INTRODUCTION:

Despite the considerable advances, cancer disorders remain the world’s leading cause of disability and hospitalization. Biomarkers, on the other hands, are multifaceted indicators of pathological disorders.

Potential biomarker discovery from biological fluids has been widely applied to several disorders. Therefore, the study of cancer disorders can benefit by the use of biomarkers because of inherent disease heterogenicity. The application of associated techniques for the identification of biomarker signatures in cancer disorders through hyphenated analysis is less well established. Hence, we aimed to standardize by describing together multiple experimental approaches for sample preparation, instrumentation, acquisition parameters and processing of data related cancer disorders. Using the standardized approach, high quality data for biological fluids analysis will be generating biomarkers either exploration or diagnostic the desired analytical goals of classification for diagnosis, pattern finding and bio-markers investigation in cancer disorders will be accomplished. Furthermore, disorders in this field by biomarker discovery and validation in cancer disorders, future development of this area is being explored. Cancer is one of the leading life-threatening disease all over the world with over 20 types identified and higher than 1500 deaths occurring every day¹. The risk factor for cancer mainly include aging, exposure to harmful environmental factors, and adverse lifestyles. Although researches have studied cancer for decades, the early diagnosis and detection of cancer and improvements in the survival time and quality of prognosis remain a major challenge². Cancer is responsible for an estimated 9.6million deaths in 2018 globally about 1 in 6 deaths due to cancer. Approximately 70% of death from cancer occur in low-and middle-income countries. Around one third of deaths from cancer are due to the 5 leading behavioural and dietary risks, higher body mass index, low fruit and vegetable intake, lack of physical activity, tobacco use, and alcohol use³. Tobacco use is the most important risk factor for cancer and is responsible for approximately 22% of cancer deaths. Cancer causing infections, such as hepatitis and human papilloma virus (HPV) are responsible for up to 25% of cancer cases in low and middle-income countries⁴. The economic impact of cancer is significant and is increasing. The total annual economic cost of cancer in 2010 was estimated at approximately us 1.16 trillion⁵.

Classification of Cancers:

From a histological stand point there are hundreds of different cancers, which are grouped into six major categories.

a. Carcinoma:

Carcinoma refers to a malignant neoplasm of epithelial origin or cancer of the internal or external lining of the body. Carcinomas, malignancies of epithelial tissue, account for 80 to 90% of all cancer cases. Carcinomas are divided into two major sub types.

· Adenocarcinomas: which develops in an organ or gland.

· Squamous cell carcinoma which originates in the squamous epithelium.

b. Sarcoma:

Sarcoma refers to cancer that originates in supportive and connective tissues such as bones, tendons, cartilage, muscle and fat.

Generally occurring in young and the most common sarcoma often developed as a painful mass on the bone.

Ex:

Osteosarcoma (bone)

Chondrosarcoma (cartilage)

Leiomyosarcoma (smooth muscle)

Rhabdomyosarcoma (skeletal muscle)

c. Myeloma:

Myeloma is cancer that originates in the plasma cell of bone marrow. The plasma cell produces some of the proteins found in blood.

d. Leukaemia:

Leukaemia is cancer of the bone marrow. This is called as the liquid cancer or blood cancer. The disease is often associated with the over production of immature white blood cells. Leukaemia also effects red blood cells and can cause poor blood clotting and fatigue due to anomia.

Ex:

Amylogenesis or granulocytic leukaemia

Lymphatic, lymphocytic or lymphoblastic leukaemia

Poly leukaemia vera or erythraemia

e. Lymphoma:

Lymphomas developed in the glands or nodes of the lymphatic system, a network of vessels, nodes, and organs that purify body fluids and produce infection fighting with blood cells or lymphocytes.

f. Mixed types:

The types components may be within one category or from different categories.

Ex:

Adenoaquamous carcinoma

Mixed mesodermal tumour

Carcinosarcoma

Teratocarcinoma

Clinical manifestations and investigations:

Most cancers are symptomatic before diagnosis, but the role of lower risk symptoms and of clinical findings potentially available in general practice is un clear. In 164 (62%) of 263 cancer cases, the general practise reported symptoms that helped diagnose cancer. The % rose to78%when clinical findings and test results were added. Lower risk symptoms were reported in 31 (12%) patients and lower risk in 19 (7%) patients. Among patients where clinical sings or testes contributed to diagnosis symptoms were absent in 39% and in 42% respectively showing the sensitivity of complementing reported symptoms with examinations and tests⁶.

Specific tests for clinical cancers examination include

I. Physical examination,

II. Laboratory tests

Ex:Blood protein test

Tumour marker tests

Circulating tumour cell test

Complete blood count

III. Imaging tests:

Ex:Ct scan, mri, nuclear scan, bone scan, pet scan, ultra-scan, x-ray.

IV. Biopsy

· With a needle

· With endoscopy

Ex: Colonoscopy

Bronchoscopy

· With surgery

· With anaesthesia

Ex: Local anaesthesia

Regional anaesthesia

General anaesthesia

· Punch biopsy

· Shave biopsy

· Skin biopsy

V. Genetic tests

· Testing for mutations

· Susceptibility gen testing

1. Biomarkers need:

Biological markers are bio-chemical, cellular or molecular alterations that can be measured in biological media. Biomarkers include technologies and tools that help in the understanding of prediction cause diagnosis, regression, progression and outcome of the treatment of disseise. Figure -1. Shows, classified biomarkers in four categories on the sequence of events on exposure to disease such as predictive, diagnostic, prognostic and monitoring of the pharmacodynamics response of drugs after the delivery of drugs. Biomarkers also help to determine treatment response on the proposed target and whether the drug has altered the cause of the disease⁷.

Figure-1. Classification of biomarkers

2. Methods of collecting samples:

Sample collection and storage⁹:

Traditionally, plasma, serum and urine where mainly the samples employed for cancer studies because they reflect an individual’s global metabolic status and the collection process or minimally invasive. However, these complex samples are easily diluted by small metabolic changes from a specific part of the body. All of the samples collection methods are summarized in Table-3.

Table. 1: Biomarkers in cancer disease^8-17

Sample	Collection	storage	preparations
Blood/plasma/serum	Collected in heparin tubes Centrifuged	80^oC	Centrifuged after thawing at 4^oc Add duterium oxide (to lock) Add ACN (for protien precipitation)
Urine	Collected in a sterile cap Aliquots of approximately 1mL were transferred into sterial cryovials	80^oC	Centrifuged after thawing at 4^oc Remve cells and other precipitated materials Add buffer to urine
BALF	Divided into 1mL aliquots in eppendorf tubes	80^oC	Add duteruim oxide to BALF Add methanol/chloroform extraction
CSF	Obtain during surgery Snap -frozen in liquid nitrogen	80^oC	Add duteriumoxide to CSF
EBC	The device used for smapling directly collects Concenses the EBC in disposable polyethylene bags at -20 C	80^oC	Add duteriumoxide to EBC
Tissues	Retrived from surgical specimens Snap-frogen in liquid nitrogen	80^oC	Add a few drops of duterium oxide Add saline and cooled tissue Add methanol/ chloroform extraction to tissue
Sweat	Use the sweat inducer to collect sweat The macroduct collecter converts the skin to collect sweat Trasfer the sweat to micro tubes	80^oC	Add 0.1% formic acid to sweat Add duterium oxide to sweat

Table-2. Biomarkers identified with hyphenated techniques in cancer diseases ^18-27

Cancer type	Panel of protein markers	Technique
Lung	carcinoembryonic antigen, retinol binding protein alpha 1-antitrypsin squamous cell carcinoma antigen	2D-DIGE and MALDI-TOF
Ovarian	Corticosteroid-binding globulin (CBG) serum amyloid p component (SAP) complement factor B (CFAB)	lectin array and quantitative LC-MS/MS
Head and neck	14-3-3 protein zeta/delta (YWHAZ), stratifin S100-A7	Multidimensional LC-MS/MS
Pancreatic Cancer	α-1-antichymotrypsin (AACT) thrombospondin-1 (THBS1) haptoglobin (HPT)	Label-free and TMT strategies LC-MS/MS

Table-3. Samples collection methods^28-35

Biomarker	Modality	Decision-making role	Notes
ACR BI-RADS breast morphology	Mammography	Diagnostic in breast cancer	Used worldwide
Clinical TNM stage	XR, CT, MRI, PET, SPECT, US, Endoscopy	Prognostic in nearly all cancers	• Used worldwide • Guides management of nearly every patient with a solid tumour • Extensively validated and qualified
Bone scan index	SPECT	Prognostic in prostate cancer	• Continuous variable data converted to ordered categorical IB • Calculation uses software requiring regulatory approval
Left ventricular ejection fraction	Scintigraphy, US	• Safety biomarker • Guides therapy	• Guides management of a substantial number of patients (for example, trastuzumab) • Decrease in LVEF of >10% confirmed with repeated imaging
T-score	DXA	• Safety biomarker • Guides prescription of bisphosphonates to patients with breast cancer and bone loss induced by therapy	• Number of standard deviations below mean bone density • Calculation uses software requiring regulatory approval
Uptake of ¹¹¹ In- pentetreotide, ⁶⁸Ga-dotatate octreotide conjugates	SPECT, PET	• Identification of primary or residual neuroendocrine lesions • Prescriptionof¹⁷⁷Lu-dotatate- octreotide ablationtherapy	IBisSUV_max(targetlesion)> SUV_max(back ground liver or bone marrow)
^99mTc-tilmanocept uptake above cut-off	SPECT	Intraoperative detection of sentinel lymph nodes	• Biomarker cut-of fisback ground radioactivity counts > 3standard deviations from the mean background count level, with background counts determined from tissue at least 200mm distal to the injection site • Approved for use in patients with breast cancer or melanoma
Split renal function measured by ^99mTc-mertiatide (MAG3)	SPECT	Determination of split renal function prior to nephrectomy, which guides surgical decision-making	NA
MARIBS category	MRI	Determination of risk of breast cancer in patients harbouring genetic risk factors such as mutations in BRCA1 or BRCA2	Approved by NICE for clinical use in UK
Objective response	CT, MRI, PET	Guides decision to continue, discontinue, or switch therapy	• Used worldwide to guide management of nearly every patient with a solid tumour • Extensively validated and qualified
Circumferential resection margin status	MRI	Determination of whether circumferential resection margin is clear in rectal cancer with pre-operative high-resolution MRI scan	Prognostic value in rectal cancer; now approved for clinical use
Objective response	CT, MRI, PET	• End point in phase II trials • Contribution to PFS determination	PFS end point is heavily based on objective response as well as serology and clinical markers
Splenic volume	CT, MRI	Assessments of responsein patients with myelofibrosis	Used in FDA approval of ruxolitinib
Biomarker	Modality	Decision-making role	Notes
ACR BI-RADS breast morphology	Mammography	Diagnostic in breast cancer	Used worldwide
Clinical TNM stage	XR, CT, MRI, PET, SPECT, US, Endoscopy	Prognostic in nearly all cancers	• Used worldwide • Guides management of nearly every patient with a solid tumour • Extensively validated and qualified
Bone scan index	SPECT	Prognostic in prostate cancer	• Continuous variable data converted to ordered categorical IB • Calculation uses software requiring regulatory approval
Left ventricular ejection fraction	Scintigraphy, US	• Safety biomarker • Guides therapy	• Guides management of a substantial number of patients (for example, trastuzumab) • Decrease in LVEF of >10% confirmed with repeated imaging
T-score	DXA	• Safety biomarker • Guides prescription of bisphosphonates to patients with breast cancer and bone loss induced by therapy	• Number of standard deviations below mean bone density • Calculation uses software requiring regulatory approval
Uptake of ¹¹¹In- pentetreotide, ⁶⁸Ga-dotatate octreotide conjugates	SPECT, PET	• Identification of primary or residual neuroendocrine lesions • Prescription of¹⁷⁷Lu-dotatate- octreotide ablation therapy	IBisSUV_max(targetlesion)>SUV_max(background liver or bone marrow)
^99mTc-tilmanocept uptake above cut-off	SPECT	Intraoperative detection of sentinel lymph nodes	• Biomarker cut-off is background radioactivity counts>3 standard deviations from the mean background count level, with background counts determined from tissue at least 200mm distal to the injection site • Approved for use in patients with breast cancer or melanoma
Split renal function measured by ^99mTc-mertiatide (MAG3)	SPECT	Determination of split renal function prior to nephrectomy, which guides surgical decision-making	NA
MARIBS category	MRI	Determination of risk of breast cancer in patients harbouring genetic risk factors such as mutations in BRCA1 or BRCA2	Approved by NICE for clinical use in UK
Objective response	CT, MRI, PET	Guides decision to continue, discontinue, or switch therapy	• Used worldwide to guide management of nearly every patient with a solid tumour • Extensively validated and qualified
Circumferential resection margin status	MRI	Determination of whether circumferential resection margin is clear in rectal cancer with pre-operative high-resolution MRI scan	Prognostic value in rectal cancer; now approved for clinical use
Objective response	CT, MRI, PET	• End point in phase II trials • Contribution to PFS determination	PFS end point is heavily based on objective response as well as serology and clinical markers
Splenic volume	CT, MRI	Assessments of response in patients with myelofibrosis	Used in FDA approval of ruxolitinib

Table-4. Comparison of bioanalytic assay and biomarker assay validation variables^47-60

Variable	Bioanalytic (GLP)assay	Biomarker assay
Assaymethodcategory	Most are definitivequantitative	Most are relative or quasi-quantitative
Regulatoryrequirement	GLP	No specific guidelines
Natureofanalyte	Exogenous	Endogenous
Stability	Drug standards, QCs, sampleanalyte	Stability of standards and matrix
stability often good		analytes often poor
Stabilitytesting	Freeze/thaw,benchtop,longtermmeasured	Freeze/thaw, bench top, storage stability
by spiking biological matrix with drug		with study samples
Standards/calibrators	Standardspreparedinstudymatrix;certified	Standards/calibrators made in matrix
standard readily available		different than study samples; certified
		standards not available
Calibrationmodel	Mostlylinear	Choose appropriate calibration model fitting
		method and tools
QCs	Certified standard and blankpatient	Certified standard or blank matrix usually not
sample matrix available		available; substitute with surrogate matrices
VS andQCmeasurements	Made in study matrix. 4-5 VSlevels	Made in study matrix. At least 5 VS levels and
and 3 QC levels		3 QC levels. If study matrix is limited may
		use surrogate matrix
Assayacceptancecriteria	4-6-15 rule (for smallmolecules)	4-6-X rule or establish confidence interval
Precision/accuracy	Robust technology with acceptancecriteria	Variable; no acceptance criteria
Specificity/selectivity	Drugs not present in samplematrix;	Specificity issues: biomarkers present in
samples are subject to cleanup and		sample matrix; samples not subject to
analyte recovery		cleanup; assess matrix effects and minimize;
		investigate sources of interference
Sensitivity	LLOQ defined by acceptancecriteria	Limited sensitivity and dynamic range;
		LLOQ and LOD defined based on working criteria
Abbreviation: LOD, limit of detection.

CONCLUSION:

The ultimate goal of this review was the development of biomarkers, which allowed the identification of cancer diseases. The several advantages of hyphenated techniques of biofluids for disease detection includes, possibility of two profile chromatographic disease related changes of fluid composition and the analysis methods that are suitable for automation.Yet, several challenges remain to the compared, which includes the integration of these techniques into clinical practices. Moreover, close collaboration of clinicians with hyphenated technicians will turn the use of hyphenated to biofluid classification into a valuable diagnostic and screening tool in clinical practice. However, large multi cantered randomized controlled studies with the gold standard of sample handling protocols and current diagnostic method will help in the validation of cancer biomarker.

REFERENCES:

1. Bray F., Ferlay J., Soerjomataram I., Siegel R.L., Torre L.A., Jemal A. Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018; 68: 394–424.

2. Fidler M.M., Bray F., Soerjomataram I. The global cancer burden and human development: A review. Scand. J. Public Health. 2018; 46: 27–36.

3. Richards M.A. The size of the prize for earlier diagnosis of cancer in england. Br. J. Cancer. 2009; 101(Suppl. 2): S125–S129.

4. Bax C., Taverna G., Eusebio L., Sironi S., Grizzi F., Guazzoni G., Capelli L. Innovative diagnostic methods for early prostate cancer detection through urine analysis: A review. Cancers. 2018; 10: 123.

5. Di Lena M., Porcelli F., Altomare D.F. Volatile organic compounds as new biomarkers for colorectal cancer: A review. Colorectal Dis. 2016; 18: 654–663.

6. Das V., Kalita J., Pal M. Predictive and prognostic biomarkers in colorectal cancer: A systematic review of recent advances and challenges. Biomed. Pharmacother. 2017; 87: 8–19.

7. Capelli L., Taverna G., Bellini A., Eusebio L., Buffi N., Lazzeri M., Guazzoni G., Bozzini G., Seveso M., Mandressi A., et al. Application and uses of electronic noses for clinical diagnosis on urine samples: A review. Sensors. 2016; 16: 1708.

8. Asimakopoulos A.D., Del Fabbro D., Miano R., Santonico M., Capuano R., Pennazza G., D’Amico A., Finazzi-Agro E. Prostate cancer diagnosis through electronic nose in the urine headspace setting: A pilot study. Prostate Cancer Prostatic Dis. 2014;17: 206–211.

9. de Meij T.G., Larbi I.B., van der Schee M.P., Lentferink Y.E., Paff T., Terhaar Sive Droste J.S., Mulder C.J., van Bodegraven A.A., de Boer N.K. Electronic nose can discriminate colorectal carcinoma and advanced adenomas by fecal volatile biomarker analysis: Proof of principle study. Int. J. Cancer. 2014; 134: 1132–1138

10. Kort S., Brusse-Keizer M., Schouwink H., De Jongh F., Citgez E., Gerritsen J.W., Van Der Palen J. Detection of small cell lung cancer by electronic nose. Eur. Respir. J. 2018; 52

11. Peng G., Hakim M., Broza Y.Y., Billan S., Abdah-Bortnyak R., Kuten A., Tisch U., Haick H. Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors. Br. J. Cancer. 2010; 103: 542–551.

12. Lippi G., Plebani M. Diabetes alert dogs: A narrative critical overview. Clin. Chem. Lab. Med. 2019; 57: 452–458.

13. Jadoon S., Karim S., Akram M.R., Kalsoom Khan A., Zia M.A., Siddiqi A.R., Murtaza G. Recent developments in sweat analysis and its applications. Int. J. Anal. Chem. 2015; 2015: 7.

14. Bosch S., Berkhout D.J., Ben Larbi I., de Meij T.G., de Boer N.K. Fecal volatile organic compounds for early detection of colorectal cancer: Where are we now? J. Cancer Res. Clin. Oncol. 2019; 145: 223–234.

15. Rooney N.J., Guest C.M., Swanson L.C.M., Morant S.V. How effective are trained dogs at alerting their owners to changes in blood glycaemic levels? Variations in performance of glycaemia alert dogs. PLoS ONE. 2019; 14:e0210092.

16. Elliker K.R., Sommerville B.A., Broom D.M., Neal D.E., Armstrong S., Williams H.C. Key considerations for the experimental training and evaluation of cancer odour detection dogs: Lessons learnt from a double-blind, controlled trial of prostate cancer detection. BMC Urol. 2014; 14: 22.

17. Gordon R.T., Schatz C.B., Myers L.J., Kosty M., Gonczy C., Kroener J., Tran M., Kurtzhals P., Heath S., Koziol J.A., et al. The use of canines in the detection of human cancers. J. Altern. Complement. Med. (New York, NY) 2008; 14: 61–67.

18. Bernabei M., Pennazza G., Santonico M., Corsi C., Roscioni C., Paolesse R., Di Natale C., D’Amico A. A preliminary study on the possibility to diagnose urinary tract cancers by an electronic nose. Sens. Actuators B Chem. 2008; 131: 1–4.

19. Cornu J.N., Cancel-Tassin G., Ondet V., Girardet C., Cussenot O. Olfactory detection of prostate cancer by dogs sniffing urine: A step forward in early diagnosis. Eur. Urol. 2011; 59: 197–201.

20. Fischer-Tenhagen C., Johnen D., Nehls I., Becker R. A proof of concept: Are detection dogs a useful tool to verify potential biomarkers for lung cancer? Front. Vet. Sci. 2018; 5: 52.

21. Worapot Suntornsuk, Leena Suntornsuk, Recent applications of paper‐based point‐of‐care devices for biomarker detection, Electrophoresis, 10.1002/elps.201900258, 41, 5-6, (287-305), (2019).

22. Yuan Li, Sihui Su, Yingzhe Zhang, Shiyao Liu, Hongyu Jin, Qianqing Zeng, Lei Cheng, Accuracy of Raman spectroscopy in discrimination of nasopharyngeal carcinoma from normal samples: a systematic review and meta-analysis, Journal of Cancer Research and Clinical Oncology, 10.1007/s00432-019-02934-y, (2019).

23. Guoyu Jiang, Wenping Zhu, Qingqing Chen, Xinbo Li, Guanxin Zhang, Yongdong Li, Xiaolin Fan, Jianguo Wang, Selective fluorescent probes for spermine and 1-adamantanamine based on the supramolecular structure formed between AIE-active molecule and cucurbit[n]urils, Sensors and Actuators B: Chemical, 10.1016/j.snb.2018.01.197, 261, (602-607), (2018).

24. Xin Xiong, Yuanyuan Zhang, Wenjing Zhang, Simultaneous determination of twelve polar pteridines including dihydro‐ and tetrahydropteridine in human urine by hydrophilic interaction liquid chromatography with tandem mass spectrometry, Biomedical Chromatography, 10.1002/bmc.4244, 32, 8, (2018).

25. Jie Wang, Wei Li, Lin Ban, Wei Du, Xiaojun Feng, Bi-Feng Liu, A paper-based device with an adjustable time controller for the rapid determination of tumor biomarkers, Sensors and Actuators B: Chemical, 10.1016/j.snb.2017.07.192, 254, (855-862), (2018).

26. Prabhpreet Singh, Lalit Singh Mittal, Gaurav Bhargava, Subodh Kumar, Ionic Self‐Assembled Platform of Perylenediimide–Sodium Dodecylsulfate for Detection of Spermine in Clinical Samples, Chemistry – An Asian Journal, 10.1002/asia.201700120, 12, 8, (890-899), (2017).

27. Shangyuan Feng, Zuci Zheng, Yuanji Xu, Jinyong Lin, Guannan Chen, Cuncheng Weng, Duo Lin, Sufang Qiu, Min Cheng, Zufang Huang, Lan Wang, Rong Chen, Shusen Xie, Haishan Zeng, A noninvasive cancer detection strategy based on gold nanoparticle surface-enhanced raman spectroscopy of urinary modified nucleosides isolated by affinity chromatography, Biosensors and Bioelectronics, 10.1016/j.bios.2017.01.006, 91, (616-622), (2017

28. Bennett, B.D.; Kimball, E.H.; Gao, M.; Osterhout, R.; Van Dien, S.J.; Rabinowitz, J.D. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat. Chem. Biol., 2009, 5(8), 593-599.

29. Amantonico, A.; Urban, P.L.; Zenobi, R. Analytical techniques for single-cell metabolomics: state of the art and trends. Anal. Bioanal. Chem., 2010, 398(6), 2493-2504.

30. Blow, N. Metabolomics: Biochemistry’s new look. Nature, 2008, 455(7213), 697-700.

31. Griffin, J.L.; Shockcor, J.P. Metabolic profiles of cancer cells. Nat. Rev. Cancer, 2004, 4(7), 551-561.

32. Costello, L.C.; Franklin, R.B. ‘Why do tumour cells glycolyse?’: from glycolysis through citrate to lipogenesis. Mol. Cell. Biochem., 2005, 280(1-2), 1-8.

33. Glunde, K.; Serkova, N.J. Therapeutic targets and biomarkers identified in cancer choline phospholipid metabolism, 2006.

34. Warburg, O. On the origin of cancer cells. Science, 1956, 123(3191), 309-314.

35. Armitage, E.G.; Barbas, C. Metabolomics in cancer biomarker discovery: current trends and future perspectives. J. Pharm. Biomed. Anal., 2014, 87, 1-11.

36. Patel, S.; Ahmed, S. Emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery. J. Pharm. Biomed. Anal., 2015, 107, 63-74.

37. Gika, H.G.; Theodoridis, G.A.; Plumb, R.S.; Wilson, I.D. Current practice of liquid chromatography-mass spectrometry in metabolomics and metabonomics. J. Pharm. Biomed. Anal., 2014, 87, 12-25.

38. Amann, A. Costello, Bde.L.; Miekisch, W.; Schubert, J.; Buszewski, B.; Pleil, J.; Ratcliffe, N.; Risby, T. The human volatilome: Volatile Organic Compounds (VOCs) in exhaled breath, skin emanations, urine, feces and saliva. J. Breath Res., 2014, 8(3), 034001.

39. Zhang, T.; Watson, D.G.; Wang, L.; Abbas, M.; Murdoch, L.; Bashford, L.; Ahmad, I.; Lam, N-Y.; Ng, A.C.; Leung, H.Y. Application of holistic liquid chromatography-high resolution mass spectrometry based urinary metabolomics for prostate cancer detection and biomarker discovery. PLoS One, 2013, 8(6), e65880.

40. Zhang, A.; Sun, H.; Wang, P.; Han, Y.; Wang, X. Modern analytical techniques in metabolomics analysis. Analyst (Lond.), 2012, 137(2), 293-300.

41. Boyland, E.; Williams, D. The estimation of tryptophan metabolites in the urine of patients with cancer of the bladder. Biochem. J., 1955, 60, p. 60(Annual General Meeting), v.

42. Haverback, B.J.; Sjoerdsma, A.; Terry, L.L. Urinary excretion of the serotonin metabolite, 5-hydroxyindoleacetic acid, in various clinical conditions. N. Engl. J. Med., 1956, 255(6), 270-272.

43. Monteiro, M.; Carvalho, M.; Henrique, R.; Jerónimo, C.; Moreira, N.; de Lourdes Bastos, M.; de Pinho, P.G. Analysis of volatile human urinary metabolome by solid-phase microextraction in combination with gas chromatography-mass spectrometry for biomarker discovery: application in a pilot study to discriminate patients with renal cell carcinoma. Eur. J. Cancer, 2014, 50(11), 1993-2002.

44. Bouatra, S.; Aziat, F.; Mandal, R.; Guo, A.C.; Wilson, M.R.; Knox, C.; Bjorndahl, T.C.; Krishnamurthy, R.; Saleem, F.; Liu, P.; Dame, Z.T.; Poelzer, J.; Huynh, J.; Yallou, F.S.; Psychogios, N.; Dong, E.; Bogumil, R.; Roehring, C.; Wishart, D.S. The human urine metabolome. PLoS One, 2013, 8(9), e73076.

45. Emwas, A-H.; Luchinat, C.; Turano, P.; Tenori, L.; Roy, R.; Salek, R.M.; Ryan, D.; Merzaban, J.S.; Kaddurah-Daouk, R.; Zeri, A.C.; Nagana Gowda, G.A.; Raftery, D.; Wang, Y.; Brennan, L.; Wishart, D.S. Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review. Metabolomics, 2015, 11(4), 872-894.

46. Chan, E.C.Y.; Pasikanti, K.K.; Nicholson, J.K. Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry. Nat. Protoc., 2011, 6(10), 1483-1499.

47. Dudley, E.; Tuytten, R.; Lemiere, F.; Esmans, E.E.; Newton, R.P. The bioanalysis of urinary modified nucleosides by mass spectrometry: their study as potential metabolomic biomarkers of cancer development. Collect. Czech. Chem. Commun., 2015, 10, 229-233.

48. Contrepois, K.; Jiang, L.; Snyder, M. Optimized Analytical Procedures for the Untargeted Metabolomic Profiling of Human Urine and Plasma by Combining Hydrophilic Interaction (HILIC) and Reverse-Phase Liquid Chromatography (RPLC)-Mass Spectrometry. Mol. Cell. Proteomics, 2015, 14(6), 1684-1695.

49. Beckonert, O.; Keun, H.C.; Ebbels, T.M.; Bundy, J.; Holmes, E.; Lindon, J.C.; Nicholson, J.K. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc., 2007, 2(11), 2692-2703.

50. Miao, Z.; Jin, M.; Liu, X.; Guo, W.; Jin, X.; Liu, H.; Wang, Y. The application of HPLC and microprobe NMR spectroscopy in the identification of metabolites in complex biological matrices. Anal. Bioanal. Chem., 2015, 407(12), 3405-3416.

51. Vinther, J.M.; Wubshet, S.G.; Staerk, D. NMR-based Metabolomics and Hyphenated NMR Techniques: A Perfect Match in Natural Products Research. Ethnopharmacology, 2015, 2500, 63.

52. Arjun Patidar, S.C.Shivhare, Umesh Ateneriya, Sonu Choudhary. A Comprehensive Review on Breast Cancer. Asian J. Nur. Edu. & Research 2(1): Jan.-March 2012; Page 28-32.

53. Sampoornam. W. Stress and Quality of Life among Breast Cancer Patients. Asian J. Nur. Edu. & Research 4(3): July- Sept., 2014; Page 325-327.

54. Sedigheh Iranmanesh, Ala Shamsi. The Relationship between Type of Cancer and Parent's Psychosocial Risks. Asian J. Nur. Edu. and Research 4(4): Oct.- Dec., 2014; Page 495-501.

55. Somsubhra Ghosh, Arnab Jana, Beduin Mahanti. An Updated Review on Medical Detection of Dog. Asian J. Pharm. Ana. 6(1): January- March, 2016; Page 47-52.

56. Pramod K., Amar Deep A., Pooja K., Mahendra Singh A. An Overview: LC-MS as Tool of sample Extraction and Quantification in Bioanalytical Laboratories. Asian J. Pharm. Ana. 2020; 10(3):165-172.

57. Banerjee S, Bonde CG, Merukar SS, Patil YR. Advanced Hyphenated Techniques in Analytical Chemistry. Asian J. Research Chem. 2(4):Oct.-Dec. 2009 page 380-387.

58. Sarav A. Desai , Prakash S. Sukhramani, Maulik P. Suthar, Vipul P. Patel. Biological Cytotoxicity Evaluation of Sulfonamide Derivatives as Anti-Lung and Anti-Breast Cancer Activity. Asian J. Research Chem. 4(4): April, 2011; Page 671-677.

59. Rajendra Jangde. An Overview of Resealed Erythrocyte for Cancer Therapy. Asian J. Res. Pharm. Sci. 1(4): Oct.-Dec. 2011; Page 83-92.

60. Rayate Yogita, Shaikh Samina, Sakhare Pooja, Gandhi Jyotsana. Review on Skin Cancer. Asian J. Res. Pharm. Sci. 2018; 8(2):100-106.

Received on 13.03.2021 Modified on 26.04.2021

Asian J. Pharm. Ana. 2021; 11(3):235-242.

DOI: 10.52711/2231-5675.2021.00041