Dataset Information

Identification of the novel progression factors in renal cell carcinoma through global proteomics

ABSTRACT: Renal cell carcinoma (RCC) is among the top 8 most diagnosed cancer in United States, which 79,000 patients newly were diagnosed and 13,920 deaths [1]. The clear-cell RCC (ccRCC) is the most common histopathological subtype representing approximately 85~90% in all diagnosed case [2]. In renal cancer treatment, metastasis is considered the most serious problem. Metastatic RCC (mRCC) is a fetal disease. It is known that about 25~30% of patients diagnosed with early kidney cancer have metastasized [3, 4]. The 5-year survival rate of patients with mRCC is 11.7%, and the prognosis is very poor [5]. Approximately 30% of patients have recurrence despite removal of the primary tumor, including 10-15% of patients found in T1 and clinically removed [4]. The mRCC patients show poor response to chemotherapy and radiotherapy, the rate of disappointing for treatment is increased from 15% to 25% [6, 7]. The common sites of RCC metastasis is known to the lung (40%), bone (30%), lymph node (22%) and liver (20%) [8].

INSTRUMENT(S): LTQ Orbitrap

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Kidney Epithelial Cell

DISEASE(S): Malignant Neoplasm Of Ovary

SUBMITTER: Ann-Yae Na

LAB HEAD: Sangkyu Lee

PROVIDER: PXD039207 | Pride | 2024-01-30

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	20220506_SILAC_Kac_Global_RCC_F0-1_3-23_.raw	Raw
	20220506_SILAC_Kac_Global_RCC_F0-2_3-23_.raw	Raw
	20220506_SILAC_Kac_Global_RCC_F0-3_3-23_.raw	Raw
	20220506_SILAC_Kac_Global_RCC_F1-1_3-23_.raw	Raw
	20220506_SILAC_Kac_Global_RCC_F1-2_3-23_.raw	Raw

Items per page:

1 - 5 of 28

Publications

Using Comparative Proteomics to Identify Protein Signatures in Clear Cell Renal Cell Carcinoma.

Park Juhee J Lee Eun Hye EH Sim Hyunchae H Na Ann-Yae AY Choi So Young SY Chung Jae-Wook JW Ha Yun-Sok YS Kwon Tae Gyun TG Lee Sangkyu S Lee Jun Nyung JN

Cancer genomics & proteomics 20231101 6

<h4>Background/aim</h4>Renal cell carcinoma (RCC) is one of the most commonly diagnosed cancers in the world. Approximately 25-30% of patients identified with initial kidney cancer will have metastasized tumors, thus 5-year survival rates for these patients are poor. Therefore, biomarker research is required to identify and predict molecular signatures in RCC.<h4>Materials and methods</h4>To address this, we used a mass spectrometry (MS)-based proteomics approach to identify proteins related to ...[more]

PMID: 37889066

Similar Datasets

Project description:The objective was to characterize differences in the secretome of human omental compared with subcutaneous adipose tissue using global gene expression profiling. Gene expression was measured using Affymetrix microarrays in subcutaneous and omental adipose tissue (n=5 independent subjects; 10 arrays). Predictive bioinformatic algorithms were employed to identify those differentially expressed genes that code for secreted proteins and to identify common pathways between these proteins. The time course data was not included in the publication and has yet to be published. Patients undergoing surgery were weight stable one month prior to the operation and were not on any medication. All patients provided informed written consent before inclusion in the study which was approved by the North of Scotland Research Ethics Committee (NOSREC). The subcutaneous and omental white adipose tissue biopsies were analysed from five obese patients (BMI 44.5 +/-10 kg/m2; age 42.7 +/- 10 years) who had fasted overnight. Subcutaneous and omental white adipose tissue biopsies were taken at the time of surgery for vertical banded gastroplasty from n=5 independent subjects who had fasted overnight. (10 arrays). Further subcutaneous adipose tissue samples were taken at approximately four months post vertical banded gastroplasty surgery as the volunteers actively lost weight and at 12 months when the volunteers became weight stable (n=5 independent subjects, three time points 0, 4, and 12 months; 15 arrays). Therefore we report on 20 arrays in total including the arrays used to analyse the omental adipose tissue. Adipose tissue samples were obtained within 5 minutes of the tissue being extracted from the patients and frozen immediately in liquid nitrogen. Subjects had fasted overnight prior to surgery. Total RNA was extracted using the RNeasy Lipid tissue mini kit followed by incubation with RNase free DNase (Qiagen, Crawley, West Sussex) for DNA-free RNA, following the manufacturer's instructions (Qiagen, Crawley, West Sussex). RNA was quantified on the Agilent 2100 Bioanalyser (Agilent Technologies, South Queensferry, West Lothian). This also shows the quality of the RNA extracted from the tissues. Only RNA showing no degradation (RIN greater than 9.0) was processed further. The DNA-free RNA from the subcutaneous and omental adipose tissue samples from the patients was then sent to a commercial company for analyses. The RNA from five volunteers (20 arrays ) was fragmented and hybridised to custom Human NuGo (Nutrigenomics; http://www.nugo.org/everyone/35788) Affymetrix (Santa Clara, CA) gene chips HS 1a520180 following the Affymetrix protocol by Service XS (Leiden, The Netherlands). Briefly, the Affymetrix one-cycle target labelling and control reagents were used to synthesize Biotin-labelled cRNA. The concentration of the cRNA was measured using a Nanodrop (Thermoscientific) and Agilent bioanalyser to rule out the possibility that only very short cRNA products were formed. This could have caused a 3’-5’ bias and influenced the data analysis. Approximately 20µg cRNA was used for further fragmentation and finally 10µg was used for the hybridisations. The Affymetrix protocols were followed for the hybridisations, washing, staining and scanning of the chips

Project description:The objective was to characterize differences in the secretome of human omental compared with subcutaneous adipose tissue using global gene expression profiling. Gene expression was measured using Affymetrix microarrays in subcutaneous and omental adipose tissue (n=3 independent subjects; 6 arrays). Predictive bioinformatic algorithms were employed to identify those differentially expressed genes that code for secreted proteins and to identify common pathways between these proteins. All patients provided informed written consent before inclusion in the study which was approved by the North of Scotland Research Ethics Committee (NOSREC). The subcutaneous and omental white adipose tissue biopsies were analysed from three obese patients (BMI 39.1+/-1.9 kg/m2; age 36 +/- 8 years) who had fasted overnight. Subcutaneous and omental white adipose tissue biopsies were taken at the time of surgery for vertical banded gastroplasty from n=3 independent subjects who had fasted overnight. (6 arrays). Adipose tissue samples were obtained within 5 minutes of the tissue being extracted from the patients and frozen immediately in liquid nitrogen. Subjects had fasted overnight prior to surgery. Total RNA was extracted using the RNeasy Lipid tissue mini kit followed by incubation with RNase free DNase (Qiagen, Crawley, West Sussex) for DNA-free RNA, following the manufacturer's instructions (Qiagen, Crawley, West Sussex). RNA was quantified on the Agilent 2100 Bioanalyser (Agilent Technologies, South Queensferry, West Lothian). This also shows the quality of the RNA extracted from the tissues. Only RNA showing no degradation (RIN greater than 9.0) was processed further. The DNA-free RNA from the subcutaneous and omental adipose tissue samples from the patients was then sent to a commercial company for analyses. The RNA from three volunteers (6 arrays ) was fragmented and hybridised to Human Affymetrix U133 plus 2 (Santa Clara, CA) gene chips following the Affymetrix protocol by the MRC Geneservice (MRC HGMP Resource centre, Babraham, Cambridge). Briefly, the Affymetrix one-cycle target labelling and control reagents were used to synthesize Biotin-labelled cRNA. The concentration of the cRNA was measured using a Nanodrop (Thermoscientific) and Agilent bioanalyser to rule out the possibility that only very short cRNA products were formed. This could have caused a 3’-5’ bias and influenced the data analysis. Approximately 20µg cRNA was used for further fragmentation and finally 10µg was used for the hybridisations. The Affymetrix protocols were followed for the hybridisations, washing, staining and scanning of the chips