Dataset Information

Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data.

ABSTRACT: Alternatively-activated pathways have been observed in biological experiments in cancer studies, but the concept had not been fully explored in computational cancer system biology. Therefore, an alternatively-activated pathway identification method was proposed and applied to primary breast cancer and breast cancer liver metastasis research using microarray data. Interestingly, the results show that cytokine-cytokine receptor interaction and calcium signaling were significantly enriched under both conditions. TGF beta signaling was found to be the hub in network topology analysis. In total, three types of alternatively-activated pathways were recognized. In the cytokine-cytokine receptor interaction pathway, four active alteration patterns in gene pairs were noticed. Thirteen cytokine-cytokine receptor pairs with inverse activity changes of both genes were verified by the literature. The second type was that some sub-pathways were active under only one condition. For the third type, nodes were significantly active in both conditions, but with different active genes. In the calcium signaling and TGF beta signaling pathways, node E2F5 and E2F4 were significantly active in primary breast cancer and metastasis, respectively. Overall, our study demonstrated the first time using microarray data to identify alternatively-activated pathways in breast cancer liver metastasis. The results showed that the proposed method was valid and effective, which could be helpful for future research for understanding the mechanism of breast cancer metastasis.

SUBMITTER: Wang L

PROVIDER: S-EPMC6826985 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data.

Wang Limei L Li Jin J Liu Enze E Kinnebrew Garrett G Zhang Xiaoli X Stover Daniel D Huo Yang Y Zeng Zhi Z Jiang Wanli W Cheng Lijun L Feng Weixing W Li Lang L

Genes 20190925 10

Alternatively-activated pathways have been observed in biological experiments in cancer studies, but the concept had not been fully explored in computational cancer system biology. Therefore, an alternatively-activated pathway identification method was proposed and applied to primary breast cancer and breast cancer liver metastasis research using microarray data. Interestingly, the results show that cytokine-cytokine receptor interaction and calcium signaling were significan ...[more]

PMID: 31557971

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Project description:BackgroundNowadays, many public repositories containing large microarray gene expression datasets are available. However, the problem lies in the fact that microarray technology are less powerful and accurate than more recent Next Generation Sequencing technologies, such as RNA-Seq. In any case, information from microarrays is truthful and robust, thus it can be exploited through the integration of microarray data with RNA-Seq data. Additionally, information extraction and acquisition of large number of samples in RNA-Seq still entails very high costs in terms of time and computational resources.This paper proposes a new model to find the gene signature of breast cancer cell lines through the integration of heterogeneous data from different breast cancer datasets, obtained from microarray and RNA-Seq technologies. Consequently, data integration is expected to provide a more robust statistical significance to the results obtained. Finally, a classification method is proposed in order to test the robustness of the Differentially Expressed Genes when unseen data is presented for diagnosis.ResultsThe proposed data integration allows analyzing gene expression samples coming from different technologies. The most significant genes of the whole integrated data were obtained through the intersection of the three gene sets, corresponding to the identified expressed genes within the microarray data itself, within the RNA-Seq data itself, and within the integrated data from both technologies. This intersection reveals 98 possible technology-independent biomarkers. Two different heterogeneous datasets were distinguished for the classification tasks: a training dataset for gene expression identification and classifier validation, and a test dataset with unseen data for testing the classifier. Both of them achieved great classification accuracies, therefore confirming the validity of the obtained set of genes as possible biomarkers for breast cancer. Through a feature selection process, a final small subset made up by six genes was considered for breast cancer diagnosis.ConclusionsThis work proposes a novel data integration stage in the traditional gene expression analysis pipeline through the combination of heterogeneous data from microarrays and RNA-Seq technologies. Available samples have been successfully classified using a subset of six genes obtained by a feature selection method. Consequently, a new classification and diagnosis tool was built and its performance was validated using previously unseen samples.

Project description:Cancer of unknown primary (CUP) is an umbrella term used to classify a heterogeneous group of metastatic cancers based on the absence of an identifiable primary tumor. Clinically, CUPs are characterized by a set of distinct features comprising early metastatic dissemination in an atypical pattern, an aggressive clinical course, poor response to empiric chemotherapy and, consequently, a short life expectancy. Two opposing strategies to change the dismal prognosis for the better are pursued. On the one hand, following the traditional tissue-gnostic approach, more and more sophisticated tissue-of-origin (TOO) classifier assays are employed to push identification of the putative primary to its limits with the clear intent of allowing tumor-site specific treatment. However, robust evidence supporting its routine clinical use is still lacking, notably with two recent randomized clinical trials failing to show a patient benefit of TOO-prediction based site-specific treatment over empiric chemotherapy in CUP. On the other hand, with regards to a tissue-agnostic strategy, precision medicine approaches targeting actionable genomic alterations have already transformed the treatment for many known tumor types. Yet, an unmet need remains for well-designed clinical trials to scrutinize its potential role in CUP beyond anecdotal case reports. In the absence of practice changing results, we believe that the emphasis on finding the presumed unknown primary tumor at all costs, implicit in the term CUP, has biased recent research in the field. Focusing on the distinct clinical features shared by all CUPs, we advocate adopting the term primary metastatic cancer (PMC) to denominate a distinct cancer entity instead. In our view, PMC should be considered the archetype of metastatic disease and as such, despite accounting for a mere 2-3% of malignancies, unraveling the mechanisms at play goes beyond improving the prognosis of patients with PMC and promises to greatly enhance our understanding of the metastatic process and carcinogenesis across all cancer types.

Dataset Information

Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data.

Publications

Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data.

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