Prognostic role of <i>EGR1</i> in breast cancer: a systematic review

Saha, Subbroto Kumar; Islam, Saiful; Saha, Tripti; Nishat, Afsana; Biswas, Polash Kumar; Gil, Minchan; Nkenyereye, Lewis; El–Sappagh, Shaker; Cho, Ssang-Goo

doi:10.5483/bmbrep.2021.54.10.087

Cited by 13 publications

(9 citation statements)

References 65 publications

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“…Several previous studies showed that EGR1 exhibits prominent tumor suppressor function in glioma (44), non-smallcell lung cancer (45), colon cancer (46), papillary thyroid carcinoma (47), and breast cancer (48). Saha et al indicated that low levels of EGR1 expression were positively correlated with poor survival for RFS and distant metastasis-free survival (DMFS) in breast cancer (49). Ribonucleotide reductase small subunit M2 (RRM2), as a master driver of aggressive prostate cancer (50), showed significant prognostic value of RRM2 in prostate cancer (51).…”

Section: Discussionmentioning

confidence: 99%

“…Saha et al. indicated that low levels of EGR1 expression were positively correlated with poor survival for RFS and distant metastasis-free survival (DMFS) in breast cancer ( 49 ). Ribonucleotide reductase small subunit M2 (RRM2), as a master driver of aggressive prostate cancer ( 50 ), showed significant prognostic value of RRM2 in prostate cancer ( 51 ).…”

Section: Discussionmentioning

confidence: 99%

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Computational Recognition of a Regulatory T-cell-specific Signature With Potential Implications in Prognosis, Immunotherapy, and Therapeutic Resistance of Prostate Cancer

Fan

Zou

et al. 2022

Front. Immunol.

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Prostate cancer, recognized as a “cold” tumor, has an immunosuppressive microenvironment in which regulatory T cells (Tregs) usually play a major role. Therefore, identifying a prognostic signature of Tregs has promising benefits of improving survival of prostate cancer patients. However, the traditional methods of Treg quantification usually suffer from bias and variability. Transcriptional characteristics have recently been found to have a predictive power for the infiltration of Tregs. Thus, a novel machine learning-based computational framework has been presented using Tregs and 19 other immune cell types using 42 purified immune cell datasets from GEO to identify Treg-specific mRNAs, and a prognostic signature of Tregs (named “TILTregSig”) consisting of five mRNAs (SOCS2, EGR1, RRM2, TPP1, and C11orf54) was developed and validated to monitor the prognosis of prostate cancer using the TCGA and ICGC datasets. The TILTregSig showed a stronger predictive power for tumor immunity compared with tumor mutation burden and glycolytic activity, which have been reported as immune predictors. Further analyses indicate that the TILTregSig might influence tumor immunity mainly by mediating tumor-infiltrating Tregs and could be a powerful predictor for Tregs in prostate cancer. Moreover, the TILTregSig showed a promising potential for predicting cancer immunotherapy (CIT) response in five CIT response datasets and therapeutic resistance in the GSCALite dataset in multiple cancers. Our TILTregSig derived from PBMCs makes it possible to achieve a straightforward, noninvasive, and inexpensive detection assay for prostate cancer compared with the current histopathological examination that requires invasive tissue puncture, which lays the foundation for the future development of a panel of different molecules in peripheral blood comprising a biomarker of prostate cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Computational Recognition of a Regulatory T-cell-specific Signature With Potential Implications in Prognosis, Immunotherapy, and Therapeutic Resistance of Prostate Cancer

Fan

Zou

et al. 2022

Front. Immunol.

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“…EGR1 (Early Growth Response 1) has long been dysregulated in many cancers and is known to regulate tumor progression, making it an attractive target for cancer therapy ( Saha et al, 2021 ). EGR1-activated LINC01503 epigenetically silences the DUSP5/CDKN1A expression, mediating cell cycle progression and tumorigenesis ( Su et al, 2019 ; Ma et al, 2021b ).…”

Section: Discussionmentioning

confidence: 99%

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

Feng

Wang²,

Liu³

et al. 2022

Front. Pharmacol.

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Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.

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“…EGR1 is an important transcription factor that has been widely studied in the areas of oncology, neuropsychiatric disease and diabetic kidney disease (DKD) [ 24 , 25 , 26 ], but it is not clear whether EGR1 is associated with hair growth. EGR1 encodes a protein with a zinc-binding finger structure, which acts as a DNA-binding domain in several transcriptional regulatory proteins [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

The Effect of EGR1 on the Proliferation of Dermal Papilla Cells

Wang

Cao

et al. 2022

Genes

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Early growth response factor 1 (EGR1) is a zinc-finger transcription factor that plays a vital role in the development of hair follicles. According to our previous studies, EGR1 is a transcriptional promoter of the bone morphogenetic protein 7 (BMP7), a candidate gene involved in the proliferation of dermal papilla cells. Since hair follicles are the basis of lambskin pattern formation and dermal papilla cells (DPCs) act on hair follicle growth, in order to elucidate the role of EGR1 and hair follicles, this study aimed to investigate the biological role of EGR1 in DPCs. In our study, the EGR1 coding sequence (CDS) region was firstly cloned by polymerase chain reaction, and bioinformatics analysis was performed. Then, the function of EGR1 was detected by 5-ethynyl-2’-deoxyuridine (EDU) and Cell Counting Kit-8 (CCK8), and Western blot (WB) was conducted to analyze the cellular effect of EGR1 on DPCs. The proliferative effect of EGR1 on DPCs was also further confirmed by detecting its expression by qPCR and WB on marker genes of proliferation, including PCNA and CDK2. The sequence of the EGR1 CDS region of a lamb was successfully cloned, and its nucleic acid sequence was analyzed and found to be highly homologous to Rattus norvegicus, Mus musculus, Bos taurus and Homo sapiens. Predictive analysis of the protein encoded by EGR1 revealed that it is an extra-membrane protein, and not a secretory protein, with subcellular localization in the nucleus and cytoplasm. The proliferative effect of DPCs was significantly stronger (p < 0.01) in EGR1 up-regulated DPCs compared to the controls, while the opposite result was observed in EGR1 down-regulated DPCs. Markers of proliferation including PCNA and CDK2 also appeared to be differentially upregulated in EGR1 gene overexpression compared to the controls, with the opposite result in EGR1 gene downregulation. In summary, our study revealed that EGR1 promotes the proliferation of DPCs, and we speculate that EGR1 may be closely associated with hair follicle growth and development.

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Prognostic role of EGR1 in breast cancer: a systematic review

Cited by 13 publications

References 65 publications

Computational Recognition of a Regulatory T-cell-specific Signature With Potential Implications in Prognosis, Immunotherapy, and Therapeutic Resistance of Prostate Cancer

Computational Recognition of a Regulatory T-cell-specific Signature With Potential Implications in Prognosis, Immunotherapy, and Therapeutic Resistance of Prostate Cancer

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

The Effect of EGR1 on the Proliferation of Dermal Papilla Cells

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