Proteomics provides individualized options of precision medicine for patients with gastric cancer

[Exploring the mechanism of IgA vasculitis pathogenesis through the interaction of thrombin and inflammatory factors using urinary proteomics]

OBJECTIVES

To explore the evidence, urinary biomarkers, and partial mechanisms of hypercoagulability in the pathogenesis of IgA vasculitis (IgAV).

METHODS

Differential expression of proteins in the urine of 10 healthy children and 10 children with IgAV was screened using high-performance liquid chromatography-tandem mass spectrometry, followed by Reactome pathway analysis. Protein-protein interaction (PPI) network analysis was conducted using STRING and Cytoscape software. In the validation cohort, 15 healthy children and 25 children with IgAV were included, and the expression levels of differential urinary proteins were verified using enzyme-linked immunosorbent assay.

RESULTS

A total of 772 differential proteins were identified between the IgAV group and the control group, with 768 upregulated and 4 downregulated. Reactome pathway enrichment results showed that neutrophil degranulation, platelet activation, and hemostasis pathways were involved in the pathogenesis of IgAV. Among the differential proteins, macrophage migration inhibitory factor (MIF) played a significant role in neutrophil degranulation and hemostasis, while thrombin was a key protein in platelet activation and hemostasis pathways. PPI analysis indicated that thrombin directly interacted with several proteins involved in inflammatory responses, and these interactions involved MIF. Validation results showed that compared to healthy children, children with IgAV had significantly higher urine thrombin/creatinine and urine MIF/creatinine levels (P<0.05).

CONCLUSIONS

Thrombin contributes to the pathogenesis of IgAV through interactions with inflammatory factors. Urinary thrombin and MIF can serve as biomarkers reflecting the hypercoagulable and inflammatory states in children with IgAV.

Proteomic Stratification of Prognosis and Treatment Options for Small Cell Lung Cancer

Small cell lung cancer (SCLC) is a highly malignant and heterogeneous cancer with limited therapeutic options and prognosis prediction models. Here, we analyzed formalin-fixed, paraffin-embedded (FFPE) samples of surgical resections by proteomic profiling, and stratified SCLC into three proteomic subtypes (S-I, S-II, and S-III) with distinct clinical outcomes and chemotherapy responses. The proteomic subtyping was an independent prognostic factor and performed better than current tumor-node-metastasis or Veterans Administration Lung Study Group staging methods. The subtyping results could be further validated using FFPE biopsy samples from an independent cohort, extending the analysis to both surgical and biopsy samples. The signatures of the S-II subtype in particular suggested potential benefits from immunotherapy. Differentially overexpressed proteins in S-III, the worst prognostic subtype, allowed us to nominate potential therapeutic targets, indicating that patient selection may bring new hope for previously failed clinical trials. Finally, analysis of an independent cohort of SCLC patients who had received immunotherapy validated the prediction that the S-II patients had better progression-free survival and overall survival after first-line immunotherapy. Collectively, our study provides the rationale for future clinical investigations to validate the current findings for more accurate prognosis prediction and precise treatments.

In-depth urinary and exosome proteome profiling analysis identifies novel biomarkers for diabetic kidney disease

Diabetic kidney disease (DKD) is a major microvascular complication of type 2 diabetes mellitus (T2DM). Monitoring the early diagnostic period and disease progression plays a crucial role in treating DKD. In this study, to comprehensively elucidate the molecular characteristics of urinary proteins and urinary exosome proteins in type 2 DKD, we performed large-scale urinary proteomics (n=144) and urinary exosome proteomics (n=44) analyses on T2DM patients with albuminuria in varying degrees. The dynamics analysis of the urinary and exosome proteomes in our study provides a valuable resource for discovering potential urinary biomarkers in patients with DKD. A series of potential biomarkers, such as SERPINA1 and transferrin (TF), were detected and validated to be used for DKD diagnosis or disease monitoring. The results of our study comprehensively elucidated the changes in the urinary proteome and revealed several potential biomarkers reflecting the progression of DKD, which provide a reference for DKD biomarker screening.

The ionome and proteome landscape of aging in laying hens and relation to egg white quality

The ionome is essential for maintaining body function and health status by participating in diverse key biological processes. Nevertheless, the distribution and utilization of ionome among different organs and how aging impacts the ionome leading to a decline in egg white quality remain unknown. Thus, we used inductively coupled plasma mass spectrometry (ICP-MS) to analyze 35 elements and their isotopic contents in eight organs of laying hens at 35, 72, and 100 weeks. Moreover, the magnum proteome, amino acids in egg white, and egg white quality were analyzed in laying hens at three different ages using 4D proteomics techniques, an amino acid analyzer, and an egg quality analyzer. Across the organs, we identified varying distribution patterns among macroelements (Mg24, Ca43/44, K39, and P31), transition metals (Zn64/66, Cu63/65, Fe56/57, and Mn55), and toxic elements (Pb208, Ba137, and Sr86). We observed an organ-specific aging pattern characterized by the accumulation of toxic elements (Pb208, Ba137, and Sr86) and calcification in the small intestine. Additionally, a decrease in the utilization of essential trace elements selenium (Se78/82) and manganese (Mn55) was noted in the oviduct. By analyzing ionome in tandem with egg quality, egg white amino acids, and proteome, we unveiled that the reduction of selenium and manganese concentrations in the magnum during the aging process affected amino acid metabolism, particularly tryptophan metabolism, thereby inhibiting the amino acid synthesis in the magnum. Furthermore, it accelerated the senescence of magnum cells through necroptosis activation, leading to a decline in the albumen secretion function of the magnum and subsequently reducing egg white quality. Overall, this study provides insights into the evolution of 35 elements and their isotopes across 8 organs of laying hens with age. It also reveals the elemental composition, interactions, and utilization patterns of these organs, as well as their correlation with egg white quality. The present study highlights the significance of ionome and offers a comprehensive perspective on the selection of ionome for regulating the aging of laying hens.

Expanding individualized therapeutic options via genoproteomics

Clinical next-generation sequencing (NGS)² tests have enabled treatment recommendations for cancer patients with driver gene mutations. Targeted therapy options for patients without driver gene mutations are currently unavailable. Herein, we performed NGS and proteomics tests on 169 formalin-fixed paraffin-embedded (FFPE)³ samples of non-small cell lung cancers (NSCLC, 65),⁴ colorectal cancers (CRC, 61),⁵ thyroid carcinomas (THCA, 14),⁶ gastric cancers (GC, 2),⁷ gastrointestinal stromal tumors (GIST, 11),⁸ and malignant melanomas (MM, 6).⁹ Of the 169 samples, NGS detected 14 actionable mutated genes in 73 samples, providing treatment options for 43% of the patients. Proteomics identified 61 actionable clinical drug targets approved by the FDA or undergoing clinical trials in 122 samples, providing treatment options for 72% of the patients. In vivo experiments demonstrated that the Mitogen-Activated Protein Kinase (MEK)¹⁰ inhibitor induced the overexpression of MEK1 (Map2k1) to block lung tumor growth in mice. Therefore, protein overexpression is a potentially feasible indicator for guiding targeted therapies. Collectively, our analysis suggests that combining NGS and proteomics (genoproteomics) could expand the targeted treatment options to 85% of cancer patients.

Piceatannol enhances Beclin-1 activity to suppress tumor progression and its combination therapy strategy with everolimus in gastric cancer

The effects and regulation of Beclin-1-an autophagy-related protein-have not been fully defined, however, a negative correlation has been reported between Beclin-1 expression and carcinogenesis. Meanwhile, no compound has been shown to directly inhibit its activity. Here, we evaluate piceatannol, a naturally occurring polyphenolic compound, as a potential targeting agonist of Beclin-1, to assess its efficacy as an antitumor agent against gastric cancer. More specifically, we determine the effects of piceatannol treatment on cell viability using a monitoring system and colony forming assay. Piceatannol was found to efficiently inhibit the proliferation of several human gastric cancer cell lines. Autophagic flux is increased by piceatannol treatment, and correlates with inhibition of cell proliferation and colony formation. Additionally, microscale thermophoresis and surface plasmon resonance results show a direct interaction between piceatannol and Beclin-1, which reduces the phosphorylation activity of Beclin-1 at the Ser-295 site. Notably, piceatannol impairs the binding of Beclin-1 to Bcl-2 and enhances the recruitment of binding of UV radiation resistance-associated gene protein, which further triggers Beclin-1-dependent autophagy signaling. An increase in autophagic activity via treatment with the mTOR inhibitor, everolimus, effectively sensitizes piceatannol-induced antitumor effects. Xenograft models confirmed that piceatannol inhibits tumor development and elicits a potent synergistic effect with everolimus in vivo. Taken together, the findings of this study strongly support the application of combinatorial piceatannol and everolimus therapy in future clinical trials for gastric cancer patients.

Combining neoadjuvant chemotherapy with PD-1/PD-L1 inhibitors for locally advanced, resectable gastric or gastroesophageal junction adenocarcinoma: A systematic review and meta-analysis

Background

Immune checkpoint inhibitors (ICIs) have shown promising prospects in locally advanced, resectable gastric or gastroesophageal junction adenocarcinoma (GC/GEJC) immunotherapy, but their efficacy in neoadjuvant settings remains unclear. This study aimed to assess the efficacy and safety of integrating programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) inhibitors into neoadjuvant chemotherapy (NACT) of GC/GEJC treatment.

Methods

PubMed, Cochrane Library, Embase, ClinicalTrials.gov, and main oncology conference databases were systematically searched up to 19 November 2022, and randomized controlled trials (RCTs) and cohort studies that evaluated the efficacy and safety of PD-1/PD-L1 inhibitors plus NACT were included. The main outcomes were pathological complete response (pCR), major pathological response (MPR), R0 resection rate, and treatment-related adverse events (TRAEs).

Results

A total of 753 patients from 20 prospective studies were included in this meta-analysis. The pooled pCR and MPR rates from studies reporting were 21.7% [95% confidence interval (CI), 18.1%-25.5%] and 44.0% (95% CI, 34.1%-53.8%), respectively. The pooled incidence rate of total TRAEs was 89.1% (95% CI, 82.7%-94.3%), and the incidence rate of grade 3 to 4 TRAEs was 34.4% (95% CI, 17.8%-66.5%). The pooled R0 resection rate was reported to be 98.9% (95% CI, 97.0%-99.9%). Subgroup analysis has not found significant differences in efficacy and safety among different PD-1/PD-L1 inhibitors. Moreover, the efficacy in patients with positive PD-L1 expression (combined positive score ≥1) was comparable with that in the entire study population [pCR, 22.5% vs. 21.2% (p > 0.05); MPR, 48.6% vs. 43.7% (p > 0.05)].

Conclusion

This systematic review and meta-analysis found that PD-1/PD-L1 inhibitors combined with NACT for locally advanced GC/GEJC were well tolerated and may confer therapeutic advantages. The integration of ICIs into NACT has shown the potential for application in any PD-L1 expression population.

High-throughput proteomics: a methodological mini-review

Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other 'omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery.

Genotypic and phenotypic characteristics of 12 chinese children with glycogen storage diseases

Background: Glycogen storage diseases (GSDs) are known as a group of disorders characterized by genetic errors leading to accumulation of glycogen in various tissues. Since different types of GSD can sometimes be clinically indistinguishable, next generation sequencing is becoming a powerful tool for clinical diagnosis.

Methods: 12 patients with suspected GSDs and their parents were enrolled in this study. The clinical and laboratory data of the patients were reviewed. Causative gene variants were identified in the patients using whole exome sequencing (WES) and verified by Sanger sequencing.

Results: Genetic testing and analysis showed that 7 patients were diagnosed with GSD II (Pompe disease), 2 patients with GSD III, 1 patient with GSD VI, and 2 patients with GSD IXα. A total number of 18 variants were identified in 12 patients including 11 variants in GAA gene, 3 variants in AGL gene, 2 variants in PYGL gene and 2 variants in PHKA2 gene, of which 9 variants were reported and 9 variants were novel. SIFT, Polyphen-2, Mutation Taster, and REVEL predicted the novel variants (except GAA c.1052_1075 + 47del) to be disease-causing. The 3D structures of wild/mutant type GAA protein were predicted indicating that variants p. Trp621Gly, p. Pro541Leu, p. Ser800Ile and p. Gly293Trp might affect the proteins function via destroying hydrogen bonds or conformational constraints. Neither liver size nor laboratory findings allow for a differentiation among GSD III, GSD VI and GSD IXα.

Conclusion: Our study expanded the variation spectrum of genes associated with GSDs. WES, in combination with clinical, biochemical, and pathological hallmarks, could provide accurate results for diagnosing and sub-typing GSD and related diseases in clinical setting.

Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.