Biomarkers
A biomarker, short for “biological marker,” is a measurable substance or characteristic found in an organism that can indicate the presence of a particular disease, condition, or physiological state. Biomarkers can be molecules, genes, proteins, hormones, cells, or other measurable indicators that provide information about the health status or the progression of a disease.
Biomarkers serve several important purposes in medicine and research:
Diagnosis: Biomarkers can help identify the presence of a disease or condition. For example, elevated levels of certain proteins in the blood can be indicative of specific diseases like cancer.
see Blood Biomarkers.
see Diagnostic biomarkers.
see Prognostic markers.
Classification
Biomarkers can be classified into different categories based on their characteristics and applications. Here are some common biomarker classifications:
Diagnostic Biomarkers:
Definition: Biomarkers used to identify the presence or absence of a disease or condition. Example: Prostate-specific antigen (PSA) for prostate cancer. Prognostic Biomarkers:
Definition: Biomarkers that provide information about the likely course or outcome of a disease. Example: HER2/neu status in breast cancer is a prognostic biomarker. Predictive Biomarkers:
Definition: Biomarkers that help predict the response to a specific treatment or intervention. Example: Estrogen receptor (ER) status in breast cancer predicts response to hormonal therapies. Surrogate Biomarkers:
Definition: Biomarkers used as substitutes for clinically meaningful endpoints to predict treatment effects. Example: Reduction in blood pressure as a surrogate for the prevention of cardiovascular events. Monitoring or Response Biomarkers:
Definition: Biomarkers used to assess the response to a treatment or intervention during the course of therapy. Example: Blood glucose levels monitored in diabetic patients to assess the response to insulin therapy. Screening Biomarkers:
Definition: Biomarkers used in early detection or screening of diseases in asymptomatic individuals. Example: Blood cholesterol levels as a screening biomarker for cardiovascular disease. Safety Biomarkers:
Definition: Biomarkers used to assess the safety and potential side effects of a treatment. Example: Liver enzyme levels to monitor potential liver toxicity of a drug.
Genetic Biomarkers:
Definition: Biomarkers based on genetic information, including DNA, RNA, and gene expression profiles. Example: BRCA1 and BRCA2 gene mutations as genetic biomarkers for breast and ovarian cancer risk. Proteomic Biomarkers:
Definition: Biomarkers related to the study of proteins, including their expression, modifications, and interactions. Example: PSA (Prostate-Specific Antigen) in prostate cancer is a proteomic biomarker.
Prognosis: Biomarkers can provide information about the likely course of a disease. Some biomarkers can predict how aggressive a cancer might be or how a person with a certain condition might respond to treatment.
Monitoring: Biomarkers can be used to track the progression of a disease or the effects of treatment. Changes in biomarker levels over time can help doctors assess the effectiveness of therapies.
Therapeutic Targets: Biomarkers can identify specific molecules or pathways involved in a disease, which can guide the development of targeted treatments.
Personalized Medicine: Biomarkers enable personalized treatment approaches by helping doctors tailor therapies to individual patients based on their unique biomarker profiles.
Research: Biomarkers are crucial in research for understanding disease mechanisms, developing new treatments, and evaluating the safety and efficacy of drugs.
Examples
PSA (Prostate-Specific Antigen): Used as a biomarker for prostate cancer. Elevated levels may indicate the presence of prostate cancer.
Blood Glucose Level: Used to monitor diabetes. High blood glucose levels are indicative of poor blood sugar control.
HER2 (Human Epidermal Growth Factor Receptor 2): A protein used as a biomarker in breast cancer. Overexpression of HER2 can influence treatment decisions.
C-reactive Protein (CRP): An indicator of inflammation. Elevated levels can be associated with a range of inflammatory conditions.
BRCA1 and BRCA2 Genes: Mutations in these genes are associated with an increased risk of breast and ovarian cancers.
Alzheimer's Disease Biomarkers: Biomarkers like beta-amyloid and tau proteins in the brain can help diagnose and track the progression of Alzheimer's disease.
1p/19q co-deletion
With the emergence of the molecular era and retreat of the histology epoch in malignant glioma, it is becoming increasingly necessary to research diagnostic/prognostic/therapeutic biomarkers and their related regulatory mechanisms.
While accumulating studies have investigated coding gene-associated biomarkers in malignant glioma, research on comprehensive coding and noncoding RNA-associated biomarkers is lacking. Furthermore, few studies have illustrated the crosstalk signalling pathways among these biomarkers and mechanisms in detail.
Huang et al. identified differentially expressed genes and Competing endogenous RNA (ceRNA) networks in malignant glioma and then constructed Cox/Lasso regression models to further identify the most valuable genes through stepwise refinement. Top-down comprehensive integrated analysis, including functional enrichment, SNV, immune infiltration, transcription factor binding site, and molecular docking analyses, further revealed the regulatory maps among these genes. The results revealed a novel and accurate model (AUC of 0.91 and C-index of 0.84 in the whole malignant gliomas, AUC of 0.90 and C-index of 0.86 in LGG, and AUC of 0.75 and C-index of 0.69 in Glioblastoma) that includes twelve ncRNAs, 1 MicroRNA and 6 coding genes. Stepwise logical reasoning based on top-down comprehensive integrated analysis and references revealed cross-talk signalling pathways among these genes that were correlated with the circadian rhythm, tumour immune microenvironment and cellular senescence pathways. In conclusion, our work reveals a novel model where the newly identified biomarkers may contribute to a precise diagnosis/prognosis and subclassification of malignant glioma, and the identified cross-talk signalling pathways would help to illustrate the noncoding RNA-associated epigenetic regulatory mechanisms of glioma tumorigenesis and aid in targeted therapy 1).
Importance in neurooncology
Since the introduction of integrated histological and molecular diagnoses by the 2016 World Health Organization (WHO) Classification of Tumors of the Nervous System, an increasing number of molecular markers have been found to have prognostic significance in infiltrating gliomas, many of which have now become incorporated as diagnostic criteria in the 2021 WHO Classification. This has increased the applicability of targeted-next generation sequencing in the diagnostic work-up of neuropathology specimens and in addition, raises the question of whether targeted sequencing can, in practice, reliably replace older, more traditional diagnostic methods such as immunohistochemistry and Fluorescence in situ hybridization. Slocum et al. demonstrated that the Oncomine Cancer Gene Mutation Panel v2 assay targeted-next generation sequencing panel for solid tumors is not only superior to IHC in detecting mutation in IDH1/2 and TP53 but can also predict 1p/19q co-deletion with high sensitivity and specificity relative to Fluorescence in situ hybridization by looking at average copy number of genes sequenced on 1p, 1q, 19p, and 19q. Along with detecting the same molecular data obtained from older methods, targeted-next generation sequencing with an RNA sequencing component provides additional information regarding the presence of RNA based alterations that have diagnostic significance and possible therapeutic implications. They advocate for expanded use of targeted-next generation sequencing over more traditional methods for the detection of important molecular alterations as a part of the standard diagnostic work up for CNS neoplasms 2)
Biomarker status now guides clinical decisions in some subtypes of gliomas, including anaplastic oligodendroglioma and glioblastoma in the elderly.
1p/19q co-deletion, O6 methylguanine DNA methyltransferase (MGMT) promoter methylation and isocitrate dehydrogenase (IDH) 1/2 mutations – are known to have important diagnostic, prognostic and predictive roles in glioma treatment.
Long non-coding RNA are aberrantly expressed in gliomas and exert diverse functions.
They are associated with tumor size, WHO grade, and prognosis in glioma patients. lncRNAs could function as potential molecular biomarkers of the clinicopathology and prognosis of glioma 3).