Dataset Information

Identification of a Six-lncRNA Signature With Prognostic Value for Breast Cancer Patients.

ABSTRACT: Breast cancer (BRCA) is the most common cancer and a major cause of death in women. Long non-coding RNAs (lncRNAs) are emerging as key regulators and have been implicated in carcinogenesis and prognosis. In this study, we aimed to develop a lncRNA signature of BRCA patients to improve risk stratification. In the training cohort (GSE21653, n = 232), 17 lncRNAs were identified by univariate Cox proportional hazards regression, which were significantly associated with patients' survival. The least absolute shrinkage and selection operator-penalized Cox proportional hazards regression analysis was used to identify a six-lncRNA signature. According to the median of the signature risk score, patients were divided into a high-risk group and a low-risk group with significant disease-free survival differences in the training cohort. A similar phenomenon was observed in validation cohorts (GSE42568, n = 101; GSE20711, n = 87). The six-lncRNA signature remained as independent prognostic factors after adjusting for clinical factors in these two cohorts. Furthermore, this signature significantly predicted the survival of grade III patients and estrogen receptor-positive patients. Furthermore, in another cohort (GSE19615, n = 115), the low-risk patients that were treated with tamoxifen therapy had longer disease-free survival than those who underwent no therapy. Overall, the six-lncRNA signature can be a potential prognostic tool used to predict disease-free survival of patients and to predict the benefits of tamoxifen treatment in BRCA, which will be helpful in guiding individualized treatments for BRCA patients.

SUBMITTER: Zhao E

PROVIDER: S-EPMC7396575 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of a Six-lncRNA Signature With Prognostic Value for Breast Cancer Patients.

Zhao Erjie E Lan Yujia Y Quan Fei F Zhu Xiaojing X A Suru S Wan Linyun L Xu Jinyuan J Hu Jing J

Frontiers in genetics 20200724

Breast cancer (BRCA) is the most common cancer and a major cause of death in women. Long non-coding RNAs (lncRNAs) are emerging as key regulators and have been implicated in carcinogenesis and prognosis. In this study, we aimed to develop a lncRNA signature of BRCA patients to improve risk stratification. In the training cohort (GSE21653, <i>n</i> = 232), 17 lncRNAs were identified by univariate Cox proportional hazards regression, which were significantly associated with patients' survival. The ...[more]

PMID: 32849766

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Project description:BackgroundBreast cancer (BRCA) is a malignant tumor with high morbidity and mortality, which is a threat to women's health worldwide. Ferroptosis is closely related to the occurrence and development of breast cancer. Here, we aimed to establish a ferroptosis-related prognostic gene signature for predicting patients' survival.MethodsGene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis model was utilized to construct a multigene signature. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and single-sample gene set enrichment analysis (ssGSEA) were performed for patients between the high-risk and low-risk groups divided by the median value of risk score.ResultsWe constructed a prognostic signature consisted of nine ferroptosis-related genes (ALOX15, CISD1, CS, GCLC, GPX4, SLC7A11, EMC2, G6PD and ACSF2). The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the ROC curves manifested that the ferroptosis-related signature had moderate predictive power. GO and KEGG functional analysis revealed that immune-related responses were largely enriched, and immune cells, including activated dendritic cells (aDCs), dendritic cells (DCs), T-helper 1 (Th1), were higher in high-risk groups (p < 0.001). Oppositely, type I IFN response and type II IFN response were lower in high-risk groups (p < 0.001).ConclusionOur study indicated that the ferroptosis-related prognostic signature gene could serve as a novel biomarker for predicting breast cancer patients' prognosis. Furthermore, we found that immunotherapy might play a vital role in therapeutic schedule based on the level and difference of immune-related cells and pathways in different risk groups for breast cancer patients.

Project description:Given the importance of solute carrier (SLC) proteins in maintaining cellular metabolic homeostasis and that their dysregulation contributes to cancer progression, here we constructed a robust SLC family signature for lung adenocarcinoma (LUAD) patient stratification. Transcriptomic profiles and relevant clinical information of LUAD patients were downloaded from the TCGA and GEO databases. SLC family genes differentially expressed between LUAD tissues and adjacent normal tissues were identified using limma in R. Of these, prognosis-related SLC family genes were further screened out and used to construct a novel SLC family-based signature in the training cohort. The accuracy of the prognostic signature was assessed in the testing cohort, the entire cohort, and the external GSE72094 cohort. Correlations between the prognostic signature and the tumor immune microenvironment and immune cell infiltrates were further explored. We found that seventy percent of SLC family genes (279/397) were differentially expressed between LUAC tissues and adjacent normal. Twenty-six genes with p-values < 0.05 in univariate Cox regression analysis and Kaplan-Meier survival analysis were regarded as prognosis-related SLC family genes, six of which were used to construct a prognostic signature for patient classification into high- and low-risk groups. Kaplan-Meier survival analysis in all internal and external cohorts revealed a better overall survival for patients in the low-risk group than those in the high-risk group. Univariate and multivariate Cox regression analyses indicated that the derived risk score was an independent prognostic factor for LUAD patients. Moreover, a nomogram based on the six-gene signature and clinicopathological factors was developed for clinical application. High-risk patients had lower stromal, immune, and ESTIMATE scores and higher tumor purities than those in the low-risk group. The proportions of infiltrating naive CD4 T cells, activated memory CD4 T cells, M0 macrophages, resting dendritic cells, resting mast cells, activated mast cells, and eosinophils were significantly different between the high- and low-risk prognostic groups. In all, the six-gene SLC family signature is of satisfactory accuracy and generalizability for predicting overall survival in patients with LUAD. Furthermore, this prognostics signature is related to tumor immune status and distinct immune cell infiltrates in the tumor microenvironment.

Project description:IntroductionCervical cancer has high mortality, high recurrence and poor prognosis. Although prognostic biomarkers such as clinicopathological features have been proposed, their accuracy and precision are far from satisfactory. Therefore, novel biomarkers are urgently needed for disease surveillance, prognosis prediction and treatment selection.MaterialsDifferentially expressed genes (DEGs) between cervical cancer and normal tissues from three microarray datasets extracted from the Gene Expression Omnibus platform were identified and screened. Based on these DEGs, a six-gene prognostic signature was constructed using cervical squamous cell carcinoma and endocervical adenocarcinoma data from The Cancer Genome Atlas. Next, the molecular functions and related pathways of the six genes were investigated through gene set enrichment analysis and co-expression analysis. Additionally, immunophenoscore analysis and the QuartataWeb Server were employed to explore the therapeutic value of the six-gene signature.ResultsWe discovered 178 overlapping DEGs in three microarray datasets and established a six-gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) prognostic signature with stable and excellent performance in predicting overall survival in different subgroups. Intriguingly, the six-gene signature was closely associated with the immune response and tumour immune microenvironment. The six-gene signature might be used for predicting response to immune checkpoint inhibitors (ICIs) and the six genes may serve as new drug targets for cervical cancer.ConclusionOur study established a novel six-gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) signature that was closely associated with the immune response and tumour immune microenvironment. The six-gene signature was indicative of aggressive features of cervical cancer and therefore might serve as a promising biomarker for predicting not only overall survival but also ICI treatment effectiveness. Moreover, three genes (UPP1, ISG20 and GLTP) within the six-gene signature have the potential to become novel drug targets.

Project description:BackgroundRobust and precise molecular prognostic predictors for luminal breast cancer are required. This study aimed to identify key methylation sites in luminal breast cancer, as well as precise molecular tools for predicting prognosis.MethodsWe compared methylation levels of normal and luminal breast cancer samples from The Cancer Genome Atlas dataset. The relationships among differentially methylated sites, corresponding mRNA expression levels and prognosis were further analysed. Differentially expressed genes in normal and cancerous samples were analysed, followed by the identification of prognostic signature genes. Samples were divided into low- and high-risk groups based on the signature genes. Prognoses of low- and high-risk groups were compared. The Gene Expression Omnibus dataset were used to validate signature genes for prognosis prediction. Prognosis of low- and high-risk groups in Luminal A and Luminal B samples from the TCGA and the Metabric cohort dataset were analyzed. We also analysed the correlation between clinical features of low- and high- risk groups as well as their differences in gene expression.ResultsFourteen methylation sites were considered to be related to luminal breast cancer prognosis because their methylation levels, mRNA expression and prognoses were closely related to each other. The methylation level of SOSTDC1 was used to divide samples into hypo- and hyper-methylation groups. We also identified an mRNA signature, comprising eight transcripts, ESCO2, PACSIN1, CDCA2, PIGR, PTN, RGMA, KLK4 and CENPA, which was used to divide samples into low- and high-risk groups. The low-risk group showed significantly better prognosis than the high-risk group. A correlation analysis revealed that the risk score was an independent prognostic factor. Low- and high- risk groups significantly correlated with the survival ratio in Luminal A samples, but not in Luminal B samples on the basis of the TCGA and the Metabric cohort dataset. Further functional annotation demonstrated that the differentially expressed genes were mainly involved in cell cycle and cancer progression.ConclusionsWe identified several key methylation sites and an mRNA signature for predicting luminal breast cancer prognosis. The signature exhibited effective and precise prediction of prognosis and may serve as a prognostic and diagnostic marker for luminal breast cancer.

Dataset Information

Identification of a Six-lncRNA Signature With Prognostic Value for Breast Cancer Patients.

Publications

Identification of a Six-lncRNA Signature With Prognostic Value for Breast Cancer Patients.

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