Dataset Information

LncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis.

ABSTRACT: Background: Breast cancer is intrinsically heterogeneous and is commonly classified into four main subtypes associated with distinct biological features and clinical outcomes. However, currently available data resources and methods are limited in identifying molecular subtyping on protein-coding genes, and little is known about the roles of long non-coding RNAs (lncRNAs), which occupies 98% of the whole genome. lncRNAs may also play important roles in subgrouping cancer patients and are associated with clinical phenotypes. Methods: The purpose of this project was to identify lncRNA gene signatures that are associated with breast cancer subtypes and clinical outcomes. We identified lncRNA gene signatures from The Cancer Genome Atlas (TCGA )RNAseq data that are associated with breast cancer subtypes by an optimized 1-Norm SVM feature selection algorithm. We evaluated the prognostic performance of these gene signatures with a semi-supervised principal component (superPC) method. Results: Although lncRNAs can independently predict breast cancer subtypes with satisfactory accuracy, a combined gene signature including both coding and non-coding genes will give the best clinically relevant prediction performance. We highlighted eight potential biomarkers (three from coding genes and five from non-coding genes) that are significantly associated with survival outcomes. Conclusion: Our proposed methods are a novel means of identifying subtype-specific coding and non-coding potential biomarkers that are both clinically relevant and biologically significant.

SUBMITTER: Zhang S

PROVIDER: S-EPMC5852561 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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lncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis.

Zhang Silu S Wang Junqing J Ghoshal Torumoy T Wilkins Dawn D Mo Yin-Yuan YY Chen Yixin Y Zhou Yunyun Y

Genes 20180126 2

<b>Background:</b> Breast cancer is intrinsically heterogeneous and is commonly classified into four main subtypes associated with distinct biological features and clinical outcomes. However, currently available data resources and methods are limited in identifying molecular subtyping on protein-coding genes, and little is known about the roles of long non-coding RNAs (lncRNAs), which occupies 98% of the whole genome. lncRNAs may also play important roles in subgrouping cancer patients and are a ...[more]

PMID: 29373522

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Project description:BackgroundCuproptosis, a novel form of programmed cell death, plays an essential role in various cancers. However, studies of the function of cuproptosis lncRNAs (CRLs) in colorectal cancer (CRC) remain limited. Thus, this study aims to identify the cuprotosis-related lncRNAs (CRLs) in CRC and to construct the potential prognostic CRLs signature model in CRC.MethodsFirst, we downloaded RNA-Seq data and clinical information of CRC patients from TCGA database and obtained the prognostic CRLs based on typical expression analysis of cuproptosis-related genes (CRGs) and univariate Cox regression. Then, we constructed a prognostic model using the Least Absolute Shrinkage and Selection Operator algorithm combined with multiple Cox regression methods (Lasso-Cox). Next, we generated Kaplan-Meier survival and receiver operating characteristic curves to estimate the performance of the prognostic model. In addition, we also analysed the relationships between risk signatures and immune infiltration, mutation, and drug sensitivity. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT -PCR) to verify the prognostic model.ResultLasso-Cox analysis revealed that four CRLs, SNHG16, LENG8-AS1, LINC0225, and RPARP-AS1, were related to CRC prognosis. Receiver operating characteristic (ROC) and Kaplan-Meier analysis curves indicated that this model performs well in prognostic predictions of CRC patients. The DCA results also showed that the model included four gene signatures was better than the traditional model. In addition, GO and KEGG analyses revealed that DE-CRLs are enriched in critical signalling pathway, such as chemical carcinogenesis-DNA adducts and basal cell carcinoma. Immune infiltration analysis revealed significant differences in immune infiltration cells between the high-risk and low-risk groups. Furthermore, significant differences in somatic mutations were noted between the high-risk and low-risk groups. Finally, we also validated the expression of four CRLs in FHCs cell lines and CRC cell lines using qRT-PCR.ConclusionThe signature composed of SNHG16, LENG8-AS1, LINC0225, and RPARP-AS1, which has better performance in predicting colorectal cancer prognosis and are promising biomarkers for prognosis prediction of CRC.

Project description:BackgroundSeveral gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n?=?123) and test set (n?=?81), respectively. Gene sets from eleven previously published gene signatures are included in the study.Principal findingsTo investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p?=?0.005; breast cancer death: p?=?0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p?=?0.003; breast cancer death: p?=?0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction.ConclusionCombining the predictive strength of multiple gene signatures improves prediction of breast cancer survival. The presented methodology is broadly applicable to breast cancer risk assessment using any new identified gene set.

Dataset Information

LncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis.

Publications

lncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis.

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