Label-Free Quantitative Proteomics Identifies Novel Biomarkers for Distinguishing Tuberculosis Pleural Effusion from Malignant Pleural Effusion

Eugenol modulates the NOD1-NF-κB signaling pathway via targeting NF-κB protein in triple-negative breast cancer cells

BACKGROUND

The most aggressive subtype of breast cancer, triple-negative breast cancer (TNBC), has a worse prognosis and a higher probability of relapse since there is a narrow range of treatment options. Identifying and testing potential therapeutic targets for the treatment of TNBC is of high priority.

METHODS

Using a transcriptional signature of triple-negative breast cancer collected from Gene Expression Omnibus (GEO), CMap was utilized to reposition compounds for the treatment of TNBC. CCK8 and colony formation experiments were performed to detect the effect of the candidate drug on the proliferation of TNBC cells. Meanwhile, transwell and wound healing assay were implemented to detect cell metastasis change caused by the candidate drug. Moreover, the proteomic approach was presently ongoing to evaluate the underlying mechanism of the candidate drug in TNBC. Furthermore, drug affinity responsive target stability (DARTS) coupled with LC-MS/MS was carried out to explore the potential drug target candidate in TNBC cells.

RESULTS

We found that the most widely used medication, eugenol, reduced the growth and metastasis of TNBC cells. According to the underlying mechanism revealed by proteomics, eugenol could inhibit TNBC cell proliferation and metastasis via the NOD1-NF-κB signaling pathway. DARTS experiment further revealed that eugenol may bind to NF-κB in TNBC cells.

CONCLUDES

Our findings pointed out that eugenol was a potential candidate drug for the treatment of TNBC.

Assessment of a Large-Scale Unbiased Malignant Pleural Effusion Proteomics Study of a Real-Life Cohort

Simple Summary

Pleural effusion (PE) occurs as a consequence of various pathologies. Malignant effusion due to lung cancer is one of the most frequent causes. A method for accurate differentiation of malignant from benign PE is an unmet clinical need. Proteomics profiling of PE has shown promising results. However, mass spectrometry (MS) analysis typically involves the tedious elimination of abundant proteins before analysis, and clinical annotation of proteomics profiled cohorts is limited. This study compares the proteomes of malignant PE and nonmalignant PE, identifies lung cancer malignant markers in agreement with other studies, and identifies markers strongly associated with patient survival.

Abstract

Background: Pleural effusion (PE) is common in advanced-stage lung cancer patients and is related to poor prognosis. Identification of cancer cells is the standard method for the diagnosis of a malignant PE (MPE). However, it only has moderate sensitivity. Thus, more sensitive diagnostic tools are urgently needed. Methods: The present study aimed to discover potential protein targets to distinguish malignant pleural effusion (MPE) from other non-malignant pathologies. We have collected PE from 97 patients to explore PE proteomes by applying state-of-the-art liquid chromatography-mass spectrometry (LC-MS) to identify potential biomarkers that correlate with immunohistochemistry assessment of tumor biopsy or with survival data. Functional analyses were performed to elucidate functional differences in PE proteins in malignant and benign samples. Results were integrated into a clinical risk prediction model to identify likely malignant cases. Sensitivity, specificity, and negative predictive value were calculated. Results: In total, 1689 individual proteins were identified by MS-based proteomics analysis of the 97 PE samples, of which 35 were diagnosed as malignant. A comparison between MPE and benign PE (BPE) identified 58 differential regulated proteins after correction of the p-values for multiple testing. Furthermore, functional analysis revealed an up-regulation of matrix intermediate filaments and cellular movement-related proteins. Additionally, gene ontology analysis identified the involvement of metabolic pathways such as glycolysis/gluconeogenesis, pyruvate metabolism and cysteine and methionine metabolism. Conclusion: This study demonstrated a partial least squares regression model with an area under the curve of 98 and an accuracy of 0.92 when evaluated on the holdout test data set. Furthermore, highly significant survival markers were identified (e.g., PSME1 with a log-rank of 1.68 × 10⁻⁶).

Diagnosing pleural effusions using mass spectrometry-based multiplexed targeted proteomics quantitating mid- to high-abundance markers of cancer, infection/inflammation and tuberculosis

Pleural effusion (PE) is excess fluid in the pleural cavity that stems from lung cancer, other diseases like extra-pulmonary tuberculosis (TB) and pneumonia, or from a variety of benign conditions. Diagnosing its cause is often a clinical challenge and we have applied targeted proteomic methods with the aim of aiding the determination of PE etiology. We developed a mass spectrometry (MS)-based multiple reaction monitoring (MRM)-protein-panel assay to precisely quantitate 53 established cancer-markers, TB-markers, and infection/inflammation-markers currently assessed individually in the clinic, as well as potential biomarkers suggested in the literature for PE classification. Since MS-based proteomic assays are on the cusp of entering clinical use, we assessed the merits of such an approach and this marker panel based on a single-center 209 patient cohort with established etiology. We observed groups of infection/inflammation markers (ADA2, WARS, CXCL10, S100A9, VIM, APCS, LGALS1, CRP, MMP9, and LDHA) that specifically discriminate TB-PEs and other-infectious-PEs, and a number of cancer markers (CDH1, MUC1/CA-15-3, THBS4, MSLN, HPX, SVEP1, SPINT1, CK-18, and CK-8) that discriminate cancerous-PEs. Some previously suggested potential biomarkers did not show any significant difference. Using a Decision Tree/Multiclass classification method, we show a very good discrimination ability for classifying PEs into one of four types: cancerous-PEs (AUC: 0.863), tuberculous-PEs (AUC of 0.859), other-infectious-PEs (AUC of 0.863), and benign-PEs (AUC: 0.842). This type of approach and the indicated markers have the potential to assist in clinical diagnosis in the future, and help with the difficult decision on therapy guidance.