A panel of selected serum protein biomarkers for the detection of aggressive prostate cancer

Multiplexed Opto-Microfluidic Biosensing: Advanced Platform for Prostate Cancer Detection

Cancer stands as a prominent global cause of mortality, necessitating early detection to augment survival rates and alleviate economic burdens on healthcare systems. In particular, prostate cancer (PCa), impacting 1.41 million men globally in 2020, accentuates the demand for sensitive and cost-effective detection methods beyond traditional prostate-specific antigen (PSA) testing. While clinical techniques exhibit limitations, biosensors emerge as compact, user-friendly alternatives to traditional laboratory approaches. However, existing biosensors predominantly concentrate on PSA detection, prompting the necessity for advancing toward multiplex sensing platforms. This study introduces a compact opto-microfluidic sensor featuring a substrate of gold nanospikes, fabricated via electrodeposition, for enhanced sensitivity. Embedded within a microfluidic chip, this nanomaterial enables the precise and concurrent measurement of PSA, alongside two complementary PCa biomarkers, matrix metalloproteinase-2 (MMP-2) and anti-α-methylacyl-CoA racemase (anti-AMACR) in diluted human plasma, offering a comprehensive approach to PSA analysis. Taking advantage of the localized surface plasmon resonance principle, this biosensor offers robustness and sensitivity in real sample analysis without the need for labeling agents. With the limit of detection at 0.22, 0.37, and 0.18 ng/mL for PSA, MMP-2, and anti-AMACR, respectively, this biosensing platform holds promise for point-of-care analysis, underscoring its potential impact on medical diagnostics.

Development of an Integrated Platform for the Simultaneous Enrichment and Characterization of N- and O-Linked Intact Glycopeptides

Both N-linked glycosylation and O-linked glycosylation play essential roles in the onset and progression of various diseases including cancer, and N-/O-linked site-specific glycans have been proven to be promising biomarkers for the discrimination of cancer. However, the micro-heterogeneity and low abundance nature of N-/O-linked glycosylation, as well as the time-consuming and tedious procedures for the enrichment of O-linked intact glycopeptides, pose great challenges for their efficient and accurate characterization. In this study, we developed an integrated platform for the simultaneous enrichment and characterization of N- and O-linked intact glycopeptides from the same serum sample. By fine-tuning the experimental conditions, we demonstrated that this platform allowed the selective separation of N- and O-linked intact glycopeptides into two fractions, with 85.1% O-linked intact glycopeptides presented in the first fraction and 93.4% N-linked intact glycopeptides presented in the second fraction. Determined with high reproducibility, this platform was further applied to the differential analysis of serum samples of gastric cancer and health control, which revealed 17 and 181 significantly changed O-linked and N-linked intact glycopeptides. Interestingly, five glycoproteins containing both significant regulation of N- and O-glycosylation were observed, hinting potential co-regulation of different types of glycosylation during tumor progress. In summary, this integrated platform opened a potentially useful avenue for the global analysis of protein glycosylation and can serve as a useful tool for the characterization of N-/O-linked intact glycopeptides at the proteomics scale.

Canine-Inspired Chemometric Analysis of Volatile Organic Compounds in Urine Headspace to Distinguish Prostate Cancer in Mice and Men

Canines can identify prostate cancer with high accuracy by smelling volatile organic compounds (VOCs) in urine. Previous studies have identified VOC biomarkers for prostate cancer utilizing solid phase microextraction (SPME) gas chromatography-mass spectrometry (GC-MS) but have not assessed the ability of VOCs to distinguish aggressive cancers. Additionally, previous investigations have utilized murine models to identify biomarkers but have not determined if the results are translatable to humans. To address these challenges, urine was collected from mice with prostate cancer and men undergoing prostate cancer biopsy and VOCs were analyzed by SPME GC-MS. Prior to analysis, SPME fibers/arrows were compared, and the fibers had enhanced sensitivity toward VOCs with a low molecular weight. The analysis of mouse urine demonstrated that VOCs could distinguish tumor-bearing mice with 100% accuracy. Linear discriminant analysis of six VOCs in human urine distinguished prostate cancer with sensitivity = 75% and specificity = 69%. Another panel of seven VOCs could classify aggressive cancer with sensitivity = 78% and specificity = 85%. These results show that VOCs have moderate accuracy in detecting prostate cancer and a superior ability to stratify aggressive tumors. Furthermore, the overlap in the structure of VOCs identified in humans and mice shows the merit of murine models for identifying biomarker candidates.

Clinical application of serum-based proteomics technology in human tumor research

The rapid development of proteomics technology in the past decades has led to further human understanding of tumor research, and in some ways, the technology plays a very important supporting role in the early detection of tumors. Human serum has been shown to contain a variety of proteins closely related to life activities, and the dynamic change in proteins can often reflect the physiological and pathological conditions of the body. Serum has the advantage of easy extraction, so the application of proteomics technology in serum has become a hot spot and frontier area for the study of malignant tumors. However, there are still many difficulties in the standardized use of proteomic technologies, which inevitably limit the clinical application of proteomic technologies due to the heterogeneity of human proteins leading to incomplete whole proteome populations, in addition to most serum protein markers being now not highly specific in aiding the early detection of tumors. Nevertheless, further development of proteomics technologies will greatly increase our understanding of tumor biology and help discover more new tumor biomarkers with specificity that will enable medical technology.

Plasma GDF15 levels associated with circulating immune cells predict the efficacy of PD-1/PD-L1 inhibitor treatment and prognosis in patients with advanced non-small cell lung cancer

PURPOSE

Although increased plasma growth differentiation factor-15 (GDF15) levels have been reported in patients with various cancers, the predictive role of PD-1/PD-L1 inhibitors in advanced cancers remains unknown. This study aimed to investigate GDF15 levels as a predictive marker in advanced non-small cell lung cancer (NSCLC) treated with PD-1/PD-L1 inhibitors and analyze their association with immune cell populations.

METHODS

This study included 87 patients with advanced NSCLC receiving anti-PD-1/PD-L1 inhibitors between March 2018 and May 2020. Blood samples were obtained immediately before and months after PD-1/PD-L1 inhibitor administration.

RESULTS

The objective response rate (ORR) was significantly higher in the low GDF15 than in the high GDF15 group (39.2% vs. 15.3%, P = 0.013). The median progression-free survival (PFS) was significantly longer in the low GDF15 than in the high GDF15 group (13.2 [95% CI 7.6-18.9] vs. 7.2 [95% CI 4.8-9.6] months, P = 0.048). Moreover, plasma GDF15 levels negatively correlated with PD-1⁺/CD8⁺ T cells (r = - 0.399, P = 0.003) and positively with PD-1⁺/Treg cells (r = 0.507, P < 0.001) and PD-1⁺Treg/CD4⁺ T cells (r = 0.439, P < 0.001). The ORR was significantly higher in the group with decreased GDF15 from baseline than in the increased GDF15 group (37.2% vs. 10.0%, P = 0.026). The median PFS was significantly longer in the decreased GDF15 group (14.8 [95% CI 10.4-19.2] vs. 5.9 [95% CI 2.8-9.0] months, P = 0.002). Plasma GDF15 levels were associated with PD-1⁺CD8⁺ T cells and PD-1⁺ Treg cells.

CONCLUSION

Plasma GDF15 could be a potential biomarker for predicting the efficacy and survival benefit of immunotherapy in advanced NSCLC.

Mass Spectrometry-Based Proteomics Analysis of Human Substantia Nigra From Parkinson's Disease Patients Identifies Multiple Pathways Potentially Involved in the Disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra (SN) of the brain. Despite decades of studies, the precise pathogenic mechanism of PD is still elusive. An unbiased proteomic analysis of PD patient's brain allows the identification of critical proteins and molecular pathways that lead to dopamine cell death and α-synuclein deposition and the resulting devastating clinical symptoms. In this study, we conducted an in-depth proteome analysis of human SN tissues from 15 PD patients and 15 healthy control individuals combining Orbitrap mass spectrometry with the isobaric tandem mass tag-based multiplexing technology. We identified 10,040 proteins with 1140 differentially expressed proteins in the SN of PD patients. Pathway analysis showed that the ribosome pathway was the most enriched one, followed by gamma-aminobutyric acidergic synapse, retrograde endocannabinoid signaling, cell adhesion molecules, morphine addiction, Prion disease, and PD pathways. Strikingly, the majority of the proteins enriched in the ribosome pathway were mitochondrial ribosomal proteins (mitoribosomes). The subsequent protein-protein interaction analysis and the weighted gene coexpression network analysis confirmed that the mitoribosome is the most enriched protein cluster. Furthermore, the mitoribosome was also identified in our analysis of a replication set of ten PD and nine healthy control SN tissues. This study provides potential disease pathways involved in PD and paves the way to study further the pathogenic mechanism of PD.

Mass spectrometry-based proteomics analysis of human globus pallidus from progressive supranuclear palsy patients discovers multiple disease pathways

BACKGROUND

Progressive supranuclear palsy (PSP) is a neurodegenerative disorder clinically characterized by progressive postural instability, supranuclear gaze palsy, parkinsonism, and cognitive decline caused by degeneration in specific areas of the brain including globus pallidus (GP), substantia nigra, and subthalamic nucleus. However, the pathogenetic mechanism of PSP remains unclear to date.Unbiased global proteome analysis of patients' brain samples is an important step toward understanding PSP pathogenesis, as proteins serve as workhorses and building blocks of the cell.

METHODS

In this study, we conducted unbiased mass spectrometry-based global proteome analysis of GP samples from 15 PSP patients, 15 Parkinson disease (PD) patients, and 15 healthy control (HC) individuals. To analyze 45 samples, we conducted 5 batches of 11-plex isobaric tandem mass tag (TMT)-based multiplexing experiments. The identified proteins were subjected to statistical analysis, such as a permutation-based statistical analysis in the significance analysis of microarray (SAM) method and bootstrap receiver operating characteristic curve (ROC)-based statistical analysis. Subsequently, we conducted bioinformatics analyses using gene set enrichment analysis, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) protein-protein interaction (PPI) analysis, and weighted gene co-expression network analysis (WGCNA).

RESULTS

We have identified 10,231 proteins with ∼1,000 differentially expressed proteins. The gene set enrichment analysis results showed that the PD pathway was the most highly enriched, followed by pathways for oxidative phosphorylation, Alzheimer disease, Huntington disease, and non-alcoholic fatty liver disease (NAFLD) when PSP was compared to HC or PD. Most of the proteins enriched in the gene set enrichment analysis were mitochondrial proteins such as cytochrome c oxidase, NADH dehydrogenase, acyl carrier protein, succinate dehydrogenase, ADP/ATP translocase, cytochrome b-c1 complex, and/or ATP synthase. Strikingly, all of the enriched mitochondrial proteins in the PD pathway were downregulated in PSP compared to both HC and PD. The subsequent STRING PPI analysis and the WGCNA further supported that the mitochondrial proteins were the most highly enriched in PSP.

CONCLUSION

Our study showed that the mitochondrial respiratory electron transport chain complex was the key proteins that were dysregulated in GP of PSP, suggesting that the mitochondrial respiratory electron transport chain complex could potentially be involved in the pathogenesis of PSP. This is the first global proteome analysis of human GP from PSP patients, and this study paves the way to understanding the mechanistic pathogenesis of PSP.

Serum multi-cytokines screening identifies TRAIL and IL-10 as probable new biomarkers for prostate health index diagnostic utility adjustment in grey zone aggressive prostate cancer detection: A single-center data in China

Objective

To identify less invasive and easily applicable serum cytokine-derived biomarkers which contribute to the diagnostic utility and risk assessment ability of the prostate health index (PHI) based multivariable model in grey zone aggressive prostate cancer (AG PCa) early detection.

Methods

Serum 45 cytokines screening was performed in a small training cohort consisting of 10 sera by Luminex liquid array-based multiplexed immunoassays and identified TRAIL and IL-10 as new biomarkers for PHI diagnostic utility adjustment for further validation with a multivariable predictive model in a cohort including 79 aggressive prostate cancer patients and 209 benign prostatic hyperplasia or indolent PCa patients within the PSA grey zone.

Results

TRAIL and IL-10 were identified as potential serum biomarkers for AG PCa detection by the result of multi-cytokines screening in the univariate analysis, while multivariable logistic regression confirmed the AUC of the full risk predictive model (0.915) including tPSA, fPSA, PHI, TRAIL, and IL-10 was higher than various diagnostic strategies. DCA suggested a superior net benefit and indicated a good discriminative ability of the full risk model consistently with the result of the nomogram.

Conclusion

We suggest a significant advantage for the PHI-based multivariate combinations of serum TRAIL and IL-10 comparing to PHI or other serum-derived biomarkers alone in the detection and risk stratification of grey zone AG PCa.

A Distinct Glucose Metabolism Signature of Lung Adenocarcinoma With Prognostic Value

Background: Lung adenocarcinoma (LUAD) remains the most common type of lung cancer and is the main cause of cancer-related death worldwide. Reprogramming of glucose metabolism plays a crucial role in tumorigenesis and progression. However, the regulation of glucose metabolism is still being explored in LUAD. Determining the underlying clinical value of glucose metabolism will contribute in increasing clinical interventions. Our study aimed to conduct a comprehensive analysis of the landscape of glucose metabolism-related genes in LUAD and develop a prognostic risk signature.

Methods: We extracted the RNA-seq data and relevant clinical variants from The Cancer Genome Atlas (TCGA) database and identified glucose metabolism-related genes associated with the outcome by correlation analysis. To generate a prognostic signature, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed.

Results: Finally, ten genes with expression status were identified to generate the risk signature, including FBP2, ADH6, DHDH, PRKCB, INPP5J, ABAT, HK2, GNPNAT1, PLCB3, and ACAT2. Survival analysis indicated that the patients in the high-risk group had a worse survival than those in the low-risk group, which is consistent with the results in validated cohorts. And receiver operating characteristic (ROC) curve analysis further validated the prognostic value and predictive performance of the signature. In addition, the two risk groups had significantly different clinicopathological characteristics and immune cell infiltration status. Notably, the low-risk group is more likely to respond to immunotherapy.

Conclusion: Overall, this study systematically explored the prognostic value of glucose metabolism and generated a prognostic risk signature with favorable efficacy and accuracy, which help select candidate patients and explore potential therapeutic approaches targeting the reprogrammed glucose metabolism in LUAD.

Validation of Serum Biomarkers That Complement CA19-9 in Detecting Early Pancreatic Cancer Using Electrochemiluminescent-Based Multiplex Immunoassays

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy; its early detection is critical for improving prognosis. Electrochemiluminescent-based multiplex immunoassays were developed with high analytical performance. All proteins were analyzed in sera of patients diagnosed with PDAC (n = 138), benign pancreatic conditions (111), and healthy controls (70). The clinical performance of these markers was evaluated individually or in combination for their complementarity to CA19-9 in detecting early PDAC. Logistic regression modeling including sex and age as cofactors identified a two-marker panel of CA19-9 and CA-125 that significantly improved the performance of CA19-9 alone in discriminating PDAC (AUC: 0.857 vs. 0.766), as well as early stage PDAC (0.805 vs. 0.702) from intraductal papillary mucinous neoplasm (IPMN). At a fixed specificity of 80%, the panel significantly improved sensitivities (78% vs. 41% or 72% vs. 59%). A two-marker panel of HE4 and CEA significantly outperformed CA19-9 in separating IPMN from chronic pancreatitis (0.841 vs. 0.501). The biomarker panels evaluated by assays demonstrated potential complementarity to CA19-9 in detecting early PDAC, warranting additional clinical validation to determine their role in the early detection of pancreatic cancer.

Lectins applied to diagnosis and treatment of prostate cancer and benign hyperplasia: A review

Environmental factors, as well as genetic factors, contribute to the increase in prostate cancer cases (PCa), the second leading cause of cancer death in men. This fact calls for the development of more reliable, quick and low-cost early detection tests to distinguish between malignant and benign cases. Abnormal cell glycosylation pattern is a promising PCa marker for this purpose. Proteins, such as lectins can decode the information contained in the glycosylation patterns. Several studies have reported on applications of plant lectins as diagnostic tools for PCa considering the ability to differentiate it from benign cases. In addition, they can be used to detect, separate and differentiate the glycosylation patterns of cells or proteins present in serum, urine and semen. Herein, we present an overview of these studies, showing the lectins that map glycans differentially expressed in PCa, as well as benign hyperplasia (BPH). We further review their applications in biosensors, histochemical tests, immunoassays, chromatography, arrays and, finally, their therapeutic potential. This is the first study to review vegetable lectins applied specifically to PCa.

Data-Independent Acquisition-Based Mass Spectrometry (DIA-MS) for Quantitative Analysis of Intact N-Linked Glycopeptides

N-linked protein glycosylation is a key regulator in various biological functions. Previous studies have shown that aberrant glycosylation is associated with many diseases. Therefore, it is essential to elucidate protein modifications of glycosylation by quantitatively profiling intact N-linked glycopeptides. Data-independent acquisition (DIA) mass spectrometry (MS) is a cost-effective, flexible, and high-throughput method for global proteomics. However, substantial challenges are still present in the quantitative analysis of intact glycopeptides with high accuracy at high throughput. In this study, we have established a novel integrated platform for the DIA analysis of intact glycopeptides isolated from complex samples. The established analysis platform utilizes a well-designed DIA-MS method for raw data collection, a spectral library constructed specifically for intact glycopeptide quantification providing accurate results by the inclusion of Y ions for quantification and filtering of quantified intact glycopeptides with low-quality MS2 spectra automatically using a set of criteria. Intact glycopeptides isolated from human serum were used to evaluate the performance of the integrated platform. By utilizing 100 isolation windows for DIA data acquisition, a well-constructed human serum spectral library containing 1123 nonredundant intact glycopeptides with Y ions, and automated data inspection, 620 intact glycopeptides were quantified with high confidence from DIA-MS. In summary, our integrated platform can serve as a reliable quantitative tool for characterizing intact glycopeptides isolated from complex biological samples to assist our understanding of biological functions of N-linked glycosylation.