Dataset Information

Liquid chromatography-mass spectrometry based tissue metabolic profiling reveals major metabolic pathway alterations and potential biomarkers of lung cancer

ABSTRACT: MS spectrum of lung tissue metabolic analysis in 131 patients with lung cancer or benign lung tumor. These include lung cancer tissues/benign tumor tissues, adjacent normal tissues and distal normal tissues.

ORGANISM(S): Cancer

SUBMITTER: Guowang Xu

PROVIDER: PXD019969 | iProX | Fri Jun 19 00:00:00 BST 2020

REPOSITORIES: iProX

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Publications

Liquid Chromatography-Mass Spectrometry-Based Tissue Metabolic Profiling Reveals Major Metabolic Pathway Alterations and Potential Biomarkers of Lung Cancer.

You Lei L Fan Yingying Y Liu Xinyu X Shao Shujuan S Guo Lei L Noreldeen Hamada A A HAA Li Zaifang Z Ouyang Yang Y Li Enyou E Pan Xue X Liu Tianyang T Tian Xin X Ye Fei F Li Xiangnan X Xu Guowang G

Journal of proteome research 20200731 9

Unclarified molecular mechanism and lack of practical diagnosis biomarkers hinder the effective treatment of non-small-cell lung cancer. Herein, we performed liquid chromatography-mass spectrometry-based nontargeted metabolomics analysis in 131 patients with their lung tissue pairs to study the metabolic characteristics and disordered metabolic pathways in lung tumor. A total of 339 metabolites were identified in metabolic profiling. Also, 241 differential metabolites were found between lung car ...[more]

PMID: 32693607

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Project description:A data set of normal epithelium, serous ovarian surface epithelial-stromal tumors (benign and type II malignancies), stroma distal to tumor, and stroma adjacent to tumor (50 samples total). Additional cel files are included which represent replicate sampling from patients, and cel files that failed quality control but may be bioinformatically interesting. Additional replicate or failed cel files were not included in the final analysis (and so these samples were not included in the matrix). Background: Ovarian cancer is the most lethal gynecologic cancer in the United States. If caught in early stages, patient survival rate can reach 94%, when diagnosed at late stages survival rates drop to 28%. Correct diagnosis depends on the presence of definite symptoms: while ~90% of diagnosed ovarian cancers have symptoms, they tend to be unfocused and subacute. A definitive and early molecular signature of disease is thus desired. To further progress towards this goal, we present an Affymetrix™ human exon array data set measuring ovarian tumor expression, assembled using best practices. Method: Samples were collected from patients with benign and malignant (type II) serous ovarian surface epithelial-stromal tumors. Normal epithelium, tumor, stroma adjacent to tumor, and distal stroma were selected based on histopathology, and isolated using laser capture microdissection. Nugen products were used to perform random-primed mRNA amplification procedures (for full transcript capture) before hybridization to Affymetrix exon chips. Tumor expression and paracrine signaling was assessed using GC-RMA and a two-way Model I ANOVA. Single enrichment ontological analysis and gene network construction were performed to guide inferences about biological context. Results: In total, across 50 microarrays, ~270 million measurements were obtained. Based on comparisons to known ovarian cancer properties as established in molecular genetics literature, the initial analysis presented emphasizes data quality. Major trends between sample classes included: apical surface and tight junction activity, mitotic activity, benign tumor suppression, epithelial-mesenchymal transitioning, tumor oncogene activity, and paracrine signaling. A list of differentially expressed transcripts has been produced to enable rapid comparisons with published biomarker lists, but it is expected that detailed alternative transcript analysis will refine these predictions. Conclusions: A data set of 50 arrays, from carefully dissected serous ovarian surface epithelial-stromal tumors, has been produced, from which high quality measurements were obtained. While relatively small in number, this represents an important addition to the community pool of ovarian tumor samples, and the chosen platform enables bridging between 3' expression and exome sequencing data sets. This represents a significant contribution to the ovarian cancer genetics community. A total of 50 human ovary samples were used in analysis: 23 tissue samples laser capture microdissected from an ovary with a benign serous tumor (specifically 4 normal epithelium samples, 5 tumor samples, 6 stroma samples adjacent to the tumor, and 8 stroma samples distal of the tumor), and 27 tissue samples laser capture microdissected from an ovary with a malignant serous tumor (specifically 5 normal epithelium samples, 8 tumor samples, 7 stroma samples adjacent to the tumor, and 7 stroma samples distal of the tumor). Additional cel files were provided which, although were used in the quality control of the data set, were not used in the final experimental analysis. 8 replicates are included as cel files (1 from each cohort previously listed). 14 cel files were also included which failed quality control. Although the replicate and failed cel files were not used in the final analysis, they may still be interesting in other research.

Dataset Information

Liquid chromatography-mass spectrometry based tissue metabolic profiling reveals major metabolic pathway alterations and potential biomarkers of lung cancer

Publications

Liquid Chromatography-Mass Spectrometry-Based Tissue Metabolic Profiling Reveals Major Metabolic Pathway Alterations and Potential Biomarkers of Lung Cancer.

Similar Datasets

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