Editorial
Disease Biomakers in Serum: Analytical Methods of Identification and Quantification
Mbah CJ*
Corresponding Author: Mbah CJ, Department of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka, Enugu State, Nigeria
Received: February 18, 2019; Revised: July 11, 2019; Accepted: February 19, 2019
Citation: Mbah CJ. (2019) Disease Biomakers in Serum: Analytical Methods of Identification and Quantification. J Pharm Drug Res, 2(4): 131-133.
Copyrights: ©2019 Mbah CJ. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Share :
  • 625

    Views & Citations
  • 10

    Likes & Shares


Biomarker is a biomolecule that is objectively measured and evaluated as an indicator of normal biological processes, pathological processes, or pharmacological responses to a therapeutic drug treatment [1,2]. Alternatively, it can be defined as a chemical, its metabolite, or the product of an interaction between a chemical and some target molecule (genes, gene products, enzymes or hormones, etc.) or cell that is measured in the human body. The effectiveness of a biomarker is determined by the degree to which biomarker reflect clinical outcomes. Therefore, an ideal biomarker is expected to be: (i) able to detect a fundamental feature of a specific disease, validated in and confirmed by those specific disease cases; (ii) able to detect the early stages of this specific disease and differentiate it from other similar disease cases or family members of that disease; (iii) precise, accurate, sensitive, specific, non-invasive and inexpensive [3].

Disease-related biomarkers [4] indicate the probable effect of treatment on patient (predictive biomarkers), if a disease already exists (diagnostic biomarker), or how such a disease may develop in an individual case regardless of the type of treatment (prognostic biomarker).

Biomarkers can be classified into:

1.       Electrolytes and ions - sodium (Na+), potassium (K+), chloride (Cl-), carbondioxide (CO2), calcium (Ca2+), phosphorus (phosphate, PO43-), magnesium (Mg2+), iron (Fe2+).

2.       Small molecules and metabolites (under a molecular weight of 1000) - those that reflect nutritional status (glucose, vitamin B12, folic acid, etc.), those that reflect the elimination of waste products (bilirubins, lactic acid, creatinine, uric acid, urea nitrogen, ammonia, etc.) and those that reflect metabolic control (thyroid stimulating hormone, estrogen, testosterone, beta-human chorionic gonadotropin, etc.)

3.       Large molecules and metabolites (molecular weights ranging from 30,000 to over 500,000) - plasma proteins (albumin, globulins, prealbumin), transport proteins (ferritin, transferring, haptoglobin ceruloplasmin), defense proteins (immunoglobulins IgA, IgG, IgM, IgE, complements C3, C4), clotting proteins (fibrinogen, D-dimer), enzymes (alanine aminotransferase ALT, aspartate aminotransferase AST, alanine phosphatase ALP, gamma-glutamyltransferase, lactate hydrogenase LD, creatine kinase CK, amylase, lipase and pseudocholinesterase), tumor markers (prostate specific antigen PSA, carcinoembryonic antigen CEA, cancer antigen 125 CA125, cancer antigen 15-3 CA15-3, alpha-fetoprotein AFP, rheumatoid factor RF, C-reactive protein CRP, high sensitivity C-reactive protein hsCRP, beta natriuretic peptide β-NP and antistreptolysin-O ASO).

4.       Lipids and lipoproteins - (total cholesterol, high density lipoprotein HDL cholesterol, low density lipoprotein LDL cholesterol, triglycerides, lipoprotein a, apopoproptein A and B).

5.       Hypothesis-driven - quiescin Q6 sulfhydryl oxidase 1 (QSOX-1). It is a protein and the most promising candidate to identify patients with acute decompensated heart failure (ADHF).

6.       Genetic - These biomarkers are based on the determination of genetic polymorphisms and can be either of intake or of effect (metabolism) or as disease risk. They can be determined in the DNA of any biological sample that contains cells with a nucleus.

7.       Environmental - biomarkers that measure exposure in the human body (cotinine in blood or urine for second-hand tobacco smoke, benzene metabolites in urine for traffic-related pollution, etc.); biomarker of effect that is associated with an established or possible health impairment or disease (DNA adducts) and biomarker of susceptibility that is associated with inherent or acquired ability of an organism to respond to the challenge of exposure to a specific chemical substance (glucose-6-phosphate dehydrogenase G6PD deficiency).

These biomarkers can be found in biological samples such as blood (whole blood, plasma or serum), urine, saliva,  cerebrospinal fluid, amniotic fluid, synovial fluid, pleural fluid, pericardial fluid, peritoneal fluid (ascetic fluid), faeces, hair, nails, adipose tissue and other specific tissues depending on the aims of the study.

A human serum is the clear portion of the human’s body fluid that separates from blood upon clotting. The serum contains proteins (60-80 mg/ml) in addition to various small molecules such as amino acids, lipids, salts and sugars [5]. Human serum contains numerous biomarkers for a number of diseases such as cancer, cardiovascular, rheumatoid arthritis, respiratory, neurodegenerative, etc. Typical examples are prostatic acid phosphatase [6] and PSA [7] for prostate cancer; carcinoembryonic antigen CEA [8], for colorectal, lung, breast, liver, pancrease, bladder cancers and CA 125 [9] for ovarian cancer, etc. Serum biomarkers for cardiovascular disease are B-type natriuretic peptide, nesiritide [10], N-terminal proB-type natriuretic peptide [11] and C-reactive protein [12], etc. Rheumatoid arthritis has stromelysin-1 [13], interleukin-15 [14] and  cytokines tumor necrosis factor-α, interleukins-12, -15, and -18 [15] as serum biomarkers. The respiratory serum biomarkers are urinary-trypsin-inhibitor [16], eosinophil cationic protein [17] while cystic fibrosis (CF) has serum CA 19-9 [18], trypsinogen [19] and prolyl hydroxylase serum [20], etc. as biomarkers.

Due to the high abundance of albumin and heterogeneity of plasma lipoproteins and glycoproteins, biomarkers are difficult to identify and quantify in human serum. Therefore, analytical method to be adopted has to be accurate, precise, selective, specific and sensitive. Biomarkers that are proteins have been separated, identified and quantified from crude biological samples by using analytical methods which exploit the physicochemical properties (isoelectric point, hydrophobicity and molecular mass and size) of proteins. Separation based on isolelectric point is done using ion exchange chromatography [21] as well as gel electrophoresis [22,23]. Separation based on hydrophobicity is carried out using reversed phase high performance liquid chromatography, RP-HPLC [24]. Separation based on molecular mass and size is done using gel electrophoresis as well as size exclusion chromatography [25]. Other methods utilized for protein analysis are immunological techniques [26], mass spectrometry techniques [27], tandem-mass spectrometry [28], liquid chromatography-mass spectrometry [29]. In addition, analytical methodologies for the determination serum biomarkers that are not proteins include atomic absorption spectrometry, inductively coupled plasma spectrometry, liquid chromatography (LC), gas chromatography (GC), mass spectrometry(MS) and hyphenated systems (GC-MS, LC-MS/MS techniques), etc.

In conclusion, biomarkers (chemical, physical or biological) play major roles in medicinal biology and help in early diagnosis, disease prevention, drug target identification and drug response. They are useful in measuring the progress of disease, evaluating the most effective therapeutic regimes for a particular disease and establish a long-term susceptibility to disease or its recurrence. Currently, techniques such as genomics, proteomics, metabolomics, lipidomics, glycomics, secretomics are also being employed to accurately measure the disease biomarker levels and establish criteria for disease diagnosis and prognosis. Finally, to enhance future serum biomarker identification and quantification, techniques should be improved and combinations of different technologies and statistical analysis are required to increase the accuracy, sensitivity, reproducibility and specificity of biomarker detection.

1.       Malchow S, Loosse C, Sickmann A, Lorenz C (2017) Quantification of cardiovascular disease biomarkers in human platelets by targeted mass spectrometry. Proteomes 5: 31-44.

2.       Atkinson AJ, Colburn WA, DeGruttola VG. (2001). Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin Pharmacol Ther 69: 89-95.

3.       de Vries J, Antoine JM, Burzykowski T, Chiodini A, Gibney M, et al. (2013) Markers for nutrition studies: Review of criteria for the evaluation of markers. Eur J Nutr 52: 1685-1699.

4.       Tevak Z, Kondratovich M, Mansfield E (2010) In vitro diagnostic regulatory perspective. Personalized Medicine 7: 517-530.

5.       Anderson NL, Anderson NG (2002). The human plasma proteome - History, character and diagnostic prospects. Mol Cell Proteomics 1: 845-867.

6.       Yam LT (1974) Clinical significance of the human acid phosphatases: Review. Am J Med 56: 604-616.

7.       Miyata Y, Sakai H, Hayashi T (2003) Serum insulin-like growth factor binding protein-3/prostate-specific antigen ratio is a useful predictive marker in patients with advanced prostate cancer. Prostate 54: 125-132.

8.       Williams MR, Turkes A, Pearson D, Twining P, Griffiths K, et al. (1988) The use of serum carcinoembryonic antigen to assess therapeutic response in locally advanced and metastatic breast-cancer: A prospective-study with external review. Eur J Surg Oncol 14: 417-422.

9.       Whitehouse C, Solomon E (2003) Current status of the molecular characterization of the ovarian cancer antigen CA125 and implications for its use in clinical screening. Gynecol Oncol 88: S152-S157.

10.    McCullough PA, Nowak RM, McCord J, Hollander JE, Herrmann HC, et al. (2002) B-type natriuretic peptide and clinical judgment in emergency diagnosis of heart failure - Analysis from breathing not properly (BNP) multinational study. Circulation 106: 416-422.

11.    Nasser N, Bar-Oz B, Nir A (2005) Natriuretic peptides and heart disease in infants and children. J Pediatr 147: 248-253.

12.    Rifai N, Ridker PM (2001) High-sensitivity C-reactive protein: A novel and promising marker of coronary heart disease. Clin Chem 47: 403-411.

13.    Yamanaka H, Matsuda Y, Tanaka M, Sendo W, Nakajima H, et al. (2000) Serum matrix metalloproteinase 3 as a predictor of the degree of joint destruction during the six months after measurement, in patients with early rheumatoid arthritis. Arthritis Rheum 43: 852-858.

14.    Cao LF, Lu YM, Ma M, Xue HY, Zhao Y, et al. (2006) Levels of serum interleukin-15 and the expression of T-helper lymphocyte subsets in peripheral blood of children with juvenile rheumatoid arthritis. Chinese J Contemp Pediatr 8: 9-12.

15.    Petrovic-Rackov L (2006) Evaluation of the degree of clinical rheumatoid arthritis activity based on the concentrations of cytokines TNF-alpha, IL-12, IL-15 and IL-18 in serum and synovial fluid. Vojnosanit Pregl 63: 21-26.

16.    Yasui K, Kanda H, Iwanami T, Komiyama A (2003) Increased serum concentration of urinary trypsin inhibitor with asthma exacerbation. Eur Respir J 22: 739-742.

17.    Sorkness C, McGill K, Busse WW (2002) Evaluation of serum eosinophil cationic protein as a predictive marker for asthma exacerbation in patients with persistent disease. Clin Exptal Allerg 32: 1355-1359.

18.    Duffy MJ, Osullivan F, McDonnell TJ, FitzGerald MX (1985) Increased concentrations of the antigen Ca-19–9 in serum of cystic-fibrosis patients. Clin Chem 31: 1245-1246.

19.    Cleghorn G, Benjamin L, Corey M, Forstner G, Dati F, et al. (1985) Age-related alterations in immunoreactive pancreatic lipase and cationic trypsinogen in young-children with cystic-fibrosis. J Pediatr 107: 377-381.

20.    Pereira TN, Lewindon PJ, Smith JL, Murphy TL, Lincoln DJ, et al. (2004) Serum markers of hepatic fibrogenesis in cystic fibrosis liver disease. J Hepatol 41: 576-583.

21.    Shan L, Anderson DJ (2002) Gradient chromatofocusing. Versatile pH gradient separation of proteins in ion-exchange HPLC: Characterization studies. Anal Chem 74: 5641-5649.

22.    Sahab Ziad J, Suh Y, Sang Qing-Xiang A (2005). Isoelectric point-based prefractionation of proteins from crude biological samples prior to two-dimensional gel electrophoresis. J Proteome Res 4: 2266-2272.

23.    Lilley KS, Razzaq A, Dupree P (2002) Two-dimensional gel electrophoresis: recent advances in sample preparation, detection and quantitation. Curr Opin Chem Biol 6: 46-50.

24.    Sheng S, Chen D, Van Eyk JE (2006) Multidimensional liquid chromatography separation of intact proteins by chromatographic focusing and reversed phase of the human serum proteome - Optimization and protein database. Mol Cell Proteomics 5: 26-34.

25.    Bloustine J, Berejnov V, Fraden S (2003). Measurements of protein-protein interactions by size exclusion chromatography. Biophys J 85: 2619-2623.

26.    Moody MD, Van Arsdell SW, Murphy KP, Orencole SF, Burns C (2001) Array-based ELISAs for high-throughput analysis of human cytokines. Biotechnique 31: 186-187.

27.    Heck AJR, Krijgsveld J (2004) Mass spectrometry-based quantitative proteomics. Exp Rev Proteomics 1: 317-326.

28.    Dongre AR, Eng JK, Yates JR (1997). Emerging tandem mass spectrometry techniques for the rapid identification of proteins. Trends Biotechnol 15: 418-425.

29.    Fung KYC, Askovic S, Basile F, Duncan MW (2004) A simple and inexpensive approach to interfacing high-performance liquid chromatography and matrix-assisted laser desorption/ionization-time of flight-mass spectrometry. Proteomics 4: 3121-3127.