Gene expression signatures in early breast cancer: Better together with clinicopathological features

Oliveira, Leandro Jonata de Carvalho; Amorim, Larissa Costa; Megid, Thais Baccili Cury; Resende, Cristiano Augusto Andrade de; Mano, Max Senna

doi:10.1016/j.critrevonc.2022.103708

Cited by 12 publications

(9 citation statements)

References 92 publications

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“…Accumulated evidence has proved that prognostic gene signatures have vast capabilities to aid clinical decision-making ( Yu et al, 2019 ; James et al, 2022 ; Oliveira et al, 2022 ). We constructed and verified a prognostic 9-gene signature to predict the response of stage II/III CRC.…”

Section: Discussionmentioning

confidence: 99%

A novel 9-gene signature for the prediction of postoperative recurrence in stage II/III colorectal cancer

Cheng

Lai

Ji³

et al. 2023

Front. Genet.

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Background: Individualized recurrence risk prediction in patients with stage II/III colorectal cancer (CRC) is crucial for making postoperative treatment decisions. However, there is still a lack of effective approaches for identifying patients with stage II and III CRC at a high risk of recurrence. In this study, we aimed to establish a credible gene model for improving the risk assessment of patients with stage II/III CRC.Methods: Recurrence-free survival (RFS)-related genes were screened using Univariate Cox regression analysis in GSE17538, GSE39582, and GSE161158 cohorts. Common prognostic genes were identified by Venn diagram and subsequently subjected to least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis for signature construction. Kaplan-Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy and superiority of our risk model. Single-sample gene set enrichment analysis (ssGSEA) was employed to investigate the relationship between the infiltrative abundances of immune cells and risk scores. Genes significantly associated with the risk scores were identified to explore the biological implications of the 9-gene signature.Results: Survival analysis identified 347 RFS-related genes. Using these genes, a 9-gene signature was constructed, which was composed of MRPL41, FGD3, RBM38, SPINK1, DKK1, GAL3ST4, INHBB, CTB-113P19.1, and FAM214B. K-M curves verified the survival differences between the low- and high-risk groups classified by the 9-gene signature. The area under the curve (AUC) values of this signature were close to or no less than the previously reported prognostic signatures and clinical factors, suggesting that this model could provide improved RFS prediction. The ssGSEA algorithm estimated that eight immune cells, including regulatory T cells, were aberrantly infiltrated in the high-risk group. Furthermore, the signature was associated with multiple oncogenic pathways, including cell adhesion and angiogenesis.Conclusion: A novel RFS prediction model for patients with stage II/III CRC was constructed using multicohort validation. The proposed signature may help clinicians better manage patients with stage II/III CRC.

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Section: Discussionmentioning

confidence: 99%

A novel 9-gene signature for the prediction of postoperative recurrence in stage II/III colorectal cancer

Cheng

Lai

Ji³

et al. 2023

Front. Genet.

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“…Microarray-based molecular classifications of breast cancer patients are used to predict recurrence risk and chemotherapy response. 28 , 29 …”

Section: The Evaluation Of Molecular Techniques For Biomarker Analysismentioning

confidence: 99%

Cancer biomarkers: Emerging trends and clinical implications for personalized treatment

Passaro,

Al Bakir,

Hamilton

et al. 2024

Cell

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“…OncotypeDx is a genomics-based 21-gene Recurrence Score assay that has been validated for prognosis and prediction of chemotherapy benefit or recurrence in early stage, ER+ breast cancer ( 90 ). The assay has been used to guide treatment decisions, is widely included in clinical trials, and its implementation is recommended in ASCO and NCCN treatment guidelines ( 91 , 92 ). Omics-based assays and big data analysis have paved the way for personalized medicine as a whole and in knowledge-based mathematical/AI models.…”

Section: Beyond the Benchtop: Mathematical Models And Artificial Inte...mentioning

confidence: 99%

Functional Drug Screening in the Era of Precision Medicine

2022

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The focus of precision medicine is providing the right treatment to each unique patient. This scientific movement has incited monumental advances in oncology including the approval of effective, targeted agnostic therapies. Yet, precision oncology has focused largely on genomics in the treatment decision making process, and several recent clinical trials demonstrate that genomics is not the only variable to be considered. Drug screening in three dimensional (3D) models, including patient derived organoids, organs on a chip, xenografts, and 3D-bioprinted models provide a functional medicine perspective and necessary complement to genomic testing. In this review, we discuss the practicality of various 3D drug screening models and each model’s ability to capture the patient’s tumor microenvironment. We highlight the potential for enhancing precision medicine that personalized functional drug testing holds in combination with genomic testing and emerging mathematical models.

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Gene expression signatures in early breast cancer: Better together with clinicopathological features

Cited by 12 publications

References 92 publications

A novel 9-gene signature for the prediction of postoperative recurrence in stage II/III colorectal cancer

A novel 9-gene signature for the prediction of postoperative recurrence in stage II/III colorectal cancer

Cancer biomarkers: Emerging trends and clinical implications for personalized treatment

Functional Drug Screening in the Era of Precision Medicine

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