Identification of Aberrant Chromosomal Regions in Human Breast Cancer Using Gene Expression Data and Related Gene Information

Wang, Hongjiu; Zhou, Meng; Li, Jia; Sun, Jie; Shi, Hang; Liu, Shulin; Wang, Zhenzhen

doi:10.12659/msm.894887

Cited by 6 publications

(5 citation statements)

References 38 publications

(33 reference statements)

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“…Overexpression of MAP2K6 predicts a worse prognosis of patients with nasopharyngeal carcinoma [40]. However, Wang et al [41] reported that MAP2K6 gene had a low expression in breast cancer compared with control. The possible reason is that the expression of MAP2K6 in different cancers is different, and its function is complicated.…”

Section: Discussionmentioning

confidence: 99%

Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis

et al. 2020

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Background This study intended to determine important genes related to the prognosis and recurrence of breast cancer. Methods Gene expression data of breast cancer patients were downloaded from TCGA database. Breast cancer samples with recurrence and death were defined as poor disease-free survival (DFS) group, while samples without recurrence and survival beyond 5 years were defined as better DFS group. Another gene expression profile dataset (GSE45725) of breast cancer was downloaded as the validation data. Differentially expressed genes (DEGs) were screened between better and poor DFS groups, which were then performed function enrichment analysis. The DEGs that were enriched in the GO function and KEGG signaling pathway were selected for cox regression analysis and Logit regression (LR) model analysis. Finally, correlation analysis between LR model classification and survival prognosis was analyzed. Results Based on the breast cancer gene expression profile data in TCGA, 540 DEGs were screened between better DFS and poor DFS groups, including 177 downregulated and 363 upregulated DEGs. A total of 283 DEGs were involved in all GO functions and KEGG signaling pathways. Through LR model screening, 10 important feature DEGs were identified and validated, among which, ABCA3, CCL22, FOXJ1, IL1RN, KCNIP3, MAP2K6, and MRPL13, were significantly expressed in both groups in the two data sets. ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 were good prognostic factors, while KCNIP3 and MRPL13 were poor prognostic factors. Conclusion ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 may serve as good prognostic factors, while KCNIP3 and MRPL13 may be poor prognostic factors for the prognosis of breast cancer.

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Section: Discussionmentioning

confidence: 99%

Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis

et al. 2020

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“…Similar aberrations in these chromosomes have been associated with invasive ductal carcinoma of breast and other subtypes [67][68][69]. Among these, chromosomes 8, 14, 4, 18, X, 3, 10, 20, 9 and 1 have also been observed to contain aberrant regions in breast cancer patients [70].…”

Section: Discussionmentioning

confidence: 59%

In silico pathway analysis based on Chromosomal Instability in Breast Cancer Patients

KOUR

Sambyal

Guleria

et al. 2020

Preprint

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Background: Complex genomic changes that arise in tumors are a consequence of chromosomal instability. In tumor cells genomic aberrations disrupt core signaling pathways involving various genes, thus delineating of signaling pathways can help understand the pathogenesis of cancer. The bioinformatics tools can further help in identifying networks of interactions between the genes to get a greater biological context of all genes affected by chromosomal instability. Methods: Karyotypic analyses was done in 150 clinically confirmed breast cancer patients and 150 age and gender matched healthy controls after 72 hours Peripheral lymphocyte culturing and GTG-banding. Reactome database from Cytoscape software version 3.7.1 was used to perform. in-silico analysis (functional interaction and gene enrichment).Results: Frequency of chromosomal aberrations (structural and numerical) was found to be significantly higher in patients as compared to controls. The genes harbored by chromosomal regions showing increased aberration frequency in patients were further analyzed in-silico. Pathway analysis on a set of genes that were not linked together revealed that genes HDAC3, NCOA1, NLRC4, COL1A1, RARA, WWTR1, and BRCA1 were enriched in the RNA Polymerase II Transcription pathway which is involved in recruitment, initiation, elongation and dissociation during transcription. Conclusion: The current study employs the information inferred from chromosomal instability analysis in a non-target tissue for determining the genes and the pathways associated with breast cancer. These results can be further extrapolated by performing either mutation analysis in the genes/pathways deduced or expression analysis which can pinpoint the relevant functional impact of chromosomal instability.

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“…Similar aberrations in these chromosomes have also been associated with invasive ductal carcinoma of breast and other subtypes [67][68][69]. Among these, chromosomes 8, 14, 4, 18, X, 3, 10, 20, 9 and 1 have also been observed to contain aberrant regions in breast cancer patients [70].…”

Section: Discussionmentioning

confidence: 62%

In silico pathway analysis based on Chromosomal Instability in Breast Cancer Patients

KOUR

Sambyal

Guleria

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Complex genomic changes that arise in tumors are a consequence of chromosomal instability. In tumor cells genomic aberrations disrupt core signaling pathways involving various genes, thus delineating of signaling pathways can help understand the pathogenesis of cancer. The bioinformatics tools can further help in identifying networks of interactions between the genes to get a greater biological context of all genes affected by chromosomal instability. Results: Karyotypic analyses was done in 150 clinically confirmed breast cancer patients and 150 age and gender matched healthy controls after 72 hours Peripheral lymphocyte culturing and GTG-banding. Karyotypic analyses found more frequent chromosomal aberrations (structural and numerical) on chromosome 1, 2, 3, 4, 5, 8, 9, 17 and X. The data of genes harbored by chromosomal regions showing increased aberration frequency was retrieved from online databases. Pathway analysis on a set of genes that were not linked together revealed that genes HDAC3, NCOA1, NLRC4, COL1A1, RARA, WWTR1, and BRCA1 were enriched in the RNA Polymerase II Transcription pathway which is involved in recruitment, initiation, elongation and dissociation during transcription. Conclusion: The chromosomal instability analysis in a non-target in cancer patients thus can be used to identify genes and decipher the pathway involved in tumorigenesis. The bioinformatics approach can help in identifying aberrant genes in transcription pathways and their relation with breast cancer progression.

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Identification of Aberrant Chromosomal Regions in Human Breast Cancer Using Gene Expression Data and Related Gene Information

Cited by 6 publications

References 38 publications

Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis

Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis

In silico pathway analysis based on Chromosomal Instability in Breast Cancer Patients

In silico pathway analysis based on Chromosomal Instability in Breast Cancer Patients

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