Integrated Omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact

Li, Lei; Wei, Yuhong; To, Christine; Zhu, Chang-Qi; Tong, Jiefei; Pham, Nhu-An; Taylor, Paul; Ignatchenko, Vladimir; Ignatchenko, Alex; Zhang, Wen; Wang, Dennis; Yanagawa, Naoki; Li, Ming; Pintilie, Melania; Liu, Geoffrey; Muthuswamy, Lakshmi; Shepherd, Frances A.; Tsao, Ming‐Sound; Kislinger, Thomas; Moran, Michael F.

doi:10.1038/ncomms6469

Cited by 96 publications

(119 citation statements)

References 68 publications

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“…Many researchers have applied quantitative proteomic approaches or combined proteomic and genomic analyses for the systematic discovery of potential lung cancer biomarkers (13,14,(22)(23)(24)(25). We compared the 133 potential lung cancer biomarkers identified in the current study with six lung cancer biomarker-related proteome data sets.…”

Section: Discussionmentioning

confidence: 99%

“…S4 and supplemental Table S10. Recently, Li et al identified 1240 proteins that were up-regulated in NSCLC primary tumors compared with matched normal lung tissues (22), and 77 proteins were found in our list of candidate markers. Kikuchi et al established a comprehensive tissue proteome data set comprising 3621 proteins from an analysis of pooled human samples of squamous cell carcinoma, ADC, and control specimens using a label-free proteomic approach (14).…”

Section: Discussionmentioning

confidence: 99%

“…An alternative strategy for biomarker discovery is the use of tissue specimens, which may both reflect the progression of cancers in vivo and serve as prognostic markers. Several groups have successfully used tissue proteomic technologies to identify potential biomarkers in lung cancer (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25). Recently, Kikuchi et al performed an in-depth proteomic analysis of 3621 proteins from squamous cell carcinoma and ADC tissues using label-free quantitative proteomic approaches (14).…”

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confidence: 99%

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Identification and Characterization of Potential Biomarkers by Quantitative Tissue Proteomics of Primary Lung Adenocarcinoma

Hsu

Hsueh

et al. 2016

Molecular & Cellular Proteomics

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Lung cancer is the leading cause of cancer-related death worldwide. Both diagnostic and prognostic biomarkers are urgently needed to increase patient survival. In this study, we identified/quantified 1763 proteins from paired adenocarcinoma (ADC) tissues with different extents of lymph node (LN) involvement using an iTRAQ-based quantitative proteomic analysis. Based on a bioinformatics analysis and literature search, we selected six candidates (ERO1L, PABPC4, RCC1, RPS25, NARS, and TARS) from a set of 133 proteins that presented a 1.5-fold increase in expression in ADC tumors without LN metastasis compared with adjacent normal tissues. These six proteins were further verified using immunohistochemical staining and Western blot analyses. The protein levels of these six candidates were higher in tumor tissues compared with adjacent normal tissues. The ERO1L and NARS levels were positively associated with LN metastasis. Importantly, ERO1L overexpression in patients with early-stage ADC was positively correlated with poor survival, suggesting that ERO1L overexpression in primary sites of early-stage cancer tissues indicates a high risk for cancer micrometastasis. Moreover, we found that knockdown of either ERO1L or NARS reduced the viability and migration ability of ADC cells. Our results collectively provide a potential biomarker data set for ADC diagnosis/prognosis and reveal novel roles of ERO1L and NARS in ADC progression. Molecular & Cellular Proteomics 15:

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Identification and Characterization of Potential Biomarkers by Quantitative Tissue Proteomics of Primary Lung Adenocarcinoma

Hsu

Hsueh

et al. 2016

Molecular & Cellular Proteomics

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“…Node degree (or degree centrality) is one of the basic methods of sorting the nodes [20][21][22][23][24][25][26][27][28][29]. Other common methods are also based on node degree [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it may require verification by other methods [20] or other network attributes [27,28]. Closeness centrality and betweenness centrality are often given with degree centrality for comparison purpose [27,28].…”

Section: Introductionmentioning

confidence: 99%

High-Order Degree and Combined Degree in Complex Networks

Wang

Song

et al. 2018

Mathematical Problems in Engineering

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We define several novel centrality metrics: the high-order degree and combined degree of undirected network, the high-order out-degree and in-degree and combined out out-degree and in-degree of directed network. Those are the measurement of node importance with respect to the number of the node neighbors. We also explore those centrality metrics in the context of several best-known networks. We prove that both the degree centrality and eigenvector centrality are the special cases of the high-order degree of undirected network, and both the in-degree and PageRank algorithm without damping factor are the special cases of the high-order in-degree of directed network. Finally, we also discuss the significance of high-order out-degree of directed network. Our centrality metrics work better in distinguishing nodes than degree and reduce the computation load compared with either eigenvector centrality or PageRank algorithm.

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Proteomic analysis enables distinction of early‐ versus advanced‐stage lung adenocarcinomas

Kelemen

Plá

Sánchez

et al. 2020

Clinical & Translational Med

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Background A gel‐free proteomic approach was utilized to perform in‐depth tissue protein profiling of lung adenocarcinoma (ADC) and normal lung tissues from early and advanced stages of the disease. The long‐term goal of this study is to generate a large‐scale, label‐free proteomics dataset from histologically well‐classified lung ADC that can be used to increase further our understanding of disease progression and aid in identifying novel biomarkers. Methods and results Cases of early‐stage (I‐II) and advanced‐stage (III‐IV) lung ADCs were selected and paired with normal lung tissues from 22 patients. The histologically and clinically stratified human primary lung ADCs were analyzed by liquid chromatography‐tandem mass spectrometry. From the analysis of ADC and normal specimens, 4863 protein groups were identified. To examine the protein expression profile of ADC, a peak area‐based quantitation method was used. In early‐ and advanced‐stage ADC, 365 and 366 proteins were differentially expressed, respectively, between normal and tumor tissues (adjusted P ‐value < .01, fold change ≥ 4). A total of 155 proteins were dysregulated between early‐ and advanced‐stage ADCs and 18 were suggested as early‐specific stage ADC. In silico functional analysis of the upregulated proteins in both tumor groups revealed that most of the enriched pathways are involved in mRNA metabolism. Furthermore, the most overrepresented pathways in the proteins that were unique to ADC are related to mRNA metabolic processes. Conclusions Further analysis of these data may provide an insight into the molecular pathways involved in disease etiology and may lead to the identification of biomarker candidates and potential targets for therapy. Our study provides potential diagnostic biomarkers for lung ADC and novel stage‐specific drug targets for rational intervention.

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Integrated Omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact

Cited by 96 publications

References 68 publications

Identification and Characterization of Potential Biomarkers by Quantitative Tissue Proteomics of Primary Lung Adenocarcinoma

Identification and Characterization of Potential Biomarkers by Quantitative Tissue Proteomics of Primary Lung Adenocarcinoma

High-Order Degree and Combined Degree in Complex Networks

Proteomic analysis enables distinction of early‐ versus advanced‐stage lung adenocarcinomas

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