Identification of a six-gene signature associated with tumor mutation burden for predicting prognosis in patients with invasive breast carcinoma

Wang, Feiran; Tang, Chong; Gao, Xuesong; Xu, Jiacheng

doi:10.21037/atm.2020.04.02

Cited by 17 publications

(17 citation statements)

References 29 publications

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“…Table 4 showed that the AUCs of four prognostic signature including 12 stemness-related lncRNA signature (0.813 at 5 years) ( 47 ), 11 immune-related lncRNA signature (0.836 at 5 years) ( 52 ), 27 immune-related gene signature (0.844 at 5 years) ( 54 ) and four methylated gene signature (0.791 at 5 years) ( 61 ) were distinctly higher than that of other biomarkers. Moreover, our signature also performed better in the prediction of BC patients’ OS than the signature based on the hallmarks related to autophagy ( 48 ), tumor microenvironment (immune, stromal, and proliferation) ( 49 ), tumor mutation burden ( 50 ), hypoxia ( 51 ), DNA repair ( 55 ), lncRNA ( 56 ) and miRNA ( 57 , 58 ). The larger the AUC value of the biomarkers, the better the predictive ability of the hallmarks.…”

Section: Resultsmentioning

confidence: 92%

“…Furthermore, a nomogram combining the prediction model and clinical factors was constructed, which could be a useful tool to predict prognosis and guide clinical practice. (48) 2020 12 autophagy-related gene signature 0.739(1-year), 0.727(3-year), 0.742(5-year), Wang J, et al (49) 2020 four ISP gene signature 0.742 (5-year) Wang F, et al (50) 2020 six gene TMB-based signature 0.705 (5-year) Wang J, et al (51) 202014-gene hypoxia−related signature 0.728 (1-year), 0.726 (3-year), 0.736 (5-year) Shen Y, et al (52) 2020 11 immune-related lncRNA signature 0.836 (5-year) Xu H, et al (53) 2020 eight immune-related gene signature 0.753 (3-year), 0.72 (5-year) Zhao Y, et al (54) 2020 27 immune-related gene signature 0.844 (5-year) Zhang D, et al (55) 2020 eight DNA repair-related gene signature 0.708 (3-year), 0.704 (5-year) Sun M, et al (56) 2019 eight lncRNA signature 0.725 (1-year), 0.727 (3-year), 0.721 (5-year) Kawaguchi, et al (57) 2019 three miRNA signature 0.71 (5-year) Lai J, et al (58) 2019 six microRNA model 0.705 (3-year), 0.701 (5-year) Liu L, et al (59) 2019 seven RNA signature 0.705 (5-year) Tao C, et al (60) 2019 seven DNA methylation site signature 0.704 (5-year) Feng L, et al (61) 2018 four methylated gene signature 0.791 (5-year) OS, overall survival; ISP, immune, stromal, and proliferation; TMB, tumor mutation burden.…”

Section: Resultsmentioning

confidence: 99%

“…The studies (47,48,52,54,61) we included were that the model was built based on the entire TCGA cohort and involved all types of breast cancer, not a certain subtype. The final results showed that our signature and another four prognostic signature including 12 stemness-related lncRNA signature (47), 11 immune-related lncRNA signature (52), 27 immune-related gene signature (54) and four methylated gene signature (61) performed better in the prediction of BC patients' OS than the signature based on the hallmarks related to autophagy (48), tumor microenvironment (immune, stromal, and proliferation) (49), tumor mutation burden (50), hypoxia (51), DNA repair (55), lncRNA (56) and miRNA (57,58). Considering that the clinical application cost of our model may be lower than that of the two gene models [12 stemness-related lncRNA signature (47) and 27 immune-related gene signature (54)] and glycolysis is closely related to the prognosis of BC, our signature may be necessary to enrich the clinical prediction methods.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

et al. 2021

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To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1,632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO, and multiple Cox regression analysis. Finally, an 11-gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan–Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC, and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it’s an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.

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Section: Resultsmentioning

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

et al. 2021

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“…As shown by previous studies, combining multiple indicators can improve the accuracy of prediction. For example, ( Wang et al, 2020 ) demonstrated that a six-gene signature associated with TMB acts as a biological marker and improves the prognosis prediction for BC patients. Another study showed that a three-long non-coding RNA-based model could be used for the diagnosis of triple-negative BC ( Liu et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Systemic Analysis of the Prognosis-Associated Alternative Polyadenylation Events in Breast Cancer

Zhang

Wang

et al. 2020

Front. Genet.

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Alternative polyadenylation (APA) is a post-translational modification that occurs during mRNA maturation in humans. Studies suggested that abnormal APA events are associated with the genesis and progression of malignant tumors. Here, we aimed to comprehensively evaluate the prognostic value of APA events involved in breast cancer (BC). Both APA events and clinical information for BC patients were downloaded from The Cancer Genome Atlas (TCGA) database to identify prognosis-related APA events in BC. A total of 462 APA events and 374 APA events were shown to be significantly related to overall survival (OS) and relapse-free survival (RFS), respectively, of BC patients. The TCGA set was randomly divided into a training and a test set. Key prognosis-related APA events were selected by LASSO regression to build prediction signatures for OS and RFS by multivariate Cox regression analysis in the training, test, and whole set. BC patients were stratified into high-risk and low-risk groups based on median risk scores. Kaplan-Meier survival analysis demonstrated that low-risk groups had better OS and RFS than high-risk groups in all three sets. The time-dependent receiver operating characteristic (ROC) curves showed that our signatures had a good predictive ability for survival and recurrence for BC patients in all three sets. The independent prognostic indicators-based nomogram model had excellent performance and considerable net benefit for predicting the OS and RFS in BC. A PPI network was constructed between key prognosis and core regulators associated with APA, consisting of 48 nodes and 244 edges. Functional enrichment analysis also revealed their association with RNA processing and RNA synthesis. Collectively, our data indicate that prognostic signatures based on APA events may be powerful prognostic predictors for OS and RFS in BC.

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“…Similarly, Lai et al [ 30 ] established a panel of 4 autophagy-related genes (ARG) signatures consisting of SERPINA1, ATG4A, NRG1 and IFNG to predict the prognosis of breast cancer, which can help clinicians make judgments and decisions on determining effective treatment strategies. Wang et al [ 31 ] identified a six differentially-expressed genes (DEGs) model consisting of IGHA2, SERPINA1, GFALS, SPDYC, PAX7, and ADRB1 by using Cox regression survival modeling for breast cancer. In another study [ 32 ], the authors constructed a prognostic risk scoring system containing 6 genes (SCUBE3, RDH16, SPC24, SPC25, CCDC69 and DGAT2), suggesting that these mRNAs may serve a driving role in the progression of Her2-positive BC.…”

Section: Discussionmentioning

confidence: 99%

Identification and development of an independent immune-related genes prognostic model for breast cancer

et al. 2021

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Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

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Identification of a six-gene signature associated with tumor mutation burden for predicting prognosis in patients with invasive breast carcinoma

Cited by 17 publications

References 29 publications

Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

Systemic Analysis of the Prognosis-Associated Alternative Polyadenylation Events in Breast Cancer

Identification and development of an independent immune-related genes prognostic model for breast cancer

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