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Background: Eosinophilic granulomatosis with polyangiitis (EGPA) is characterized by asthma-like attacks in its early stage, which is easily misdiagnosed as severe asthma. Therefore, new biomarkers for the early diagnosis of EGPA are needed, especially for differentiating the diagnosis of asthma. Objectives: To identify serum biomarkers that can be used for early diagnosis of EGPA and to distinguish EGPA from severe asthma. Method: Data-independent acquisition (DIA) analysis was performed to identify 45 healthy controls (HC), severe asthma (S-A), and EGPA patients in a cohort to screen biomarkers for early diagnosis of EGPA and to differentiate asthma diagnosis. Subsequently, parallel reaction monitoring (PRM) analysis was applied to a validation cohort of 71 HC, S-A, and EGPA patients. Result: Four candidate biomarkers were identified from DIA and PRM analysis—i.e., serum amyloid A1 (SAA1), fibrinogen-α (FGA), and serum amyloid P component (SAP)—and were upregulated in the EGPA group, while cholesteryl ester transfer protein (CETP) was downregulated in the EGPA group compared with the S-A group. Receiver operating characteristics analysis shows that, as biomarkers for early diagnosis of EGPA, the combination of SAA1, FGA, and SAP has an area under the curve (AUC) of 0.947, a sensitivity of 82.35%, and a specificity of 100%. The combination of SAA1, FGA, SAP, and CETP as biomarkers for differential diagnosis of asthma had an AUC of 0.921, a sensitivity of 78.13%, and a specificity of 100%, which were all larger than single markers. Moreover, SAA1, FGA, and SAP were positively and CETP was negatively correlated with eosinophil count. Conclusion: DIA-PRM combined analysis screened and validated four previously unexplored but potentially useful biomarkers for early diagnosis of EGPA and differential diagnosis of asthma. [ABSTRACT FROM AUTHOR]

Background: Early lung cancer detection remains a clinical challenge for standard diagnostic biopsies due to insufficient tumor morphological evidence. As epigenetic alterations precede morphological changes, expression alterations of certain imprinted genes could serve as actionable diagnostic biomarkers for malignant lung lesions. Results: Using the previously established quantitative chromogenic imprinted gene in situ hybridization (QCIGISH) method, elevated aberrant allelic expression of imprinted genes GNAS, GRB10, SNRPN and HM13 was observed in lung cancers over benign lesions and normal controls, which were pathologically confirmed among histologically stained normal, paracancerous and malignant tissue sections. Based on the differential imprinting signatures, a diagnostic grading model was built on 246 formalin-fixed and paraffin-embedded (FFPE) surgically resected lung tissue specimens, tested against 30 lung cytology and small biopsy specimens, and blindly validated in an independent cohort of 155 patients. The QCIGISH diagnostic model demonstrated 99.1% sensitivity (95% CI 97.5–100.0%) and 92.1% specificity (95% CI 83.5–100.0%) in the blinded validation set. Of particular importance, QCIGISH achieved 97.1% sensitivity (95% CI 91.6–100.0%) for carcinoma in situ to stage IB cancers with 100% sensitivity and 91.7% specificity (95% CI 76.0–100.0%) noted for pulmonary nodules with diameters ≤ 2 cm. Conclusions: Our findings demonstrated the diagnostic value of epigenetic imprinting alterations as highly accurate translational biomarkers for a more definitive diagnosis of suspicious lung lesions. [ABSTRACT FROM AUTHOR]

Asthma is the most common chronic respiratory disease and has been declared a global public health problem by the World Health Organization. Due to the high heterogeneity and complexity, asthma can be classified into different 'phenotypes' and it is still difficult to assess the phenotypes and stages of asthma by traditional methods. In recent years, mass spectrometry-based proteomics studies have made significant progress in sensitivity and accuracy of protein identification and quantitation, and are able to obtain differences in protein expression across samples, which provides new insights into the mechanisms and classification of asthma. In this article, we summarize research strategies in quantitative proteomics, including labeled, label-free and targeted quantification, and highlight the advantages and disadvantages of each. In addition, new applications of quantitative proteomics and the current status of research in asthma have also been discussed. In this study, online resources such as PubMed and Google Scholar were used for literature retrieval. The application of quantitative proteomics in asthma has an important role in identifying asthma subphenotypes, revealing potential pathogenesis and therapeutic targets. But the proteomic studies on asthma are not sufficient, as most of them are in the phase of biomarker discovery. [ABSTRACT FROM AUTHOR]

Background: Epigenetic alterations are involved in most cancers, but its application in cancer diagnosis is still limited. More practical and intuitive methods to detect the aberrant expressions from clinical samples using highly sensitive biomarkers are needed. In this study, we developed a novel approach in identifying, visualizing, and quantifying the biallelic and multiallelic expressions of an imprinted gene panel associated with cancer status. We evaluated the normal and aberrant expressions measured using the imprinted gene panel to formulate diagnostic models which could accurately distinguish the imprinting differences of normal and benign cases from cancerous tissues for each of the ten cancer types. Results: The Quantitative Chromogenic Imprinted Gene In Situ Hybridization (QCIGISH) method developed from a 1013-case study which provides a visual and quantitative analysis of non-coding RNA allelic expressions identified the guanine nucleotide-binding protein, alpha-stimulating complex locus (GNAS), growth factor receptor-bound protein (GRB10), and small nuclear ribonucleoprotein polypeptide N (SNRPN) out of five tested imprinted genes as efficient epigenetic biomarkers for the early-stage detection of ten cancer types. A binary algorithm developed for cancer diagnosis showed that elevated biallelic expression (BAE), multiallelic expression (MAE), and total expression (TE) measurements for the imprinted gene panel were associated with cell carcinogenesis, with the formulated diagnostic models achieving consistently high sensitivities (91–98%) and specificities (86–98%) across the different cancer types. Conclusions: The QCIGISH method provides an innovative way to visually assess and quantitatively analyze individual cells for cancer potential extending from hyperplasia and dysplasia until carcinoma in situ and invasion, which effectively supplements standard clinical cytologic and histopathologic diagnosis for early cancer detection. In addition, the diagnostic models developed from the BAE, MAE, and TE measurements of the imprinted gene panel GNAS, GRB10, and SNRPN could provide important predictive information which are useful in early-stage cancer detection and personalized cancer management. [ABSTRACT FROM AUTHOR]

Aim High-resolution magic-angle spinning proton nuclear magnetic resonance (HRMAS 1H NMR) spectroscopy technique was employed to analyze the metabonomic characterizations of lung cancer tissues in hope to identify potential diagnostic biomarkers for malignancy detection and staging research of lung tissues. Methods HRMAS 1H NMR spectroscopy technique can rapidly provide important information for accurate diagnosis and staging of cancer tissues owing to its noninvasive nature and limited requirement for the samples, and thus has been acknowledged as an excellent tool to investigate tissue metabolism and provide a more realistic insight into the metabonomics of tissues when combined with multivariate data analysis (MVDA) such as component analysis and orthogonal partial least squares-discriminant analysis in particular. Results HRMAS 1H NMR spectra displayed the metabonomic differences of 32 lung cancer tissues at the different stages from 32 patients. The significant changes ( P < 0.05) of some important metabolites such as lipids, aspartate and choline-containing compounds in cancer tissues at the different stages had been identified. Furthermore, the combination of HRMAS 1H NMR spectroscopy and MVDA might potentially and precisely provided for a high sensitivity, specificity, prediction accuracy in the positive identification of the staging for the cancer tissues in contrast with the pathological data in clinic. Conclusion This study highlighted the potential of metabonomics in clinical settings so that the techniques might be further exploited for the diagnosis and staging prediction of lung cancer in future. [ABSTRACT FROM AUTHOR]

Background Lung adenocarcinoma has one of the poorest outcomes of any cancer worldwide, in part due to the lack of a reliable means of early detection. Long noncoding RNAs (lncRNAs) have been shown to be deregulated in some types of cancer; however, the contributions of lncRNAs to lung adenocarcinoma remain unknown. Methods We described the expression profile of lncRNAs in human lung adenocarcinoma at an early stage and the corresponding adjacent nontumorous tissues (NT) by microarray. From the microarray analysis, a total of 1170 lncRNAs were significantly differentially expressed in three early stage lung adenocarcinoma tissues compared with NT (fold-change ≥ 2.0, p ≤ 0.05). Candidate biomarkers were selected from the significantly differentially expressed lncRNAs based on our established filtering pipeline; subsequently, marker optimization and validation by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) on a total of 102 pairs of early stage lung adenocarcinoma and NT samples. Results A panel of 5-lncRNA was identified that could distinguish early stage lung adenocarcinoma from NT samples with high sensitivity and specificity. The area under the receiver operating characteristic curve (AUC) for tumor identification in the training and validation sets were 0.978 and 0.987, respectively. Conclusions Our results are the first to reveal differentially expressed lncRNAs in early stage lung adenocarcinoma and suggest that lncRNAs may be novel candidate biomarkers for the early detection of this disease. [ABSTRACT FROM AUTHOR]