Bioinformatics Tools and Workflow to Select Blood Biomarkers for Early Cancer Diagnosis: An Application to Pancreatic Cancer

Integrating OMICS-based platforms and analytical tools for diagnosis and management of pancreatic cancer: a review

Cancer remains the second leading cause of death worldwide, surpassed only by cardiovascular disease. From the different types of cancer, pancreatic cancer (PaC) has one of the lowest survival rates, with a survival rate of about 20% after the first year of diagnosis and about 8% after 5 years. The lack of highly sensitive and specific biomarkers, together with the absence of symptoms in the early stages, determines a late diagnosis, which is associated with a decrease in the effectiveness of medical intervention, regardless of its nature - surgery and/or chemotherapy. This review provides an updated overview of recent studies combining multi-OMICs approaches (e.g., proteomics, metabolomics) with analytical tools, highlighting the synergy between high-throughput molecular data generation and precise analytical tools such as LC-MS, GC-MS and MALDI-TOF MS. This combination significantly improves the detection, quantification and identification of biomolecules in complex biological systems and represents the latest advances in understanding PaC management and the search for effective diagnostic tools. Large-scale data analysis coupled with bioinformatics tools enables the identification of specific genetic mutations, gene expression patterns, pathways, networks, protein modifications and metabolic signatures associated with PaC pathogenesis, progression and treatment response through the integration of multi-OMICs data.

Unlocking the Promise of Decellularized Pancreatic Tissue: A Novel Approach to Support Angiogenesis in Engineered Tissue

Advancements in regenerative medicine have highlighted the potential of decellularized extracellular matrix (ECM) as a scaffold for organ bioengineering. Although the potential of ECM in major organ systems is well-recognized, studies focusing on the angiogenic effects of pancreatic ECM are limited. This study investigates the capabilities of pancreatic ECM, particularly its role in promoting angiogenesis. Using a Triton-X-100 solution, porcine pancreas was successfully decellularized, resulting in a significant reduction in DNA content (97.1% removal) while preserving key pancreatic ECM components. A three-dimensional ECM hydrogel was then created from this decellularized tissue and used for cell culture. Biocompatibility tests demonstrated enhanced adhesion and proliferation of mouse embryonic stem cell-derived endothelial cells (mES-ECs) and human umbilical vein endothelial cells (HUVECs) in this hydrogel compared to conventional scaffolds. The angiogenic potential was evaluated through tube formation assays, wherein the cells showed superior tube formation capabilities in ECM hydrogel compared to rat tail collagen. The RT-PCR analysis further confirmed the upregulation of pro-angiogenic genes in HUVECs cultured within the ECM hydrogel. Specifically, HUVECs cultured in the ECM hydrogel exhibited a significant upregulation in the expression of MMP2, VEGF and PAR-1, compared to those cultured in collagen hydrogel or in a monolayer condition. The identification of ECM proteins, specifically PRSS2 and Decorin, further supports the efficacy of pancreatic ECM hydrogel as an angiogenic scaffold. These findings highlight the therapeutic promise of pancreatic ECM hydrogel as a candidate for vascularized tissue engineering application.

Proteomics: Unraveling the Cross Talk Between Innate Immunity and Disease Pathophysiology, Diagnostics, and Treatment Options

Inflammation is crucial in diseases, and proteins play a key role in the interplay between innate immunity and pathology. This review explores how proteomics helps understanding this relationship, focusing on diagnosis and treatment. We explore the dynamic innate response and the significance of proteomic techniques in deciphering the complex network of proteins involved in prevalent diseases, including infections, cancer, autoimmune and neurodegenerative disorders. Proteomics identifies key proteins in host-pathogen interactions, shedding light on infection mechanisms and inflammation. These discoveries hold promise for diagnostic tools, therapies, and vaccines. In cancer research, proteomics reveals innate signatures associated with tumor development, immune evasion, and therapeutic response. Additionally, proteomic analysis has unveiled autoantigens and dysregulation of the innate immune system in autoimmunity, offering opportunities for early diagnosis, disease monitoring, and new therapeutic targets. Moreover, proteomic analysis has identified altered protein expression patterns in neurodegenerative diseases like Alzheimer's and Parkinson's, providing insights into potential therapeutic strategies. Proteomics of the innate immune system provides a comprehensive understanding of disease mechanisms, identifies biomarkers, and enables effective interventions in various diseases. Despite still in its early stages, this approach holds great promise to revolutionize innate immunity research and significantly improve patient outcomes across a wide range of diseases.

Management of Metastatic Pancreatic Cancer-Comparison of Global Guidelines over the Last 5 Years

Pancreatic cancer (PC) is usually diagnosed at an advanced stage of its development, which results in lower overall survival (OS). Prognosis is also poor even with curative-intent surgery. Approximately 80% of patients with localized PDAC have micrometastases at the time of diagnosis, which leads to a worse prognosis than in other cancers. The objective of this study is to present the progress in the treatment of metastatic pancreatic cancer based on the recommendations of oncological scientific societies, such as ESMO, NCCN, ASCO, NICE and SEOM, over the last 5 years. Combined FOLFIRINOX therapy is mostly a recommended therapy among patients with good performance statuses, while gemcitabine is recommended for more fragile patients as a first-line treatment. The newest guidelines suggest that molecular profiling of the tumor should be the first step in determining the course of treatment. The use of modern molecular therapies in patients with specific gene mutations should extend the survival of patients with this disease.

PRSS2 regulates EMT and metastasis via MMP-9 in gastric cancer

Serine protease 2 (PRSS2) is upregulated in gastric cancer tissues, correlates with poor prognosis and promotes migration and invasion of gastric cancer cells. However, the exact mechanism by which PRSS2 promotes metastasis in gastric cancer is unclear. We examined serum PRSS2 levels in healthy controls and gastric cancer patients by enzyme linked immunosorbent assay (ELISA) and analyzed the correlation between PRSS2 serum level with the clinicopathological characteristics of gastric cancer patients and matrix metalloproteinase-9 (MMP-9) expression. A lentiviral MMP-9 overexpression vector was constructed and used to transfect gastric cancer cells with stable silencing of PRSS2, and migration, invasion and epithelial-mesenchymal transition (EMT) of gastric cancer cells were examined. High serum PRSS2 levels were detected in gastric cancer patients and associated with lymphatic metastasis and TNM stage. Serum PRSS2 was positively correlated with serum MMP-9 level. PRSS2 silencing inhibited EMT, and knock-down of PRSS2 partially abrogated cell metastasis and EMT caused by overexpression of MMP-9. These results suggest that PRSS2 promotes the migration and invasion of gastric cancer cells through EMT induction by MMP-9. Our findings suggest that PRSS2 may be a potential early diagnostic marker and therapeutic target of gastric cancer.

Identification of potential extracellular vesicle protein markers altered in osteosarcoma from public databases

PURPOSE

Extracellular vesicles (EVs) have become promising biomarkers for cancer management. Particularly, the molecular cargo such as proteins carried by EVs are similar to their cells of origin, providing important information that can be used for cancer diagnostics, prognosis, and treatment monitoring. However, to date, molecular analysis on EVs is still challenging, limited by the availability of efficient analytical technologies, largely due to the small size of EVs. In this work, we developed a computational workflow for in silico identification of potential EV protein markers from genomic and proteomic databases, and applied it for the discovery of osteosarcoma (OS) EV protein markers.

EXPERIMENTAL DESIGN

Both mRNA and protein data were computed and compared from publicly accessible databases, and top markers with high differential expression levels were selected.

RESULTS

Thirty nine markers were identified overexpressed and seven found to be downregulated. These identified markers have been found to be associated with OS on different aspects in literature, demonstrating the usability of this workflow.

CONCLUSIONS AND CLINICAL RELEVANCE

This work provides a list of potential EV protein markers that are either overexpressed or downregulated in OS for further experimental validation for improved clinical management of OS.

A novel quantitative prognostic model for initially diagnosed non-small cell lung cancer with brain metastases

Background

The prognosis of non-small cell lung cancer (NSCLC) with brain metastases (BMs) had been researched in some researches, but the combination of clinical characteristics and serum inflammatory indexes as a noninvasive and more accurate model has not been described.

Methods

We retrospectively screened patients with BMs at the initial diagnosis of NSCLC at Sun Yat-Sen University Cancer Center. LASSO-Cox regression analysis was used to establish a novel prognostic model for predicting OS based on blood biomarkers. The predictive accuracy and discriminative ability of the prognostic model was compared to Adjusted prognostic Analysis (APA), Recursive Partition Analysis (RPA), and Graded Prognostic Assessment (GPA) using concordance index (C-index), time-dependent receiver operating characteristic (td-ROC) curve, Decision Curve Analysis(DCA), net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI).

Results

10-parameter signature's predictive model for the NSCLC patients with BMs was established according to the results of LASSO-Cox regression analysis. The C-index of the prognostic model to predict OS was 0.672 (95% CI = 0.609 ~ 0.736) which was significantly higher than APA,RPA and GPA. The td-ROC curve and DCA of the predictive model also demonstrated good predictive accuracy of OS compared to APA, RPA and GPA. Moreover, NRI and IDI analysis indicated that the prognostic model had improved prediction ability compared with APA, RPA and GPA.

Conclusion

The novel prognostic model demonstrated favorable performance than APA, RPA, and GPA for predicting OS in NSCLC patients with BMs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02671-2.

In-silico selection of cancer blood plasma proteins by integrating genomic and proteomic databases

Blood protein markers have been studied for the clinical management of cancer. Due to the large number of the proteins existing in blood, it is often necessary to pre-select potential protein markers before experimental studies. However, to date there is a lack of automated method for in-silico selection of cancer blood proteins that integrates the information from both genetic and proteomic studies in a cancer-specific manner. In this work, we synthesized both genomic and proteomic information from several open access databases and established a bioinformatic pipeline for in-silico selection of blood plasma proteins overexpressed in specific type of cancer. We demonstrated the workflow of this pipeline with an example of breast cancer, while the methodology was applicable for other cancer types. With this pipeline we obtained 10 candidate biomarkers for breast cancer. The proposed pipeline provides a useful and convenient tool for in-silico selection of candidate blood protein biomarkers for a variety of cancer research.

ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.

Advances and Utility of the Human Plasma Proteome

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.

Mass Spectrometry-Based Proteomics Reveal Alcohol Dehydrogenase 1B as a Blood Biomarker Candidate to Monitor Acetaminophen-Induced Liver Injury

Acute liver injury (ALI) is a severe disorder resulting from excessive hepatocyte cell death, and frequently caused by acetaminophen intoxication. Clinical management of ALI progression is hampered by the dearth of blood biomarkers available. In this study, a bioinformatics workflow was developed to screen omics databases and identify potential biomarkers for hepatocyte cell death. Then, discovery proteomics was harnessed to select from among these candidates those that were specifically detected in the blood of acetaminophen-induced ALI patients. Among these candidates, the isoenzyme alcohol dehydrogenase 1B (ADH1B) was massively leaked into the blood. To evaluate ADH1B, we developed a targeted proteomics assay and quantified ADH1B in serum samples collected at different times from 17 patients admitted for acetaminophen-induced ALI. Serum ADH1B concentrations increased markedly during the acute phase of the disease, and dropped to undetectable levels during recovery. In contrast to alanine aminotransferase activity, the rapid drop in circulating ADH1B concentrations was followed by an improvement in the international normalized ratio (INR) within 10–48 h, and was associated with favorable outcomes. In conclusion, the combination of omics data exploration and proteomics revealed ADH1B as a new blood biomarker candidate that could be useful for the monitoring of acetaminophen-induced ALI.

Identification of genes associated with Kikuchi-Fujimoto disease using RNA and exome sequencing

Kikuchi-Fujimoto disease (KFD) is an extremely rare disease, and although it is reported to have a worldwide distribution, young Asian women are most likely to be affected. Although this disease is generally benign and self-limiting, distinguishing it from other diseases that cause lymphadenopathy (e.g., leukemia, lymphoma, and infectious diseases) is challenging. A lymph node biopsy is a definitive diagnostic technique for KFD and only requires skillful pathologists. There are no specific symptoms or laboratory tests for KFD, and more than 50% of KFD patients have suffered from being misdiagnosed with lymphoma, which leads to improper treatment. In this study, lymph node tissue samples from KFD patients were used to reveal their exomes and transcriptomes using a high-throughput nucleotide sequencer. Fourteen single nucleotide polymorphisms (SNPs) were identified as candidate KFD markers and were compared with a healthy lymph node exome dataset. The mutation of these genes caused disruptive impact in the proteins. Several SNPs associated with KFD involve genes related to human cancers, olfaction, and osteoblast differentiation. According to the transcriptome data, there were 238 up-regulated and 1,519 down-regulated genes. RANBP2-like and ribosomal protein L13 were the most up-regulated and down-regulated genes in KFD patients, respectively. The altered gene expression involved in the human immune system, chromatin remodeling, and gene transcription. A comparison of KFD and healthy datasets of exomes and transcriptomes may allow further insights into the KFD phenotype. The results may also facilitate future KFD diagnosis and treatment.

Discovery and Validation of Circulating EVL mRNA as a Prognostic Biomarker in Pancreatic Cancer

Background

Circulating plasma mRNAs can be analyzed to identify putative cancer biomarkers. This study was conducted in an effort to detect plasma mRNA biomarkers capable of predicting pancreatic cancer (PACA) patient prognosis. Material and Methods. First, prognostic mRNAs that were differentially expressed in PACA in The Cancer Genome Atlas (TCGA) were established, after which microarray expression profiles from PACA patient plasma samples were utilized to specifically identify potential prognostic plasma mRNA biomarkers associated with this cancer type. In total, plasma samples were then collected from 79 PACA patients and 19 healthy controls to confirm differential mRNA expression via qPCR, while Kaplan–Meier analyses were used to examine the link between mRNA expression and patient overall survival.

Results

In total, three prognostic differentially expressed genes were identified in PACA patient plasma samples, including SMAP2, PTPN6, and EVL (Ena/VASP-like). Plasma EVL levels were confirmed via qPCR to be correlated with tumor pathology (p < 0.01), while the overall survival of patients with low plasma EVL levels was poor (p < 0.01). Multivariate Cox regression analyses further confirmed that plasma EVL levels were independent predictors of PACA patient prognosis.

Conclusion

We found that PACA is associated with the downregulation of plasma EVL mRNA levels, indicating that this mRNA may be a viable biomarker associated with patient prognosis.

Protein biomarker elucidation for the verification of biological agents in the taxonomic group of Gammaproteobacteria using tandem mass spectrometry

Some pathogenic microbes can be used for nefarious applications and instigate population-based fear. In a bio-threat scenario, rapid and accurate methods to detect biological agents in a wide range of complex environmental and clinical matrices, is of paramount importance for the implementation of mitigation protocols and medical countermeasures. This study describes targeted and shot-gun tandem MS based approaches for the verification of biological agents from the environmental samples. The marker proteins and peptides were elucidated by an exhaustive literature mining, in silico analysis of prioritized proteins, and MS/MS analysis of abundant proteins from selected bacterial species. For the shot-gun methodology, tandem MS analysis of abundant peptides was carried from spiked samples. The validation experiments employing a combination of shot-gun tandem MS analysis and a targeted search reported here is a proof of concept to show the applicability of the methodology for the unambiguous verification of biological agents at sub-species level, even with limited fractionation of crude protein extracts from environmental samples.

Sexual differences in mitochondrial and related proteins in rat cerebral microvessels: A proteomic approach

Sex differences in mitochondrial numbers and function are present in large cerebral arteries, but it is unclear whether these differences extend to the microcirculation. We performed an assessment of mitochondria-related proteins in cerebral microvessels (MVs) isolated from young, male and female, Sprague-Dawley rats. MVs composed of arterioles, capillaries, and venules were isolated from the cerebrum and used to perform a 3 versus 3 quantitative, multiplexed proteomics experiment utilizing tandem mass tags (TMT), coupled with liquid chromatography/mass spectrometry (LC/MS). MS data and bioinformatic analyses were performed using Proteome Discoverer version 2.2 and Ingenuity Pathway Analysis. We identified a total of 1969 proteins, of which 1871 were quantified by TMT labels. Sixty-four proteins were expressed significantly (p < 0.05) higher in female samples compared with male samples. Females expressed more mitochondrial proteins involved in energy production, mitochondrial membrane structure, anti-oxidant enzyme proteins, and those involved in fatty acid oxidation. Conversely, males had higher expression levels of mitochondria-destructive proteins. Our findings reveal, for the first time, the full extent of sexual dimorphism in the mitochondrial metabolic protein profiles of MVs, which may contribute to sex-dependent cerebrovascular and neurological pathologies.

GO Enrichment Analysis for Differential Proteomics Using ProteoRE

With the increased simplicity of producing proteomics data, the bottleneck has now shifted to the functional analysis of large lists of proteins to translate this primary level of information into meaningful biological knowledge. Tools implementing such approach are a powerful way to gain biological insights related to their samples, provided that biologists/clinicians have access to computational solutions even when they have little programming experience or bioinformatics support. To achieve this goal, we designed ProteoRE (Proteomics Research Environment), a unified online research service that provides end-users with a set of tools to interpret their proteomics data in a collaborative and reproducible manner. ProteoRE is built upon the Galaxy framework, a workflow system allowing for data and analysis persistence, and providing user interfaces to facilitate the interaction with tools dedicated to the functional and the visual analysis of proteomics datasets. A set of tools relying on computational methods selected for their complementarity in terms of functional analysis was developed and made accessible via the ProteoRE web portal. In this chapter, a step-by-step protocol linking these tools is designed to perform a functional annotation and GO-based enrichment analyses applied to a set of differentially expressed proteins as a use case. Analytical practices, guidelines as well as tips related to this strategy are also provided. Tools, datasets, and results are freely available at http://www.proteore.org , allowing researchers to reuse them.

Advances on diagnostic biomarkers of pancreatic ductal adenocarcinoma: A systems biology perspective

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is usually diagnosed at an advanced stage when curative surgery is no longer an option. Robust diagnostic biomarkers with high sensitivity and specificity for early detection are urgently needed. Systems biology provides a powerful tool for understanding diseases and solving challenging biological problems, allowing biomarkers to be identified and quantified with increasing accuracy, sensitivity, and comprehensiveness. Here, we present a comprehensive overview of efforts to identify biomarkers of PDAC using genomics, transcriptomics, proteomics, metabonomics, and bioinformatics. Systems biology perspective provides a crucial “network” to integrate multi-omics approaches to biomarker identification, shedding additional light on early PDAC detection.

The Functionally Unannotated Proteome of Human Male Tissues: A Shared Resource to Uncover New Protein Functions Associated with Reproductive Biology

In the context of the Human Proteome Project, we built an inventory of 412 functionally unannotated human proteins for which experimental evidence at the protein level exists (uPE1) and which are highly expressed in tissues involved in human male reproduction. We implemented a strategy combining literature mining, bioinformatics tools to collate annotation and experimental information from specific molecular public resources, and efficient visualization tools to put these unknown proteins into their biological context (protein complexes, tissue and subcellular location, expression pattern). The gathered knowledge allowed pinpointing five uPE1 for which a function has recently been proposed and which should be updated in protein knowledge bases. Furthermore, this bioinformatics strategy allowed to build new functional hypotheses for five other uPE1s in link with phenotypic traits that are specific to male reproductive function such as ciliogenesis/flagellum formation in germ cells (CCDC112 and TEX9), chromatin remodeling (C3orf62) and spermatozoon maturation (CCDC183). We also discussed the enigmatic case of MAGEB proteins, a poorly documented cancer/testis antigen subtype. Tools used and computational outputs produced during this study are freely accessible via ProteoRE (http://www.proteore.org), a Galaxy-based instance, for reuse purposes. We propose these five uPE1s should be investigated in priority by expert laboratories and hope that this inventory and shared resources will stimulate the interest of the community of reproductive biology.

Elucidation of protein biomarkers for verification of selected biological warfare agents using tandem mass spectrometry

Some pathogens and toxins have the potential to be used as weapons of mass destruction and instigate population-based fear. Efforts to mitigate biothreat require development of efficient countermeasures which in turn relies on fast and accurate methods to detect the biological agents in a range of complex matrices including environmental and clinical samples. We report here an mass spectrometry (MS) based methodology, employing both targeted and shot-gun approaches for the verification of biological agents from the environmental samples. Our shot-gun methodology relied on tandem MS analysis of abundant peptides from the spiked samples, whereas, the targeted method was based on an extensive elucidation of marker proteins and unique peptides resulting in the generation of an inclusion list of masses reflecting relevant peptides for the unambiguous identification of nine bacterial species [listed as priority agents of bioterrorism by Centre for Disease Control and Prevention (CDC)] belonging to phylogenetically diverse genera. The marker peptides were elucidated by extensive literature mining, in silico analysis, and tandem MS (MS/MS) analysis of abundant proteins of the cultivated bacterial species in our laboratory. A combination of shot-gun MS/MS analysis and the targeted search using a panel of unique peptides is likely to provide unambiguous verification of biological agents at sub-species level, even with limited fractionation of crude protein extracts from environmental samples. The comprehensive list of peptides reflected in the inclusion list, makes a valuable resource for the multiplex analysis of select biothreat agents and further development of targeted MS/MS assays.

Surveillance of Individuals with a Family History of Pancreatic Cancer and Inherited Cancer Syndromes: A Strategy for Detecting Early Pancreatic Cancers

A family history of pancreatic cancer (PC) is a risk factor of PC, and risk levels increase as affected families grow in number and/or develop PC at younger ages. Familial pancreatic cancer (FPC) is defined as a client having at least two PC cases in a first degree relatives. In the narrow sense, FPC does not include some inherited cancer syndromes that are known to increase the risks of PC, such as Peutz-Jeghers syndrome (PJS), hereditary pancreatitis (HP), hereditary breast ovarian cancer syndrome (HBOC), and so on. FPC accounts for 5%-10% of total PC diagnoses and is marked by several features in genetic, epidemiological, and clinicopathological findings that are similar to or distinct from conventional PC. Recent advances in genetic medicine have led to an increased ability to identify germline variants of cancer-associated genes. To date, high-risk individuals (HRIs) in many developed countries, including FPC kindreds and inherited cancer syndromes, are screened clinically to detect and treat early-stage PC. This article highlights the concept of FPC and the most recent data on its detection.