A panel of 8-lncRNA predicts prognosis of breast cancer patients and migration of breast cancer cells

Long-Noncoding-RNA HOTAIR Upregulation is Associated with Poor Breast Cancer Outcome: A Systematic Review and Meta Analysis

BACKGROUND

Breast cancer is the most frequent cancer among women worldwide with significant disproportionate mortality rates in developing countries. Although clinical management of breast cancer has been immensely improved, refinement in the prognostic and recurrent markers is still needed. Long non-coding RNAs (lncRNA) HOTAIR has recently been associated with poor outcome and is potentially used as a prognostic marker in breast cancer.

METHODS

We comprehensively reviewed studies evaluating lncRNA HOTAIR in association with overall and disease-free survivals in breast cancers. Systematic searches were performed in Pubmed, ProQuest, ScienceDirect, Scopus, Google Scholar, Semantic Scholar, Springer, Nature, Sage Journals, and Wiley databases using combination keywords "long non-coding RNA," "lncRNA," "HOX transcript antisense RNA," "HOTAIR," "breast can-cer," "carcinoma mammae," "prognosis," and "survival." Risk of bias score was used to assess quality of studies, I2 test was conducted to assess heterogeneity. Meta-analysis was performed to compare HOTAIR expression with breast cancer survival rates using STATA v.17 software.

RESULTS

Of the total 1,504 screened studies, seven studies were included in the meta-analysis involving 533 patients. High expression of HOTAIR was associated with poor survival rates (pooled HR: 1.69; 95%CI: 1.11-2.59; p=0.015), shorter overall survival (OS) (pooled HR: 1.33; 95%CI: 0.78-2.26; p=0.455), poor disease-free survival (DFS) (pooled HR: 2.40; 95%CI: 1.63-3.53; p<0.001), poor distant metastatic-free survival (MFS) (HR: 1.75; 95%CI: 1.13-2.71; p=0.012). In addition, overexpression of HOTAIR was associated with positive lymph node infiltration (pooled OR: 2.38; 95%CI: 0.53-10.69; p=0.26) and ductal type cancer (pooled OR: 3.27; 95%CI: 1.15-9.30; p=0.03).

CONCLUSION

Upregulation of lncRNA HOTAIR is associated with worse DFS aand MFS that can potentially be used as a prognostic marker in breast cancer patients.

miR-30d-5p inhibits proliferation, invasion and migration of breast cancer cells by targeting SERPINE1 and promoting fatty acid β-oxidation

Breast cancer (BC) is among the top three most prevalent cancers across the world, especially in women, and its pathogenesis is still unknown. Fatty acid β-oxidation is highly associated with breast cancer. Serpin family E member 1 (SERPINE1)-induced down-regulation of fatty acid β-oxidation can facilitate BC cell proliferation, invasion, and metastasis. In this paper, the difference of miR-30d-5p expressions in both cancerous tissues and para-carcinoma tissues was first detected. Next, the expressions of SERPINE1, long-chain acyl-CoA dehydrogenase (LCAD) and medium-chain acyl-CoA dehydrogenase (MCAD) in the aforementioned tissues were analyzed. Finally, miR-30d-5p mimics were supplemented to breast cancer cells to observe the miR-30d-5p effect upon breast cancer cells. Via immunofluorescence assay and Western blotting, it was found that cancerous tissues had lower expressions of miR-30d-5p, MCAD and LCAD and a higher expression of SERPINE1 than para-carcinoma tissues. The miR-30d-5p mimic group had a decreased SERPINE1 expression and increased MCAD and LCAD expressions compared with the NC group, thus inhibiting BC cell proliferation, invasion, and metastasis. To sum up, miR-30d-5p blocks the cell proliferation, invasion and metastasis by targeting SERPINE1 and promoting fatty acid β-oxidation. Preclinical studies are further required to establish a fatty acid β-oxidation-targeting therapy for breast cancer.

Integrated Bioinformatics Analysis to Identify a Novel Four-Gene Prognostic Model of Breast Cancer and Reveal Its Association with Immune Infiltration

Liquid-liquid phase separation (LLPS) impact immune signaling in cancer and related genes have shown prognostic value in breast cancer (BRCA). However, the crosstalk between LLPS and immune infiltration in BRCA remain unclear. Therefore, we aimed to develop a novel prognostic model of BRCA related to LLPS and immune infiltration. BRCA-related, liquid-liquid phase separation (LLPS)-related genes, and differentially expressed genes (DEGs) were identified using public databases. Mutation and drug sensitivity analyses were performed using Gene Set Cancer Analysis database. Univariate cox regression and LASSO Cox regression were used for the construction and verification of prognostic model. Kaplan-Meier analysis was performed to evaluate overall survival (OS). Gene set variation analysis was conducted to analyze key pathways. CIBERSORT was used to assess immune infiltration and its correlation with prognostic genes was determined through Pearson analysis. A total of 6056 BRCA-associated genes, 3775 LLPS-associated genes, and 4049 DEGs, resulting in 314 overlapping genes. Twenty-eight prognostic genes were screened, and some of them were mutational and related to drug sensitivity Subsequently, a prognostic model comprising L1CAM, EVL, FABP7, and CST1 was built. Patients in high-risk group had shorter OS than those in low-risk group. The infiltrating levels of CD8+ T cells, macrophages M0, macrophages M2, dendritic cells activated, and mast cells resting was altered in high-risk group of breast cancer patients compared to low-risk group. L1CAM, EVL, FABP7, and CST1 were related to these infiltrating immune cells. L1CAM, EVL, FABP7, and CST1 were potential diagnostic biomarkers and therapeutic targets for BRCA.

The Clinical Utility of lncRNAs and Their Application as Molecular Biomarkers in Breast Cancer

Given their tumor-specific and stage-specific gene expression, long non-coding RNAs (lncRNAs) have demonstrated to be potential molecular biomarkers for diagnosis, prognosis, and treatment response. Particularly, the lncRNAs DSCAM-AS1 and GATA3-AS1 serve as examples of this because of their high subtype-specific expression profile in luminal B-like breast cancer. This makes them candidates to use as molecular biomarkers in clinical practice. However, lncRNA studies in breast cancer are limited in sample size and are restricted to the determination of their biological function, which represents an obstacle for its inclusion as molecular biomarkers of clinical utility. Nevertheless, due to their expression specificity among diseases, such as cancer, and their stability in body fluids, lncRNAs are promising molecular biomarkers that could improve the reliability, sensitivity, and specificity of molecular techniques used in clinical diagnosis. The development of lncRNA-based diagnostics and lncRNA-based therapeutics will be useful in routine medical practice to improve patient clinical management and quality of life.

T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing

Background

Hepatocellular carcinoma (HCC), the third most prevalent cause of cancer-related death, is a frequent primary liver cancer with a high rate of morbidity and mortality. T-cell depletion (TEX) is a progressive decline in T-cell function due to continuous stimulation of the TCR in the presence of sustained antigen exposure. Numerous studies have shown that TEX plays an essential role in the antitumor immune process and is significantly associated with patient prognosis. Hence, it is important to gain insight into the potential role of T cell depletion in the tumor microenvironment. The purpose of this study was to develop a trustworthy TEX-based signature using single-cell RNA-seq (scRNA-seq) and high-throughput RNA sequencing, opening up new avenues for evaluating the prognosis and immunotherapeutic response of HCC patients.

Methods

The International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases were used to download RNA-seq information for HCC patients. The 10x scRNA-seq. data of HCC were downloaded from GSE166635, and UMAP was used for clustering descending, and subgroup identification. TEX-related genes were identified by gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). Afterward, we established a prognostic TEX signature using LASSO-Cox analysis. External validation was performed in the ICGC cohort. Immunotherapy response was assessed by the IMvigor210, GSE78220, GSE79671, and GSE91061cohorts. In addition, differences in mutational landscape and chemotherapy sensitivity between different risk groups were investigated. Finally, the differential expression of TEX genes was verified by qRT-PCR.

Result

11 TEX genes were thought to be highly predictive of the prognosis of HCC and substantially related to HCC prognosis. Patients in the low-risk group had a greater overall survival rate than those in the high-risk group, according to multivariate analysis, which also revealed that the model was an independent predictor of HCC. The predictive efficacy of columnar maps created from clinical features and risk scores was strong.

Conclusion

TEX signature and column line plots showed good predictive performance, providing a new perspective for assessing pre-immune efficacy, which will be useful for future precision immuno-oncology studies.

Construction of a lncRNA-mediated ceRNA network and a genomic-clinicopathologic nomogram to predict survival for breast cancer patients

Breast cancer (BC) is the most common cancer among women and a leading cause of cancer-related deaths worldwide. The diagnosis of early patients and the prognosis of advanced patients have not improved over the past several decades. The purpose of the present study was to identify the lncRNA-related genes based on ceRNA network and construct a credible model for prognosis in BC. Based on The Cancer Genome Atlas (TCGA) database, prognosis-related differently expressed genes (DEGs) and a lncRNA-associated ceRNA regulatory network were obtained in BC. The patients were randomly divided into a training group and a testing group. A ceRNA-related prognostic model as well as a nomogram was constructed for further study. A total of 844 DElncRNAs, 206 DEmiRNAs and 3295 DEmRNAs were extracted in BC, and 12 RNAs (HOTAIR, AC055854.1, ST8SIA6-AS1, AC105999.2, hsa-miR-1258, hsa-miR-7705, hsa-miR-3662, hsa-miR-4501, CCNB1, UHRF1, SPC24 and SHCBP1) among them were recognized for the construction of a prognostic risk model. Patients were then assigned to high-risk and low-risk groups according to the risk score. The Kaplan-Meier (K-M) analysis demonstrated that the high-risk group was closely associated with poor prognosis. The predictive nomogram combined with clinical features showed performance in clinical practice. In a nutshell, our ceRNA-related gene model and the nomogram graph are accurate and reliable tools for predicting prognostic outcomes of BC patients, and may make great contributions to modern precise medicine.

Predictive and Prognostic Value of Non-Coding RNA in Breast Cancer

For decades since the central dogma, cancer biology research has been focusing on the involvement of genes encoding proteins. It has been not until more recent times that a new molecular class has been discovered, named non-coding RNA (ncRNA), which has been shown to play crucial roles in shaping the activity of cells. An extraordinary number of studies has shown that ncRNAs represent an extensive and prevalent group of RNAs, including both oncogenic or tumor suppressive molecules. Henceforth, various clinical trials involving ncRNAs as extraordinary biomarkers or therapies have started to emerge. In this review, we will focus on the prognostic and diagnostic role of ncRNAs for breast cancer.

Amino Acid Metabolism-Related lncRNA Signature Predicts the Prognosis of Breast Cancer

Background and Purpose: Breast cancer (BRCA) is the most frequent female malignancy and is potentially life threatening. The amino acid metabolism (AAM) has been shown to be strongly associated with the development and progression of human malignancies. In turn, long noncoding RNAs (lncRNAs) exert an important influence on the regulation of metabolism. Therefore, we attempted to build an AAM-related lncRNA prognostic model for BRCA and illustrate its immune characteristics and molecular mechanism.

Experimental Design: The RNA-seq data for BRCA from the TCGA-BRCA datasets were stochastically split into training and validation cohorts at a 3:1 ratio, to construct and validate the model, respectively. The amino acid metabolism-related genes were obtained from the Molecular Signature Database. A univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression, and a multivariate Cox analysis were applied to create a predictive risk signature. Subsequently, the immune and molecular characteristics and the benefits of chemotherapeutic drugs in the high-risk and low-risk subgroups were examined.

Results: The prognostic model was developed based on the lncRNA group including LIPE-AS1, AC124067.4, LINC01655, AP005131.3, AC015802.3, USP30-AS1, SNHG26, and AL589765.4. Low-risk patients had a more favorable overall survival than did high-risk patients, in accordance with the results obtained for the validation cohort and the complete TCGA cohort. The elaborate results illustrated that a low-risk index was correlated with DNA-repair–associated pathways; a low TP53 and PIK3CA mutation rate; high infiltration of CD4⁺ T cells, CD8⁺ T cells, and M1 macrophages; active immunity; and less-aggressive phenotypes. In contrast, a high-risk index was correlated with cancer and metastasis-related pathways; a high PIK3CA and TP53 mutation rate; high infiltration of M0 macrophages, fibroblasts, and M2 macrophages; inhibition of the immune response; and more invasive phenotypes.

Conclusion: In conclusion, we attempted to shed light on the importance of AAM-associated lncRNAs in BRCA. The prognostic model built here might be acknowledged as an indispensable reference for predicting the outcome of patients with BRCA and help identify immune and molecular characteristics.

An Immune-Related Long Noncoding RNA Signature as a Prognostic Biomarker for Human Endometrial Cancer

Background

Endometrial cancer is among the most common malignant tumors threatening the health of women. Recently, immunity and long noncoding RNA (lncRNA) have been widely examined in oncology and shown to play important roles in oncology. Here, we searched for immune-related lncRNAs as prognostic biomarkers to predict the outcome of patients with endometrial cancer.

Methods

RNA sequencing data for 575 endometrial cancer samples and immune-related genes were downloaded from The Cancer Genome Atlas (TCGA) database and gene set enrichment analysis (GSEA) gene sets, respectively. Immune-related lncRNAs showing a coexpression relationship with immune-related genes were obtained, and Cox regression analysis was performed to construct the prognostic model. Survival, independent prognostic, and clinical correlation analyses were performed to evaluate the prognostic model. Immune infiltration of endometrial cancer samples was also evaluated. Functional annotation of 12 immune-related lncRNAs was performed using GSEA software. Prognostic nomogram and survival analysis for independent prognostic risk factors were performed to evaluate the prognostic model and calculate the survival time based on the prognostic model.

Results

Twelve immune-related lncRNAs (ELN-AS1, AC103563.7, PCAT19, AF131215.5, LINC01871, AC084117.1, NRAV, SCARNA9, AL049539.1, POC1B-AS1, AC108134.4, and AC019080.5) were obtained, and a prognostic model was constructed. The survival rate in the high-risk group was significantly lower than that in the low-risk group. Patient age, pathological grade, the International Federation of Gynecology and Obstetrics (FIGO) stage, and risk status were the risk factors. The 12 immune-related lncRNAs correlated with patient age, pathological grade, and FIGO stage. Principal component analysis and functional annotation showed that the high-risk and low-risk groups separated better, and the immune status of the high-risk and low-risk groups differed. Nomogram and receiver operating characteristic (ROC) curves effectively predicted the prognosis of endometrial cancer. Additionally, age, pathological grade, FIGO stage, and risk status were all related to patient survival.

Conclusion

We identified 12 immune-related lncRNAs affecting the prognosis of endometrial cancer, which may be useful as therapeutic targets and molecular biomarkers.

ARHGEF19 promotes the growth of breast cancer in vitro and in vivo by the MAPK pathway

OBJECTIVE

To assess the expression of ARHGEF19 in human breast cancer, investigate its role in breast cancer, and clarify the mechanism.

METHODS

Bioinformatics analysis, immunoblot, quantitative PCR, and immunohistochemical (IHC) assays were performed to assess ARHGEF19 expression in breast cancer. CCK-8 and Edu assays were conducted to reveal its role in breast cancer cell proliferation. Flow cytometry (FCM) assays and immunoblot were performed to confirm its effects on breast cancer apoptosis. Immunoblot was also performed to clarify the mechanism. Finally, tumor growth assays were aimed to confirm the role of ARHGEF19 in mice.

RESULTS

We observed that ARHGEF19 was highly expressed in human breast cancer. ARHGEF19 promoted breast cancer cell growth in vitro, and suppressed apoptosis. In addition, we found that ARHGEF19 could activate the MAPK pathway in breast cancer cells. Our findings further confirmed that ARHGEF19 contributed to breast cancer growth in mice.

CONCLUSION

We observed that ARHGEF19 promoted the growth of breast cancer in vitro and in vivo via MAPK pathway, and presume it could serve as a breast cancer therapeutic target.