-glycosylation is a potential biomarker for the accurate classification of different LDs.