Proteomics: A new perspective for cancer

Plasma-based proteomic profiling identifies the distinct regulation of proteins in hyperplasia and endometrial cancer

BACKGROUND

Among gynaecological malignancies, endometrial cancer (EC) is the most prevalent type of uterine cancer affecting women. This study explored the proteomic profiles of plasma samples obtained from EC patients, those with hyperplasia (Hy), and a control group (CO). A combination of techniques, such as 2D-DIGE, mass spectrometry, and bioinformatics, including pathway analysis, was used to identify proteins with modified expression levels, biomarkers and their associated metabolic pathways in these groups.

METHODS

Thirty-four patients, categorized into three groups-10 with EC, 12 with Hy, and 12 CO-between the ages of 46 and 75 years old were included in the study. Untargeted proteomic analysis was carried out using two-dimensional difference in gel electrophoresis (2D-DIGE) coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS).

RESULTS

In all three groups, 114 proteins that were significantly (p ≤ 0.05 and fold change ≥ 1.5) altered were successfully identified using peptide mass fingerprints (PMFs). Compared with those in the control group (CO), the EC samples had 85 differentially expressed proteins (39 upregulated and 46 downregulated), and in the Hy group, 81 proteins were dysregulated (40 upregulated and 41 downregulated) compared to those in the CO group, while 33 proteins exhibited differential regulation (12 upregulated and 21 downregulated) in the EC plasma samples compared to those in the Hy group. Vitamin D binding protein and complement C3 distinguished Hy and EC from CO with the greatest changes in expression. Among the differentially expressed proteins identified, enzymes with catalytic activity represented the largest group (42.9%). In terms of biological processes, most of the proteins were involved in cellular processes (28.8%), followed by metabolic processes (16.7%). STRING analysis for protein interactions revealed that the significantly differentially abundant proteins in the three groups are involved in three main biological processes: signalling of complement and coagulation cascades, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), and plasma lipoprotein assembly, remodelling, and clearance.

CONCLUSION

The identified plasma protein markers have the potential to serve as biomarkers for differentiating between EC and Hy, as well as for early diagnosis and monitoring of cancer progression.

Exploring the Molecular Therapeutic Mechanisms of Gemcitabine through Quantitative Proteomics

Gemcitabine (GEM) is widely employed in the treatment of various cancers, including pancreatic cancer. Despite their clinical success, challenges related to GEM resistance and toxicity persist. Therefore, a deeper understanding of its intracellular mechanisms and potential targets is urgently needed. In this study, through mass spectrometry analysis in data-dependent acquisition mode, we carried out quantitative proteomics (three independent replications) and thermal proteome profiling (TPP, two independent replications) on MIA PaCa-2 cells to explore the effects of GEM. Our proteomic analysis revealed that GEM led to the upregulation of the cell cycle and DNA replication proteins. Notably, we observed the upregulation of S-phase kinase-associated protein 2 (SKP2), a cell cycle and chemoresistance regulator. Combining SKP2 inhibition with GEM showed synergistic effects, suggesting SKP2 as a potential target for enhancing the GEM sensitivity. Through TPP, we pinpointed four potential GEM binding targets implicated in tumor development, including in breast and liver cancers, underscoring GEM's broad-spectrum antitumor capabilities. These findings provide valuable insights into GEM's molecular mechanisms and offer potential targets for improving treatment efficacy.

Exploring the mechanism of contact-dependent cell-cell communication on chemosensitivity based on single-cell high-throughput drug screening platform

High-throughput drug screening (HTDS) has significantly reduced the time and cost of new drug development. Nonetheless, contact-dependent cell-cell communication (CDCCC) may impact the chemosensitivity of tumour cells. There is a pressing need for low-cost single-cell HTDS platforms, alongside a deep comprehension of the mechanisms by which CDCCC affects drug efficacy, to fully unveil the efficacy of anticancer drugs. In this study, we develop a microfluidic chip for single-cell HTDS and evaluate the molecular mechanisms impacted by CDCCC using quantitative mass spectrometry-based proteomics. The chip achieves high-quality drug mixing and single-cell capture, with single-cell drug screening results on the chip showing consistency with those on the 96-well plates under varying concentration gradients. Through quantitative proteomic analysis, we deduce that the absence of CDCCC in single tumour cells can enhance their chemoresistance potential, but simultaneously subject them to stronger proliferation inhibition. Additionally, pathway enrichment analysis suggests that CDCCC could impact several signalling pathways in tumour single cells that regulate vital biological processes such as tumour proliferation, adhesion, and invasion. These results offer valuable insights into the potential connection between CDCCC and the chemosensitivity of tumour cells. This research paves the way for the development of single-cell HTDC platforms and holds the promise of advancing tumour personalized treatment strategies.

Omics-based novel strategies in the diagnosis of endometriosis

Endometriosis, an enigmatic and chronic disorder, is considered a debilitating condition despite being benign. Globally, this gynecologic disorder affects up to 10% of females of reproductive age, impacting almost 190 million individuals. A variety of genetic and environmental factors are involved in endometriosis development, hence the pathophysiology and etiology of endometriosis remain unclear. The uncertainty of the etiology of the disease and its complexity along with nonspecific symptoms have led to misdiagnosis or lack of diagnosis of affected people. Biopsy and laparoscopy are referred to as the gold standard for endometriosis diagnosis. However, the invasiveness of the procedure, the unnecessary operation in disease-free women, and the dependence of the reliability of diagnosis on experience in this area are considered the most significant limitations. Therefore, continuous studies have attempted to offer a noninvasive and reliable approach. The recent advances in modern technologies have led to the generation of large-scale biological data sets, known as -omics data, resulting in the proceeding of the -omics century in biomedical sciences. Thereby, the present study critically reviews novel and noninvasive biomarkers that are based on -omics approaches from 2020 onward. The findings reveal that biomarkers identified based on genomics, epigenomics, transcriptomics, proteomics, and metabolomics are potentially able to diagnose endometriosis, predict prognosis, and stage patients, and potentially, in the near future, a multi-panel of these biomarkers will generate clinical benefits.

Protein Signatures of Parathyroid Adenoma according to Tumor Volume and Functionality

BACKGRUOUND

Parathyroid adenoma (PA) is a common endocrine disease linked to multiple complications, but the pathophysiology of the disease remains incompletely understood. The study aimed to identify the key regulator proteins and pathways of PA according to functionality and volume through quantitative proteomic analyses.

METHODS

We conducted a retrospective study of 15 formalin-fixed, paraffin-embedded PA samples from tertiary hospitals in South Korea. Proteins were extracted, digested, and the resulting peptides were analyzed using liquid chromatography-tandem mass spectrometry. Pearson correlation analysis was employed to identify proteins significantly correlated with clinical variables. Canonical pathways and transcription factors were analyzed using Ingenuity Pathway Analysis.

RESULTS

The median age of the participants was 52 years, and 60.0% were female. Among the 8,153 protein groups analyzed, 496 showed significant positive correlations with adenoma volume, while 431 proteins were significantly correlated with parathyroid hormone (PTH) levels. The proteins SLC12A9, LGALS3, and CARM1 were positively correlated with adenoma volume, while HSP90AB2P, HLA-DRA, and SCD5 showed negative correlations. DCPS, IRF2BPL, and FAM98A were the main proteins that exhibited positive correlations with PTH levels, and SLITRK4, LAP3, and AP4E1 had negative correlations. Canonical pathway analysis demonstrated that the RAN and sirtuin signaling pathways were positively correlated with both PTH levels and adenoma volume, while epithelial adherence junction pathways had negative correlations.

CONCLUSION

Our study identified pivotal proteins and pathways associated with PA, offering potential therapeutic targets. These findings accentuate the importance of proteomics in understanding disease pathophysiology and the need for further research.

C1QA and COMP: plasma-based biomarkers for early diagnosis of pancreatic neuroendocrine tumors

Pancreatic Neuroendocrine tumors (PanNET) are challenging to diagnose and often detected at advanced stages due to a lack of specific and sensitive biomarkers. This study utilized proteomics as a valuable approach for cancer biomarker discovery; therefore, mass spectrometry-based proteomic profiling was conducted on plasma samples from 12 subjects (3 controls; 5 Grade I, 4 Grade II PanNET patients) to identify potential proteins capable of effectively distinguishing PanNET from healthy controls. Data are available via ProteomeXchange with the identifier PXD045045. 13.2% of proteins were uniquely identified in PanNET, while 60% were commonly expressed in PanNET and controls. 17 proteins exhibiting significant differential expression between PanNET and controls were identified with downstream analysis. Further, 5 proteins (C1QA, COMP, HSP90B1, ITGA2B, and FN1) were selected by pathway analysis and were validated using Western blot analysis. Significant downregulation of C1QA (p = 0.001: within groups, 0.03: control vs. grade I, 0.0013: grade I vs. grade II) and COMP (p = 0.011: within groups, 0.019: control vs grade I) were observed in PanNET Grade I & II than in controls. Subsequently, ELISA on 38 samples revealed significant downregulation of C1QA and COMP with increasing disease severity. This study shows the potential of C1QA and COMP in the early detection of PanNET, highlighting their role in the search for early-stage (Grade-I and Grade-II) diagnostic markers and therapeutic targets for PanNET.

Upgrading Treatment and Molecular Diagnosis in Endometrial Cancer-Driving New Tools for Endometrial Preservation?

One emerging problem for onco-gynecologists is the incidence of premenopausal patients under 40 years of age diagnosed with stage I Endometrial Cancer (EC) who want to preserve their fertility. Our review aims to define a primary risk assessment that can help fertility experts and onco-gynecologists tailor personalized treatment and fertility-preserving strategies for fertile patients wishing to have children. We confirm that risk factors such as myometrial invasion and The International Federation of Gynecology and Obstetrics (FIGO) staging should be integrated into the novel molecular classification provided by The Cancer Genome Atlas (TCGA). We also corroborate the influence of classical risk factors such as obesity, Polycystic ovarian syndrome (PCOS), and diabetes mellitus to assess fertility outcomes. The fertility preservation options are inadequately discussed with women with a diagnosis of gynecological cancer. A multidisciplinary team of gynecologists, oncologists, and fertility specialists could increase patient satisfaction and improve fertility outcomes. The incidence and death rates of endometrial cancer are rising globally. International guidelines recommend radical hysterectomy and bilateral salpingo-oophorectomy as the standard of care for this cancer; however, fertility-sparing alternatives should be tailored to motivated women of reproductive age, establishing an appropriate cost-benefit balance between childbearing desire and cancer risk. New molecular classifications such as that of TCGA provide a robust supplementary risk assessment tool that can tailor the treatment options to the patient's needs, curtail over- and under-treatment, and contribute to the spread of fertility-preserving strategies.

Pre-diagnostic blood biomarkers for adult glioma

Glioma is one of the most common malignant primary brain tumours in adults, of which, glioblastoma is the most prevalent and malignant entity. Glioma is often diagnosed at a later stage of disease progression, which means it is associated with significant mortality and morbidity. Therefore, there is a need for earlier diagnosis of these tumours, which would require sensitive and specific biomarkers. These biomarkers could better predict glioma onset to improve diagnosis and therapeutic options for patients. While liquid biopsies could provide a cheap and non-invasive test to improve the earlier detection of glioma, there is little known on pre-diagnostic biomarkers which predate disease detection. In this review, we examine the evidence in the literature for pre-diagnostic biomarkers in glioma, including metabolomics and proteomics. We also consider the limitations of these approaches and future research directions of pre-diagnostic biomarkers for glioma.

Rapid Multivariate Analysis Approach to Explore Differential Spatial Protein Profiles in Tissue

Spatially targeted proteomics analyzes the proteome of specific cell types and functional regions within tissue. While spatial context is often essential to understanding biological processes, interpreting sub-region-specific protein profiles can pose a challenge due to the high-dimensional nature of the data. Here, we develop a multivariate approach for rapid exploration of differential protein profiles acquired from distinct tissue regions and apply it to analyze a published spatially targeted proteomics data set collected from Staphylococcus aureus-infected murine kidney, 4 and 10 days postinfection. The data analysis process rapidly filters high-dimensional proteomic data to reveal relevant differentiating species among hundreds to thousands of measured molecules. We employ principal component analysis (PCA) for dimensionality reduction of protein profiles measured by microliquid extraction surface analysis mass spectrometry. Subsequently, k-means clustering of the PCA-processed data groups samples by chemical similarity. Cluster center interpretation revealed a subset of proteins that differentiate between spatial regions of infection over two time points. These proteins appear involved in tricarboxylic acid metabolomic pathways, calcium-dependent processes, and cytoskeletal organization. Gene ontology analysis further uncovered relationships to tissue damage/repair and calcium-related defense mechanisms. Applying our analysis in infectious disease highlighted differential proteomic changes across abscess regions over time, reflecting the dynamic nature of host-pathogen interactions.

A quantitative proteomic analyses of primary myocardial cell injury induced by heat stress in chicken embryo

In this study, the model of heat stress was constructed in primary chick embryonic myocardial cells at 42 °C for 4 h. Proteome analysis using DIA identified 245 differentially expressed proteins (DEPs) (Q-value <0.05, fold change >1.5), of which 63 proteins were up-regulated and 182 proteins were down-regulated. Many were related to metabolism, oxidative stress, oxidative phosphorylation and apoptosis. Gene Ontology (GO) analysis showed that many DEPs under heat stress were involved in regulating metabolites and energy, cellular respiration, catalytic activity and stimulation. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that DEPs were enriched in metabolic pathways, oxidative phosphorylation, citrate cycle (TCA cycle), cardiac muscle contraction, and carbon metabolism. The results could help understanding of the effect of heat stress on myocardial cells and even the heart and possible action mechanism at the protein level.

Big Data in Gastroenterology Research

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

From protein biomarkers to proteomics in dementia with Lewy Bodies

Dementia with Lewy Bodies (DLB) is the second most common neurodegenerative dementia. Despite considerable research progress, there remain gaps in our understanding of the pathophysiology and there is no disease-modifying treatment. Proteomics is a powerful tool to elucidate complex biological pathways across heterogenous conditions. This review summarizes the widely used proteomic methods and presents evidence for protein dysregulation in the brain and peripheral tissues in DLB. Proteomics of post-mortem brain tissue shows that DLB shares common features with other dementias, such as synaptic dysfunction, but retains a unique protein signature. Promising diagnostic biomarkers are being identified in cerebrospinal fluid (CSF), blood, and peripheral tissues, such as serum Heart-type fatty acid binding protein. Research is needed to track these changes from the prodromal stage to established dementia, with standardized workflows to ensure replicability. Identifying novel protein targets in causative biological pathways could lead to the development of new targeted therapeutics or the stratification of participants for clinical trials.

The Transition from Cancer "omics" to "epi-omics" through Next- and Third-Generation Sequencing

Deciphering cancer etiopathogenesis has proven to be an especially challenging task since the mechanisms that drive tumor development and progression are far from simple. An astonishing amount of research has revealed a wide spectrum of defects, including genomic abnormalities, epigenomic alterations, disturbance of gene transcription, as well as post-translational protein modifications, which cooperatively promote carcinogenesis. These findings suggest that the adoption of a multidimensional approach can provide a much more precise and comprehensive picture of the tumor landscape, hence serving as a powerful tool in cancer research and precision oncology. The introduction of next- and third-generation sequencing technologies paved the way for the decoding of genetic information and the elucidation of cancer-related cellular compounds and mechanisms. In the present review, we discuss the current and emerging applications of both generations of sequencing technologies, also referred to as massive parallel sequencing (MPS), in the fields of cancer genomics, transcriptomics and proteomics, as well as in the progressing realms of epi-omics. Finally, we provide a brief insight into the expanding scope of sequencing applications in personalized cancer medicine and pharmacogenomics.

Machine learning algorithms based on proteomic data mining accurately predicting the recurrence of hepatitis B-related hepatocellular carcinoma

BACKGROUND AND AIM

Over 10% of hepatocellular carcinoma (HCC) cases recur each year, even after surgical resection. Currently, there is a lack of knowledge about the causes of recurrence and the effective prevention. Prediction of HCC recurrence requires diagnostic markers endowed with high sensitivity and specificity. This study aims to identify new key proteins for HCC recurrence and to build machine learning algorithms for predicting HCC recurrence.

METHODS

The proteomics data for analysis in this study were obtained from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) database. We analyzed different proteins based on cases with or without recurrence of HCC. Survival analysis, Cox regression analysis, and area under the ROC curves (AUROC > 0.7) were used to screen for more significant differential proteins. Predictive models for HCC recurrence were developed using four machine learning algorithms.

RESULTS

A total of 690 differentially expressed proteins between 50 relapsed and 77 non-relapsed hepatitis B-related HCC patients were identified. Seven of these proteins had an AUROC > 0.7 for 5-year survival in HCC, including BAHCC1, ESF1, RAP1GAP, RUFY1, SCAMP3, STK3, and TMEM230. Among the machine learning algorithms, the random forest algorithm showed the highest AUROC values (AUROC: 0.991, 95% CI 0.962-0.999) for identifying HCC recurrence, followed by the support vector machine (AUROC: 0.893, 95% Cl 0.824-0.956), the logistic regression (AUROC: 0.774, 95% Cl 0.672-0.868), and the multi-layer perceptron algorithm (AUROC: 0.571, 95% Cl 0.459-0.682).

CONCLUSIONS

Our study identifies seven novel proteins for predicting HCC recurrence and the random forest algorithm as the most suitable predictive model for HCC recurrence.

Prognostic value of Talin-1 in renal cell carcinoma and its association with B7-H3

METHODS: Talin-1 protein was demonstrated as a potential prognostic marker in renal cell carcinoma (RCC) using bioinformatics analysis. We, therefore, examined the protein expression levels and prognostic significance of Talin-1 with a clinical follow-up in a total of 269 tissue specimens from three important subtypes of RCC and 30 adjacent normal samples using immunohistochemistry. Then, we used combined analysis with B7-H3 to investigate higher prognostic values. RESULTS: The results showed that high membranous and cytoplasmic expression of Talin-1 was significantly associated with advanced nucleolar grade, microvascular invasion, histological tumor necrosis, and invasion to Gerota’s fascia in clear cell RCC (ccRCC). In addition, high membranous and cytoplasmic expression of Talin-1 was found to be associated with significantly poorer disease-specific survival (DSS) and progression-free survival (PFS). Moreover, increased cytoplasmic expression of Talin-1High/B7-H3High compared to the other phenotypes was associated with tumor aggressiveness and progression of the disease, and predicted a worse clinical outcome, which may be an effective biomarker to identify ccRCC patients at high risk of recurrence and metastasis. CONCLUSIONS: Collectively, these observations indicate that Talin-1 is an important molecule involved in the spread and progression of ccRCC when expressed particularly in the cytoplasm and may serve as a novel prognostic biomarker in this subtype. Furthermore, a combined analysis of Talin-1/B7-H3 indicated an effective biomarker to predict the progression of disease and prognosis in ccRCC.

Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology

In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient's prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.

Tissue-Based Proteomic Profiling in Patients with Hyperplasia and Endometrial Cancer

Uterine cancers are among the most prevalent gynecological malignancies, and endometrial cancer (EC) is the most common in this group. This study used tissue-based proteomic profiling analysis in patients with endometrial cancer and hyperplasia, and control patients. Conventional 2D gel electrophoresis, followed by a mass spectrometry approach with bioinformatics, including a network pathway analysis pipeline, was used to identify differentially expressed proteins and associated metabolic pathways between the study groups. Thirty-six patients (twelve with endometrial cancer, twelve with hyperplasia, and twelve controls) were enrolled in this study. The mean age of the participants was 46–75 years. Eighty-seven proteins were significantly differentially expressed between the study groups, of which fifty-three were significantly differentially regulated (twenty-eight upregulated and twenty-five downregulated) in the tissue samples of EC patients compared to the control (Ctrl). Furthermore, 26 proteins were significantly dysregulated (8 upregulated and 18 downregulated) in tissue samples of hyperplasia (HY) patients compared to Ctrl. Thirty-two proteins (nineteen upregulated and thirteen downregulated) including desmin, peptidyl prolyl cis-trans isomerase A, and zinc finger protein 844 were downregulated in the EC group compared to the HY group. Additionally, fructose bisphosphate aldolase A, alpha enolase, and keratin type 1 cytoskeletal 10 were upregulated in the EC group compared to those in the HY group. The proteins identified in this study were known to regulate cellular processes (36%), followed by biological regulation (16%). Ingenuity pathway analysis found that proteins that are differentially expressed between EC and HY are linked to AKT, ACTA2, and other signaling pathways. The panels of protein markers identified in this study could be used as potential biomarkers for distinguishing between EC and HY and early diagnosis and progression of EC from hyperplasia and normal patients.

Plasma protein biomarker profiling reveals major differences between acute leukaemia, lymphoma patients and controls

Aiming to accommodate the unmet need for easily accessible biomarkers with a focus on biological differences between haematological diseases, the diagnostic value of plasma proteins in acute leukaemias and lymphomas was investigated. A multiplex proximity extension assay (PEA) was used to analyze 183 proteins in diagnostic plasma samples from 251 acute leukaemia and lymphoma patients and compared with samples from 60 healthy controls. Multivariate modelling using partial least square discriminant analysis revealed highly significant differences between distinct disease subgroups and controls. The model allowed explicit distinction between leukaemia and lymphoma, with few patients misclassified. Acute leukaemia samples had higher levels of proteins associated with haemostasis, inflammation, cell differentiation and cell-matrix integration, whereas lymphoma samples demonstrated higher levels of proteins known to be associated with tumour microenvironment and lymphoma dissemination. PEA technology can be used to screen for large number of plasma protein biomarkers in low µL sample volumes, enabling the distinction between controls, acute leukaemias and lymphomas. Plasma protein profiling could help gain insights into the pathophysiology of acute leukaemia and lymphoma and the technique may be a valuable tool in the diagnosis of these diseases.

Long noncoding RNA Ftx regulates the protein expression profile in HCT116 human colon cancer cells

Background

The long noncoding RNA (lncRNA) five prime to Xist (Ftx) is involved in distant metastasis in colorectal cancer (CRC). This study aimed to investigate Ftx alteration-induced proteomic changes in the highly metastatic CRC cell line HCT116.

Methods

Tandem mass tag (TMT)-based proteomics analysis was performed to detect the differential protein expression in Ftx-overexpressing and Ftx-silenced HCT116 cells. The differentially expressed proteins were classified and characterized by bioinformatics analyses, including gene ontology (GO) annotation, GO/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway/protein domain enrichment analyses, as well as hierarchical clustering. A total of 5471 proteins were quantified, and the proteins with |fold change|≥ 1.2 and p < 0.05 were identified as differentially expressed proteins in response to Ftx overexpression or silencing.

Results

The bioinformatics analyses revealed that the differentially expressed proteins were involved in a wide range of GO terms and KEGG signaling pathways and contained multiple protein domains. These terms, pathways, and protein domains were associated with tumorigenesis and metastasis in CRC.

Conclusions

Our results indicate that the alteration of Ftx expression induces proteomic changes in highly metastatic HCT116 cells, suggesting that Ftx and its downstream molecules and signaling pathways could be potential diagnostic biomarkers and therapeutic targets for metastatic CRC.

Quantitative Proteomic Analysis of Cervical Cancer Tissues Identifies Proteins Associated With Cancer Progression

BACKGROUND/AIM

To date, several proteomics studies in cervical cancer (CC) have focused mainly on squamous cervical cancer (SCC). Our study aimed to discover and clarify differences in SCC and CAD that may provide valuable information for the identification of proteins involved in tumor progression, in CC as a whole, or specific for SCC or CAD.

MATERIALS AND METHODS

Total protein extracts from 15 individual samples corresponding to 5 different CC tissue types were compared with a non-cancerous control group using bidimensional liquid chromatography-mass spectrometry (2D LC-MS/MS), isobaric tags for relative and absolute quantitation (ITRAQ), principal component analysis (PCA) and gene set enrichment analysis (GSEA).

RESULTS

A total of 622 statistically significant different proteins were detected. Exocytosis-related proteins were the most over-represented, accounting for 25% of the identified and quantified proteins. Based on the experimental results, reticulocalbin 3 (RCN3) and Ras-related protein Rab-14 (RAB14) were chosen for further downstream in vitro and vivo analyses. RCN3 was overexpressed in all CC tissues compared to the control and RAB14 was overexpressed in squamous cervical cancer (SCC) compared to invasive cervical adenocarcinoma (CAD). In the tumor xenograft experiment, RAB14 protein expression was positively correlated with increased tumor size. In addition, RCN3-expressing HeLa cells induced a discrete size increment compared to control, at day 47 after inoculation.

CONCLUSION

RAB14 and RCN3 are suggested as potential biomarkers and therapeutic targets in the treatment of CC.

Experimental Nuclear Medicine Meets Tumor Biology

Personalized treatment of cancer patients demands specific and validated biomarkers for tumor diagnosis and therapy. The development and validation of such require translational preclinical models that recapitulate human diseases as accurately as possible. Moreover, there is a need for convergence of different (pre)clinical disciplines that openly share their knowledge and methodologies. This review sheds light on the differential perception of biomarkers and gives an overview of currently used models in tracer development and approaches for biomarker discovery.

Co-expression of cancer-testis antigens of MAGE-A6 and MAGE-A11 is associated with tumor aggressiveness in patients with bladder cancer

Melanoma antigen gene (MAGE)-A6 and MAGE-A11 are two of the most cancer-testis antigens overexpressed in various types of cancers. However, the clinical and prognosis value of MAGE-A6 and MAGE-A11 co-expression in the pathophysiology of the bladder is unknown. Three studies were selected from GEO databases in order to introduce the common genes that are involved in bladder cancer. Then immunohistochemical analysis for staining pattern and clinicopathological significance of suggested markers, MAGE-A6 and MAGE-A11, were performed in 199 and 213 paraffin-embedded bladder cancer with long adjacent normal tissues, respectively. A significant and positive correlation was found between both nuclear and cytoplasmic expressions of MAGE-A6 as well as expression of cytoplasmic MAGE-A11 with histological grade, PT stage, lamina propria invasion, and LP/ muscularis (L/M) involvement (all of the p-values in terms of H-score were < 0.0001). Additionally, significant differences were found between both nuclear and cytoplasmic MAGE-A6/MAGE-A11 phenotypes with tumor size (P = 0.007, P = 0.043, respectively), different histological grades, PT stage, LP involvement, and L/M involvement (all of the p-values for both phenotypes were < 0.0001). The current study added the value of these novel markers to the bladder cancer clinical settlement that might be considered as an admirable target for immunotherapy.

Systems biology–the transformative approach to integrate sciences across disciplines

Life science is the study of living organisms, including bacteria, plants, and animals. Given the importance of biology, chemistry, and bioinformatics, we anticipate that this chapter may contribute to a better understanding of the interdisciplinary connections in life science. Research in applied biological sciences has changed the paradigm of basic and applied research. Biology is the study of life and living organisms, whereas science is a dynamic subject that as a result of constant research, new fields are constantly emerging. Some fields come and go, whereas others develop into new, well-recognized entities. Chemistry is the study of composition of matter and its properties, how the substances merge or separate and also how substances interact with energy. Advances in biology and chemistry provide another means to understand the biological system using many interdisciplinary approaches. Bioinformatics is a multidisciplinary or rather transdisciplinary field that encourages the use of computer tools and methodologies for qualitative and quantitative analysis. There are many instances where two fields, biology and chemistry have intersection. In this chapter, we explain how current knowledge in biology, chemistry, and bioinformatics, as well as its various interdisciplinary domains are merged into life sciences and its applications in biological research.

Immune-Proteome Profiling in Classical Hodgkin Lymphoma Tumor Diagnostic Tissue

In classical Hodgkin Lymphoma (cHL), immunoediting via protein signaling is key to evading tumor surveillance. We aimed to identify immune-related proteins that distinguish diagnostic cHL tissues (=diagnostic tumor lysates, n = 27) from control tissues (reactive lymph node lysates, n = 30). Further, we correlated our findings with the proteome plasma profile between cHL patients (n = 26) and healthy controls (n = 27). We used the proximity extension assay (PEA) with the OlinkTM multiplex Immuno-Oncology panel, consisting of 92 proteins. Univariate, multivariate-adjusted analysis and Benjamini–Hochberg’s false discovery testing (=Padj) were performed to detect significant discrepancies. Proteins distinguishing cHL cases from controls were more numerous in plasma (30 proteins) than tissue (17 proteins), all Padj < 0.05. Eight of the identified proteins in cHL tissue (PD-L1, IL-6, CCL17, CCL3, IL-13, MMP12, TNFRS4, and LAG3) were elevated in both cHL tissues and cHL plasma compared with control samples. Six proteins distinguishing cHL tissues from controls tissues were significantly correlated to PD-L1 expression in cHL tissue (IL-6, MCP-2, CCL3, CCL4, GZMB, and IFN-gamma, all p ≤0.05). In conclusion, this study introduces a distinguishing proteomic profile in cHL tissue and potential immune-related markers of pathophysiological relevance.

Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery

Proteomics has become an important field in molecular sciences, as it provides valuable information on the identity, expression levels, and modification of proteins. For example, cancer proteomics unraveled key information in mechanistic studies on tumor growth and metastasis, which has contributed to the identification of clinically applicable biomarkers as well as therapeutic targets. Several cancer proteome databases have been established and are being shared worldwide. Importantly, the integration of proteomics studies with other omics is providing extensive data related to molecular mechanisms and target modulators. These data may be analyzed and processed through bioinformatic pipelines to obtain useful information. The purpose of this review is to provide an overview of cancer proteomics and recent advances in proteomic techniques. In particular, we aim to offer insights into current proteomics studies of brain cancer, in which proteomic applications are in a relatively early stage. This review covers applications of proteomics from the discovery of biomarkers to the characterization of molecular mechanisms through advances in technology. Moreover, it addresses global trends in proteomics approaches for translational research. As a core method in translational research, the continued development of this field is expected to provide valuable information at a scale beyond that previously seen.

Malignant Ascites in Ovarian Cancer: Cellular, Acellular, and Biophysical Determinants of Molecular Characteristics and Therapy Response

Ascites refers to the abnormal accumulation of fluid in the peritoneum resulting from an underlying pathology, such as metastatic cancer. Among all cancers, advanced-stage epithelial ovarian cancer is most frequently associated with the production of malignant ascites and is the leading cause of death from gynecologic malignancies. Despite decades of evidence showing that the accumulation of peritoneal fluid portends the poorest outcomes for cancer patients, the role of malignant ascites in promoting metastasis and therapy resistance remains poorly understood. This review summarizes the current understanding of malignant ascites, with a focus on ovarian cancer. The first section provides an overview of heterogeneity in ovarian cancer and the pathophysiology of malignant ascites. Next, analytical methods used to characterize the cellular and acellular components of malignant ascites, as well the role of these components in modulating cell biology, are discussed. The review then provides a perspective on the pressures and forces that tumors are subjected to in the presence of malignant ascites and the impact of physical stress on therapy resistance. Treatment options for malignant ascites, including surgical, pharmacological and photochemical interventions are then discussed to highlight challenges and opportunities at the interface of drug discovery, device development and physical sciences in oncology.

TYK2 in Cancer Metastases: Genomic and Proteomic Discovery

Simple Summary

Cancer deaths are predominantly due to metastases rather than the primary tumors, and thus there is an urgent need for the discovery of more effective drug therapies for metastatic cancer. Recent genomics, transcriptomics, and proteomics studies have identified tyrosine kinase 2 (TYK2) as an oncogene that is frequently mutated or overexpressed in many types of cancer and metastases. A member of the Janus kinase (JAK) family, TYK2 mediates the signals of numerous cytokines involved in immune and inflammatory signaling. In cancer cells, activation of TYK2 can lead to decreased cell death as well as increased cell growth and invasion. Multiple drugs that specifically block TYK2 or JAKs are currently FDA-approved or in clinical trials. In this review, we provide an overview of the screening, molecular, and animal studies that have characterized the role of TYK2 in cancer and metastases, and the potential of TYK2 inhibitors as effective cancer therapies.

Abstract

Advances in genomic analysis and proteomic tools have rapidly expanded identification of biomarkers and molecular targets important to cancer development and metastasis. On an individual basis, personalized medicine approaches allow better characterization of tumors and patient prognosis, leading to more targeted treatments by detection of specific gene mutations, overexpression, or activity. Genomic and proteomic screens by our lab and others have revealed tyrosine kinase 2 (TYK2) as an oncogene promoting progression and metastases of many types of carcinomas, sarcomas, and hematologic cancers. TYK2 is a Janus kinase (JAK) that acts as an intermediary between cytokine receptors and STAT transcription factors. TYK2 signals to stimulate proliferation and metastasis while inhibiting apoptosis of cancer cells. This review focuses on the growing evidence from genomic and proteomic screens, as well as molecular studies that link TYK2 to cancer prevalence, prognosis, and metastasis. In addition, pharmacological inhibition of TYK2 is currently used clinically for autoimmune diseases, and now provides promising treatment modalities as effective therapeutic agents against multiple types of cancer.

Post-translational cleavage of HMW-GS Dy10 allele improves the cookie-making quality in common wheat (Triticum aestivum)

Wheat is a major staple food crop worldwide because of the unique properties of wheat flour. High molecular weight glutenin subunits (HMW-GSs), which are among the most critical determinants of wheat flour quality, are responsible for the formation of glutenin polymeric structures via interchain disulfide bonds. We herein describe the identification of a new HMW-GS Dy10 allele (Dy10-m619SN). The amino acid substitution (serine-to-asparagine) encoded in this allele resulted in a partial post-translational cleavage that produced two new peptides. These new peptides disrupted the interactions among gluten proteins because of the associated changes to the number of available cysteine residues for interchain disulfide bonds. Consequently, Dy10-m619SN expression decreased the size of glutenin polymers and weakened glutens, which resulted in wheat dough with improved cookie-making quality, without changes to the glutenin-to-gliadin ratio. In this study, we clarified the post-translational processing of HMW-GSs and revealed a new genetic resource useful for wheat breeding.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01238-9.

Multi-Omics Model Applied to Cancer Genetics

In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and transcriptomics to understand the phases of multi-omics approach. We consider how to create a multi-omics model. In the last section, we describe the new frontiers and future perspectives of this field.

Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine

Over the last decades, transcriptome profiling emerged as one of the most powerful approaches in oncology, providing prognostic and predictive utility for cancer management. The development of novel technologies, such as revolutionary next-generation sequencing, enables the identification of cancer biomarkers, gene signatures, and their aberrant expression affecting oncogenesis, as well as the discovery of molecular targets for anticancer therapies. Transcriptomics contribute to a change in the holistic understanding of cancer, from histopathological and organic to molecular classifications, opening a more personalized perspective for tumor diagnostics and therapy. The further advancement on transcriptome profiling may allow standardization and cost reduction of its analysis, which will be the next step for transcriptomics to become a canon of contemporary cancer medicine.

The tumor genetics of acral melanoma: What should a dermatologist know?

Dermatologists stand at the gateway of individualization of classification, treatment, and outcomes of acral melanoma patients. The acral melanoma genetic landscape differs in vital ways from that of other cutaneous melanomas. These differences have important implications in understanding pathogenesis, treatment, and prognosis. The selection of molecularly targeted therapy must be adapted for acral melanoma. It is also critical to recognize that tumor development is far more complex than an isolated event, reliably treated by a medication acting on a single target. Tumors exhibit intratumor genetic heterogeneity, metastasis may have different genetic or epigenetic features than primary tumors, and tumor resistance may develop because of the activation of alternative genetic pathways. Microenvironmental, immune, and epigenetic events contribute and sustain tumors in complex ways. Treatment strategies with multiple targets are required to effectively disrupt the tumor ecosystem. This review attempts to translate the current molecular understanding of acral melanoma into digestible concepts relevant to the practice of dermatology. The focus is tumor genetics defining potentially treatable cancer pathways, contextualized within the relevant pathologic and molecular features.

Implementation of MALDI Mass Spectrometry Imaging in Cancer Proteomics Research: Applications and Challenges

Studying the proteome–the entire set of proteins in cells, tissues, organs and body fluids—is of great relevance in cancer research, as differential forms of proteins are expressed in response to specific intrinsic and extrinsic signals. Discovering protein signatures/pathways responsible for cancer transformation may lead to a better understanding of tumor biology and to a more effective diagnosis, prognosis, recurrence and response to therapy. Moreover, proteins can act as a biomarker or potential drug targets. Hence, it is of major importance to implement proteomic, particularly mass spectrometric, approaches in cancer research, to provide new crucial insights into tumor biology. Recently, mass spectrometry imaging (MSI) approaches were implemented in cancer research, to provide individual molecular characteristics of each individual tumor while retaining molecular spatial distribution, essential in the context of personalized disease management and medicine.

Personalized Graphene Oxide-Protein Corona in the Human Plasma of Pancreatic Cancer Patients

The protein corona (PC) that forms around nanomaterials upon exposure to human biofluids (e.g., serum, plasma, cerebral spinal fluid etc.) is personalized, i.e., it depends on alterations of the human proteome as those occurring in several cancer types. This may relevant for early cancer detection when changes in concentration of typical biomarkers are often too low to be detected by blood tests. Among nanomaterials under development for in vitro diagnostic (IVD) testing, Graphene Oxide (GO) is regarded as one of the most promising ones due to its intrinsic properties and peculiar behavior in biological environments. While recent studies have explored the binding of single proteins to GO nanoflakes, unexplored variables (e.g., GO lateral size and protein concentration) leading to formation of GO-PC in human plasma (HP) have only marginally addressed so far. In this work, we studied the PC that forms around GO nanoflakes of different lateral sizes (100, 300, and 750 nm) upon exposure to HP at several dilution factors which extend over three orders of magnitude from 1 (i.e., undiluted HP) to 10³. HP was collected from 20 subjects, half of them being healthy donors and half of them diagnosed with pancreatic ductal adenocarcinoma (PDAC) a lethal malignancy with poor prognosis and very low 5-year survival rate after diagnosis. By dynamic light scattering (DLS), electrophoretic light scattering (ELS), sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and nano liquid chromatography tandem mass spectrometry (nano-LC MS/MS) experiments we show that the lateral size of GO has a minor impact, if any, on PC composition. On the other side, protein concentration strongly affects PC of GO nanoflakes. In particular, we were able to set dilution factor of HP in a way that maximizes the personalization of PC, i.e., the alteration in the protein profile of GO nanoflakes between cancer vs. non-cancer patients. We believe that this study shall contribute to a deeper understanding of the interactions among GO and HP, thus paving the way for the development of IVD tools to be used at every step of the patient pathway, from prognosis, screening, diagnosis to monitoring the progression of disease.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

Proteomic Biomarkers for the Detection of Endometrial Cancer

Endometrial cancer is the leading gynaecological malignancy in the western world and its incidence is rising in tandem with the global epidemic of obesity. Early diagnosis is key to improving survival, which at 5 years is less than 20% in advanced disease and over 90% in early-stage disease. As yet, there are no validated biological markers for its early detection. Advances in high-throughput technologies and machine learning techniques now offer unique and promising perspectives for biomarker discovery, especially through the integration of genomic, transcriptomic, proteomic, metabolomic and imaging data. Because the proteome closely mirrors the dynamic state of cells, tissues and organisms, proteomics has great potential to deliver clinically relevant biomarkers for cancer diagnosis. In this review, we present the current progress in endometrial cancer diagnostic biomarker discovery using proteomics. We describe the various mass spectrometry-based approaches and highlight the challenges inherent in biomarker discovery studies. We suggest novel strategies for endometrial cancer detection exploiting biologically important protein biomarkers and set the scene for future directions in endometrial cancer biomarker research.

Neurotheranostics as personalized medicines

The discipline of neurotheranostics was forged to improve diagnostic and therapeutic clinical outcomes for neurological disorders. Research was facilitated, in largest measure, by the creation of pharmacologically effective multimodal pharmaceutical formulations. Deployment of neurotheranostic agents could revolutionize staging and improve nervous system disease therapeutic outcomes. However, obstacles in formulation design, drug loading and payload delivery still remain. These will certainly be aided by multidisciplinary basic research and clinical teams with pharmacology, nanotechnology, neuroscience and pharmaceutic expertise. When successful the end results will provide "optimal" therapeutic delivery platforms. The current report reviews an extensive body of knowledge of the natural history, epidemiology, pathogenesis and therapeutics of neurologic disease with an eye on how, when and under what circumstances neurotheranostics will soon be used as personalized medicines for a broad range of neurodegenerative, neuroinflammatory and neuroinfectious diseases.

Recursive model for dose-time responses in pharmacological studies

Background

Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. On the other hand, one can use Gompertz equation to model the temporal behaviors at each dose without capturing the evolution of time curves across dosage.

Results

In this article, we propose a parametric model for dose-time responses that follows Gompertz law in time and Hill equation across dose approximately. We derive a recursion relation for dose-response curves over time capturing the temporal evolution and then specify a regression model connecting the parameters controlling the dose-time responses with individual level proteomic data. The resultant joint model allows us to predict the dose-response curves over time for new individuals.

Conclusion

We have compared the efficacy of our proposed Recursive Hybrid model with individual dose-response predictive models at desired time points. We note that our proposed model exhibits a superior performance compared to the individual ones for both synthetic data and actual pharmacological data. For the desired dose-time varying genetic characterization and drug response values, we have used the HMS-LINCS database and demonstrated the effectiveness of our model for all available anticancer compounds.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2831-4) contains supplementary material, which is available to authorized users.

Identification of the Altered Proteins Related to Colon Carcinogenesis by iTRAQ-based Quantitative Proteomic Analysis

Background:

The molecular mechanisms or valuable biomarkers for early diagnosis of colorectal cancer (CRC) are not fully elucidated yet.

Objective:

To understand the proteomic changes at the global level in the carcinogenesis of CRC, differentially expressed proteins between normal intestinal epithelial cells CCD841 and colorectal cancer cells HCT116 were identified.

Method:

The isobaric tags for relative and absolute quantitation (iTRAQ) coupled with 2D LC-MS/MS proteomic approach were performed for screening the altered proteins between cells CCD841 and HCT116.

Results:

A total of 1947 proteins were identified after filtering and using a 1% false discovery rate. Based on a final cutoff (> 3.16 and < 0.32), 229 proteins were found to be significantly altered, among which 95 (41%) were up-regulated while 134 (59%) were down-regulated. Gene Ontology analysis revealed that the differentially expressed proteins were mainly cell part proteins involved in cellular process and binding in terms of subcellular distribution, biological process, and molecular function. KEGG analysis indicated that the differentially expressed proteins were significantly involved in the process of focal adhesion, pathogenic Escherichia coli infection, leukocyte transendothelial migration, bacterial invasion of epithelial cells, regulation of actin cytoskeleton, DNA replication and so on.

Conclusion:

Collectively, our data identified differentially expressed proteins in colon cancer carcinogenesis, which could provide the clues on unraveling the molecular mechanism of CRC.

Proteome Imaging: From Classic to Modern Mass Spectrometry-Based Molecular Histology

In order to overcome the limitations of classic imaging in Histology during the actually era of multiomics, the multi-color "molecular microscope" by its emerging "molecular pictures" offers quantitative and spatial information about thousands of molecular profiles without labeling of potential targets. Healthy and diseased human tissues, as well as those of diverse invertebrate and vertebrate animal models, including genetically engineered species and cultured cells, can be easily analyzed by histology-directed MALDI imaging mass spectrometry. The aims of this review are to discuss a range of proteomic information emerging from MALDI mass spectrometry imaging comparative to classic histology, histochemistry and immunohistochemistry, with applications in biology and medicine, concerning the detection and distribution of structural proteins and biological active molecules, such as antimicrobial peptides and proteins, allergens, neurotransmitters and hormones, enzymes, growth factors, toxins and others. The molecular imaging is very well suited for discovery and validation of candidate protein biomarkers in neuroproteomics, oncoproteomics, aging and age-related diseases, parasitoproteomics, forensic, and ecotoxicology. Additionally, in situ proteome imaging may help to elucidate the physiological and pathological mechanisms involved in developmental biology, reproductive research, amyloidogenesis, tumorigenesis, wound healing, neural network regeneration, matrix mineralization, apoptosis and oxidative stress, pain tolerance, cell cycle and transformation under oncogenic stress, tumor heterogeneity, behavior and aggressiveness, drugs bioaccumulation and biotransformation, organism's reaction against environmental penetrating xenobiotics, immune signaling, assessment of integrity and functionality of tissue barriers, behavioral biology, and molecular origins of diseases. MALDI MSI is certainly a valuable tool for personalized medicine and "Eco-Evo-Devo" integrative biology in the current context of global environmental challenges.

Onco-Multi-OMICS Approach: A New Frontier in Cancer Research

The acquisition of cancer hallmarks requires molecular alterations at multiple levels including genome, epigenome, transcriptome, proteome, and metabolome. In the past decade, numerous attempts have been made to untangle the molecular mechanisms of carcinogenesis involving single OMICS approaches such as scanning the genome for cancer-specific mutations and identifying altered epigenetic-landscapes within cancer cells or by exploring the differential expression of mRNA and protein through transcriptomics and proteomics techniques, respectively. While these single-level OMICS approaches have contributed towards the identification of cancer-specific mutations, epigenetic alterations, and molecular subtyping of tumors based on gene/protein-expression, they lack the resolving-power to establish the casual relationship between molecular signatures and the phenotypic manifestation of cancer hallmarks. In contrast, the multi-OMICS approaches involving the interrogation of the cancer cells/tissues in multiple dimensions have the potential to uncover the intricate molecular mechanism underlying different phenotypic manifestations of cancer hallmarks such as metastasis and angiogenesis. Moreover, multi-OMICS approaches can be used to dissect the cellular response to chemo- or immunotherapy as well as discover molecular candidates with diagnostic/prognostic value. In this review, we focused on the applications of different multi-OMICS approaches in the field of cancer research and discussed how these approaches are shaping the field of personalized oncomedicine. We have highlighted pioneering studies from “The Cancer Genome Atlas (TCGA)” consortium encompassing integrated OMICS analysis of over 11,000 tumors from 33 most prevalent forms of cancer. Accumulation of huge cancer-specific multi-OMICS data in repositories like TCGA provides a unique opportunity for the systems biology approach to tackle the complexity of cancer cells through the unification of experimental data and computational/mathematical models. In future, systems biology based approach is likely to predict the phenotypic changes of cancer cells upon chemo-/immunotherapy treatment. This review is sought to encourage investigators to bring these different approaches together for interrogating cancer at molecular, cellular, and systems levels.

Preprocessing of 2-Dimensional Gel Electrophoresis Images Applied to Proteomic Analysis: A Review

Various methods and specialized software programs are available for processing two-dimensional gel electrophoresis (2-DGE) images. However, due to the anomalies present in these images, a reliable, automated, and highly reproducible system for 2-DGE image analysis has still not been achieved. The most common anomalies found in 2-DGE images include vertical and horizontal streaking, fuzzy spots, and background noise, which greatly complicate computational analysis. In this paper, we review the preprocessing techniques applied to 2-DGE images for noise reduction, intensity normalization, and background correction. We also present a quantitative comparison of non-linear filtering techniques applied to synthetic gel images, through analyzing the performance of the filters under specific conditions. Synthetic proteins were modeled into a two-dimensional Gaussian distribution with adjustable parameters for changing the size, intensity, and degradation. Three types of noise were added to the images: Gaussian, Rayleigh, and exponential, with signal-to-noise ratios (SNRs) ranging 8-20 decibels (dB). We compared the performance of wavelet, contourlet, total variation (TV), and wavelet-total variation (WTTV) techniques using parameters SNR and spot efficiency. In terms of spot efficiency, contourlet and TV were more sensitive to noise than wavelet and WTTV. Wavelet worked the best for images with SNR ranging 10-20 dB, whereas WTTV performed better with high noise levels. Wavelet also presented the best performance with any level of Gaussian noise and low levels (20-14 dB) of Rayleigh and exponential noise in terms of SNR. Finally, the performance of the non-linear filtering techniques was evaluated using a real 2-DGE image with previously identified proteins marked. Wavelet achieved the best detection rate for the real image.

Proteomic Analysis of Combined Gemcitabine and Birinapant in Pancreatic Cancer Cells

Pancreatic cancer is characterized by mutated signaling pathways and a high incidence of drug resistance. Comprehensive, large-scale proteomic analysis can provide a system-wide view of signaling networks, assist in understanding drug mechanisms of action and interactions, and serve as a useful tool for pancreatic cancer research. In this study, liquid chromatography-mass spectrometry-based proteomic analysis was applied to characterize the combination of gemcitabine and birinapant in pancreatic cancer cells, which was shown previously to be synergistic. A total of 4069 drug-responsive proteins were identified and quantified in a time-series proteome analysis. This rich dataset provides broad views and accurate quantification of signaling pathways. Pathways relating to DNA damage response regulations, DNA repair, anti-apoptosis, pro-migration/invasion were implicated as underlying mechanisms for gemcitabine resistance and for the beneficial effects of the drug combination. Promising drug targets were identified for future investigation. This study also provides a database for systems mathematical modeling to relate drug effects and interactions in various signaling pathways in pancreatic cancer cells.