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Expression Analysis of Long Non-Coding RNAs Related With FOXM1, GATA3, FOXA1 and ESR1 in Breast Tissues.

ABSTRACT: Breast cancer is the most common neoplasm among females. Estrogen receptor (ESR) signaling has a prominent impact in the pathogenesis of breast cancer. Among the transcription factors associated with ESR signaling, FOXM1, GATA3, FOXA1 and ESR1 have been suggested as a candidate in the pathogenesis of this neoplasm. In the current project, we have designed an in silico approach to find long non-coding RNAs (lncRNAs) that regulate these transcription factors. Then, we used clinical samples to carry out validation of our in silico findings. Our systems biology method led to the identification of APTR, AC144450.1, linc00663, ZNF337.AS1, and RAMP2.AS1 lncRNAs. Subsequently, we assessed the expression of these genes in breast cancer tissues compared with the adjacent non-cancerous tissues (ANCTs). Expression of GATA3 was significantly higher in breast cancer tissues compared with ANCTs (Ratio of mean expressions (RME) = 4.99, P value = 3.12E-04). Moreover, expression levels of APTR, AC144450.1, and ZNF337.AS1 were elevated in breast cancer tissues compared with control tissues (RME = 2.27, P value = 5.40E-03; Ratio of mean expressions = 615.95, P value = 7.39E-19 and RME = 1.78, P value = 3.40E-02, respectively). On the other hand, the expression of RAMP2.AS1 was lower in breast cancer tissues than controls (RME = 0.31, P value = 1.87E-03). Expression levels of FOXA1, ESR1, and FOXM1 and linc00663 were not significantly different between the two sets of samples. Expression of GATA3 was significantly associated with stage (P value = 4.77E-02). Moreover, expressions of FOXA1 and RAMP2.AS1 were associated with the mitotic rate (P values = 2.18E-02 and 1.77E-02, respectively). Finally, expressions of FOXM1 and ZNF337.AS1 were associated with breastfeeding duration (P values = 3.88E-02 and 4.33E-02, respectively). Based on the area under receiver operating characteristics curves, AC144450.1 had the optimal diagnostic power in differentiating between cancerous and non-cancerous tissues (AUC = 0.95, Sensitivity = 0.90, Specificity = 0.96). The combination of expression levels of all genes slightly increased the diagnostic power (AUC = 0.96). While there were several significant pairwise correlations between expression levels of genes in non-tumoral tissues, the most robust correlation was identified between linc00663 and RAMP2.AS1 (r = 0.61, P value = 3.08E-8). In the breast cancer tissues, the strongest correlations were reported between FOXM1/ZNF337.AS1 and FOXM1/RAMP2.AS1 pairs (r = 0.51, P value = 4.79E-5 and r = 0.51, P value = 6.39E-5, respectively). The current investigation suggests future assessment of the functional role of APTR, AC144450.1 and ZNF337.AS1 in the development of breast neoplasms.

SUBMITTER: Hassani B

PROVIDER: S-EPMC8171254 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Breast cancer, the second leading cause of cancer death in women, is a highly heterogeneous disease, characterized by distinct genomic and transcriptomic profiles. Transcriptome analyses prevalently assessed protein-coding genes; however, the majority of the mammalian genome is expressed in numerous non-coding transcripts. Emerging evidence supports that many of these non-coding RNAs are specifically expressed during development, tumorigenesis, and metastasis. The focus of this study was to investigate the expression features and molecular characteristics of long non-coding RNAs (lncRNAs) in breast cancer. We investigated 26 breast tumor and 5 normal tissue samples utilizing a custom expression microarray enclosing probes for mRNAs as well as novel and previously identified lncRNAs. We identified more than 19,000 unique regions significantly differentially expressed between normal versus breast tumor tissue, half of these regions were non-coding without any evidence for functional open reading frames or sequence similarity to known proteins. The identified non-coding regions were primarily located in introns (53%) or in the intergenic space (33%), frequently orientated in antisense-direction of protein-coding genes (14%), and commonly distributed at promoter-, transcription factor binding-, or enhancer-sites. Analyzing the most diverse mRNA breast cancer subtypes Basal-like versus Luminal A and B resulted in 3,025 significantly differentially expressed unique loci, including 682 (23%) for non-coding transcripts. A notable number of differentially expressed protein-coding genes displayed non-synonymous expression changes compared to their nearest differentially expressed lncRNA, including an antisense lncRNA strongly anticorrelated to the mRNA coding for histone deacetylase 3 (HDAC3), which was investigated in more detail. Previously identified chromatin-associated lncRNAs (CARs) were predominantly downregulated in breast tumor samples, including CARs located in the protein-coding genes for CALD1, FTX, and HNRNPH1. In conclusion, a number of differentially expressed lncRNAs have been identified with relation to cancer-related protein-coding genes.

Project description:BackgroundThe clinical outcomes of breast cancer (BC) are unpredictable due to the high level of heterogeneity and complex immune status of the tumor microenvironment (TME). When set up, multiple long non-coding RNA (lncRNA) signatures tended to be employed to appraise the prognosis of BC. Nevertheless, predicting immunotherapy responses in BC is still essential. LncRNAs play pivotal roles in cancer development through diverse oncogenic signal pathways. Hence, we attempted to construct an oncogenic signal pathway-based lncRNA signature for forecasting prognosis and immunotherapy response by providing reliable signatures.MethodsWe preliminarily retrieved RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) database and extracted lncRNA profiles by matching them with GENCODE. Following this, Gene Set Variation Analysis (GSVA) was used to identify the lncRNAs closely associated with 10 oncogenic signaling pathways from the TCGA-BRCA (breast-invasive carcinoma) cohort and was further screened by the least absolute shrinkage and selection operator Cox regression model. Next, an lncRNA signature (OncoSig) was established through the expression level of the final 29 selected lncRNAs. To examine survival differences in the stratification described by the OncoSig, the Kaplan-Meier (KM) survival curve with the log-rank test was operated on four independent cohorts (n = 936). Subsequently, multiple Cox regression was used to investigate the independence of the OncoSig as a prognostic factor. With the concordance index (C-index), the time-dependent receiver operating characteristic was employed to assess the performance of the OncoSig compared to other publicly available lncRNA signatures for BC. In addition, biological differences between the high- and low-risk groups, as portrayed by the OncoSig, were analyzed on the basis of statistical tests. Immune cell infiltration was investigated using gene set enrichment analysis (GSEA) and deconvolution tools (including CIBERSORT and ESTIMATE). The combined effect of the Oncosig and immune checkpoint genes on prognosis and immunotherapy was elucidated through the KM survival curve. Ultimately, a pan-cancer analysis was conducted to attest to the prevalence of the OncoSig.ResultsThe OncoSig score stratified BC patients into high- and low-risk groups, where the latter manifested a significantly higher survival rate and immune cell infiltration when compared to the former. A multivariate analysis suggested that OncoSig is an independent prognosis predictor for BC patients. In addition, compared to the other four publicly available lncRNA signatures, OncoSig exhibited superior predictive performance (AUC = 0.787, mean C-index = 0.714). The analyses of the OncoSig and immune checkpoint genes clarified that a lower OncoSig score meant significantly longer survival and improved response to immunotherapy. In addition to BC, a high OncoSig score in several other cancers was negatively correlated with survival and immune cell infiltration.ConclusionsOur study established a trustworthy and discriminable prognostic signature for BC patients with similar clinical profiles, thus providing a new perspective in the evaluation of immunotherapy responses. More importantly, this finding can be generalized to be applicable to the vast majority of human cancers.

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Expression Analysis of Long Non-Coding RNAs Related With FOXM1, GATA3, FOXA1 and ESR1 in Breast Tissues.

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