An application of the 2-nitrobenzenesulfenyl method to proteomic profiling of human colorectal carcinoma: A novel approach for biomarker discovery

In the development of novel biomarkers, the proteomic approach is advantageous because using it the cancer-associated proteins can be directly identified. We previously developed a 2-nitrobenzenesulfenyl (NBS) method to improve quantitative proteome analysis. Here, we applied this method to proteomic profiling of colorectal carcinoma (CRC) to identify novel proteins with altered expression in CRC. Each pair of tumor and normal tissue specimens from 12 CRC patients was analyzed, and approximately 5000 NBS-labeled paired peaks were quantified. Peaks with altered signal intensities (>1.5-fold) and occurring frequently in the samples (>70%) were selected, and 128 proteins were identified by MS/MS analyses as differentially expressed proteins in CRC tissues. Many proteins were newly revealed to be CRC related; 30 were reported in earlier studies of CRC. Six proteins that were up-regulated in CRC (ZYX, RAN, RCN1, AHCY, LGALS1, and VIM) were further characterized and validated by Western blot and immunohistochemistry. All six were found to be CRC-localized, either in cancer cells or in stroma cells near the cancer cells. These results indicate that the proteins identified in this study are novel candidates for CRC markers, and that the NBS method is useful in proteome mining to discover novel biomarkers.     

Quantitative mass spectrometry for colorectal cancer proteomics

This review documents the uses of quantitative MS applied to colorectal cancer (CRC) proteomics for biomarker discovery and molecular pathway profiling. Investigators are adopting various labeling and label-free MS approaches to quantitate differential protein levels in cells, tumors, and plasma/serum. We comprehensively review recent uses of this technology to examine mouse models of CRC, CRC cell lines, their secretomes and subcellular fractions, CRC tumors, CRC patient plasma/serum, and stool samples. For biomarker discovery these approaches are uncovering proteins with potential diagnostic and prognostic utility, while in vitro cell culture experiments are characterizing proteomic and phosphoproteomic responses to disrupted signaling pathways due to mutations or to inhibition of drugable enzymes.     

Biomarker discovery by proteomics‐based approaches for early detection and personalized medicine in colorectal cancer

About one million people per year develop colorectal cancer (CRC) and approximately half of them die. The extent of the disease (i.e. local invasion at the time of diagnosis) is a key prognostic factor. The 5‐year survival rate is almost 90% in the case of delimited CRC and 10% in the case of metastasized CRC. Hence, one of the great challenges in the battle against CRC is to improve early diagnosis strategies. Large‐scale proteomic approaches are widely used in cancer research to search for novel biomarkers. Such biomarkers can help in improving the accuracy of the diagnosis and in the optimization of personalized therapy. Herein, we provide an overview of studies published in the last 5 years on CRC that led to the identification of protein biomarkers suitable for clinical application by using proteomic approaches. We discussed these findings according to biomarker application, including also the role of protein phosphorylation and cancer stem cells in biomarker discovery. Our review provides a cross section of scientific approaches and can furnish suggestions for future experimental strategies to be used as reference by scientists, clinicians and researchers interested in proteomics for biomarker discovery.     

The development of multiple reaction monitoring assays for liver-derived plasma proteins

There is wide interpatient variability in toxicity to chemotherapeutic drugs and a lack of routine clinical tests for prospectively identifying patients at risk of developing toxicity from chemotherapy. An empirically driven MS strategy has been developed to monitor liver-derived plasma proteins as potential biomarkers of early toxicity. Multiple reaction monitoring (MRM) has been used to assess 46 candidate peptides from 18 liver-derived proteins. Following an iterative process of assay design, optimisation and assessment we selected 29 MRM assays (median CV 4.6%, range 1.2-11.6%) and monitored changes in levels of plasma proteins from a small number of colorectal cancer (CRC) patients undergoing chemotherapy. We demonstrated MRM assay robustness, and show that patients undergo minor elevation in plasma proteins when profiled on Day 3 of the chemotherapeutic regime. The MRM assays were in general agreement with 2-D DIGE-based quantitation from the same patient samples. The data supports the application of MRM-based methods as facile, highly reproducible, medium-throughput techniques that warrant expanded investigation for clinical utility in identifying patients at risk of developing chemotoxicity.     

Detection of colorectal adenoma and cancer based on transthyretin and C3a-desArg serum levels

Colorectal cancer is the second leading cause of cancer death, and it develops from benign colorectal adenomas in over 95% of patients. Early detection of these cancer precursors by screening tests and their removal can potentially eradicate more than 95% of colorectal cancers before they develop. To discover sensitive and specific biomarkers for improvement of pre‐clinical diagnosis of colorectal adenoma and cancer, we analysed in two independent studies (n = 87 and n = 83 patients) serum samples from colorectal cancer (stage III), colorectal adenoma and control patients using SELDI‐TOF‐MS. Extensive statistical analysis was performed to establish homogeneous patient groups based on their clinical data. Two biomarkers that were each able to distinguish control patients from either colorectal adenoma or colorectal cancer patients (p<0.001) were identified as transthyretin (pre‐albumin) and C3a‐desArg by MS/MS and were further validated by antibody‐based assays (radial immunodiffusion, ELISA). A combination of both proteins clearly indicated the presence of colorectal adenoma or carcinoma. Using a cut‐off of <0.225 g/L for transthyretin and >1974 ng/mL for C3a‐desArg, we found a sensitivity and specificity for colorectal adenoma of 96% and 70%, respectively.     

Overexpression and elevated plasma level of tumor-associated antigen 90K/Mac-2 binding protein in colorectal carcinoma

The cancer cell secretome may contain potentially useful biomarkers. Previously, we have analyzed the colorectal carcinoma (CRC) cell secretome. In this study, tumor‐associated antigen 90K (TAA90K)/Mac‐2 binding protein (Mac‐2BP), one of the CRC cell secreted proteins, was chosen for evaluation as a potential CRC biomarker because its mRNA level was also found to be significantly elevated in CRC tissues and in a more metastatic CRC cell line from the analysis of two public domain array‐based datasets. Immunohistochemical analysis of 241 CRC specimens showed that TAA90K/Mac‐2BP was positively detected in 52.7% of the tumors, but weakly or not detected in over 95% of the adjacent nontumor epithelial cells. The plasma TAA90K/Mac‐2BP levels were significantly higher in CRC patients (N = 280) versus healthy controls (N = 147) (7.77 ± 3.49 vs. 5.72 ± 2.67 μg/mL, p<0.001). Moreover, combination of TAA90K/Mac‐2BP and carcinoembryonic antigen (CEA) could outperform CEA alone in discriminating CRC patients from healthy persons in this case‐control study. Our results collectively indicate that analysis of cancer cell secretome is a feasible strategy for identifying cancer biomarker candidates, and the TAA90K/Mac‐2BP may be a potential CRC biomarker.     

Identification of cancer-associated proteins by proteomics and downregulation of β-tropomyosin expression in colorectal adenoma and cancer

Elucidating the molecular mechanism underlying the development of adenoma, the major precursor lesion of colorectal cancer (CRC), would provide a basis for early detection, prevention as well as treatment of CRC. Using the highly sensitive 2-D DIGE method coupled with MS, we identified 24 differentially expressed proteins in adenoma tissues compared with matched normal colonic mucosa and CRC tissues. Fifteen proteins were downregulated and three proteins were upregulated in adenoma tissues when compared with individual-matched normal colonic mucosa. Five proteins were downregulated, while one protein was upregulated in adenoma tissues when compared with matched CRC tissues. A protein, β-tropomyosin (TM-β), recently suggested to be a biomarker of esophageal squamous carcinoma, was downregulated in both adenoma and CRC tissues. Additionally, the reduction in the level of TM-β in adenoma and CRC tissues was further validated by Western blotting (p<0.05) and RT-PCR (p<0.001). Our findings suggest that downregulation of TM-β is involved in the early development of CRC and that differentially expressed proteins might serve as potential biomarkers for detection of CRC.     

Cell‐Free DNA Blood Collection Tubes Are Appropriate for Clinical Proteomics: A Demonstration in Colorectal Cancer

Background

Optimized blood collection tubes (BCT) have been developed to expand the utility of plasma cell‐free DNA (cfDNA) and are in clinical use. The appropriateness of plasma collected and stored in these tubes for proteomic analysis is unknown.

Methods

Paired blood samples were collected in BCT and traditional K3EDTA (EDTA) tubes from healthy controls and from colorectal cancer (CRC) patients before and after surgery, and stored for between 45 min and 48 h at room temperature. Plasma proteins were analyzed following high‐abundant plasma protein depletion in quantitative discovery and targeted proteomics by liquid chromatography tandem‐mass spectrometry (LC‐MS/MS).

Results

BCT reduced cellular protein contamination in healthy controls over time, and increased the number of high confident low‐abundant protein identifications in CRC blood samples compared to matched samples collected in EDTA tubes. The known CRC plasma protein biomarker, carcinoembryonic antigen (CEA), showed elevated levels across patients pre‐operatively when collected and stored in BCT compared to EDTA tubes. Emerging CRC biomarkers, Dickkopf‐3 (DKK3) and Gelsolin (GSN), showed elevated levels pre‐operatively when collected in BCT.

Conclusions

Optimized BCT are appropriate for low‐abundant plasma protein analysis and can be used with confidence for clinical proteomics.

     

Developing proteomics-based biomarkers for colorectal neoplasms for clinical practice: Opportunities and challenges

Colorectal cancer (CRC) arises from the normal colon epithelium through the accumulation of genetic mutations and epigenetic alterations that are associated with progression along the histological adenoma-adenocarcinoma sequence. Elucidating the molecular alterations underlying disease progression will not only provide insight into the behavior of the tumors, but also could lead to the discovery of useful biomarkers for diagnosis, monitoring treatment responsiveness, or predicting disease outcomes. In the past a few years, there have been several evaluating differentially expressed protein biomarkers by employing proteomics technologies coupled with mass spectrometry. In the current review, we will briefly summarize the results from selected recent studies using tissue or serum samples from CRC patients in the past 5 years and discuss the opportunities and challenges in translating these findings from the research setting to clinical practice.     

Proteomics-based identification of HSP60 as a tumor-associated antigen in colorectal cancer

Patients with cancer frequently develop autoantibodies. The identification of tumor autoantigens may have utility in early cancer diagnosis and immunotherapy. In this study, we used serological proteomics analysis (SERPA) to identify tumor proteins that elicit humoral response in colorectal cancer (CRC). The CRC cell line HCT116 was used as a source of proteins for 2-DE and subsequent Western blot analysis in which individual serum from patients with CRC was analyzed for autoantibodies. An autoantibody against HSP60 identified by MS was detected in 13 out of 25 patients with CRC and 1 out of 15 healthy subjects. In addition, the HSP60 expressions in tumor tissues collected from 40 patients with CRC were assessed by immunohistochemistry, and serum specimens from 100 patients with cancer and 30 healthy controls were screened for antibody titer to HSP60 by ELISA. The results showed that expressions of HSP60 in tumor tissue and serum antibody titer to HSP60 were significantly higher in patients with CRC than in healthy subjects. Thus, we conclude that the SERPA is an excellent assay for the identification of tumor-associated antigens and tumor markers. The detection of HSP60 may have clinical utility in CRC screening, diagnosis, and immunotherapy.     

Modulated T-complex protein 1 ζ and peptidyl-prolyl cis-trans isomerase B are two novel indicators for evaluating lymph node metastasis in colorectal cancer: Evidence from proteomics and bioinformatics

Lymph node metastasis (LNM) is an important indicator for systematic therapy, which could increase the survival of colorectal cancer (CRC) patients. However, effective clinical evaluation for LNM is still absent to date. In this study, protein expression profiles of CRC tissues were compared between patients with and without LNM. Based on average expression level, 12 proteins were found to be differentially expressed in the CRC tissues with LNM, whose discrimination reliability was confirmed by PCA. With stepwise linear discriminant analysis, T-complex protein 1 ζ subunit and peptidyl-prolyl cis-trans isomerase B (PPIB) were identified as two main contributors for separating CRC tissues with positive LNM from those negative ones in both original-grouped and cross-validated-grouped cases, which was also supported in subsequent linear support vector machine analysis. In addition, the expression alterations of the two proteins were verified by Western blot and immunohistochemistry. Functional studies also confirmed the role of PPIB in migration and invasion of cancer cells. Taken together, the down-regulated T-complex protein 1 ζ subunit and up-regulated PPIB were identified as two promising indicators for the clinical evaluation of LNM in CRC patients.     

The role of AI technology in prediction,diagnosis and treatment of colorectal cancer

Artificial intelligence (AI) is a fascinating new technology that incorporates machine learning and neural networks to improve existing technology or create new ones. Potential applications of AI are introduced to aid in the fight against colorectal cancer (CRC). This includes how AI will affect the epidemiology of colorectal cancer and the new methods of mass information gathering like GeoAI, digital epidemiology and real-time information collection. Meanwhile, this review also examines existing tools for diagnosing disease like CT/MRI, endoscopes, genetics, and pathological assessments also benefitted greatly from implementation of deep learning. Finally, how treatment and treatment approaches to CRC can be enhanced when applying AI is under discussion. The power of AI regarding the therapeutic recommendation in colorectal cancer demonstrates much promise in clinical and translational field of oncology, which means better and personalized treatments for those in need.

     

Application of glycoproteomics for the discovery of biomarkers in lung cancer

Lung cancer is the leading cause of cancer-related deaths in the United States. Approximately 40-60% of lung cancer patients present with locally advanced or metastatic disease at the time of diagnosis. Lung cancer development and progression are a multistep process that is characterized by abnormal gene and protein expressions ultimately leading to phenotypic change. Glycoproteins have long been recognized to play fundamental roles in many physiological and pathological processes, particularly in cancer genesis and progression. In order to improve the survival rate of lung cancer patients, the discovery of early diagnostic and prognostic biomarkers is urgently needed. Herein, we reviewed the recent technological developments of glycoproteomics and published data in the field of glycoprotein biomarkers in lung cancer, and discussed their utility and limitations for the discovery of potential biomarkers in lung cancer. Although numerous papers have already acknowledged the importance of the discovery of cancer biomarkers, the systemic study of glycoproteins in lung cancer using glycoproteomic approaches is still suboptimal. Recent development in the glycoproteomics will provide new platforms for identification of potential protein biomarkers in lung cancers.     

Colorectal cancer proteomics, molecular characterization and biomarker discovery

Colorectal cancer (CRC) is a widespread disease, whose major genetic changes and mutations have been well characterized in the sporadic form. Much less is known at the protein and proteome level. Still, CRC has been the subject of multiple proteomic studies due to the urgent necessity of finding clinically relevant markers and to elucidate the molecular mechanisms underlying the progression of the disease. These proteomic approaches have been limited by different technical issues, mainly related with sensitivity and reproducibility. However, recent advances in proteomic techniques and MS systems have rekindled the quest for new biomarkers in CRC and an improved molecular characterization. In this review, we will discuss the application of different proteomic approaches to the identification of differentially expressed proteins in CRC. In particular, we will make a critical assessment about the use of 2-D DIGE, MS and protein microarray technologies, in their different formats, to identify up- or downregulated proteins and/or autoantibodies profiles that could be useful for CRC characterization and diagnosis. Despite a wide list of potential biomarkers, it is clear that more scientific efforts and technical advances are still needed to cover the range of low-abundant proteins, which may play a key role in CRC diagnostics and progression.     

基于聚类算法的结构变异及其形成机制识别

在人类基因组上存在着涉及到不同序列长度的结构变异,这些结构变异对癌症的发生和发展产生了显著的影响。随着新一代测序技术的发展以及测序成本的降低使得在全基因组水平研究结构变异变得可能,基于聚类算法对千人基因组三个不同地区的样本以及ＣＧＨｕｂ数据库中结直肠癌样本进行了结构变异识别,并基于间断点处的序列同源性对结构变异的形成机制进行了分析;利用方差分析及非参数检验分析了结构变异和癌症的关系以及结构变异与地域之间的关系。最后,探讨了该领域未来的发展趋势。     

Development of an optimized multi-biomarker panel for the detection of lung cancer based on principal component analysis and artificial neural network modeling

Lung cancer is a public health priority worldwide due to the high mortality rate and the costs involved. Early detection of lung cancer is important for increasing the survival rate, however, frequently its diagnosis is not made opportunely, since detection methods are not sufficiently sensitive and specific. In recent years serum biomarkers have been proposed as a method that might enhance diagnostic capabilities and complement imaging studies. However, when used alone they show low sensitivity and specificity because lung cancer is a heterogeneous disease. Recent reports have shown that simultaneous analysis of biomarkers has the potential to separate lung cancer patients from control subjects. However, it has become clear that a universal biomarker panel does not exist, and optimized panels need to be developed and validated in each population before their application in a clinical setting. In this study, we selected 14 biomarkers from literature, whose diagnostic or prognostic value had been previously demonstrated for lung cancer, and evaluated them in sera from 63 patients with lung cancer and 87 non-cancer controls (58 Chronic Obstructive Pulmonary Disease (COPD) patients and 29 current smokers). Principal component analysis and artificial neural network modeling allowed us to find a reduced biomarker panel composed of Cyfra 21.1, CEA, CA125 and CRP. This panel was able to correctly classify 135 out of 150 subjects, showing a correct classification rate for lung cancer patients of 88.9%, 93.3% and 90% in training, validation and testing phases, respectively. Thus, sensitivity was increased 18.31% (sensitivity 94.5% at specificity 80%) with respect to the best single marker Cyfra 21.1. This optimized panel represents a potential tool for assisting lung cancer diagnosis, therefore it merits further consideration.     

Targeted detection of prostate cancer proteins in serum using heavy‐isotope‐labeled‐peptide standards and MALDI‐TOF/TOF

Proteins released from cancer tissues to patient sera can potentially be used to achieve sensitive, specific, and early detection of cancer by means of blood tests. In this study, we used a platform that combines glycopeptide capture, heavy‐isotope‐labeled‐peptide standards, and liquid chromatography coupled to tandem mass spectrometry to determine which glycoproteins from prostate cancer can be detected in sera from patients with early‐stage prostate cancer. The detection limit for prostate‐specific antigen in serum was 3.44 ng/mL; thus, direct identification of low abundance, cancer‐specific proteins was achieved using our platform. We showed that prostatic acid phosphatase and membrane metallo‐endopeptidase that were detected in sera were preferentially expressed in prostate cancer tissues. Levels of these two proteins were elevated in biopsy‐positive patients but not biopsy‐negative groups. Therefore, these two proteins are candidate biomarkers for analysis of patient samples with levels of prostate‐specific antigen in the diagnostic gray zone.     

Modulation of cancer cell line differentiation: A neglected proteomic analysis with potential implications in pathophysiology, diagnosis, prognosis, and therapy of cancer

The recent development of compounds that induce cell differentiation in various types of cancer cells has enabled the molecular mechanisms governing this kind of induced cancer regression to be investigated. Moreover, this approach to investigating the pathophysiology of neoplasia represents a promising experimental model for proteomic analysis of cancer cells. Modulating neoplastic cell differentiation grade may reveal cytodifferentiation-related protein expression changes, and doing so in vitro has the advantage of less biological variation. Hence, this analysis brings attention to molecular targets of the so-called differentiating factors (i.e., retinoids, hybrid polar compounds, tyrosine kinase inhibitors, etc.) as well as proteins that are frequently associated with differentiation/dedifferentiation processes. The in vitro study of these proteins and of their pathogenetic roles in cancer may ultimately result in the discovery of cancer biomarkers with diagnostic, prognostic, and therapeutic applications.     

Discovering cancer biomarkers from clinical samples by protein microarrays

Cancer biomarkers are of potential use in early cancer diagnosis, anticancer therapy development, and monitoring the responses to treatments. Protein-based cancer biomarkers are major forms in use, as they are much easier to be monitored in body fluids or tissues. For cancer biomarker discovery, high-throughput techniques such as protein microarrays hold great promises, because they are capable of global unbiased monitoring but with a miniaturized format. In doing so, novel and cancer type specific biomarkers can be systematically discovered at an affordable cost. In this review, we give a relatively complete picture on protein microarrays applied to clinical samples for cancer biomarker discovery, and conclude this review with the future perspectives.     

Towards a comprehensive proteome of normal and malignant human colon tissue by 2-D-LC-ESI-MS and 2-DE proteomics and identification of S100A12 as potential cancer biomarker

The aim of this study was to characterize the proteome of normal and malignant colonic tissue. We previously studied the colon proteome using 2‐DE and MALDI‐MS and identified 734 proteins (Roeßler, M., Rollinger, W., Palme S., Hagmann, M.‐L., et al.., Clin. Cancer Res. 2005, 11, 6550–6557). Here we report the identification of additional colon proteins from the same set of tissue samples using a complementary nano‐flow 2‐D‐LC‐ESI‐MS. In total, 484 proteins were identified in colon. Of these, 252 had also been identified by the 2‐DE/MALDI‐MS approach, whereas 232 proteins were unique to the 2‐D‐LC‐ESI‐MS analysis. Comparing protein expression in neoplastic and normal colon tissue indicated elevated expression of several proteins in colorectal cancer, among them the well established tumor marker carcinoembryonic antigen, as well as calnexin, 40S ribosomal protein S15a, serpin H1, and S100A12. Overexpression of these proteins was confirmed by immunoblotting. Serum levels of S100A12 were determined by ELISA and were found to be strongly elevated in colorectal cancer patients compared to healthy individuals. We conclude, that 2‐D‐LC‐ESI‐MS is a powerful approach to identify and compare protein profiles of tissue samples, that it is complementary to 2‐DE/MALDI‐MS approaches and has the potential to identify novel biomarkers.