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

Wippel, Helisa Helena; Santos, Marlon D.M.; Clasen, Milan A; Kurt, Louise Ulrich; Nogueira, Fábio César Sousa; Carvalho, Carlos Eduardo; Mccormick, Thaís Messias; Neto, Guilherme Pinto Bravo; Alves, Lysangela Ronalte; Carvalho, Maria da Glória da Costa; Carvalho, Paulo C.; Fischer, Juliana de Saldanha da Gama

doi:10.1016/j.jprot.2017.10.005

Cited by 12 publications

(11 citation statements)

References 57 publications

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“…However, this precept can often be a chicken-and-egg problem in that it is often difficult to finalize a standard until it has been well tested by several implementations, and yet it is difficult to convince software developers to implement a format that has not yet been finalized. PEFF has finally achieved critical mass with one major search engine implementation (Comet), several major exporters (neXtProt and UniProt) supporting PEFF, and emerging research citing the use of PEFF in the workflow 48 . As a key point, several software libraries now support PEFF.…”

Section: Discussionmentioning

confidence: 99%

Proteomics Standards Initiative Extended FASTA Format

et al. 2019

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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.

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Section: Discussionmentioning

confidence: 99%

Proteomics Standards Initiative Extended FASTA Format

et al. 2019

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“…Using clusterin isoform 1, the highest AUC values to distinguish the advanced or early gastric cancer from normal controls are 0.94 and 0.88, respectively ( Yoo et al, 2017 ). In addition, the comprehensive proteomics studies had also been employed to classify gastric cancer subtypes as genomics data ( Ge et al, 2018 ; Wippel et al, 2018 ; Mun et al, 2019 ). The diffuse-type gastric cancer can be further classified into three or four distinct subtypes according to proteome profiling, respectively ( Ge et al, 2018 ; Mun et al, 2019 ).…”

Section: Proteomics and Metabolomics In Gastric Cancermentioning

confidence: 99%

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

Shi

Wei

2020

Front. Mol. Biosci.

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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 datadriven 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.

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“…All patients were diagnosed with type I meningioma, as shown in the Table 6. www.nature.com/scientificreports www.nature.com/scientificreports/ Sample preparation. The twelve tissue samples of grade I meningiomas were pulverized in liquid nitrogen, as previously described 78 . Then, protein extraction was made in a solution of 0.1% of RapiGest (w/v) in 50 mM triethylammonium bicarbonate (TEAB).…”

Section: Patients This Study Was Approved By the Ethics Committee Ofmentioning

confidence: 99%

“…Mass spectrometry analysis. We used a nanoLC Easy1000 coupled online with a Q-Exactive plus mass spectrometer to generate two proteomic profiles of each biological replicate using the same materials and methods as we previously described 78 .…”

Section: Patients This Study Was Approved By the Ethics Committee Ofmentioning

confidence: 99%

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

Silva

Wippel

Santos

et al. 2020

Sci Rep

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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. Meningioma is a high incidence tumor that typically emerges at the arachnoid cap or meningothelial cells of the meninges 1 , commonly from intracranial, intraspinal, or orbital locations, and are usually benign, slow-growing tumors 2. Resonance imaging and molecular markers are frequently used for preliminary diagnosis; yet, surgical removal of the tumor is necessary for histological diagnostic confirmation and improved life quality 1,2. When surgery for total removal of the tumor is not feasible, adjuvant radiation may be used 1. The World Health Organization (WHO) classifies meningiomas according to their histopathological characteristics, mitotic count, and brain invasion pattern in (i) grade I, also known as benign meningiomas (BMs, about 80% of termed cases); (ii) grade II, or atypical meningiomas (AMs, 17% of termed cases); and (iii) grade III, the malignant meningiomas (MMs, 3% of termed cases) 3. Although this classification is valid in terms of prognosis, it lacks information about tumor aggressiveness and recurrence rates 2. Controversially, most recurrent meningiomas correspond to BMs; their metabolic phenotype indicates an aggressive metabolism, resembling that of AM 4. Female patients present approximately double the incidence of meningiomas compared to men 1. Interestingly, the main risk factor for meningiomas is related to hormonal changes as these tumors present hormones receptors (i.e., progesterone and estrogen) 5,6. Moreover, association between meningiomas and breast cancer, mainly due to similar hormonal signaling and genetic predisposition, has also been reported 5. The fact that women diagnosed with breast cancer are more likely to develop meningioma may also justify the higher incidence of this tumor in females 7,8. In general, women usually develop the benign form while males develop the malignant type of meningioma, the aggressive grade III 6. Besides gender, other risk factors associated with this disease include...

show abstract

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

Cited by 12 publications

References 57 publications

Proteomics Standards Initiative Extended FASTA Format

Proteomics Standards Initiative Extended FASTA Format

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

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

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