Pancreatic cancer (PC) is the most lethal type of malignancy and is characterized by a highly aggressive nature with no serious symptoms in its early stages. To date, there are several methods developed to detect PC at an early stage, such as abdominal ultrasound, CT, MRI, and endoscopic ultrasound (EUS). However, these conventional methods are usually characterized by low diagnostic accuracy. Therefore, the development of new techniques for early and high-precision diagnosis is of great value. CD BioSciences is a leading custom service provider with experience in PC basic research and drug development. With advanced techniques and platforms, we are proud to offer PC diagnostic development services, including protein biomarker development for PC diagnosis.
Overview of protein biomarkers in PC
Evaluation of human proteins can reflect various aspects of patient physiology, including tumor growth and disease status. Thus, protein biomarkers or biomarker panels represent a promising source of disease indicators that can be more feasibly evaluated with less invasive diagnostic tests. PC-related protein biomarkers have been detected in patients' blood, pancreatic fluid, and tumor tissue. High-throughput screening of PC patient proteins combined with bioinformatic analysis of existing cancer genomic datasets has led to the identification of many potential new markers for PC diagnosis.
- Protein biomarker candidates for the diagnosis of PC in serum samples: CA 19-9, C4BPA, GPC1, CPA4, PFAA, MUC5AC, and OPNT+TIMP-1.
- Protein biomarker candidates for the diagnosis of PC in urine samples: LYVE1, REGIA, TFFI, and neutrophil gelatinase-associated lipocalin (NGAL).
- Protein biomarker candidates for the diagnosis of PC in pancreatic juice and bile samples: anterior gradient-2 (ARG2), ligand-binding repeats (sLR11)
Protein biomarker development services for PC
In recent years, proteomics mass spectrometry (MS) has been widely used for biomarker development. In particular, the MS-based targeted proteomics approach enables the measurement of hundreds of proteins. In addition to its powerful high-throughput capabilities, MS-based targeted proteomics methods maintain high sensitivity, selectivity, and reproducibility simultaneously. Moreover, multiple reaction monitoring-mass spectrometry (MRM-MS), as an MS-based targeted proteomics method, allows specific detection of peptides of interest and their corresponding fragments and is therefore largely independent of matrix effects and other contaminants. Based on MS-based targeted proteomics methods, enzyme-linked immunosorbent assay (ELISA), immunohistochemistry, and tissue microarray technology, we aim to help our clients achieve efficient protein biomarker development for PC, specifically including protein biomarker discovery, and verification and validation. Our services include,
- Experimental design and consultation
- Protein biomarker discovery and identification
- Protein biomarker verification and validation
- A protein-based multi-marker panel development
The ideal diagnostic biomarkers for effective screening are those that can be obtained in a minimally invasive manner and that can distinguish PC from healthy individuals or other benign pancreatic diseases with satisfactory sensitivity and specificity. If you are interested in learning more about our PC vaccine development services, would like to learn more about our services and opportunities to participate in market research, or are interested in a potential partnership or collaboration, please don't hesitate to contact us. Our professional and patient staff will contact you as soon as possible.
- Wu, Haotian, et al. "Advances in biomarkers and techniques for pancreatic cancer diagnosis." Cancer Cell International 22.1 (2022): 1-12.
- Liu, Xiaohui, et al. "A new panel of pancreatic cancer biomarkers discovered using a mass spectrometry-based pipeline." British journal of cancer 117.12 (2017): 1846-1854.
- Hu, Dingyuan, et al. "Proteomic analyses identify prognostic biomarkers for pancreatic ductal adenocarcinoma." Oncotarget 9.11 (2018): 9789.