Diagnostic biomarkers differentiating metastatic melanoma patients from healthy controls identified by an integrated MALDI-TOF mass spectrometry/bioinformatic approach

Proteomics as a tool to improve novel insights into skin diseases: what we know and where we should be going

Background

Biochemical processes involved in complex skin diseases (skin cancers, psoriasis, and wound) can be identified by combining proteomics analysis and bioinformatics tools, which gain a next-level insight into their pathogenesis, diagnosis, and therapeutic targets.

Methods

Articles were identified through a search of PubMed, Embase, and MEDLINE references dated to May 2022, to perform system data mining, and a search of the Web of Science (WoS) Core Collection was utilized to conduct a visual bibliometric analysis.

Results

An increased trend line revealed that the number of publications related to proteomics utilized in skin diseases has sharply increased recent years, reaching a peak in 2021. The hottest fields focused on are skin cancer (melanoma), inflammation skin disorder (psoriasis), and skin wounds. After deduplication and title, abstract, and full-text screening, a total of 486 of the 7,822 outcomes met the inclusion/exclusion criteria for detailed data mining in the field of skin disease tooling with proteomics, with regard to skin cancer. According to the data, cell death, metabolism, skeleton, immune, and inflammation enrichment pathways are likely the major part and hotspots of proteomic analysis found in skin diseases. Also, the focuses of proteomics in skin disease are from superficial presumption to depth mechanism exploration within more comprehensive validation, from basic study to a combination or guideline for clinical applications. Furthermore, we chose skin cancer as a typical example, compared with other skin disorders. In addition to finding key pathogenic proteins and differences between diseases, proteomic analysis is also used for therapeutic evaluation or can further obtain in-depth mechanisms in the field of skin diseases.

Conclusion

Proteomics has been regarded as an irreplaceable technology in the study of pathophysiological mechanism and/or therapeutic targets of skin diseases, which could provide candidate key proteins for the insight into the biological information after gene transcription. However, depth pathogenesis and potential clinical applications need further studies with stronger evidence within a wider range of skin diseases.

Imaging Mass Spectrometry-Based Proteomic Analysis to Differentiate Melanocytic Nevi and Malignant Melanoma

The discrimination of malignant melanoma from benign nevi may be difficult in some cases. For this reason, immunohistological and molecular techniques are included in the differential diagnostic toolbox for these lesions. These methods are time consuming when applied subsequently and, in some cases, no definitive diagnosis can be made. We studied both lesions by imaging mass spectrometry (IMS) in a large cohort (n = 203) to determine a different proteomic profile between cutaneous melanomas and melanocytic nevi. Sample preparation and instrument setting were tested to obtain optimal results in term of data quality and reproducibility. A proteomic signature was found by linear discriminant analysis to discern malignant melanoma from benign nevus (n = 113) with an overall accuracy of >98%. The prediction model was tested in an independent set (n = 90) reaching an overall accuracy of 93% in classifying melanoma from nevi. Statistical analysis of the IMS data revealed mass-to-charge ratio (m/z) peaks which varied significantly (Area under the receiver operating characteristic curve > 0.7) between the two tissue types. To our knowledge, this is the largest IMS study of cutaneous melanoma and nevi performed up to now. Our findings clearly show that discrimination of melanocytic nevi from melanoma is possible by IMS.

Development of a Bioinformatics Framework for Identification and Validation of Genomic Biomarkers and Key Immunopathology Processes and Controllers in Infectious and Non-infectious Severe Inflammatory Response Syndrome

Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requiring admission to an intensive care unit (ICU). The causative agents and processes involved are multifactorial but are characterized by an overarching inflammatory response, sharing elements in common with severe inflammatory response syndrome (SIRS) of non-infectious origin. Sepsis presents with a range of pathophysiological and genetic features which make clinical differentiation from SIRS very challenging. This may reflect a poor understanding of the key gene inter-activities and/or pathway associations underlying these disease processes. Improved understanding is critical for early differential recognition of sepsis and SIRS and to improve patient management and clinical outcomes. Judicious selection of gene biomarkers suitable for development of diagnostic tests/testing could make differentiation of sepsis and SIRS feasible. Here we describe a methodologic framework for the identification and validation of biomarkers in SIRS, sepsis and septic shock patients, using a 2-tier gene screening, artificial neural network (ANN) data mining technique, using previously published gene expression datasets. Eight key hub markers have been identified which may delineate distinct, core disease processes and which show potential for informing underlying immunological and pathological processes and thus patient stratification and treatment. These do not show sufficient fold change differences between the different disease states to be useful as primary diagnostic biomarkers, but are instrumental in identifying candidate pathways and other associated biomarkers for further exploration.

Liquid biomarkers in melanoma: detection and discovery

A vast array of tumor-derived genetic, proteomic and cellular components are constantly released into the circulation of cancer patients. These molecules including circulating tumor DNA and RNA, proteins, tumor and immune cells are emerging as convenient and accurate liquid biomarkers of cancer. Circulating cancer biomarkers provide invaluable information on cancer detection and diagnosis, prognosticate patient outcomes, and predict treatment response. In this era of effective molecular targeted treatments and immunotherapies, there is now an urgent need to implement use of these circulating biomarkers in the clinic to facilitate personalized therapy. In this review, we present recent findings in circulating melanoma biomarkers, examine the challenges and promise of evolving technologies used for liquid biomarker discovery, and discuss future directions and perspectives in melanoma biomarker research.

In Silico Analysis Validates Proteomic Findings of Formalin-fixed Paraffin Embedded Cutaneous Squamous Cell Carcinoma Tissue

BACKGROUND

Cutaneous squamous cell carcinoma (cSCC) is a common type of skin cancer but there are no comprehensive proteomic studies on this entity.

MATERIALS AND METHODS

We employed liquid chromatography coupled with tandem mass spectrometry (MS/MS) using formalin-fixed paraffin-embedded (FFPE) cSCC material to study the tumor and normal skin tissue proteomes. Ingenuity Pathway Analysis (IPA) was used to interpret the role of altered proteins in cSCC pathophysiology. Results were validated using the Human Protein Atlas and Oncomine database in silico.

RESULTS

Of 1,310 unique proteins identified, expression of an average of 144 and 88 proteins were significantly (p<0.05) increased and decreased, respectively, in the tumor samples compared to their normal counterparts. IPA analysis revealed disruptions in proteins associated with cell proliferation, apoptosis, and migration. In silico analysis confirmed that proteins corresponding to 12 antibodies, and genes corresponding to 18 proteins were differentially expressed between the two categories, validating our proteomic measurements.

CONCLUSION

Label-free MS-based proteomics is useful for analyzing FFPE cSCC tissues.

Correlation between phylogroups and intracellular proteomes of Propionibacterium acnes and differences in the protein expression profiles between anaerobically and aerobically grown cells

Propionibacterium acnes is one of the dominant commensals on the human skin and also an opportunistic pathogen in relation to acne, sarcoidosis, prostate cancer, and various infections. Recent investigations using housekeeping and virulence genes have revealed that the species consists of three major evolutionary clades (types I, II, and III). In order to investigate protein expression differences between these phylogroups, proteomic profiles of 21 strains of P. acnes were investigated. The proteins extracted from cells cultured under anaerobic and aerobic conditions were analysed using a SELDI-TOF mass spectrometer, high-resolution capillary gel electrophoresis, and LC-MS/ MS. The SELDI spectral profiles were visualised as a heat map and a dendrogram, which resulted in four proteomic groups. Strains belonging to type I were represented in the proteome Group A, while Group B contained type III strains. Groups C and D contained mixtures of types I and II. Each of these groups was not influenced by differences in culture conditions. Under anoxic growth conditions, a type IB strain yielded high expressions of some proteins, such as methylmalonyl-CoA epimerase and the Christie-Atkins-Munch-Petersen (CAMP) factor. The present study revealed good congruence between genomic and proteomic data suggesting that the microenvironment of each subtype may influence protein expression.

Identification of novel breast cancer-associated transcripts by UniGene database mining and gene expression analysis in normal and malignant cells

Breast cancer is a heterogeneous and complex disease. Although the use of tumor biomarkers has improved individualized breast cancer care, i.e., assessment of risk, diagnosis, prognosis, and prediction of treatment outcome, new markers are required to further improve patient clinical management. In the present study, a search for novel breast cancer-associated genes was performed by mining the UniGene database for expressed sequence tags (ESTs) originating from human normal breast, breast cancer tissue, or breast cancer cell lines. Two hundred and twenty-eight distinct breast-associated UniGene Clusters (BUC1-228) matched the search criteria. Four BUC ESTs (BUC6, BUC9, BUC10, and BUC11) were subsequently selected for extensive in silico database searches, and in vitro analyses through sequencing and RT-PCR based assays on well-characterized cell lines and tissues of normal and cancerous origin. BUC6, BUC9, BUC10, and BUC11 are clustered on 10p11.21-12.1 and showed no homology to any known RNAs. Overall, expression of the four BUC transcripts was high in normal breast and testis tissue, and in some breast cancers; in contrast, BUC was low in other normal tissues, peripheral blood mononuclear cells (PBMCs), and other cancer cell lines. Results to-date suggest that BUC11 and BUC9 translate to protein and BUC11 cytoplasmic and nuclear protein expression was detected in a large cohort of breast cancer samples using immunohistochemistry. This study demonstrates the discovery and expression analysis of a tissue-restricted novel transcript set which is strongly expressed in breast tissue and their application as clinical cancer biomarkers clearly warrants further investigation.

Novel biomarkers and therapeutic targets for optimizing the therapeutic management of melanomas

Cutaneous malignant melanoma is the most aggressive form of skin cancer with an extremely poor survival rate for the patients diagnosed with locally invasive and metastatic disease states. Intensive research has led in last few years to an improvement of the early detection and curative treatment of primary cutaneous melanomas that are confined to the skin by tumor surgical resection. However, locally advanced and disseminated melanomas are generally resistant to conventional treatments, including ionizing radiation, systemic chemotherapy, immunotherapy and/or adjuvant stem cell-based therapies, and result in the death of patients. The rapid progression of primary melanomas to locally invasive and/or metastatic disease states remains a major obstacle for an early effective diagnosis and a curative therapeutic intervention for melanoma patients. Importantly, recent advances in the melanoma research have led to the identification of different gene products that are often implicated in the malignant transformation of melanocytic cells into melanoma cells, including melanoma stem/progenitor cells, during melanoma initiation and progression to locally advanced and metastatic disease states. The frequent deregulated genes products encompass the oncogenic B-RafV600E and N-RasQ61R mutants, different receptor tyrosine kinases and developmental pathways such as epidermal growth factor receptor (EGFR), stem cell-like factor (SCF) receptor KIT, hedgehog, Wnt/β-catenin, Notch, stromal cell-derived factor-1 (SDF-1)/CXC chemokine receptor-4 (CXCR4) and vascular endothelial growth factor (VEGF)/VEGFR receptor. These growth factors can cooperate to activate distinct tumorigenic downstream signaling elements and epithelial-mesenchymal transition (EMT)-associated molecules, including phosphatidylinositol 3’-kinase (PI3K)/Akt/ molecular target of rapamycin (mTOR), nuclear factor-kappaB (NF-κB), macrophage inhibitory cytokine-1 (MIC-1), vimentin, snail and twist. Of therapeutic relevance, these deregulated signal transduction components constitute new potential biomarkers and therapeutic targets of great clinical interest for improving the efficacy of current diagnostic and prognostic methods and management of patients diagnosed with locally advanced, metastatic and/or relapsed melanomas.

MicroRNA signature analysis in colorectal cancer: identification of expression profiles in stage II tumors associated with aggressive disease

PURPOSE

Colorectal cancer (CRC) is a clinically diverse disease whose molecular etiology remains poorly understood. The purpose of this study was to identify miRNA expression patterns predictive of CRC tumor status and to investigate associations between microRNA (miRNA) expression and clinicopathological parameters.

METHODS

Expression profiling of 380 miRNAs was performed on 20 paired stage II tumor and normal tissues. Artificial neural network (ANN) analysis was applied to identify miRNAs predictive of tumor status. The validation of specific miRNAs was performed on 102 tissue specimens of varying stages.

RESULTS

Thirty-three miRNAs were identified as differentially expressed in tumor versus normal tissues. ANN analysis identified three miRNAs (miR-139-5p, miR-31, and miR-17-92 cluster) predictive of tumor status in stage II disease. Elevated expression of miR-31 (p = 0.004) and miR-139-5p (p < 0.001) and reduced expression of miR-143 (p = 0.016) were associated with aggressive mucinous phenotype. Increased expression of miR-10b was also associated with mucinous tumors (p = 0.004). Furthermore, progressively increasing levels of miR-10b expression were observed from T1 to T4 lesions and from stage I to IV disease.

CONCLUSION

Association of specific miRNAs with clinicopathological features indicates their biological relevance and highlights the power of ANN to reliably predict clinically relevant miRNA biomarkers, which it is hoped will better stratify patients to guide adjuvant therapy.

A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis: stem cell and melanoma cancer studies

INTRODUCTION

Raw spectral data from matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) with MS profiling techniques usually contains complex information not readily providing biological insight into disease. The association of identified features within raw data to a known peptide is extremely difficult. Data preprocessing to remove uncertainty characteristics in the data is normally required before performing any further analysis. This study proposes an alternative yet simple solution to preprocess raw MALDI-TOF-MS data for identification of candidate marker ions. Two in-house MALDI-TOF-MS data sets from two different sample sources (melanoma serum and cord blood plasma) are used in our study.

METHOD

Raw MS spectral profiles were preprocessed using the proposed approach to identify peak regions in the spectra. The preprocessed data was then analysed using bespoke machine learning algorithms for data reduction and ion selection. Using the selected ions, an ANN-based predictive model was constructed to examine the predictive power of these ions for classification.

RESULTS

Our model identified 10 candidate marker ions for both data sets. These ion panels achieved over 90% classification accuracy on blind validation data. Receiver operating characteristics analysis was performed and the area under the curve for melanoma and cord blood classifiers was 0.991 and 0.986, respectively.

CONCLUSION

The results suggest that our data preprocessing technique removes unwanted characteristics of the raw data, while preserving the predictive components of the data. Ion identification analysis can be carried out using MALDI-TOF-MS data with the proposed data preprocessing technique coupled with bespoke algorithms for data reduction and ion selection.

Serum biomarkers which correlate with failure to respond to immunotherapy and tumor progression in a murine colorectal cancer model

PURPOSE

To advance our understanding of mechanisms involved in tumor progression/regression, a CT26 colorectal mouse model treated intra-tumorally with DISC-herpes simplex virus as immunotherapy was used in the discovery and validation phases to investigate and ultimately identify biomarkers correlating with the failure to respond to immunotherapy.

EXPERIMENTAL DESIGN

For the discovery phase, serum protein/peptide profiles of a retrospective sample collection (total n=70) were analyzed using MALDI-TOF-MS combined with artificial neural networks. Following identification of the key predictive peptides using ESI-MS/MS, validation of the identified proteins was carried out on serum and tissues collected in an independent sample set (total n=60).

RESULTS

Artificial neural network analysis resulted in four discriminatory peaks with an accuracy of 86%, sensitivity of 90% and specificity of 81% between the progressor/regressor groups. Three of the identified discriminatory markers were upregulated and demonstrated a positive correlation with tumor progression following DISC-herpes simplex virus therapy. Immunovalidation studies corroborated the MALDI-TOF-MS findings. Immunohistochemistry revealed that serum amyloid A-1 and serum amyloid P produced in the liver localized intracellularly in CT26 tumor tissue.

CONCLUSIONS

MALDI-TOF-MS and BI analysis of the serum proteome of tumor-bearer mice undergoing immunotherapy, identified biomarkers associating with "failure to respond" and biological arrays confirmed these findings.

Mass spectrometry based proteomics profiling as diagnostic tool in oncology: current status and future perspective

Proteomics analysis has been heralded as a novel tool for identifying new and specific biomarkers that may improve diagnosis and monitoring of various disease states. Recent years have brought a number of proteomics profiling technologies. Although proteomics profiling has resulted in the detection of disease-associated differences and modification of proteins, current proteomics technologies display certain limitations that are hampering the introduction of these new technologies into clinical laboratory diagnostics and routine applications. In this review, we summarize current advances in mass spectrometry based biomarker discovery. The promises and challenges of this new technology are discussed with particular emphasis on diagnostic perspectives of mass-spectrometry based proteomics profiling for malignant diseases.

Cancer-specific MALDI-TOF profiles of blood serum and plasma: biological meaning and perspectives

MALDI-TOF mass-spectrometry has become a popular tool of cancer research during the last decade. High throughput and relative simplicity of this technology have made it attractive for biomarker discovery and validation across various platforms in blood serum/plasma. Many technical approaches have been developed for plasma/serum profiling including protein-chip based SELDI-TOF mass-spectrometry, purification of serum on magnetic beads, analysis of carrier-associated fraction and mass-spectrometric immunoassays. Extensive data about the identity of differential features detected on mass-spectra up to now makes it possible to draw conclusions about potency and perspectives of MALDI-TOF mass-spectrometry in this field. A great majority of identified differentially expressed proteins are either house-keeping or inflammatory proteins as well as their modifications or fragments. Discriminating ability of mass-spectra is likely to be based on differential modification and fragmentation patterns of abundant serum proteins reflecting activity of enzymes including proteases and their inhibitors.

MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neu receptor status in breast cancer

INTRODUCTION

Breast cancer is a heterogeneous disease encompassing a number of phenotypically diverse tumours. Expression levels of the oestrogen, progesterone and HER2/neu receptors which characterize clinically distinct breast tumours have been shown to change during disease progression and in response to systemic therapies. Mi(cro)RNAs play critical roles in diverse biological processes and are aberrantly expressed in several human neoplasms including breast cancer, where they function as regulators of tumour behaviour and progression. The aims of this study were to identify miRNA signatures that accurately predict the oestrogen receptor (ER), progesterone receptor (PR) and HER2/neu receptor status of breast cancer patients to provide insight into the regulation of breast cancer phenotypes and progression.

METHODS

Expression profiling of 453 miRNAs was performed in 29 early-stage breast cancer specimens. miRNA signatures associated with ER, PR and HER2/neu status were generated using artificial neural networks (ANN), and expression of specific miRNAs was validated using RQ-PCR.

RESULTS

Stepwise ANN analysis identified predictive miRNA signatures corresponding with oestrogen (miR-342, miR-299, miR-217, miR-190, miR-135b, miR-218), progesterone (miR-520g, miR-377, miR-527-518a, miR-520f-520c) and HER2/neu (miR-520d, miR-181c, miR-302c, miR-376b, miR-30e) receptor status. MiR-342 and miR-520g expression was further analysed in 95 breast tumours. MiR-342 expression was highest in ER and HER2/neu-positive luminal B tumours and lowest in triple-negative tumours. MiR-520g expression was elevated in ER and PR-negative tumours.

CONCLUSIONS

This study demonstrates that ANN analysis reliably identifies biologically relevant miRNAs associated with specific breast cancer phenotypes. The association of specific miRNAs with ER, PR and HER2/neu status indicates a role for these miRNAs in disease classification of breast cancer. Decreased expression of miR-342 in the therapeutically challenging triple-negative breast tumours, increased miR-342 expression in the luminal B tumours, and downregulated miR-520g in ER and PR-positive tumours indicates that not only is dysregulated miRNA expression a marker for poorer prognosis breast cancer, but that it could also present an attractive target for therapeutic intervention.

Serum biomarker profiling in cancer studies: a question of standardisation?

Companion animals are exposed to similar environmental conditions and carcinogens as humans. In some animal cancers, there also appears to be the same genetic changes associated as in humans. However, little work has been carried out in cancer biomarker identification in animals. The recent dramatic advances in molecular medicine, genomics, proteomics and translational research will allow biomarker identification, which may provide the best strategies for veterinarians and clinicians to combat disease by early diagnosis and administration of effective treatments. Proteomics may have important applications in cancer diagnosis, prognosis and predictive clinical outcome that could directly change clinical practice by affecting critical elemen-ts of care and management. This review summarizes the advances in proteomics that has propelled us to this exciting age of clinical proteomics, and highlights the future work that is required for this to become a reality. In this review, we will discuss the available proteomic technologies and their limitations, and highlight the key areas of research and how they have been used to discover cancer biomarkers. The principles described here are equally applicable to human and animal disease, but implementation of 'omic' technologies requires stringent guidelines for collection of clinical material, the application of analytical techniques and interpretation of the data.

An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies

Applications of genomic and proteomic technologies have seen a major increase, resulting in an explosion in the amount of highly dimensional and complex data being generated. Subsequently this has increased the effort by the bioinformatics community to develop novel computational approaches that allow for meaningful information to be extracted. This information must be of biological relevance and thus correlate to disease phenotypes of interest. Artificial neural networks are a form of machine learning from the field of artificial intelligence with proven pattern recognition capabilities and have been utilized in many areas of bioinformatics. This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments. As such, neural networks have been applied to problems such as disease classification and identification of biomarkers. This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods. This should form the necessary background knowledge and information enabling researchers with an interest in these methodologies, but not necessarily from a machine learning background, to apply the concepts to their own datasets, thus maximizing the information gain from these complex biological systems.

A qualitative proteome investigation of the sediment portion of human urine: Implications in the biomarker discovery process

Inherent to the biomarker discovery process is a comparative analysis of physiological states. It is therefore critical that the proteome detection protocol does not bias the analysis. With urine, the sediment portion, obtained upon thawing frozen urine, is routinely discarded prior to proteome analysis. However, our results demonstrate that such a practice inadvertently induces bias, having significant implications in the biomarker discovery process. We present the first proteome investigation of human urinary sediments, identifying 60 proteins in this phase by MS. Many sediment proteins were also detected in the urinary supernatant, indicating that several proteins partition between the two phases. This partitioning is dependant on the pH of the sample, as well as the degree of sample agitation. As a consequence of discarding the sediment portion of urine, the concentration of potential candidate biomarkers in the supernatant phase will be altered or, in other instances, may be completely removed from the sample. To minimize this, the pH of all samples should first be normalized, and the samples vigorously vortexed prior to discarding the sediments. For more comprehensive biomarker investigations, we suggest that urinary sediments be analyzed along with the supernatant proteins.