A six-long non-coding RNAs signature as a potential prognostic marker for survival prediction of ER-positive breast cancer patients

Non-coding RNA's prevalence as biomarkers for prognostic, diagnostic, and clinical utility in breast cancer

Noncoding RNAs (ncRNAs), which make up a significant portion of the mammalian transcriptome and plays crucial regulatory roles in expression of genes and other biological processes, have recently been found. The most extensively researched of the sncRNAs, microRNAs (miRNAs), have been characterized in terms of their synthesis, roles, and significance in the tumor development. Its crucial function in the stem cell regulation, another class of sncRNAs known as aspirRNAs, has attracted attention in cancer research. The investigations have shown that long non-coding RNAs have a crucial role in controlling developmental stages, such as mammary gland development. Additionally, it has been discovered that lncRNA dysregulation precedes the development of several malignancies, including breast cancer. The functions of sncRNAs (including miRNAs and piRNAs) and lncRNAs in the onset and development of the breast cancer are described in this study. Additionally, future perspectives of various ncRNA-based diagnostic, prognostic, and therapeutic approaches also discussed.

LncRNA STK4 antisense RNA 1 (STK4-AS1) promoted osteosarcoma by inhibiting p53 expression

BACKGROUND

LncRNA STK4 antisense RNA 1 (STK4-AS1) has been identified as a potential biomarker associated with multiple cancers. We proposed that STK4-AS1 plays a role in the proliferation of osteosarcoma by regulating the cell cycle.

METHODS

We compared the expression of STK4-AS1, p53, and p21 in osteosarcoma vs normal samples in clinical tissues and cell lines. We determined the effect of overexpression and knockdown of STK4-AS1 in p53 expressing osteosarcoma cells U2OS, p53 muted osteosarcoma cells MG63, and osteoblast cells hFOB on p53 and p21 expression and the cell viability. For U2OS and MG63, the cell cycle was analyzed and the expression of cyclin proteins was determined. We overexpressed p53 or p21 in STK4-AS1 overexpressed cells to explore the association of STK4-AS1 and p53 in U2OS.

RESULTS

The STK4-AS1 expression was higher and p53 and p21 expression were lower in osteosarcoma tissue and cells than in their non-cancer counterparts. The expression of STK4-AS1 was negatively correlated with the expression of p53 or p21. Knockdown of STK4-AS1 in U2OS decreased the cell viability, increased cells in the G0/G1 phase, decreased cells in the S and G2/M phase, decreased expression of cyclin A and B, increased p53 and p21, and had no effect on cyclin D and cyclin E, while overexpression of STK4-AS1 did the opposes. Overexpression of p53 or p21 recovered some changes caused by STK4-AS1 overexpression in U2OS. MG63 expressed no p53 and the expression of p21, cyclin A, and cyclin B, cell viability, and cell cycle were not affected by altered STK4-AS1 levels. In hFOB cells, the expression of p53 and p21 was decreased and the cell viability was increased when STK4-AS1 was overexpressed, but they were not affected when STK4-AS1 was knocked down.

CONCLUSION

LncRNA STK4-AS1 promoted the cell cycle of osteosarcoma cells by inhibiting p53 expression.

Coding roles of long non-coding RNAs in breast cancer: Emerging molecular diagnostic biomarkers and potential therapeutic targets with special reference to chemotherapy resistance

Dysregulation of epigenetic mechanisms have been depicted in several pathological consequence such as cancer. Different modes of epigenetic regulation (DNA methylation (hypomethylation or hypermethylation of promotor), histone modifications, abnormal expression of microRNAs (miRNAs), long non-coding RNAs, and small nucleolar RNAs), are discovered. Particularly, lncRNAs are known to exert pivot roles in different types of cancer including breast cancer. LncRNAs with oncogenic and tumour suppressive potential are reported. Differentially expressed lncRNAs contribute a remarkable role in the development of primary and acquired resistance for radiotherapy, endocrine therapy, immunotherapy, and targeted therapy. A wide range of molecular subtype specific lncRNAs have been assessed in breast cancer research. A number of studies have also shown that lncRNAs may be clinically used as non-invasive diagnostic biomarkers for early detection of breast cancer. Such molecular biomarkers have also been found in cancer stem cells of breast tumours. The objectives of the present review are to summarize the important roles of oncogenic and tumour suppressive lncRNAs for the early diagnosis of breast cancer, metastatic potential, and chemotherapy resistance across the molecular subtypes.

Transcriptomic signature associated with RNA-binding proteins for survival stratification of laryngeal cancer

RNA-binding proteins (RBPs) have been suggested as important prognostic indicators in different human cancers. This study was designed to search the prognostic value of RBPs of laryngeal squamous cell carcinoma (LSCC). Differentially expressed RBPs (DERBPs) were screened via The Cancer Genome Atlas (TCGA). Bioinformatics methods were used to identify prognostic DERBPs. Expression profiling of training cohort were calculated to develop a transcriptomic signature, which was validated by three independent cohorts (TCGA cohort, GSE65858 cohort and GSE27020 cohort). We identified DERBPs and a set of signatures (GTPBP3, KHDRBS3 and RBM38) were confirmed as prognosis-related hub DERBPs in LSCC, which was also tested and verified by bioinformatics method and molecular biology experiment. The role of immune cell infiltration and drug resistance between subgroups was explored. Furthermore, the risk score based on transcriptomic signature was turned out to be an independent prognostic indicator for LSCC. Finally, a nomogram for further clinical application was established. Our study demonstrated that the transcriptomic signature we constructed could serve as a novel therapeutic target and biomarker for LSCC from the perspective of RBPs.

Importance of the long non-coding RNA (lncRNA) transcript HULC for the regulation of phenylalanine hydroxylase and treatment of phenylketonuria

More than 1280 variants in the phenylalanine hydroxylase (PAH) gene are responsible for a broad spectrum of phenylketonuria (PKU) phenotypes. While the genotype-phenotype correlation is reaching 88%, for some inconsistent phenotypes with the same genotype additional factors like tetrahydrobiopterin (BH₄), the PAH co-chaperone DNAJC12, phosphorylation of the PAH residues or epigenetic factors may play an important role. Very recently an additional player, the long non-coding RNA (lncRNA) transcript HULC, was described to regulate PAH activity and enhance residual enzyme activity of some PAH variants (e.g., the most common p.R408W) by using HULC mimics. In this review we present an overview of the lncRNA function and in particular the interplay of the HUCL transcript with the PAH and discuss potential applications for the future treatment of some PKU patients.

Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis

Background

Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice.

Results

In this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation.

Conclusions

The experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04215-3.

Construction of a prognostic model based on nine immune-related genes and identification of small molecule drugs for hepatocellular carcinoma (HCC)

This study aimed to develop a prognostic model for hepatocellular carcinoma (HCC) based on immune-related genes and to identify new potential small-molecule drugs. A differential gene expression analysis of high-throughput microarray data from The Cancer Genome Atlas (TCGA) was performed to identify immune-related genes. By comparison with an immune-related genome, nine genes with important prognostic value for HCC were identified. The prognostic characteristics were established based on univariate and multivariate COX and Lasso regression analyzes. Subsequently, immune-related HCC risk signatures were constructed based on these identified nine immune-related genes and patients were classified as being at high or low risk according to these signatures. The overall survival (OS) time of high-risk patients was significantly shorter than that of low-risk patients. When studied as an independent prognostic factor of HCC, the significant prognostic value of this feature can be seen in the stratified cohorts. For clinical application, it was developed a nomogram that includes nine clinical risk factors and the prognostic model built based on the identified immune-related genes. Internal and external verification on 243 HCC tissues through International Cancer Genome Consortium (ICGC) database were performed to make this model more accurate and reliable. In addition, it was observed a positive regulation between the identified immune-related genes and their transcription factors found in HCC patients. Moreover, physiological function and signaling pathway of identified immune-related genes were studied by GO and KEGG enrichment analysis. Finally, several new small molecular drugs with potential for the treatment of HCC have been identified in the CMap database.

Long non-coding RNA (lncRNA) transcriptional landscape in breast cancer identifies LINC01614 as non-favorable prognostic biomarker regulated by TGFβ and focal adhesion kinase (FAK) signaling

Long non-coding RNAs (lncRNAs) represent a class of epigenetic regulators implicated in a number of physiological and pathological conditions. Herein, we characterized the lncRNA expression portrait from 837 patients with invasive breast cancer and 105 normals from the cancer genome atlas (TCGA), which revealed eighteen upregulated and forty-six downregulated lncRNAs. Clustering analysis revealed distinct lncRNA profile for the triple negative breast cancer (TNBC) and normal breast tissue, while less separation was observed among the HER2⁺HR⁺, HER2⁺HR⁻, HER2⁻HR⁺ molecular subtypes. LINC01614, and LINC01235 correlated with worse disease-free survival (DFS), while the expression of lnc-LRR1–1, lnc-ODF3B-2, AC015712.5, lnc-LAMB3–1, lnc-SPP2–3, and lnc-MAP9–2 correlated with better DFS. The expression of LINC01235 correlated with worse overall survival (OS), while the expression of MIR205HG, lnc-MAP2K6–5, FGF14-AS2, lnc-SPP2–3 correlated with better OS. Highest expression of LINC01614 was observed in progesterone receptor (PR)⁺, Estrogen receptor (PR)⁺, and HER2⁺ tumors, while lowest expression was in TNBC. Concordantly, LINC01614 was highly expressed in the luminalB/HER2⁺ subtype from the SRP062132 dataset. Elevated expression of LINC01614 was subsequently validated in primary breast cancer tissue and breast cancer cell lines. Bioinformatics and pathway analyses on LINC01614^high vs. LINC01614^low BC tissue revealed TGFβ1 and ECM as the most activated networks in LINC01614^high tumors. Concordantly, strong correlation between the expression of LINC01614 and COL10A1 (R² = 0.6929), SPOCK1 (R² = 0.5156), ZEB1 (R² = 0.3372), TGFBI (R² = 0.2978), TGFB1 (R² = 0.1985), ACTA2 (R² = 0.1833), and TAGLN (R² = 0.1909) was observed. Mechanistically, exogenous TGFB1 induced LINC01614 expression in the BT474 triple positive BC model, while small-molecule inhibition of transforming growth factor β (TGFβ, SB-431542) or focal adhesion kinase (FAK, PF-573228) abrogated LINC01614 expression. Our data revealed the lncRNA transcription landscape in breast cancer and its molecular subtypes. Our data provide novel insight implicating LINC01614 as unfavorable prognostic marker in BC, its association with the HR⁺/HER2⁺ BC molecular subtype and its regulation by TGFβ and FAK signaling.

Identification and validation of an individualized autophagy-clinical prognostic index in bladder cancer patients

Purpose: Autophagy is a major catabolic system by which eukaryotic cells undergo self-degradation of damaged, defective, or unwanted intracellular components. An abnormal autophagic level is implicated in the pathogenesis of multiple diseases, including cancers. The aim of this study is to explore the prognostic value of autophagy in bladder cancer (BC), which is a major cause of cancer-related death globally.

Patients and methods: First, 27 differentially expressed autophagy-related genes (ARGs) were identified in BC patients based on The Cancer Genome Atlas (TCGA) database. Functional enrichment analyses hinted that autophagy may act in a tumor-suppressive role in the initiation of BC. Then, the Cox proportional hazard regression model were employed to identify three key prognostic ARGs (JUN, MYC, and ITGA3), which were related with overall survival (OS) significantly in BC. The three genes represented important clinical significance and prognostic value in BC. Then a prognostic index (PI) was constructed.

Results: The PI was constructed based on the three genes, and significantly stratified BC patients into high- and low-risk groups in terms of OS (HR=1.610, 95% CI=1.200–2.160, P=0.002). PI remained as an independent prognostic factor in multivariate analyses (HR=2.355, 95% CI=1.483–3.739, P<0.001). When integrated with clinical characteristics of age and stage, an autophagy-clinical prognostic index (ACPI) was finally validated, which had improved performance in predicting OS of BC patients (HR=2.669, 95% CI=1.986–3.587, P<0.001). The ACPI was confirmed in datasets of GSE13507 (HR=7.389, 95% CI=3.645–14.980, P<0.001) and GSE31684 (HR=1.665, 95% CI=0.872–3.179, P=0.122).

Conclusion: This study provides a potential prognostic signature for predicting prognosis of BC patients and molecular insights of autophagy in BC.

Long noncoding RNAs in cancer cells

Long noncoding RNA (lncRNA) has recently been investigated as key modulators that regulate many biological processes in human cancers via diverse mechanisms. LncRNAs can interact with macromolecules such as DNA, RNA, or protein to exert cellular effects and to act as either tumor promoters or tumor suppressors in various malignancies. Moreover, the aberrant expression of lncRNAs may be detected in multiple cancer phenotypes by employing the rapidly developing modern gene chip technology and bioinformatics analysis. Herein, we highlight the mechanisms of action of lncRNAs, their functional cellular roles and their involvement in cancer progression. Finally, we provide an overview of recent progress in the lncRNA field and future potential for lncRNAs as cancer diagnostic markers and therapeutics.

Long non-coding RNAs differential expression in breast cancer subtypes: What do we know?

Breast Cancer (BC) is the most commonly diagnosed cancer and is the leading cause of cancer deaths in women. BC is a heterogeneous disease with different clinical and genetic features. According to immunohistochemical markers, BC is subdivided into four main subtypes: luminal A, luminal B, ERBB2 positive and triple negative. Long non-coding RNAs (lncRNAs) are transcripts with more than 200 nucleotides and deregulated lncRNAs are associated with human diseases, including BC. In order to improve BC molecular classification, non-coding RNAs (ncRNAs), including lncRNAs, have been used. In this review, we focus on lncRNAs with differential expression in BC subtypes and how these RNAs may act to contribute to BC heterogeneity. We also emphasize the potential of these lncRNAs as biomarkers.

Long non-coding RNAs defining major subtypes of B cell precursor acute lymphoblastic leukemia

BACKGROUND

Long non-coding RNAs (lncRNAs) have emerged as a novel class of RNA due to its diverse mechanism in cancer development and progression. However, the role and expression pattern of lncRNAs in molecular subtypes of B cell acute lymphoblastic leukemia (BCP-ALL) have not yet been investigated. Here, we assess to what extent lncRNA expression and DNA methylation is driving the progression of relapsed BCP-ALL subtypes and we determine if the expression and DNA methylation profile of lncRNAs correlates with established BCP-ALL subtypes.

METHODS

We performed RNA sequencing and DNA methylation (Illumina Infinium microarray) of 40 diagnosis and 42 relapse samples from 45 BCP-ALL patients in a German cohort and quantified lncRNA expression. Unsupervised clustering was applied to ascertain and confirm that the lncRNA-based classification of the BCP-ALL molecular subtypes is present in both our cohort and an independent validation cohort of 47 patients. A differential expression and differential methylation analysis was applied to determine the subtype-specific, relapse-specific, and differentially methylated lncRNAs. Potential functions of subtype-specific lncRNAs were determined by using co-expression-based analysis on nearby (cis) and distally (trans) located protein-coding genes.

RESULTS

Using an integrative Bioinformatics analysis, we developed a comprehensive catalog of 1235 aberrantly dysregulated BCP-ALL subtype-specific and 942 relapse-specific lncRNAs and the methylation profile of three subtypes of BCP-ALL. The 1235 subtype-specific lncRNA signature represented a similar classification of the molecular subtypes of BCP-ALL in the independent validation cohort. We identified a strong correlation between the DUX4-specific lncRNAs and genes involved in the activation of TGF-β and Hippo signaling pathways. Similarly, Ph-like-specific lncRNAs were correlated with genes involved in the activation of PI3K-AKT, mTOR, and JAK-STAT signaling pathways. Interestingly, the relapse-specific lncRNAs correlated with the activation of metabolic and signaling pathways. Finally, we found 23 promoter methylated lncRNAs epigenetically facilitating their expression levels.

CONCLUSION

Here, we describe a set of subtype-specific and relapse-specific lncRNAs from three major BCP-ALL subtypes and define their potential functions and epigenetic regulation. The subtype-specific lncRNAs are reproducible and can effectively stratify BCP-ALL subtypes. Our data uncover the diverse mechanism of action of lncRNAs in BCP-ALL subtypes defining which lncRNAs are involved in the pathogenesis of disease and are relevant for the stratification of BCP-ALL subtypes.

Integrated analysis of long noncoding RNA associated-competing endogenous RNA as prognostic biomarkers in clear cell renal carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant carcinomas and its molecular mechanisms remain unclear. Long noncoding RNA (lncRNA) could bind sites of miRNA which affect the expression of mRNA according to the competing endogenous (ceRNA) theory. The aim of the present study was to construct a ceRNA network and to identify key lncRNA to predict survival prognosis. We identified differentially expressed mRNA, lncRNA and miRNA between tumor tissues and normal tissues from The Cancer Genome Atlas database. Then, using bioinformatics tools, we explored the connection of 89 lncRNA, 10 miRNA and 22 mRNA, and we constructed the ceRNA network. Furthermore, we analyzed the functions and pathways of 22 differentially expressed mRNA. Then, univariate and multivariate Cox regression analyses of these 89 lncRNA and overall survival were explored. Nine lncRNA were finally screened out in the training group. The patients were divided into high‐risk and low‐risk groups according to the 9 lncRNA and low‐risk scores having better clinical overall survival (P < .01). Furthermore, the receiver operating characteristic curve demonstrates the predicted role of the 9 lncRNA. The 9‐lncRNA signature was successfully proved in the testing group and the entire group. Finally, multivariate Cox regression analysis and stratification analysis further proved that the 9‐lncRNA signature was an independent factor to predict survival. In summary, the present study provides a deeper understanding of the lncRNA‐related ceRNA network in ccRCC and suggests that the 9‐lncRNA signature could serve as an independent biomarker to predict survival in ccRCC patients.

A prognostic 4-gene expression signature for patients with HER2-negative breast cancer receiving taxane and anthracycline-based chemotherapy

Breast cancer is a heterogeneous group of diseases with diverse clinicopathological and molecular features. At present, chemo-resistance still poses a major obstacle to successful treatment of HER-2 negative breast cancer. Reliable biomarkers are urgently needed to accurately predict the therapeutic sensitivity and prognosis of such patients. In this study, we identified 3145 distant relapse-free survival (DRFS) associated genes in 310 patients with HER-2 negative breast cancer receiving taxane and anthracycline-based chemotherapy in the GSE25055 dataset using univariate survival analysis. Four genes (SRPK1, PCCA, PRLR and FBP1) were further selected by a robust likelihood-based survival model. A risk score model was then constructed with the regression coefficients of the four signature genes. Patients in the training set were successfully divided into high- and low-risk groups with significant differences in DRFS between the two groups. The predictive value was further validated in GSE25065 dataset and similar results were observed. Moreover, the 4-gene signature was proved to have superior prognostic power compared with several clinical signatures such as tumor size, lymph node invasion, TNM stage and PAM50 signature. Our findings indicated that the 4-gene signature was a robust prognostic marker with a good prospect of clinical application for HER-2 negative breast cancer patients receiving taxane-anthracycline combination therapy.

Trends of long noncoding RNA research from 2007 to 2016: a bibliometric analysis

Purpose

This study aims to analyze the scientific output of long noncoding RNA (lncRNA) research and construct a model to evaluate publications from the past decade qualitatively and quantitatively.

Methods

Publications from 2007 to 2016 were retrieved from the Web of Science Core Collection database. Microsoft Excel 2016 and CiteSpace IV software were used to analyze publication outputs, journals, countries, institutions, authors, citation counts, ESI top papers, H-index, and research frontiers.

Results

A total of 3,008 papers on lncRNA research were identified published by June 17, 2017. The journal, Oncotarget (IF2016, 5.168) ranked first in the number of publications. China had the largest number of publications (1,843), but the United States showed its dominant position in both citation frequency (45,120) and H-index (97). Zhang Y (72 publications) published the most papers, and Guttman M (1,556 citations) had the greatest co-citation counts. The keyword “database” ranked first in research frontiers.

Conclusion

The annual number of publications rapidly increased in the past decade. China showed its significant progress in lncRNA research, but the United States was the actual leading country in this field. Many Chinese institutions engaged in lncRNA research but significant collaborations among them were not noted. Guttman M, Mercer TR, Rinn JL, and Gupta RA were identified as good candidates for research collaboration. “Database,” “Xist RNA,” and “Genome-wide association study” should be closely observed in this field.