Gene expression signatures: A tool for analysis of breast cancer prognosis and therapy

Latha, Neetha Rajan; Rajan, A. R.; Nadhan, Revathy; Achyutuni, Sarada; Sengodan, Satheesh Kumar; Hemalatha, S; Varghese, Geetu Rose; Thankappan, Ratheeshkumar; Krishnan, Neethu; Patra, Dipyaman; Warrier, Arathy; Srinivas, Priya

doi:10.1016/j.critrevonc.2020.102964

Cited by 28 publications

(14 citation statements)

References 66 publications

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“…However, they have limited predictive accuracy in prognostic predictions and are limited to early-stage BC, which are characterized by a small size or without lymph node metastasis. 4 For example, while recurrence and death are often seen in patients with early-stage BC within a short time of diagnosis, patients staged III/IV or with pathological grade III breast tumors can survive for over 5 years. 5 Although the tumor-nodemetastasis (TNM) staging system is commonly used to predict individual prognosis, it just contain clinical factors and neglects genetic characteristics.…”

Section: Introductionmentioning

confidence: 99%

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Breast Cancer

Yuan

Duan

Zhai

et al. 2021

IJWH

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Section: Introductionmentioning

confidence: 99%

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Breast Cancer

Yuan

Duan

Zhai

et al. 2021

IJWH

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“…Over the past decades, in-depth gene sequencing and bioinformatics provided us the chance to identify novel diagnostic parameters and guide the individual treatment optimization for various illnesses (13)(14)(15)(16). Gene expression microarray was an effective method to show large-scale data at genomic levels, and rapid progression of bioinformatics make it possible to mine more reliable biomarkers (17).…”

Section: Introductionmentioning

confidence: 99%

Identification and Validation of a Five-Gene Signature Associated With Overall Survival in Breast Cancer Patients

Wang

Chen

et al. 2021

Front. Oncol.

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BackgroundRecent years, the global prevalence of breast cancer (BC) was still high and the underlying molecular mechanisms remained largely unknown. The investigation of prognosis-related biomarkers had become an urgent demand.ResultsIn this study, gene expression profiles and clinical information of breast cancer patients were downloaded from the TCGA database. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A risk score formula involving five novel prognostic associated biomarkers (EDN2, CLEC3B, SV2C, WT1, and MUC2) were then constructed by LASSO. The prognostic value of the risk model was further confirmed in the TCGA entire cohort and an independent external validation cohort. To explore the biological functions of the selected genes, in vitro assays were performed, indicating that these novel biomarkers could markedly influence breast cancer progression.ConclusionsWe established a predictive five-gene signature, which could be helpful for a personalized management in breast cancer patients.

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“…GEP has been extensively used to classify BC into subtypes, identifying transcriptional signatures for estrogen receptor + (ER + , luminal), human epidermal growth factor receptor 2 + (HER2 + ) ERBB2-amplified, and ER − , progesterone receptor − (PR − ) and HER2 − (basal) BC [ 21 ]. This has resulted in the development of kits for GEP of BC samples, such as Mammaprint, Oncotype Dx Breast, Prosigna PAM50 Breast Cancer Prognostic Gene Signature Assay, Breast Cancer Index, and EndoPredict [ 22 ]. Moreover, GEP provided an overview of multi-gene interactions, as such painting a bigger picture of changes at the level of molecular pathways and networks.…”

Section: Introductionmentioning

confidence: 99%

Overcoming the Challenges of High Quality RNA Extraction from Core Needle Biopsy

et al. 2021

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The use of gene expression profiling (GEP) in cancer management is rising, as GEP can be used for disease classification and diagnosis, tailoring treatment to underlying genetic determinants of pharmacological response, monitoring of therapy response, and prognosis. However, the reliability of GEP heavily depends on the input of RNA in sufficient quantity and quality. This highlights the need for standard procedures to ensure best practices for RNA extraction from often small tumor biopsies with variable tissue handling. We optimized an RNA extraction protocol from fresh-frozen (FF) core needle biopsies (CNB) from breast cancer patients and from formalin-fixed paraffin-embedded (FFPE) tissue when FF CNB did not yield sufficient RNA. Methods to avoid ribonucleases andto homogenize or to deparaffinize tissues and the impact of tissue composition on RNA extraction were studied. Additionally, RNA’s compatibility with the nanoString nCounter® technology was studied. This technology platform enables GEP using small RNA fragments. After optimization of the protocol, RNA of high quality and sufficient quantity was obtained from FF CNB in 92% of samples. For the remaining 8% of cases, FFPE material prepared by the pathology department was used for RNA extraction. Both resulting RNA end products are compatible with the nanoString nCounter® technology.

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Gene expression signatures: A tool for analysis of breast cancer prognosis and therapy

Cited by 28 publications

References 66 publications

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Breast Cancer

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Breast Cancer

Identification and Validation of a Five-Gene Signature Associated With Overall Survival in Breast Cancer Patients

Overcoming the Challenges of High Quality RNA Extraction from Core Needle Biopsy

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