Identification of co-expression modules and potential biomarkers of breast cancer by WGCNA

Recent studies have reported that dishevelled-associated activator of morphogenesis 1 (DAAM1) is remarkably essential for mediating cell migration and invasion in breast cancer (BrCa). Nonetheless, the definite expression profile of DAAM1 in BrCa patients and the impact on metastasis of BrCa in vivo have not been explored up to now. The differential expression of DAAM1 in BrCa and adjacent tissues was assessed via immunohistochemistry (IHC) staining. The metastatic capacities of BrCa SUM-1315 cells were examined in BALB/c nude mice. Besides, the prognostic values of DAAM1 mRNA in BrCa were explored based on Kaplan-Meier (KM) plotter. The expression of DAAM1 protein was notably overexpressed in BrCa tissues compared with that in paired normal breast tissues. The high expression of DAAM1 in BrCa tissues was significantly associated with lymph-node metastasis. Furthermore, DAAM1 overexpression promoted the invasive capacity of BrCa cells and stimulated lung metastatic extent in vivo. We also found that overexpressed DAAM1 mRNA was significantly associated with poor relapse-free survival (RFS), overall survival (OS), distance-metastasis-free survival (DMFS), and post-progression survival (PPS). Our findings reveal that DAAM1 might be a novel therapeutic target to manage the deteriorated metastasis of BrCa and identified DAAM1 as a promising biomarker for unfavorable prognosis in BrCa patients.

Overexpressed DAAM1 correlates with metastasis and predicts poor prognosis in breast cancer.

Breast cancer (BC) is a malignancy with high incidence among women in the world. This study aims to screen key genes and potential prognostic biomarkers for BC using bioinformatics analysis. Total 58 normal tissues and 203 cancer tissues were collected from three Gene Expression Omnibus (GEO) gene expression profiles, and then the differential expressed genes (DEGs) were identified. Subsequently, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway were analyzed to investigate the biological function of DEGs. Additionally, hub genes were screened by constructing a protein-protein interaction (PPI) network. Then, we explored the prognostic value and molecular mechanism of these hub genes using Kaplan-Meier (KM) curve and Gene Set Enrichment Analysis (GSEA). As a result, 42 up-regulated and 82 down-regulated DEGs were screened out from GEO datasets. The DEGs were mainly related to cell cycles and cell proliferation by GO and KEGG pathway analysis. Furthermore, 12 hub genes (FN1, AURKA, CCNB1, BUB1B, PRC1, TPX2, NUSAP1, TOP2A, KIF20A, KIF2C, RRM2, ASPM) with a high degree were identified initially, among which, 11 hub genes were significantly correlated with the prognosis of BC patients based on the Kaplan-Meier-plotter. GSEA reviewed that these hub genes correlated with KEGG_CELL_CYCLE and HALLMARK_P53_PATHWAY. In conclusion, this study identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis. This finding will improve our knowledge of the BC progress and mechanisms.

https://www.tandfonline.com/doi/pdf/10.1080/21655979.2021.1960775

Identification of candidate biomarkers correlated with poor prognosis of breast cancer based on bioinformatics analysis.

BACKGROUND Breast cancer has a high mortality rate and is the most common cancer of women worldwide. Our gene co-expression network analysis identified the genes closely related to the pathological stage of breast cancer. MATERIAL AND METHODS We performed weighted gene co-expression network analysis (WGCNA) from the Gene Expression Omnibus (GEO) database, and performed pathway enrichment analysis on genes from significant modules. RESULTS A non-metastatic sample (374) of breast cancer from GSE102484 was used to construct the gene co-expression network. All 49 hub genes have been shown to be upregulated, and 19 of the 49 hub genes are significantly upregulated in breast cancer tissue. The roles of the genes CASC5, CKAP2L, FAM83D, KIF18B, KIF23, SKA1, GINS1, CDCA5, and MCM6 in breast cancer are unclear, so in order to better reveal the staging of breast cancer markers, it is necessary to study those hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes indicated that 49 hub genes were enriched to sister chromatid cohesion, spindle midzone, microtubule motor activity, cell cycle, and something else. Additionally, there is an independent data set - GSE20685 - for module preservation analysis, survival analysis, and gene validation. CONCLUSIONS This study identified 49 hub genes that were associated with pathologic stage of breast cancer, 19 of which were significantly upregulated in breast cancer. Risk stratification, therapeutic decision making, and prognosis predication might be improved by our study results. This study provides new insights into biomarkers of breast cancer, which might influence the future direction of breast cancer research.

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Identification of Hub Genes Using Co-Expression Network Analysis in Breast Cancer as a Tool to Predict Different Stages.

Background Lung cancer (LC) has become the top cause responsible for cancer-related deaths. Cell division cycle-associated (CDCA) genes exert an important role in the life process. Dysregulation in the process of cell division may lead to malignancy. Methods Transcriptional data on CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases. Results CDCA1/2/3/4/5/7/8 expression levels were higher in lung adenocarcinoma tissues, and the CDCA1/2/3/4/5/6/7/8 expression levels were increased in squamous cell LC tissues compared with those in noncarcinoma lung tissues. The expression levels of CDCA1/2/3/4/5/8 showed correlation with tumor classification. The Kaplan-Meier Plotter database was employed to carry out survival analysis, indicating that increased CDCA1/2/3/4/5/6/7/8 expression levels were increased in squamous cell LC tissues compared with those in noncarcinoma lung tissues. The expression levels of P < 0.05). Only LC patients with increased CDCA3/4/5/8 expression were significantly related to lower post-progression survival (PPS) (P < 0.05). Only LC patients with increased CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases. CDCA8, INCENP, AURKB, and BIRC5); CORUM: 127: NDC80 kinetochore complex; M129: the PID PLK1 pathway; and GO: 0007080: mitotic metaphase plate congression, all of which were remarkably modulated since the alterations affected CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases. Conclusions Upregulated CDCA genes' expression levels in LC tissues probably play a crucial part in LC oncogenesis. The upregulated CDCA genes' expression levels are used as the potential prognostic markers to improve patient survival and the LC prognostic accuracy. CDCA genes probably exert their functions in tumorigenesis through the PLK1 pathway.CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases. CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases. CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases.

https://downloads.hindawi.com/journals/bmri/2020/6412593.pdf

Analysis of the Expression of Cell Division Cycle-Associated Genes and Its Prognostic Significance in Human Lung Carcinoma: A Review of the Literature Databases.

Breast cancer is one of the most common endocrine cancers among females worldwide. Distant metastasis of breast cancer is causing an increasing number of breast cancer-related deaths. However, the potential mechanisms of metastasis and candidate biomarkers remain to be further explored. The gene expression profiles of GSE102484 were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to screen for the most potent gene modules associated with the metastatic risk of breast cancer, and a total of 12 modules were identified based on the analysis. In the most significant module (R2 = 0.68), 21 network hub genes (MM > 0.90) were retained for further analyses. Next, protein-protein interaction (PPI) networks were used to further explore the biomarkers with the most interactions in gene modules. According to the PPI networks, five hub genes (TPX2, KIF2C, CDCA8, BUB1B, and CCNA2) were identified as key genes associated with breast cancer progression. Furthermore, the prognostic value and differential expression of these genes were validated based on data from The Cancer Genome Atlas (TCGA) and Kaplan-Meier (KM) Plotter. Receiver operating characteristic (ROC) curve analysis revealed that the mRNA expression levels of these five hub genes showed excellent diagnostic value for breast cancer and adjacent tissues. Moreover, these five hub genes were significantly associated with worse distant metastasis-free survival (DMFS) in the patient cohort based on KM Plotter. Five hub genes (TPX2, KIF2C, CDCA8, BUB1B, and CCNA2) associated with the risk of distant metastasis were extracted for further research, which might be used as biomarkers to predict distant metastasis of breast cancer.

/pdf/identification-of-five-hub-genes-as-monitoring-biomarkers-19bl4mjwkn.pdf

Identification of five hub genes as monitoring biomarkers for breast cancer metastasis in silico

Angiotensin-converting enzyme 2 (ACE2) is known as a host cell receptor for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is identified to be dysregulated in multiple tumors. Although the characterization of abnormal ACE2 expression in malignancies has been preliminarily explored, in-depth analysis of ACE2 in breast cancer (BRCA) has not been elucidated. A systematic pan-cancer analysis was conducted to assess the expression pattern and immunological role of ACE2 based on RNA-sequencing (RNA-seq) data downloaded from The Cancer Genome Atlas (TCGA). Next, correlations between ACE2 expression immunological characteristics in the BRCA tumor microenvironment (TME) were evaluated. Also, the role of ACE2 in predicting the clinical features and the response to therapeutic options in BRCA was estimated. These findings were subsequently validated in another public transcriptomic cohort as well as a recruited cohort. ACE2 was lowly expressed in most cancers compared with adjacent tissues. ACE2 was positively correlated with immunomodulators, tumor-infiltrating immune cells (TIICs), cancer immunity cycles, immune checkpoints, and tumor mutation burden (TMB). Besides, high ACE2 levels indicated the triple-negative breast cancer (TNBC) subtype of BRCA, lower response to endocrine therapy and higher response to chemotherapy, anti-ERBB therapy, antiangiogenic therapy and immunotherapy. To sum up, ACE2 correlates with an inflamed TME and identifies immuno-hot tumors, which may be used as an auxiliary biomarker for the identification of immunological characteristics in BRCA.

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Yun Cai

Papers

Identification of five hub genes as monitoring biomarkers for breast cancer metastasis in silico

SARS-CoV-2 receptor ACE2 identifies immuno-hot tumors in breast cancer