DJ-1 may contribute to metastasis of non-small cell lung cancer

Lung cancer is a leading cause of cancer-related death, about 40% human non-small cell lung cancer (NSCLC) patients showed lymph node involvements. However, the precise mechanism for the metastasis is still not fully understood. This study was to analyze the potential molecular mechanism for lung cancer metastasis. In the current study, proteomics analysis by two-dimensional electrophoresis (2-DE) was performed first to identify the differentially expressed protein between the higher metastasis lung adenocarcinoma cell line Anip973 and the lower metastasis lung adenocarcinoma cell line AGZY83-a. We confirmed the result by RT-PCR, immunoblotting and immunocytochemistry analyses in these two cell lines. Then we examined the expression of the differentially expressed protein in tumor tissues of NSCLC patients by immunoblotting and immunohistochemistry analyses. Using 2-DE analysis, we have identified DJ-1 was expressed higher in the higher metastasis Anip973 compared to the parental cell line AGZY83-a, that was confirmed by RT-PCR, immunoblotting and immunocytochemistry analyses. In NSCLC patients?? tumor tissues study, immunoblotting data showed that, DJ-1 expression level was significantly higher in 72.2% (13/18) of NSCLC tissue samples compared to that in paired normal lung tissues (P?=?0.044). Immunohistochemistry analysis demonstrated increased DJ-1 expression in 85 NSCLC tumor tissue samples compared with 7 normal lung tissue samples (P?=?0.044). DJ-1 expression was also found to be significantly correlated with cancer lymphatic metastasis (P?=?0.039). DJ-1 might contribute to the metastasis of NSCLC.     

MALDI Profiling of Human Lung Cancer Subtypes

Background

Proteomics is expected to play a key role in cancer biomarker discovery. Although it has become feasible to rapidly analyze proteins from crude cell extracts using mass spectrometry, complex sample composition hampers this type of measurement. Therefore, for effective proteome analysis, it becomes critical to enrich samples for the analytes of interest. Despite that one-third of the proteins in eukaryotic cells are thought to be phosphorylated at some point in their life cycle, only a low percentage of intracellular proteins is phosphorylated at a given time.

Methodology/Principal Findings

In this work, we have applied chromatographic phosphopeptide enrichment techniques to reduce the complexity of human clinical samples. A novel method for high-throughput peptide profiling of human tumor samples, using Parallel IMAC and MALDI-TOF MS, is described. We have applied this methodology to analyze human normal and cancer lung samples in the search for new biomarkers. Using a highly reproducible spectral processing algorithm to produce peptide mass profiles with minimal variability across the samples, lineal discriminant-based and decision tree–based classification models were generated. These models can distinguish normal from tumor samples, as well as differentiate the various non–small cell lung cancer histological subtypes.

Conclusions/Significance

A novel, optimized sample preparation method and a careful data acquisition strategy is described for high-throughput peptide profiling of small amounts of human normal lung and lung cancer samples. We show that the appropriate combination of peptide expression values is able to discriminate normal lung from non-small cell lung cancer samples and among different histological subtypes. Our study does emphasize the great potential of proteomics in the molecular characterization of cancer.     

Gene Expression-Based Classification of Non-Small Cell Lung Carcinomas and Survival Prediction

Background

Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy.

Methodology and Principal Findings

A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier survival curves show that the two groups display a significant difference in post-surgery survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts.

Conclusions

The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the prediction of clinical outcome.     

Discovery of lung cancer biomarkers by profiling the plasma proteome with monoclonal antibody libraries

A challenge in the treatment of lung cancer is the lack of early diagnostics. Here, we describe the application of monoclonal antibody proteomics for discovery of a panel of biomarkers for early detection (stage I) of non-small cell lung cancer (NSCLC). We produced large monoclonal antibody libraries directed against the natural form of protein antigens present in the plasma of NSCLC patients. Plasma biomarkers associated with the presence of lung cancer were detected via high throughput ELISA. Differential profiling of plasma proteomes of four clinical cohorts, totaling 301 patients with lung cancer and 235 healthy controls, identified 13 lung cancer-associated (p < 0.05) monoclonal antibodies. The monoclonal antibodies recognize five different cognate proteins identified using immunoprecipitation followed by mass spectrometry. Four of the five antigens were present in non-small cell lung cancer cells in situ. The approach is capable of generating independent antibodies against different epitopes of the same proteins, allowing fast translation to multiplexed sandwich assays. Based on these results, we have verified in two independent clinical collections a panel of five biomarkers for classifying patient disease status with a diagnostics performance of 77% sensitivity and 87% specificity. Combining CYFRA, an established cancer marker, with the panel resulted in a performance of 83% sensitivity at 95% specificity for stage I NSCLC.     

Integrative Proteomics and Tissue Microarray Profiling Indicate the Association between Overexpressed Serum Proteins and Non-Small Cell Lung Cancer

Lung cancer is the leading cause of cancer deaths worldwide. Clinically, the treatment of non-small cell lung cancer (NSCLC) can be improved by the early detection and risk screening among population. To meet this need, here we describe the application of extensive peptide level fractionation coupled with label free quantitative proteomics for the discovery of potential serum biomarkers for lung cancer, and the usage of Tissue microarray analysis (TMA) and Multiple reaction monitoring (MRM) assays for the following up validations in the verification phase. Using these state-of-art, currently available clinical proteomic approaches, in the discovery phase we confidently identified 647 serum proteins, and 101 proteins showed a statistically significant association with NSCLC in our 18 discovery samples. This serum proteomic dataset allowed us to discern the differential patterns and abnormal biological processes in the lung cancer blood. Of these proteins, Alpha-1B-glycoprotein (A1BG) and Leucine-rich alpha-2-glycoprotein (LRG1), two plasma glycoproteins with previously unknown function were selected as examples for which TMA and MRM verification were performed in a large sample set consisting about 100 patients. We revealed that A1BG and LRG1 were overexpressed in both the blood level and tumor sections, which can be referred to separate lung cancer patients from healthy cases.     

Identification of Five Candidate Lung Cancer Biomarkers by Proteomics Analysis of Conditioned Media of Four Lung Cancer Cell Lines

Detection of lung cancer at an early stage is necessary for successful therapy and improved survival rates. We performed a bottom-up proteomics analysis using a two-dimensional LC-MS/MS strategy on the conditioned media of four lung cancer cell lines of different histological backgrounds (non-small cell lung cancer: H23 (adenocarcinoma), H520 (squamous cell carcinoma), and H460 (large cell carcinoma); small cell lung cancer: H1688) to identify secreted or membrane-bound proteins that could be useful as novel lung cancer biomarkers. Proteomics analysis of the four conditioned media allowed identification of 1,830 different proteins (965, 871, 726, and 847 from H1688, H23, H460, and H520, respectively). All proteins were assigned a subcellular localization, and 38% were classified as extracellular or membrane-bound. We successfully identified the internal control proteins (also detected by ELISA), kallikrein-related peptidases 14 and 11, and IGFBP2. We also identified known or putative lung cancer tumor markers such as squamous cell carcinoma antigen, carcinoembryonic antigen, chromogranin A, creatine kinase BB, progastrin-releasing peptide, neural cell adhesion molecule, and tumor M2-PK. To select the most promising candidates for validation, we performed tissue specificity assays, functional classifications, literature searches for association to cancer, and a comparison of our proteome with the proteome of lung-related diseases and serum. Five novel lung cancer candidates, ADAM-17, osteoprotegerin, pentraxin 3, follistatin, and tumor necrosis factor receptor superfamily member 1A were preliminarily validated in the serum of patients with lung cancer and healthy controls. Our results demonstrate the utility of this cell culture proteomics approach to identify secreted and shed proteins that are potentially useful as serological markers for lung cancer.Lung cancer is the leading cause of cancer-related mortality worldwide in both men and women. An estimated 213,000 news cases and 160,000 deaths from lung cancer occur in the United States every year (National Cancer Institute). According to the World Health Organization, lung cancers are largely classified into two histologically distinct types, based on the size and appearance of the malignant cells: small cell (SCLC)1 and non-small cell lung cancer (NSCLC). NSCLC, which comprises more than 80% of lung cancers, can be further divided into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma.Despite advances in treatments such as surgery, chemotherapy, and radiotherapy, the clinical outcome for patients with lung cancer still remains poor. The overall 5-year survival rate is only 10–15% (1) mainly because, at the time of diagnosis, most lung cancer patients are at advanced stages. In this context, there is a critical need to detect lung cancer earlier by improving the current diagnostic methods such as computed tomography and chest x-ray and by discovering useful diagnostic and prognostic biomarkers. To date, a number of serum biomarkers for lung cancer have been studied, including CEA, squamous cell carcinoma (SCC)-Ag, neuron-specific enolase, tissue polypeptide antigen, CYFRA21-1 (cytokeratin 19 fragment), and pro-GRP. They are elevated in serum of patients with lung cancer, but they are not sensitive or specific enough, alone or in combination, to reliably diagnose asymptomatic patients with lung cancer.Recently, new approaches in clinical proteomics have been developed to identify novel biomarkers of lung pathology (COPD, asthma, pleural effusion, and cancer) and to gain insights into disease mechanisms in which proteins play a major role. Some proteomics analyses of various biological fluids associated with the human airway have been reported, including nasal lavage fluid (2–4), bronchoalveolar lavage fluid (5, 6), and saliva (7, 8). By using a combination of 2DE analysis and Gel electrophoresis coupled with LC-MS/MS, Nicholas et al. (9) identified 258 proteins in human sputum, and among them, 191 were of human origin. Proteins included lower and upper airway secretory products, cellular products, and inflammatory cell-derived products. In addition, Casado et al. (10) used capillary column LC-ESI-Q/TOF-MS to investigate the proteome profiles of hypertonic saline-induced sputum samples from healthy smokers and patients with COPD of different severity. A total of 203 unique proteins were identified of which some may be markers of COPD severity. The proteomics profile of human pleural effusion from 43 lung adenocarcinoma was also studied using a 2D nano-HPLC-ESI-MS/MS system (11). The results revealed 1,415 unique proteins of which 124 were identified with higher confidence (at least two unique peptide sequences matched). However, there are inherent limitations of using MS for biomarker discovery in complex biological mixtures such as fluids or serum (12, 13), requiring methodologies for depletion of high abundance proteins such as albumin and immunoglobulins. These limitations illustrate the need to find other sources to mine for biomarker discovery.One approach to overcome this limitation posed by complex mixtures is by using a cell culture model, in which cells are grown in serum-free medium, to perform proteomics analysis. This model offers various advantages over the traditional cultures in serum-supplemented medium: it reduces complexity by avoiding interferences from nutritional proteins present in the medium, increases the reproducibility, and allows detection of low abundance proteins. This strategy has been successfully used in our laboratory for the discovery of novel breast and prostate biomarkers (14, 15). This technique was also reported in lung-related proteomics approaches. Tachibana et al. (16) reported the regulatory roles of β1 integrin in morphological differentiation in CADO LC6 cells, an SCLC cell line cultured in serum-free medium. To explore serum biomarkers of lung cancer at early stage, M-BE, an SV40T-transformed human bronchial epithelial cell line with the phenotypic features of early tumorigenesis at high passage, was cultured, and the conditioned medium was used to collect its secretory proteins (17). Proteins secreted from different passages of M-BE cells were extracted and then separated by 2DE followed by MALDI-TOF/TOF mass spectrometry. The authors identified 47 proteins, including cathepsin D, that exhibited increased abundance in culture media or cells during passaging. Moreover, Xiao et al. (18) analyzed the proteins released into the serum-free medium from the tumor microenvironment with short time-cultured lung cancer and adjacent normal bronchial epithelial cells, thus demonstrating the versatility of this approach.In this study, we performed a shotgun proteomics analysis of the conditioned media of four lung cancer cell lines of differing histotypes. Our aim was to identify secreted or membrane-bound proteins that could be useful as novel lung cancer biomarkers. Five proteins were elevated in serum of lung cancer patients, suggesting that they may represent lung cancer biomarkers that are worth validating in the future.     

Comparative proteomic analysis for the insoluble fractions of colorectal cancer patients

We used label-free quantitative proteomics with the insoluble fractions from colorectal cancer (CRC) patients to gain further insight into the utility of profiling altered protein expression as a potential biomarker for cancer. The insoluble fractions were prepared from paired tumor/normal biopsies from 13 patients diagnosed with CRC (stages I to IV). Fifty-six proteins identified in data pooled from the 13 cases were differentially expressed between the tumor and adjacent normal tissue. The connections between these proteins are involved in reciprocal networks related to tumorigenesis, cancer incidence based on genetic disorder, and skeletal and muscular disorders. To assess their potential utility as biomarkers, the relative expression levels of the proteins were validated using personal proteomics and a heat map to compare five individual CRC samples with five normal tissue samples. Further validation of a panel of proteins (KRT5, JUP, TUBB, and COL6A1) using western blotting confirmed the differential expression. These proteins gave specific network information for CRC, and yielded a panel of novel markers and potential targets for treatment. It is anticipated that the experimental approach described here will increase our understanding of the membrane environment in CRC, which may provide direction for making diagnoses and prognoses through molecular biomarker targeting.     

Urinary exosomal long noncoding RNAs serve as biomarkers for early detection of non-small cell lung cancer

Objective: Increasing the efficiency of early diagnosis using noninvasive biomarkers is crucial for enhancing the survival rate of lung cancer patients. We explore the differential expression of non-small cell lung cancer (NSCLC)-related long noncoding RNAs (lncRNAs) in urinary exosomes in NSCLC patients and normal controls to diagnose lung cancer.Methods: A differential expression analysis between NSCLC patients and healthy controls was performed using microarrays. Gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to predict potential functions of lncRNAs in NSCLC. quantitative real-time PCR (QT-PCR) was used to verify microarray results.Results: A total of 640 lncRNAs (70 up- and 570 down-regulated) were differentially expressed in NSCLC patients in comparison to healthy controls. Six lncRNAs were detected by QT-PCR. GO term and KEGG pathway analyses showed that differential lncRNAs were enriched in cellular component organization or biogenesis, as well as other biological processes and signaling pathways, such as the PI3K-AKT, FOXO, p53, and fatty acid biosynthesis.Conclusions: The differential lncRNAs in urinary exosomes are potential diagnostic biomarkers of NSCLC. The lncRNAs enriched in specific pathways may be associated with tumor cell proliferation, tumor cell apoptosis, and the cell cycle involved in the pathogenesis of NSCLC.     

Differences in miRNA Expression in Early Stage Lung Adenocarcinomas that Did and Did Not Relapse

Relapse of adenocarcinoma, the most common non-small cell lung cancer (NSCLC), is a major clinical challenge to improving survival. To gain insight into the early molecular events that contribute to lung adenocarcinoma relapse, and taking into consideration potential cell type specificity, we used stringent criteria for sample selection. We measured miRNA expression only from flash frozen stage I lung adenocarcinomas, excluding other NSCLC subtypes. We compared miRNA expression in lung adenocarcinomas that relapsed within two years to those that did not relapse within three years after surgical resection prior to adjuvant therapy. The most significant differences in mRNA expression for recurrent tumors compared to non-recurrent tumors were decreases in miR-106b*, -187, -205, -449b, -774* and increases in miR-151-3p, let-7b, miR-215, -520b, and -512-3p. A unique comparison between adjacent normal lung tissue from relapse and non-relapse groups revealed dramatically different miRNA expression, suggesting dysregulation of miRNA in the environment around the tumor. To assess patient-to-patient variability, miRNA levels in the tumors were normalized to levels in matched adjacent normal lung tissue. This analysis revealed a different set of significantly altered miRNA in tumors that recurred compared to tumors that did not. Together our analyses elucidated miRNA not previously linked to lung adenocarcinoma that likely have important roles in its development and progression. Our results also highlight the differences in miRNA expression in normal lung tissue in adenocarcinomas that do and do not recur. Most notably, our data identified those miRNA that distinguish early stage tumors likely to relapse prior to treatment and miRNA that could be further studied for use as biomarkers for prognosis, patient monitoring, and/or treatment decisions.     

Identification of isocitrate dehydrogenase 1 as a potential diagnostic and prognostic biomarker for non-small cell lung cancer by proteomic analysis

Lung cancer is the leading cause of cancer-related death in the world. To explore tumor biomarkers for clinical application, two-dimensional fluorescence difference gel electrophoresis and subsequent MALDI-TOF/TOF mass spectrometry were performed to identify proteins differentially expressed in 12 pairs of lung squamous cell tumors and their corresponding normal tissues. A total of 28 nonredundant proteins were identified with significant alteration in lung tumors. The up-regulation of isocitrate dehydrogenase 1 (IDH1), superoxide dismutase 2, 14-3-3ε, and receptor of activated protein kinase C1 and the down-regulation of peroxiredoxin 2 in tumors were validated by RT-PCR and Western blot analysis in independent 15 pairs of samples. Increased IDH1 expression was further verified by the immunohistochemical study in extended 73 squamous cell carcinoma and 64 adenocarcinoma clinical samples. A correlation between IDH1 expression and poor overall survival of non-small cell lung cancer (NSCLC) patients was observed. Furthermore, ELISA analysis showed that the plasma level of IDH1 was significantly elevated in NSCLC patients compared with benign lung disease patients and healthy individuals. In addition, knockdown of IDH1 by RNA interference suppressed the proliferation of NSCLC cell line and decreased the growth of xenograft tumors in vivo. These observations suggested that IDH1, as a protein promoting tumor growth, could be used as a plasma biomarker for diagnosis and a histochemical biomarker for prognosis prediction of NSCLC.     

Unlocking biomarker discovery: large scale application of aptamer proteomic technology for early detection of lung cancer

Background

Lung cancer is the leading cause of cancer deaths worldwide. New diagnostics are needed to detect early stage lung cancer because it may be cured with surgery. However, most cases are diagnosed too late for curative surgery. Here we present a comprehensive clinical biomarker study of lung cancer and the first large-scale clinical application of a new aptamer-based proteomic technology to discover blood protein biomarkers in disease.

Methodology/Principal Findings

We conducted a multi-center case-control study in archived serum samples from 1,326 subjects from four independent studies of non-small cell lung cancer (NSCLC) in long-term tobacco-exposed populations. Sera were collected and processed under uniform protocols. Case sera were collected from 291 patients within 8 weeks of the first biopsy-proven lung cancer and prior to tumor removal by surgery. Control sera were collected from 1,035 asymptomatic study participants with ≥10 pack-years of cigarette smoking. We measured 813 proteins in each sample with a new aptamer-based proteomic technology, identified 44 candidate biomarkers, and developed a 12-protein panel (cadherin-1, CD30 ligand, endostatin, HSP90α, LRIG3, MIP-4, pleiotrophin, PRKCI, RGM-C, SCF-sR, sL-selectin, and YES) that discriminates NSCLC from controls with 91% sensitivity and 84% specificity in cross-validated training and 89% sensitivity and 83% specificity in a separate verification set, with similar performance for early and late stage NSCLC.

Conclusions/Significance

This study is a significant advance in clinical proteomics in an area of high unmet clinical need. Our analysis exceeds the breadth and dynamic range of proteome interrogated of previously published clinical studies of broad serum proteome profiling platforms including mass spectrometry, antibody arrays, and autoantibody arrays. The sensitivity and specificity of our 12-biomarker panel improves upon published protein and gene expression panels. Separate verification of classifier performance provides evidence against over-fitting and is encouraging for the next development phase, independent validation. This careful study provides a solid foundation to develop tests sorely needed to identify early stage lung cancer.     

Proteomic profiling of endothelial cells in human lung cancer

Genomic and proteomic analysis of normal and diseased tissues have yielded an abundance of molecular information for diagnostic and potential therapeutic targets. Changing the target of analysis from poorly accessible cells within tissues to easily accessible vascular endothelium has theoretical advantages in tissue-specific targeting. In this study, we sought to map a large-scale proteome of microvascular endothelium in human non-small cell lung cancer (NSCLC) and normal lung tissues, and identify lung cancer-related endothelial cell (EC)-selective proteins. Endothelial cells were isolated within NSCLC tissues and adjacent-normal lung tissue of lung cancer patients by using CD31-immunomagnetic beads. The complex proteins from the ECs were separated by one-dimensional gel electrophoresis, and the proteins in each gel band were digested by trypsin. Peptides were separated by online reverse-phase liquid-chromatography and analyzed by electrospray ionization (ESI) ion trap tandem mass spectrometry. Approximately 600-1000 proteins were identified in each individual sample. Five patient cases of paired individual data, extracted from the protein identification data sets of both normal- and cancer-derived ECs, were analyzed by subtractive proteomics. An average of 300 proteins was specifically identified from each lung cancer-derived EC isolate, compared to normal lung-derived ECs. With the use of several comparative analyses, we identified among those 300 proteins, 16 common candidate proteins that were detected in at least 3 of 5 cases specific to lung cancer-derived ECs. Proteins selectively identified in cancer-derived ECs, including coatomer protein complex, subunit gamma (COPG), and peroxiredoxin 4 (PRDX4), were validated by Western blot analysis. In an additional experiment in which 16 cancer samples were analyzed by immunohistochemistry, PRDX4, thymopoietin (TMPO), and COPG were confirmed to be abundantly expressed in lung cancer-derived ECs and in cancerous lung cells. Further ongoing analysis of these 16 candidate proteins will determine their potential applicability to NSCLC-specific diagnosis and therapeutics.     

Quantification of Normal Cell Fraction and Copy Number Neutral LOH in Clinical Lung Cancer Samples Using SNP Array Data

Background

Technologies based on DNA microarrays have the potential to provide detailed information on genomic aberrations in tumor cells. In practice a major obstacle for quantitative detection of aberrations is the heterogeneity of clinical tumor tissue. Since tumor tissue invariably contains genetically normal stromal cells, this may lead to a failure to detect aberrations in the tumor cells.

Principal Finding

Using SNP array data from 44 non-small cell lung cancer samples we have developed a bioinformatic algorithm that accurately models the fractions of normal and tumor cells in clinical tumor samples. The proportion of normal cells in combination with SNP array data can be used to detect and quantify copy number neutral loss-of-heterozygosity (CNNLOH) in the tumor cells both in crude tumor tissue and in samples enriched for tumor cells by laser capture microdissection.

Conclusion

Genome-wide quantitative analysis of CNNLOH using the CNNLOH Quantifier method can help to identify recurrent aberrations contributing to tumor development in clinical tumor samples. In addition, SNP-array based analysis of CNNLOH may become important for detection of aberrations that can be used for diagnostic and prognostic purposes.     

An Outlook on Ovarian Cancer and Borderline Ovarian Tumors: Focus on Genomic and Proteomic Findings

Among the gynaecological malignancies, ovarian cancer is one of the neoplastic forms with the poorest prognosis and with the bad overall and disease-free survival rates than other gynaecological cancers. Ovarian tumors can be classified on the basis of the cells of origin in epithelial, stromal and germ cell tumors. Epithelial ovarian tumors display great histological heterogeneity and can be further subdivided into benign, intermediate or borderline, and invasive tumors. Several studies on ovarian tumors, have focused on the identification of both diagnostic and prognostic markers for applications in clinical practice. High-throughput technologies have accelerated the process of biomolecular study and genomic discovery; unfortunately, validity of these should be still demonstrated by extensive researches on sensibility and sensitivity of ovarian cancer novel biomarkers, determining whether gene profiling and proteomics could help differentiate between patients with metastatic ovarian cancer and primary ovarian carcinomas, and their potential impact on management. Therefore, considerable interest lies in identifying molecular and protein biomarkers and indicators to guide treatment decisions and clinical follow up. In this review, the current state of knowledge about the genoproteomic and potential clinical value of gene expression profiling in ovarian cancer and ovarian borderline tumors is discussed, focusing on three main areas: distinguishing normal ovarian tissue from ovarian cancers and borderline tumors, identifying different genotypes of ovarian tissue and identifying proteins linked to cancer or tumor development. By these targets, authors focus on the use of novel molecules, developed on the proteomics and genomics researches, as potential protein biomarkers in the management of ovarian cancer or borderline tumor, overlooking on current state of the art and on future perspectives of researches.Key Words: Ovarian cancer, borderline ovarian tumors, markers, genomics, proteomics, oncogenes.     

Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.     

Comparative proteomic approach identifies PKM2 and cofilin-1 as potential diagnostic, prognostic and therapeutic targets for pulmonary adenocarcinoma

Lung cancer is the leading cause of cancer-related death in the world. Non-small cell lung carcinomas (Non-SCLC) account for almost 80% of lung cancers, of which 40% were adenocarcinomas. For a better understanding of the molecular mechanisms behind the development and progression of lung cancer, particularly lung adenocarcinoma, we have used proteomics technology to search for candidate prognostic and therapeutic targets in pulmonary adenocarcinoma. The protein profile changes between human pulmonary adenocarcinoma tissue and paired surrounding normal tissue were analyzed using two-dimensional polyacrylamide gel electrophoresis (2-DE) based approach. Differentially expressed protein-spots were identified with ESI-Q-TOF MS/MS instruments. As a result, thirty two differentially expressed proteins (over 2-fold, p<0.05) were identified in pulmonary adenocarcinoma compared to normal tissues. Among them, two proteins (PKM2 and cofilin-1), significantly up-regulated in adenocarcinoma, were selected for detailed analysis. Immunohistochemical examination indicated that enhanced expression of PKM2 and cofilin-1 were correlated with the severity of epithelial dysplasia, as well as a relatively poor prognosis. Knockdown of PKM2 expression by RNA interference led to a significant suppression of cell growth and induction of apoptosis in pulmonary adenocarcinoma SPC-A1 cells in vitro, and tumor growth inhibition in vivo xenograft model (P<0.05). In addition, the shRNA expressing plasmid targeting cofilin-1 significantly inhibited tumor metastases and prolonged survival in LL/2 metastatic model. While additional works are needed to elucidate the biological significance and molecular mechanisms of these altered proteins identified in this study, PKM2 and cofilin-1 may serve as potential diagnostic and prognostic biomarkers, as well as therapeutic targets for pulmonary adenocarcinoma.     

Tissue proteomics for cancer biomarker development: laser microdissection and 2D-DIGE

Novel cancer biomarkers are required to achieve early diagnosis and optimized therapy for individual patients. Cancer is a disease of the genome, and tumor tissues are a rich source of cancer biomarkers as they contain the functional translation of the genome, namely the proteome. Investigation of the tumor tissue proteome allows the identification of proteomic signatures corresponding to clinico-pathological parameters, and individual proteins in such signatures will be good biomarker candidates. Tumor tissues are also a rich source for plasma biomarkers, because proteins released from tumor tissues may be more cancer specific than those from non-tumor cells. Two-dimensional difference gel electrophoresis (2D-DIGE) with novel ultra high sensitive fluorescent dyes (CyDye DIGE Fluor satulation dye) enables the efficient protein expression profiling of laser-microdissected tissue samples. The combined use of laser microdissection allows accurate proteomic profiling of specific cells in tumor tissues. To develop clinical applications using the identified biomarkers, collaboration between research scientists, clinicians and diagnostic companies is essential, particularly in the early phases of the biomarker development projects. The proteomics modalities currently available have the potential to lead to the development of clinical applications, and channeling the wealth of produced information towards concrete and specific clinical purposes is urgent.