Comparative proteomic profiling identifies potential prognostic factors for human clear cell renal cell carcinoma

Sun, Xiang; Zhang, Hongwei; Luo, Long-Hua; Zhong, Kezhao; Ma, Yu‐Shui; Fan, Linlin; Fu, Da; Wan, Lijuan

doi:10.3892/or.2016.5159

Cited by 22 publications

(33 citation statements)

References 17 publications

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“…In our study, we performed a quantitative proteomic analysis on 16 patients including NSCLC and paired adjacent non-tumor tissues using label-free quantitative proteomics and LC-MS/MS to identify differentially expressed proteins. Proteomic-based approaches carry out analyses mainly at translational levels and complex post-translational levels, which are not conducted by gene analysis [ 8 ]. These analyses at translational levels and complex post-translational levels perform an important role in detecting the complex cancer-related biological processes and potential prognostic biomarkers for cancers.…”

Section: Discussionmentioning

confidence: 99%

Combination of CALR and PDIA3 is a potential prognostic biomarker for non-small cell lung cancer

Wang

Chen

et al. 2017

Oncotarget

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Proteomic-based approaches for biomarker discovery are promising strategies used in cancer research. In this study, we performed quantitative proteomic analysis on 16 paired samples of non-small cell lung cancer (NSCLC) and adjacent non-tumor lung tissues using label-free quantitative proteomics and liquid chromatography-tandem mass spectrometry/mass spectrometry (LC-MS/MS) to identify differentially expressed proteins. A total of 91 proteins were differentially expressed in NSCLC compared with adjacent non-tumor lung tissues among 4047 identified proteins (fold change > 1.5 or < 0.67, P < 0.05). Gene ontology (GO) analysis, Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis and ingenuity pathway analysis (IPA) of 91 dysregulated proteins showed that they were related to the cancer-associated biological processes. We confirmed that the candidate proteins, calreticulin (CALR) and protein disulfide isomerase family A member 3 (PDIA3) were overexpressed in NSCLC by real-time PCR using 20 paired samples and western blot using 5 paired samples. PDIA3 expression was highly associated with CALR expression (Spearman r = 0.345, P = 0.001) and they were co-localized and interacted with each other in A549 and H460 cells. Moreover, survival analysis performed in tissue microarray with 88 samples indicated that low expression of both CALR and PDIA3 in NSCLC was positively associated with poor overall survival. Combination of CALR and PDIA3 might serve as an efficient biomarker and improved the prediction of NSCLC prognosis significantly (P = 0.023). Our results collectively provide a potential biomarker dataset for NSCLC prognosis, especially the prognostic value of combined expression of CALR and PDIA3.

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Section: Discussionmentioning

confidence: 99%

Combination of CALR and PDIA3 is a potential prognostic biomarker for non-small cell lung cancer

Wang

Chen

et al. 2017

Oncotarget

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“…PCK1 is a key enzyme in the gluconeogenesis pathway. PCK1 is downregulated in hepatocellular carcinoma (HCC) and clear cell renal cell carcinoma and its reduced expression predicts poor prognosis [56,57]. Overexpression of PCK1 decreased viability, induced apoptosis, and inhibited migration of HCC cell lines [58].…”

Section: Serbp1 As a New Regulator Of Cancer Metabolismmentioning

confidence: 99%

The RNA-binding protein SERBP1 functions as a novel oncogenic factor in glioblastoma by bridging cancer metabolism and epigenetic regulation

Kosti

Araújo

et al. 2020

Genome Biol

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Background: RNA-binding proteins (RBPs) function as master regulators of gene expression. Alterations in RBP expression and function are often observed in cancer and influence critical pathways implicated in tumor initiation and growth. Identification and characterization of oncogenic RBPs and their regulatory networks provide new opportunities for targeted therapy. Results: We identify the RNA-binding protein SERBP1 as a novel regulator of glioblastoma (GBM) development. High SERBP1 expression is prevalent in GBMs and correlates with poor patient survival and poor response to chemo-and radiotherapy. SERBP1 knockdown causes delay in tumor growth and impacts cancer-relevant phenotypes in GBM and glioma stem cell lines. RNAcompete identifies a GC-rich region as SERBP1-binding motif; subsequent genomic and functional analyses establish SERBP1 regulation role in metabolic routes preferentially used by cancer cells. An important consequence of these functions is SERBP1 impact on methionine production. SERBP1 knockdown decreases methionine levels causing a subsequent reduction in histone methylation as shown for H3K27me3 and upregulation of genes associated with neurogenesis, neuronal differentiation, and function. Further analysis demonstrates that several of these genes are downregulated in GBM, potentially through epigenetic silencing as indicated by the presence of H3K27me3 sites. Conclusions: SERBP1 is the first example of an RNA-binding protein functioning as a central regulator of cancer metabolism and indirect modulator of epigenetic regulation in GBM. By bridging these two processes, SERBP1 enhances glioma stem cell phenotypes and contributes to GBM poorly differentiated state.

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“…The peptide samples were then desalted onto a solid-phase extraction cartridge (Empore 7 mm/3 ml). The lyophilized peptide mixture was re-suspended in water with 0.1% formic acid (v/v) and its content was estimated by UV light spectral density at 280 nm, then 3 μg of the digest peptides were analyzed by nano-liquid chromatography-tandem mass spectrometry (LC-MS/MS) on LTQ Orbitrap Velos Pro mass spectrometer [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

Proteogenomic characterization and integrative analysis of glioblastoma multiforme

Song

Zhang

et al. 2017

Oncotarget

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Glioblastoma multiforme (GBM), the most aggressive and lethal primary brain tumor, is characterized by very low life expectancy. Understanding the genomic and proteogenomic characteristics of GBM is essential for devising better therapeutic approaches.Here, we performed proteomic profiling of 8 GBM and paired normal brain tissues. In parallel, comprehensive integrative genomic analysis of GBM was performed in silico using mRNA microarray and sequencing data. Two whole transcript expression profiling cohorts were used - a set of 3 normal brain tissues and 22 glioma tissue samples and a cohort of 5 normal brain tissues and 49 glioma tissue samples. A validation cohort included 529 GBM patients from The Cancer Genome Atlas datasets. We identified 36 molecules commonly changed at the level of the gene and protein, including up-regulated TGFBI and NES and down-regulated SNCA and HSPA12A. Single amino acid variant analysis identified 200 proteins with high mutation rates in GBM samples. We further identified 14 differentially expressed genes with high-level protein modification, among which NES and TNC showed differential expression at the protein level. Moreover, higher expression of NES and TNC mRNAs correlated with shorter overall survival, suggesting that these genes constitute potential biomarkers for GBM.

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Comparative proteomic profiling identifies potential prognostic factors for human clear cell renal cell carcinoma

Cited by 22 publications

References 17 publications

Combination of CALR and PDIA3 is a potential prognostic biomarker for non-small cell lung cancer

Combination of CALR and PDIA3 is a potential prognostic biomarker for non-small cell lung cancer

The RNA-binding protein SERBP1 functions as a novel oncogenic factor in glioblastoma by bridging cancer metabolism and epigenetic regulation

Proteogenomic characterization and integrative analysis of glioblastoma multiforme

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