Cancer proteomics

Construction and validation of classification models for predicting the response to concurrent chemo-radiotherapy of patients with esophageal squamous cell carcinoma based on multi-omics data

BACKGROUND

Concurrent chemo-radiotherapy (CCRT) is the preferred non-surgical treatment for patients with locally advanced esophageal squamous cell carcinoma (ESCC). Unfortunately, some patients respond poorly, which leads to inappropriate or excessive treatment and affects patient survival. To accurately predict the response of ESCC patients to CCRT, we developed classification models based on the clinical, serum proteomic and radiomic data.

METHODS

A total of 138 ESCC patients receiving CCRT were enrolled in this study and randomly split into a training cohort (n = 92) and a test cohort (n = 46). All patients were classified into either complete response (CR) or incomplete response (non-CR) groups according to RECIST1.1. Radiomic features were extracted by 3Dslicer. Serum proteomic data was obtained by Olink proximity extension assay. The logistic regression model with elastic-net penalty and the R-package "rms" v6.2-0 were applied to construct classification and nomogram models, respectively. The area under the receiver operating characteristic curves (AUC) was used to evaluate the predictive performance of the models.

RESULTS

Seven classification models based on multi-omics data were constructed, of which Model-COR, which integrates five clinical, five serum proteomic, and seven radiomic features, achieved the best predictive performance on the test cohort (AUC = 0.8357, 95 % CI: 0.7158-0.9556). Meanwhile, patients predicted to be CR by Model-COR showed significantly longer overall survival than those predicted to be non-CR in both cohorts (Log-rank P = 0.0014 and 0.027, respectively). Furthermore, two nomogram models based on multi-omics data also performed well in predicting response to CCRT (AUC = 0.8398 and 0.8483, respectively).

CONCLUSION

We developed and validated a multi-omics based classification model and two nomogram models for predicting the response of ESCC patients to CCRT, which achieved the best prediction performance by integrating clinical, serum Olink proteomic, and radiomic data. These models could be useful for personalized treatment decisions and more precise clinical radiotherapy and chemotherapy for ESCC patients.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

Using proteomics for stratification and risk prediction in patients with solid tumors

Proteomics, the study of proteins and their functions, has greatly evolved due to advances in analytical chemistry and computational biology. Unlike genomics or transcriptomics, proteomics captures the dynamic and diverse nature of proteins, which play crucial roles in cellular processes. This is exemplified in cancer, where genomic and transcriptomic information often falls short in reflecting actual protein expression and interactions. Liquid chromatography-mass spectrometry (LC-MS) is pivotal in proteomic data generation, enabling high-throughput analysis of protein samples. The MS-based workflow involves protein digestion, chromatographic separation, ionization, and fragmentation, leading to peptide identification and quantification. Computational biostatistics, particularly using tools in R (R Foundation for Statistical Computing, Vienna, Austria; www.R-project.org ), aid in data analysis, revealing protein expression patterns and correlations with clinical variables. Proteomic studies can be explorative, aiming to characterize entire proteomes, or targeted, focusing on specific proteins of interest. The integration of proteomics with genomics addresses database limitations and enhances peptide identification. Case studies in intrahepatic cholangiocarcinoma, glioblastoma multiforme, and pancreatic ductal adenocarcinoma highlight proteomics' clinical applications, from subtyping cancers to identifying diagnostic markers. Moreover, proteomic data augment molecular tumor boards by providing deeper insights into pathway activities and genomic mutations, supporting personalized treatment decisions. Overall, proteomics contributes significantly to advancing our understanding of cellular biology and improving clinical care.

In-Depth Analysis of the N-Glycome of Colorectal Cancer Cell Lines

Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer deaths worldwide. A well-known hallmark of cancer is altered glycosylation. Analyzing the N-glycosylation of CRC cell lines may provide potential therapeutic or diagnostic targets. In this study, an in-depth N-glycomic analysis of 25 CRC cell lines was conducted using porous graphitized carbon nano-liquid chromatography coupled to electrospray ionization mass spectrometry. This method allows for the separation of isomers and performs structural characterization, revealing profound N-glycomic diversity among the studied CRC cell lines with the elucidation of a number of 139 N-glycans. A high degree of similarity between the two N-glycan datasets measured on the two different platforms (porous graphitized carbon nano-liquid chromatography electrospray ionization tandem mass spectrometry (PGC-nano-LC-ESI-MS) and matrix-assisted laser desorption/ionization time of flight-mass spectrometry (MALDI-TOF-MS)) was discovered. Furthermore, we studied the associations between glycosylation features, glycosyltransferases (GTs), and transcription factors (TFs). While no significant correlations between the glycosylation features and GTs were found, the association between TF CDX1 and (s)Le antigen expression and relevant GTs FUT3/6 suggests that CDX1 contributes to the expression of the (s)Le antigen through the regulation of FUT3/6. Our study provides a comprehensive characterization of the N-glycome of CRC cell lines, which may contribute to the future discovery of novel glyco-biomarkers of CRC.

The Role of Proteomics and Phosphoproteomics in the Discovery of Therapeutic Targets and Biomarkers in Acquired EGFR-TKI-Resistant Non-Small Cell Lung Cancer

The discovery of potent EGFR-tyrosine kinase inhibitors (EGFR-TKIs) has revolutionized the treatment of EGFR-mutated lung cancer. Despite the fact that EGFR-TKIs have yielded several significant benefits for lung cancer patients, the emergence of resistance to EGFR-TKIs has been a substantial impediment to improving treatment outcomes. Understanding the molecular mechanisms underlying resistance is crucial for the development of new treatments and biomarkers for disease progression. Together with the advancement in proteome and phosphoproteome analysis, a diverse set of key signaling pathways have been successfully identified that provide insight for the discovery of possible therapeutically targeted proteins. In this review, we highlight the proteome and phosphoproteomic analyses of non-small cell lung cancer (NSCLC) as well as the proteome analysis of biofluid specimens that associate with acquired resistance in response to different generations of EGFR-TKI. Furthermore, we present an overview of the targeted proteins and potential drugs that have been tested in clinical studies and discuss the challenges of implementing this discovery in future NSCLC treatment.

Integration of pharmacoproteomic and computational approaches reveals the cellular signal transduction pathways affected by apatinib in gastric cancer cell lines

Apatinib is known to be a highly selective vascular endothelial growth factor receptor 2 (VEGFR2) inhibitor with anti-angiogenic and anti-tumor properties. In a phase III study, the objective response rate to apatinib was low. It remains unclear why the effectivity of apatinib varies among patients and what type of patients are candidates for the treatment. In this study, we investigated the anti-tumor efficacy of apatinib against 13 gastric cancer cell lines and found that it differed depending on the cell line. Using integrated wet and dry approaches, we showed that apatinib was a multi-kinase inhibitor of c-Kit, RAF1, VEGFR1, VEGFR2, and VEGFR3, predominantly inhibiting c-Kit. Notably, KATO-III, which was the most apatinib-sensitive among the gastric cancer cell lines investigated, was the only cell line expressing c-Kit, RAF1, VEGFR1, and VEGFR3 but not VEGFR2. Furthermore, we identified SNW1 as a molecule affected by apatinib that plays an important role in cell survival. Finally, we identified the molecular network related to SNW1 that was affected by treatment with apatinib. These results suggest that the mechanism of action of apatinib in KATO-III cells is independent of VEGFR2 and that the differential efficacy of apatinib was due to differences in expression patterns of receptor tyrosine kinases. Furthermore, our results suggest that the differential efficacy of apatinib in gastric cell lines may be attributed to SNW1 phosphorylation levels at a steady state. These findings contribute to a deeper understanding of the mechanism of action of apatinib in gastric cancer cells.

Label-free-based quantitative proteomic analysis of the inhibition of cisplatin-resistant ovarian cancer cell proliferation by cucurbitacin B

BACKGROUND

Ovarian cancer is a serious threat to women's health, and resistance to chemotherapeutic drugs constitutes one of the principal reasons for ovarian cancer recurrence and the low overall survival rate. Therefore, it is of paramount importance to develop additional and more-effective drugs to combat resistance to chemotherapeutic drugs. Cucurbitacin B (CuB) is a natural compound found in food plants such as bitter gourd and pumpkin, and it manifests favorable antitumor effects on a variety of malignant tumors.

PURPOSE

The present study aimed to determine the mechanism effects of CuB overcomes tumor-drug resistance in ovarian cancer.

METHODS

We used CCK-8, Edu, flow cytometric assays and cisplatin-resistant ovarian cancer xenograft mouse model to evaluate the cellular proliferation, cellular apoptosis.and tumor growth. We subsequently applied a pharmacoproteomic approach to analyze the molecular mechanisms by which CuB inhibited the proliferation of cisplatin-resistant ovarian cancer cells. We also employed western blot and molecular docking experiments to verify elements of PI3K/Akt/mTOR pathway expression.

RESULTS

We found that CuB inhibited cellular proliferation and promoted apoptosis in cisplatin-resistant ovarian cancer cell lines. We discerned that CuB inhibited tumor growth of xenograft mouse tumors. We ascertained that treatment of A2780-DDP cells with CuB resulted in the differential expression of 305 proteins, with 202 proteins downregulated and 103 proteins upregulated. Of these proteins, the mTOR protein was significantly downregulated in the drug-treated group. We also found that CuB inhibited PI3K, Akt, and mTOR and that it activated cGAS expression upstream of PI3K and inhibited ATR expression. Molecular docking experiments revealed that CuB was hydrogen-bonded to mTOR proteins at Gly (2142) and Thr (2207), with a binding force of -10.2 kcal/mol.

CONCLUSION

Our study confirmed that cucurbitacin B inhibits the PI3K/Akt/mTOR signaling pathway, targets mTOR, suppresses the proliferation of cisplatin-resistant ovarian cancer cells.And we also found that cucurbitacin B induces DNA damage, activates cGASA and recruits IKBα,playing a crucial role in eliciting anti-tumor immunity. We herein uncovered a new use for CuB in inhibiting tumor-drug resistance, providing a novel approach to overcoming chemotherapeutic drug resistance in ovarian cancer.

Construction and validation of a novel and superior protein risk model for prognosis prediction in esophageal cancer

Esophageal cancer (EC) is recognized as one of the most common malignant tumors in the word. Based on the biological process of EC occurrence and development, exploring molecular biomarkers can provide a good guidance for predicting the risk, prognosis and treatment response of EC. Proteomics has been widely used as a technology that identifies, analyzes and quantitatively acquires the composition of all proteins in the target tissues. Proteomics characterization applied to construct a prognostic signature will help to explore effective biomarkers and discover new therapeutic targets for EC. This study showed that we established a 8 proteins risk model composed of ASNS, b-Catenin_pT41_S45, ARAF_pS299, SFRP1, Vinculin, MERIT40, BAK and Atg4B via multivariate Cox regression analysis of the proteome data in the Cancer Genome Atlas (TCGA) to predict the prognosis power of EC patients. The risk model had the best discrimination ability and could distinguish patients in the high- and low-risk groups by principal component analysis (PCA) analysis, and the high-risk patients had a poor survival status compared with the low-risk patients. It was confirmed as one independent and superior prognostic predictor by the receiver operating characteristic (ROC) curve and nomogram. K-M survival analysis was performed to investigate the relationship between the 8 proteins expressions and the overall survival. GSEA analysis showed KEGG and GO pathways enriched in the risk model, such as metabolic and cancer-related pathways. The high-risk group presented upregulation of dendritic cells resting, macrophages M2 and NK cells activated, downregulation of plasma cells, and multiple activated immune checkpoints. Most of the potential therapeutic drugs were more appropriate treatment for the low-risk patients. Through adequate analysis and verification, this 8 proteins risk model could act as a great prognostic evaluation for EC patients and provide new insight into the diagnosis and treatment of EC.

Cancer-Associated Membrane Protein as Targeted Therapy for Bladder Cancer

Bladder cancer (BC) recurrence is one of the primary clinical problems encountered by patients following chemotherapy. However, the mechanisms underlying their resistance to chemotherapy remain unclear. Alteration in the pattern of membrane proteins (MPs) is thought to be associated with this recurrence outcome, often leading to cell dysfunction. Since MPs are found throughout the cell membrane, they have become the focus of attention for cancer diagnosis and treatment. Identifying specific and sensitive biomarkers for BC, therefore, requires a major collaborative effort. This review describes studies on membrane proteins as potential biomarkers to facilitate personalised medicine. It aims to introduce and discuss the types and significant functions of membrane proteins as potential biomarkers for future medicine. Other types of biomarkers such as DNA-, RNA- or metabolite-based biomarkers are not included in this review, but the focus is mainly on cell membrane surface protein-based biomarkers.

The Role of Novel Bladder Cancer Diagnostic and Surveillance Biomarkers-What Should a Urologist Really Know?

The aim of this review is to analyze and describe the current landscape of bladder cancer diagnostic and surveillance biomarkers. We researched the literature from 2016 to November 2021 to find the most promising new molecules and divided them into seven different subgroups based on their function and location in the cell. Although cystoscopy and cytology are still the gold standard for diagnosis and surveillance when it comes to bladder cancer (BCa), their cost is quite a burden for national health systems worldwide. Currently, the research is focused on finding a biomarker that has high negative predictive value (NPV) and can exclude with a certainty the presence of the tumor, considering missing it could be disastrous for the patient. Every subgroup has its own advantages and disadvantages; for example, protein biomarkers cost less than genomic ones, but on the other hand, they seem to be less precise. We tried to simplify this complicated topic as much as possible in order to make it comprehensible to doctors and urologists that are not as familiar with it, as well as encourage them to actively participate in ongoing research.

Early detection of cancer

Survival improves when cancer is detected early. However, ~50% of cancers are at an advanced stage when diagnosed. Early detection of cancer or precancerous change allows early intervention to try to slow or prevent cancer development and lethality. To achieve early detection of all cancers, numerous challenges must be overcome. It is vital to better understand who is at greatest risk of developing cancer. We also need to elucidate the biology and trajectory of precancer and early cancer to identify consequential disease that requires intervention. Insights must be translated into sensitive and specific early detection technologies and be appropriately evaluated to support practical clinical implementation. Interdisciplinary collaboration is key; advances in technology and biological understanding highlight that it is time to accelerate early detection research and transform cancer survival.

Identification of Significant Modules and Targets of Xian-Lian-Jie-Du Decoction Based on the Analysis of Transcriptomics, Proteomics and Single-Cell Transcriptomics in Colorectal Tumor

Purpose

Colorectal cancer (CRC) remains the third most common tumor worldwide. Ulcerative colitis (UC) could cause chronic inflammation and ulcers in the colon and rectum. UC is a risk factor for a high incidence of CRC, and the incidence of UC-associated CRC (UC-CRC) is still increasing. Chinese medicine prescription, Xian-Lian-Jie-Du decoction (XLJDD), has been proven its efficacy in some UC-CRC patients. However, the mechanism of XLJDD in treating UC-CRC remains unknown. This study aimed to investigate the mechanism of XLJDD in treating UC-CRC.

Methods

We constructed an AOM/DSS mouse model that could simulate the various stages of UC-CRC in humans. XLJDD and its 5 main components are used to treat the AOM/DSS model, respectively. With the power of high-throughput sequencing technology, we described the mechanism of XLJDD from transcriptomics, proteomics, and single-cell transcriptomics.

Results

Our results showed that XLJDD could effectively suppress the occurrence and development of colorectal tumors. Using the weighted correlation network analysis (WGCNA), several mRNA and protein modules that respond to XLJDD have been identified. Moreover, two essential genes, Mfsd2a and Ccdc85c, were caught our attention. They were prognostic markers in CRC patients, and their expression could be significantly modulated by XLJDD, showing their potential as effective targets of XLJDD. In addition, we also discovered that XLJDD could affect the cell composition of the colorectal tumor environment, especially in the infiltration of B cells.

Conclusion

We demonstrated that XLJDD could prevent the initiation and development of colorectal tumors by modulating the expression of Mfsd2a and Ccdc85c and reducing the infiltration of B cells in the tumor microenvironment of colorectal tumor.

Advances in the DNA Nanotechnology for the Cancer Biomarkers Analysis: Attributes and Applications

The most commonly used clinical methods are enzyme-linked immunosorbent assay (ELISA) and quantitative PCR (qPCR) in which ELISA was applied for the detection of protein biomarkers and qPCR was especially applied for nucleic acid biomarker analysis. Although these constructed methods have been applied in wide range, they also showed some inherent shortcomings such as low sensitivity, large sample volume and complex operations. At present, many methods have been successfully constructed on the basis of DNA nanotechnology with the merits of high accuracy, rapid and simple operation for cancer biomarkers assay. In this review, we summarized the bioassay strategies based on DNA nanotechnology from the perspective of the analytical attributes for the first time and discussed and the feasibility of the reported strategies for clinical application in the future.

Integrated approaches for precision oncology in colorectal cancer: The more you know, the better

Colorectal cancer (CRC) is one of the most common human malignancies accounting for approximately 10 % of worldwide cancer incidence and mortality. While early-stage CRC is mainly a preventable and curable disease, metastatic colorectal cancer (mCRC) remains an unmet clinical need. Moreover, about 25 % of CRC cases are diagnosed only at the metastatic stage. Despite the extensive molecular and functional knowledge on this disease, systemic therapy for mCRC still relies on traditional 5-fluorouracil (5-FU)-based chemotherapy regimens. On the other hand, targeted therapies and immunotherapy have shown effectiveness only in a limited subset of patients. For these reasons, there is a growing need to define the molecular and biological landscape of individual patients to implement novel, rationally driven, tailored therapies. In this review, we explore current and emerging approaches for CRC management such as genomic, transcriptomic and metabolomic analysis, the use of liquid biopsies and the implementation of patients' preclinical avatars. In particular, we discuss the contribution of each of these tools in elucidating patient specific features, with the aim of improving our ability in advancing the diagnosis and treatment of colorectal tumors.

Research status and prospects of biomarkers for nasopharyngeal carcinoma in the era of high‑throughput omics (Review)

As a malignant tumor type, nasopharyngeal carcinoma (NPC) is characterized by distinct geographical, ethnic and genetic differences; presenting a major threat to human health in many countries, especially in Southern China. At present, no accurate and effective methods are available for the early diagnosis, efficacious evaluation or prognosis prediction for NPC. As such, a large number of patients have locoregionally advanced NPC at the time of initial diagnosis. Many patients show toxic reactions to overtreatment and have risks of cancer recurrence and distant metastasis owing to insufficient treatment. To solve these clinical problems, high‑throughput '‑omics' technologies are being used to screen and identify specific molecular biomarkers for NPC. Because of the lack of comprehensive descriptions regarding NPC biomarkers, the present study summarized the research progress that has been made in recent years to discover NPC biomarkers, highlighting the existing problems that require exploration. In view of the lack of authoritative reports at present, study design factors that affect the screening of biomarkers are also discussed here and prospects for future research are proposed to provide references for follow‑up studies of NPC biomarkers.

Integrated Proteomic and Phosphoproteomics Analysis of DKK3 Signaling Reveals Activated Kinase in the Most Aggressive Gallbladder Cancer

DKK3 is a secreted protein, which belongs to a family of Wnt antagonists and acts as a potential tumor suppressor in gallbladder cancer. To further understand its tumor suppressor functions, we overexpressed DKK3 in 3 GBC cell lines. We have employed high-resolution mass spectrometry and tandem mass tag (TMT) multiplexing technology along with immobilized metal affinity chromatography to enrich phosphopeptides to check the downstream regulators. In this study, we reported for the first time the alteration in the phosphorylation of 14 kinases upon DKK3 overexpression. In addition, we observed DKK3 induced hyper phosphorylation of 2 phosphatases: PPP1R12A and PTPRA, which have not been reported previously. Canonical pathway analysis of altered molecules indicated differential enrichment of signaling cascades upon DKK3 overexpression in all the 3 cell lines. Protein kinase A signaling, Sirtuin signaling pathway, and Cell Cycle Control of Chromosomal Replication were observed to be differentially activated in the GBC cell lines. Our study revealed, DKK3 overexpression has differential effect based on the aggressive behavior of the cell lines. This study expands the understanding of DKK3-mediated signaling events and can be used as a primary factor for understanding the complex nature of this molecule.

RNA-binding protein KHSRP promotes tumor growth and metastasis in non-small cell lung cancer

BACKGROUND

KH-type splicing regulatory protein (KHSRP) plays an important role in cancer invasion, but the relevant mechanism is not well known. In the present study, we investigated the function and potential molecular mechanism of KHSRP in non-small cell lung cancer (NSCLC) metastasis and elucidated its clinical significance.

METHODS

Isobaric tags for relative and absolute quantitation and the SWATH™ approach were combined with nanoliquid chromatography-tandem mass spectrometry analysis to identify metastasis-associated nucleoproteins in NSCLC. Real-time PCR and Western blot were used to screen for metastasis-associated candidate molecules. Gene knockdown and overexpression were used to investigate their functions and molecular mechanisms in lung cancer cells. Coimmunoprecipitation (Co-IP) experiments were performed to identify the interactions between candidate molecules and their interacting proteins. Gene expression and its association with multiple clinicopathologic characteristics were analyzed by immunohistochemistry (IHC) and Western blot in human lung cancer specimens.

RESULTS

KHSRP was identified as a metastasis-associated candidate molecule. In NSCLC cell lines, knockdown of KHSRP significantly reduced lung cancer cell proliferation, migration, and invasion in vitro and in vivo, whereas overexpression of KHSRP did the opposite. Mechanistically, the protein heterogeneous nuclear ribonucleoprotein C (C1/C2) (HNRNPC) was identified to interact with KHSRP using Co-IP experiments. In NSCLC cell lines, overexpression of HNRNPC significantly promoted lung cancer cell proliferation, migration, and invasion in vitro and in vivo. KHSRP and HNRNPC may induce human lung cancer cell invasion and metastasis by activating the IFN-α-JAK-STAT1 signaling pathway. Drastically higher expression levels of KHSRP and HNRNPC were observed in lung cancer tissues compared to those in adjacent noncancerous tissues. Increased KHSRP and HNRNPC expression was significantly associated with advanced tumor stages and metastasis (both lymph node and distant). Kaplan-Meier survival analysis showed that patients with high KHSRP and HNRNPC expression levels were predicted to have the shortest survival times and to have a poor prognosis.

CONCLUSIONS

KHSRP plays an important role in NSCLC metastasis and may serve as a potential prognostic marker and novel therapeutic target for lung cancer metastasis treatment.

Molecular insights into cancer drug resistance from a proteomics perspective

INTRODUCTION

Resistance to chemotherapy and development of specific and effective molecular targeted therapies are major obstacles facing current cancer treatment. Comparative proteomic approaches have been employed for the discovery of putative biomarkers associated with cancer drug resistance and have yielded a number of candidate proteins, showing great promise for both novel drug target identification and personalized medicine for the treatment of drug-resistant cancer. Areas covered: Herein, we review the recent advances and challenges in proteomics studies on cancer drug resistance with an emphasis on biomarker discovery, as well as understanding the interconnectivity of proteins in disease-related signaling pathways. In addition, we highlight the critical role that post-translational modifications (PTMs) play in the mechanisms of cancer drug resistance. Expert opinion: Revealing changes in proteome profiles and the role of PTMs in drug-resistant cancer is key to deciphering the mechanisms of treatment resistance. With the development of sensitive and specific mass spectrometry (MS)-based proteomics and related technologies, it is now possible to investigate in depth potential biomarkers and the molecular mechanisms of cancer drug resistance, assisting the development of individualized therapeutic strategies for cancer patients.

Experimental Design in Clinical 'Omics Biomarker Discovery

This tutorial highlights some issues in the experimental design of clinical 'omics biomarker discovery, how to avoid bias and get as true quantities as possible from biochemical analyses, and how to select samples to improve the chance of answering the clinical question at issue. This includes the importance of defining clinical aim and end point, knowing the variability in the results, randomization of samples, sample size, statistical power, and how to avoid confounding factors by including clinical data in the sample selection, that is, how to avoid unpleasant surprises at the point of statistical analysis. The aim of this Tutorial is to help translational clinical and preclinical biomarker candidate research and to improve the validity and potential of future biomarker candidate findings.

Urine proteome analysis by C18 plate-matrix-assisted laser desorption/ionization time-of-flight mass spectrometry allows noninvasive differential diagnosis and prediction of diabetic nephropathy

Diabetic nephropathy (DN) is one of the most common complications in diabetic patients. New noninvasive markers are still needed for the early detection of DN before identifiable alternations in kidney function or urine albumin excretion occurs. A C18 plate and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) were used to compare the urinary protein profiles of 238 subjects from the following 4 groups: patients with type 2 diabetic (T2D) with microalbuminuria, patients with DM without micro- or macroalbuminuria, patients with micro- or macroalbuminuria due to nondiabetic disease, and healthy controls. β2-microglobulin (B2M) and Clara-cell protein (CC16) were found to be highly released in the urine of patients with proteinuria due to nondiabetic or diabetic diseases. In differentiating nephropathy from healthy subject, the B2M and CC16 markers have a combined sensitivity and specificity of 77.3% and 91.8%, respectively. In distinguishing T2D with microalbuminuria from T2D patients, the combined markers have sensitivity and specificity of 66% and 73%, respectively. The predictive ability of B2M and CC16 for early renal functional decline (ERFD) was validated in 125 T2D patients with a follow-up times. The odds ratio (OR) of combined B2M and CC16 markers for developing ERFD was 7.59 (95% CI: 1.97-29.24). The detection of B2M and CC16 with the C18 plate-MALDI-TOF MS approach could be an attractive and practical assay for rapid diagnosis of nephropathy in nondiabetic/diabetic patients and as a predictor of ERFD among T2D patients who had not manifested significant kidney disease at baseline.

Recent advances in mass spectrometry-based glycoproteomics

Protein glycosylation plays fundamental roles in many biological processes as one of the most common, and the most complex, posttranslational modification. Alterations in glycosylation profile are now known to be associated with many diseases. As a result, the discovery and detailed characterization of glycoprotein disease biomarkers is a primary interest of biomedical research. Advances in mass spectrometry (MS)-based glycoproteomics and glycomics are increasingly enabling qualitative and quantitative approaches for site-specific structural analysis of protein glycosylation. While the complexity presented by glycan heterogeneity and the wide dynamic range of clinically relevant samples like plasma, serum, cerebrospinal fluid, and tissue make comprehensive analyses of the glycoproteome a challenging task, the ongoing efforts into the development of glycoprotein enrichment, enzymatic digestion, and separation strategies combined with novel quantitative MS methodologies have greatly improved analytical sensitivity, specificity, and throughput. This review summarizes current MS-based glycoproteomics approaches and highlights recent advances in its application to cancer biomarker and neurodegenerative disease research.

Differentially expressed and survival-related proteins of lung adenocarcinoma with bone metastasis

Despite recent advances in targeted and immune‐based therapies, the poor prognosis of lung adenocarcinoma (LUAD) with bone metastasis (BM) remains a challenge. First, two‐dimensional gel electrophoresis (2‐DE) was used to identify proteins that were differentially expressed in LUAD with BM, and then matrix‐assisted laser desorption/ionization time of flight mass spectrometry (MALDI‐TOF‐MS) was used to identify these proteins. Second, the Cancer Genome Atlas (TCGA) was used to identify mutations in these differentially expressed proteins and Kaplan–Meier plotter (KM Plotter) was used to generate survival curves for the analyzed cases. Immunohistochemistry (IHC) was used to check the expression of proteins in 28 patients with BM and nine patients with LUAD. Lastly, the results were analyzed with respect to clinical features and patient's follow‐up. We identified a number of matched proteins from 2‐DE. High expression of enolase 1 (ENO1) (HR = 1.67, logrank P = 1.9E‐05), ribosomal protein lateral stalk subunit P2 (RPLP2) (HR = 1.77, logrank P = 2.9e‐06), and NME/NM23 nucleoside diphosphate kinase 2 (NME1‐NME2) (HR = 2.65, logrank P = 3.9E‐15) was all significantly associated with poor survival (P < 0.05). Further, ENO1 was upregulated (P = 0.0004) and calcyphosine (CAPS1) was downregulated (P = 5.34E‐07) in TCGA LUAD RNA‐seq expression data. IHC revealed that prominent ENO1 staining (OR = 7.5, P = 0.034) and low levels of CAPS1 (OR = 0.01, P < 0.0001) staining were associated with BM incidence. Finally, we found that LUAD patients with high expression of ENO1 and RPLP2 had worse overall survival. This is the first instance where the genes ENO1, RPLP2, NME1‐NME2 and CAPS1 were associated with disease severity and progression in LUAD patients with BM. Thus, with this study, we have identified potential biomarkers and therapeutic targets for this disease.

A Timely Shift from Shotgun to Targeted Proteomics and How It Can Be Groundbreaking for Cancer Research

The fact that cancer is a leading cause of death all around the world has naturally sparked major efforts in the pursuit of novel and more efficient biomarkers that could better serve as diagnostic tools, prognostic predictors, or therapeutical targets in the battle against this type of disease. Mass spectrometry-based proteomics has proven itself as a robust and logical alternative to the immuno-based methods that once dominated the field. Nevertheless, intrinsic limitations of classic proteomic approaches such as the natural gap between shotgun discovery-based methods and clinically applicable results have called for the implementation of more direct, hypothesis-based studies such as those made available through targeted approaches, that might be able to streamline biomarker discovery and validation as a means to increase survivability of affected patients. In fact, the paradigm shifting potential of modern targeted proteomics applied to cancer research can be demonstrated by the large number of advancements and increasing examples of new and more useful biomarkers found during the course of this review in different aspects of cancer research. Out of the many studies dedicated to cancer biomarker discovery, we were able to devise some clear trends, such as the fact that breast cancer is the most common type of tumor studied and that most of the research for any given type of cancer is focused on the discovery diagnostic biomarkers, with the exception of those that rely on samples other than plasma and serum, which are generally aimed toward prognostic markers. Interestingly, the most common type of targeted approach is based on stable isotope dilution-selected reaction monitoring protocols for quantification of the target molecules. Overall, this reinforces that notion that targeted proteomics has already started to fulfill its role as a groundbreaking strategy that may enable researchers to catapult the number of viable, effective, and validated biomarkers in cancer clinical practice.

Advancement of mass spectrometry-based proteomics technologies to explore triple negative breast cancer

Understanding the complexity of cancer biology requires extensive information about the cancer proteome over the course of the disease. The recent advances in mass spectrometry-based proteomics technologies have led to the accumulation of an incredible amount of such proteomic information. This information allows us to identify protein signatures or protein biomarkers, which can be used to improve cancer diagnosis, prognosis and treatment. For example, mass spectrometry-based proteomics has been used in breast cancer research for over two decades to elucidate protein function. Breast cancer is a heterogeneous group of diseases with distinct molecular features that are reflected in tumour characteristics and clinical outcomes. Compared with all other subtypes of breast cancer, triple-negative breast cancer is perhaps the most distinct in nature and heterogeneity. In this review, we provide an introductory overview of the application of advanced proteomic technologies to triple-negative breast cancer research.

Clinical proteomics in cancer: Where we are

Proteomics has emerged as a promising field in the post-genomic era. Notwithstanding the great advances provided by gene expression analysis in cancer, the lack of a correlation between gene expression and protein levels has highlighted the need for a proteomic focus on cancer. Although the increasing knowledge regarding cancer biology, a reliable marker to improve diagnosis, prognosis and treatment for cancer patients is not a reality at present. In this review, we address the main considerations regarding proteomics-based studies and their clinical applications on cancer research, highlighting some considerations related to strengths and limitations of proteomics-based studies and its application to clinical practice.

Diagnostic and prognostic significance of serum apolipoprotein C-I in triple-negative breast cancer based on mass spectrometry

Women with triple-negative breast cancer (TNBC) have poor prognosis because of the aggressive nature of the tumor, delayed diagnosis and non-specific symptoms in the early stages. Identification of novel specific TNBC serum biomarkers for screening and therapeutic purposes therefore remains an urgent clinical requirement.We obtained serum samples from a total of 380 recruited individuals split into mining and testing sets, with the aim of screening for reliable protein biomarkers from TNBC and non-TNBC (NTNBC) sera. Samples were assessed using mass spectrometry, followed by receiver operating characteristic (ROC), survival and hazard function curve as well as multivariate Cox regression analyses to ascertain the potential of the protein constituents as diagnostic and prognostic biomarkers for TNBC.We identified upregulated apolipoprotein C-I (apoC-I) with a validated positive effect on TNBC tumorigenesis, with confirmation in an independent test set and minimization of systematic bias by pre-analytical parameters. The apoC-I protein had superior diagnostic ability in distinguishing between TNBC and NTNBC cases. Moreover, the protein presented a more robust potential prognostic factor for TNBC than NTNBC. The apoC-I protein identified in this study presents an effective novel diagnostic and prognostic biomarker for TNBC, indicating that measurement of the peak intensity at 7785 Da in serum samples could facilitate improved early detection and estimation of postoperative survival prognosis for TNBC.

Diagnostic and prognostic role of serum protein peak at 6449 m/z in gastric adenocarcinoma based on mass spectrometry

Background:

Gastric cancer (GC) is a highly aggressive cancer type associated with significant mortality owing to delayed diagnosis and non-specific symptoms observed in the early stages. Therefore, identification of novel specific GC serum biomarkers for screening purposes is an urgent clinical requirement.

Methods:

This study recruited a total of 432 serum samples from 296 GC patients split into the mining and testing sets. We aimed to screen for reliable protein biomarkers from matched serum samples based on mass spectrometry, followed by comparison with three representative conventional markers using receiver operating characteristic and survival curve analyses to ascertain their potential values as diagnostic and prognostic biomarkers for GC.

Results:

We identified an apoC-III fragment with confirmation in an independent test set from a second hospital. We found that the diagnostic ability of this fragment performed better than current standard GC diagnostic biomarkers both individually and in combination in distinguishing patients with GC from healthy individuals. Moreover, we found that this apoC-III protein fragment represents a more robust potential prognostic factor for GC than the three conventional markers.

Conclusions:

In view of these findings, we suggest that apoC-III protein fragment is a novel diagnostic and prognostic biomarker, a complement to conventional biomarkers in detecting GC.

Clinical proteomics-driven precision medicine for targeted cancer therapy: current overview and future perspectives

Cancer is a common disease that is a leading cause of death worldwide. Currently, early detection and novel therapeutic strategies are urgently needed for more effective management of cancer. Importantly, protein profiling using clinical proteomic strategies, with spectacular sensitivity and precision, offer excellent promise for the identification of potential biomarkers that would direct the development of targeted therapeutic anticancer drugs for precision medicine. In particular, clinical sample sources, including tumor tissues and body fluids (blood, feces, urine and saliva), have been widely investigated using modern high-throughput mass spectrometry-based proteomic approaches combined with bioinformatic analysis, to pursue the possibilities of precision medicine for targeted cancer therapy. Discussed in this review are the current advantages and limitations of clinical proteomics, the available strategies of clinical proteomics for the management of precision medicine, as well as the challenges and future perspectives of clinical proteomics-driven precision medicine for targeted cancer therapy.

Novel insight into MALAT-1 in cancer: Therapeutic targets and clinical applications

Long non-protein-coding RNAs (lncRNAs) are emerging as important gene expression regulators that are linked to various biological processes at the post-transcriptional and transcriptional levels. lncRNAs are known to be important in cell proliferation, cell differentiation, apoptosis and metastasis. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT-1), a novel lncRNA, is highly conserved amongst mammals. In addition, it has been considered to act as an oncogene, depending on the tumor system. An increasing number of studies have indicated that MALAT-1 may be detected in certain types of human tumors, including lung and bladder cancer and hepatocellular carcinoma. MALAT-1 silencing may be an effective therapeutic approach against tumors. The present study reviews the current knowledge on the functional role of MALAT-1 in the control of various cancers.

Proteomics in cancer research: Are we ready for clinical practice?

Although genomics has delivered major advances in cancer prognostics, treatment and diagnostics, it still only provides a static image of the situation. To study more dynamic molecular entities, proteomics has been introduced into the cancer research field more than a decade ago. Currently, however, the impact of clinical proteomics on patient management and clinical decision-making is low and the implementations of scientific results in the clinic appear to be scarce. The search for cancer-related biomarkers with proteomics however, has major potential to improve risk assessment, early detection, diagnosis, prognosis, treatment selection and monitoring. In this review, we provide an overview of the transition of oncoproteomics towards translational oncology. We describe which lessons are learned from currently approved protein biomarkers and previous proteomic studies, what the pitfalls and challenges are in clinical proteomics applications, and how proteomic research can be successfully translated into medical practice.

Cancer proteomics: developments in technology, clinical use and commercialization

In the last two decades, advances in genomic, transcriptomic and proteomic methods have enabled us to identify and classify cancers by their molecular profiles. Many anticipate that a molecular taxonomy of cancer will not only lead to more effective subtyping of cancers but also earlier diagnoses, more informative prognoses and more targeted treatments. This article reviews recent technological developments in the field of proteomics, recent discoveries in proteomic cancer biomarker research and trends in clinical use. Readers are also informed of examples of successful commercialization, and the future of proteomics in cancer diagnostics.

Autoimmune Response to IGF2 mRNA-Binding Protein 2 (IMP2/p62) in Breast Cancer

The purpose of this study was to understand the autoimmune response and immunogenicity of a tumour-associated antigen IMP2/p62 in breast cancer. Autoantibody responses to IMP2/p62 were evaluated by enzyme-linked immunosorbent assay (ELISA), Western blotting and indirect immunofluorescence assay in sera from patients with breast cancer, benign breast tumour and normal human individuals. Immunohistochemistry (IHC) study with breast cancer tissues was also performed to analyse protein expression of IMP2/p62. The results have demonstrated that IMP2/p62 can induce a relatively higher frequency of autoantibody response in breast cancer (14.3%, 7/49) compared to patients with benign breast tumour (5.6%, 2/36) and normal individuals (2.2%, 1/44). The frequency of IMP2/p62 expression in breast cancer tissues was significantly higher than that in normal tissues (P < 0.01). The data suggest that autoantibody against IMP2/p62 may be a useful serum biomarker for early-stage breast cancer screening and diagnosis.

Proteomics in pharmaceutical research and development

In the 20 years since its inception, the evolution of proteomics in pharmaceutical industry has mirrored the developments within academia and indeed other industries. From initial enthusiasm and subsequent disappointment in global protein expression profiling, pharma research saw the biggest impact when relating to more focused approaches, such as those exploring the interaction between proteins and drugs. Nowadays, proteomics technologies have been integrated in many areas of pharmaceutical R&D, ranging from the analysis of therapeutic proteins to the monitoring of clinical trials. Here, we review the development of proteomics in the drug discovery process, placing it in a historical context as well as reviewing the current status in light of the contributions to this special issue, which reflect some of the diverse demands of the drug and biomarker pipelines.

Cancer stratification by molecular imaging

The lack of specificity of traditional cytotoxic drugs has triggered the development of anticancer agents that selectively address specific molecular targets. An intrinsic property of these specialized drugs is their limited applicability for specific patient subgroups. Consequently, the generation of information about tumor characteristics is the key to exploit the potential of these drugs. Currently, cancer stratification relies on three approaches: Gene expression analysis and cancer proteomics, immunohistochemistry and molecular imaging. In order to enable the precise localization of functionally expressed targets, molecular imaging combines highly selective biomarkers and intense signal sources. Thus, cancer stratification and localization are performed simultaneously. Many cancer types are characterized by altered receptor expression, such as somatostatin receptors, folate receptors or Her2 (human epidermal growth factor receptor 2). Similar correlations are also known for a multitude of transporters, such as glucose transporters, amino acid transporters or hNIS (human sodium iodide symporter), as well as cell specific proteins, such as the prostate specific membrane antigen, integrins, and CD20. This review provides a comprehensive description of the methods, targets and agents used in molecular imaging, to outline their application for cancer stratification. Emphasis is placed on radiotracers which are used to identify altered expression patterns of cancer associated markers.

Autoantibodies response to MDM2 and p53 in the immunodiagnosis of esophageal squamous cell carcinoma

The human homologue of the mouse double minute 2 (MDM2) is known to be overexpressed in a variety of human malignancies. As one of E3 ubiquitin-protein ligases, MDM2 interacts with the tumour suppressor p53 by mediating ubiquitination and degradation of p53. Since abnormally expressed proteins can induce autoimmune response, to further examine whether sera from patients with esophageal squamous cell carcinoma (ESCC) exhibited immunoreactivity against MDM2 and p53, autoantibody responses to MDM2 and p53 were evaluated by enzyme-linked immunosorbent assay (ELISA) in sera from patients with ESCC and normal individuals. Positive results were also confirmed by Western blotting and indirect immunofluorescence assay. The results demonstrated that the positive rate of autoantibody against p53 and MDM2 in ESCC sera was 22.9% (36/157) and 14.0% (22/157), whereas this rate was 0% (0/85) and 1.2% (1/85), respectively, in normal individuals. Some of the sera with antibodies specific for MDM2 also contained antibodies against p53. And there was an increase of positive antibody reactions reaching a frequency of 35% (55/157) combination with MDM2 and p53. This was significantly higher than the frequency of antibodies in normal individuals (P < 0.01). Our preliminary results suggest that autoantibodies against MDM2 and p53 may be useful serum biomarkers in the immunodiagnosis of ESCC.

Implementation of proteomics for cancer research: past, present, and future

Cancer is the leading cause of the death, accounts for about 13% of all annual deaths worldwide. Many different fields of science are collaborating together studying cancer to improve our knowledge of this lethal disease, and find better solutions for diagnosis and treatment. Proteomics is one of the most recent and rapidly growing areas in molecular biology that helps understanding cancer from an omics data analysis point of view. The human proteome project was officially initiated in 2008. Proteomics enables the scientists to interrogate a variety of biospecimens for their protein contents and measure the concentrations of these proteins. Current necessary equipment and technologies for cancer proteomics are mass spectrometry, protein microarrays, nanotechnology and bioinformatics. In this paper, we provide a brief review on proteomics and its application in cancer research. After a brief introduction including its definition, we summarize the history of major previous work conducted by researchers, followed by an overview on the role of proteomics in cancer studies. We also provide a list of different utilities in cancer proteomics and investigate their advantages and shortcomings from theoretical and practical angles. Finally, we explore some of the main challenges and conclude the paper with future directions in this field.

Mass spectrometry-based N-glycoproteomics for cancer biomarker discovery

Glycosylation is estimated to be found in over 50% of human proteins. Aberrant protein glycosylation and alteration of glycans are closely related to many diseases. More than half of the cancer biomarkers are glycosylated-proteins, and specific glycoforms of glycosylated-proteins may serve as biomarkers for either the early detection of disease or the evaluation of therapeutic efficacy for treatment of diseases. Glycoproteomics, therefore, becomes an emerging field that can make unique contributions to the discovery of biomarkers of cancers. The recent advances in mass spectrometry (MS)-based glycoproteomics, which can analyze thousands of glycosylated-proteins in a single experiment, have shown great promise for this purpose. Herein, we described the MS-based strategies that are available for glycoproteomics, and discussed the sensitivity and high throughput in both qualitative and quantitative manners. The discovery of glycosylated-proteins as biomarkers in some representative diseases by employing glycoproteomics was also summarized.

Mass spectrometry based biomarker discovery, verification, and validation--quality assurance and control of protein biomarker assays

In its early years, mass spectrometry (MS)-based proteomics focused on the cataloging of proteins found in different species or different tissues. By 2005, proteomics was being used for protein quantitation, typically based on "proteotypic" peptides which act as surrogates for the parent proteins. Biomarker discovery is usually done by non-targeted "shotgun" proteomics, using relative quantitation methods to determine protein expression changes that correlate with disease (output given as "up-or-down regulation" or "fold-increases"). MS-based techniques can also perform "absolute" quantitation which is required for clinical applications (output given as protein concentrations). Here we describe the differences between these methods, factors that affect the precision and accuracy of the results, and some examples of recent studies using MS-based proteomics to verify cancer-related biomarkers.

Overexpression of HCC1/CAPERα may play a role in lung cancer carcinogenesis

HCC1/CAPERα is considered to be a novel human tumor-associated antigen, and the tumor-specific immunity of HCC1/CAPERα has been reported in several types of cancer. However, there was very limited evidence indicating its function in tumorigenesis. In the present study, to elucidate the roles and underlying molecular mechanism of HCC1/CAPERα in lung cancer, we examined the expression of HCC1/CAPERα in human non-small cell lung cancer (NSCLC) cell line and NSCLC tissue microarray (TMA). Immunohistochemistry with TMA was performed to detect HCC1/CAPERα expression in NSCLC and adjacent lung tissues. NSCLC cell line constitutively transfected by pcDNA3.1-HCC1/CAPERα, and empty pcDNA3.1 vector were used. These cells were analyzed by Western blot, MTT, immunofluorescence, wound healing assay, and transwell assays. It was found that HCC1/CAPERα was mainly localized in the nucleus of the lung cancer cells and overexpression of HCC1/CAPERα may promote lung cancer cells proliferation and increase cells migration. The frequency of HCC1/CAPERα expression in NSCLC tissues was significantly higher than that in adjacent and normal tissues (P < 0.01). Our data suggest that overexpression of HCC1/CAPERα may increase the proliferation and migration of NSCLC cells, and HCC1/CAPERα could be a promising biomarker for lung cancer.

Proteome Analyses of Hepatocellular Carcinoma

Proteomics has evolved into a powerful and widely used bioanalytical technique in the study of cancer, especially hepatocellular carcinoma (HCC). In this review, we provide an up to date overview of feasible proteome-analytical techniques for clinical questions. In addition, we present a broad summary of proteomic studies of HCC utilizing various technical approaches for the analysis of samples derived from diverse sources like HCC cell lines, animal models, human tissue and body fluids.

MRM for the verification of cancer biomarker proteins: recent applications to human plasma and serum

Accurate cancer biomarkers are needed for early detection, disease classification, prediction of therapeutic response and monitoring treatment. While there appears to be no shortage of candidate biomarker proteins, a major bottleneck in the biomarker pipeline continues to be their verification by enzyme linked immunosorbent assays. Multiple reaction monitoring (MRM), also known as selected reaction monitoring, is a targeted mass spectrometry approach to protein quantitation and is emerging to bridge the gap between biomarker discovery and clinical validation. Highly multiplexed MRM assays are readily configured and enable simultaneous verification of large numbers of candidates facilitating the development of biomarker panels which can increase specificity. This review focuses on recent applications of MRM to the analysis of plasma and serum from cancer patients for biomarker verification. The current status of this approach is discussed along with future directions for targeted mass spectrometry in clinical biomarker validation.

Free-flow electrophoresis in proteome sample preparation

An aim of proteome research is to identify the entire complement of proteins expressed in defined cell types of humans, animals, plants, and microorganisms. The approach requires searching for low abundant or even rarely expressed proteins in many cell types, as well as the determination of the protein expression levels in subcellular compartments and organelles. In recent years, rather powerful MS technologies have been developed. At this stage of MS device development, it is of highest interest to purify intact cell types or isolate subcellular compartments, where the proteins of interest are originating from, which determine the final composition of a peptide mixture. Free-flow electrophoresis proved to be useful to prepare meaningful peptide mixtures because of its improved capabilities in particle electrophoresis and the enhanced resolution in protein separation. Sample preparation by free-flow electrophoresis mediated particle separation was preferentially performed for purification of either organelles and their subspecies or major protein complexes. Especially, the introduction of isotachophoresis and interval zone electrophoresis improved the purity of the gained analytes of interest. In addition, free-flow IEF proved to be helpful, when proteins of low solubility, obtained, e.g. from cell membranes, were investigated.

Comparative analysis of the surface exposed proteome of two canine osteosarcoma cell lines and normal canine osteoblasts

Background

Osteosarcoma (OSA) is the most common primary bone tumor of dogs and carries a poor prognosis despite aggressive treatment. An improved understanding of the biology of OSA is critically needed to allow for development of novel diagnostic, prognostic, and therapeutic tools. The surface-exposed proteome (SEP) of a cancerous cell includes a multifarious array of proteins critical to cellular processes such as proliferation, migration, adhesion, and inter-cellular communication. The specific aim of this study was to define a SEP profile of two validated canine OSA cell lines and a normal canine osteoblast cell line utilizing a biotinylation/streptavidin system to selectively label, purify, and identify surface-exposed proteins by mass spectrometry (MS) analysis. Additionally, we sought to validate a subset of our MS-based observations via quantitative real-time PCR, Western blot and semi-quantitative immunocytochemistry. Our hypothesis was that MS would detect differences in the SEP composition between the OSA and the normal osteoblast cells.

Results

Shotgun MS identified 133 putative surface proteins when output from all samples were combined, with good consistency between biological replicates. Eleven of the MS-detected proteins underwent analysis of gene expression by PCR, all of which were actively transcribed, but varied in expression level. Western blot of whole cell lysates from all three cell lines was effective for Thrombospondin-1, CYR61 and CD44, and indicated that all three proteins were present in each cell line. Semi-quantitative immunofluorescence indicated that CD44 was expressed at much higher levels on the surface of the OSA than the normal osteoblast cell lines.

Conclusions

The results of the present study identified numerous differences, and similarities, in the SEP of canine OSA cell lines and normal canine osteoblasts. The PCR, Western blot, and immunocytochemistry results, for the subset of proteins evaluated, were generally supportive of the mass spectrometry data. These methods may be applied to other cell lines, or other biological materials, to highlight unique and previously unrecognized differences between samples. While this study yielded data that may prove useful for OSA researchers and clinicians, further refinements of the described techniques are expected to yield greater accuracy and produce a more thorough SEP analysis.

Reversible protein affinity-labelling using bromomaleimide-based reagents

Reversible protein biotinylation is readily affected via conjugation with a bromomaleimide-based reagent followed by reductive cleavage. The intermediate biotinylated protein constructs are stable at physiological temperature and pH 8.0. Quantitative reversibility is elegantly delivered under mild conditions of using a stoichiometric amount of a bis-thiol, thus providing an approach that will be of general interest in chemical biology and proteomics.

A call for a fresh new look at the plasma proteome

Serum and plasma from which serum is derived represent a substantial challenge for proteomics due to their complexity. A landmark plasma proteome study was initiated a decade ago by the Human Proteome Organization (HUPO) that had as an objective to examine the capabilities of existing technologies. Given the advances in proteomics and the continued interest in the plasma proteome, it would timely reassess the depth and breadth of analysis of plasma that can be achieved with current methodology and instrumentation. A collaborative project to define the plasma proteome and its variation, with a plan to build a plasma proteome database would be timely.

Networks in proteomics analysis of cancer

Proteomics provides direct biological information on proteins but is still a limited platform. Borrowing from genomics, its cancer-specific applications can be broadly categorized as (1) pure diagnostics, (2) biomarkers, (3) identification of root causes and (4) identification of cancer-specific network rewirings. Biological networks capture complex relationships between proteins and provide an appropriate means of contextualization. While playing significantly larger roles, especially in 1 and 3, progress in proteomics-specific network-based methods is lagging as compared to genomics. Rapid hardware advances and improvements in proteomic identification and quantification have given rise to much better quality data alongside advent of new network-based analysis methods. However, a tighter integration between analytics and hardware is still essential for network analysis to play more significant roles in proteomics analysis.