Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis

Ma, Jiulong; Chen, Chen; Liu, Shan; Ji, Jiahua; Wu, Di; Huang, Peng; Wei, Dexian; Zhang, Fan; Ren, Liqun

doi:10.1038/s41417-022-00473-2

Cited by 19 publications

(17 citation statements)

References 45 publications

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“…With the advent of the era of precision medicine and the development of bioinformatics, an increasing amount of research has focused on identifying markers or models that can accurately predict prognosis and provide individualized treatment for patients (20,21). For example, Ma J et al identified 5 prognosis-related markers in triple-negative breast cancer and provided potential therapeutic targets through multiomics and bioinformatics (22). Zhou L et al developed an 8-gene signature to predict overall survival in colon cancer patients (23).…”

Section: Discussionmentioning

confidence: 99%

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Identification and validation of a 17-gene signature to improve the survival prediction of gliomas

Tong

Xia²,

Xu³

et al. 2022

Front. Immunol.

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Gliomas are one of the most frequent types of nervous system tumours and have significant morbidity and mortality rates. As a result, it is critical to fully comprehend the molecular mechanism of glioma to predict prognosis and target gene therapy. The goal of this research was to discover the hub genes of glioma and investigate their prognostic and diagnostic usefulness. In this study, we collected mRNA expression profiles and clinical information from glioma patients in the TCGA, GTEx, GSE68848, and GSE4920 databases. WGCNA and differential expression analysis identified 170 DEGs in the collected datasets. GO and KEGG pathway analyses revealed that DEGs were mainly enriched in gliogenesis and extracellular matrix. LASSO was performed to construct prognostic signatures in the TCGA cohort, and 17 genes were used to build risk models and were validated in the CGGA database. The ROC curve confirmed the accuracy of the prognostic signature. Univariate and multivariate Cox regression analyses showed that all independent risk factors for glioma except gender. Next, we performed ssGSEA to demonstrate a high correlation between risk score and immunity. Subsequently, 7 hub genes were identified by the PPI network and found to have great drug targeting potential. Finally, RPL39, as one of the hub genes, was found to be closely related to the prognosis of glioma patients. Knockdown of RPL39 in vitro significantly inhibited the proliferation and migration of glioma cells, whereas overexpression of RPL39 had the opposite effect. And we found that knockdown of RPL39 inhibited the polarization and infiltration of M2 phenotype macrophages. In conclusion, our new prognosis-related model provides more potential therapeutic strategies for glioma patients.

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Section: Discussionmentioning

confidence: 99%

“…For example, Ma J et al. identified 5 prognosis-related markers in triple-negative breast cancer and provided potential therapeutic targets through multiomics and bioinformatics ( 22 ). Zhou L et al.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of a 17-gene signature to improve the survival prediction of gliomas

Tong

Xia²,

Xu³

et al. 2022

Front. Immunol.

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“…TOP2A encodes topoisomerase (DNA) II alpha, somatic mutations in a TOP2A immunohistochemical score may be important in predicting response to immunotherapy treatment for triple-negative breast cancer ( Jiang et al, 2021 ). Several studies have proposed TOP2A as a prognostic marker and therapeutic target for TNBC through well-established integrated bioinformatics methods ( Qiu et al, 2021 ; Wei et al, 2021 ; Ma et al, 2022 ). The gene expression of TOP2A and EGFR identifies efficacy of docetaxel plus epirubicin as neoadjuvant chemotherapy in TNBC patients ( Liu et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

“…Transcriptomics analysis is one of the most popular platforms for identifying disease-driving genes ( Li et al, 2021 ; Yuan et al, 2021 ; Alam et al, 2022a ; Alam et al, 2022b ; Alam et al, 2022c ; Ma et al, 2022 ). In this study, we created transcriptomics datasets of large sample sizes from NCBI-GEO and TCGA databases to identify more consistent TNBC-driving KGs for diagnosis at an early-stage.…”

Section: Introductionmentioning

confidence: 99%

Gene expression profile analysis to discover molecular signatures for early diagnosis and therapies of triple-negative breast cancer

Alam

Sultana

Wang

et al. 2022

Front. Mol. Biosci.

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Triple-negative breast cancer (TNBC) is one of the most lethal subtypes of breast cancer (BC), and it accounts for approximately 10%–20% of all invasive BCs diagnosed worldwide. The survival rate of TNBC in stages III and IV is very low, and a large number of patients are diagnosed in these stages. Therefore, the purpose of this study was to identify TNBC-causing molecular signatures and anti-TNBC drug agents for early diagnosis and therapies. Five microarray datasets that contained 304 TNBC and 109 control samples were collected from the Gene Expression Omnibus (GEO) database, and RNA-Seq data with 116 tumor and 124 normal samples were collected from TCGA database to identify differentially expressed genes (DEGs) between TNBC and control samples. A total of 64 DEGs were identified, of which 29 were upregulated and 35 were downregulated, by using the statistical limma R-package. Among them, seven key genes (KGs) were commonly selected from microarray and RNA-Seq data based on the high degree of connectivity through PPI (protein–protein interaction) and module analysis. Out of these seven KGs, six KGs (TOP2A, BIRC5, AURKB, ACTB, ASPM, and BUB1B) were upregulated and one (EGFR) was downregulated. We also investigated their differential expression patterns with different subtypes and progression stages of BC by the independent datasets of RNA-seq profiles from UALCAN database, which indicated that they may be potential biomarkers for early diagnosis. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses with the proposed DEGs were performed using the online Enrichr database to investigate the pathogenetic processes of TNBC highlighting KGs. Then, we performed gene regulatory network analysis and identified three transcriptional (SOX2, E2F4, and KDM5B) and three post-transcriptional (hsa-mir-1-3p, hsa-mir-124-3p, and hsa-mir-34a-5p) regulators of KGs. Finally, we proposed five KG-guided repurposable drug molecules (imatinib, regorafenib, pazopanib, teniposide, and dexrazoxane) for TNBC through network pharmacology and molecular docking analyses. These drug molecules also showed significant binding performance with some cancer-related PTM-sites (phosphorylation, succinylation, and ubiquitination) of top-ranked four key proteins (EGFR, AURKB, BIRC5, and TOP2A). Therefore, the findings of this computational study may play a vital role in early diagnosis and therapies against TNBC by wet-lab validation.

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“…We did the meta-analysis using protein expression data; then we also need to verify at mRNA level the differential expression of HIF1A in PDAC and normal pancreas despite the fact that, there is somehow an intermediate correlation (medium consistency) between mRNA levels of HIF-1α and protein levels in PDAC (39). For the purpose, GEPIA database is a famous webserver used for gene profiling and integrative analysis, and cited in high quality papers (40)(41)(42). The differential analysis of HIF1A expression through GEPIA webserver showed a significant difference in the expression of HIF1A between PDAC and normal pancreas.…”

Section: Discussionmentioning

confidence: 99%

High expression of hypoxia-inducible factor 1-alpha predicts poor prognosis in pancreatic ductal adenocarcinoma: a meta-analysis and database validation protocol

Zoa¹,

Yang²,

Huang³

et al. 2022

Transl Cancer Res TCR

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Background: Hypoxia-inducible factor 1-alpha (HIF-1α) is overexpressed in pancreatic ductal adenocarcinomas (PDACs). However, the prognosis of high expression of HIF-1α in PDACs remains controversial because of lacking a solid support. A meta-analysis may help for a better understanding of the role of HIF-1α in the prognosis of PDACs.Methods: By using a systematic approach, we conducted a meta-analysis from current literature. We performed an advanced search in PubMed, Embase, Cochrane Library and Web of Science databases.Recorded studies were published between September 1, 2001, and February 26, 2021, in English and related to the expression of HIF-1α in PDAC. We pooled and combined hazard ratios (HRs) and 95% confidence intervals (CIs) to show the effect of HIF-1α expression on overall survival (OS). We pooled also risk ratios (RRs) and 95% CIs to assess the correlation between HIF-1α expression and clinicopathological characteristics in PDAC. We evaluated publication bias among included studies through the Begg's test and Egger's test. From "Expression Plots" modules in the Gene Expression Profiling Interactive Analysis (GEPIA) database, we showed the difference of mRNA level for HIF1A between 179 pancreatic adenocarcinomas (PAADs) and 171 normal pancreatic tissues.Results: This meta-analysis included 11 studies and 764 patients. High expression of HIF-1α was associated with shorter OS compared to low HIF-1α expression in PDAC (HR =1.74, 95% CI: 1.49-2.04, P<0.001). Patients with high expression of HIF-1α tended to have an increased risk of earlier lymph node metastasis (LNM) (RR =1.63, 95% CI: 1.36-1.95, P<0.001), and more advanced clinical stage (RR =1.64, 95% CI: 1.38-1.97, P<0.001) compared to those with low HIF-1α expression. Expression plots from GEPIA database showed HIF1A overexpressed in PDAC tissues compared to normal tissues (Log 2 FC =2, P<0.01).Conclusions: High HIF-1α expression associated with worse prognosis of PDACs compared to low HIF-1α expression. Since HIF-1α expression is greater in PDAC than normal pancreas, it could serve as a prognostic factor and potential therapeutic target. However, due to the complex role of HIF-1α in physiology and pathology, therapeutic intervention should be considered with caution.

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Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis

Cited by 19 publications

References 45 publications

Identification and validation of a 17-gene signature to improve the survival prediction of gliomas

Identification and validation of a 17-gene signature to improve the survival prediction of gliomas

Gene expression profile analysis to discover molecular signatures for early diagnosis and therapies of triple-negative breast cancer

High expression of hypoxia-inducible factor 1-alpha predicts poor prognosis in pancreatic ductal adenocarcinoma: a meta-analysis and database validation protocol

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