Comparing intestinal versus diffuse gastric cancer using a PEFF-oriented proteomic pipeline

Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite

The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.

Identification and validation of PGLS as a metabolic target for early screening and prognostic monitoring of gastric cancer

BACKGROUND

Gastric cancer is the third leading cause of cancer-related death in the world. The purpose of the present study is to investigate the expression and prognostic significance of 6-phosphogluconolactonase (PGLS) in gastric cancer.

METHODS

The protein extracted from a panel of four pairs of gastric cancer tissues and adjacent tissues, labeled with iTRAQ (8-plex) reagents, and followed by LC-ESI-MS/MS. The expressions of proteins were further validated by immunohistochemistry analysis. The expression levels of mRNA were analyzed and validated in the Oncomine database. The correlations of PGLS with prognostic outcomes were evaluated with Kaplan-Meier plotter database.

RESULTS

The present study found that PGLS was significantly up-regulated in gastric cancer by using iTRAQ-based proteomics and immunohistochemistry analysis. The sensitivity of PGLS in gastric cancer was 72.9%. The high expression of PGLS was significantly correlated with TNM staging in gastric cancer (p = 0.02). The overexpression of PGLS predicts worse overall survival (OS) and post-progression survival (PPS) for gastric cancer (OS, HR = 1.48, p = 2.1e-05; PPS, HR = 1.35, p = 0.015). Specifically, the high PGLS expression predicts poor OS, PPS in male gastric cancer patients, in patients with lymph node metastasis and in patients with Her-2 (-).

CONCLUSIONS

These findings suggested that PGLS was aberrantly expressed in gastric cancer and predicts poor overall survival, post-progression survival for gastric cancer patients. The present study collectively supported that PGLS is an important target for early determining and follow-up monitoring for gastric cancer.

Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment

Gastric cancer is the fifth most diagnosed cancer in the world, affecting more than a million people and causing nearly 783,000 deaths each year. The prognosis of advanced gastric cancer remains extremely poor despite the use of surgery and adjuvant therapy. Therefore, understanding the mechanism of gastric cancer development, and the discovery of novel diagnostic biomarkers and therapeutics are major goals in gastric cancer research. Here, we review recent progress in application of omics technologies in gastric cancer research, with special focus on the utilization of systems biology approaches to integrate multi-omics data. In addition, the association between gastrointestinal microbiota and gastric cancer are discussed, which may offer insights in exploring the novel microbiota-targeted therapeutics. Finally, the application of data-driven systems biology and machine learning approaches could provide a predictive understanding of gastric cancer, and pave the way to the development of novel biomarkers and rational design of cancer therapeutics.

MinProtMaxVP: Generating a minimized number of protein variant sequences containing all possible variant peptides for proteogenomic analysis

Identifying single-amino-acid variants (SAVs) from mass spectrometry-based experiments is critical for validating single-nucleotide variants (SNVs) at the protein level to facilitate biomedical research. Currently, two approaches are usually applied to convert SNV annotations into SAV-harboring protein sequences. One approach generates one sequence containing exactly one SAV, and the other all SAVs. However, they may neglect the possibility of SAV combinations, e.g., haplotypes, existing in bio-samples. Therefore, it is necessary to consider all SAV combinations of a protein when generating SAV-harboring protein sequences. In this paper, we propose MinProtMaxVP, a novel approach which selects a minimized number of SAV-harboring protein sequences generated from the exhaustive approach, while still accommodating all possible variant peptides, by solving a classic set covering problem. Our study on known haplotype variations of TAS2R38 justifies the necessity for MinProtMaxVP to consider all combinations of SAVs. The performance of MinProtMaxVP is demonstrated by an in silico study on OR2T27 with five SAVs and real experimental data of the HEK293 cell line. Furthermore, assuming simulated somatic and germline variants of OR2T27 in tumor and normal tissues demonstrates that when adopting the appropriate somatic and germline SAV integration strategy, MinProtMaxVP is adaptable to labeling and label-free mass spectrometry-based experiments. SIGNIFICANCE: We present MinProtMaxVP, a novel approach to generate SAV-harboring protein sequences for constructing a customized protein sequence database, which is used in database searching for variant peptide identification. This approach outperforms the existing approaches in generating all possible variant peptides to be included in protein sequences and possibly leading to identification of more variant peptides in proteogenomic analysis.

Thrombopoietin is associated with a prognosis of gastric adenocarcinoma

OBJECTIVE Thrombopoietin (THPO) is well-known as a megakaryocyte growth and development factor (MGDF) involved in megakaryocyte proliferation and maturation. To explore the biological effects of THPO in gastric adenocarcinoma, we conducted this study. Methods: By accessing the TCGA database, the expression level of THPO was determined in tumor tissues. The association between THPO expression and clinical features, or prognostic significance was described by Cox regression analysis and Kaplan-Meier. The SiRNA method was used to decline the THPO expression; then cell viability, invasion, and migration were detected to verify the effects of the knockdown of THPO. qPCR and western blotting were implemented to examine the expression level of THPO. Results: The expression of THPO was increased in tumor tissue and cells, its high-regulation was associated with a poor prognosis in patients with gastric adenocarcinoma. Cell viability, invasion, and migration were suppressed in AGS with the down-regulation of THPO. Furthermore, on the basis of si-THPO transfection, E-cadherin was promoted while N-cadherin and Vimentin were attenuated. CONCLUSION Our results revealed that THPO may be a potent marker of gastric adenocarcinoma, providing a novel potential screening method for gastric adenocarcinoma.

Proteomics pinpoints alterations in grade I meningiomas of male versus female patients

Meningiomas are among the most common primary tumors of the central nervous system (CNS) and originate from the arachnoid or meningothelial cells of the meninges. Surgery is the first option of treatment, but depending on the location and invasion patterns, complete removal of the tumor is not always feasible. Reports indicate many differences in meningiomas from male versus female patients; for example, incidence is higher in females, whereas males usually develop the malignant and more aggressive type. With this as motivation, we used shotgun proteomics to compare the proteomic profile of grade I meningioma biopsies of male and female patients. Our results listed several differentially abundant proteins between the two groups; some examples are S100-A4 and proteins involved in RNA splicing events. For males, we identified enriched pathways for cell-matrix organization and for females, pathways related to RNA transporting and processing. We believe our findings contribute to the understanding of the molecular differences between grade I meningiomas of female and male patients.

Proteomics Standards Initiative Extended FASTA Format

Mass-spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs) in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI extended FASTA format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backward compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available at http://www.psidev.info/peff .

Improving deep proteome and PTMome coverage using tandem HILIC-HPRP peptide fractionation strategy

Despite being orthogonal to reverse-phase separation and valuable for posttranslational modification (PTM) pre-enrichment, hydrophilic interaction liquid chromatography (HILIC) has not been widely adopted for large-scale proteomic applications. Here, we first evaluated the performance of HILIC in comparison with the popular high-pH reverse-phase (HPRP) separation, as the first dimension for tryptic peptide fractionation in a shotgun workflow to characterize the complex 293T cell proteome. The data indicated that the complementary nature of HILIC and HPRP for peptide separation was mainly due to different hydrophobicity preferences. Realizing that uncaptured components from one mode can be resolved in the other mode, we then designed and compared two multidimensional separation schemes using HILIC and HPRP in tandem for peptide prefractionation, in terms of identification efficiency and coverage at peptide, protein, and PTM levels. A total of 22,604 and 23,566 peptides corresponding to 4481 and 4436 proteins from 293T cell lysate were detected using HILIC-HPRP- and HPRP-HILIC-based shotgun proteomics workflow, respectively. In addition, without assistance of enrichment techniques, the tandem fractionation methods aided to identify 46 different PTMs from over 10,000 of spectra using blind modification search algorithm. We concluded that HILIC is a valuable alternative option for peptide prefractionation in a large-scale proteomic study, but can be further augmented with the use of a secondary HPRP separation.

Identification of putative drugs for gastric adenocarcinoma utilizing differentially expressed genes and connectivity map

Gastric adenocarcinoma (GAC) is a challenging disease with dim prognosis even after surgery; hence, novel treatments for GAC are in urgent need. The aim of the present study was to explore new potential compounds interfering with the key pathways related to GAC progression. The differentially expressed genes (DEGs) between GAC and adjacent tissues were identified from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. Connectivity Map (CMap) was performed to screen candidate compounds for treating GAC. Subsequently, pathways affected by compounds were overlapped with those enriched by the DEGs to further identify compounds which had anti-GAC potential. A total of 843 DEGs of GAC were identified. Via Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, 13 pathways were significantly enriched. Moreover, 78 compounds with markedly negative correlations with DEGs were revealed in CMap database (P<0.05 and Enrichment <0). Subpathways of cell cycle and p53 signaling pathways, and core genes of these compounds, cyclin B1 (CCNB1) and CDC6, were identified. This study further revealed seven compounds that may be effective against GAC; in particular methylbenzethonium chloride and alexidine have never yet been reported for GAC treatment. In brief, the candidate drugs identified in this study may provide new options to improve the treatment of patients with GAC. However, the biological effects of these drugs need further investigation.