Dataset Information

Alteration of specific cytokine expression patterns in patients with breast cancer.

ABSTRACT: Systemic inflammation has been associated with aggressive tumor growth, invasion, and metastasis. Here we performed a comprehensive analysis of 26 kinds of inflammatory cytokine expression patterns among 185 patients with breast cancer and 54 healthy volunteers followed by chemometric analysis. We identified the specific cytokine expression patterns of breast cancer patients compared to healthy volunteers with (1) VEGF, IL-9, GM-CSF, IL-13, IL-4, and IFN?, (2) IL-8, IL-10, IL-12, IL-5, IL-7, IL-1?, GCSF, IL-1?, and TNF? and (3) IL-2, Eotaxin, MIP1?, MIP1?, IL-17, and bFGF. Among the patients with breast cancer, we identified the specific cytokine signature of metastatic patients compared to non-metastatic patients. We also established a mathematical model for distinguishing patients with breast cancer from healthy volunteers and metastatic patients from non-metastatic patients. This cytokine network analysis could provide new insights into early intervention and effective therapeutic strategy for patients with breast cancer.

SUBMITTER: Kawaguchi K

PROVIDER: S-EPMC6393524 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Alteration of specific cytokine expression patterns in patients with breast cancer.

Kawaguchi Kosuke K Sakurai Masashi M Yamamoto Yasuko Y Suzuki Eiji E Tsuda Moe M Kataoka Tatsuki R TR Hirata Masahiro M Nishie Mariko M Nojiri Takashi T Kumazoe Motofumi M Saito Kuniaki K Toi Masakazu M

Scientific reports 20190227 1

Systemic inflammation has been associated with aggressive tumor growth, invasion, and metastasis. Here we performed a comprehensive analysis of 26 kinds of inflammatory cytokine expression patterns among 185 patients with breast cancer and 54 healthy volunteers followed by chemometric analysis. We identified the specific cytokine expression patterns of breast cancer patients compared to healthy volunteers with (1) VEGF, IL-9, GM-CSF, IL-13, IL-4, and IFNγ, (2) IL-8, IL-10, IL-12, IL-5, IL-7, IL- ...[more]

PMID: 30814616

Similar Datasets

Project description:Though xenografts are used extensively for drug development in breast cancer, how well xenografts reflect the breadth of primary breast tumor subtypes has not been well characterized. Moreover, few studies have compared the gene expression of xenograft tumors to the primary tumors from which they were derived. Here we investigate whether the ability of human breast tumors (n = 20) to create xenografts in immune-deficient mice is associated with breast cancer immunohistochemical (IHC) and intrinsic subtype. We also characterize how precisely the gene expression of xenografts reprises that of parent breast tumors, using hierarchical clustering and other correlation-based techniques applied to Agilent 44K gene expression data from 16 samples including four matched primary tumor-xenograft pairs. Of the breast tumors studied, 25 % (5/20) generated xenografts. Receptor and intrinsic subtype were significant predictors of xenograft success, with all (4/4) triple-negative (TN) tumors and no (0/12) HR+Her2- tumors forming xenografts (P = 0.0005). Tumor cell expression of ALDH1, a stem cell marker, trended toward successful engraftment (P = 0.14), though CDK5/6, a basal marker, did not. Though hierarchical clustering across the 500 most variable genes segregated human breast tumors from xenograft tumors, when clustering was performed over the PAM50 gene set the primary tumor-xenograft pairs clustered together, with all IHC subtypes clustered in distinct groups. Greater similarity between primary tumor-xenograft pairs relative to random pairings was confirmed by calculation of the within-pair between-pair scatter ratio (WPBPSR) distribution (P = 0.0269), though there was a shift in the xenografts toward more aggressive features including higher proliferation scores relative to the primary. Triple-negative breast tumors demonstrate superior ability to create xenografts compared to HR+ tumors, which may reflect higher proliferation or relatively stroma-independent growth of this subtype. Xenograft tumors' gene expression faithfully resembles that of their parent tumors, yet also demonstrates a shift toward more aggressive molecular features.

Project description:Although many methods have been proposed to reconstruct gene regulatory network, most of them, when applied in the sample-based data, can not reveal the gene regulatory relations underlying the phenotypic change (e.g. normal versus cancer). In this paper, we adopt phenotype as a variable when constructing the gene regulatory network, while former researches either neglected it or only used it to select the differentially expressed genes as the inputs to construct the gene regulatory network. To be specific, we integrate phenotype information with gene expression data to identify the gene dependency pairs by using the method of conditional mutual information. A gene dependency pair (A,B) means that the influence of gene A on the phenotype depends on gene B. All identified gene dependency pairs constitute a directed network underlying the phenotype, namely gene dependency network. By this way, we have constructed gene dependency network of breast cancer from gene expression data along with two different phenotype states (metastasis and non-metastasis). Moreover, we have found the network scale free, indicating that its hub genes with high out-degrees may play critical roles in the network. After functional investigation, these hub genes are found to be biologically significant and specially related to breast cancer, which suggests that our gene dependency network is meaningful. The validity has also been justified by literature investigation. From the network, we have selected 43 discriminative hubs as signature to build the classification model for distinguishing the distant metastasis risks of breast cancer patients, and the result outperforms those classification models with published signatures. In conclusion, we have proposed a promising way to construct the gene regulatory network by using sample-based data, which has been shown to be effective and accurate in uncovering the hidden mechanism of the biological process and identifying the gene signature for phenotypic change.

Project description:PIWI-interacting RNAs (piRNAs) are a novel class of small ncRNAs initially isolated from germ line cells; although recent studies report that they are expressed also in somatic cells. To elucidate the role of piRNAs in somatic cells, in particular from breast cancer, we performed the first extensive next generation sequencing expression analysis of small RNA transcriptomes of hormone responsive breast cancer cell lines in different culture conditions. In addition, to understand the behavior of piRNAs with respect to miRNAs in breast tumor tissues, small RNA sequence data set available in Gene Expression Omnibus (GSE39162) database was used. Results led to the identification of ~100 and ~150 human piRNAs in the breast cancerous cell lines and tumors respectively, several of which differentially expressed in cell lines under different experimental conditions tested or in response to ERβ and in tumor tissues. Western blotting and Q-PCR analysis also revealed the presence in breast cell lines of PIWIL (PIWI Like) subfamily members proteins encoded in the human genome (PIWIL2/HILI and PIWIL4/HIWI2) and of other components of the piRNA biogenesis pathways, suggesting that this might indeed be functional in somatic cells. These results show that piRNAs are expressed in human somatic cells, in particular in cancer, where their expression is influenced by neoplastic transformation, growth conditions and estrogen receptor beta. More important, we demonstrate for the first time a distinct pattern of piRNAs expression in cancerous vs normal breast tissues, which suggests a potential role of these epigenetic modulators in mammary carcinogenesis and maintenance of the cancer cell phenotype. In addition, to understand the behavior of piRNAs with respect to miRNAs in breast tumor tissues, small RNA sequence data set available in Gene Expression Omnibus (GEO; GSM957192 TAX577740T ,GSM957194 TAX577740N, GSM957195 TAX577453T, GSM957197 TAX577453N, GSM957198 TAX577745T, GSM957200 TAX577745N, GSM957201 TAX577579T, GSM957203 TAX577579N) was used.

Dataset Information

Alteration of specific cytokine expression patterns in patients with breast cancer.

Publications

Alteration of specific cytokine expression patterns in patients with breast cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets