Histopathological classification of lung cancer has important implications in the application of clinical practice guidelines and the prediction of patient prognosis. Thus, we focused on discovering glycobiomarker candidates to classify the types of lung cancer tissue. First, we performed lectin microarray analysis of lung cancer tissue specimens and cell lines and identified Aleuria aurantia lectin (AAL), Hippeastrum hybrid lectin (HHL), and Concanavalia ensiformis agglutinin (ConA) as lectin probes specific to non-small cell lung carcinoma (NSCLC). LC-MS-based analysis was performed for the comprehensive identification of glycoproteins and N-linked glycosylation sites using lectin affinity capture of NSCLC-specific glycoforms of glycoproteins. This analysis identified 1092 AAL-bound glycoproteins (316 gene symbols) and 948 HHL/ConA-bound glycoproteins (279 gene symbols). The lectin microarray-assisted verification using 15 lung cancer cell lines revealed the NSCLC-specific expression of fibronectin. The glycosylation profiling of fibronectin indicated that the peanut agglutinin (PNA) signal appeared to differentiate two NSCLC types, adenocarcinoma and large cell carcinoma, whereas the protein expression level was similar between these types. Our glycoproteomics approach together with the concurrent use of an antibody and lectin is applicable to the quantitative and qualitative monitoring of variations in glycosylation of fibronectin specific to certain types of lung cancer tissue.

Background/Aims: The identification of predictive biomarkers for Alzheimer’s disease (AD) from urine would aid in screening for the disease, but information about biological and pathophysiological changes in the urine of AD patients is limited. This study aimed to explore the comprehensive profile and molecular network relations of urinary proteins in AD patients. Methods: Urine samples collected from 18 AD patients and 18 age- and sex-matched cognitively normal controls were analyzed by mass spectrometry and semiquantified with the normalized spectral index method. Bioinformatics analyses were performed on proteins which significantly increased by more than 2-fold or decreased by less than 0.5-fold compared to the control (p < 0.05) using DAVID bioinformatics resources and KeyMolnet software. Results: The levels of 109 proteins significantly differed between AD patients and controls. Among these, annotation clusters related to lysosomes, complement activation, and gluconeogenesis were significantly enriched. The molecular relation networks derived from these proteins were mainly associated with pathways of lipoprotein metabolism, heat shock protein 90 signaling, matrix metalloproteinase signaling, and redox regulation by thioredoxin. Conclusion: Our findings suggest that changes in the urinary proteome of AD patients reflect systemic changes related to AD pathophysiology.

Introduction: Biomarkers of Alzheimer’s disease (AD) that can easily be measured in routine health checkups are desirable. Urine is a source of biomarkers that can be collected easily and noninvasively. We previously reported on the comprehensive profile of the urinary proteome of AD patients and identified proteins estimated to be significantly increased or decreased in AD patients by a label-free quantification method. The present study aimed to validate urinary levels of proteins that significantly differed between AD and control samples from our proteomics study (i.e., apolipoprotein C3 [ApoC3], insulin-like growth factor-binding protein 3 [Igfbp3], and apolipoprotein D [ApoD]). Methods: Enzyme-linked immunosorbent assays (ELISAs) were performed using urine samples from the same patient and control groups analyzed in the previous proteomics study (18 AD and 18 controls, set 1) and urine samples from an independent group of AD patients and controls (13 AD, 5 mild cognitive impairment [MCI], and 32 controls) from the National Center for Geriatrics and Gerontology Biobank (set 2). Results: In set 1, the crude urinary levels of ApoD, Igfbp3, and creatinine-adjusted ApoD were significantly higher in the AD group relative to the control group (p = 0.003, p = 0.002, and p = 0.019, respectively), consistent with our previous proteomics results. In set 2, however, the crude urinary levels of Igfbp3 were significantly lower in the AD+MCI group than in the control group (p = 0.028), and the levels of ApoD and ApoC3 did not differ significantly compared to the control group. Combined analysis of all samples revealed creatinine-adjusted ApoC3 levels to be significantly higher in the AD+MCI group (p = 0.015) and the AD-only group (p = 0.011) relative to the control group. Conclusion: ApoC3 may be a potential biomarker for AD, as validated by ELISA. Further analysis of ApoC3 as a urinary biomarker for AD is warranted.

In an attempt to complete human proteome project (HPP), Chromosome-Centric Human Proteome Project (C-HPP) launched the journey of missing protein (MP) investigation in 2012. However, 2579 and 572 protein entries in the neXtProt (2017-1) are still considered as missing and uncertain proteins, respectively. Thus, in this study, we proposed a pipeline to analyze, identify, and validate human missing and uncertain proteins in open-access transcriptomics and proteomics databases. Analysis of RNA expression pattern for missing proteins in Human protein Atlas showed that 28% of them, such as Olfactory receptor 1I1 ( O60431 ), had no RNA expression, suggesting the necessity to consider uncommon tissues for transcriptomic and proteomic studies. Interestingly, 21% had elevated expression level in a particular tissue (tissue-enriched proteins), indicating the importance of targeting such proteins in their elevated tissues. Additionally, the analysis of RNA expression level for missing proteins showed that 95% had no or low expression level (0-10 transcripts per million), indicating that low abundance is one of the major obstacles facing the detection of missing proteins. Moreover, missing proteins are predicted to generate fewer predicted unique tryptic peptides than the identified proteins. Searching for these predicted unique tryptic peptides that correspond to missing and uncertain proteins in the experimental peptide list of open-access MS-based databases (PA, GPM) resulted in the detection of 402 missing and 19 uncertain proteins with at least two unique peptides (≥9 aa) at <(5 × 10)% FDR. Finally, matching the native spectra for the experimentally detected peptides with their SRMAtlas synthetic counterparts at three transition sources (QQQ, QTOF, QTRAP) gave us an opportunity to validate 41 missing proteins by ≥2 proteotypic peptides.

Secretogranin III (SgIII) is a member of the chromogranin/secretogranin family of neuroendocrine secretory proteins. Granins are expressed in endocrine and neuroendocrine cells and subsequently processed into bioactive hormones. Although granin-derived peptide expression is correlated with neuroendocrine carcinomas, little is known about SgIII. We previously identified SgIII by a comparative glycoproteomics approach for elucidation of glycobiomarker candidates in lung carcinoma. Here, we examined the expression, secretion, and glycosylation of SgIII to identify novel biomarkers of small cell lung carcinoma (SCLC). In comparative immunohistochemical analysis and secretion profiling, SgIII was observed in all types of lung cancer. However, low-molecular-weight SgIII (short-form SgIII) was specifically found in SCLC culture medium. Glycoproteomics analysis showed that a fucosylated glycan was attached to the first of three potential N-glycosylation sites and an unfucosylated glycan was detected on the second site; however, the third site was not glycosylated. Next, we performed lectin capture with a fucose-binding lectin and detected short-form SgIII specifically in the sera of patients with SCLC. The results suggested an association between the fucosylated glycoform of short-form SgIII and SCLC. Thus, fucosylated short-form SgIII may be a valuable biomarker for SCLC and could be used to monitor development of the disease. All MS data are available via ProteomeXchange and jPOST with identifiers PXD007626 and JPST000313, respectively.