Bioinformatics analysis of key genes in triple negative breast cancer and validation of oncogene PLK1

Ren, Yi; Deng, Rong; Zhang, Qian; Li, Jing; Han, Baohui

doi:10.21037/atm-20-6873

Cited by 18 publications

(16 citation statements)

References 54 publications

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“…GEPIA is an internet database that may be utilized to do online research. A substantial amount of sequencing data is available on the GEPIA website, including ndings from 8587 tumors samples and 9736 normal tissues (34).…”

Section: Validation Analysis Of Hub Genesmentioning

confidence: 99%

Identification of hub genes affecting triple-negative breast cancer using bioinformatics analysis strategies

Liu

Kan

Wang

et al. 2022

Preprint

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BackgroundBreast cancer is one of the most serious malignancies in women throughout the world, with an increasing number of people being diagnosed and dying from it. Triple-negative breast cancer (TNBC), which has a dismal prognosis, the estrogen receptor (ER), progesterone receptor (PR), and human growth factor receptor type 2 (Her2) immunohistochemistry test results are all negative. MethodsTo find differentially expressed genes (DEGs), two datasets from the Gene Expression Omnibus (GEO) were used to search for TNBC and not triple-negative breast cancer (not-TNBC) differentially expressed genes. Using weighted gene co-expression network analysis (WGCNA), the breast cancer data from the Cancer Genome Atlas (TCGA) was utilized to study the relationship between genes and TNBC features and to identify the hub genes in the most important linked modules. The Gene Ontology (GO) and Kyoto Encyclopedia of the Genes and Genomes (KEGG) enrichment analyses were performed on the differential genes in the GEO dataset and the hub genes in the TCGA dataset to study the nature of genes and to find gene enrichment pathways. A protein-protein interaction network (PPI) was created for each of the genes considered. To discover the most important genes in PPI networks, utilize the cytoHubba plugin in Cytoscape. Following that, the gene expression profiling interaction analysis (GEPIA) was used to confirm the hub gene's expression. Finally, data sets from the cBioPortal database were utilized for survival analysis. ResultsWe detected 7 genes that were up-regulated and 2 genes that were down-regulated by combining the GEO and TCGA databases.PGR, ELF5, AGR2, and GATA3 prolong life expectancy, while ESR1, TFF1, FOXA1, and AR shorten life expectancy.ConclusionOur study confirmed that PGR, ELF5, AGR2, GATA3, ESR1, TFF1, FOXA1, and AR may be potential therapeutic targets and biomarkers for TNBC.

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Section: Validation Analysis Of Hub Genesmentioning

confidence: 99%

Identification of hub genes affecting triple-negative breast cancer using bioinformatics analysis strategies

Liu

Kan

Wang

et al. 2022

Preprint

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show abstract

“…Nowadays, use of gene expression profile to define as prognostic biomarkers of TNBC patients have been still under-investigation. Although other studies found the genes related to poor prognosis, those finding have not applied in clinical practice (10)(11)(12)(13). To develop the new prognostic indicators, more detailed research is required owing to the complex tumor heterogeneity and complicated molecular regulatory mechanism of TNBC.…”

Section: Introductionmentioning

confidence: 99%

Integrated bioinformatic analysis of potential biomarkers of poor prognosis in triple-negative breast cancer

Bissanum¹,

Kamolphiwong²,

Navakanitworakul³

et al. 2022

Transl Cancer Res TCR

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Background: Triple-negative breast cancer (TNBC) is a heterogeneous disease associated with late-stage diagnosis and high metastatic rates. However, a gene signature for reliable TNBC biomarkers is not available yet. We aimed to identify potential key genes and their association with poor prognosis in TNBC through integrated bioinformatics.Methods: Microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database.Differentially expressed genes (DEGs) in TNBC vs. non-TNBC and TNBC vs. normal tissues were analyzed. Overlapping upregulated and downregulated DEGs were selected as inputs for Gene Ontology and pathway enrichment analyses using Metascape. Then, UALCAN and Kaplan-Meier plotter were employed to analyze the prognostic values of all overlapping DEGs. Results:We identified 21 upregulated and 24 downregulated overlapping DEGs in TNBC vs. non-TNBC and TNBC vs. normal breast tissue. The upregulated overlapping DEGs were mainly enriched in various pathways including chromosome segregation, cell cycle phase transition, and cell division, whereas overlapping DEGs were significantly downregulated in pathways, such as multicellular organismal homeostasis, tissue homeostasis, and negative regulation of cell population proliferation. Key genes were identified by association with poor overall survival (OS). Our results showed that high expression of CENPW and HORMAD1 was associated with poor OS of TNBC patients. Conversely, the low expression of PIP, APOD, and ZNF703 indicated worse OS. Conclusions:We identified key genes (CENPW, HORMAD1, APOD, PIP, and ZNF703) associated with poor OS. Thus, these genes might serve as candidate prognostic markers for TNBC.

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“…Bioinformatics can conduct in-depth analysis of open biological databases such as tissues, cell genes, and proteins, and provide a potential theoretical basis for cancer early diagnosis and treatment ( Wang Y. et al, 2021 ). Many scholars have used the database to analyze differentially expressed genes (DEGs) in the progression of lung cancer, gastric cancer, breast cancer, and other tumors ( Ren et al, 2020 ; Wang H. et al, 2021 ; Shan et al, 2021 ). The proteins encoded by these DEGs could be used as molecular markers for tumor diagnosis and prognosis ( Zhao et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Anti-POSTN and Anti-TIMP1 Autoantibodies as Diagnostic Markers in Esophageal Squamous Cell Carcinoma

et al. 2022

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Esophageal cancer is one of the most commonly diagnosed malignant gastrointestinal tumors. The aim of the study was to explore the diagnostic values of anti-POSTN and anti-TIMP1 autoantibodies in esophageal squamous cell carcinoma (ESCC). Differentially expressed genes (DEGs) associated with esophageal cancer were screened out by the LIMMA method in the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Search Tool for the Retrieval of Interacting Genes (STRING) was used to construct the protein–protein interaction (PPI) based on highly DEGs. The candidate hub genes were the intersection genes calculated based on degree and Maximal Clique Centrality (MCC) algorithms via Cytoscape. A total of 370 participants including 185 ESCC patients and 185 matched normal controls were enrolled in enzyme-linked immunosorbent assay (ELISA) to detect the expression levels of autoantibodies corresponding to POSTN and TIMP1 proteins. A total of 375 DEGs with high expression were obtained in esophageal cancer. A total of 20 hub genes were acquired using the cytoHubba plugin by degree and MCC algorithms. The expression levels of anti-POSTN and anti-TIMP1 autoantibodies were higher in the sera of ESCC patients (p < 0.05). Anti-POSTN autoantibody can diagnose ESCC patients with an AUC of 0.638 at the specificity of 90.27% and sensitivity of 27.57%, and anti-TIMP1 autoantibody can diagnose ESCC patients with an AUC of 0.585 at the specificity of 90.27% and sensitivity of 20.54% (p < 0.05). In addition, anti-POSTN and anti-TIMP1 autoantibodies can distinguish ESCC patients from normal controls in most clinical subgroups (p < 0.05). In conclusion, anti-POSTN and anti-TIMP1 autoantibodies may be considered the potential biomarkers in the clinical diagnosis of ESCC.

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Bioinformatics analysis of key genes in triple negative breast cancer and validation of oncogene PLK1

Cited by 18 publications

References 54 publications

Identification of hub genes affecting triple-negative breast cancer using bioinformatics analysis strategies

Identification of hub genes affecting triple-negative breast cancer using bioinformatics analysis strategies

Integrated bioinformatic analysis of potential biomarkers of poor prognosis in triple-negative breast cancer

Anti-POSTN and Anti-TIMP1 Autoantibodies as Diagnostic Markers in Esophageal Squamous Cell Carcinoma

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