Original Article Unclassified renal cell carcinoma: a clinicopathological, comparative genomic hybridization, and whole-genome exon sequencing study

Abstract: Unclassified renal cell carcinoma (URCC) is a rare variant of RCC, accounting for only 3-5% of all cases. Studies on the molecular genetics of URCC are limited, and hence, we report on 2 cases of URCC analyzed using comparative genome hybridization (CGH) and the genome-wide human exon GeneChip technique to identify the genomic alterations of URCC. Both URCC patients (mean age, 72 years) presented at an advanced stage and died within 30 months post-surgery. Histologically, the URCCs were composed of undifferentiated, multinucleated, giant cells with eosinophilic cytoplasm. Immunostaining revealed that both URCC cases had strong p53 protein expression and partial expression of cluster of differentiation-10 and cytokeratin. The CGH profiles showed chromosomal imbalances in both URCC cases: gains were observed in chromosomes 1p11-12, 1q12-13, 2q20-23, 3q22-23, 8p12, and 16q11-15, whereas losses were detected on chromosomes 1q22-23, 3p12-22, 5p30-ter, 6p, 11q, 16q18-22, 17p12-14, and 20p. Compared with 18 normal renal tissues, 40 mutated genes were detected in the URCC tissues, including 32 missense and 8 silent mutations. Functional enrichment analysis revealed that the missense mutation genes were involved in 11 different biological processes and pathways, including cell cycle regulation, lipid localization and transport, neuropeptide signaling, organic ether metabolism, and ATP-binding cassette transporter signaling. Our findings indicate that URCC may be a highly aggressive cancer, and the genetic alterations identified herein may provide clues regarding the tumorigenesis of URCC and serve as a basis for the development of targeted therapies against URCC in the future

D4.2 Final report on trade-off investigations

Research activities in METIS WP4 include several as pects related to the network-level of future wireless communication networks. Thereby, a large variety of scenarios is considered and solutions are proposed to serve the needs envis ioned for the year 2020 and beyond. This document provides vital findings about several trade-offs that need to be leveraged when designing future network-level solutions. In more detail, it elaborates on the following trade- offs: • Complexity vs. Performance improvement • Centralized vs. Decentralized • Long time-scale vs. Short time-scale • Information Interflow vs. Throughput/Mobility enha ncement • Energy Efficiency vs. Network Coverage and Capacity Outlining the advantages and disadvantages in each trade-off, this document serves as a guideline for the application of different network-level solutions in different situations and therefore greatly assists in the design of future communication network architectures.Aydin, O.; Ren, Z.; Bostov, M.; Lakshmana, TR.; Sui, Y.; Svensson, T.; Sun, W.... (2014). D4.2 Final report on trade-off investigations. http://hdl.handle.net/10251/7676

Protein target discovery of drug and its reactive intermediate metabolite by using proteomic strategy

Identifying protein targets of bioactive compounds is an effective approach to discover unknown protein functions, identify molecular mechanisms of drug action, and obtain information for optimization of lead compounds. At the same time, metabolic activation of a drug can lead to cytotoxicities. Therefore, it is very important to systemically characterize the drug and its reactive intermediate. Mass spectrometry-based proteomic approach has emerged as the most efficient to study protein functions and modifications. This review will discuss method development for the drug target discovery and the application in different fields including the drug toxicity mechanism caused by reactive metabolites

Glycoproteomic Analysis of Urinary Extracellular Vesicles for Biomarkers of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) accounts for the most common form of primary liver cancer cases and constitutes a major health problem worldwide. The diagnosis of HCC is still challenging due to the low sensitivity and specificity of the serum α-fetoprotein (AFP) diagnostic method. Extracellular vesicles (EVs) are heterogeneous populations of phospholipid bilayer-enclosed vesicles that can be found in many biological fluids, and have great potential as circulating biomarkers for biomarker discovery and disease diagnosis. Protein glycosylation plays crucial roles in many biological processes and aberrant glycosylation is a hallmark of cancer. Herein, we performed a comprehensive glycoproteomic profiling of urinary EVs at the intact N-glycopeptide level to screen potential biomarkers for the diagnosis of HCC. With the control of the spectrum-level false discovery rate ≤1%, 756 intact N-glycopeptides with 154 N-glycosites, 158 peptide backbones, and 107 N-glycoproteins were identified. Out of 756 intact N-glycopeptides, 344 differentially expressed intact N-glycopeptides (DEGPs) were identified, corresponding to 308 upregulated and 36 downregulated N-glycopeptides, respectively. Compared to normal control (NC), the glycoproteins LG3BP, PIGR and KNG1 are upregulated in HCC-derived EVs, while ASPP2 is downregulated. The findings demonstrated that specific site-specific glycoforms in these glycoproteins from urinary EVs could be potential and efficient non-invasive candidate biomarkers for HCC diagnosis

Glycoproteomic Analysis of Urinary Extracellular Vesicles for Biomarkers of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) accounts for the most common form of primary liver cancer cases and constitutes a major health problem worldwide. The diagnosis of HCC is still challenging due to the low sensitivity and specificity of the serum α-fetoprotein (AFP) diagnostic method. Extracellular vesicles (EVs) are heterogeneous populations of phospholipid bilayer-enclosed vesicles that can be found in many biological fluids, and have great potential as circulating biomarkers for biomarker discovery and disease diagnosis. Protein glycosylation plays crucial roles in many biological processes and aberrant glycosylation is a hallmark of cancer. Herein, we performed a comprehensive glycoproteomic profiling of urinary EVs at the intact N-glycopeptide level to screen potential biomarkers for the diagnosis of HCC. With the control of the spectrum-level false discovery rate ≤1%, 756 intact N-glycopeptides with 154 N-glycosites, 158 peptide backbones, and 107 N-glycoproteins were identified. Out of 756 intact N-glycopeptides, 344 differentially expressed intact N-glycopeptides (DEGPs) were identified, corresponding to 308 upregulated and 36 downregulated N-glycopeptides, respectively. Compared to normal control (NC), the glycoproteins LG3BP, PIGR and KNG1 are upregulated in HCC-derived EVs, while ASPP2 is downregulated. The findings demonstrated that specific site-specific glycoforms in these glycoproteins from urinary EVs could be potential and efficient non-invasive candidate biomarkers for HCC diagnosis

Recent advances in mass spectrometry-based peptidome analysis

The peptidome, which is the low-molecular-weight subset of the proteome, has attracted increasing attention in recent years. However, with the interference of high-abundance protein components in complex biological mixtures (e.g., serum), selective extraction of endogenous peptides is the first and most important step before analyzing the peptidome. A number of methods and technologies have now been developed for the selective enrichment, fractionation, quantitative analysis of the endogenous peptides and their application in the potential biomarker discovery. This review will cover the methods and technologies developed in recent years for the peptidome analysis on the selective extraction, multidimensional separation and quantitative analysis, as well as their application for clinical diagnosis and biomarker discovery. The future prospects of the peptidome are also discussed

Quantitative MS analysis of therapeutic mAbs and their glycosylation for pharmacokinetics study

Therapeutic mAbs play an important role in the treatment of a wide range of diseases. Due to their complexity, comprehensive evaluation of their pharmacokinetics has yet to be fully achieved. It is crucial to develop sensitive, accurate, reliable, and reproducible methods for quantitation of mAbs in complex samples. In addition, it is also important to evaluate the PTMs which can affect their safety and/or effectiveness. MS-based methods provide an emerging approach for quantitation of proteins and their modification forms. In this review, we give a brief overview of quantification analysis of mAbs in complex biological samples and the characterization of N-glycosylation by biological MS