7-lncRNA Assessment Model for Monitoring and Prognosis of Breast Cancer Patients: Based on Cox Regression and Co-expression Analysis

Li, Huayao; Gao, Chundi; Liu, Lijuan; Zhuang, Jing; Yang, Jing; Liu, Cun; Zhou, Chao; Feng, Fubin

doi:10.3389/fonc.2019.01348

Cited by 36 publications

(29 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three-year AUC values for the time-dependent ROC curve in the training, test, and entire sets were 0.760, 0.717, and 0.741, respectively, indicating outstanding performance in survival prediction. Recently, Zhu et al [ 11 ] and Li et al [ 21 ] have proposed a breast cancer prognosis model based on RNA-seq, and three-year AUC values of their models are 0.641 and 0.711 in the training set, respectively. Therefore, the performance of our model outperforms these two models.…”

Section: Discussionmentioning

confidence: 99%

Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database

Zhou

Pang

Yao

et al. 2020

Analytical Cellular Pathology

View full text Add to dashboard Cite

Long noncoding RNA (lncRNA) plays a critical role in the development of tumors. The aim of our study was construction of a lncRNA signature model to predict breast cancer (BRCA) patient survival. We downloaded RNA-seq data and relevant clinical information from the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNA were computed using the “edgeR” package and subjected to the univariate and multivariate Cox regression analysis. Corresponding protein-coding genes were used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis. Finally, 521 differentially expression lncRNA were obtained. We constructed a ten-lncRNA signature model (LINC01208, RP5-1011O1.3, LINC01234, LINC00989, RP11-696F12.1, RP11-909N17.2, CTC-297N7.9, CTA-384D8.34, CTC-276P9.4, and MAPT-IT1) to predict BRCA patient survival using the multivariate Cox proportional hazard regression model. The C-index was 0.712, and AUC scores of training, test, and entire sets were 0.746, 0.717, and 0.732, respectively. Univariate Cox regression analysis indicated that age, tumor status, N status, M status, and risk score were significantly related to overall survival in patients with BRCA. Further, the multivariate analysis showed that risk score and M status had outstanding independent prognostic values, both with p<0.001. The Gene Ontology (GO) function and KEEG pathway analysis was primarily enriched in immune response, receptor binding, external surface of plasma membrane, signal transduction, cytokine-cytokine receptor interaction, and cell adhesion molecules (CAMs). Finally, we constructed a ten-lncRNA signature model that can serve as an independent prognostic model to predict BRCA patient survival.

show abstract

Section: Discussionmentioning

confidence: 99%

Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database

Zhou

Pang

Yao

et al. 2020

Analytical Cellular Pathology

View full text Add to dashboard Cite

show abstract

“…Since that study, various models have been constructed to predict prognoses in cancer patients. [46][47][48] Nevertheless, there have been few prediction models combining lncRNA information with CRC clinical features. In our study, we identified a prognostic model with two CRC lncRNAs, and we constructed a nomogram and risk classification system.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis

Shi

Guo

Yang

et al. 2022

Cancer Biotherapy and Radiopharmaceuticals

View full text Add to dashboard Cite

Background: Knowledge about the prognostic role of long noncoding RNA (lncRNA) in colorectal cancer (CRC) is limited. Therefore, we constructed a lncRNA-related prognostic model based on data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Materials and Methods: CRC transcriptome and clinical data were downloaded from the GSE20916 dataset and the TCGA database, respectively. R software was used for data processing and analysis. The differential lncRNA expression within the two datasets was first screened, and then intersections were measured. Cox regression and the Kaplan-Meier method were used to evaluate the effects of various factors on prognosis. The area under the curve (AUC) of the receiver operating characteristic curve and a nomogram based on multivariate Cox analysis were used to estimate the prognostic value of the lncRNA-related model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to elucidate the significantly involved biological functions and pathways. Results: A total of 11 lncRNAs were crossed. The univariate Cox analysis screened out two lncRNAs, which were analyzed in the multivariate Cox analysis. A nomogram based on the two lncRNAs and other clinicopathological risk factors was constructed. The AUC of the nomogram was 0.56 at 3 years and 0.71 at 5 years. The 3-year nomogram model was compared with the ideal model, which showed that some indices of the 3-year model were consistent with the ideal model, suggesting that our model was highly accurate. The GO and KEGG enrichment analyses showed that positive regulation of secretion by cells, positive regulation of secretion, positive regulation of exocytosis, endocytosis, and the calcium signaling pathway were differentially enriched in the two-lncRNA-associated phenotype. Conclusions: A two-lncRNA prognostic model of CRC was constructed by bioinformatics analysis. The model had moderate prediction accuracy, LncRNA BBOX1-AS1 and lncRNA FOXP4-AS1 were identified as prognostic biomarkers.

show abstract

“…In the other hand, lncRNAs have attracted increasing attention with the development of next-generation sequencing over the last decade. Accumulating research has demonstrated that that lncRNAs play an important role in the development and progression of the breast cancer, and different lncRNA signatures can predict the prognosis of breast cancer [22][23][24][25][26][27][28]. However, the studies about lncRNAs and TNBC without gBRCAm are limited.…”

Section: Discussionmentioning

confidence: 99%

An 8-lncRNA Signature Predicts Survival of Triple-Negative Breast Cancer Patients Without Germline BRCA1/2 Mutation

Liu

Dai

Zhu

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Triple-negative breast cancer (TNBC) is a particular breast cancer subtype with poor prognosis due to its aggressive biological behavior and strong heterogeneity. TNBC with germline BRCA1/2 mutation (gBRCAm) have higher sensitivity to DNA damaging agents including platinum-based chemotherapy and PARP inhibitors. But the treatment of TNBC without gBRCAm remains challenging. This study aimed to develop a long non-coding RNA (lncRNA) signature of TNBC patients without gBRCAm to improve risk stratification and optimize individualized treatment.Methods: 98 TNBC patients without gBRCAm were acquired from The Cancer Genome Atlas (TCGA) database. The univariable Cox regression analysis and LASSO Cox regression model were applied to establish an lncRNA signature in the training cohort (N = 59). Then Kaplan–Meier survival curve and time-dependent ROC curve were used to validate the prognostic ability of the signature. The signature related mRNAs were identified using the Pearson correlation. Functional enrichment analysis of related mRNA was performed using the Metascape. The qPCR assay was performed to confirm the expressions and clinicopathological correlationsof two potential lncRNAs HAGLROS and TONSL-AS1 in 30 paired clinical triple-negative breast cancer samples without gBRCAm.Results:We developed an 8-lncRNA signature in the training cohort including HAGLROS, AL139002.1, AL391244.2, AP000696.1, AL391056.1, AL513304.1, TONSL-AS1 and AL031008.1. In both the training and validation cohort, patients with higher risk scores showed significantly worse overall survival compared to those with lower risk scores(P=0.00018 and P =0.0068 respectively). 1, 5, 8-year AUC in the training cohort were 1.000, 1.000 and 0.908 respectively, in the validation cohort were 0.785, 0.790 and 0.892 respectively indicating that our signature has a good prognostic capacity. Signature related mRNA mainly enriched in terms include RNA metabolic process, DNA repair pathways, and so on. Two potential lncRNAs HAGLROS and TONSL-AS1 were found frequently overexpressed in TNBC without gBRCAm, and significantly associated with tumor grade and invasion.Conclusions: We constructed a novel 8-lncRNA signaturewhich significantly associated with the overall survival of TNBC patients without gBRCAm. Among those 8lncRNAs, HAGLROS and TONSL-AS1 may be potential therapeutic targetswhich function needed further exploration.

show abstract

7-lncRNA Assessment Model for Monitoring and Prognosis of Breast Cancer Patients: Based on Cox Regression and Co-expression Analysis

Cited by 36 publications

References 38 publications

Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database

Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis

An 8-lncRNA Signature Predicts Survival of Triple-Negative Breast Cancer Patients Without Germline BRCA1/2 Mutation

Contact Info

Product

Resources

About