A Novel Approach for Discovering Condition-Specific Correlations of Gene Expressions within Biological Pathways by Using Cloud Computing Technology

Microarrays are widely used to assess gene expressions. Most microarray studies focus primarily on identifying differential gene expressions between conditions (e.g., cancer versus normal cells), for discovering the major factors that cause diseases. Because previous studies have not identified the correlations of differential gene expression between conditions, crucial but abnormal regulations that cause diseases might have been disregarded. This paper proposes an approach for discovering the condition-specific correlations of gene expressions within biological pathways. Because analyzing gene expression correlations is time consuming, an Apache Hadoop cloud computing platform was implemented. Three microarray data sets of breast cancer were collected from the Gene Expression Omnibus, and pathway information from the Kyoto Encyclopedia of Genes and Genomes was applied for discovering meaningful biological correlations. The results showed that adopting the Hadoop platform considerably decreased the computation time. Several correlations of differential gene expressions were discovered between the relapse and nonrelapse breast cancer samples, and most of them were involved in cancer regulation and cancer-related pathways. The results showed that breast cancer recurrence might be highly associated with the abnormal regulations of these gene pairs, rather than with their individual expression levels. The proposed method was computationally efficient and reliable, and stable results were obtained when different data sets were used. The proposed method is effective in identifying meaningful biological regulation patterns between conditions.

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Horizontal meta-analysis identifies common deregulated genes across AML subgroups providing a robust prognostic signature

Blood Advances ◽

10.1182/bloodadvances.2020002042 ◽

2020 ◽

Vol 4 (20) ◽

pp. 5322-5335

Author(s):

Ali Nehme ◽

Hassan Dakik ◽

Frédéric Picou ◽

Meyling Cheok ◽

Claude Preudhomme ◽

...

Keyword(s):

Gene Expression ◽

Microarray Data ◽

Meta Analysis ◽

Prognostic Score ◽

Gene Expression Omnibus ◽

Response To Therapy ◽

Data Sets ◽

Dna Hypermethylation ◽

Transcriptomic Data ◽

Differential Gene

Abstract Advances in transcriptomics have improved our understanding of leukemic development and helped to enhance the stratification of patients. The tendency of transcriptomic studies to combine AML samples, regardless of cytogenetic abnormalities, could lead to bias in differential gene expression analysis because of the differential representation of AML subgroups. Hence, we performed a horizontal meta-analysis that integrated transcriptomic data on AML from multiple studies, to enrich the less frequent cytogenetic subgroups and to uncover common genes involved in the development of AML and response to therapy. A total of 28 Affymetrix microarray data sets containing 3940 AML samples were downloaded from the Gene Expression Omnibus database. After stringent quality control, transcriptomic data on 1534 samples from 11 data sets, covering 10 AML cytogenetically defined subgroups, were retained and merged with the data on 198 healthy bone marrow samples. Differentially expressed genes between each cytogenetic subgroup and normal samples were extracted, enabling the unbiased identification of 330 commonly deregulated genes (CODEGs), which showed enriched profiles of myeloid differentiation, leukemic stem cell status, and relapse. Most of these genes were downregulated, in accordance with DNA hypermethylation. CODEGs were then used to create a prognostic score based on the weighted sum of expression of 22 core genes (CODEG22). The score was validated with microarray data of 5 independent cohorts and by quantitative real time-polymerase chain reaction in a cohort of 142 samples. CODEG22-based stratification of patients, globally and into subpopulations of cytologically healthy and elderly individuals, may complement the European LeukemiaNet classification, for a more accurate prediction of AML outcomes.

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Decision theory for precision therapy of breast cancer

Scientific Reports ◽

10.1038/s41598-021-82418-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Michael Kenn ◽

Dan Cacsire Castillo-Tong ◽

Christian F. Singer ◽

Rudolf Karch ◽

Michael Cibena ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Decision Theory ◽

Data Science ◽

Gene Expression Omnibus ◽

Hormone Receptor Status ◽

Breast Cancer Patients ◽

Receptor Status ◽

Kaplan Meier ◽

Precision Therapy

AbstractCorrectly estimating the hormone receptor status for estrogen (ER) and progesterone (PGR) is crucial for precision therapy of breast cancer. It is known that conventional diagnostics (immunohistochemistry, IHC) yields a significant rate of wrongly diagnosed receptor status. Here we demonstrate how Dempster Shafer decision Theory (DST) enhances diagnostic precision by adding information from gene expression. We downloaded data of 3753 breast cancer patients from Gene Expression Omnibus. Information from IHC and gene expression was fused according to DST, and the clinical criterion for receptor positivity was re-modelled along DST. Receptor status predicted according to DST was compared with conventional assessment via IHC and gene-expression, and deviations were flagged as questionable. The survival of questionable cases turned out significantly worse (Kaplan Meier p < 1%) than for patients with receptor status confirmed by DST, indicating a substantial enhancement of diagnostic precision via DST. This study is not only relevant for precision medicine but also paves the way for introducing decision theory into OMICS data science.

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Differential Gene Expression Analysis of Breast Cancer by Combining Sequence-Tagged Reverse-Transcription PCR with Pyrosequencing

Springer Protocols Handbooks - Advances and Clinical Practice in Pyrosequencing ◽

10.1007/978-1-4939-3308-2_31 ◽

2016 ◽

pp. 361-369

Author(s):

Xiaodan Zhang ◽

Haiping Wu ◽

Zhiyao Chen ◽

Bingjie Zou ◽

Qinxin Song ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Differential Gene Expression ◽

Expression Analysis ◽

Reverse Transcription ◽

Gene Expression Analysis ◽

Differential Gene Expression Analysis ◽

Reverse Transcription Pcr ◽

Differential Gene

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Response to FEC Chemotherapy and Oncolytic HSV-1 Is Associated with Macrophage Polarization and Increased Expression of S100A8/A9 in Triple Negative Breast Cancer

Cancers ◽

10.3390/cancers13215590 ◽

2021 ◽

Vol 13 (21) ◽

pp. 5590

Author(s):

Alyssa Vito ◽

Nader El-Sayes ◽

Omar Salem ◽

Yonghong Wan ◽

Karen L. Mossman

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Macrophage Polarization ◽

Gene Signature ◽

Immune Checkpoint Blockade ◽

Differential Gene ◽

Microarray Analyses ◽

Tumor Cell Killing

The era of immunotherapy has seen an insurgence of novel therapies driving oncologic research and the clinical management of the disease. We have previously reported that a combination of chemotherapy (FEC) and oncolytic virotherapy (oHSV-1) can be used to sensitize otherwise non-responsive tumors to immune checkpoint blockade and that tumor-infiltrating B cells are required for the efficacy of our therapeutic regimen in a murine model of triple-negative breast cancer. In the studies herein, we have performed gene expression profiling using microarray analyses and have investigated the differential gene expression between tumors treated with FEC + oHSV-1 versus untreated tumors. In this work, we uncovered a therapeutically driven switch of the myeloid phenotype and a gene signature driving increased tumor cell killing.

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High Moesin Expression Is a Predictor of Poor Prognosis of Breast Cancer: Evidence From a Systematic Review With Meta-Analysis

Frontiers in Oncology ◽

10.3389/fonc.2021.650488 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiaoli Hu ◽

Yang Liu ◽

Zhitong Bing ◽

Qian Ye ◽

Chengcheng Li

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Systematic Review ◽

Cancer Patients ◽

Poor Prognosis ◽

Data Extraction ◽

Meta Analysis ◽

Gene Expression Omnibus ◽

Breast Cancer Patients ◽

Node Metastases

Owing to metastases and drug resistance, the prognosis of breast cancer is still dismal. Therefore, it is necessary to find new prognostic markers to improve the efficacy of breast cancer treatment. Literature shows a controversy between moesin (MSN) expression and prognosis in breast cancer. Here, we aimed to conduct a systematic review and meta-analysis to evaluate the prognostic relationship between MSN and breast cancer. Literature retrieval was conducted in the following databases: PubMed, Web of Science, Embase, and Cochrane. Two reviewers independently performed the screening of studies and data extraction. The Gene Expression Omnibus (GEO) database including both breast cancer gene expression and follow-up datasets was selected to verify literature results. The R software was employed for the meta-analysis. A total of 9 articles with 3,039 patients and 16 datasets with 2,916 patients were ultimately included. Results indicated that there was a significant relationship between MSN and lymph node metastases (P < 0.05), and high MSN expression was associated with poor outcome of breast cancer patients (HR = 1.99; 95% CI 1.73–2.24). In summary, there is available evidence to support that high MSN expression has valuable importance for the poor prognosis in breast cancer patients.Systematic Review Registrationhttps://inplasy.com/inplasy-2020-8-0039/.

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