How many cancer biomarkers

RESULTS: Depending on the epitope specificity of the capture monoclonal mAb, we were either unable to distinguish the control from LC-groups or showed a higher level of LRG1 and IgG autoantibody containing immunocomplexes in the plasma of non-small cell lung cancer and small cell lung cancer subgroups of lung cancer patients than in the plasma of control subjects. Article Type: Correction. Abstract: Hepatocellular carcinoma HCC is a cancer with relatively high mortality, yet little attention has been devoted for related prognostic biomarkers.

K-means consensus clustering analysis was implemented to subdivide samples. Independent prognostic factors were screened by univariate and multivariate Cox regression analyses.

HCC samples were classified into 3 subgroups through clustering analysis according to the expression mode of m5C RNA methyltransferase-related genes.

It was also discovered that patients in different subgroups presented significant differences in survival rate and distribution of grade.

Through regression analyses combined with various clinical pathological factors, it was displayed that NSUN4 could work as an independent prognostic factor. Altogether, our study preliminarily developed a novel biomarker that could be independently used in prognosis of HCC, which may provide a new direction for the study of related molecular mechanism or treatment regimen.

Pathogenesis of laryngeal cancer is multifactorial and the association of SIRT1 expression with the clinical characteristics and prognosis of LSCC has not been fully identified. Therefore; this study was designed in order to address the relation between CXCL concentrations levels and markers of severity in CLL patients.

A positive association existed between TLR3 expression and the abundance of immune cells and the expression of various immune biomarkers. Keywords: Biomarker, bioinformatics analysis, immune cell infiltration, stomach adenocarcinoma, Toll-like receptor. We conclude that blood as a sample source could be used instead of biopsy for early detection of HCC. Despite major developments in medical sciences and technologies, the incidence and mortality rates of BC cases are still increasing.

One of the reasons for this increase is the absence of an easy to perform early-diagnostic tool. Although there are defined BC biomarkers routinely used for diagnostic and prognostic purposes, none of these biomarkers is useful for early diagnosis.

Therefore, early diagnosis of BC remains an important challenge and there is a great need for the early-diagnostic biomarker s. Shibboleth log in. IOS Press, Inc. For editorial issues, like the status of your submitted paper or proposals, write to [email protected].

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Please enable Javascript for this site to function properly. Therefore, they can be used as biomarkers for the cancer detection or predicting responses to various treatments [ 15 — 17 ]. Comprehensive understanding of the altered molecular mechanisms and cellular processes underlying carcinogenesis or hallmarks of cancer may link cancer biomarkers and their clinical utility in cancer patient. Genetic, molecular, and metabolic biomarker may be identified through applying the sequential of events occurring in cancer cells from gene mutation following its effects on cellular proliferation and metabolism [ 18 ], as illustrated in Figure 1.

One of the major challenges for oncology research is to establish the definite relationship between cancer biomarkers and cancer pathology, as well as, to detect cancer in early stage beside the development of targeted therapies targeting the exact altered gene or cellular process [ 16 ].

Identification of biomarkers in the process of carcinogenesis modified from Bhatt et al. Understanding mechanisms of carcinogenesis could explain the production and release of CB in cancerous cells, blood or various body fluid and hence release of those molecules and elevation during cancer initiation, development, and progression or metastasizing.

Mechanisms for elevation of CB levels in any of the biological fluid could be explained by three mechanisms. The first mechanism is overexpression or amplification of gene product, or enhancement of epigenetic changes affect gene expression as DNA methylation with release of such CB as protein human epididymal secretory protein 4 HE4 in ovarian cancer. HE4 is overexpressed in ovarian carcinoma and could be also detected in serum [ 19 — 21 ].

However, clinical evaluation of HE4 revealed that it is also overexpressed in endometrial, breast, and bronchial adenocarcinoma [ 22 ]. The second mechanism of elevation could be typically applied on serum biomarkers, which is the secretion of cellular proteins or shedding of membrane proteins.

An example of such serum biomarker is alpha-fetoprotein AFP ; an oncofetal protein with altered single peptide that is elevated in circulation in patient with hepatocellular carcinoma [ 23 ] and HER2-neu, a cell membrane surface-bound tyrosine kinase, released and elevated in the serum of breast cancer patients after being cleaved by proteolysis.

The third mechanism is cell invasion and angiogenesis as occur with prostate-specific antigen PSA. It is expressed normally by prostatic epithelium but elevation of PSA levels occurs due to distorted basement membrane of prostatic cell and lymph angiogenesis [ 25 ].

Of even much importance are hidden cancers that are not easily accessible, for example, nasopharyngeal, ovarian, and pancreatic cancers.

However, there is mandatory need for validation of such biomarkers [ 26 ]. CB could be detected in cancerous cells or tissue of origin in solid tumors, bone marrow, and lymph node or as circulating cells. CB could be detected in biological body fluid such as serum, ascetic fluid, pleural fluid, or urine representing noninvasive specimens or samples. In addition, it was postulated that prostate cancer antigen 3 PCA3 is another promising new molecular marker for diagnosis and follow-up of cancer prostate [ 28 ].

Stool for colorectal cancer, nipple aspirate fluid, ductal lavage, and cyst fluid for breast cancer are other examples for biological fluid sources for discovery or clinical application tool for CB [ 29 ].

Because of a correlation between marker concentration and active tumor mass, tumor markers are useful in the management of cancer patients. Markers, which are available for most cancer cases, are additional, valuable tools in patient prognosis, surveillance, and therapy monitoring, whereas they are presently not applicable for screening.

Sero-diagnostic measurements of markers should emphasize relative trends instead of absolute values and cut-off levels. PSA is one of the serum biomarker currently used consistently in primary care to assess the risk of underlying prostate cancer.

Cancer antigen CA can be a biomarker of ovarian cancer risk or an indicator of malignancy, but it has low sensitivity and specificity. CEA is another biomarker that is elevated in patients with colorectal, breast, lung, or pancreatic cancer [ 31 ].

A major challenge is to develop promising CB for the stratification of cancer patients not only to predict outcome or response for therapy, providing customized treatment, but also for personalized therapeutic strategies of cancer patients.

Among promising biomarkers in that field is survivin and HER2-neu [ 32 , 33 ]. As being released from tumor cells, or body cells in response to the tumor, CB can be detected in any of the body fluids, secretions, or tumor tissue and cells. CB can be detected in serum, plasma, or whole blood, also in whole excretions as urine, sputum, or CSF.

Therefore, CB could be assessed in noninvasive and in serial manner. Evaluation of cancer biomarker in tissue or cells requires tissue biopsy or more invasive technique than serum biomarkers. CB can be detected in tissues by special techniques but in an invasive manner than serum or urine biomarkers. Genetic biomarkers could be detected in DNA derived from tumor tissue, whole blood, or buccal mucosa cells [ 34 ]. Evaluation of diagnostic value of any test or marker is usually performed with referral to the terms of sensitivity and specificity of that marker.

Specificity means that ability of the marker to detect non-diseased subjects whereas sensitivity refers to the ability of that test to identify diseased subjects patients [ 35 ]. At definitive cutoff value, a test or biomarker may be found above that value positive , but actually not all positives are diseased subjects. Therefore, sensitivity is calculated, as the ratio of the all positives who are found by that test, above the cutoff value to the total number of abnormals known to have the disease true positive ; simply sensitivity is the true positive rate TPR.

Similarly, by applying the same cutoff value for the same test, some people with normal results below cutoff value are actually normal true negative but not all of them are not having the disease false negative. Therefore, the true negative rate or specificity could be calculated as the ratio of the all negatives who found by the test below cutoff value to the total number of normals known not to have the disease true negative [ 36 ].

Consequently, sensitivity and specificity could be computed across all possible cutoff or threshold values and both are inversely related to each other [ 38 ]. Cancer biomarker range of results among cancer and non-cancerous patients. Comparative analysis of different sensitivities and specificities at different thresholds would be very effective to judge the accuracy of diagnostic test.

ROC curve was introduced by the British during World War II in order to identify accurate radar detectors and was used later in performance evaluation of radiological tests [ 39 ]. ROC curve is simply defined as performance indicator of a test or biomarker by plotting its sensitivity along the y axis and its 1-specificity or FPR false positive rate along the x axis to assess the diagnostic ability of such biomarker and in discrimination of the diseased from the healthy subjects [ 40 ].

ROC curves have been extensively used for evaluation of the accuracy of diagnostic tests with meaningful interpretations. Several indices could be derived from it such as the area under the curve AUC that determines the average of the sensitivity values for all possible specificity values and includes whole area underneath the entire ROC curve [ 36 ]. AUC could have a range between one and zero because values of the x and y axes probably having values ranging from zero to one as well.

The closer the value of AUC to one the better is the clinical performance of that test [ 40 ]. Comparing AUC areas of different tests can be used to compare their diagnostic performance as AUC is a measure of their overall performance.

The test with bigger AUC value is of better overall performance. On comparison of two tests and if both AUC areas are equal, this indicates same diagnostic performance of both tests, but non-necessarily mean identical ROC curves [ 41 ].

Figure 2 represents the CB levels among cancer and non-cancer cases, while Figure 3 illustrates ROC curve and area under the curve. ROC curve analysis and comparison of area under the curve. Measurement of sensitivity and specificity of a biomarker at a range of cutoff values could be of an important impact for evaluation of CB as we may chose a definitive cutoff value that achieves the highest sensitivity and specificity.

Increment of cutoff point will definitely lead to increase of specificity of the test or false negative patients but on the other hand, this will decrease number of false positives; this indicate a highly specific but low sensitive biomarker. Similarly, if the cutoff point is low that indicates a highly sensitive but low-specific biomarker, as there are fewer false negatives but more false positive subjects.

Indeed, pairs of sensitivities and specificities may describe accuracy of the biomarker and its ability to discriminate between healthy normal and diseased.

The decision threshold must be chosen to be used in patient care, but not for assessment of accuracy. Indeed assessment for performance at definitive point may be misleading or this may results in bias for comparison between tests [ 42 ]. Ideal biomarker must be strictly able to differentiate between cancerous from benign cases, aggressive tumors from insignificant one; it should be of high specificity and sensitivity. Furthermore, it should be a noninvasive and inexpensive [ 30 , 43 ].

The characteristic features of an ideal biomarker are variable and relay to some extent on the application and classification of CB. Mostly, CB have to fulfill the following general properties to be considered ideal. Organ or tissue specific. Proportional to tumor burden or volume: quantitatively proportionate to tumor volume or disease progression.

Short half-life : reflecting quickly any early changes in tumor burden for proper monitoring of therapy. Present if any at low levels in the serum of healthy individuals and those with benign disease. Sharply discriminating metastasis.

Exist in quantitative, standardized, reproducible, and validated assay. Inexpensive or low coasting method. Obtained in a noninvasive manner: detected in serum, body fluids, or in easily accessible tissue. Conventionally used tumor markers or CB may be either proteins or glycoproteins, being probably not involved in carcinogenesis or development of cancer process, rather are likely to be by-products of malignant transformation.

Low molecular weight, small molecules or nucleic acids markers as gene mutations or polymorphisms and quantitative gene expression analysis, peptides, proteins, lipids metabolites, and other small molecules are promising and recently being evaluated as potential clinically useful tumor markers, the patterns of gene expression and genetic alterations and defects may be the framework of the molecular classification of CB [ 11 ].

There are several classification s for CB depending on different aspects related to their chemical nature, proposed mechanisms for their release and applications. Six years ago, a unique classification proposed by Mishra and Verma [ 45 ] with an emphasis on clinical utility of CB.

They classified CB into prediction biomarkers as DNA biomolecules, detection biomarkers as RNA molecules, diagnostic biomarkers as protein biomarkers, and prognosis biomarkers as glyco-biomarkers. Clinical applications and uses of CB, as simply illustrated in Figure 4 are screening and early detection, diagnostic confirmation, prognosis and prediction of therapeutic response, and monitoring disease and recurrence [ 46 ].

Another use of CB includes cancer susceptibility and risk assessment markers which include the identification of individuals who are at a high risk of developing cancer or candidates for screening programs and early preventive studies [ 47 ]. Risk or susceptibility assessment markers include markers of inflammation, oxidative stress and single-nucleotide polymorphisms SNPs , and mutations in certain genes [ 48 , 49 ]. Table 1 illustrates most of traditional, the FDA approved, and clinically relevant CB with their uses in various cancer types.

Clinical utility and uses of cancer biomarkers. Earlier efficient treatment must lead to better outcome compared with the treatment available at later cancer stages or symptomatic patients.

Screening aim was to detect disease when subjects are asymptomatic which differ from diagnosis of symptomatic patients. Objectives of screening and early detection of cancer were to detect cancer at curable and better outcome state and even before appearance of symptoms.

Therefore, screening CB should be able to detect cancer in an early stage or asymptomatic stage and consequently will result in increase of survival rate and decrease complications or morbidities. Screening test must be highly specific to minimize false positives as less as possible. High specificity is mandatory for screening biomarker because even a small false-positive rate could result in large number of unnecessary other invasive diagnostic procedures that may be unneeded with the associated psychological burden and excess costs.

Ideal screening programs have to be noninvasive and inexpensive and definitely lead to obvious reduction in morbidity and mortality and increase in survival rate. Usually, screening programs are directed for highly prevalent cancers and further treatment and follow-up are mandatory [ 34 ]. Other limiting factors for screening biomarker are the low diagnostic sensitivity and specificity of most of the currently used biomarkers to serve as screening markers and being elevated later in the course of cancer.

PSA was cleared by the FDA as a screening biomarker for prostate cancer; however, false positive elevation of PSA levels can be found in individuals with benign or inflammatory conditions as benign prostatic hyperplasia and prostatitis [ 53 ]. Contribution of PSA screening in decreasing mortality is still being a matter of contraverse [ 54 , 55 ].

A diagnostic biomarker would be applied only for symptomatic patients in contrast to screening biomarker that would be applicable only for symptomatic individuals. Interestingly, the characteristics of an ideal diagnostic biomarker are similar to the characteristics for screening.

Notably, most of well-established biomarkers for screening could be used as diagnostic markers and PSA is well-recognized example. PSA, in combination with a digital rectal examination DRE , is the most commonly used diagnostic tool for prostate cancer [ 56 ]. Regarding encountered limitations for diagnostic biomarkers, current available cancer biomarkers are still having low diagnostic sensitivity and specificity; however, diagnostic biomarkers must be of high sensitivity in order to be a good diagnostic biomarker [ 57 ].

For example, Bence-Jones protein in urine remains one of the strongest, well-established diagnostic indicators of multiple myeloma [ 29 ]. Use of panel of CB in order to increase sensitivity and specificity of CB in diagnosis has been used to confirm diagnosis of certain cancers.

In , Mor et al. Other attempts to improve diagnostic sensitivity and specificity included combination of CA with ultrasonography for diagnosis of ovarian cancer [ 60 ]. Prognosis is the probability of cure or likely outcome of any patient. In recent years, scientists have started to look at patterns of gene expression and changes in DNA as cancer biomarkers. There are many types of cancer biomarkers, and they each work differently within the body and react differently to treatments.

In general, cancer biomarkers are classified by their different functions:. An example of this type of biomarker is the HER2 protein, which helps to control cell growth. This condition can possibly cause the cells to grow more quickly and increase their chances of metastasizing spreading to other parts of the body.

SPARC helps bring albumin — a type of protein found in blood, egg whites, milk, and other substances — into cells. Therefore, an overexpression of SPARC helps treatments bound with albumin work more effectively by bringing the treatment right into the cell. Some chemotherapeutic drugs are made with platinum to disrupt tumor DNA.

Even within the above biomarker categories, there is variety. But all cancers do have biomarkers, including genetic biomarkers. In order to determine if, and at what levels, certain biomarkers are present in your cancer, your doctor will need to take a sample of tumor tissue or bodily fluid and send it to a laboratory to conduct a series of advanced pathology and molecular profiling tests.