Nuclear matrix proteins as biomarkers for breast cancer

A Label-free "Lock-key" Fluorescence Aptasensing Based on Triplex-helix DNA and G-quadruplex for CA15-3 Detection

Herein, we designed a label-free fluorescent aptasensor based on triple-helix DNA and G-quadruplex for carbohydrate antigen (CA15-3) detection. The triplex-helix structure can be formed with inserted G-rich DNA (IG) and aptamer DNA (Apt), which like a "lock" to lock the G-rich sequences. The CA15-3 was the "key", which specifically combined with aptamer sequences of Apt, resulting in liberating IG from the triplex-helix "lock". Then, the G-rich sequences of IG were formed into G-quadruplex and specifically interacted with N-methylmesoporphyrin IX (NMM), which greatly enhanced the fluorescence of the solution. However, when the "key" did not exist, the "lock" was fastened and fluorescence intensity did not change. With this proposed method, the concentration of CA15-3 can be effectively detected from 0.01 to 5 U mL^-1 with a detection limit (LOD) of 0.01 U mL^-1. Furthermore, this proposed biosensor can be applied to spiked human serum with great precision and reproducibility.

Breast cancer diagnosis: Imaging techniques and biochemical markers

Breast cancer is a complex disease which is found as the second cause of cancer-associated death among women. Accumulating of evidence indicated that various factors (i.e., gentical and envirmental factors) could be associated with initiation and progression of breast cancer. Diagnosis of breast cancer patients in early stages is one of important aspects of breast cancer treatment. Among of various diagnosis platforms, imaging techniques are main diagnosis approaches which could provide valuable data on patients with breast cancer. It has been showed that various imaging techniques such as mammography, magnetic resonance imaging (MRI), positron-emission tomography (PET), Computed tomography (CT), and single-photon emission computed tomography (SPECT) could be used for diagnosis and monitoring patients with breast cancer in various stages. Beside, imaging techniques, utilization of biochemical biomarkers such as proteins, DNAs, mRNAs, and microRNAs could be employed as new diagnosis and therapeutic tools for patients with breast cancer. Here, we summarized various imaging techniques and biochemical biomarkers could be utilized as diagnosis of patients with breast cancer. Moreover, we highlighted microRNAs and exosomes as new diagnosis and therapeutic biomarkers for monitoring patients with breast cancer.

Thoc1 inhibits cell growth via induction of cell cycle arrest and apoptosis in lung cancer cells

THO complex 1 (Thoc1) is a human nuclear matrix protein that binds to the retinoblastoma tumor suppressor retinoblastoma protein (pRb). While some studies suggest that Thoc1 has characteristics of a tumor suppressor protein, whether Thoc1 can inhibit lung cancer cell growth is not clear. In the present study, we observed that Thoc1 is lowly expressed in the lung cancer cell lines SPC-A1 and NCI-H1975. Then, we investigated the potential effects of Thoc1 on lung cancer cell proliferation, cell cycle and apoptosis after stable transfection of these lines with a Thoc1 expression vector. We found that overexpression of Thoc1 can inhibit cell proliferation, induce G2/M cell cycle arrest and promote apoptosis. Further investigation indicated that overexpression of Thoc1 is involved in the inhibition of cell cycle-related proteins cyclin A1 and B1 and of pro-apoptotic factors Bax and caspase-3. In vivo experiments showed that tumors overexpressing Thoc1 display a slower growth rate than the control xenografts and show reduced expression of the protein Ki-67, which localized on the nuclear membrane. Taken together, our data show that in lung cancer cells, Thoc1 inhibits cell growth through induction of cell cycle arrest and apoptosis. These results indicate that Thoc1 may be used as a novel therapeutic target for human lung cancer treatment.

Breast cancer biomarkers and molecular medicine: part II

In this second part of the two-part review of breast cancer biomarkers and molecular medicine, the first section will consider additional breast cancer prognostic factors, including oncogenes, tumor suppressor genes, cell adhesion molecules, invasion-associated proteins and proteases, hormone receptor proteins, drug resistance proteins, apoptosis regulators, transcription factors, telomerase, DNA repair and methylation and transcriptional profiling using high-density genomic microarrays. The second section will consider the prediction of therapy response using the techniques of pharmacogenetics and pharmacogenomics.

Nuclear mechanics in disease

Over the past two decades, the biomechanical properties of cells have emerged as key players in a broad range of cellular functions, including migration, proliferation, and differentiation. Although much of the attention has focused on the cytoskeletal networks and the cell's microenvironment, relatively little is known about the contribution of the cell nucleus. Here, we present an overview of the structural elements that determine the physical properties of the nucleus and discuss how changes in the expression of nuclear components or mutations in nuclear proteins can not only affect nuclear mechanics but also modulate cytoskeletal organization and diverse cellular functions. These findings illustrate that the nucleus is tightly integrated into the surrounding cellular structure. Consequently, changes in nuclear structure and composition are highly relevant to normal development and physiology and can contribute to many human diseases, such as muscular dystrophy, dilated cardiomyopathy, (premature) aging, and cancer.

Isotropic 3D nuclear morphometry of normal, fibrocystic and malignant breast epithelial cells reveals new structural alterations

Background

Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria.

Methodology

We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure.

Principal Findings

We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations.

Conclusions

Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.

Gene regulation by SMAR1: Role in cellular homeostasis and cancer

Changes in the composition of nuclear matrix associated proteins contribute to alterations in nuclear structure, one of the major phenotypes of malignant cancer cells. The malignancy-induced changes in this structure lead to alterations in chromatin folding, the fidelity of genome replication and gene expression programs. The nuclear matrix forms a scaffold upon which the chromatin is organized into periodic loop domains called matrix attachment regions (MAR) by binding to various MAR binding proteins (MARBPs). Aberrant expression of MARBPs modulates the chromatin organization and disrupt transcriptional network that leads to oncogenesis. Dysregulation of nuclear matrix associated MARBPs has been reported in different types of cancers. Some of these proteins have tumor specific expression and are therefore considered as promising diagnostic or prognostic markers in few cancers. SMAR1 (scaffold/matrix attachment region binding protein 1), is one such nuclear matrix associated protein whose expression is drastically reduced in higher grades of breast cancer. SMAR1 gene is located on human chromosome 16q24.3 locus, the loss of heterozygosity (LOH) of which has been reported in several types of cancers. This review elaborates on the multiple roles of nuclear matrix associated protein SMAR1 in regulating various cellular target genes involved in cell growth, apoptosis and tumorigenesis.

Non-invasive proteomics-thinking about personalized breast cancer screening and treatment

The early diagnosis of breast cancer in potentially curable stages improves prognosis and consecutively reduces mortality of breast cancer patients. Established screening programs have an unfavorable connotation due to significant rates of false negative as well as false positive results leading to overdiagnosis and overtherapy. The combination of a non-invasive breast-cancer-suspectability-biomarker with established clinical diagnostics could help to increase the acceptance of population based breast cancer screening programs by creating an individual risk profile, which is irrespective of mammography quality and interpretation. Recently, non-invasive proteomic biomarkers obtained from blood, saliva or nipple aspiration fluid have been extensively investigated and might play a future role in the personalized management of breast cancer screening. A simple, robust and inexpensive, non-invasive test for screening and diagnosis could easily be performed in every medical practice leading to an affordable, high-throughput instrument. This review describes recently investigated proteomic screening biomarkers that could improve the early diagnosis of breast cancer in the following years.

Prediction of breast cancer and lymph node metastatic status with tumour markers using logistic regression models

AIMS

Early detection of breast cancer can improve disease mortality. The aim of this study was to evaluate the effectiveness of serum biomarkers in the detection of primary breast cancer and lymph node metastatic status.

METHODS

Serum samples were obtained from 55 female patients with breast cancer and 39 women without breast cancer. For these subjects, clinicopathological data were collected and serum levels of carcinoembryonic antigen, breast cancer-specific cancer antigen 15.3 (CA15-3), tissue polypeptide-specific antigen (TPS), soluble interleukin-2 receptor (sIL-2R) and insulin-like growth factor binding protein-3 (IGFBP-3) were assayed. Univariate and multivariate logistic regression were performed to evaluate the association between biomarkers and breast cancer, as well as lymph node metastatic status.

RESULTS

For breast cancer prediction, the serum level of TPS had the best predictive value, with a sensitivity of 80% at an optimal cut-off value of 69.1 U L(-1). The combination of TPS, CA15-3 and IGFBP-3 with logistic regression model increased the sensitivity to 85%. For lymph node metastasis prediction, the serum level of sIL-2R had the best predictive value, with a sensitivity of 66% at an optimal cut-off value of 286 U mL(-1). The combination of sIL-2R and TPS with logistic regression model increased the sensitivity to 69%.

CONCLUSION

TPS may be useful in the detection of primary breast cancer, while sIL-2R may be useful in lymph node metastasis prediction. The combination of more than one biomarker with logistic regression model can improve the predictive sensitivity.

Chromatin organization and nuclear microenvironments in cancer cells

Nuclear morphometric descriptors such as nuclear size, shape, DNA content and chromatin organization are used by pathologists as diagnostic markers for cancer. However, our knowledge of events resulting in changes in nuclear shape and chromatin organization in cancer cells is limited. Nuclear matrix proteins, which include lamins, transcription factors (Sp1) and histone modifying enzymes (histone deacetylases), and histone modifications (histone H3 phosphorylation) have roles in organizing chromatin in the interphase nucleus, regulating gene expression programs and determining nuclear shape. Histone H3 phosphorylation, a downstream target of the Ras-mitogen activated protein kinase pathway, is involved in neoplastic transformation. This article will review genetic and epigenetic events that alter chromatin organization in cancer cells and the role of the nuclear matrix in determining nuclear morphology.

Comparison of nuclear matrix proteins between gastric cancer and normal gastric tissue

AIM: To study the alteration of nuclear matrix proteins (NMPs) in gastric cancer.

METHODS: The NMPs extracted from 22 cases of gastric cancer and normal gastric tissues were investigated by SDS-PAGE technique and the data were analyzed using Genetools analysis software.

RESULTS: Compared with normal gastric tissue, the expression of 30 ku and 28 ku NMPs in gastric cancer decreased significantly (P = 0.002, P = 0.001, P < 0.05). No significant difference was found in the expression of the two NMPs between the various differentiated grades (P = 0.947, P = 0.356) and clinical stages of gastric cancer (P = 0.920, P = 0.243, P > 0.05).

CONCLUSION: The results suggested that the alteration of NMPs in gastric cancer occurred at the early stage of gastric cancer development.

Monitoring of Intracellular Enzyme Kinetic Characteristics of Peripheral Mononuclear Cells in Breast Cancer Patients

A new methodology for the detection of functional response of peripheral blood mononuclear cells against breast cancer (BC) antigens was developed. The method is based on cellular enzymatic activity measurements, using a fluorogenic substrate. We used this method to estimate the kinetic activity of lymphocytes derived from cancer patients and healthy donors. The aim of the study was to determine a possible correlation between the basic characteristics (K(m) and V(max)) of biochemical enzymatic reactions in live peripheral white mononuclear cells and common clinical-pathological characteristics in BC patients. Our method shows that the enzymatic activity, upon interaction with mitogen or tumor antigens, of the peripheral blood cells in BC patients is different from the enzymatic reactions in healthy individuals. This holds true in the early stages, and the difference persists throughout all of the stages of the disease. This difference is manifested, primarily, by an increase in the K(m) values after cell incubation with tumor tissue. It was also demonstrated that higher K(m) values of tumor tissue-activated peripheral blood mononuclear cells are associated with a better prognostic status of the BC patients (lymph node-negative tumors, hormone receptor preservation, and the absence of Her-2/neu protein overexpression). Thus, the present methodology may serve as an additional criterion for prognosis and monitoring, both in BC patients, and in individuals associated with high cancer risk.

Proteomics in biomarker discovery and drug development

Proteomics is a research field aiming to characterize molecular and cellular dynamics in protein expression and function on a global level. The introduction of proteomics has been greatly broadening our view and accelerating our path in various medical researches. The most significant advantage of proteomics is its ability to examine a whole proteome or sub-proteome in a single experiment so that the protein alterations corresponding to a pathological or biochemical condition at a given time can be considered in an integrated way. Proteomic technology has been extensively used to tackle a wide variety of medical subjects including biomarker discovery and drug development. By complement with other new technique advances in genomics and bioinformatics, proteomics has a great potential to make considerable contribution to biomarker identification and to revolutionize drug development process. This article provides a brief overview of the proteomic technologies and their application in biomarker discovery and drug development.

Mass spectrometry-based clinical proteomics

In recent years, mass spectrometry (MS) has been recognized as a 'Gold Standard' tool for the identification and analysis of individual proteins in expression proteomics studies. Moreover, MS has proven useful for the analysis of nucleic acids for single nucleotide polymorphism (SNP) genotyping purposes. With the increased usage of MS as a standard tool for life science applications and the advancement of MS instrumentation, sample preparation and bioinformatics, MS technology has entered novel screening and discovery application areas that are beyond the traditional protein identification and characterization applications. The areas of clinical diagnostics and predictive medicine are just two prime examples of these fields. Predictive markers or biomarkers for early diagnosis of diseases are of growing importance for the human healthcare community. The goal of using MS in clinical proteomics is to generate protein profiles (mass to charge [m/z] ratio versus signal intensity) from readily available body fluids like serum, saliva and urine to detect changes in protein levels that reflect changes in the disease states. Whereas the results originating from individual protein markers may be intriguing, data resulting from the analysis of complex, multiple biomarker patterns may be unequivocal. These biomarker patterns are hidden in complex mass spectra and are not always obvious to the human eye. Sophisticated bioinformatics algorithms have to be applied to determine these unique biomarker patterns. Here, we review the latest developments concerning the use of MS for the discovery of biomarker patterns and for the identification of individual biomarkers in the field of clinical proteomics applications.

Role of proteomics in diagnosis of cancer

Proteomic technologies have emerged as an important addition to the genomic and antibody-based technologies for the diagnosis of cancer. Important technologies include 2-D gel electrophoresis, mass spectrometry, laser capture microdissection, detection of molecular markers of cancer and protein patterns. For clinical applications, the most likely technologies to be used widely are protein biochips. Application of these technologies to various cancers are described. Proteomic technologies have a potential in developing molecular diagnostics and markers for the early detection of cancer. However, information from various diagnostic technologies should be integrated to obtain the optimal information required for clinical management of a patient.