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Due to the invasive procedure associated with Pap smears for diagnosing cervical cancer and the conservative culture of developing countries, identifying less invasive biomarkers is of great interest. Quantitative label-free mass spectrometry was performed to identify potential biomarkers in the urine samples of patients with cervical cancer. This technique was used to study the differential expression of urinary proteomes between normal individuals and cancer patients. The alterations in the levels of urinary proteomes in normal and cancer patients were analyzed by Progenesis label-free software and the results revealed that 60 proteins were upregulated while 73 proteins were downregulated in patients with cervical cancer. This method could enrich high molecular weight proteins from 100 kDa. The protein-protein interactions were obtained by Search Tool for the Retrieval of Interacting Genes/Proteins analysis and predicted the biological pathways involving various functions including cell-cell adhesion, blood coagulation, metabolic processes, stress response and the regulation of morphogenesis. Two notable upregulated urinary proteins were leucine-rich α-2-glycoprotein (LRG1) and isoform-1 of multimerin-1 (MMRN1), while the 3 notable downregulated proteins were S100 calcium-binding protein A8 (S100A8), serpin B3 (SERPINB3) and cluster of differentiation-44 antigen (CD44). The validation of these 5 proteins was performed by western blot analysis and the biomarker sensitivity of these proteins was analyzed individually and in combination with receiver operator characteristic curve (ROC) analysis. Quantitative mass spectrometry analysis may allow for the identification of urinary proteins of high molecular weight. The proteins MMRN1 and LRG1 were presented, for the first time, to be highly expressed urinary proteins in cervical cancer. ROC analysis revealed that LRG1 and SERPINB3 could be individually used, and these 5 proteins could also be combined, to detect the occurrence of cervical cancer.

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Cholangiocarcinoma (CCA), derived from the bile duct, occurs with a relatively high incidence in Northeast Thailand. Early diagnosis is still hampered by the lack of sufficient biomarkers. In recent years, biomarker discovery using secretomes has provided interesting results, including our studies on CCA secretomes, especially with three-dimensional cell cultures. Thus, cells cultured using the hollow fiber bioreactor (HFB) with 20 kDa molecular weight cut-off (MWCO) yielded higher quality and quantity of secretomes than those from conditioned media of the monolayer culture (MNC) system. In this study, we employed the HFB culture system with 5 kDa MWCO and compared conditioned media from the HFB and MNC systems using two-dimensional gel electrophoresis, followed by identifying proteins of interest by liquid chromatography and mass spectrometry (LC/MS/MS). Two out of 4 spots of NGAL or lipocalin-2 were found to show highest increase in expression of 19.93-fold and 18.79-fold in HFB compared to MNC. Interestingly, all 14 proteasome subunits including proteasome subunit α type-1 to type-7 and β type-1 to type-7 showed 2.92-fold to 12.13-fold increased expression in HFB. The protein-protein interactions of upregulated proteins were predicted, and one of the main interaction clusters involved 20S proteasome subunits. Proteasome activity in the HFB conditioned media was also found to be higher than that in MNC conditioned media. Three types of proteasome subunit were also validated by immunoblotting and showed higher expression in the HFB system compared to MNC system. Proteasome subunit α type-3 (PSMA3) showed the highest level in plasma of cholangiocarcinoma patients compared to normal and hepatocellular carcinoma patients by immunodetection, and is of interest as a potential biomarker for cholangiocarcinoma.

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Cholangiocarcinoma (CCA) is a highly lethal disease because most patients are asymptomatic until they progress to advanced stages. Current CCA diagnosis relies on clinical imaging tests and tissue biopsy, while specific CCA biomarkers are still lacking. This study employed a translational proteomic approach for the discovery, validation, and development of a multiplex CCA biomarker assay. In the discovery phase, label-free proteomic quantitation was performed on nine pooled plasma specimens derived from nine CCA patients, nine disease controls (DC), and nine normal individuals. Seven proteins (S100A9, AACT, AFM, and TAOK3 from proteomic analysis, and NGAL, PSMA3, and AMBP from previous literature) were selected as the biomarker candidates. In the validation phase, enzyme-linked immunosorbent assays (ELISAs) were applied to measure the plasma levels of the seven candidate proteins from 63 participants: 26 CCA patients, 17 DC, and 20 normal individuals. Four proteins, S100A9, AACT, NGAL, and PSMA3, were significantly increased in the CCA group. To generate the multiplex biomarker assays, nine machine learning models were trained on the plasma dynamics of all seven candidates (All-7 panel) or the four significant markers (Sig-4 panel) from 45 of the 63 participants (70%). The best-performing models were tested on the unseen values from the remaining 18 (30%) of the 63 participants. Very strong predictive performances for CCA diagnosis were obtained from the All-7 panel using a support vector machine with linear classification (AUC = 0.96; 95% CI 0.88–1.00) and the Sig-4 panel using partial least square analysis (AUC = 0.94; 95% CI 0.82–1.00). This study supports the use of the composite plasma biomarkers measured by clinically compatible ELISAs coupled with machine learning models to identify individuals at risk of CCA. The All-7 and Sig-4 assays for CCA diagnosis should be further validated in an independent prospective blinded clinical study.