A targeted proteomics approach reveals a serum protein signature as diagnostic biomarker for resectable gastric cancer

Plasma immune profiling combined with machine learning contributes to diagnosis and prognosis of active pulmonary tuberculosis

Tuberculosis (TB) remains one of the deadliest chronic infectious diseases globally. Early diagnosis not only prevents the spread of TB but also ensures effective treatment. However, the absence of non-sputum-based diagnostic tests often leads to delayed TB diagnoses. Inflammation is a hallmark of TB, we aimed to identify biomarkers associated with TB based on immune profiling. We collected 222 plasma samples from healthy controls (HCs), disease controls (non-TB pneumonia; PN), patients with TB (TB), and cured TB cases (RxTB). A high-throughput protein detection technology, multiplex proximity extension assays (PEA), was applied to measure the levels of 92 immune proteins. Based on differential analysis and the correlation with TB severity, we selected 9 biomarkers (CXCL9, PDL1, CDCP1, CCL28, CCL23, CCL19, MMP1, IFNγ and TRANCE) and explored their diagnostic capabilities through 7 machine learning methods. We identified combination of these 9 biomarkers that distinguish TB cases from controls with an area under the receiver operating characteristic curve (AUROC) of 0.89-0.99, with a sensitivity of 82-93% at a specificity of 88-92%. Moreover, the model excels in distinguishing severe TB cases, achieving AUROC exceeding 0.95, sensitivities and specificities exceeding 93.3%. In summary, utilizing targeted proteomics and machine learning, we identified a 9 plasma proteins signature that demonstrates significant potential for accurate TB diagnosis and clinical outcome prediction.

Machine Learning Identifies Key Proteins in Primary Sclerosing Cholangitis Progression and Links High CCL24 to Cirrhosis

Primary sclerosing cholangitis (PSC) is a rare, progressive disease, characterized by inflammation and fibrosis of the bile ducts, lacking reliable prognostic biomarkers for disease activity. Machine learning applied to broad proteomic profiling of sera allowed for the discovery of markers of disease presence, severity, and cirrhosis and the exploration of the involvement of CCL24, a chemokine with fibro-inflammatory activity. Sera from 30 healthy controls and 45 PSC patients were profiled with proximity extension assay, quantifying the expression of 2870 proteins, and used to train an elastic net model. Proteins that contributed most to the model were tested for correlation to enhanced liver fibrosis (ELF) score and used to perform pathway analysis. Statistical modeling for the presence of cirrhosis was performed with principal component analysis (PCA), and receiver operating characteristics (ROC) curves were used to assess the useability of potential biomarkers. The model successfully predicted the presence of PSC, where the top-ranked proteins were associated with cell adhesion, immune response, and inflammation, and each had an area under receiver operator characteristic (AUROC) curve greater than 0.9 for disease presence and greater than 0.8 for ELF score. Pathway analysis showed enrichment for functions associated with PSC, overlapping with pathways enriched in patients with high levels of CCL24. Patients with cirrhosis showed higher levels of CCL24. This data-driven approach to characterize PSC and its severity highlights potential serum protein biomarkers and the importance of CCL24 in the disease, implying its therapeutic potential in PSC.

Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis.

METHODS

We quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models.

RESULTS

In the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively.

CONCLUSIONS

This study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.

The chemokine CCL20 can assist AFP in serological diagnosis of hepatocellular carcinoma

The chemokine 20 (CCL20) is a member of the CC chemokine family and plays a role in tumor immunity and autoimmune disease. This work investigated the value of CCL20 as a serum diagnostic marker for primary hepatocellular carcinoma (HCC). Based on the data of hepatocellular carcinoma patients in the TCGA database, the up-regulated genes encoding secretory proteins were analyzed in each pathological stage, and the candidate marker CCL20 gene was selected. Serum concentrations of CCL20 in patients with primary HCC, benign liver disease, and healthy subjects were analyzed by enzyme-linked immunosorbent assay (ELISA). The ROC curve evaluated the efficacy of CCL20 alone or in combination with AFP in the diagnosis of HCC. It was found the expression of CCL20 in HCC patients was significantly higher than that in the benign liver disease group and healthy controls (P < 0.05); The AUC of ROC curve to distinguish HCC patients from healthy controls was 0.859, the sensitivity was 73.42%, and the specificity was 86.84%. After combination with AFP, the AUC increased to 0.968, the sensitivity was 88.16%, and the specificity was 97.37%. Although CCL20 was increased in the serum of patients with benign liver diseases, combined with AFP, the AUC to distinguish HCC patients from non-HCC cohorts (benign liver disease group and healthy control group) was 0.902, with a sensitivity of 91.67% and a specificity of 75.26%. Collectively, serum CCL20 is closely related to the occurrence of HCC, and detection of serum CCL20 can assist AFP in improving the diagnostic sensitivity of HCC.

ADAMTS Proteases: Their Multifaceted Role in the Regulation of Cancer Metastasis

Cancer leads to nearly 10 million deaths worldwide per year. The tumour microenvironment (TME) is fundamental for tumour growth and progression. A key component of the TME, the extracellular matrix (ECM) has recently become a focus of interest in cancer research. Dysregulation of ECM synthesis and proteolysis leads to uncontrolled tumour growth and metastasis. Matrix remodelling enzymes, secreted by cancer cells and stromal cells, modify the overall structure and organisation of ECM proteins, therefore influencing biochemical interactions, tissue integrity and tissue turnover. While A Disintegrin and Metalloproteinases (ADAMs)' and matrix metalloproteinases' role in cancer has been deeply investigated, other proteolytic enzymes, like ADAMs with thrombospondin(-like) motifs (ADAMTSs) have been gaining interest due to their roles in modulating cancer cell-ECM interactions and oncogenic signalling pathways. In this review, we will discuss the dysregulation of ADAMTSs in cancer and their roles in regulating cancer development and progression, via ECM remodelling and cell signalling modulation.

Integrated meta-analysis of colorectal cancer public proteomic datasets for biomarker discovery and validation

The cancer biomarker field has been an object of thorough investigation in the last decades. Despite this, colorectal cancer (CRC) heterogeneity makes it challenging to identify and validate effective prognostic biomarkers for patient classification according to outcome and treatment response. Although a massive amount of proteomics data has been deposited in public data repositories, this rich source of information is vastly underused. Here, we attempted to reuse public proteomics datasets with two main objectives: i) to generate hypotheses (detection of biomarkers) for their posterior/downstream validation, and (ii) to validate, using an orthogonal approach, a previously described biomarker panel. Twelve CRC public proteomics datasets (mostly from the PRIDE database) were re-analysed and integrated to create a landscape of protein expression. Samples from both solid and liquid biopsies were included in the reanalysis. Integrating this data with survival annotation data, we have validated in silico a six-gene signature for CRC classification at the protein level, and identified five new blood-detectable biomarkers (CD14, PPIA, MRC2, PRDX1, and TXNDC5) associated with CRC prognosis. The prognostic value of these blood-derived proteins was confirmed using additional public datasets, supporting their potential clinical value. As a conclusion, this proof-of-the-concept study demonstrates the value of re-using public proteomics datasets as the basis to create a useful resource for biomarker discovery and validation. The protein expression data has been made available in the public resource Expression Atlas.

Circulating Proteins as Diagnostic Markers in Gastric Cancer

Gastric cancer (GC) is a highly malignant disease affecting humans worldwide and has a poor prognosis. Most GC cases are detected at advanced stages due to the cancer lacking early detectable symptoms. Therefore, there is great interest in improving early diagnosis by implementing targeted prevention strategies. Markers are necessary for early detection and to guide clinicians to the best personalized treatment. The current semi-invasive endoscopic methods to detect GC are invasive, costly, and time-consuming. Recent advances in proteomics technologies have enabled the screening of many samples and the detection of novel biomarkers and disease-related signature signaling networks. These biomarkers include circulating proteins from different fluids (e.g., plasma, serum, urine, and saliva) and extracellular vesicles. We review relevant published studies on circulating protein biomarkers in GC and detail their application as potential biomarkers for GC diagnosis. Identifying highly sensitive and highly specific diagnostic markers for GC may improve patient survival rates and contribute to advancing precision/personalized medicine.

PPARδ dysregulation of CCL20/CCR6 axis promotes gastric adenocarcinoma carcinogenesis by remodeling gastric tumor microenvironment

BACKGROUND

Peroxisome proliferator-activated receptor delta (PPARδ) promotes inflammation and carcinogenesis in many organs, but the underlying mechanisms remains elusive. In stomachs, PPARδ significantly increases chemokine Ccl20 expression in gastric epithelial cells while inducing gastric adenocarcinoma (GAC). CCR6 is the sole receptor of CCL20. Here, we examine the role of PPARδ-mediated Ccl20/Ccr6 signaling in GAC carcinogenesis and investigate the underlying mechanisms.

METHODS

The effects of PPARδ inhibition by its specific antagonist GSK3787 on GAC were examined in the mice with villin-promoter-driven PPARδ overexpression (PpardTG). RNAscope Duplex Assays were used to measure Ccl20 and Ccr6 levels in stomachs and spleens. Subsets of stomach-infiltrating immune cells were measured via flow cytometry or immunostaining in PpardTG mice fed GSK3787 or control diet. A panel of 13 optimized proinflammatory chemokines in mouse sera were quantified by an enzyme-linked immunosorbent assay.

RESULTS

GSK3787 significantly suppressed GAC carcinogenesis in PpardTG mice. PPARδ increased Ccl20 level to chemoattract Ccr6+ immunosuppressive cells, including tumor-associated macrophages, myeloid-derived suppressor cells and T regulatory cells, but decreased CD8+ T cells in gastric tissues. GSK3787 suppressed PPARδ-induced gastric immunosuppression by inhibiting Ccl20/Ccr6 axis. Furthermore, Ccl20 protein levels increased in sera of PpardTG mice starting at the age preceding gastric tumor development and further increased with GAC progression as the mice aged. GSK3787 decreased the PPARδ-upregulated Ccl20 levels in sera of the mice.

CONCLUSIONS

PPARδ dysregulation of Ccl20/Ccr6 axis promotes GAC carcinogenesis by remodeling gastric tumor microenvironment. CCL20 might be a potential biomarker for the early detection and progression of GAC.

A targeted antibody-based array reveals a serum protein signature as biomarker for adolescent idiopathic scoliosis patients

BACKGROUND

Evident adolescent idiopathic scoliosis (AIS) incurs high treatment costs, low quality of life, and many complications. Early screening of AIS is essential to avoid progressing to an evident stage. However, there is no valid serum biomarker for AIS for early screening.

METHODS

Antibody-based array is a large-scale study of proteins, which is expected to reveal a serum protein signature as biomarker for AIS. There are two segments of the research, including biomarkers screening and validation. In the biomarkers screening group, a total of 16 volunteers participated in this study, and we carried out differentially expressed proteins screening via protein array assay between No-AIS group and the AIS group, through which GeneSet enrichment analysis was performed. In the validation group with a total of 62 volunteers, the differentially expressed proteins from screening group were verified by Enzyme-Linked immunosorbent assay (ELISA), and then multiple regression analysis.

RESULTS

In our study, there were twenty-nine differentially expressed proteins in AIS, through Protein array assay and GeneSet enrichment analysis in the biomarkers screening group. Then the expression of FAP, CD23 and B2M decreased as the degree of AIS increased via ELISA in validation group (FAP, p < 0.0001; CD23, p = 0.0002; B2M, p < 0.0001). Further, the results of multiple regression analysis showed that FAP, CD23 are linked to Cobb angle, whereas B2M were excluded because of multicollinearity.

CONCLUSIONS

Altogether, we found that serum protein FAP and CD23 are intimately related to AIS, suggesting FAP and CD23 are expected to serve as the serum biomarkers, which significantly facilitate frequent longitudinal monitoring as to keep track of disease progression and tailor treatment accordingly.

Data-driven decision-making for precision diagnosis of digestive diseases

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.

Exploring molecular markers and drug candidates for colorectal cancer through comprehensive bioinformatics analysis

Colorectal cancer (CRC) often has a poor prognosis and identifying useful and novel agents for treating CRC is urgently required. This study aimed to examine molecular markers associated with CRC prognosis and to identify potential drug candidates. The differentially expressed genes (DEGs) of CRC in TCGA were identified. The genes associated with CRC, summarized from NCBI-gene, OMIM, and the DEGs, were used to construct a co-expression network by WGCNA. Moreover, the co-expression genes from modules of interest were used to carry out functional enrichment. A total of 2742 DEGs, including 1674 upregulated and 1068 downregulated genes, were identified. Thirteen co-expression modules were constructed with WGCNA. Brown and blue co-expression modules with significant differences in disease phenotype were found. Functional enrichment analysis showed that genes in the brown module were mainly related to cell cycle, cell proliferation, DNA replication, and RNA transport. The genes in the blue module were mainly associated with fatty acid degradation, sulfur metabolism, PPAR signaling pathway and bile secretion. In addition, both the genes in brown and blue were associated with tumor staging. Some prognostic markers and candidate small molecules drugs for CRC treatment were identified. In conclusion, we revealed molecular biomarker profiles in CRC by systematic bioinformatics analysis, constructed regulatory networks of mRNA, ncRNA and transcriptional regulators (TFs), and identified potential drugs targeting hub proteins and TFs.

Cerebrospinal Fluid Protein Biomarker Discovery in CLN3

Syndromic CLN3-Batten is a fatal, pediatric, neurodegenerative disease caused by variants in CLN3, which encodes the endolysosomal transmembrane CLN3 protein. No approved treatment for CLN3 is currently available. The protracted and asynchronous disease presentation complicates the evaluation of potential therapies using clinical disease progression parameters. Biomarkers as surrogates to measure the progression and effect of potential therapeutics are needed. We performed proteomic discovery studies using cerebrospinal fluid (CSF) samples from 28 CLN3-affected and 32 age-similar non-CLN3 individuals. Proximal extension assay (PEA) of 1467 proteins and untargeted data-dependent mass spectrometry [MS; MassIVE FTP server (ftp://MSV000090147@massive.ucsd.edu)] were used to generate orthogonal lists of protein marker candidates. At an adjusted p-value of <0.1 and threshold CLN3/non-CLN3 fold-change ratio of 1.5, PEA identified 54 and MS identified 233 candidate biomarkers. Some of these (NEFL, CHIT1) have been previously linked with other neurologic conditions. Others (CLPS, FAM217B, QRICH2, KRT16, ZNF333) appear to be novel. Both methods identified 25 candidate biomarkers, including CHIT1, NELL1, and ISLR2 which had absolute fold-change ratios >2. NELL1 and ISLR2 regulate axonal development in neurons and are intriguing new candidates for further investigation in CLN3. In addition to identifying candidate proteins for CLN3 research, this study provides a comparison of two large-scale proteomic discovery methods in CSF.

The diagnostic value of serum insulin-like growth factor binding protein 7 in gastric cancer

Backgrounds

Early detection might help in reducing the burden and promoting the survival rate of gastric cancers. Herein, we tried to explore the diagnostic value of insulin-like growth factor binding protein 7 (IGFBP7) in gastric cancers.

Methods

In this study, we first analyzed the expression levels and prognostic value of IGFBP7 mRNA in gastric cancers from The Cancer Genome Atlas (TCGA) database. Then, we recruited 169 gastric cancer patients and 100 normal controls as training cohort, and 55 gastric cancer patients and 55 normal controls as independent validation cohort. Enzyme-linked immunosorbent assay was applied to test the serum levels of IGFBP7. The receiver operating characteristic curve (ROC) and the area under the curve (AUC) were applied to evaluation the diagnostic value.

Results

TCGA showed that IGFBP7 mRNA was dysregulated and associated with prognosis in gastric cancer patients. Then, we examined the expression of serum IGFBP7 and found that serum IGFBP7 expressed lower in gastric cancer patients than normal controls both in training and independent validation cohorts (p < 0.0001). In training cohort, with the cutoff value of 1.515 ng/ml, the AUC for distinguishing gastric cancer patients was 0.774 (95% CI [0.713-0.836]) with sensitivity of 36.7% (95% CI [29.5-44.5]) and specificity of 90.0% (95% CI [82.0-94.8]). As for early-stage EJA, the AUC was 0.773 (95% CI [0.701-0.845]) with the sensitivity of 33.3% (95% CI [14.4-58.8]). In independent validation cohort, with the same cutoff value, the AUC reached to 0.758 (95% CI [0.664-0.852]). Similarly, for early-stage gastric cancer diagnosis in the independent validation cohort, the AUC value was 0.778 (95% CI [0.673-0.882]).

Conclusions

This study indicated that serum IGFBP7 might act as a potential early diagnostic marker for gastric cancers.

The Human Gastric Juice: A Promising Source for Gastric Cancer Biomarkers

Gastric cancer (GC) is a major public health problem worldwide, with high mortality rates due to late diagnosis and limited treatment options. Biomarker research is essential to improve the early detection of GC. Technological advances and research methodologies have improved diagnostic tools, identifying several potential biomarkers for GC, including microRNA, DNA methylation markers, and protein-based biomarkers. Although most studies have focused on identifying biomarkers in biofluids, the low specificity of these markers has limited their use in clinical practice. This is because many cancers share similar alterations and biomarkers, so obtaining them from the site of disease origin could yield more specific results. As a result, recent research efforts have shifted towards exploring gastric juice (GJ) as an alternative source for biomarker identification. Since GJ is a waste product during a gastroscopic examination, it could provide a "liquid biopsy" enriched with disease-specific biomarkers generated directly at the damaged site. Furthermore, as it contains secretions from the stomach lining, it could reflect changes associated with the developmental stage of GC. This narrative review describes some potential biomarkers for gastric cancer screening identified in gastric juice.

Multiplex immune profiling reveals the role of serum immune proteomics in predicting response to preoperative chemotherapy of gastric cancer

Responses toward preoperative chemotherapy are heterogeneous in patients with gastric adenocarcinoma. Existing studies in the field focus heavily on the tumor microenvironment (TME), whereas little is known about the relationship between systemic immunity and chemotherapy response. In this study, we collect serum samples from patients with gastric adenocarcinoma before, on, and after preoperative chemotherapy and study their immune proteomics using an antibody-based proteomics panel. We also collect surgically resected tumor samples and incorporate multiple methods to assess their TME. We find that both local and systemic immune features are associated with treatment response. Preoperative chemotherapy induces a sophisticated systemic immune response indicated by dynamic serum immune proteomics. A pretreatment serum protein scoring system is established for response prediction. Together, these findings highlight the fundamental but largely underestimated role of systemic immunity in the treatment of gastric cancer, suggesting a patient stratification strategy based on pretreatment serum immune proteomics.

Are the results from a multiplex proteomic assay and a conventional immunoassay for NT-proBNP and GDF-15 comparable?

BACKGROUND

We aimed to compare absolute plasma concentrations of N-terminal pro-brain natriuretic peptide (NT-proBNP) and growth differentiation factor 15 (GDF-15) obtained by a conventional immunoassay with the corresponding relative concentrations from a proximity extension assay (PEA) and compare the prognostic impact of the protein levels obtained from these assays.

METHODS

We evaluated 437 patients with peripheral arterial disease (PAD) and a population-based cohort of 643 individuals without PAD. Correlations were calculated using Spearman's rank correlation coefficients (rho). The discriminatory accuracy of the protein levels to predict future cardiovascular events was analyzed with Cox regression and presented as time-dependent areas under the receiver-operator-characteristic curves (tdAUCs).

RESULTS

For NT-proBNP, the two assays correlated with rho 0.93 and 0.93 in the respective cohort. The PEA values leveled off at higher values in both cohorts. The corresponding correlations for GDF-15 were 0.91 and 0.89. At 5 years follow-up, the tdAUCs in the patient cohort were similar for NT-proBNP and GDF-15 regardless of assay used (0.65-0.66). The corresponding tdAUCs in the population-based cohort were between 0.72 and 0.77.

CONCLUSION

Except for the highest levels of NT-proBNP, we suggest that PEA data for NT-proBNP and GDF-15 reliably reflects absolute plasma levels and contains similar prognostic information.

Biomarkers of gastric cancer: current advancement

Gastric cancer (GC) is one of the most prevalent malignant types worldwide, especially in East Asia. Due to its frequently advanced stage at diagnosis, the mortality from GC is high and the prognosis is still unsatisfactory. Thus, early detection using effective screening approaches is vital to decrease the morbidity and mortality of GC. Interestingly, biomarkers can be used for diagnosis, prediction of sensitivity to treatment, and prognosis in GC. The potential biomarkers detectable in liquid biopsies such as circulating tumor cells (CTCs), long non-coding RNAs (lncRNAs), cell-free DNA (cfDNA), microRNAs, and exosomes reveal numerous information regarding the early prediction and the outcomes for GC patients. Additionally, using the novel serum biomarkers has opened up new opportunities for diagnosing and monitoring patients with GC. This review mainly summarizes the novel progress and approaches in GC biomarkers, which could be potentially used for early diagnosis and therapy monitoring. Meanwhile, we also discussed the advantages, disadvantages, and future perspectives of GC biomarkers.

ZNF395 facilitates macrophage polarization and impacts the prognosis of glioma

The immune microenvironment of glioma attributes to the initiation and development of glioma; however, the underlying mechanisms of tumor microenvironment formation have not been fully understood. In this study, we revealed that Zinc Finger Protein 395 (ZNF395), a member of the Kruppel C2H2-type zinc-finger protein family and also known as a common transcription factor, was aberrantly overexpressed in glioma and positively associated with the poor clinicopathological features and the prognosis of patients with glioma based on the analyses of TCGA, CGGA and other datasets. Further in vitro experimental data demonstrated that the upregulation of ZNF395 promoted the proliferation of glioma cells. In addition, functional enrichment analysis showed that ZNF395 was involved in immune processes and correlated with macrophage infiltration and polarization. Moreover, C-C Motif Chemokine Ligand 20 (CCL20), one of the ZNF395 co-expressed genes, was validated as the downstream factor under the transcriptional regulation of ZNF395. Importantly, cell co-culture experiments confirmed that ZNF395 upregulated both the intracellular and secreted CCL20 level of glioma cells and induced M2 macrophage polarization which is known to promote the malignant progression of glioma. Taken together, our findings suggested that ZNF395 might play an essential role in glioma development, and inhibition of ZNF395 might be a plausible strategy for glioma therapy.

The Mechanism and Role of ADAMTS Protein Family in Osteoarthritis

Osteoarthritis (OA) is a principal cause of aches and disability worldwide. It is characterized by the inflammation of the bone leading to degeneration and loss of cartilage function. Factors, including diet, age, and obesity, impact and/or lead to osteoarthritis. In the past few years, OA has received considerable scholarly attention owing to its increasing prevalence, resulting in a cumbersome burden. At present, most of the interventions only relieve short-term symptoms, and some treatments and drugs can aggravate the disease in the long run. There is a pressing need to address the safety problems due to osteoarthritis. A disintegrin-like and metalloprotease domain with thrombospondin type 1 repeats (ADAMTS) metalloproteinase is a kind of secretory zinc endopeptidase, comprising 19 kinds of zinc endopeptidases. ADAMTS has been implicated in several human diseases, including OA. For example, aggrecanases, ADAMTS-4 and ADAMTS-5, participate in the cleavage of aggrecan in the extracellular matrix (ECM); ADAMTS-7 and ADAMTS-12 participate in the fission of Cartilage Oligomeric Matrix Protein (COMP) into COMP lyase, and ADAMTS-2, ADAMTS-3, and ADAMTS-14 promote the formation of collagen fibers. In this article, we principally review the role of ADAMTS metalloproteinases in osteoarthritis. From three different dimensions, we explain how ADAMTS participates in all the following aspects of osteoarthritis: ECM, cartilage degeneration, and synovial inflammation. Thus, ADAMTS may be a potential therapeutic target in osteoarthritis, and this article may render a theoretical basis for the study of new therapeutic methods for osteoarthritis.

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.

Multi-omics approaches for biomarker discovery in early ovarian cancer diagnosis

Ovarian cancer (OC) is a heterogeneous disease with the highest mortality rate and the poorest prognosis among gynecological malignancies. Because of the absence of specific early symptoms, most OC patients are often diagnosed at late stages. Thus, improved biomarkers of OC for use in research and clinical practice are urgently needed. The last decade has seen increasingly rapid advances in sequencing and biotechnological methodologies. Consequently, multiple omics technologies, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra, have been widely applied to analyze tissue- and liquid-derived samples from OC patients. The integration of multi-omics data has increased our knowledge of the disease and identified valuable OC biomarkers. In this review, we summarize the recent advances and perspectives in the use of multi-omics technologies in OC research and highlight potential applications of multi-omics for identifying novel biomarkers and improving clinical assessments.

Is Cadmium Toxicity Tissue-Specific? Toxicogenomics Studies Reveal Common and Specific Pathways in Pulmonary, Hepatic, and Neuronal Cell Models

Several harmful modifications in different tissues-organs, leading to relevant diseases (e.g., liver and lung diseases, neurodegeneration) are reported after exposure to cadmium (Cd), a wide environmental contaminant. This arises the question whether any common molecular signatures and/or Cd-induced modifications might represent the building block in initiating or contributing to address the cells towards different pathological conditions. To unravel possible mechanisms of Cd tissue-specificity, we have analyzed transcriptomics data from cell models representative of three major Cd targets: pulmonary (A549), hepatic (HepG2), and neuronal (SH-SY-5Y) cells. Further, we compared common features to identify any non-specific molecular signatures. The functional analysis of dysregulated genes (gene ontology and KEGG) shows GO terms related to metabolic processes significantly enriched only in HepG2 cells. GO terms in common in the three cell models are related to metal ions stress response and detoxification processes. Results from KEGG analysis show that only one specific pathway is dysregulated in a significant way in all cell models: the mineral absorption pathway. Our data clearly indicate how the molecular mimicry of Cd and its ability to cause a general metal ions dyshomeostasis represent the initial common feature leading to different molecular signatures and alterations, possibly responsible for different pathological conditions.

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.

Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer

Background: The management of gastric cancer (GC) still lacks tumor markers with high specificity and sensitivity. The goal of current research is to find effective diagnostic and prognostic markers and to clarify their related mechanisms.

Methods: In this study, we integrated GC DNA methylation data from publicly available datasets obtained from TCGA and GEO databases, and applied random forest and LASSO analysis methods to screen reliable differential methylation sites (DMSs) for GC diagnosis. We constructed a diagnostic model of GC by logistic analysis and conducted verification and clinical correlation analysis. We screened credible prognostic DMSs through univariate Cox and LASSO analyses and verified a prognostic model of GC by multivariate Cox analysis. Independent prognostic and biological function analyses were performed for the prognostic risk score. We performed TP53 correlation analysis, mutation and prognosis analysis on eleven-DNA methylation driver gene (DMG), and constructed a multifactor regulatory network of key genes.

Results: The five-DMS diagnostic model distinguished GC from normal samples, and diagnostic risk value was significantly correlated with grade and tumor location. The prediction accuracy of the eleven-DMS prognostic model was verified in both the training and validation datasets, indicating its certain potential for GC survival prediction. The survival rate of the high-risk group was significantly lower than that of the low-risk group. The prognostic risk score was an independent risk factor for the prognosis of GC, which was significantly correlated with N stage and tumor location, positively correlated with the VIM gene, and negatively correlated with the CDH1 gene. The expression of CHRNB2 decreased significantly in the TP53 mutation group of gastric cancer patients, and there were significant differences in CCDC69, RASSF2, CHRNB2, ARMC9, and RPN1 between the TP53 mutation group and the TP53 non-mutation group of gastric cancer patients. In addition, CEP290, UBXN8, KDM4A, RPN1 had high frequency mutations and the function of eleven-DMG mutation related genes in GC patients is widely enriched in multiple pathways.

Conclusion: Combined, the five-DMS diagnostic and eleven-DMS prognostic GC models are important tools for accurate and individualized treatment. The study provides direction for exploring potential markers of GC.

Prediction of Blood miRNA-mRNA Regulatory Network in Gastric Cancer

Background

The aim of the study was to suggest a high specific and sensitive blood biomarker for early GC diagnosis.

Methods

the expression data of miRNAs and mRNAs were collected from the blood samples of the GC patients based on literature mining. Bioinformatics tools and databases (PANTHER, TargetScan, miRTarBase, miRDB, STRING, and Cytoscape) were used to predict the regulatory relationship. Subsequently, expression level of the selected miRNA was evaluated in the blood samples of gastritis patients to recognize the common miRNA between the GC and gastritis patients.

Results

Analysis of 40 target genes by MCODE (installed in Cytoscape software) indicated 4 hub genes (WWP1, SKP2, KLHL42, and FBXO11) as a significant cluster in the PPI network related to miR-21, with Node Score Cutoff: 0.2, Degree Cutoff: 2 and K-Core: 2. In addition, the miRNA RT-qPCR results showed that, the expression level of miR-21 was significantly higher in gastritis group compared to the healthy group (p< 0.05).

Conclusion

the present study clearly demonstrated the increasing level of blood miR-21 among the gastritis patients infected by H. pylori. Therefore, the altered miRNAs, especially overexpression of onco-miRs, may identify a potential link between miRNAs and pathogenesis of the H. pylori-related complications.

Evaluation of serum insulin-like growth factor 1 and its significance in thyroid cancer: An observational study

Serum insulin-like growth factor 1 (IGF-1) is elevated in different cancers. However, relationships between serum IGF-1 and thyroid cancer (TC) are scarce. The present study aimed to investigate the clinical significance of serum IGF-1 in TC.Serum was collected from 124 TC patients, 50 patients with benign nodules, and 50 healthy controls. Serum IGF-1 levels were measured and compared. Relationships were investigated between IGF-1 and clinical characteristics. A receiver operating characteristic (ROC) curve was plotted to explore the diagnostic value of IGF-1 in TC.Serum IGF-1 levels were significantly higher in TC than that of healthy controls and benign nodules (P = .003; P < .001). Serum IGF-1 levels were higher in TC patients with advanced stage than early stage (P = .029). Higher serum IGF-1 levels were found in patients with lymph node metastasis present and (tumor size >1 cm) than that of patients without lymph node metastasis (P = .018) and (tumor size ≤1 cm) (P = .031). Serum IGF-1 levels were higher in patients with a solitary nodule than multinodular nodules (P = .043). The serum IGF-1 cutoff value for a TC diagnosis was 216 ng/mL with a sensitivity of 53.2%, a specificity of 74.0%, a positive predictive value (PPV) of 83.5%, and an area under the curve was of 0.71.Serum IGF-1 was significantly correlated with tumor stage, size, and lymph node metastasis. Serum IGF-1 shows great potential as a laboratory marker for TC.

Large-scale plasma proteomic profiling identifies a high-performance biomarker panel for Alzheimer's disease screening and staging

Introduction

Blood proteins are emerging as candidate biomarkers for Alzheimer's disease (AD). We systematically profiled the plasma proteome to identify novel AD blood biomarkers and develop a high‐performance, blood‐based test for AD.

Methods

We quantified 1160 plasma proteins in a Hong Kong Chinese cohort by high‐throughput proximity extension assay and validated the results in an independent cohort. In subgroup analyses, plasma biomarkers for amyloid, tau, phosphorylated tau, and neurodegeneration were used as endophenotypes of AD.

Results

We identified 429 proteins that were dysregulated in AD plasma. We selected 19 “hub proteins” representative of the AD plasma protein profile, which formed the basis of a scoring system that accurately classified clinical AD (area under the curve  = 0.9690–0.9816) and associated endophenotypes. Moreover, specific hub proteins exhibit disease stage‐dependent dysregulation, which can delineate AD stages.

Discussion

This study comprehensively profiled the AD plasma proteome and serves as a foundation for a high‐performance, blood‐based test for clinical AD screening and staging.

Use of a Recombinant Biomarker Protein DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins

Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma samples, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e., <10 ng/mL) proteins. To mitigate justified FDR concerns, and to replicate a more typical library creation approach, the DDA rPSL library was merged with a human plasma DDA library and SWATH identification repeated using such a merged library. The majority (33/36) of the low abundance plasma proteins added from the rPSL were still able to be identified using such a merged library when high-stringency HPP Guidelines v3.0 protein inference criteria were applied to our data set. The MS data set has been deposited to ProteomeXchange Consortium via the PRIDE partner repository (PXD022361).

Interleukin (IL)-7 Signaling in the Tumor Microenvironment

Interleukin (IL)-7 plays an important immunoregulatory role in different types of cells. Therefore, it attracts researcher's attention, but despite the fact, many aspects of its modulatory action, as well as other functionalities, are still poorly understood. The review summarizes current knowledge on the interleukin-7 and its signaling cascade in context of cancer development. Moreover, it provides a cancer-type focused description of the involvement of IL-7 in solid tumors, as well as hematological malignancies.The interleukin has been discovered as a growth factor crucial for the early lymphocyte development and supporting the growth of malignant cells in certain leukemias and lymphomas. Therefore, its targeting has been explored as a treatment modality in hematological malignancies, while the unique ability to expand lymphocyte populations selectively and without hyperinflammation has been used in experimental immunotherapies in patients with lymphopenia. Ever since the early research demonstrated a reduced growth of solid tumors in the presence of IL-7, the interleukin application in boosting up the anticancer immunity has been investigated. However, a growing body of evidence indicative of IL-7 upregulation in carcinomas, facilitating tumor growth and metastasis and aiding drug-resistance, is accumulating. It therefore becomes increasingly apparent that the response to the IL-7 stimulus strongly depends on cell type, their developmental stage, and microenvironmental context. The interleukin exerts its regulatory action mainly through phosphorylation events in JAK/STAT and PI3K/Akt pathways, while the significance of MAPK pathway seems to be limited to solid tumors. Given the unwavering interest in IL-7 application in immunotherapy, a better understanding of interleukin role, source in tumor microenvironment, and signaling pathways, as well as the identification of cells that are likely to respond should be a research priority.

miR-7641 depletion suppresses proliferation of gastric cancer cells by targeting ARID1A

Gastric cancer (GC) is lethal and there is an urgent need for improved understanding of this disease. Recent studies have reported that microRNAs (miRNAs) play increasingly important roles in the regulation of GC. In this study, we explored the target genes and effects of miR-7641 in GC. Our data showed that high miR-7641 expression was associated with low expression of ARID1A in GC tissue. miR-7641 expression promoted GC cell proliferation and colony formation. Luciferase reporter assay results confirmed that ARID1A was a target gene of miR-7641. Furthermore, downregulation of ARID1A expression caused a significant increase in GC cell proliferation. In vivo depletion of miR-7641 reduced tumor volume and weight and increased ARID1A and Ki67 expression as well as a decreased terminal-deoxynucleotidyl transferase-mediated nick end labeling in mouse tumor tissues. Conversely, ARID1A silencing reversed the suppressive effects of miR-7641 inhibitors on GC cells. Overall, these findings indicate that miR-7641 is a promising novel prognostic biomarker of GC and may represent a novel target for clinical management of GC.

The CDCP1 Signaling Hub: A Target for Cancer Detection and Therapeutic Intervention

CUB-domain containing protein 1 (CDCP1) is a type I transmembrane glycoprotein that is upregulated in malignancies of the breast, lung, colorectum, ovary, kidney, liver, pancreas, and hematopoietic system. Here, we discuss CDCP1 as an important hub for oncogenic signaling and its key roles in malignant transformation and summarize approaches focused on exploiting it for cancer diagnosis and therapy. Elevated levels of CDCP1 are associated with progressive disease and markedly poorer survival. Predominantly located on the cell surface, CDCP1 lies at the nexus of key tumorigenic and metastatic signaling cascades, including the SRC/PKCδ, PI3K/AKT, WNT, and RAS/ERK axes, the oxidative pentose phosphate pathway, and fatty acid oxidation, making important functional contributions to cancer cell survival and growth, metastasis, and treatment resistance. These findings have stimulated the development of agents that target CDCP1 for detection and treatment of a range of cancers, and results from preclinical models suggest that these approaches could be efficacious and have manageable toxicity profiles.

Immune-related protein signature in serum stratify relapsed mantle cell lymphoma patients based on risk

Background

Response to modern treatment strategies, which combine cytotoxic compounds with immune stimulatory agents and targeted treatment is highly variable among MCL patients. Thus, providing prognostic and predictive markers for risk adapted therapy is warranted and molecular information that can help in patient stratification is a necessity. In relapsed MCL, biopsies are rarely available and molecular information from tumor tissue is often lacking. Today, the main tool to access risk is the MCL international prognostic index (MIPI), which does not include detailed biological information of relevance for different treatment options. To enable continuous monitoring of patients, non-invasive companion diagnostic tools are needed which can further reduce cost and patient distress and enable efficient measurements of biological markers.

Methods

We have assessed if serum-based protein profiling can identify immune related proteins that stratify relapsed MCL patients based on risk. Overall, 371 scFv targeting 158 proteins were assessed using an antibody microarray platform. We profiled patients (n = 44) who had been treated within the MCL6-Philemon trial combining targeted and immune-modulatory treatment.

Results

The downstream processing led to the identification of the relapsed immune signature (RIS) consisting of 11 proteins with potential to stratify patients with long and short overall survival (OS). Moreover, in this population, MIPI alone failed to separate high, intermediate and low risk patients, but a combined index based on MIPI together with RIS, MIPI_ris, showed improved performance and significantly stratified all three risk groups based on OS.

Conclusions

Our results show that addition of biological parameters to previous prognostic indices improves patient stratification among patients treated with BTK inhibitor triplet combination, particularly, in the identification of an extreme high risk group.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07678-4.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

Study on the Diagnosis of Gastric Cancer by Magnetic Beads Extraction and Mass Spectrometry

Objective. This study constructed a model for the early diagnosis of gastric cancer by comparing the serum peptides profiles of patients with advanced gastric cancer and healthy people. And that model may be the potential to be applied for the efficacy evaluation and recurrence monitoring in gastric cancer. Methods. Serums of 30 healthy people and 30 advanced gastric cancer patients were matched by age and gender were collected. The serum peptide spectrum was obtained by MB-WCX concentration and MALDI-TOF MS analysis. Based on the analysis of the efficiency of differential peptides in the diagnosis of gastric cancer, we first established a model for the diagnosis of gastric cancer based on differential peptides and then carried out external verification. The diagnostic reliability of this model was further tested by compared with carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9). Results. In this present study, we found the expression of two peptide peaks with a molecular weight of 2863 Da and 2953 Da were significantly increased in gastric cancer serum, while the expression of two peptide peaks with a molecular weight of 1945 Da and 2082 Da were significantly decreased. Depending on the characteristics of peptide expression, we constructed a diagnostic model, we compared the sensitivity and specificity of the model established by 2953 Da/1945 Da, and found this model is significantly higher than CEA and CA19-9. Conclusion. There were some differences in serum peptides profiles between patients with advanced gastric cancer and healthy people. The serum peptide diagnostic models based on 2953 Da and 1945 Da have high diagnostic efficiency for advanced gastric cancer. Our result indicated that this model was well worth further validation for clinical diagnosis.

Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis

Osteosarcoma (OS) is a malignant disease that develops rapidly and is associated with poor prognosis. Immunotherapy may provide new insights into clinical treatment strategies for OS. The purpose of this study was to identify immune-related genes that could predict OS prognosis. The gene expression profiles and clinical data of 84 OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. According to non-negative matrix factorization, two molecular subtypes of immune-related genes, C1 and C2, were acquired, and 597 differentially expressed genes between C1 and C2 were identified. Univariate Cox analysis was performed to get 14 genes associated with survival, and 4 genes (GJA5, APBB1IP, NPC2, and FKBP11) obtained through least absolute shrinkage and selection operator (LASSO)-Cox regression were used to construct a 4-gene signature as a prognostic risk model. The results showed that high FKBP11 expression was correlated with high risk (a risk factor), and that high GJA5, APBB1IP, or NPC2 expression was associated with low risk (protective factors). The testing cohort and entire TARGET cohort were used for internal verification, and the independent GSE21257 cohort was used for external validation. The study suggested that the model we constructed was reliable and performed well in predicting OS risk. The functional enrichment of the signature was studied through gene set enrichment analysis, and it was found that the risk score was related to the immune pathway. In summary, our comprehensive study found that the 4-gene signature could be used to predict OS prognosis, and new biomarkers of great significance for understanding the therapeutic targets of OS were identified.

Mining cancer biology through bioinformatic analysis of proteomic data

Introduction: Discovery proteomics for cancer research generates complex datasets of diagnostic, prognostic, and therapeutic significance in human cancer. With the advent of high-resolution mass spectrometers, able to identify thousands of proteins in complex biological samples, only the application of bioinformatics can lead to the interpretation of data which can be relevant for cancer research. Areas covered: Here, we give an overview of the current bioinformatic tools used in cancer proteomics. Moreover, we describe their applications in cancer proteomics studies of cell lines, serum, and tissues, highlighting recent results and critically evaluating their outcomes. Expert opinion: The use of bioinformatic tools is a fundamental step in order to manage the large amount of proteins (from hundreds to thousands) that can be identified and quantified in a cancer biological samples by proteomics. To handle this challenge and obtain useful data for translational medicine, it is important the combined use of different bioinformatic tools. Moreover, a particular attention to the global experimental design, and the integration of multidisciplinary skills are essential for best setting of tool parameters and best interpretation of bioinformatics output.