Dataset Information

Identification of Gene-Expression Signatures and Protein Markers for Breast Cancer Grading and Staging.

ABSTRACT: The grade of a cancer is a measure of the cancer's malignancy level, and the stage of a cancer refers to the size and the extent that the cancer has spread. Here we present a computational method for prediction of gene signatures and blood/urine protein markers for breast cancer grades and stages based on RNA-seq data, which are retrieved from the TCGA breast cancer dataset and cover 111 pairs of disease and matching adjacent noncancerous tissues with pathologists-assigned stages and grades. By applying a differential expression and an SVM-based classification approach, we found that 324 and 227 genes in cancer have their expression levels consistently up-regulated vs. their matching controls in a grade- and stage-dependent manner, respectively. By using these genes, we predicted a 9-gene panel as a gene signature for distinguishing poorly differentiated from moderately and well differentiated breast cancers, and a 19-gene panel as a gene signature for discriminating between the moderately and well differentiated breast cancers. Similarly, a 30-gene panel and a 21-gene panel are predicted as gene signatures for distinguishing advanced stage (stages III-IV) from early stage (stages I-II) cancer samples and for distinguishing stage II from stage I samples, respectively. We expect these gene panels can be used as gene-expression signatures for cancer grade and stage classification. In addition, of the 324 grade-dependent genes, 188 and 66 encode proteins that are predicted to be blood-secretory and urine-excretory, respectively; and of the 227 stage-dependent genes, 123 and 51 encode proteins predicted to be blood-secretory and urine-excretory, respectively. We anticipate that some combinations of these blood and urine proteins could serve as markers for monitoring breast cancer at specific grades and stages through blood and urine tests.

SUBMITTER: Yao F

PROVIDER: S-EPMC4573873 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Identification of Gene-Expression Signatures and Protein Markers for Breast Cancer Grading and Staging.

Yao Fang F Zhang Chi C Du Wei W Liu Chao C Xu Ying Y

PloS one 20150916 9

The grade of a cancer is a measure of the cancer's malignancy level, and the stage of a cancer refers to the size and the extent that the cancer has spread. Here we present a computational method for prediction of gene signatures and blood/urine protein markers for breast cancer grades and stages based on RNA-seq data, which are retrieved from the TCGA breast cancer dataset and cover 111 pairs of disease and matching adjacent noncancerous tissues with pathologists-assigned stages and grades. By ...[more]

PMID: 26375396

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Project description:Breast cancer in young women is more aggressive with a poorer prognosis and overall survival compared to older women diagnosed with the disease. Despite recent research, the underlying biology and molecular alterations that drive the aggressive nature of breast tumors associated with breast cancer in young women have yet to be elucidated. In this study, we performed transcriptomic profile and network analyses of breast tumors arising in Middle Eastern women to identify age-specific gene signatures. Moreover, we studied molecular alterations associated with cancer progression in young women using cross-species comparative genomics approach coupled with copy number alterations (CNA) associated with breast cancers from independent studies. We identified 63 genes specific to tumors in young women that showed alterations distinct from two age cohorts of older women. The network analyses revealed potential critical regulatory roles for Myc, PI3K/Akt, NF-?B, and IL-1 in disease characteristics of breast tumors arising in young women. Cross-species comparative genomics analysis of progression from pre-invasive ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) revealed 16 genes with concomitant genomic alterations, CCNB2, UBE2C, TOP2A, CEP55, TPX2, BIRC5, KIAA0101, SHCBP1, UBE2T, PTTG1, NUSAP1, DEPDC1, HELLS, CCNB1, KIF4A, and RRM2, that may be involved in tumorigenesis and in the processes of invasion and progression of disease. Array findings were validated using qRT-PCR, immunohistochemistry, and extensive in silico analyses of independently performed microarray datasets. To our knowledge, this study provides the first comprehensive genomic analysis of breast cancer in Middle Eastern women in age-specific cohorts and potential markers for cancer progression in young women. Our data demonstrate that cancer appearing in young women contain distinct biological characteristics and deregulated signaling pathways. Moreover, our integrative genomic and cross-species analysis may provide robust biomarkers for the detection of disease progression in young women, and lead to more effective treatment strategies.

Project description:Purpose: There is a quest for novel non-invasive diagnostic markers for the detection of breast cancer. The goal of this study is to identify circulating microRNA signatures using a cohort of Asian Chinese breast cancer patients, and to compare microRNA profiles between tumour and serum samples. Experimental design: MicroRNAs from paired breast cancer tumours, normal tissue and serum samples derived from 32 patients were comprehensively profiled using microarrays (1300 microRNAs against tumour and normal tissues) or LNA RT-PCR panels (742 microRNAs against serum samples). Serum samples from healthy individuals (n=22) were also employed as normal controls. Significant serum microRNAs, identified by logistic regression, were validated in an independent set of serum samples from patients (n=82) and healthy controls (n=53). Results: The 20 most significant microRNAs differentially expressed in breast cancer tumours included miR-21, miR-10b, and miR-145, previously shown to be dysregulated in breast cancer. Interestingly, 16 of the 20 most significant microRNAs differentially expressed in serum samples were novel. MiR-1, miR-92a, miR-133a and miR-133b were identified as the most important diagnostic markers, and were successfully validated; receiver operating characteristic curves derived from combinations of these microRNAs exhibited areas under the curves of 0.944-0.946. Only seven microRNAs were overexpressed in both tumours and serum, suggesting that microRNAs may be released into the serum selectively. Conclusion: The clinical employment of microRNA signatures as a non-invasive diagnostic strategy is promising, but should be further validated for different subtypes of breast cancers. "_A" and "_B" are two tissue sections of the same sample; "_1" and "_2" represents 2 runs of the same sample; na = not available All tissue samples were histologically confirmed by a pathologist using hematoxylin and eosin staining of cryosectioned specimens. One tumour sample was rejected due to failure to detect any tumour cells. Except for two samples (with 30% and 40% tumour cells), all tumour tissues employed had a minimum of 60% tumour cells, as estimated microscopically. Overall, the breast cancer tumour samples had an average of 71% tumour cells. The criteria for adjacent normal tissue were absence of tumour cells and presence of epithelial cells. Hence, after histological confirmation, 31 breast cancer tumours and 23 matched normal tissues were employed for microRNA extraction and profiling using microarray.

Dataset Information

Identification of Gene-Expression Signatures and Protein Markers for Breast Cancer Grading and Staging.

Publications

Identification of Gene-Expression Signatures and Protein Markers for Breast Cancer Grading and Staging.

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