Esophageal cancer (EC) is one of the malignant tumors with a poor prognosis. The early stage of EC is asymptomatic, so identification of cancer biomarkers is important for early detection and clinical practice. In this study, we compared the protein expression profiles in esophageal squamous cell carcinoma (ESCC) tissues and adjacent normal esophageal tissues from five patients through high-resolution label-free mass spectrometry. Through bioinformatics analysis, we found the differentially expressed proteins of ESCC. To perform the rapid identification of biomarkers, we adopted a high-throughput protein identification technique of Quantitative Dot Blot (QDB). Meanwhile, the QDB results were verified by classical immunohistochemistry. In total 2297 proteins were identified, out of which 308 proteins were differentially expressed between ESCC tissues and normal tissues. By bioinformatics analysis, the four up-regulated proteins (PTMA, PAK2, PPP1CA, HMGB2) and the five down-regulated proteins (Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin) were selected and validated in ESCC by Western Blot. Furthermore, we performed the QDB and IHC analysis in 64 patients and 117 patients, respectively. The PTMA expression was up-regulated gradually along the progression of ESCC, and the PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression. Therefore, we suggest that PTMA might be a potential candidate biomarker for ESCC. In this study, label-free quantitative proteomics combined with QDB revealed that PTMA expression was up-regulated in ESCC tissues, and PTMA might be a potential candidate for ESCC. Since Western Blot cannot achieve rapid and high-throughput screening of mass spectrometry results, the emergence of QDB meets this demand and provides an effective method for the identification of biomarkers.

Purpose: Scotopic vision impairment as an early event is found in diabetic retinopathy. However, the underlying mechanisms behind hyperglycemia-induced scotopic vision impairment remain unclear. This study aims to identify that Grm6 is associated with glutamate accumulation–induced scotopic vision impairment under hyperglycemia. Methods: In this study, diabetic mice with impaired scotopic vision were induced by streptozotocin, and the retinal electrical activity was evaluated using electroretinography. Label-free quantitative proteomic analysis was used to identify differentially expressed proteins in the retinas of diabetic mice. A retinal transcriptome-wide association analysis and correlation screening were performed in BXD mice strains to explore the potential genes associated with hyperglycemia. Gene function enrichment analysis was used to evaluate gene function and to construct the correlation network. Results: In total, 151 proteins were significantly altered in the retina of diabetic mice. Among these 151 candidates, 22 genes presented a significant correlation with blood glucose level (p<0.05), which were enriched in alanine, aspartate, and glutamate metabolism (p=0.003). Moreover, the glutamate catabolism-related genes Slc1a2, Gad1, and Glud1 were significantly negatively correlated with blood glucose at the transcript and proteome levels, which led to glutamate accumulation under hyperglycemia. Among eight types of metabotropic glutamate receptors, Grm6 had the most significant correlation with blood glucose level (R=–0.5291, p=0.0003). Moreover, Grm6 expression was significantly decreased at both the mRNA and protein levels in the diabetic retina. Gene coexpression network analysis further identified that Grm6 was correlated with Rgs9, suggesting that hyperglycemia may impair scotopic vision via the phototransduction pathway. Conclusions: Our study confirms that Grm6 is associated with scotopic vision impairment induced by glutamate accumulation in diabetic mice and provides an efficient strategy for exploring critical biomarkers and pathways through a combination of proteomics and transcriptome-wide association analysis.