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

Efficient weighted univariate clustering maps outstanding dysregulated genomic zones in human cancers

Motivation

Chromosomal patterning of gene expression in cancer can arise from aneuploidy, genome disorganization or abnormal DNA methylation. To map such patterns, we introduce a weighted univariate clustering algorithm to guarantee linear runtime, optimality and reproducibility.

Results

We present the chromosome clustering method, establish its optimality and runtime and evaluate its performance. It uses dynamic programming enhanced with an algorithm to reduce search-space in-place to decrease runtime overhead. Using the method, we delineated outstanding genomic zones in 17 human cancer types. We identified strong continuity in dysregulation polarity—dominance by either up- or downregulated genes in a zone—along chromosomes in all cancer types. Significantly polarized dysregulation zones specific to cancer types are found, offering potential diagnostic biomarkers. Unreported previously, a total of 109 loci with conserved dysregulation polarity across cancer types give insights into pan-cancer mechanisms. Efficient chromosomal clustering opens a window to characterize molecular patterns in cancer genome and beyond.

Availability and implementation

Weighted univariate clustering algorithms are implemented within the R package ‘Ckmeans.1d.dp’ (4.0.0 or above), freely available at https://cran.r-project.org/package=Ckmeans.1d.dp.

Supplementary information

Supplementary data are available at Bioinformatics online.

In silico pathway analysis based on chromosomal instability in breast cancer patients

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 h 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.

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

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.

G protein subunit α q regulates gastric cancer growth via the p53/p21 and MEK/ERK pathways

Genetic alterations in G protein subunit α q (GNAQ) have been reported in numerous types of human cancer. However, the role of GNAQ in human gastric cancer (GC) has not been explored. In the present study, we found that GNAQ was highly expressed in GC patient samples and GNAQ expression was related to patient age, GC differentiation status and adjuvant therapy, as determined by immunohistochemical assay. Lentivirus delivery of short hairpin RNA (shRNA) targeting GNAQ was used to explore the function of GNAQ in GC cells. Silencing of GNAQ markedly suppressed proliferation and colony formation in GC cells, and arrested the cell cycle at the S phase. Mechanistic analysis revealed that knockdown of GNAQ significantly increased the expression of p53 and p21, and decreased cyclin A and p-CDK2 protein expression. Moreover, the phosphorylation of ERK and MEK was also decreased after knockdown of GNAQ as determined by western blotting assay. Overall, our results suggest that GNAQ plays a critical role in regulating GC cell growth and survival via canonical oncogenic signaling pathways including MAPK and p53, and therefore serves as a promising new therapeutic target in GC.