Dataset Information

A lncRNA prognostic signature associated with immune infiltration and tumour mutation burden in breast cancer.

ABSTRACT: Current studies have shown that long non-coding RNAs (lncRNAs) may serve as prognostic biomarkers in multiple cancers. Therefore, we postulated that expression patterns of multiple lncRNAs combined into a single signature could improve clinicopathological risk stratification and prediction of overall survival rate for breast cancer patients. Two algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to select candidate lncRNAs. Univariate and multivariate Cox regression analyses were employed to construct a seven-lncRNA signature for breast cancer. Stratified analysis revealed that the signature was significantly associated with multiple clinicopathological risk factors. For clinical use, we developed a nomogram model to predict overall survival and odds of death for breast cancer patients. Single-sample gene set enrichment analysis (ssGSEA), CIBERSORT algorithm and ESTIMATE method were employed to assess the relative immune cell infiltrations of each sample. Differentially infiltration of immune cells and diverse tumour mutation burden (TMB) scores might give rise to the efficacy of lncRNA signature for predicting the overall survival of patients. Correlation analysis implied that LINC01215 was associated with multiple immune-related signalling pathways. A seven-lncRNA prognostic signature is a reliable tool to predict the prognosis of breast cancer patients.

SUBMITTER: Liu Z

PROVIDER: S-EPMC7687003 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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A lncRNA prognostic signature associated with immune infiltration and tumour mutation burden in breast cancer.

Liu Zijian Z Mi Mi M Li Xiaoqian X Zheng Xin X Wu Gang G Zhang Liling L

Journal of cellular and molecular medicine 20200923 21

Current studies have shown that long non-coding RNAs (lncRNAs) may serve as prognostic biomarkers in multiple cancers. Therefore, we postulated that expression patterns of multiple lncRNAs combined into a single signature could improve clinicopathological risk stratification and prediction of overall survival rate for breast cancer patients. Two algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to sel ...[more]

PMID: 32967061

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Project description:BackgroundKidney renal clear cell carcinoma (KIRC) lacks effective prognostic biomarkers and the role and mechanism of N6-methyladenosine (m6A) modification of long noncoding RNAs (lncRNAs) in KIRC remain unclear.MethodsWe extracted standard mRNA-sequencing and clinical data from the TCGA database. The prognostic risk model was obtained by Lasso regression and Cox regression. We randomly divided the samples into training and test sets, each taking half of the cases. Based on Lasso regression and Cox regression for training set, the prognostic risk signature was constructed; risk scores were calculated with the R package "glmnet." Based on the median value of the prognostic risk score, risk scores were calculated for each patient and we divided all KIRC samples into high-risk and low-risk groups. Then, high- and low-risk subtypes were established and their prognosis, clinical features, and immune infiltration microenvironment were evaluated in test set and the entire sampled data set. The reliability of the prognostic model was confirmed by receiver operating characteristic curve analysis.ResultsWe found 28 prognostic m6A-related lncRNAs and established a m6A-related lncRNAs prognostic signature. Risk score=AC015813.1∗(0.0086)+EMX2OS∗(-0.0101)+LINC00173∗(0.0309)+PWAR5∗(-0.0146)+SNHG1∗(0.0043). The signature showed a better predictive ability than other clinical indicators, including tumor node metastasis classification (TNM), histological, and pathological stages. In the high-risk group, M0 macrophages, CD8+ T cells, and regulatory T cells had significantly higher scores. Contrarily, in the low-risk group, activated dendritic cells, M1 macrophages, mast resting cells, and monocytes had significantly higher scores. In the high-risk group, LSECtin was overexpressed. In the low-risk group, PD-L1 was overexpressed. Moreover, high-risk patients may benefit more from AZ628.ConclusionsIn conclusion, prognosis prediction of patients with KIRC and new insights for immunotherapy are provided by the m6A-related lncRNA prognostic signature.

Project description:Background: Sirtuin 7 (SIRT7), a protein-coding gene whose abnormal expression and function are associated with carcinogenesis. However, the prognosis of SIRT7 in different breast cancer subtypes and its correlation with tumor-infiltrating lymphocytes remain unclear. Methods: The expression and survival data of SIRT7 in patients with breast cancer were analyzed using Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interaction Analysis (GEPIA), The Human Protein Atlas (HPA), UALCAN, Breast Cancer Gene-Expression Miner (BC-GenExMiner), and Kaplan-Meier plotter databases. Also, the expression correlations between SIRT7 and immune infiltration gene markers were analyzed using TIMER and further verified the results using immunohistochemistry. Results: SIRT7 exhibited higher expression levels in breast cancer tissues than the adjacent normal tissues. SIRT7 expression was significantly correlated with sample type, subclass, cancer stage, menopause status, age, nodal status, estrogen receptor (ER), progesterone receptor (PR), and triple-negative status. High SIRT7 expression was associated with poor prognosis in breast cancer-luminal A [overall survival (OS): hazard ratio (HR) = 1.54, p = 1.70e-02; distant metastasis-free survival (DMFS): HR = 1.56, p = 2.60e-03]. Moreover, the expression of SIRT7 was positively correlated with the expression of IRF5 (M1 macrophages marker, r = 0.165, p = 1.13e-04) and PD1 (T cell exhaustion marker, r = 0.134, p = 1.74e-03). These results suggested that the expression of SIRT7 was related to M1 macrophages and T cell exhaustion infiltration in breast cancer-luminal. Conclusions: These findings demonstrate that the high expression of SIRT7 indicates poor prognosis in breast cancer as well as increased immune infiltration levels of M1 macrophages and T cell exhaustion in breast cancer-luminal. Thus, SIRT7 may serve as a candidate prognostic biomarker for determining prognosis associated with immune infiltration in breast cancer-luminal.

Dataset Information

A lncRNA prognostic signature associated with immune infiltration and tumour mutation burden in breast cancer.

Publications

A lncRNA prognostic signature associated with immune infiltration and tumour mutation burden in breast cancer.

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