Long non-coding RNA, LINC01614 as a potential biomarker for prognostic prediction in breast cancer

Background Dysregulated long non-coding RNAs (lncRNAs) may serve as potential biomarkers of cancers including breast cancer (BRCA). This study aimed to identify lncRNAs with strong prognostic value for BRCA. Methods LncRNA expression profiles of 929 tissue samples were downloaded from TANRIC database. We performed differential expression analysis between paired BRCA and adjacent normal tissues. Survival analysis was used to identify lncRNAs with prognostic value. Univariate and multivariate Cox regression analyses were performed to confirm the independent prognostic value of potential lncRNAs. Dysregulated signaling pathways associated with lncRNA expression were evaluated using gene set enrichment analysis. Results We found that a total of 398 lncRNAs were significantly differentially expressed between BRCA and adjacent normal tissues (adjusted P value <= 0.0001 and |logFC| >= 1). Additionally, 381 potential lncRNAs were correlated Overall Survival (OS) (P value < 0.05). A total of 48 lncRNAs remained when differentially expressed lncRNAs overlapped with lncRNAs that had prognostic value. Among the 48 lncRNAs, one lncRNA (LINC01614) had stronger prognostic value and was highly expressed in BRCA tissues. LINC01614 expression was validated as an independent prognostic factor using univariate and multivariate analyses. Higher LINC01614 expression was observed in several molecular subgroups including estrogen receptors+, progesterone receptors+ and human epidermal growth factor receptor 2 (HER2)+ subgroup, respectively. Also, BRCA carrying one of four gene mutations had higher expression of LINC01614 including AOAH, CIT, HER2 and ODZ1. Higher expression of LINC01614 was positively correlated with several gene sets including TGF-β1 response, CDH1 signals and cell adhesion pathways. Conclusions A novel lncRNA LINC01614 was identified as a potential biomarker for prognosis prediction of BRCA. This study emphasized the importance of LINC01614 and further research should be focused on it.

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Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽

10.7717/peerj.7821 ◽

2019 ◽

Vol 7 ◽

pp. e7821 ◽

Cited By ~ 7

Author(s):

Xiaoming Zhang ◽

Jing Zhuang ◽

Lijuan Liu ◽

Zhengguo He ◽

Cun Liu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Prognostic Value ◽

Early Stage ◽

Expression Profiles ◽

Diagnostic Value ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

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Identification of a prognostic long non-coding RNA signature in breast cancer

10.21203/rs.3.rs-27286/v1 ◽

2020 ◽

Author(s):

Gaochen Lan ◽

Xiaoling Yu ◽

Yanna Zhao ◽

Jinjian Lan ◽

Wan Li ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Prognostic Biomarker ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

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MicroRNA-99b predicts clinical outcome of osteosarcoma and suppresses tumor cell proliferation, migration and invasion

Diagnostic Pathology ◽

10.1186/s13000-019-0889-y ◽

2019 ◽

Vol 14 (1) ◽

Cited By ~ 1

Author(s):

Xin Shi ◽

Xingfa Guan

Keyword(s):

Cell Proliferation ◽

Cell Lines ◽

Prognostic Value ◽

Cox Regression ◽

Expression Profiles ◽

Migration And Invasion ◽

Aberrant Expression ◽

Cox Regression Analysis ◽

Normal Tissues

Abstract Background Osteosarcoma (OS) is a malignancy predominantly occurred in children and adolescents. Numerous microRNAs are involved in the pathogenesis of various cancers. This study aimed to investigate the expression profiles of miR-99b and its prognostic value in OS patients, and further analyze the biological function of miR-99b in the tumor progression by using OS cells. Methods Expression of miR-99b was measured using quantitative real-time PCR. Kaplan-Meier survival curves and Cox regression analysis were performed to evaluate the prognostic value of miR-99b. OS cell lines were used to investigate the effects of miR-99b on cell proliferation, migration and invasion. Results A significant decreased expression of miR-99b was observed in the OS tissues and cell lines respectively compared with the normal tissues and cells. Aberrant expression of miR-99b was associated with the patients’ metastasis and TNM stage, and could be used to predict the prognosis of OS. The expression of miR-99b was regulated in vitro by cell transfection, and we found that the overexpression of miR-99b led to suppressed cell proliferation, migration and invasion, whereas the knockdown of miR-99b resulted in the opposite results. Conclusions In one word, the aberrantly expressed miR-99b serves a prognostic biomarker for OS patients. OS cell proliferation, migration and invasion can be inhibited by the overexpression of miR-99b, suggesting that the methods to increase miR-99b expression may be novel therapeutic strategies in OS.

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Integrated analysis of autophagy-related genes(ATGs) reveals the prognostic value of oral squamous cell carcinoma

10.21203/rs.3.rs-17099/v1 ◽

2020 ◽

Author(s):

Guangzhao Huang ◽

Zhi-yun Li ◽

Yu Rao ◽

Xiao-zhi Lv

Keyword(s):

Survival Analysis ◽

Signaling Pathway ◽

Risk Score ◽

Prognostic Value ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

Differentially Expressed ◽

Integrated Analysis ◽

Score Model

Abstract Background: Increasing evidence demonstrated that autophagy paly a crucial role in initiation and progression of OSCC. The aim of this study was to explore the prognostic value of autophagy-related genes(ATGs) in patients with OSCC. RNA-seq and clinical data were downloaded from TCGA database following extrating ATGs expression profiles. Then, differentially expressed analysis was performed in R software EdgeR package, and the potential biological function of differentially expressed ATGs were explored by GO and KEGG enrichment analysis. Furthermore, a risk score model based on ATGs was constructed to predict the overall survival. Moreover, univariate, multivariate cox regression and survival analysis were used to select autophagy related biomarkers which were identified by RT-qPCR in OSCC cell lines, OSCC tissues and matched normal mucosal tissues. Results: Total of 232 ATGs were extrated and 37 genes were differentially expressed in OSCC. GO and KEGG analysis indicated that these differentially expressed genes were mainly located in autophagosome membrane, and associated with apoptosis, platinum drug resistance, ErbB signaling pathway and TNF signaling pathway. Furthermore, a risk score model including 9 variables was constructed and subsequently identified with univariate, multivariate cox regression, survival analysis and Receiver Operating Characteristic curve(ROC). Moreover, ATG12 and BID were identified as potential autophagy related biomakers. Conclusion: This study successfully constructed a risk model to predict the prognosis of patients with OSCC, and the risk score may be as a independent prognostic biomarker in OSCC. ATG12 and BID were identified as potential biomarkers in tumor diagnosis and treatment of OSCC.

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Identification of a Nine Immune-Related lncRNA Signature as a Novel Diagnostic Biomarker for Hepatocellular Carcinoma

BioMed Research International ◽

10.1155/2021/9798231 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Mengqin Yuan ◽

Yanqing Wang ◽

Qinqian Sun ◽

Shiyi Liu ◽

Shu Xian ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Risk Score ◽

Prognostic Value ◽

Expression Profiles ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Potential Biomarker ◽

Increased Risk

Hepatocellular carcinoma (HCC) ranks fifth among common cancers and is the second most common cause of cancer-related mortality worldwide. This study is aimed at identifying an immune-related long noncoding RNA (lncRNA) signature as a potential biomarker with prognostic value to improve early diagnosis and provide potential therapeutic targets for HCC patients. The subjects of this study were HCC samples with complete transcriptome data and clinical information downloaded from The Cancer Genome Atlas (TCGA) database. We then extracted the immune-related mRNA and lncRNA expression profiles. Based on the expression profiles of immune-related lncRNAs, we identified a nine-lncRNA signature that was related to the progression of HCC. The risk score was calculated based on the expression level of the nine lncRNAs of each sample, which divided patients into high-risk and low-risk groups. We found that the increased risk score was associated with a poor prognosis of HCC patients. To assess the accuracy of the survival model, we calculated a receiver operating characteristic (ROC) for validation. The curve showed that the area under the curve (AUC) for the risk score was 0.792. Besides, both principal component analysis (PCA) and gene set enrichment analysis (GSEA) were further used for functional annotation. We found that the distribution patterns were different between the low-risk and high-risk groups in PCA, and the underlying mechanism by which the nine lncRNAs promoted the progression of HCC involved an abnormal immune status. Finally, we analyzed the infiltration of twenty-nine kinds of immune cells and the activation of immune function in HCC using the ssGSEA algorithm. The results showed that aDCs, iDCs, macrophages, Tfh, Th1, Treg, and NK cells were correlated with the progress of HCC patients. And the immune functions including APC costimulation, CCR, check point, HLA, MHC class I, and Type II IFN responses were also significantly different between the high-risk and low-risk groups. In conclusion, our study identified a nine-lncRNA signature with potential prognostic value for patients with HCC, which could be used as a new biomarker for the diagnosis and immunotherapy of HCC.

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A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer

BMC Cancer ◽

10.1186/s12885-021-08687-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yingchun Liang ◽

Fangdie Ye ◽

Chenyang Xu ◽

Lujia Zou ◽

Yun Hu ◽

...

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognosis Prediction

Abstract Background The effective treatment and prognosis prediction of bladder cancer (BLCA) remains a medical problem. Ferroptosis is an iron-dependent form of programmed cell death. Ferroptosis is closely related to tumour occurrence and progression, but the prognostic value of ferroptosis-related genes (FRGs) in BLCA remains to be further clarified. In this study, we identified an FRG signature with potential prognostic value for patients with BLCA. Methods The corresponding clinical data and mRNA expression profiles of BLCA patients were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to extract FRGs related to survival time, and a Cox regression model was used to construct a multigene signature. Both principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were performed for functional annotation. Results Clinical traits were combined with FRGs, and 15 prognosis-related FRGs were identified by Cox regression. High expression of CISD1, GCLM, CRYAB, SLC7A11, TFRC, ACACA, ZEB1, SQLE, FADS2, ABCC1, G6PD and PGD was related to poor survival in BLCA patients. Multivariate Cox regression was used to construct a prognostic model with 7 FRGs that divided patients into two risk groups. Compared with that in the low-risk group, the overall survival (OS) of patients in the high-risk group was significantly lower (P < 0.001). In multivariate regression analysis, the risk score was shown to be an independent predictor of OS (HR = 1.772, P < 0.01). Receiver operating characteristic (ROC) curve analysis verified the predictive ability of the model. In addition, the two risk groups displayed different immune statuses in ssGSEA and different distributed patterns in PCA. Conclusion Our research suggests that a new gene model related to ferroptosis can be applied for the prognosis prediction of BLCA. Targeting FRGs may be a treatment option for BLCA.

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Identification and validation of glycolysis-related signatures for prognosis in patients with breast cancer

10.21203/rs.3.rs-35764/v1 ◽

2020 ◽

Author(s):

Ming Zhang ◽

Mengying Xing ◽

Yunfei Ma ◽

Bing Yao ◽

Xiang Chen ◽

...

Keyword(s):

Breast Cancer ◽

Cell Lines ◽

Risk Score ◽

Molecular Mechanisms ◽

Cox Regression ◽

Expression Patterns ◽

Gene Set Enrichment Analysis ◽

Altered Expression ◽

Normal Tissues ◽

Tumor Tissues

Abstract Background: Emerging evidence has demonstrated roles of glycolysis in the tumorigenesis and progression of human tumors. However, their underlying clinical implications have not been well elucidated in breast cancer. In present study, we aimed to generate a risk-score from glycolysis-related signatures to predict prognosis of patients with breast cancer. Methods: We acquired mRNAs expression and clinical datasets in patients with breast cancer from The Cancer Genome Atlas (TCGA), then identifying glycolysis-related mRNAs by Gene Set Enrichment Analysis (GSEA), followed by construction of prognostic risk-score. The altered expression of glycolysis-related mRNAs was identified as candidates for further investigation. We constructed a risk-score from the prognostic glycolysis-related mRNAs by Cox regression. Receiver Operating Characteristic (ROC) and clinical subgroups analysis were performed to evaluate the values of risk-score to predict prognosis of breast cancer. Besides, we also compared the expression patterns of the signatures in breast cancer tissues and cell lines.Results: Total of 1208 cases were obtained, including 112 normal tissues and 1096 tumor tissues. We found 4 glycolysis-related pathways significantly involved in breast cancer. And 298 mRNAs involved in the 4 pathways were defined as glycolysis-related mRNAs; of these, 241 dysregulated mRNAs were candidates for further exploration. Then we constructed a risk-score from the 5 candidates (IL13RA1, PGK1, SDC3, NUP43 and SDC1). The area under the curve (AUC) for the risk-score to predict prognosis was 0.729. Patients with high-risk score had poor prognosis among overall or clinical subgroups ( P <0.05). And IL13RA1, PGK1, NUP43 and SDC1 were up-regulated in tumor tissues and cell lines (MDA-MB-231 and BT474) as compared to normal tissues and cell line (MCF-10A), while SDC3 was down-regulated.Conclusions: We construct a risk-score based on 5 glycolysis-related signatures, which can well predict prognosis in breast cancer. Additionally, our findings further unveil the molecular mechanisms of glycolysis in cancer, providing promising directions for the prognostic and therapeutic biomarkers for breast cancer.

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Transcriptome Analysis Derives a Novel Prognostic 7-mRNA Signature in Early-Stage Triple-Negative Breast Cancer

10.21203/rs.3.rs-389386/v1 ◽

2021 ◽

Author(s):

Yun-Song Yang ◽

Yi-Xing Ren ◽

Shuang Hao ◽

Xiao-En Xu ◽

Xi Jin ◽

...

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Prognostic Value ◽

Triple Negative ◽

Cox Regression ◽

Early Stage ◽

Distant Recurrence ◽

Cancer Center ◽

Transcriptome Data ◽

Normal Tissues

Abstract Background: Triple-negative breast cancer (TNBC) is a highly heterogeneous disease and patients with early-stage TNBCs have distinct likelihood of distant recurrence. Methods: In this study, We extracted transcriptome data for 189 pathologically confirmed pT1-2 node-negative TNBC patients at Fudan University Shanghai Cancer Center. Candidate mRNAs were filtered, which was followed by differential expressed mRNAs analysis, survival analysis, and LASSO Cox regression model. All-subsets regression program was used for constructing a multi-mRNA signature in the training set (n=159); the accuracy and prognostic value were then validated using an independent validation set (n=158). Results: Here, we profiled the transcriptome data from 189 early-stage TNBC patients along with 50 paired normal tissues, and developed a prognostic signature based on seven mRNAs (ACAN, KRT5, TMEM101, LCA5, RPP40, LAGE3, CDKL2).In both the training (n=159) and validation cohorts (n=158), the signature could identify patients with relatively high recurrence risks and serve as an independent prognostic factor. Furthermore, the signature had better prognostic value than traditional clinicopathological features in both sets. Among the seven mRNAs, TMEM101 was identified as a prognostic biomarker of early-stage TNBC. Additional cell experiments suggested that TMEM101 could facilitate migration and proliferation of TNBC cells. Conclusions: Our 7-mRNA signature could accurately predict recurrence risks of early-stage TNBCs. Clinical and genomic low risk TNBC patients may safely avoid adjuvant chemotherapy.

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Long non-coding RNA expression profiles in neutrophils revealed potential biomarker for prediction of renal involvement in SLE patients

Rheumatology ◽

10.1093/rheumatology/keaa575 ◽

2020 ◽

Author(s):

Bin Cai ◽

Jingyi Cai ◽

Zhihua Yin ◽

Xiaoyue Jiang ◽

Chao Yao ◽

...

Keyword(s):

Disease Activity ◽

Expression Profiles ◽

Quantitative Polymerase Chain Reaction ◽

Renal Involvement ◽

Differentially Expressed ◽

Healthy Controls ◽

Lncrna Expression ◽

Non Coding Rna ◽

Potential Biomarker ◽

Long Non Coding Rna

Abstract Objective The long non-coding RNA plays an important role in inflammation and autoimmune diseases. The aim of this study is to screen and identify abnormally expressed lncRNAs in peripheral blood neutrophils of SLE patients as novel biomarkers and to explore the relationship between lncRNAs levels and clinical features, disease activity and organ damage. Methods RNA-seq technology was used to screen differentially expressed lncRNAs in neutrophils from SLE patients and healthy donors. Based on the results of screening, candidate lncRNA levels in neutrophils of 88 SLE patients, 35 other connective disease controls, and 78 healthy controls were qualified by real-time quantitative polymerase chain reaction. Results LncRNA expression profiling revealed 360 up-regulated lncRNAs and 224 down-regulated lncRNAs in neutrophils of SLE patients when compared with healthy controls. qPCR assay validated that the expression of Lnc-FOSB-1:1 was significantly decreased in neutrophils of SLE patients when compared with other CTD patients or healthy controls. It correlated negatively with SLE Disease Activity Index 2000 (SLEDAI-2K) score (r = −0.541, P < 0.001) and IFN scores (r = −0.337, P = 0.001). More importantly, decreased Lnc-FOSB-1:1 expression was associated with lupus nephritis. Lower baseline Lnc-FOSB-1:1 level was associated with higher risk of future renal involvement (within an average of 2.6 years) in patients without renal disease at baseline (P = 0.019). Conclusion LncRNA expression profile in neutrophils of SLE patients revealed differentially expressed lncRNAs. Validation study on Lnc-FOSB-1:1 suggest that it is a potential biomarker for prediction of near future renal involvement.

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Individualized lncRNA differential expression profile reveals heterogeneity of breast cancer

Oncogene ◽

10.1038/s41388-021-01883-6 ◽

2021 ◽

Author(s):

Zhangxiang Zhao ◽

YingYing Guo ◽

Yaoyao Liu ◽

Lichun Sun ◽

Bo Chen ◽

...

Keyword(s):

Breast Cancer ◽

Differential Expression ◽

Epithelial Mesenchymal Transition ◽

Expression Profiles ◽

Breast Cancer Subtype ◽

Differential Expression Analysis ◽

Differentially Expressed ◽

Migration And Invasion ◽

Response Analysis ◽

Mesenchymal Transition

AbstractLong non-coding RNAs (lncRNAs) play key regulatory roles in breast cancer. However, population-level differential expression analysis methods disregard the heterogeneous expression of lncRNAs in individual patients. Therefore, we individualized lncRNA expression profiles for breast invasive carcinoma (BRCA) using the method of LncRNA Individualization (LncRIndiv). After evaluating the robustness of LncRIndiv, we constructed an individualized differentially expressed lncRNA (IDElncRNA) profile for BRCA and investigated the subtype-specific IDElncRNAs. The breast cancer subtype-specific IDElncRNA showed frequent co-occurrence with alterations of protein-coding genes, including mutations, copy number variation and differential methylation. We performed hierarchical clustering to subdivide TNBC and revealed mesenchymal subtype and immune subtype for TNBC. The TNBC immune subtype showed a better prognosis than the TNBC mesenchymal subtype. LncRNA PTOV1-AS1 was the top differentially expressed lncRNA in the mesenchymal subtype. And biological experiments validated that the upregulation of PTOV1-AS1 could downregulate TJP1 (ZO-1) and E-Cadherin, and upregulate Vimentin, which suggests PTOV1-AS1 may promote epithelial-mesenchymal transition and lead to migration and invasion of TNBC cells. The mesenchymal subtype showed a higher fraction of M2 macrophages, whereas the immune subtype was more associated with CD4 + T cells. The immune subtype is characterized by genomic instability and upregulation of immune checkpoint genes, thereby suggesting a potential response to immunosuppressive drugs. Last, drug response analysis revealed lncRNA ENSG00000230082 (PRRT3-AS1) is a potential resistance biomarker for paclitaxel in BRCA treatment. Our analysis highlights that IDElncRNAs can characterize inter-tumor heterogeneity in BRCA and the new TNBC subtypes indicate novel insights into TNBC immunotherapy.

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