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Discovery of Novel Serum Peptide Biomarkers for CCA Recurrence Through MALDI-TOF and LC-MSMS Peptidome Analysis

ABSTRACT: Cholangiocarcinoma (CCA) is an aggressive cancer originating from bile duct epithelial cells, with a high rate of recurrence following surgical resection. Recurrence is categorized as early linked to aggressive tumor biology than late recurrence. This study aimed to identify novel peptide mass fingerprints (PMFs) and potential biomarker penel in the serum of CCA patients with early and late recurrence using mass spectrometry.

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Attapol Titapun

PROVIDER: PXD057396 | JPOST Repository | Fri Oct 31 00:00:00 GMT 2025

REPOSITORIES: jPOST

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			Action	DRS
	Pool%20Serum%20Early_GC5_01_37590.mgf	Mgf
	Pool%20Serum%20Early_GC5_01_37590.mzXML	Mzxml
	Pool%20Serum%20Early_GC5_01_37591.mgf	Mgf
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	Pool%20Serum%20Early_GC5_01_37592.mgf	Mgf

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Discovery of Novel Peptide-based Biomarkers in Serum for Cholangiocarcinoma Diagnosis and Staging Using MALDI-TOF MS and LC-MS/MS

Project description:Cholangiocarcinoma (CCA) is a highly aggressive malignancy often diagnosed at an advanced stage due to the absence of specific early symptoms, thereby limiting treatment options and resulting in a poor prognosis. Current cancer staging primarily depends on postoperative histopathological evaluation, requiring surgical resection for accurate staging. This reliance causes delays in staging and subsequently affects treatment planning. Given the challenges in early diagnosis and accurate staging of CCA, there is an urgent need for non-invasive and precise diagnostic tools to improve patient outcomes. To address this gap, this study identified serum peptide biomarkers for cholangiocarcinoma and other hepatobiliary diseases through MALDI-TOF MS and LC-MS/MS, integrated with machine learning models

2025-03-19 | PXD061091 | JPOST Repository

Rapid Detection of COVID-19 Using MALDI-TOF-Based Serum Peptidome Profiling.

Project description:The outbreak of coronavirus disease 2019 (COVID-19) caused by SARS CoV-2 is ongoing and a serious threat to global public health. It is essential to detect the disease quickly and immediately to isolate the infected individuals. Nevertheless, the current widely used PCR and immunoassay-based methods suffer from false negative results and delays in diagnosis. Herein, a high-throughput serum peptidome profiling method based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is developed for efficient detection of COVID-19. We analyzed the serum samples from 146 COVID-19 patients and 152 control cases (including 73 non-COVID-19 patients with similar clinical symptoms, 33 tuberculosis patients, and 46 healthy individuals). After MS data processing and feature selection, eight machine learning methods were used to build classification models. A logistic regression machine learning model with 25 feature peaks achieved the highest accuracy (99%), with sensitivity of 98% and specificity of 100%, for the detection of COVID-19. This result demonstrated a great potential of the method for screening, routine surveillance, and diagnosis of COVID-19 in large populations, which is an important part of the pandemic control.

| S-EPMC7945584 | biostudies-literature

Serum peptidome patterns of colorectal cancer based on magnetic bead separation and MALDI-TOF mass spectrometry analysis.

Project description:Background. Colorectal cancer (CRC) is one of the most common cancers in the world, identification of biomarkers for early detection of CRC represents a relevant target. The present study aims to determine serum peptidome patterns for CRC diagnosis. Methods. The present work focused on serum proteomic analysis of 32 health volunteers and 38 CRC by ClinProt Kit combined with mass spectrometry. This approach allowed the construction of a peptide patterns able to differentiate the studied populations. An independent group of serum (including 33 health volunteers, 34 CRC, 16 colorectal adenoma, 36 esophageal carcinoma, and 31 gastric carcinoma samples) was used to verify the diagnostic and differential diagnostic capability of the peptidome patterns blindly. An immunoassay method was used to determine serum CEA of CRC and controls. Results. A quick classifier algorithm was used to construct the peptidome patterns for identification of CRC from controls. Two of the identified peaks at m/z 741 and 7772 were used to construct peptidome patterns, achieving an accuracy close to 100% (>CEA, P < 0.05). Furthermore, the peptidome patterns could differentiate validation group with high accuracy. Conclusions. These results suggest that the ClinProt Kit combined with mass spectrometry yields significantly higher accuracy for the diagnosis and differential diagnosis of CRC.

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Utilizing MALDI-TOF mass spectrometry and liquid chromatography-tandem mass spectrometry to access serum peptidome-based biomarkers in canine oral tumors

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Discovery of novel serum peptide biomarkers for cholangiocarcinoma recurrence through MALDI-TOF MS and LC-MS/MS peptidome analysis.

Project description:Cholangiocarcinoma (CCA) is an aggressive cancer originating from bile duct epithelial cells, with a high rate of recurrence following surgical resection. Recurrence is categorized as early linked to aggressive tumor biology than late recurrence. This study aimed to identify novel peptide mass fingerprints (PMFs) and potential biomarker panels in the serum of CCA patients with early and late recurrence using mass spectrometry. Serum samples of 81 CCA patients were analyzed using MALDI-TOF MS and LC-MS/MS, with statistical analysis correlating peptide profiles with clinical outcomes like disease-free survival (DFS) and overall survival (OS). A 365-day DFS cut-off effectively distinguished early from late recurrence, with early recurrence linked to poorer survival outcomes. The PMFs from MALDI-TOF MS differentiated recurrence types based on specific mass signatures. LC-MS/MS analysis identified 95 peptides associated with cancer progression in early recurrence and 60 in late recurrence. Distinct protein associations were found: ATR, POLA1, BLM, SP100, and PPP1R15A for early recurrence, and SERPINA1, TGFB2, SERPING1, and CAD for late recurrence, with strong interactions with chemotherapeutic drugs. This study successfully demonstrated the use of PMFs for rapid discrimination between early and late recurrence in CCA and identified potential serum peptide biomarkers to improve accuracy in recurrence classification.

| S-EPMC11746940 | biostudies-literature

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Project description:Cholangiocarcinoma (CCA) is a highly aggressive cancer originating from the bile duct epithelium. It has been on the rise globally, especially in Asian countries where the liver ûuke infection is prevalent. Current chemotherapy treatments for CCA are often ineûective due to drug resistance. Thus, there is a pressing need for new, eûective anticancer agents. This study explores the potential of 5'-deoxy-5'-methylthioadenosine (MTA), a naturally occurring nucleoside, in treating CCA. MTA has shown promise in inhibiting cell proliferation and invasion in various cancer types, but its eûects on CCA have not been studied.

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Discovering the anticancer mechanism of 52-deoxy-52- methylthioadenosine in cholangiocarcinoma cells using quantitative proteomics analysis

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