Potential early clinical stage colorectal cancer diagnosis using a proteomics blood test panel

Recombinant Protein Spectral Library (rPSL) DIA-MS method improves identification and quantification of low-abundance cancer-associated and kynurenine pathway proteins

Data-independent acquisition mass spectrometry (DIA-MS) is a powerful tool for quantitative proteomics, but a well-constructed reference spectral library is crucial to optimize DIA analysis, particularly for low-abundance proteins. In this study, we evaluate the efficacy of a recombinant protein spectral library (rPSL), generated from tryptic digestion of 42 human recombinant proteins, in enhancing the detection and quantification of lower-abundance cancer-associated proteins. Additionally, we generated a combined sample-specific biological-rPSL by integrating the rPSL with a spectral library derived from pooled biological samples. We compared the performance of these libraries for DIA data extraction with standard methods, including sample-specific biological spectral library and library-free DIA methods. Our specific focus was on quantifying cancer-associated proteins, including key enzymes involved in kynurenine pathway, across patient-derived tissues and cell lines. Both rPSL and biological-rPSL-DIA approaches provided significantly improved coverage of lower-abundance proteins, enhancing sensitivity and more consistent protein quantification across matched tumour and adjacent noncancerous tissues from breast and colorectal cancer patients and in cancer cell lines. Overall, our study demonstrates that rPSL and biological-rPSL coupled with DIA-MS workflows, can address the limitations of both biological library-based and library-free DIA methods, offering a robust approach for quantifying low-abundance cancer-associated proteins in complex biological samples.

Comparative Analysis of HMC3 and C20 Microglial Cell Lines Reveals Differential Myeloid Characteristics and Responses to Immune Stimuli

Microglia are the primary resident immune cells of the central nervous system (CNS) that respond to injury and infections. Being critical to CNS homeostasis, microglia also have been shown to contribute to neurodegenerative diseases and brain cancer. Hence, microglia are regarded as a potential therapeutic target in CNS diseases, resulting in an increased demand for reliable in vitro models. Two human microglia cell lines (HMC3 and C20) are being used in multiple in vitro studies, however, the knowledge of their biological and immunological characteristics remains limited. Our aim was to identify and compare the biological changes in these immortalised immune cells under normal physiological and immunologically challenged conditions. Using high-resolution quantitative mass spectrometry, we have examined in-depth proteomic profiles of non-stimulated and LPS or IFN-γ challenged HMC3 and C20 cells. Our findings reveal that HMC3 cells responded to both treatments through upregulation of immune, metabolic, and antiviral pathways, while C20 cells showed a response associated with mitochondrial and immune activities. Additionally, the secretome analysis demonstrated that both cell lines release IL-6 in response to LPS, while IFN-γ treatment resulted in altered kynurenine pathway activity, highlighting distinct immune and metabolic adaptations.

Relationships between MMP-2, MMP-9, and ADAMDEC1 serum and tissue levels in patients with colorectal cancer

PURPOSE

Colorectal cancer (CRC) is the most common malignancy of the gastrointestinal system globally. Identifying specific gene expression patterns indicative of early-stage CRC could enable early diagnosis and rapid treatment initiation. Matrix metalloproteinases (MMPs) play crucial roles in extracellular matrix degradation and tissue remodeling. Among them, MMP-2 and MMP-9 have been found to be upregulated in various cancers, including CRC, and are associated with tumor invasion, metastasis, and angiogenesis. In contrast, a disintegrin and metalloproteinase like decysin 1 (ADAMDEC1) is a relatively newly discovered gene with demonstrated involvement in immune response and inflammation. This study investigated serum levels of MMP-2 and MMP-9, along with tissue expression of MMP-2, MMP-9, and ADAMDEC1, and explored potential associations with pathological and clinical factors in patients with CRC.

METHODS

This study included 100 patients with CRC and 100 control participants. Tissue and blood samples were collected. Serum MMP-2 and MMP-9 levels were analyzed using the enzyme-linked immunosorbent assay. Quantitative real-time polymerase chain reaction was employed to assess the expression levels of MMP-2, MMP-9, and ADAMDEC1 in CRC tissue samples compared to adjacent control tissue.

RESULTS

The expression levels of MMP-2, MMP-9, and ADAMDEC1 were significantly upregulated in CRC relative to adjacent control tissues. Analysis of clinicopathological features revealed statistically significant differences in the expression levels of MMP-2, MMP-9, and ADAMDEC1 between patients with CRC with and without lymphovascular invasion (P<0.001). Based on receiver operating characteristic curve analysis, these genes represent promising candidate diagnostic biomarkers for CRC.

CONCLUSION

MMP-2, MMP-9, and ADAMDEC1 levels may serve as potential diagnostic biomarkers for CRC.

A transcriptome-wide association study integrating multi-omics bioinformatics and Mendelian randomization reveals the prognostic value of ADAMDEC1 in colon cancer

An abundant amount of colon cancers is diagnosed every year, accounting for 9% of malignant tumors. Even with the progress of relevant research, the 5-year survival rate for colon cancer is still less than 60%, indicating that improving the prognosis of colon cancer is still a challenge that needs to be overcome. This study employed the algorithm "scissor" to integrate the single-cell sequencing data and bulk transcriptome data with prognosis information to predict prognosis-associated cells (PAC). Summary-data-based Mendelian randomization (SMR) analysis was conducted using expression quantitative trait loci data and GWAS data to identify genes having causal associations with prognosis phenotype in colon cancer patients and five traditional two-sample Mendelian randomization methods were utilized to confirm the results. Finally, our findings were validated based on two independent external validation datasets, GSE17536 and GSE39582. The real-world tissue dataset with corresponding immunohistochemical (IHC) experiments was utilized to confirm our findings. We determined that the majority of PACs were fibroblasts. On top of that, this study identified ADAMDEC1 as a gene that has a significant causal association with overall survival. ADAMDEC1, highly expressed in highly differentiated fibroblasts, was ascertained its high expression was linked with a better prognosis of patients with colon cancer by the related bulk transcriptome analysis. Our dataset presented that higher IHC scores were associated with a better prognosis for colon cancer, further validating our results. This study has identified ADAMDEC1 as a prognostic protective factor for patients with colon cancer, providing clues for clinical trials and drug experimental target research.

A Proteomic Examination of Plasma Extracellular Vesicles Across Colorectal Cancer Stages Uncovers Biological Insights That Potentially Improve Prognosis

BACKGROUND

Recent advancements in understanding plasma extracellular vesicles (EVs) and their role in disease biology have provided additional unique insights into the study of Colorectal Cancer (CRC).

METHODS

This study aimed to gain biological insights into disease progression from plasma-derived extracellular vesicle proteomic profiles of 80 patients (20 from each CRC stage I-IV) against 20 healthy age- and sex-matched controls using a high-resolution SWATH-MS proteomics with a reproducible centrifugation method to isolate plasma EVs.

RESULTS

We applied the High-Stringency Human Proteome Project (HPP) guidelines for SWATH-MS analysis, which refined our initial EV protein identification from 1362 proteins (10,993 peptides) to a more reliable and confident subset of 853 proteins (6231 peptides). In early-stage CRC, we identified 11 plasma EV proteins with differential expression between patients and healthy controls (three up-regulated and eight down-regulated), many of which are involved in key cancer hallmarks. Additionally, within the same cohort, we analysed EV proteins associated with tumour recurrence to identify potential prognostic indicators for CRC. A subset of up-regulated proteins associated with extracellular vesicle formation (GDI1, NSF, and TMED9) and the down-regulation of TSG101 suggest that micro-metastasis may have occurred earlier than previously anticipated.

DISCUSSION

By employing stringent proteomic analysis and a robust SWATH-MS approach, we identified dysregulated EV proteins that potentially indicate early-stage CRC and predict recurrence risk, including proteins involved in metabolism, cytoskeletal remodelling, and immune response. While our findings underline discrepancies with other studies due to differing isolation and stringency parameters, they provide valuable insights into the complexity of the EV proteome, emphasising the need for standardised protocols and larger, well-controlled studies to validate potential biomarkers.

Breast cancer metastasis progression is associated with elevated activity of kynurenine monooxygenase and kynureninase

INTRODUCTION

Metastasis remains the major cause of death in breast cancer (BrCa) and lacks specific treatment strategies. The kynurenine pathway (KP) has been suggested as a key mechanism facilitating progression of BrCa. While KP activity has been explored in primary BrCa, its role in metastasis remains unclear. To better understand this, we examined changes in the KP of BrCa with no metastasis compared to BCa that produced local or distant metastases. Given that the cancer cell secretome plays a role in metastasis, we also investigated the relationship between changes in KP activity and serum proteins of patients with local or distant metastases.

METHODS

To investigate changes in the KP in BrCa, with and without metastasis, we quantified KP metabolites in blood sera collected from patients with stage 1 BrCa (n = 34), BrCa with local metastases (n = 46), BrCa with distant metastases (n = 20) and healthy controls (n = 39). The serum protein profile of the BrCa patients with local or distant metastasis was determined before correlation analyses were carried out to examine the relationship between changes in the KP and cancer serum proteins using SPSS.

RESULTS

We found that the KP was elevated in BrCa patients with local and distant metastasis compared to healthy controls and stage 1 BrCa patients. The activity of kynurenine monooxygenase (KMO) and kynureninase (KYNU) A was positively associated with disease stage and was higher compared to healthy controls. Proteome analysis in patients with local or distant metastasis revealed the dysregulation of 14 proteins, 9 of which were up-regulated and 5 down-regulated at the distant metastasis stage. Importantly, three of these proteins have not been previously linked to BrCa metastasis. In the correlation studies between the KP profile, cancer serum proteins and metastasis status, KYNU A had the greatest number of significant associations with cancer serum protein, followed by KMO.

CONCLUSION

Our findings reveal that the KP was regulated differently at various stages of BrCa and was more dysregulated in patients with local or distant metastasis. These KP activity changes showed a significant association with cancer serum proteins in BrCa patients with local or distant metastasis, highlighting the potential role of KP in BrCa metastasis.

Plasma proteomic and polygenic profiling improve risk stratification and personalized screening for colorectal cancer

This study aims to identify colorectal cancer (CRC)-related proteomic profiles and develop a prediction model for CRC onset by integrating proteomic profiles with genetic and non-genetic factors (QCancer-15) to improve the risk stratification and estimate of personalized initial screening age. Here, using a two-stage strategy, we prioritize 15 protein biomarkers as predictors to construct a protein risk score (ProS). The risk prediction model integrating proteomic profiles with polygenic risk score (PRS) and QCancer-15 risk score (QCancer-S) shows improved performance (C-statistic: 0.79 vs. 0.71, P = 4.94E-03 in training cohort; 0.75 vs 0.69, P = 5.49E-04 in validation cohort) and net benefit than QCancer-S alone. The combined model markedly stratifies the risk of CRC onset. Participants with high ProS, PRS, or combined risk score are proposed to start screening at age 46, 41, or before 40 years old. In this work, the integration of blood proteomics with PRS and QCancer-15 demonstrates improved performance for risk stratification and clinical implication for the derivation of risk-adapted starting ages of CRC screening, which may contribute to the decision-making process for CRC screening.

Signaling pathways involved in colorectal cancer: pathogenesis and targeted therapy

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Its complexity is influenced by various signal transduction networks that govern cellular proliferation, survival, differentiation, and apoptosis. The pathogenesis of CRC is a testament to the dysregulation of these signaling cascades, which culminates in the malignant transformation of colonic epithelium. This review aims to dissect the foundational signaling mechanisms implicated in CRC, to elucidate the generalized principles underpinning neoplastic evolution and progression. We discuss the molecular hallmarks of CRC, including the genomic, epigenomic and microbial features of CRC to highlight the role of signal transduction in the orchestration of the tumorigenic process. Concurrently, we review the advent of targeted and immune therapies in CRC, assessing their impact on the current clinical landscape. The development of these therapies has been informed by a deepening understanding of oncogenic signaling, leading to the identification of key nodes within these networks that can be exploited pharmacologically. Furthermore, we explore the potential of integrating AI to enhance the precision of therapeutic targeting and patient stratification, emphasizing their role in personalized medicine. In summary, our review captures the dynamic interplay between aberrant signaling in CRC pathogenesis and the concerted efforts to counteract these changes through targeted therapeutic strategies, ultimately aiming to pave the way for improved prognosis and personalized treatment modalities in colorectal cancer.

Dysregulation of the Kynurenine Pathway, Cytokine Expression Pattern, and Proteomics Profile Link to Symptomology in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Dysregulation of the kynurenine pathway (KP) is believed to play a significant role in neurodegenerative and cognitive disorders. While some evidence links the KP to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), further studies are needed to clarify the overall picture of how inflammation-driven KP disturbances may contribute to symptomology in ME/CFS. Here, we report that plasma levels of most bioactive KP metabolites differed significantly between ME/CFS patients and healthy controls in a manner consistent with their known contribution to symptomology in other neurological disorders. Importantly, we found that enhanced production of the first KP metabolite, kynurenine (KYN), correlated with symptom severity, highlighting the relationship between inflammation, KP dysregulation, and ME/CFS symptomology. Other significant changes in the KP included lower levels of the downstream KP metabolites 3-HK, 3-HAA, QUIN, and PIC that could negatively impact cellular energetics. We also rationalized KP dysregulation to changes in the expression of inflammatory cytokines and, for the first time, assessed levels of the iron (Fe)-regulating hormone hepcidin that is also inflammation-responsive. Levels of hepcidin in ME/CFS decreased nearly by half, which might reflect systemic low Fe levels or possibly ongoing hypoxia. We next performed a proteomics screen to survey for other significant differences in protein expression in ME/CFS. Interestingly, out of the seven most significantly modulated proteins in ME/CFS patient plasma, 5 proteins have roles in maintaining gut health, which considering the new appreciation of how gut microbiome and health modulates systemic KP could highlight a new explanation of symptomology in ME/CFS patients and potential new prognostic biomarker/s and/or treatment avenues.

Mass Spectrometry Proteomics Characterization of Plasma Biomarkers for Colorectal Cancer Associated With Inflammation

Background

Colorectal cancer (CRC) prognosis is determined by the disease stage with low survival rates for advanced stages. Current CRC screening programs are mainly using colonoscopy, limited by its invasiveness and high cost. Therefore, non-invasive, cost-effective, and accurate alternatives are urgently needed.

Objective and design

This retrospective multi-center plasma proteomics study was performed to identify potential blood-based biomarkers in 36 CRC patients and 26 healthy volunteers by high-resolution mass spectrometry proteomics followed by the validation in an independent CRC cohort (60 CRC patients and 44 healthy subjects) of identified selected biomarkers.

Results

Among the 322 identified plasma proteins, 37 were changed between CRC patients and healthy volunteers and were associated with the complement cascade, cholesterol metabolism, and SERPIN family members. Increased levels in CRC patients of the complement proteins C1QB, C4B, and C5 as well as pro-inflammatory proteins, lipopolysaccharide-binding protein (LBP) and serum amyloid A4, constitutive (SAA4) were revealed for first time. Importantly, increased level of C5 was verified in an independent validation CRC cohort. Increased C4B and C8A levels were correlated with cancer-associated inflammation and CRC progression, while cancer-associated inflammation was linked to the acute-phase reactant leucine-rich alpha-2-glycoprotein 1 (LRG1) and ceruloplasmin. Moreover, a 4-protein signature including C4B, C8A, apolipoprotein C2 (APO) C2, and immunoglobulin heavy constant gamma 2 was changed between early and late CRC stages.

Conclusion

Our results suggest that C5 could be a potential biomarker for CRC diagnosis. Further validation studies will aid the application of these new potential biomarkers to improve CRC diagnosis and patient care.

Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence

Colorectal cancer (CRC) is a leading cause of cancer-related deaths. Recent advancements in genomic technologies and analytical approaches have revolutionized CRC research, enabling precision medicine. This review highlights the integration of multi-omics, spatial omics, and artificial intelligence (AI) in advancing precision medicine for CRC. Multi-omics approaches have uncovered molecular mechanisms driving CRC progression, while spatial omics have provided insights into the spatial heterogeneity of gene expression in CRC tissues. AI techniques have been utilized to analyze complex datasets, identify new treatment targets, and enhance diagnosis and prognosis. Despite the tumor's heterogeneity and genetic and epigenetic complexity, the fusion of multi-omics, spatial omics, and AI shows the potential to overcome these challenges and advance precision medicine in CRC. The future lies in integrating these technologies to provide deeper insights and enable personalized therapies for CRC patients.

Technical report: The clinically useful selection of proteins protocol: An approach to identify clinically useful proteins for validation

Clinical proteomics studies aiming to develop markers of clinical outcome or disease typically involve distinct discovery and validation stages, neither of which focus on the clinical applicability of the candidate markers studied. Our clinically useful selection of proteins (CUSP) protocol proposes a rational approach, with statistical and non-statistical components, to identify proteins for the validation phase of studies that could be most effective markers of disease or clinical outcome. Additionally, this protocol considers commercially available analysis methods for each selected protein to ensure that use of this prospective marker is easily translated into clinical practice. SIGNIFICANCE: When developing proteomic markers of clinical outcomes, there is currently no consideration at the validation stage of how to implement such markers into a clinical setting. This has been identified by several studies as a limitation to the progression of research findings from proteomics studies. When integrated into a proteomic workflow, the CUSP protocol allows for a strategically designed validation study that improves researchers' abilities to translate research findings from discovery-based proteomics into clinical practice.

Longitudinal plasma proteomic profiling of EML4-ALK positive lung cancer receiving ALK-TKIs therapy

BACKGROUND

Anaplastic lymphoma kinase-tyrosine kinase inhibitors (ALK-TKIs) has demonstrated remarkable therapeutic effects in ALK-positive non-small cell lung cancer (NSCLC) patients. Identifying prognostic biomarkers can enhance the clinical efficacy of relapsed or refractory patients.

METHODS

We profiled 737 plasma proteins from 159 pre-treatment and on-treatment plasma samples of 63 ALK-positive NSCLC patients using data-independent acquisition-mass spectrometry (DIA-MS). The consensus clustering algorithm was used to identify subtypes with distinct biological features. A plasma-based prognostic model was constructed using the LASSO-Cox method. We performed the Mfuzz analysis to classify the patterns of longitudinal changes in plasma proteins during treatment. 52 baseline plasma samples from another independent ALK-TKI treatment cohort were collected to validate the potential prognostic markers using ELISA.

RESULTS

We identified three subtypes of ALK-positive NSCLC with distinct biological features and clinical efficacy. Patients in subgroup 1 exhibited activated humoral immunity and inflammatory responses, increased expression of positive acute-phase response proteins, and the worst prognosis. Then we constructed and verified a prognostic model that predicts the efficacy of ALK-TKI therapy using the expression levels of five plasma proteins (SERPINA4, ATRN, APOA4, TF, and MYOC) at baseline. Next, we explored the longitudinal changes in plasma protein expression during treatment and identified four distinct change patterns (Clusters 1-4). The longitudinal changes of acute-phase proteins during treatment can reflect the treatment status and tumor progression of patients. Finally, we validated the prognostic efficacy of baseline plasma CRP, SAA1, AHSG, SERPINA4, and TF in another independent NSCLC cohort undergoing ALK-TKI treatment.

CONCLUSIONS

This study contributes to the search for prognostic and drug-resistance biomarkers in plasma samples for ALK-TKI therapy and provides new insights into the mechanism of drug resistance and the selection of follow-up treatment.

A proteomic classifier panel for early screening of colorectal cancer: a case control study

BACKGROUND

Diagnosis of colorectal cancer (CRC) during early stages can greatly improve patient outcome. Although technical advances in the field of genomics and proteomics have identified a number of candidate biomarkers for non-invasive screening and diagnosis, developing more sensitive and specific methods with improved cost-effectiveness and patient compliance has tremendous potential to help combat the disease.

METHODS

We enrolled three cohorts of 479 subjects, including 226 CRC cases, 197 healthy controls, and 56 advanced precancerous lesions (APC). In the discovery cohort, we used quantitative mass spectrometry to measure the expression profile of plasma proteins and applied machine-learning to select candidate proteins. We then developed a targeted mass spectrometry assay to measure plasma concentrations of seven proteins and a logistic regression classifier to distinguish CRC from healthy subjects. The classifier was further validated using two independent cohorts.

RESULTS

The seven-protein panel consisted of leucine rich alpha-2-glycoprotein 1 (LRG1), complement C9 (C9), insulin-like growth factor binding protein 2 (IGFBP2), carnosine dipeptidase 1 (CNDP1), inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3), serpin family A member 1 (SERPINA1), and alpha-1-acid glycoprotein 1 (ORM1). The panel classified CRC and healthy subjects with high accuracy, since the area under curve (AUC) of the training and testing cohort reached 0.954 and 0.958. The AUC of the two independent validation cohorts was 0.905 and 0.909. In one validation cohort, the panel had an overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 89.9%, 81.8%, 89.2%, and 82.9%, respectively. In another blinded validation cohort, the panel classified CRC from healthy subjects with a sensitivity of 81.5%, specificity of 97.9%, and overall accuracy of 92.0%. Finally, the panel was able to detect APC with a sensitivity of 49%.

CONCLUSIONS

This seven-protein classifier is a clear improvement compared to previously published blood-based protein biomarkers for detecting early-stage CRC, and is of translational potential to develop into a clinically useful assay.

Serum/Plasma Proteome in Non-Malignant Liver Disease

The liver is the central metabolic organ and produces 85-90% of the proteins found in plasma. Accordingly, the plasma proteome is an attractive source of liver disease biomarkers that reflects the different cell types present in this organ, as well as the processes such as responses to acute and chronic injury or the formation of an extracellular matrix. In the first part, we summarize the biomarkers routinely used in clinical evaluations and their biological relevance in the different stages of non-malignant liver disease. Later, we describe the current proteomic approaches, including mass spectrometry and affinity-based techniques, that allow a more comprehensive assessment of the liver function but also require complex data processing. The many approaches of analysis and interpretation and their potential caveats are delineated. While these advances hold the promise to transform our understanding of liver diseases and support the development and validation of new liver-related drugs, an interdisciplinary collaboration is needed.

Research progress on the biological basis of Traditional Chinese Medicine syndromes of gastrointestinal cancers

Gastrointestinal cancers account for 11.6 % of all cancers, and are the second most frequently diagnosed type of cancer worldwide. Traditional Chinese medicine (TCM), together with Western medicine or alone, has unique advantages for the prevention and treatment of cancers, including gastrointestinal cancers. Syndrome differentiation and treatment are basic characteristics of the theoretical system of TCM. TCM syndromes are the result of the differentiation of the syndrome and the basis of treatment. Genomics, transcriptomics, proteomics, metabolomics, intestinal microbiota, and serology, generated around the central law, are used to study the biological basis of TCM syndromes in gastrointestinal cancers. This review summarizes current research on the biological basis of TCM syndrome in gastrointestinal cancers and provides useful references for future research on TCM syndrome in gastrointestinal cancers.

Identification of GUCA2A and COL3A1 as prognostic biomarkers in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation

By 2030, it is anticipated that there will be 2.2 million new instances of colorectal cancer worldwide, along with 1.1 million yearly deaths. Therefore, it is critical to develop novel biomarkers that could help in CRC early detection. We performed an integrated analysis of four RNA-Seq data sets and TCGA datasets in this study to find novel biomarkers for diagnostic, prediction, and as potential therapeutic for this malignancy, as well as to determine the molecular mechanisms of CRC carcinogenesis. Four RNA-Seq datasets of colorectal cancer were downloaded from the Sequence Read Archive (SRA) database. The metaSeq package was used to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was constructed using the string platform, and hub genes were identified using the cytoscape software. The gene ontology and KEGG pathway enrichment analysis were performed using enrichR package. Gene diagnostic sensitivity and its association to clinicopathological characteristics were demonstrated by statistical approaches. By using qRT-PCR, GUCA2A and COL3A1 were examined in colon cancer and rectal cancer. We identified 5037 differentially expressed genes, including (4752 upregulated, 285 downregulated) across the studies between CRC and normal tissues. Gene ontology and KEGG pathway analyses showed that the highest proportion of up-regulated DEGs was involved in RNA binding and RNA transport. Integral component of plasma membrane and mineral absorption pathways were identified as containing down-regulated DEGs. Similar expression patterns for GUCA2A and COL3A1 were seen in qRT-PCR and integrated RNA-Seq analysis. Additionally, this study demonstrated that GUCA2A and COL3A1 may play a significant role in the development of CRC.

Microcystin-LR-Induced Interaction between M2 Tumor-Associated Macrophage and Colorectal Cancer Cell Promotes Colorectal Cancer Cell Migration through Regulating the Expression of TGF-β1 and CST3

Microcystin-LR (MC-LR) is a toxic secondary metabolite produced by cyanobacteria that has been demonstrated to promote colorectal cancer (CRC). However, the mechanism by which MC-LR enhances CRC in the tumor microenvironment (TME) is poorly understood. To elucidate its role in TME, a co-culture system was established using CRC cells and M2 macrophages in a Transwell chamber. The study found that MC-LR promotes CRC cell migration by upregulating TGF-β1 expression and secretion in M2 macrophages and downregulating CST3 in CRC cells. Neutralizing TGF-β1 increased CST3 expression in CRC cells, while overexpressing CST3 in CRC cells suppressed TGF-β1 expression in M2 macrophages, both of which weakened MC-LR-induced cellular motility in the co-culture system. In vivo, the mice in the MC-LR/AOM/DSS group had more tumor nodules, deeper tumor invasion, and higher M2 macrophage infiltration compared to the AOM/DSS group, and the expression of TGF-β1 and CST3 in tumors was consistent with the cellular level. Overall, this study provides insights into the regulatory mechanism of MC-LR on TME, revealing that MC-LR upregulates the expression and secretion of TGF-β1 in M2 macrophages, which in turn inhibits the expression of CST3 in CRC cells to promote migration.

Innate Immune Cells in the Tumor Microenvironment of Liver Metastasis from Colorectal Cancer: Contribution to a Comprehensive Therapy

Colorectal cancer (CRC) is the third most prevalent type of cancer, and liver metastasis is the most common site of metastatic development. In the tumor microenvironment (TME), various innate immune cells are known to influence cancer progression and metastasis occurrence. CD274 (PD-L1) and CD206 (MRC1) are proteins that have been associated with poor prognosis and disease progression. We conducted a study on tumoral and non-tumoral biopsies from 47 patients with CRC liver metastasis, using flow cytometry to phenotypically characterize innate immune cells. Our findings showed an increase in the expression of CD274 on classical, intermediate, and non-classical monocytes when comparing tumor with non-tumor samples. Furthermore, tumor samples with a desmoplastic growth pattern exhibited a significantly decreased percentage of CD274- and CD206-positive cells in all monocyte populations compared to non-desmoplastic samples. We found a correlation between a lower expression of CD206 or CD274 on classical, intermediate, and non-classical monocytes and increased disease-free survival, which points to a better prognosis for these patients. In conclusion, our study has identified potential new targets and biomarkers that could be incorporated into a personalized medicine approach to enhance the outcome for colorectal cancer patients.

Label-free quantitative proteomics reveals aberrant expression levels of LRG, C9, FN, A1AT and AGP1 in the plasma of patients with colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the major causes of cancer-related death worldwide. Although commercial biomarkers of CRC are currently available, they are still lacking in terms of sensitivity and specificity; thus, searching for reliable blood-based biomarkers are important for the primary screening of CRC.

METHODS

Plasma samples of patients with non-metastatic (NM) and metastatic (M) CRC and healthy controls were fractionated using MARS-14 immunoaffinity chromatography. The flow-through and elute fractions representing low- and high-abundant proteins, respectively, were analyzed by label-free quantitative proteomics mass spectrometry. The functional analysis of the proteins with greater than 1.5-fold differential expression level between the CRC and the healthy control groups were analyzed for their biological processes and molecular functions. In addition, the levels of plasma proteins showing large alterations in CRC patients were confirmed by immunoblotting using two independent cohorts. Moreover, receiver operating characteristic (ROC) curve analysis was performed for individual and combinations of biomarker candidates so as to evaluate the diagnostic performance of biomarker candidates.

RESULTS

From 163 refined identifications, five proteins were up-regulated and two proteins were down-regulated in NM-CRC while eight proteins were up-regulated and three proteins were down-regulated in M-CRC, respectively. Altered plasma proteins in NM-CRC were mainly involved in complement activation, while those in M-CRC were clustered in acute-phase response, complement activation, and inflammatory response. Results from the study- and validation-cohorts indicate that the levels of leucine-rich alpha-2-glycoprotein-1(LRG), complement component C9 (C9), alpha-1-acid glycoprotein 1 (AGP1), and alpha-1-antitrypsin (A1AT) were statistically increased, while fibronectin (FN) level was statistically decreased in CRC patients compared to healthy controls, with most alterations found in a metastatic stage-dependent manner. ROC analysis revealed that FN exhibited the best diagnostic performance to discriminate CRC patients and healthy controls while AGP1 showed the best discrimination between the disease stages in both cohorts. The combined biomarker candidates, FN + A1AT + AGP1, exhibited perfect discriminatory power to discriminate between the CRC population and healthy controls whereas LRG + A1AT + AGP1 was likely to be the best panel to discriminate the metastatic stages in both cohorts.

CONCLUSIONS

This study identified and quantified distinct plasma proteome profiles of CRC patients. Selected CRC biomarker candidates including FN, LRG, C9, A1AT, and AGP1 may be further applied for screening larger cohorts including disease groups from other types of cancer or other diseases.

Carbon-Based Nanomaterials as Drug Delivery Agents for Colorectal Cancer: Clinical Preface to Colorectal Cancer Citing Their Markers and Existing Theranostic Approaches

Colorectal cancer (CRC) is one of the universally established cancers with a higher incidence rate. Novel progression toward cancer prevention and cancer care among countries in transition should be considered seriously for controlling CRC. Hence, several cutting edge technologies are ongoing for high performance cancer therapeutics over the past few decades. Several drug-delivery systems of the nanoregime are relatively new in this arena compared to the previous treatment modes such as chemo- or radiotherapy to mitigate cancer. Based on this background, the epidemiology, pathophysiology, clinical presentation, treatment possibilities, and theragnostic markers for CRC were revealed. Since the use of carbon nanotubes (CNTs) for the management of CRC has been less studied, the present review analyzes the preclinical studies on the application of carbon nanotubes for drug delivery and CRC therapy owing to their inherent properties. It also investigates the toxicity of CNTs on normal cells for safety testing and the clinical use of carbon nanoparticles (CNPs) for tumor localization. To conclude, this review recommends the clinical application of carbon-based nanomaterials further for the management of CRC in diagnosis and as carriers or therapeutic adjuvants.

Potential biomarkers and immune characteristics of small bowel adenocarcinoma

Small bowel adenocarcinoma (SBA) is a gastrointestinal malignancy with low incidence but poor prognosis, and its pathogenesis is still unclear. This study aimed to explore potential disease-causing biomarkers of SBA. The gene expression datasets of SBA and normal samples were downloaded from the Gene Expression Omnibus database. First, differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were performed. Common genes (CGs) were obtained by intersection of differentially expressed genes (DEGs) and optimal modal genes of WGCNA. Subsequently, a protein‒protein interaction network was established to screen hub genes, and target genes were obtained by Lasso regression analysis of hub genes. An SBA risk prediction model was established based on target genes. The prediction accuracy of the model was evaluated by the area under the receiver operating characteristic curve (AUC). The levels of immune cell infiltration and activation of immune pathways were compared between SBA and normal samples using the "ggpubr" and "reshape2" packages. A total of 1058 DEGs were identified. WGCNA showed that the signature gene in the brown module was significantly associated with SBA (p = 7E−17), and 469 CGs were obtained. Four target genes (APOA4, APOB, COL1A2, FN1) were identified and showed excellent prediction of SBA risk (AUC = 0.965). In addition, active dendritic cells and macrophages showed higher infiltration levels in SBA. Meanwhile, the APC_co_stimulation pathway and parainflammation pathway were strongly active in SBA. Four target genes (APOA4, APOB, COL1A2, FN1) may be involved in the pathogenesis of small bowel adenocarcinoma.

Construction of 2DE Patterns of Plasma Proteins: Aspect of Potential Tumor Markers

The use of tumor markers aids in the early detection of cancer recurrence and prognosis. There is a hope that they might also be useful in screening tests for the early detection of cancer. Here, the question of finding ideal tumor markers, which should be sensitive, specific, and reliable, is an acute issue. Human plasma is one of the most popular samples as it is commonly collected in the clinic and provides noninvasive, rapid analysis for any type of disease including cancer. Many efforts have been applied in searching for “ideal” tumor markers, digging very deep into plasma proteomes. The situation in this area can be improved in two ways—by attempting to find an ideal single tumor marker or by generating panels of different markers. In both cases, proteomics certainly plays a major role. There is a line of evidence that the most abundant, so-called “classical plasma proteins”, may be used to generate a tumor biomarker profile. To be comprehensive these profiles should have information not only about protein levels but also proteoform distribution for each protein. Initially, the profile of these proteins in norm should be generated. In our work, we collected bibliographic information about the connection of cancers with levels of “classical plasma proteins”. Additionally, we presented the proteoform profiles (2DE patterns) of these proteins in norm generated by two-dimensional electrophoresis with mass spectrometry and immunodetection. As a next step, similar profiles representing protein perturbations in plasma produced in the case of different cancers will be generated. Additionally, based on this information, different test systems can be developed.

Identification of Six Genes as Diagnostic Markers for Colorectal Cancer Detection by Integrating Multiple Expression Profiles

Background

Many studies have demonstrated the promising utility of DNA methylation and miRNA as biomarkers for colorectal cancer (CRC) early detection. However, mRNA is rarely reported. This study aimed to identify novel fecal-based mRNA signatures.

Methods

The differentially expressed genes (DEGs) were first determined between CRCs and matched normal samples by integrating multiple datasets. Then, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to reduce the number of candidates of aberrantly expressed genes. Next, the potential functions were investigated for the candidate signatures and their ability to detect CRC and pan-cancers was comprehensively evaluated.

Results

We identified 1841 common DEGs in two independent datasets. Functional enrichment analysis revealed they were mainly related to extracellular structure, biosynthesis, and cell adhesion. The CRC classifier was established based on six genes screened by LASSO regression. Sensitivity, specificity, and area under the ROC curve (AUC) for CRC detection were 79.30%, 80.40%, and 0.85 (0.76–0.92) in the training set, and these indexes achieved 93.20%, 41.80%, and 0.73 (0.65–0.83) in the testing set. For validation set, the sensitivity, specificity, and AUC were 98.90%, 98.00%, and 0.97 (0.94–0.99). The average sensitivities exceeded 90.00% for CRCs with different clinical features. For adenomas detection, the sensitivity and specificity were 74.50% and 64.00%. Besides, the six genes obtained an average AUC of 0.855 for pan-cancer detection.

Conclusion

The six-gene signatures showed ability to detect CRC and pan-cancer samples, which could be served as potential diagnostic markers.

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.

Proteomics technologies for cancer liquid biopsies

Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies.Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).

Liquid Biopsy-Based Colorectal Cancer Screening via Surface Markers of Circulating Tumor Cells

Colorectal cancer (CRC) is ranked second for cancer-related deaths worldwide with approximately half of the patients being diagnosed at the late stages. The untimely detection of CRC results in advancement to the metastatic stage and nearly 90% of cancer-related deaths. The early detection of CRC is crucial to decrease its overall incidence and mortality rates. The recent introduction of circulating tumor cells (CTCs) has enabled a less invasive sampling method from liquid biopsies, besides revealing key information toward CRC metastasis. The current gold standard for CTC identification is the CellSearch® system (Veridex). This first-generation instrumentation relies on a single cell surface marker (CSM) to capture and count CTCs. Detection of CTCs allows the identification of patients at risk for metastasis, whereas CTC enumeration could improve risk assessment, monitoring of systemic therapy, and detection of therapy resistance in advanced metastatic CRC. In this review, we compared the pros and cons between single CSM-based CTC enrichment techniques and multi-marker-based systems. We also highlighted the challenges faced in the routine implementation of CSM-dependent CTC detection methods in CRC screening, prediction, prognosis, disease monitoring, and therapy selection toward precision medicine, as well as the dwelling on post-CTC analysis and characterization methods.

Clinicopathological and Fecal Proteome Evaluations in 16 Dogs Presenting Chronic Diarrhea Associated with Lymphangiectasia

Canine intestinal lymphangiectasia (IL) is a condition characterized by variably severe gastrointestinal signs, frequently associated with laboratory abnormalities; the research for markers allowing a better understanding of the severity degree and/or obtaining an early diagnosis and/or monitoring is continuously progressing. In the present study, we investigated possible new diagnostic/follow-up markers in IL dogs, namely, serum C-reactive protein, serum bacterial lipopolysaccharide, serum cleaved cytokeratin 18, serum citrulline, and zonulin (in both serum and feces). A fecal proteomic study looking for possible confirmation and/or new marker candidates was also performed. All markers in both substrates, with the exception of serum citrulline, significantly differed between diseased and control dogs. Fecal proteomics allowed the retrieval of three proteins in IL dogs (Fc fragment of IgG-binding protein; transthyretin; proproteinase E) that were not previously found in clinically healthy subjects. Although further studies are needed, C-reactive protein, bacterial lipopolysaccharide, cleaved cytokeratin 18, and zonulin (in both serum and feces) resulted as promising markers for canine IL; similarly, fecal proteomics represents a road worthy of being pursued in the search for candidate biomarkers.

Identification and validation of potential novel biomarkers for oral squamous cell carcinoma

Our study aimed to explore potential new diagnostic biomarkers in patients with oral squamous cell carcinoma (OSCC) to find new target molecules involved in the progression of OSCC. Potential novel biomarkers of OSCC were identified using a protein microarray assay. Compared with the healthy control group, there were five proteins (I309, GDF15, AXL, MMP3, and CTACK) in the serum of in situ oral cancer group. However, there were four differentially expressed proteins (MCSF, I309, MMP3, and CTACK) in the serum of the OSCC group. Receiver operating characteristic (ROC) curve analysis results suggested that these six proteins (I309, GDF15, AXL, MMP3, CTACK, and MCSF) had diagnostic value for OSCC. Based on The Cancer Genome Atlas (TCGA) database, we found that only GDF15 expression was associated with the prognosis of OSCC. Subsequently, we verified the expression levels of six proteins in HSC-3 and HaCaT cells, and the results showed that the level of these six proteins was significantly higher in HSC-3 cells than in normal HaCaT cells. Similarly, in the OSCC nude mouse model, the expression levels of these proteins were significantly upregulated in OSCC tumor tissue compared to the normal tissue. GDF15, MMP3, AXL, MCSF, I309, and CTACK may be used as biomarkers for OSCC diagnosis and provide a novel study direction for the treatment of OSCC.

Exploration of the Proteomic Landscape of Small Extracellular Vesicles in Serum as Biomarkers for Early Detection of Colorectal Neoplasia

Background

Patient participation in colorectal cancer (CRC) screening via a stool test and colonoscopy is suboptimal, but participation can be improved by the development of a blood test. However, the suboptimal detection abilities of blood tests for advanced neoplasia, including advanced adenoma (AA) and CRC, limit their application. We aimed to investigate the proteomic landscape of small extracellular vesicles (sEVs) from the serum of patients with colorectal neoplasia and identify specific sEV proteins that could serve as biomarkers for early diagnosis.

Materials and Methods

We enrolled 100 patients including 13 healthy subjects, 12 non-AAs, 13 AAs, and 16 stage-I, 15 stage-II, 16 stage-III, and 15 stage-IV CRCs. These patients were classified as normal control, early neoplasia, and advanced neoplasia. The sEV proteome was explored by liquid chromatography-tandem mass spectrometry. Generalized association plots were used to integrate the clustering methods, visualize the data matrix, and analyze the relationship. The specific sEV biomarkers were identified by a decision tree via Orange3 software. Functional enrichment analysis was conducted by using the Ingenuity Pathway Analysis platform.

Results

The sEV protein matrix was identified from the serum of 100 patients and contained 3353 proteins, of which 1921 proteins from 98 patients were finally analyzed. Compared with the normal control, subjects with early and advanced neoplasia exhibited a distinct proteomic distribution in the data matrix plot. Six sEV proteins were identified, namely, GCLM, KEL, APOF, CFB, PDE5A, and ATIC, which properly distinguished normal control, early neoplasia, and advanced neoplasia patients from each other. Functional enrichment analysis revealed that APOF⁺ and CFB⁺ sEV associated with clathrin-mediated endocytosis signaling and the complement system, which have critical implications for CRC carcinogenesis.

Conclusion

Patients with colorectal neoplasia had a distinct sEV proteome expression pattern in serum compared with those patients who were healthy and did not have neoplasms. Moreover, the six identified specific sEV proteins had the potential to discriminate colorectal neoplasia between early-stage and advanced neoplasia. Collectively, our study provided a six-sEV protein biomarker panel for CRC diagnosis at early or advanced stages. Furthermore, the implication of the sEV proteome in CRC carcinogenesis via specific signaling pathways was explored.

Identification of a metabolic signature to predict overall survival for colorectal cancer

PURPOSE

Metabolic genes are associated with the occurrence and development of tumors. Metabolic-related risk models have showed partly prognostic predictive ability in cancers. However, the correlation between metabolic-related genes (MRGs) and the outcome of colorectal cancer is still poorly understood.

PATIENTS AND METHODS

TCGA database is used as the training cohort; while GSE39582 is the verification cohort. The least absolute shrinkage and selection operator Cox regression analysis were utilized to identify the MRGs and establish a genetic risk scoring model. A nomogram by integrating MRGs risk scores with TNM stage was constructed. The potential biological mechanisms were explored using gene set enrichment analysis. Associations of the signature with immune cell infiltrations and the tumor mutation burden (TMB) were also uncovered by Spearman rank test.

RESULTS

A six-gene metabolic signature was identified. Based on the risk scoring model with the signature, patients were divided into two groups (high-risk versus low-risk). The overall survival (OS) duration of patients with high-risk were quite shorter than those of low-risk patients (TCGA: p < .001, GSE39582: p < .001). Metabolic-related pathways were major enriched in low-risk group, while the high-risk group exhibited multiple immune-related pathways. Moreover, our signature was more linear dependent with antigen-presenting cell than effector immune cells, and a positive correction were seen between our signature and TMB.

CONCLUSION

Our research has discovered a six-gene metabolic signature to predict the OS of colorectal cancer. These genes may play significant roles in colorectal cancer regulating tumor microenvironment and serving as potential biomarkers for anti-cancer therapy.

SecProCT: In Silico Prediction of Human Secretory Proteins Based on Capsule Network and Transformer

Identifying secretory proteins from blood, saliva or other body fluids has become an effective method of diagnosing diseases. Existing secretory protein prediction methods are mainly based on conventional machine learning algorithms and are highly dependent on the feature set from the protein. In this article, we propose a deep learning model based on the capsule network and transformer architecture, SecProCT, to predict secretory proteins using only amino acid sequences. The proposed model was validated using cross-validation and achieved 0.921 and 0.892 accuracy for predicting blood-secretory proteins and saliva-secretory proteins, respectively. Meanwhile, the proposed model was validated on an independent test set and achieved 0.917 and 0.905 accuracy for predicting blood-secretory proteins and saliva-secretory proteins, respectively, which are better than conventional machine learning methods and other deep learning methods for biological sequence analysis. The main contributions of this article are as follows: (1) a deep learning model based on a capsule network and transformer architecture is proposed for predicting secretory proteins. The results of this model are better than the those of existing conventional machine learning methods and deep learning methods for biological sequence analysis; (2) only amino acid sequences are used in the proposed model, which overcomes the high dependence of existing methods on the annotated protein features; (3) the proposed model can accurately predict most experimentally verified secretory proteins and cancer protein biomarkers in blood and saliva.

Proteomics, Personalized Medicine and Cancer

As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome.

Revealing the Role of High-Density Lipoprotein in Colorectal Cancer

Colorectal cancer (CRC) is a highly prevalent malignancy with multifactorial etiology, which includes metabolic alterations as contributors to disease development. Studies have shown that lipid status disorders are involved in colorectal carcinogenesis. In line with this, previous studies have also suggested that the serum high-density lipoprotein cholesterol (HDL-C) level decreases in patients with CRC, but more recently, the focus of investigations has shifted toward the exploration of qualitative properties of HDL in this malignancy. Herein, a comprehensive overview of available evidences regarding the putative role of HDL in CRC will be presented. We will analyze existing findings regarding alterations of HDL-C levels but also HDL particle structure and distribution in CRC. In addition, changes in HDL functionality in this malignancy will be discussed. Moreover, we will focus on the genetic regulation of HDL metabolism, as well as the involvement of HDL in disturbances of cholesterol trafficking in CRC. Finally, possible therapeutic implications related to HDL will be presented. Given the available evidence, future studies are needed to resolve all raised issues concerning the suggested protective role of HDL in CRC, its presumed function as a biomarker, and eventual therapeutic approaches based on HDL.

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).

Microsatellite Instability in Colorectal Cancer Liquid Biopsy-Current Updates on Its Potential in Non-Invasive Detection, Prognosis and as a Predictive Marker

Colorectal cancer (CRC) is the third most commonly-diagnosed cancer in the world and ranked second for cancer-related mortality in humans. Microsatellite instability (MSI) is an indicator for Lynch syndrome (LS), an inherited cancer predisposition, and a prognostic marker which predicts the response to immunotherapy. A recent trend in immunotherapy has transformed cancer treatment to provide medical alternatives that have not existed before. It is believed that MSI-high (MSI-H) CRC patients would benefit from immunotherapy due to their increased immune infiltration and higher neo-antigenic loads. MSI testing such as immunohistochemistry (IHC) and PCR MSI assay has historically been a tissue-based procedure that involves the testing of adequate tissue with a high concentration of cancer cells, in addition to the requirement for paired normal tissues. The invasive nature and specific prerequisite of such tests might hinder its application when surgery is not an option or when the tissues are insufficient. The application of next-generation sequencing, which is highly sensitive, in combination with liquid biopsy, therefore, presents an interesting possibility worth exploring. This review aimed to discuss the current body of evidence supporting the potential of liquid biopsy as a tool for MSI testing in CRC.

The Roadmap of Colorectal Cancer Screening

Colorectal cancer (CRC) is the third most common form of cancer in terms of incidence and the second in terms of mortality worldwide. CRC develops over several years, thus highlighting the importance of early diagnosis. National screening programs based on fecal occult blood tests and subsequent colonoscopy have reduced the incidence and mortality, however improvements are needed since the participation rate remains low and the tests present a high number of false positive results. This review provides an overview of the CRC screening globally and the state of the art in approaches aimed at improving accuracy and participation in CRC screening, also considering the need for gender and age differentiation. New fecal tests and biomarkers such as DNA methylation, mutation or integrity, proteins and microRNAs are explored, including recent investigations into fecal microbiota. Liquid biopsy approaches, involving novel biomarkers and panels, such as circulating mRNA, micro- and long-non-coding RNA, DNA, proteins and extracellular vesicles are discussed. The approaches reported are based on quantitative PCR methods that could be easily applied to routine screening, or arrays and sequencing assays that should be better exploited to describe and identify candidate biomarkers in blood samples.

Colorectal cancer screening and diagnosis: omics-based technologies for development of a non-invasive blood-based method

Introduction: Colorectal cancer (CRC) is one of the most important health problems in the Western world. In order to reduce the burden of the disease, two strategies are proposed: screening and prompt detection in symptomatic patients. Although diagnosis and prevention are mainly based on colonoscopy, fecal hemoglobin detection has been widely implemented as a noninvasive strategy. Various studies aiming to discover blood-based biomarkers have recently emerged.Areas covered: The burgeoning omics field provides diverse high-throughput approaches for CRC blood-based biomarker discovery. In this review, we appraise the most robust and commonly used technologies within the fields of genomics, transcriptomics, epigenomics, proteomics, and metabolomics, together with their targeted validation approaches. We summarize the transference process from the discovery phase until clinical translation. Finally, we review the best candidate biomarkers and their potential clinical applicability.Expert opinion: Some available biomarkers are promising, especially in the field of epigenomics: DNA methylation and microRNA. Transference requires the joint collaboration of basic researchers, intellectual property experts, technology transfer officers and clinicians. Blood-based biomarkers will be selected not only based on their diagnostic accuracy and cost but also on their reliability, applicability to clinical analysis laboratories and their acceptance by the population.

Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management

Following the introduction of routine Prostate Specific Antigen (PSA) screening in the early 1990's, Prostate Cancer (PCa) is often detected at an early stage. There are also a growing number of treatment options available and so the associated mortality rate is generally low. However, PCa is an extremely complex and heterogenous disease and many patients suffer disease recurrence following initial therapy. Disease recurrence commonly results in metastasis and metastatic PCa has an average survival rate of just 3-5 years. A significant problem in the clinical management of PCa is being able to differentiate between patients who will respond to standard therapies and those who may benefit from more aggressive intervention at an earlier stage. It is also acknowledged that for many men the disease is not life threatenting. Hence, there is a growing desire to identify patients who can be spared the significant side effects associated with PCa treatment until such time (if ever) their disease progresses to the point where treatment is required. To these important clinical needs, current biomarkers and clinical methods for patient stratification and personlised treatment are insufficient. This review provides a comprehensive overview of the complexities of PCa pathology and disease management. In this context it is possible to review current biomarkers and proteomic technologies that will support development of biomarker-driven decision tools to meet current important clinical needs. With such an in-depth understanding of disease pathology, the development of novel clinical biomarkers can proceed in an efficient and effective manner, such that they have a better chance of improving patient outcomes.

The status of proteomics as we enter the 2020s: Towards personalised/precision medicine

The last decade has seen many major advances in proteomics, with over 70,000 publications in the field since 2010. A comprehensive omics toolbox has been developed facilitating rapid in depth analysis of the human proteome. Such studies are advancing our understanding of the biology of both health and disease. The combination of proteomics with other omics platforms (the omics pipeline), in particular proteogenomics, is giving important insights to the molecular changes leading to disease, covering the spectrum from genotype to phenotype and identifying potential biomarkers for disease detection, surveillance and monitoring, and revealing potential new drug targets. Discovery-based finding are now being translated to clinical application, supporting the rollout of precision/personalised medicine. This perspective has focused on twelve areas of importance that have fuelled the field. Recent exemplars are given to illustrate this and show how, together with some emerging technologies, they are anticipated to lead to further advances in the field. However, hurdles still remain to be overcome, especially in the area of Big Data analysis.

PSMD11, PTPRM and PTPRB as novel biomarkers of pancreatic cancer progression

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate of all major cancers. Surgery is the only curative intent therapy, but the majority of patients experience disease relapse. Thus, patients who do not benefit from highly morbid surgical resection needs to be identified and offered palliative chemotherapy instead. In this pilot study, we aimed to identify differentially regulated proteins in plasma and plasma derived microparticles from PDAC patients with poor and good prognosis.

METHODS

Plasma and plasma derived microparticle samples were obtained before surgical resection from PDAC patients. Sequential Windowed Acquisition of all Theoretical fragment ion spectra - Mass Spectrometry (SWATH-MS) proteomic analysis was performed to identify and quantify proteins in these samples. Statistical analysis was performed to identify biomarkers for poor prognosis.

RESULTS

A total of 482 and 1024 proteins were identified from plasma and microparticle samples, respectively, by SWATH-MS analysis. Statistical analysis of the data further identified nine and six differentially (log₂ratio > 1, p < .05) expressed proteins in plasma and microparticles, respectively. Protein tyrosine phosphatases, PTPRM and PTPRB, were decreased in plasma of patients with poor PDAC prognosis, while proteasomal subunit PSMD11 was increased in microparticles of patients with poor prognosis.

CONCLUSION AND GENERAL SIGNIFICANCE

A novel blood-based biomarker signature for PDAC prognosis was identified.

[Quantitative proteomics and bioinformatics analyses of human coronary artery endothelial cell injury induced by Kawasaki disease]

OBJECTIVE

To study the biomarkers for human coronary artery endothelial cell (HCAEC) injury induced by Kawasaki disease (KD) using isobaric tags for relative and absolute quantitation (iTRAQ) proteomics.

METHODS

HCAECs cultured with the serum of children with KD were used as the KD group, and those cultured with the serum of healthy children was used as the healthy control group. The iTRAQ technique was used to measure the expression of proteins in two groups. The data on proteins were analyzed by bioinformatics. Western blot was used for the validation of protein markers.

RESULTS

A total of 518 significantly differentially expressed proteins were identified (with an absolute value of difference fold of >1.2, P<0.05). The gene ontology analysis showed that the differentially expressed proteins were significantly enriched in biological processes (including cellular processes, metabolic processes, and biological regulation), cellular components (including cell parts, cells, and organelles), and molecular functions (including binding, catalytic activity, and molecular function regulators). The KEGG analysis showed that the proteins were significantly enriched in the signaling pathways of ribosomes, PI3K-Akt signaling pathway, and transcriptional dysregulation in cancer. The PPI network showed that the top 9 protein markers in relation density were PWP2, MCM4, MCM7, MCM5, MCM3, MCM2, SLD5, HDAC2, and MCM6, which were selected as the protein markers for coronary endothelial injury in KD. Western blot showed that the KD group had significantly lower expression levels of the protein markers HDAC2, PWP2, and MCM2 than the healthy control group (P<0.05).

CONCLUSIONS

The serum of children with KD significantly changes the protein expression pattern of HCAECs and affects the signaling pathways associated with the cardiovascular system, which provides a new basis for the pathophysiological mechanism and therapeutic targets of KD.

[Quantitative proteomics and bioinformatics analyses of human coronary artery endothelial cell injury induced by Kawasaki disease]

OBJECTIVE

To study the biomarkers for human coronary artery endothelial cell (HCAEC) injury induced by Kawasaki disease (KD) using isobaric tags for relative and absolute quantitation (iTRAQ) proteomics.

METHODS

HCAECs cultured with the serum of children with KD were used as the KD group, and those cultured with the serum of healthy children was used as the healthy control group. The iTRAQ technique was used to measure the expression of proteins in two groups. The data on proteins were analyzed by bioinformatics. Western blot was used for the validation of protein markers.

RESULTS

A total of 518 significantly differentially expressed proteins were identified (with an absolute value of difference fold of >1.2, P<0.05). The gene ontology analysis showed that the differentially expressed proteins were significantly enriched in biological processes (including cellular processes, metabolic processes, and biological regulation), cellular components (including cell parts, cells, and organelles), and molecular functions (including binding, catalytic activity, and molecular function regulators). The KEGG analysis showed that the proteins were significantly enriched in the signaling pathways of ribosomes, PI3K-Akt signaling pathway, and transcriptional dysregulation in cancer. The PPI network showed that the top 9 protein markers in relation density were PWP2, MCM4, MCM7, MCM5, MCM3, MCM2, SLD5, HDAC2, and MCM6, which were selected as the protein markers for coronary endothelial injury in KD. Western blot showed that the KD group had significantly lower expression levels of the protein markers HDAC2, PWP2, and MCM2 than the healthy control group (P<0.05).

CONCLUSIONS

The serum of children with KD significantly changes the protein expression pattern of HCAECs and affects the signaling pathways associated with the cardiovascular system, which provides a new basis for the pathophysiological mechanism and therapeutic targets of KD.

Systemic Proteome Alterations Linked to Early Stage Pancreatic Cancer in Diabetic Patients

BACKGROUND

Diabetes is a risk factor associated with pancreatic ductal adenocarcinoma (PDAC), and new adult-onset diabetes can be an early sign of pancreatic malignancy. Development of blood-based biomarkers to identify diabetic patients who warrant imaging tests for cancer detection may represent a realistic approach to facilitate earlier diagnosis of PDAC in a risk population.

METHODS

A spectral library-based proteomic platform was applied to interrogate biomarker candidates in plasma samples from clinically well-defined diabetic cohorts with and without PDAC. Random forest algorithm was used for prediction model building and receiver operating characteristic (ROC) curve analysis was applied to evaluate the prediction probability of potential biomarker panels.

RESULTS

Several biomarker panels were cross-validated in the context of detection of PDAC within a diabetic background. In combination with carbohydrate antigen 19-9 (CA19-9), the panel, which consisted of apolipoprotein A-IV (APOA4), monocyte differentiation antigen CD14 (CD14), tetranectin (CLEC3B), gelsolin (GSN), histidine-rich glycoprotein (HRG), inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3), plasma kallikrein (KLKB1), leucine-rich alpha-2-glycoprotein (LRG1), pigment epithelium-derived factor (SERPINF1), plasma protease C1 inhibitor (SERPING1), and metalloproteinase inhibitor 1 (TIMP1), demonstrated an area under curve (AUC) of 0.85 and a two-fold increase in detection accuracy compared to CA19-9 alone. The study further evaluated the correlations of protein candidates and their influences on the performance of biomarker panels.

CONCLUSIONS

Proteomics-based multiplex biomarker panels improved the detection accuracy for diagnosis of early stage PDAC in diabetic patients.

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

Proteomic investigations into resistance in colorectal cancer

Introduction: Despite advances in screening and treatment options, colorectal cancer (CRC) remains one of the most prevalent and lethal cancer subtypes. Resistance to cytotoxic or targeted therapy has remained a constant challenge to the treatment and long-term management of patients, attracting intense worldwide investigation since the 1950s. Through extensive investigations into the proteomic mechanisms and functions that convey resistance to therapy/s, researchers have become able to implicate alterations in several signaling pathways that provide and sustain resistance to treatment.Areas covered: In this review, we summarize how protein alterations are associated with resistance to therapy, with particular emphasis on CRC. An overview of the mechanisms of therapeutic resistance is described, highlighting recent studies which endeavor to elucidate the proteomic changes that are associated with the acquisition and promulgation of therapeutic resistance.Expert opinion: While cancers such as CRC have been intensively studied for decades, unresponsiveness and the resistance to therapy remain critical obstacles in the treatment of patients. Due to the inherent biological and clinical heterogeneity of individual CRCs, proteomic methods stand to become powerful tools to provide biological insights that may guide therapeutic strategies with the ultimate goal of refining emergent immunotherapeutic treatments.

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data

The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.