Cancer Protein Detection
Cancer Protein Detection - Related Videos
10:02
Standardized SDS-PAGE Workflow for Personalized Protein Corona Profiling in Early Cancer Detection
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2025
This protocol standardizes SDS-PAGE analysis for personalized protein corona profiling on nanoparticles, enabling reproducible, scalable, and low-cost detection of cancer-specific signatures. Designed for early diagnosis of pancreatic ductal adenocarcinoma, it offers a practical, REASSURED-aligned alternative to complex proteomic methods in both research and clinical settings.
05:17
Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
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2025
This protocol describes an automated, ISO15189-accredited next-generation sequencing workflow for detecting targetable genomic alterations in non-small cell lung cancer (NSCLC) formalin-fixed paraffin-embedded tissues.
12:22
The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers
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Cited by 12 •
2013
RPPA enables the protein expression of hundreds of samples, printed on nitrocellulose slides to be interrogated simultaneously, using fluorescently labelled antibodies. This technique has been applied to study the effect of drug treatment heterogeneity within clear cell renal carcinoma.
09:00
Detection of Protein Ubiquitination
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Cited by 96 •
2009
Ubiquitination is a key posttranslational modification carried out by a set of three enzymes. Mutations of genes involved in this modification are associated with many different human diseases. Here, we describe protocols to detect protein ubiquitination in cultured cells in vivo and test tubes in vitro.
12:28
Protein Purification Technique that Allows Detection of Sumoylation and Ubiquitination of Budding Yeast Kinetochore Proteins Ndc10 and Ndc80
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Cited by 11 •
2015
This manuscript describes the detection of sumoylation and ubiquitination of kinetochore proteins, Ndc10 and Ndc80, in the budding yeast Saccharomyces cerevisiae.
FAQs
Selectivity comes from molecular recognition at the sensing interface. An antibody, aptamer, or other affinity reagent is chosen to bind a cancer-associated protein, while unbound material is separated or otherwise excluded from the measured response. The resulting target-dependent signal helps distinguish disease-related protein changes from unrelated sample components and supports more reliable measurements.
Signal choice determines how binding becomes usable data. Optical readouts measure a light-based response, electrical biosensors translate recognition into an electrical response, and mass-based workflows detect changes through measured molecular mass. These approaches share the same recognition principle but differ in the type of signal they produce, giving engineers alternatives for designing cancer protein detection systems.
Antibodies and aptamers serve the same core purpose but are distinct recognition reagents. Each can be paired with a measurement platform to target a cancer-associated protein, while other affinity reagents may also be used when appropriate. This modularity lets engineers connect molecular selectivity with optical, electrical, or mass-based readouts without changing the broader measurement goal.
Sensitivity, speed, and multiplexing are central engineering objectives rather than interchangeable outcomes. A platform may be developed to detect smaller disease-related changes, return measurements more quickly, or measure multiple proteins in one system. Improving one or more of these characteristics can strengthen diagnostic development, but the measurement must still remain accurate enough for interpretation and treatment-related decision support.
A basic workflow begins with a biological sample and a target protein, followed by exposure to an affinity reagent that can recognize that target. The platform then converts the binding event into an optical, electrical, or mass-based signal and uses that measurement to assess the protein. This sequence connects sample analysis with quantitative biomarker information.
Measurements can support several decisions across the disease course. Cancer-associated protein patterns may contribute to diagnosis or classification, while repeated measurements can help monitor disease progression or response to therapy. The same information can therefore serve both clinical decision support and research on biomarker behavior, provided the detection system produces sufficiently accurate results.
Engineering contributes by matching the assay architecture to the intended use. Immunoassays provide one implementation, while biosensors and microfluidic devices support engineered measurement platforms; mass spectrometry offers a mass-based workflow. Comparing these formats helps guide development of systems that are faster, more sensitive, or multiplexed, extending cancer protein detection beyond a single instrument design.