The nomogram based on the 6-lncRNA model can promote the prognosis prediction of patients with breast invasive carcinoma

Luo, Dankun; Yao, Wenchao; Wang, Qiang; Qiu, Yang; Liu, Xuxu; Yang, Yang; Zhang, Weihui; Xue, Dongbo; Ma, Biao

doi:10.1038/s41598-021-00364-w

Cited by 5 publications

(5 citation statements)

References 59 publications

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“…In recent years, risk models with the ability to predict prognosis have gained increasing attention. Many risk models are established based on gene expression profiles and are widely utilized in studies of various malignancies, such as breast cancer 22 and lung adenocarcinoma 23 . Hence, after 8 differentially expressed genes based on ESTIMATE scores were verified to be significantly associated with survival and the immune-infiltrating assessment of CIBERSORT, the LASSO algorithm was employed to select optimal genes.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the tumor immune profile based on a three-gene prognostic risk model in HER2 positive breast cancer

Lin

Zhao

2022

Sci Rep

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To date, there have not been great breakthroughs in immunotherapy for HER2 positive breast cancer (HPBC). This study aimed to build a risk model that might contribute to predicting prognosis and discriminating the immune landscape in patients with HPBC. We analyzed the tumor immune profile of HPBC patients from the TCGA using the ESTIMATE algorithm. Thirty survival-related differentially expressed genes were selected according to the ImmuneScore and StromalScore. A prognostic risk model consisting of PTGDR, PNOC and CCL23 was established by LASSO analysis, and all patients were classified into the high- and low-risk score groups according to the risk scores. Subsequently, the risk model was proven to be efficient and reliable. Immune related pathways were the dominantly enriched category. ssGSEA showed stronger immune infiltration in the low-risk score group, including the infiltration of TILs, CD8 T cells, NK cells, DCs, and so on. Moreover, we found that the expression of immune checkpoint genes, including PD-L1, CTLA-4, TIGIT, TIM-3 and LAG-3, was significantly upregulated in the low-risk score group. All the results were validated with corresponding data from the GEO database. In summary, our investigation indicated that the risk model composed of PTGDR, PNOC and CCL23 has potential to predict prognosis and evaluate the tumor immune microenvironment in HPBC patients. More importantly, HPBC patients with a low-risk scores are likely to benefit from immune treatment.

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Section: Discussionmentioning

confidence: 99%

Evaluating the tumor immune profile based on a three-gene prognostic risk model in HER2 positive breast cancer

Lin

Zhao

2022

Sci Rep

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“…Moreover, the predictive value was assessed in the external validation cohort through ROC analysis to verify the reliability of the AG-lnc signature. Compared with the other prognostic signature in BC established by Zhao et al, 15 Zhang et al, 26 Ping et al, 27 and Luo et al, 28 the AUC value at the 5-year OS of the risk score was the highest, illuminating the optimal ability of our risk signature in prediction of the longer survival outcomes in BC patients ( Figure 6A ). Besides, the concordance index of our risk model was also the highest, further supporting the good representation of our risk signature in BC prognosis ( Figure 6B ).…”

Section: Resultsmentioning

confidence: 65%

An Aging-Related lncRNA Signature Establishing for Breast Cancer Prognosis and Immunotherapy Responsiveness Prediction

Zhou,

Yang,

Zeng

2024

PGPM

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Purpose Emerging evidence demonstrates the vital role of aging and long non-coding RNAs (lncRNAs) in breast cancer (BC) progression. Our study intended to develop a prognostic risk model based on aging-related lncRNAs (AG-lncs) to foresee BC patients’ outcomes. Patients and Methods 307 aging-related genes (AGs) were sequenced from the TCGA project. Then, 697 AG-lncs were identified by the co-expression analysis with AGs. Using multivariate and univariate Cox regression analysis, and LASSO, 6 AG-lncs, including al136531.1, mapt-as1, al451085.2, otud6b-as1, tnfrsf14-as1 , and linc01871 , were validated to compute the risk score and establish a risk signature. Expression levels of al136531.1, mapt-as1, al451085.2, tnfrsf14-as1 , and linc01871 were higher in low-risk BC patients, whereas otud6b-as1 expression was higher in high-risk BC patients. In the training and testing set, high-risk patients performed shorter PFI, OS, and DFS than low-risk patients. Results Our risk signature had the highest concordance index among other established prognostic signatures and displayed ideal predictive ability for 1-, 3- and 5-year patient OS in the nomogram. Additionally, BC patients with different risk score levels showed different immune statuses and responses to immunotherapy via GSEA, ssGSEA, ESTIMATE algorithm, and TIDE algorithm analysis. Of note, the qRT-PCR analysis validated that these 6 AG-lncs expressed quite differentially in BC tissues at various clinical stages. Conclusion The risk signature of 6 AG-lncs might offer a novel prognostic biomarker and promisingly enhance BC immunotherapy’s effectiveness.

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“…Our nomogram is a comprehensive prognostic prediction tool that includes clinical characteristics, NMF clustering-based typing, and the risk score. Many multigene analysis-based models have been published in the last decade [36][37][38]. NMF clustering is a novel typing method that is rarely used in breast cancer and with which we can achieve a more detailed typing to predict more accurate prognoses for breast cancer patients.…”

Section: Discussionmentioning

confidence: 99%

Tumor microenvironment-related multigene prognostic prediction model for breast cancer

Hong

Zhang

Yao

et al. 2022

Aging

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Background: Breast cancer is an invasive disease with complex molecular mechanisms. Prognosis-related biomarkers are still urgently needed to predict outcomes of breast cancer patients. Methods: Original data were download from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The analyses were performed using perl-5.32 and R-x64-4.1.1. Results: In this study, 1086 differentially expressed genes (DEGs) were identified in the TCGA cohort; 523 shared DEGs were identified in the TCGA and GSE10886 cohorts. Eight subtypes were estimated using non-negative matrix factorization clustering with significant differences seen in overall survival (OS) and progression-free survival (PFS) (P < 0.01). Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed to develop a related risk score related to the 17 DEGs; this score separated breast cancer into low-and high-risk groups with significant differences in survival (P < 0.01) and showed powerful effectiveness (TCGA all group: 1-year area under the curve [AUC] = 0.729, 3-year AUC = 0.778, 5-year AUC = 0.781). A nomogram prediction model was constructed using non-negative matrix factorization clustering, the risk score, and clinical characteristics. Our model was confirmed to be related with tumor microenvironment. Furthermore, DEGs in high-risk breast cancer were enriched in histidine metabolism (normalized enrichment score [NES] = 1.49, P < 0.05), protein export (NES = 1.58, P < 0.05), and steroid hormone biosynthesis signaling pathways (NES = 1.56, P < 0.05). Conclusions: We established a comprehensive model that can predict prognosis and guide treatment.

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The nomogram based on the 6-lncRNA model can promote the prognosis prediction of patients with breast invasive carcinoma

Cited by 5 publications

References 59 publications

Evaluating the tumor immune profile based on a three-gene prognostic risk model in HER2 positive breast cancer

Evaluating the tumor immune profile based on a three-gene prognostic risk model in HER2 positive breast cancer

An Aging-Related lncRNA Signature Establishing for Breast Cancer Prognosis and Immunotherapy Responsiveness Prediction

Tumor microenvironment-related multigene prognostic prediction model for breast cancer

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