Cancer Biomarker Detection
Cancer Biomarker Detection - Related Videos
07:32
A Capillary Electrophoresis Immunoassay for Protein Biomarker Detection
0 Views •
2025
This video demonstrates a capillary electrophoresis immunoassay protocol for detecting a protein biomarker associated with amyotrophic lateral sclerosis (ALS) from human blood platelets. Confirmation of the presence of the ALS biomarker protein is achieved through size-based separation of the platelet proteins followed by chemiluminescent detection.
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.
03:04
Colorimetric Detection of a Bacterial Biomarker Using a Modified Litmus Test
0 Views •
2025
This video demonstrates a colorimetric E. coli detection method, involving an E. coli biomarker activating a DNAzyme, leading to cleavage of RNA linkage and detachment of a DNAzyme-urease conjugate. A subsequent colorimetric assay detects the E. coli biomarker through ammonia-induced pH changes.
12:24
Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
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Cited by 18 •
2012
Diffuse fluorescence tomography offers a relatively low-cost and potentially high-throughout approach to preclinical in vivo tumor imaging. The methodology of optical data collection, calibration, and image reconstruction is presented for a computed tomography-guided non-contact time-domain system using fluorescent targeting of the tumor biomarker epidermal growth factor receptor in a mouse glioma model.
10:22
Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
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Cited by 59 •
2016
High Frequency Oscillations (HFOs) have emerged as presurgical biomarkers for the identification of the epileptogenic zone in pediatric patients with medically refractory epilepsy. A methodology for the noninvasive recording, detection, and localization of HFOs with simultaneous scalp electroencephalography (EEG) and magnetoencephalography (MEG) is presented.
FAQs
Sensitivity determines how effectively an assay detects the biomarker signal, while specificity reflects how reliably it distinguishes that signal from unrelated molecular or cellular features. Both characteristics influence the usefulness of a test: higher sensitivity can support earlier detection, whereas higher specificity strengthens confidence in tumor classification or monitoring results. Bioengineering improves these properties through assay design and signal-processing strategies.
These approaches recognize different types of tumor-associated information. Antibodies target molecular features that can be measured through binding, whereas nucleic acid probes identify complementary genetic sequences. Polymerase chain reaction amplifies selected nucleic acid targets for measurement, and sequencing provides broader sequence information. Choosing among them depends on the biomarker characteristics and the intended outcome, such as classification or treatment selection.
Engineered biosensors translate a biomarker-binding event into a measurable signal, creating a direct connection between molecular recognition and analytical readout. Their value lies in integrating recognition elements with engineered signal-conversion systems, potentially supporting sensitive measurements in biological samples. Within bioengineering, biosensors complement antibody- and nucleic-acid-based assays by offering another route for detecting tumor-associated signatures.
Microfluidic platforms can be integrated with biomarker assays to support analysis of samples such as blood or tissue, while computational platforms can help interpret the resulting measurements. Together, these technologies connect sample analysis with signal evaluation rather than treating detection as an isolated step. Their integration is intended to improve assay performance and support more informative, personalized cancer-care decisions.
A basic workflow begins with a relevant sample, such as blood or tissue, followed by selection of an assay suited to the target signature. Antibodies, nucleic acid probes, polymerase chain reaction, sequencing, or an engineered biosensor can then generate measurable information. Interpreting that result in context supports decisions about detection, tumor classification, treatment selection, or disease monitoring.
Cancer biomarker detection can address several distinct needs across the disease course. It may support early detection, characterize a tumor, help select a treatment, or track disease progression and therapeutic response. The same general bioengineering framework therefore contributes to both diagnosis-related assessment and follow-up measurement, with the intended use determining which biomarker and analytical approach are most relevant.