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

Wang, Xiaolong; Li, Chen; Chen, Tong; Li, Wenhao; Zhang, Hanwen; Zhang, Dong; Liu, Ying; Han, Dianwen; Li, Yaming; Li, Zheng; Luo, Dan; Zhang, Ning; Yang, Qifeng

doi:10.3389/fonc.2021.660242

Cited by 7 publications

(5 citation statements)

References 55 publications

(54 reference statements)

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“…B4GALNT2 is down-regulated in colorectal and gastric cancers and elevated in breast cancer [ 30 ], and in CM, we report for the first time that it may act as a risk factor influencing patient prognosis. Likewise, EDN2 was revealed as a novel prognostic marker in various cancers, including prostate cancer [ 31 ] and breast cancer [ 32 ]. Here, for the first time, we identified a gene signature composed of eight genes, FBP1, NDRG1, GPI, IER3, B4GALNT2, BGN, PKP1, and EDN2 in CM, and the risk score was composed of coefficients, more than using a single biomarker alone.…”

Section: Discussionmentioning

confidence: 99%

Explore the impact of hypoxia-related genes (HRGs) in Cutaneous melanoma

Cheng

Sun

et al. 2023

BMC Med Genomics

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Background Cutaneous melanoma (CM) has an overall poor prognosis due to a high rate of metastasis. This study aimed to explore the role of hypoxia-related genes (HRGs) in CM. Methods We first used on-negative matrix factorization consensus clustering (NMF) to cluster CM samples and preliminarily analyzed the relationship of HRGs to CM prognosis and immune cell infiltration. Subsequently, we identified prognostic-related hub genes by univariate COX regression analysis and the least absolute shrinkage and selection operator (LASSO) and constructed a prognostic model. Finally, we calculated a risk score for patients with CM and investigated the relationship between the risk score and potential surrogate markers of response to immune checkpoint inhibitors (ICIs), such as TMB, IPS values, and TIDE scores. Results Through NMF clustering, we identified high expression of HRGs as a risk factor for the prognosis of CM patients, and at the same time, increased expression of HRGs also indicated a poorer immune microenvironment. Subsequently, we identified eight gene signatures (FBP1, NDRG1, GPI, IER3, B4GALNT2, BGN, PKP1, and EDN2) by LASSO regression analysis and constructed a prognostic model. Conclusion Our study identifies the prognostic significance of hypoxia-related genes in melanoma and shows a novel eight-gene signature to predict the potential efficacy of ICIs.

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Section: Discussionmentioning

confidence: 99%

Explore the impact of hypoxia-related genes (HRGs) in Cutaneous melanoma

Cheng

Sun

et al. 2023

BMC Med Genomics

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“…As the molecular mechanism of breast cancer is complex, continuous studies aim to identify better molecular typing of breast cancer. Wang et al [ 7 ] developed a five-gene (EDN2, CLEC3B, SV2C, WT1, and MUC2) prognostic signature using LASSO Cox regression analysis. Gao et al [ 6 ] developed a pyroptosis-related lncRNA-associated (AC121761.2, AC027307.2, LINC01871, U73166.1, AL513477.2, AC005034.5 and AL451085.2) predictive model using LASSO Cox regression analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Different subtypes have been confirmed to have different prognoses and drug responses. With advances in statistical analysis, multigene signatures are widely used to predict patient prognosis and drug response [6,7]. Some multigene prediction models, such as the Oncotype DX 21-gene test, Prediction Analysis of Microarray 50, and 70-gene signature (MammaPrint), have been applied in the clinic [8][9][10].…”

Section: Introductionmentioning

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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“… 27 Due to the current limitation on statistical property of conventional risk stratification systems, it was demonstrated by a number of researches that multigene signatures calculated by systematic analysis might work as more precise prognostic biomarkers than the traditional clinicopathologic features. 28 …”

Section: Discussionmentioning

confidence: 99%

A Novel lncRNA Panel for Risk Stratification and Immune Landscape in Breast Cancer Patients

Li¹,

Wang²,

Chen³

et al. 2022

IJGM

Self Cite

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Purpose In recent years, breast cancer (BC) has been a primary cause of mortality in women. However, the underlying mechanisms remain to be elucidated. Accumulating evidence has supported the hypothesis that long noncoding RNAs (lncRNAs) play central roles in the progression of cancer. We aimed to construct an immune-related lncRNA panel to predict the prognosis of patients with BC and evaluate the immune features. Methods The expression profiles of patients with BC were obtained from The Cancer Genome Atlas (TCGA) database to screen the differentially expressed lncRNAs (DELs). Pearson’s correlation analysis was employed to filter the DELs related to the immune-associated genes. Univariate Cox regression, the LASSO algorithm, and multivariate Cox regression analyses were conducted to establish the model. Functional enrichment analyses and biological experiments were performed to explore the immune activity of the lncRNA panel. Results A four-immune-related lncRNA panel (IRLP) composed of AC022196.1, ARHGAP26-AS1, DPYD-AS1 and PURPL was established in TCGA training cohort. The prognostic accuracy of the predictive model was confirmed in TCGA internal validation cohort, TCGA entire cohort and Qilu external validation cohort. Bioinformatics analyses indicated that the IRLP had a close relationship with tumour infiltrating immune cells and immunomodulatory biomarkers. The biological functions of the four immune-related lncRNAs in BC were first investigated in vitro and in vivo. PURPL was indicated to play a central role in the regulation of macrophage recruitment and polarization via CCL2. Conclusion Our study identified IRLP as a reliable prognostic indicator with great potential for clinical application in personalized immunotherapy.

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

Cited by 7 publications

References 55 publications

Explore the impact of hypoxia-related genes (HRGs) in Cutaneous melanoma

Explore the impact of hypoxia-related genes (HRGs) in Cutaneous melanoma

Tumor microenvironment-related multigene prognostic prediction model for breast cancer

A Novel lncRNA Panel for Risk Stratification and Immune Landscape in Breast Cancer Patients

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